Neutrophils-tumor cell physical crosstalk as diagnostic and therapeutic target for breast cancer aggressiveness

Abstract

The present disclosure relates to cancer diagnosis and personalized therapy, and more particularly to molecular signatures indicative of breast cancer aggressiveness. Disclosed herein are diagnostic methods for prognosing and / or detecting advanced breast cancer in a mammalian subject by determining expression levels of one or more signatory biomarkers in a biological sample to generate a tumor associated neutrophil (TAN) -score and / or a neutrophil tumor microenvironment physically interacting cell (Neu TME-PIC)-score. The resulting score(s) are evaluated to identify advanced or aggressive diseases. The disclosure further provides compositions, kits, and systems and also identifies therapeutic targets and related methods for treating aggressive breast cancer based on the disclosed signatures.

Description

[0001] NEUTROPHILS -TUMOR CELL PHYSICAL CROSSTALK AS DIAGNOSTIC AND THERAPEUTIC TARGET FOR BREAST CANCER AGGRESSIVENESS

[0002] The project leading to this application has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research.

[0003] TECHNOLOGICAL FIELD

[0004] The present disclosure relates to cancer diagnosis and therapy. More specifically, the present disclosure provides molecular signatures for breast cancer aggressiveness and uses thereof in diagnosis. The present disclosure further provides therapeutic targets for the treatment of aggressive breast cancer.

[0005] BACKGROUND ART

[0006] References considered to be relevant as background to the presently disclosed subject matter are listed below:

[0007] 1. Pal, B. et al. Single cell transcriptome atlas of mouse mammary epithelial cells across development. Breast Cancer Res 23, 69 (2021).

[0008] 2. Pal, B. et al. A single-cell RNA expression atlas of normal, preneoplastic and tumorigenic states in the human breast. EMBO J 40, 11 (2021).

[0009] 3. Valdes-Mora, F. et al. Single-cell transcriptomics reveals involution mimicry during the specification of the basal breast cancer subtype. Cell Rep 35, 108945 (2021).

[0010] 4. Albrengues, J. et al. Neutrophil extracellular traps produced during inflammation awaken dormant cancer cells in mice. Science 361, 6409 (2018).

[0011] 5. Jaillon, S. et al. Neutrophil diversity and plasticity in tumour progression and therapy. Nature Reviews Cancer 20, 485-503 (2020).

[0012] 6. Zhu, Y. P. et al. Identification of an early unipotent neutrophil progenitor with pro-tumoral activity in mouse and human bone marrow. Cell Rep 24, 2329-234 l.e8 (2018).

[0013] 7. Szczerba, B. M. et al. Neutrophils escort circulating tumour cells to enable cell cycle progression. Nature 566, 553-557 (2019).

[0014] 8. Gong, Z. et al. Immunosuppressive reprogramming of neutrophils by lung mesenchymal cells promotes breast cancer metastasis. Sci Immunol 8, eadd5204 (2023).9. Adler, O. et al. Reciprocal interactions between innate immune cells and astrocytes facilitate neuroinflammation and brain metastasis via lipocalin-2. Nat Cancer 4, 401-418 (2023).

[0015] 10. Hao, Y. et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat Biotechnol 42, 293-304 (2024).

[0016] 11. Maas, R. R. et al. The local microenvironment drives activation of neutrophils in human brain tumors. Cell 186, 4546-4566. e27 (2023).

[0017] 12. Ng, M. S. F. et al. Deterministic reprogramming of neutrophils within tumors. Science 383, 1-16 (2024).

[0018] Acknowledgement of the above references herein is not to be inferred as meaning that these are in any way relevant to the patentability of the presently disclosed subject matter.

[0019] BACKGROUND

[0020] The mammary gland, unlike most tissues, completes its maturation process postnatally during puberty and undergoes hormonal and morphological changes along estrous cycles and postpartum periods throughout adulthood [Ref 1]. This ongoing tissue re-organization is induced by alterations in cellular composition and aided by intercellular crosstalk involving immune and non-immune cells [Ref 2]. Similarly, remodeling of the tumor microenvironment (TME) during breast cancer progression is supported by reciprocal signaling between the immune compartment, the tumor cells and the stromal niche [Ref 2, 3].

[0021] Among the immune cells residing in the TME, neutrophils have been associated with inducing tumor growth [Ref 4-6], accompanying circulating tumor cells (CTCs) [Ref 7] and establishing the metastatic niche [Ref 8, 9]. Recently, technological advances have allowed more in-depth analysis of neutrophil activity and heterogeneity in cancer [Ref 11, 12].

[0022] GENERAL DESCRIPTION

[0023] A thorough understanding of neutrophil breast tissue- specific transcriptional plasticity, function and interactive potential during physiology and cancer progression is still lacking.

[0024] By applying scRNA-seq and physically interacting cell sequencing (PIC-seq) [Giladi, A. et al. Nat Biotechnol 38, 629-637 (2020)] along breast tissue development and cancer stages, the inventors found a transient expansion of neutrophils during development, and then an enrichment of neutrophils in physical interaction with cancer cells specifically during advanced carcinoma. Integrating PIC-seq, ligand-receptor analysis and functional assays uncovered a signaling nicheactivated by reciprocal crosstalk between neutrophil-tumor cell PICs, ductal macrophages and vasculature. Additionally, the physical interaction of neutrophils and tumor cells upregulated a unique pro-tumorigenic molecular signature, which is also significantly correlated with lower survival of advanced breast cancer patients. Thus, the inventors' interaction-driven approach further elucidates the key functional role of neutrophils in breast cancer progression.

[0025] A first aspect of the present disclosure relates to a diagnostic and / or prognostic method for prognosing and / or detecting and / or identifying and / or determining and / or staging, advanced breast cancer in a mammalian subject. More specifically, in some embodiments, the method comprising: in step (a), determining the expression level of at least one biomarker in at least one biological sample of the subject to obtain a tumor associated neutrophil (TAN)-score and / or a neutrophil tumor microenvironment physically interacting cell (Neu TME-PIC)-score for the sample. It should be understood that the at least one biomarker / s comprise at least one of: (i), at least one TAN-score biomarker selected from: Cystatin A (CSTA), Cathelicidin Antimicrobial Peptide (CAMP), Cluster of Differentiation 177 (CD177), Glycogenin 1 (GYG1), Interferon-Induced Transmembrane Protein 1 (IFITM1), Lipocalin 2 (LCN2), Prokineticin 2 (PR0K2), Resistin-Like Beta (RETNLB), S100 Calcium-Binding Protein A6 (S100A6), S100 Calcium-Binding Protein A8 (S100A8), Uridine Phosphorylase 1 (UPP1) and WAP Four-Disulfide Core Domain 21, Pseudogene (WFDC21P); and / or (ii) at least one Neu TME-PIC- score biomarker selected from: Arginase 1 (ARG1), Charged Multivesicular Body Protein 4C (CHMP4C), Claudin 4 (CLDN4), Chromosome 16 Open Reading Frame 91 (C16ORF91), EPH Receptor A2 (EPHA2), Interleukin 18 Receptor Accessory Protein (IL18RAP), Prolactin-Induced Protein (PIP), Prokineticin 2 (PR0K2), Radical S-Adenosyl Methionine Domain-Containing 2 (RSAD2), Secreted and Transmembrane 1 (SECTM1), Schlafen Family Member 12-Like (SLFN12L), Tescalcin (TESC), Uridine Phosphorylase 1 (UPP1) and Vascular Endothelial Growth Factor (VEGF). The next step (b), involves determining if at least one of the TAN-score and / or the Neu TME-PIC- score obtained in step (a), is positive or negative with respect to a predetermined standard TAN-score and / or a standard Neu TME-PIC- score; or to a TAN-score and / or a Neu TME-PIC- score of at least one control sample. It should be noted that a positive TAN-score and / or Neu TME-PIC-score of the sample indicates that the subject has an advanced stage of breast cancer, and / or with poor prognosis.

[0026] Another aspect of the present disclosure relates to a diagnostic composition comprising means for determining the expression level of at least one biomarker in at least one biological sample of a subject to obtain a TAN-score and / or a Neu TME-PIC-score for the sample. The biomarker / scomprise at least one of: (i), at least one TAN-score biomarker selected from: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and (ii), at least one Neu TME-PIC-score biomarker selected from:ARGl, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF. In some embodiments the disclosed composition is adapted for prognosing and / or detecting and / or identifying and / or determining and / or staging advanced breast cancer in a mammalian subject.

[0027] Another aspect of the present disclosure relates to a kit comprising: as component (a), a means for determining the expression level of at least one biomarker in at least one biological sample of a subject, to obtain a TAN-score and / or a Neu TME-PIC-score for the sample. In some embodiments, such a means comprises a means and / or reagent / s for sequencing and / or at least one detecting molecule. Each of the detecting molecule / s is specific for one of the biomarker / s. More specifically, the at least one biomarker / s comprise at least one of: (i), at least one TAN-score biomarker selected from: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and / or (ii), at least one Neu TME-PIC- score biomarker selected from: ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN 12L, TESC, UPP1 and VEGF. The disclosed kits further comprise at least one of: Component (b), software for determining the expression level of the biomarker / s in the sample based on a calculation of normalized RNA expression of the target genes compared to a similarly expressed set of control genes. Alternatively, or additionally, component (c), a predetermined standard TAN-score and / or a predetermined standard Neu TME-PIC- score; and / or component (d), at least one control sample.

[0028] The disclosed kit is in some embodiments configured for prognosing and / or detecting and / or identifying and / or determining and / or staging advanced breast cancer in a mammalian subject. A further aspect of the present disclosure relates to a prognostic method for predicting and assessing responsiveness of a mammalian subject has breast cancer, (in some embodiments, an advanced stage of breast cancer), to at least one therapeutic agent or a treatment regimen comprising the at least one therapeutic agent, and optionally for monitoring disease progression. More specifically, the prognostic methods comprising the steps of: specifically, in step (a), determining the expression level of at least one biomarker in at least one biological sample of the subject, to obtain a TAN-score and / or a Neu TME-PIC-score for the sample. In some embodiments, the at least one biomarker / s comprise at least one of: (i), at least one TAN-score biomarker selected from: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB,S100A6, S100A8, UPP1 and WFDC21P; and / or (ii), at least one Neu TME-PIC-score biomarker selected from: ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF. In step (b), classifying the subject as: (i), a responder subject to the therapeutic agent or treatment regimen, if the TAN-score and / or the Neu TME-PIC-score obtained in step (a), for at least one sample obtained after the initiation of the treatment, is negative with respect to a predetermined standard TAN-score and / or a predetermined standard Neu TME-PIC-score; or to a TAN-score and / or a Neu TME-PIC-score of at least one control sample. Alternatively, the subject is classified as (ii), a non-responder subject to the therapeutic agent or treatment regimen, if the TAN-score and / or the Neu TME-PIC-score obtained in step (a) for at least one sample obtained after the initiation of the treatment, is positive with respect to a predetermined standard TAN-score and / or a standard Neu TME-PIC-score; or to a TAN-score and / or a Neu TME-PIC-score of at least one control sample; thereby, predicting and assessing responsiveness of the subject to said therapeutic agent or treatment regimen.

[0029] Another aspect of the present disclosure relates to a method for treating, preventing, inhibiting, reducing, eliminating, protecting or delaying the onset of breast cancer in a mammalian subject. The method comprising: (a), determining the expression level of at least one biomarker in at least one biological sample of the subject to obtain a TAN-score and / or a Neu TME-PIC-score for the sample. More specifically, the at least one biomarker / s comprise at least one of: (i), at least one TAN-score biomarker selected from: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and / or (ii), at least one Neu TME-PIC-score biomarker selected from: ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF. Next, in step (b), determining if at least one of the TAN-score and / or the Neu TME-PIC- score obtained in step (a), is positive or negative with respect to a predetermined standard TAN-score and / or a standard Neu TME-PIC-score; or to a TAN-score and / or a Neu TME-PIC-score of at least one control sample. It should be noted that a positive TAN-score and / or Neu TME-PIC-score of the sample, classifies the subject as displaying advanced stage of breast cancer. The next step (c), involves the therapeutic step of administering at least one therapeutic agent to a subject classified in step (b), as displaying advanced stage of breast cancer.

[0030] A further aspect of the present disclosure relates to a method for treating, preventing, inhibiting, reducing, eliminating, protecting or delaying the onset of breast cancer in a mammalian subject. The method comprising the steps of administrating to the subject at least one therapeutic agent that: (i) reduces and / or specifically depletes at least one sub -population of neutrophils in thesubject; and / or (ii) interferes with the interaction between at least one ligand-receptor pairs of neutrophils-tumor cells. In some embodiments, the sub-population of neutrophils comprises at least one of: young TANs, TAN 1 and TAN2 and / or enriched in at least one of MHC class II (MHC-II Neut) and Ptgs2-expressing neutrophil (Ptgs2+ Neut).

[0031] A further aspect of the present disclosure relates to a screening method for identifying at least one therapeutic compound for the treatment of advanced breast cancer in a mammalian subject. The screening method comprising the steps of: (a), determining a TAN-score and / or a Neu TME-PIC-score of at least one sample contacted with a candidate compound, wherein: (i), a TAN-score is determined in a sample comprising a population of neutrophils, by determining the expression level of at least one biomarker selected from: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and / or (ii), a Neu TME-PIC-score is determined in a sample comprising neutrophil-tumor cell pairs, by determining the expression level of at least one biomarker selected from: ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF. The next step (b), involves determining that the candidate compound is a therapeutic compound for advanced breast cancer if the TAN-score and / or the Neu TME-PIC-score determined in step (a) for the sample is negative with respect to a predetermined standard TAN-score and / or a predetermined standard Neu TME-PIC-score; or to a TAN-score and / or a Neu TME-PIC-score of at least one control sample; thereby identifying a therapeutic compound for advanced breast cancer.

[0032] BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to better understand the subject matter that is disclosed herein and to exemplify how it may be carried out in practice, embodiments will now be described, by way of non-limiting example only, with reference to the accompanying drawings, in which:

[0034] Figure 1A-1E. Thorough characterization of mammary gland immune and non-immune lineages during physiological development and carcinogenesis

[0035] Fig. 1A. Experimental design. Mammary gland samples were collected at different time points from PyMT+(tumor) and PyMT’ (normal) mice. scRNA-seq and PIC-seq analysis enabled investigation of the molecular signaling in the tumor niche. lOd: mammary glands from 5-6 neonates were pooled; 3w: mammary glands from 3 mice were pooled; 6-8w: mammary glands from 2 mice were pooled; 10-12w: mammary glands from 1-2 mice were pooled in each biological replicate (total of biological replicates: PyMT' n= 18; PyMT+n=22).Fig. IB. A two-dimensional projection of 38,206 single cells, grouped into 683 metacells, based on the neighbor graph created by the MetaCell package. Cells are colored by annotation based on hallmark gene expression.

[0036] Fig. 1C. A two-dimensional projection of key cell-type- specific marker gene log normalized expression in all cells.

[0037] Fig. ID. Two-dimensional projection maps highlighting cells originating from tumor and normal mice and from three different sorted populations, CD45+, CD45' and EpCAM+.

[0038] Fig. IE. Log ratio between the population fractions of each cell type in tumor / normal mammary glands, demonstrating positive or negative enrichment in tumor vs. normal samples. The fraction was calculated as the number of cells from the relevant FACS gate that belong to each annotation, averaged first across biological replicates in each time point then across time points. P-values were determined using a two-way analysis of variance (ANOVA) followed by Tukey's post-hoc test for multiple comparisons. The test was run prior to averaging across time points, comparing the population fraction across time points and conditions. *P<0.05, **P<0.01, ***P<0.001.

[0039] Figure 2A-2G. Isolation ami characterization of immune ami non~immune populations from normal and tumor breast tissue in the MMTV-PyMT mouse model

[0040] Fig.2A. Representative FACS plots showing the gating strategy for isolation of immune (CD45+), non-immune (CD45') and epithelial (EpCAM+) cells.

[0041] Fig. 2B-2C. The distribution of total UMI counts (Fig. 2B) binned across the 3 FACS gates and (Fig. 2C) across cell states in passed QC cells.

[0042] Fig. 2D-2G. Heatmaps of log normalized expression of signature genes for (Fig. 2D) lymphoid, (Fig. 2E) myeloid, (Fig. 2F) stromal and (Fig. 2G) epithelial cells.

[0043] Figure 3A-3I. Changes in cellular composition of each cell population during breast tissue development and tumor progression

[0044] Fig.3A. FACS quantification of CD45+and EpCAM+cell percentage across time points in PyMT+and PyMT’ mammary glands. P value was calculated by the Mann-Whitney U test.

[0045] Fig. 3B-3E. Cell type distribution of (Fig. 3B) lymphoid, (Fig. 3C) myeloid, (Fig. 3D) stromal and (Fig. 3E) epithelial populations isolated from PyMT+and PyMT' mammary glands across time points. Heatmaps on the right of the annotations represent P-values for the relationship between age and condition with population fraction. P-values were determined using a two-way ANOVA followed by Tukey's post-hoc test and FDR correction for multiple comparisons.

[0046] Fig. 3F-3H. Dynamic changes in the abundance of (Fig. 3F) Schwann cells, (Fig. 3G) pericytes and (Fig. 3H) Myo / Lum in tumor and normal mammary glands across time points, represented aseach cell’s fraction out of total cells in the relevant compartment. Error bars represent the mean ± SE of fractions across biological replicates for each time point and condition. The presented P-values are the significance of the relationship between conditions (tumor vs normal) and population fraction.

[0047] Fig. 31. Representative IF staining of EpCAM+epithelial cells, F4 / 80+macrophages and DAPI+nuclei in the TME of PyMT+vs PyMT' 6w old mice. Scale bars 50-100 pm.

[0048] *P< 0.05, **P<0.01, ***P<0.001.

[0049] Figure 4A-4H. Dynamic analysis of mammary gland cellular composition revealed a tumor¬ specific cellular niche

[0050] Fig. 4A-4E. The fraction of (Fig. 4A) neutrophils, (Fig. 4B) ductal macs, (Fig. 4C) CAFs, (Fig.

[0051] 4D) endothelial Mcamhl, and (Fig. 4E) tumor cells out of the total cells in their relevant compartment across time points in PyMT+and PyMT' mice. Error bars represent the mean ± SE of fractions across biological replicates for each time point and condition. P-values were determined using a two-way ANOVA followed by Tukey's post-hoc test and FDR correction for multiple comparisons. The presented P-values are the significance of the relationship between conditions (tumor vs normal) and population fraction.

[0052] Fig. 4F. Scaled heatmap of each cell population’s fraction out of the total cells in the relevant compartment across time points and conditions, demonstrating the dynamic trend composition in tumor and normal tissues. Niches were hierarchically clustered based on similar behavior.

[0053] Fig. 4G-4H. Representative IF staining of EpCAM+epithelial cells, CD45+immune cells and DAPI+nuclei in the mammary gland TME of (Fig.4G) PyMT+vs non-carrier 6w old mice and of (Fig.4H) tumor vs normal-adjacent tissue from a human IDC patient. Scale bars, 100 pm. Figure 5A-5J. PIC-seq analysis revealed neutrophil enrichment in physical interactions with epithelial cells in the TME

[0054] Fig. 5A. A representative FACS plot of singlet immune cells (CD45+, top left dotted square), singlet epithelial cells (EpCAM+, bottom right dotted square) and immune-epithelial cell PICs (CD45+ / EpCAM+, top right dotted square), purified from PyMT+12w old mice (n=5); population frequencies represent mean ± SE.

[0055] Fig. 5B. Representative immuno staining of bulk sorted CD45+EpCAM+PICs (n=3). Scale bars, 50 pm.

[0056] Fig. 5C. Cell type distribution of the immune (CD45+) compartment in singlets and PICs across stages in PyMT+and PyMT' mice (total of biological replicates: PyMT' n=9; PyMT+n=14). P-values were determined by the Mann-Whitney U test followed by FDR correction.Fig. 5D-5F. Dynamic changes in the abundance of PICs compared to singlets of (Fig. 5D) mregDC, (Fig. 5E) ductal macs and (Fig. 5F) neutrophils out of total cells in the relevant compartment. Error bars represent the mean ± SE of fractions across biological replicates for each stage and condition. P-value was determined by the Mann-Whitney U test.

[0057] Fig. 5G. FACS quantification of Ly6G+neutrophil percentage in CD45+singlets vs EpCAM+CD45+PICs in advanced carcinoma (10-12w); P-value was determined by the Wilcoxon signed-rank test.

[0058] Fig. 5H. A representative FACS plot showing the enrichments of neutrophils-PICs (Ly6G+CD45+EpCAM+) out of the total immune / epithelium PICs (CD45+ / EpCAM+; n=4), and a representative confocal microscopy image of Ly6G+CD45+EpCAM+PICs bulk sorted from PyMT+mammary glands (n=3); population frequencies represent mean ± SE; Scale bars, 25 pm.

[0059] Fig. 51. Log normalized gene expression profile for selected singlet neutrophils and singlet epithelial genes expressed in epithelial cells, neutrophils and neutrophil-epithelial cell PICs. Fig. 5J. Representative IF staining depicting PICs of neutrophils-epithelial cells (CD45+Ly6G+EpCAM+), in PyMT+12w mammary gland. Arrows show interaction between neutrophils and epithelial cells (n=3). Scale bar, 50 pm.

[0060] *P< 0.05, **P<0.01, ***P<0.001.

[0061] Figure 6A-6J. Quality control of immune-epithelial PICs

[0062] Fig. 6A. Distribution of total UMI counts for CD45+EpCAM+PICs, on a log-scale. Solid lines signify quartiles.

[0063] Fig. 6B-6D. Accuracy of the PIC-seq pipeline was tested on simulated PICs with various total UMI sizes between the minimum UMI on the X axis and 50 UMIs above the minimum, calculating (Fig. 6B) R2of the correlation between simulated and inferred mixing factors, (Fig. 6C) fraction of correct metacell inference and (Fig. 6D) fraction of correct annotation inference.

[0064] Fig. 6E-6F. Heatmaps showing the density of correlation between true (simulated) and inferred (Fig. 6E) epithelial metacells and (Fig. 6F) immune metacells, grouped by annotation.

[0065] Fig. 6G. The correlation between mixing factor a in simulated PICs vs the inferred a.

[0066] Fig. 6H. The fraction of singlets vs simulated PICs that would be excluded from the analysis for each threshold of the difference between PIC simulation and singlet simulation, defining 1.6 as the threshold which keeps the most PICs while excluding the most singlets.

[0067] Fig. 61. Distribution of experimental PIC events excluded (dark grey) or kept (light grey), across time points and conditions.Fig. 6J. Heatmap of log normalized expression of epithelial or immune marker genes across singlets, excluded PICs and good PICs that were kept for later analysis.

[0068] Figure 7A-7C. Characterization of immune-epithelial cell PICs

[0069] Fig. 7A. A heatmap of log normalized expression for selected CD45+immune and EpCAM+epithelial genes in immune singlets, epithelial singlets and immune-epithelial PICs. The heatmap also specifies the mixing factor (a; lower bar), representing the relative share of the immune and epithelial partners in the PIC UMIs. While a=0 would imply all UMIs originate from the immune partner, a=0.5 value exhibits an even distribution for each partner and a=l value shows all UMIs originate from the epithelial partner.

[0070] Fig. 7B. Cell type distribution of EpCAM+epithelial cells in singlets and PICs across time points in tumor vs normal. P-values were determined by the Mann-Whitney U test followed by FDR correction (Total of biological replicates: early carcinoma n=3; advanced carcinoma n=8).

[0071] Fig. 7C. ImageStream representative images showing interacting neutrophil-tumor cell PICs (Ly6G+CD45+EpCAM+). Scale bar, 10 pm.

[0072] Figure 8A-8K. Neutrophil singlet isolation and analysis

[0073] Fig. 8A. A representative FACS plot of singlet neutrophils (Ly6G+) gated from CD45+EpCAM' population, following purification from PyMT+12w old mice (n=4); population frequencies represent mean ± SE.

[0074] Fig. 8B-8D. UMAPs of all CD45+and Ly6G+immune singlets, colored by (Fig. 8B) Leiden clusters, (Fig. 8C) FACS gates and (Fig. 8D) log normalized expression of neutrophil- specific genes.

[0075] Fig. 8E. Density plot of neutrophil UMI counts.

[0076] Fig. 8F. A two-dimensional map of 1,759 neutrophils generated by the MetaCell algorithm and colored by annotation.

[0077] Fig. 8G. Distribution of neutrophil annotations for each Ly6G+enriched sample from PyMT+and PyMT' mice across time points.

[0078] Fig. 8H. The Distribution of TAN gene scores assigned to single cells from each population. The distribution of the scores in TANs was significantly higher than the distribution in each of the other populations according to the T-test with FDR multiple testing correction.

[0079] Fig. 81. Kaplan-Meier curve showing survival over time of stage I and II breast cancer patients (n=396) belonging to the top and bottom quartiles of 792 patients provided a gene score based on expression of the TAN signature gene list. P-value was calculated using the Log-rank test.Fig. 8J-8K. LFCs in mean normalized expression for tumor vs blood neutrophils collected from the breast cancer model and compared to (Fig. 8J) a PDAC model and (Fig. 8K) a CRC model. In both cases, linear regression was performed to generate the dotted line, and P-value and Pearson correlation coefficient were calculated to assess the statistical significance and strength of the correlation. ***P<0.001.

[0080] Figure 9A-9H. Heterogeneous neutrophil states during mammary gland development and tumor progression

[0081] Fig. 9 A. Gene expression profiles of the 1,759 singlet neutrophils grouped into 7 transcriptional states. Top color bar indicates neutrophil annotation.

[0082] Fig. 9B. The fraction of neutrophil along the development and carcinoma stages in PyMT+mammary glands. Error bars represent the mean ± SE of fractions across biological replicates for each stage and condition. Development: lOd (n=3); early carcinoma: 6w and 8w (n=2); advanced carcinoma: lOw and 12w (n=5).

[0083] Fig.9C. FACS quantification showing percentage of neutrophil states (MHCII+, PTGS2+, LCN2+) out of CD45+Ly6G+TME-neutrophils during early (8w) and advanced (12w) carcinoma (n=3); lines represent the mean of each population. P-values were determined by T-test.

[0084] Fig. 9D. Representative IF staining showing co-expression of Ly6G+with MHCII or PTGS2 or LCN2 to identify different neutrophil states during advanced carcinoma (n=2) for each marker. Scale bars 25 pm or 50 pm.

[0085] Fig.9E. Kaplan-Meier curve showing survival over time of stage III and IV breast cancer patients (n=133) belonging to the top and bottom quartiles of 265 patients provided a gene score based on expression of the TAN signature gene list. P-value was calculated using the Log-rank test.

[0086] Fig.9F. Differential gene expression between advanced carcinoma neutrophils from the mammary gland (grey) and the peripheral blood (black). P-values were calculated using the Wald test comparing pseudo-bulked gene expression per replicate with FDR multiple testing correction. Dashed line marks P-value 0.05.

[0087] Fig. 9G. A comparison of expression of selected genes between peripheral blood neutrophils and the different mammary gland neutrophil states in advanced carcinoma. Dot size represents percentage of cells expressing the gene, dots color represents mean log normalized expression.

[0088] Fig. 9H. Heatmap comparing the LFC of selected genes between tumor and peripheral blood neutrophils in the study, as well as published datasets of pancreatic cancer and colorectal cancer neutrophils from tissue and blood. Positive LFC (red) implies overexpression in the tumor, while negative LFC (blue) implies overexpression in the blood.*P< 0.05, **P<0.01, ***P<0.001.

[0089] Figure 10A-10J. Neutrophil-tumor cell PIC analysis and TME signaling

[0090] Fig. 10A-10B. Density of correlation between true (simulated) and inferred (Fig. 10A) epithelial metacells and (Fig. 10B) neutrophil metacells, grouped by annotation.

[0091] Fig. 10C. The correlation between mixing factor a in simulated PICs vs the inferred a.

[0092] Fig. 10D. Cell type distribution of neutrophil states (CD45+Ly6G+) (right four columns) and epithelial cells (EpCAM+) (left four columns) in singlets and PICs in early and advanced carcinoma.

[0093] Fig. 10E-10G. Counts of interactions with LIANA aggregate rank < 0.05 from the (Fig. 10E) tumor niche into neutrophils, (Fig. 10F) from neutrophils to the tumor niche cells and (Fig. 10G) from the tumor niche into neutrophil-tumor cell PICs.

[0094] Fig. 10H. A heatmap showing significant ligand-receptor pairs representing potential signaling from the tumor niche to neutrophils, patterned according to LIANA aggregate rank.

[0095] Fig. 101. Total number of Ly6G+neutrophils that migrated to the bottom chamber when they are exposed to RM-MACs cultured with / wo anti-CCL3 treatment; presented as pool of 2 independent experiments. Dots represent technical replicates, lines represent the mean.

[0096] Fig. 10J. A heatmap showing significant ligand-receptor pairs representing potential signaling from neutrophil-tumor cell PICs to vasculature (endothelial Mcamhland pericytes), patterned according to LIANA aggregate rank. * aggregate rank<0.05, ** aggregate rankcO.Ol, *** aggregate rankcO.OOL

[0097] Figure 11A-11K. TAN-tumor cell PICs form a signaling niche with ductal macrophages and the perivascular compartment in the breast TME

[0098] Fig. 11A-11C. Log normalized expression of interacting ligand-receptor gene pairs selected from the incoming signaling to the neutrophils identified by the LIANA pipeline (aggregate rank < 0.05) with relation to (Fig. HA) adhesion and (Fig. 11B-11C) recruitment processes. Error bars represent the mean ± SE log normalized expression.

[0099] Fig. 11D. Experimental design of neutrophil migration assay. Regular medium, RM; tumor conditioned medium, CM.

[0100] Fig. HE. Total number of Ly6G+neutrophils that migrated to the bottom chamber when they are exposed to CM-activated and RM-non-activated macrophages (presented as pool of 3 independent experiments). Dots represent technical replicates, lines represent the mean. P-values were determined by T-test with FDR multiple testing correction.Fig. 11F. Total number of Ly6G+neutrophils that migrated to the bottom chamber when they are exposed to CM-activated macrophages with / wo anti-CCL3 treatment (presented as pool of 2 independent experiments). P-values were determined by T-test.

[0101] Fig. 11G. Counts of outgoing interactions from neutrophil-tumor cell PICs to the tumor niche with LIANA aggregate rank < 0.05.

[0102] Fig. 11H-11I. Log normalized expression of interacting ligand-receptor genes selected from the outgoing signaling from the neutrophil-tumor cell PICs. Error bars represent the mean ± SE log normalized expression.

[0103] Fig. 11J-11K. Representative IF staining of mammary glands depicting interacting neutrophiltumor cell PICs in the TME of (Fig. 11J) 12w old PyMT+mice (Ly6G+EpCAM+), and (Fig. 11K) in the TME of human IDC (S100A9+EpCAM+), in close proximity to CD31+endothelial cells. White arrows show the interaction between neutrophils and epithelial cells and yellow arrows show endothelial cells. Scale bars, 50-75 pm.

[0104] *P< 0.05, **P<0.01, ***P<0.001.

[0105] Figure 12A-12H. Molecular and functional programs induced by neutrophil-tumor cell physical crosstalk

[0106] Fig. 12A. Comparison of mean expression (normalized to median cell size) in observed vs simulated neutrophil-tumor cell PICs in advanced carcinoma mice. Highlighted genes are colored by their expected specificity to the neutrophil (green) or tumor cell (red) compartments, calculated as mean epithelial expression / (mean epithelial + mean neutrophil expression).

[0107] Fig. 12B. Representative scratch assay images at 0 hours and 12 hours of incubation in each of the conditions (n=2).

[0108] Fig. 12C. The distribution of NeuTME-PIC (Including Ptgs2+Neut, TAN 1 and TAN2) gene scores assigned to single cells from each population and to the NeuTME-PICs. P-values comparing each population to the NeuTME-PICs were assigned using the T-test with FDR multiple testing correction.

[0109] Fig. 12D. Kaplan-Meier curve showing survival over time of stage I and II breast cancer patients (n=396) belonging to the top and bottom quartiles of 792 patients provided a gene score based on expression of the NeuTME-PIC gene set. P-value was calculated using the Log-rank test.

[0110] Fig. 12E. FACS quantification of CD45+Ly6G+cells from mammary glands of PyMT+12w, comparing IgG isotype injected mice (n=7) vs. anti-Ly6G treated mice (n=8). Bars represent the mean. P-value was determined by T-test.

[0111] Fig. 12F-12G. UMAP projection of genes used to exclude problematic populations from (Fig.

[0112] 12F) epithelial cells and (Fig. 12G) endothelial cells before differential gene expression analysis.Fig. 12H. Enrichment of the Gene Ontology gene list among the analyzed gene set sorted by T-scores generated by comparing tumor cells from the anti-Ly6G treated mice vs. IgG isotype injected mice, q-value represents the FDR adjusted P-value of the enrichment.

[0113] *P<0.05, **P<0.01, ***P<0.001.

[0114] Figure 13A-13H. The physical interaction between neutrophils and cancer cells has high malignant molecular features

[0115] Fig. 13A. Mean observed (real PICs, grey bar) and expected (simulated PICs, black and white bars) expression in PICs of each subset of neutrophils with tumor cells in advanced carcinoma mice of genes related to metastasis, migration and invasion. X-axis represents neutrophil-tumor cell PICs, while neutrophil pattern is associated with neutrophil state annotations. Solid error bars indicate mean ± SE normalized expression in observed PICs; dotted error bars indicate the standard deviation of the means of the 1,000 simulated PIC datasets created. P-values were determined by calculating the Z-score for the observed mean expression as compared to the distribution of means of simulated PICs and FDR adjusted.

[0116] Fig. 13B. Percentage of wound closure at 12h of Met-1 cells seeded alone, in coculture with neutrophils (Neut), with conditioned medium of neutrophils (Neut CM) or with neutrophils pretreated with cytochalasin D. Error bars represent the mean ± SE. P-values were determined by T-test with FDR multiple testing correction (n=2).

[0117] Fig. 13C-13D. Mean observed and expected expression in PICs of genes related to (Fig. 13C) proliferation and (Fig. 13D) angiogenesis.

[0118] Fig. 13E-13F. FACS quantification of (Fig. 13E) percentage of KI67+cells and (Fig. 13F) VEGFA MFI from CD45+Ey6G+EpCAM+PICs compared to singlets in PyMT+12w. Fines represent the mean, P-values were determined by T-test with FDR multiple testing correction (n=4).

[0119] Fig. 13G. Representative IF staining of VEGFA+EpCAM+Ly6G+PICs (n=2). Scale bar, 10 pm.

[0120] Fig. 13H. Kaplan-Meier curve showing survival over time of stages III and IV breast cancer patients (n=133) belonging to the top and bottom quartiles of 265 patients provided a gene score based on expression of the NeuTME-PIC gene set (Including Ptgs2+Neut, TAN 1 and TAN2 PICs). P-value was calculated using the Fog-rank test.

[0121] *P<0.05, **P<0.01, ***P<0.001.Figure 14A-14K. The pro-tumoral effect of neutrophils in the breast TME depends on their physical interaction

[0122] Fig. 14A. Experimental design of in vitro validation assay of KI67 and VEGFA signaling produced by physical interaction. Met-1 cells were seeded in monoculture, with neutrophils in a transwell chamber or in coculture with neutrophils allowing physical interaction.

[0123] Fig. 14B-14C. FACS quantification of (Fig. 14B) percentage of KI67+cells and (Fig. 14C) VEGFA MFI in Met-1 cells and neutrophils across different in vitro conditions. Error bars represent the mean ± SE of percentage or MFI. P-values were determined by T-test with FDR multiple testing correction (n=2).

[0124] Fig. 14D. Experimental design of neutrophil depletion. MMTV-PyMT+10.5w old females were injected intraperitoneally with an anti-Ey6G antibody or with a non-active isotype daily for 9 days, until they reach 12w.

[0125] Fig. 14E. Representative FACS plots showing the gating strategy of CD45 EpCAM+epithelial singlets (bottom right dotted square), CD45+EpCAM+PICs (top right dotted square) and CD31+endothelial cells (black solid square).

[0126] Fig. 14F-14H. Comparison of percentages of KI67+cells among mice treated with anti-Ly6G+antibody or isotype of (Fig. 14F) singlet epithelial cells, (Fig. 14G) PICs and (Fig. 14H) endothelial cells. P-values were calculated using the T-test.

[0127] Fig. 14I-14J. Comparison of log normalized expression of selected genes in (Fig. 141) tumor cells and (Fig. 14J) endothelial cells of 12w PyMT+mice treated with anti-Ey6G antibody or with isotype. Error bars represent the mean ± SE log normalized expression. P-values were calculated using the T-test with FDR multiple testing correction.

[0128] Fig. 14K. Schematic overview highlighting molecular niche- signaling in the breast TME between neutrophil-tumor cell PICs, ductal macrophages and vasculature.

[0129] *P< 0.05, **P<0.01, ***P<0.001.

[0130] DETAILED DESCRIPTION OF EMBODIMENTS

[0131] Intercellular communication forms complex niche signaling networks necessary for maintaining tissue homeostasis; however, aberrant interactions in the tumor microenvironment (TME) can promote carcinogenesis both by upregulating aggressive tumor cell properties [de Visser, K. E. & Joyce, J. A. Cancer Cell 41, 374-403 (2023); Caronni, N. et al. Nature 2023623:7986623, 415— 422 (2023); Casanova-Acebes, M. et al. Nature 595, 578-584 (2021); Bayik, D. & Lathia, J. D. Nat Rev Cancer 21, 526-536 (2021) and by activating other tissue resident cells, includingmacrophages (TAMs) and fibroblasts (CAFs) [Ma, R. Y., Black, A. & Qian, B. Z. Trends Immunol 43, 546-563 (2022); Sahai, E. et al. Nat Rev Cancer 20, 174-186 (2020)]. By investigating secreted and physical interactions in the breast cancer TME, the present disclosure identified neutrophil-derived signaling as a significant inducer of tumor aggressiveness.

[0132] Neutrophils are known to take on diverse roles in the TME, ranging from anti-tumoral involvement in immune cell recruitment and antigen presentation [Linde, I. L. et al. Cancer Cell 41, 356-372. elO (2023); Pylaeva, E. et al. Cell Rep 40, 111171 (2022)] to pro-tumoral functions of immunosuppression, fatty acid metabolism, angiogenesis and metastasis [Ref 5; Quail, D. F. et al. J Exp Med 219, 39 (2022); Hedrick, C. C. & Malanchi, I. Nat Rev Immunol 22, 173-187 (2021)]. In the breast tissue, the transient appearance of singlet neutrophils was observed during development and their re-emergence in physical contact with tumor cells during advanced carcinoma. The inventors identified heterogeneous neutrophil transcriptional phenotypes similar to tumor associated neutrophil (TAN) states which have been characterized across various primary tumors, in accordance with their specific activity in the TME [Ref 11, 12; Xue, R. et al. Nature 612, 141-147 (2022); Salcher, S. et al. Cancer Cell 40, 1503-1520.e8 (2022)]. By comparing neutrophils from the TME with ones from the peripheral blood, the inventors identified a tissue-associated gene program, including the genes Ptgs2, Illb, Nfkbia, Cxcl2, and Vegfa, which has been shown to characterize mature tissue neutrophils in various healthy tissues [Ballesteros, I. et al. Cell 183, 1282-1297. el8 (2020)], primary tumors [Ref 12; Bui, T. M. et al. J Clin Invest 134, el74545 (2024)] and metastatic sites [Ref 11; Yofe, I. et al. Cancer Discov 13, 2610-2631 (2023)]. This finding is particularly interesting and suggests a shared mature neutrophil phenotype with an important role in tissue homeostasis, tumor progression and metastasis

[0133] To better understand the way neutrophils shape the breast cancer TME, a computational methodology was applied integrating single cell RNA sequencing (scRNA-seq) and physically interacting cell sequencing (PIC-seq) analytical pipelines with Ligand-Receptor (L-R) analysis and performed in vivo and in vitro validations. It was first shown by the present disclosure that neutrophils are recruited to the mammary gland by the TME macrophages, as was suggested also in brain cancer and metastasis [Ref 11]. Then, analysis of interactions between neutrophils, tumor cells and the vasculature revealed pro-angiogenic crosstalk, including VEGFA-mediated signaling, driven by neutrophil-tumor cell PICs. Expression of VEGFA, which is widely used as a target for oncological anti-angiogenic treatments [Perez-Gutierrez, L. & Ferrara, N. Nat Rev Mol Cell Biol 24, 816-834 (2023); Zhang, S. D., McCrudden, C. M. & Kwok, H. F. Oncol Lett 10, 1893 (2015)], has been previously reported in healthy tissue singlet neutrophils and to a greaterextent in cancer [Ref 11, 12; Tazzyman, S., Lewis, C. E. & Murdoch, C. Int J Exp Pathol 90, 222 (2009); Phillipson, M. & Kubes, P. Trends Immunol 40, 635-647 (2019)]. Notably, the integrated analysis of the present disclosure also showed that the physical interaction between neutrophils and tumor cells increased invasive and proliferative programs in the tumor cells [Hedrick, C. C. & Malanchi, I. Nat Rev Immunol 22, 173-187 (2021); Phillipson, M. & Kubes, P. Trends Immunol 40, 635-647 (2019); Pereira- Veiga, T., Schneegans, S., Pantel, K. & Wikman, H. Cell Rep 40, 111298 (2022)]. In vivo depletion of neutrophils further validated their role by leading to reduced proliferation of endothelial and tumor cells. These findings open a path for investigation of additional interacting pairs in cellular niches, which can reveal novel targetable candidates.

[0134] As major players in breast cancer metastasis, neutrophils have been shown to assist the communication between tumor cells and endothelial cells, inducing dissemination [Pereira- Veiga, T., Schneegans, S., Pantel, K. & Wikman, H. Cell Rep 40, 111298 (2022); Coffelt, S. B., Wellenstein, M. D. & De Visser, K. E. Nat Rev Cancer 16, 431-446 (2016)], and to promote the proliferation of murine and human circulating tumour cells (CTCs), supporting their survival in the metastatic niche [Ref 7]. Specifically, neutrophil subsets similar to TME-states Ptgs2 and Lcn2) were seen recruited to distant organs during the pre-metastatic phase to facilitate tumor colonization [Ref 8, 9; Wculek, S. K. & Malanchi, I. Nature 528, 413-417 (2015)]. In this context, the crosstalk identified by the present disclosure in the primary tumor, is regarded as part of a sequence of events spanning primary tumor development, early dissemination and the pre-metastatic phase, underscoring the critical role of neutrophil-tumor cell interactions in shaping a microenvironment conducive to breast cancer progression.

[0135] Thus, a first aspect of the present disclosure relates to a diagnostic and / or prognostic method for prognosing and / or detecting and / or identifying and / or determining and / or staging, advanced breast cancer in a mammalian subject. More specifically, in some embodiments, the method comprising: In step (a), determining the expression level of at least one biomarker in at least one biological sample of the subject to obtain a tumor associated neutrophil (TAN)-score and / or a neutrophil tumor microenvironment physically interacting cell (Neu TME-PIC)-score for the sample. It should be understood that the at least one biomarker / s comprise at least one of: (i), at least one TAN-score biomarker selected from: Cystatin A (CSTA), Cathelicidin Antimicrobial Peptide (CAMP), Cluster of Differentiation 177 (CD177), Glycogenin 1 (GYG1), Interferon-Induced Transmembrane Protein 1 (IFITM1), Lipocalin 2 (LCN2), Prokineticin 2 (PROK2), Resistin-Like Beta (RETNLB), S100 Calcium-Binding Protein A6 (S100A6), S100 Calcium-Binding Protein A8 (S100A8), Uridine Phosphorylase 1 (UPP1) and WAP Four-Disulfide Core Domain 21,Pseudogene (WFDC21P); and / or (ii) at least one Neu TME-PIC- score biomarker selected from: Arginase 1 (ARG1), Charged Multivesicular Body Protein 4C (CHMP4C), Claudin 4 (CLDN4), Chromosome 16 Open Reading Frame 91 (C16ORF91), EPH Receptor A2 (EPHA2), Interleukin 18 Receptor Accessory Protein (IE18RAP), Prolactin-Induced Protein (PIP), Prokineticin 2 (PR0K2), Radical S-Adenosyl Methionine Domain-Containing 2 (RSAD2), Secreted and Transmembrane 1 (SECTM1), Schlafen Family Member 12-Eike (SEFN12E), Tescalcin (TESC), Uridine Phosphorylase 1 (UPP1) and Vascular Endothelial Growth Factor (VEGF). The next step (b), involves determining if at least one of the TAN-score and / or the Neu TME-PIC- score obtained in step (a), is positive or negative with respect to a predetermined standard TAN-score and / or a standard Neu TME-PIC- score; or to a TAN-score and / or a Neu TME-PIC- score of at least one control sample. It should be noted that a positive TAN-score and / or Neu TME-PIC-score of the sample, indicates that the subject has an advanced stage of breast cancer, and / or with poor prognosis.

[0136] Still further, the present disclosure provides diagnostic and / or prognostic methods for prognosing and / or detecting and / or identifying and / or determining and / or staging, advanced breast cancer in a mammalian subject. More specifically, in some embodiments, the method comprises the following steps. In step (a), determining the expression level of at least one biomarker in at least one biological sample of the subject to obtain a tumor associated neutrophil (TAN)-score and / or a neutrophil tumor microenvironment physically interacting cell (Neu TME-PIC)-score for the sample. It should be understood that the at least one biomarker / s comprise at least one of: (i), at least one TAN-score biomarker selected from: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and / or (ii) at least one Neu TME-PIC- score biomarker selected from: ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF. The next step (b), involves classifying the subject as having an advanced stage of breast cancer, and / or with poor prognosis if at least one of the TAN-score and / or the Neu TME-PIC- score obtained or determined in step (a), is positive with respect to a predetermined standard TAN-score and / or a standard Neu TME-PIC-score, or to a TAN-score and / or a Neu TME-PIC- score of at least one control sample. It should be appreciated that the TAN score biomarkers and the Neu TME-PIC score biomarkers are also referred to herein as the signatory biomarkers, or the biomarkers of the signature / s of the present disclosure.In some embodiments, the expression level of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, and at least twelve of the TAN-score biomarkers is determined by the disclosed methods.

[0137] In yet some further embodiments the expression level of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, and at least twelve, at least thirteen, at least fourteen of the Neu TME-PIC-score biomarkers is determined by the disclosed methods.

[0138] In some embodiments, the expression level of at least at least six of the TAN-score biomarkers, is determined by the disclosed methods, specifically, at least six of CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, and / or the expression level of at least six of the Neu TME-PIC-score biomarkers is determined by the disclosed methods, specifically, at least six of ARG1, CHMP4C, CEDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SEFN12E, TESC, UPP1 and VEGF.

[0139] In some embodiments, the expression level of at least at least seven of the TAN-score biomarkers, is determined by the disclosed methods, specifically, at least seven of CSTA, CAMP, CD 177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, and / or the expression level of at least seven of the Neu TME-PIC-score biomarkers is determined by the disclosed methods, specifically, at least seven of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

[0140] In some embodiments, the expression level of at least at least eight of the TAN-score biomarkers, is determined by the disclosed methods, specifically, at least eight of CSTA, CAMP, CD 177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, and / or the expression level of at least eight of the Neu TME-PIC-score biomarkers is determined by the disclosed methods, specifically, at least eight of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

[0141] In some embodiments, the expression level of at least at least nine of the TAN-score biomarkers, is determined by the disclosed methods, specifically, at least nine of CSTA, CAMP, CD 177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, and / or the expression level of at least nine of the Neu TME-PIC-score biomarkers is determined by the disclosed methods, specifically, at least nine of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

[0142] In some embodiments, the expression level of at least at least ten of the TAN-score biomarkers, is determined by the disclosed methods, specifically, at least ten of CSTA, CAMP, CD177, GYG1,IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, and / or the expression level of at least ten of the Neu TME-PIC-score biomarkers is determined by the disclosed methods, specifically, at least ten of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

[0143] In some embodiments, the expression level of at least at least eleven of the TAN-score biomarkers, is determined by the disclosed methods, specifically, at least eleven of CSTA, CAMP, CD 177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, and / or the expression level of at least eleven of the Neu TME-PIC-score biomarkers is determined by the disclosed methods, specifically, at least eleven of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

[0144] In some embodiments, the expression level of at least at least twelve of the TAN-score biomarkers, is determined by the disclosed methods, specifically, at least twelve of CSTA, CAMP, CD 177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, and / or the expression level of at least twelve of the Neu TME-PIC-score biomarkers is determined by the disclosed methods, specifically, at least twelve of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

[0145] In some embodiments, the expression level of at least at least one to twelve of the TAN-score biomarkers, is determined by the disclosed methods, specifically, at least one to twelve of CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, and / or the expression level of at least thirteen of the Neu TME-PIC-score biomarkers is determined by the disclosed methods, specifically, at least thirteen of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

[0146] In some embodiments, the expression level of at least at least one to twelve of the TAN-score biomarkers, is determined by the disclosed methods, specifically, at least one to twelve of CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, and / or the expression level of at least fourteen of the Neu TME-PIC-score biomarkers is determined by the disclosed methods, specifically, at least fourteen of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

[0147] In more specific embodiments, the expression level of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at leastseventeen, at least eighteen, at least nineteen, at least twenty, at least twenty-one, at least twenty-two, at least twenty-three, and at least twenty-four, of the TAN-score biomarkers and of the Neu TME-PIC-score biomarkers is determined by the disclosed methods. This is to obtain the TAN score and the Neu TME-PIC scores of the tested sample. It should be understood that additional biomarkers and control reference genes or proteins may be further determined by the disclosed methods, and included in the compositions and kits of the present disclosure, provided that no more than 100 biomarkers and control references are used, specifically, additional 1 to 50, specifically, additional 1 to 10 biomarkers and / or additional 1 to 10 control references may be used. In some embodiments, additional 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50 or more biomarkers and / or control references may be further used with the at least one of the TAN-score biomarkers and / or with at least one of the Neu TME-PIC-score biomarkers. In yet some further embodiments, up to, a total of, and no more than 150, 200, 250, 300, 350, 400, 450, or 500 biomarkers, including the signatory biomarkers (e.g., the TAN score biomarkers and / or the Neu TME-PIC biomarkers), additional biomarkers, and the control references are used. Accordingly, in some embodiments, the disclosed methods may involve in step (a), the determination of the expression level of at least one of, or at least 1 to 12 of the TAN-score biomarkers, specifically, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, of CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, and / or at least one of, or at least 1 to 14 of the Neu TME-PIC-score biomarkers, specifically, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 of the ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF; and optionally, 1 to 476 (in case all 24 signatory biomarkers are used, or up to 499, if only one of the signatory biomarkers are used), additional biomarkers, and control references.

[0148] As mentioned above, the present disclosure relates to a diagnostic and / or prognostic method for prognosing, detecting, identifying, determining and / or staging, advanced breast cancer in a mammalian subject. As used herein, the term "diagnosing" or "diagnostic" refers to the process of detecting or identifying the presence or absence of a disease or pathological condition in a subject. In some embodiments, the detection of the presence of a primary tumor. In the present case, the disclosed methods are applicable for diagnosing breast cancer, specifically, advanced breast cancer. The term "prognosing" or "prognostic" refers to predicting the likely course, progression, or outcome of the disease in a subject. As used herein, "detecting" or "detection" refers to identifying the presence or indication of the condition, biomarker, or change in biologicalstate that is associated with advanced breast cancer. The term "identifying" or "identification" refers to the process of distinguishing or characterizing a specific condition, subtype, stage, or biomarker associated with advanced breast cancer. "Determining" or "determination" refers to assessing, measuring, or evaluating a clinical or biological parameter that informs the diagnosis, prognosis, or classification of advanced breast cancer. As indicated herein after in more detail, the disclosed methods further provide a tool for staging and grading the disease. "Staging" or "staging of a disease" as used herein refers to the classification of disease extent and severity within the body. This process provides a framework for prognosis assessment, treatment planning, and comparative research across patient populations. Staging and grading in the context of breast cancer are defined herein later.

[0149] For diagnosing, prognosing, detecting, identifying, determining and / or staging advanced breast cancer in a mammalian subject, the expression level of at least one biomarker in at least one biological sample of the subject is determined to obtain a tumor associated neutrophil (TAN)-score and / or a neutrophil tumor microenvironment physically interacting cell (Neu TME-PIC)-score.

[0150] "Tumor-associated neutrophils (TANs)" refers herein to a distinct population of neutrophilic granulocytes found within the tumor microenvironment (TME). This neutrophilic population contributes to tumor progression, immune modulation, and therapeutic response. Unlike circulating neutrophils, TANs are recruited to the TME by tumor- secreted chemokines, such as CXCL1 and CXCL8, and undergo functional reprogramming influenced by local factors, including cytokines like TGF-P, G-CSF, and IL-6. This reprogramming results in phenotypic and functional plasticity, enabling TANs to exhibit both tumor-promoting and tumor-suppressing roles depending on the specific context of the TME.-In their tumor-promoting (N2) phenotype, TANs enhance cancer progression by secreting pro-angiogenic factors such as VEGF, matrix metalloproteinases (MMPs) that facilitate tissue remodeling, and immunosuppressive cytokines that inhibit anti-tumor immune responses. Conversely, under certain conditions, such as IFN-y stimulation, TANs may adopt a tumor-suppressing (Nl) phenotype characterized by increased cytotoxic activity and promotion of anti-tumor immunity. The dual functionality of TANs makes them critical players in cancer biology and potential targets for therapeutic interventions.

[0151] "Neutrophil Tumor Microenvironment-Physically Interacting Cells" or "Neu TME-PIC" , refers to neutrophils within the tumor microenvironment (TME) that directly engage with other cellular components within the TME through physical interactions, influencing tumor progression, immune regulation, and therapeutic outcomes. These interactions occur via cell-cell contact mediated by receptor-ligand binding (e.g., CXCR2-CXCL8), adhesion molecules (e.g., ICAM-1,VCAM-1), or integrins (e.g., LFA-1). Neutrophils interact with tumor cells, endothelial cells, stromal fibroblasts, and immune cells, playing a pivotal role in shaping the dynamics of the TME. As an example, the physical interactions between neutrophils and tumor cells can promote tumor cell invasion and migration by facilitating matrix degradation through the release of neutrophil extracellular traps (NETs) or matrix metalloproteinases (MMPs). Contact with endothelial cells can support for example angiogenesis by enhancing vascular remodeling, while interactions with fibroblasts can contribute to extracellular matrix deposition and stiffness, creating a pro-tumorigenic niche. Additionally, neutrophils can interact with T cells and macrophages, modulating immune responses and promoting immunosuppression within the TME.

[0152] In some embodiments, the Neu TME-PIC refers to a Ly6G+CD45+EpCAM+population or to other equivalent Neu TME-PIC mammalian population (e.g. CD15+CD45+EpCAM+, CD 16+CD45+EpC AM+, CD66b+CD45+EpC AM+) .

[0153] The TAN-score and / or the Neu TME-PIC-score are evaluated by determining the level of at least one biomarker, specifically, the expression level. The term "biomarker", or "signatory biomarker" refers to a biological molecule, gene, gene product, metabolite, cell type, protein, nucleic acid, or any measurable indicator that reflects a normal or pathological biological process, or a response to a therapeutic intervention. More specifically, a "TAN-score biomarker" refers herein to a biomarker that is used to determine the TAN score, while a "Neu TME-PIC-score biomarker " refers to a biomarker that is used to determine the Neu TME-PIC-score.

[0154] As shown by Example 6, the TAN score biomarkers were selected as follows: to best capture the signature of the TANs (defined here as TAN1 and TAN2), both highly expressed and differentially expressed genes (DEGs), were included and the TAN metacells were compared to MHCII Neut and Ptgs2+ Neut metacells. Using Seurat's FindMarkers method with default parameters [Ref 10], the top 20 genes with the highest LFC and a P-value under 0.05 were selected. Then, for each of the mentioned metacells, the top 15 most highly expressed genes were calculated, and genes that were unique to the TAN metacells were selected. The two lists were merged to create the final TAN score genes. The gene list was manually converted from mouse genes to their human homologues using the MGI website, resulting in a list of the TAN score biomarkers. In some embodiments, the TAN-score biomarkers comprise at least one of CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P.

[0155] The Neu TME-PIC-score biomarkers were selected as follows: for the neutrophil states most abundant in the physical crosstalk (TAN1, TAN2 and Ptgs2+ Neut), the top 20 genes with the highest LFC and a P-value under 0.05 were selected from the PIC-seq gene analysis. Finally, thegene list was filtered to only keep genes with a mean expression in PICs higher than all other populations. The gene list was manually converted from mouse genes to their human homologues using the MGI website, resulting in a list of Neu TME-PIC-score biomarkers. In some embodiments, the Neu TME-PIC-score biomarkers comprise at least one of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

[0156] The expression level of at least one TAN score biomarker and / or at least one Neu TME-PIC-score biomarker is used to obtain the "TAN score” and / or the "Neu TME-PIC score", respectively, as follows: to quantify the expression of each gene signature across single cells and PICs, the Seurat function AddModuleScore (v5.0.1) may be used [Ref 10]. This function computes a module score for each cell by averaging the expression levels of the genes in the input list and subtracting the aggregated expression of control gene sets with matched expression levels. The resulting score reflects the relative enrichment of the gene module in each cell or PIC, normalized against background gene expression. This method may be applied to both the TAN gene list and the PIC-enriched neutrophil gene list, to obtain the "TAN score” and "Neu TME-PIC score” , respectively. The TAN-score and / or the Neu TME-PIC-score of a sample is classified as positive or negative by comparison to a predetermined reference value, such as a standard TAN-score and / or a standard Neu TME-PIC-score or to a quantile threshold of the TAN-score and / or a Neu TME-PIC-score of positive control samples, specifically, TAN-score and / or the Neu TME-PIC-score calculated from one or more samples, specifically, samples of several advance breast cancer subjects. A positive TAN-score and / or Neu TME-PIC score in the tested subject's sample is indicative of an advanced stage of breast cancer and / or is associated with a poor prognosis.

[0157] It should be understood that determination of a "positive" or alternatively "negative" score with respect to a standard score or a quantile threshold (specifically, a value derived from the distribution of a dataset that serves as a cutoff to classify, filter, or normalize data based on its relative position within the dataset) may involve in some embodiments comparison of the TAN-score and / or the Neu TME-PIC-score of the examined sample as obtained in step (a) of the disclosed methods, with the TAN-score and / or the Neu TME-PIC-score obtained for several advanced breast cancer samples and / or , in some embodiments, the TAN-score and / or the Neu TME-PIC-score obtained from at least one control sample, or from any established or predetermined score (e.g., a standard TAN-score and / or a standard Neu TME-PIC-score) obtained from a known control (negative control e.g. healthy controls, or a positive control that are advanced breast cancer samples). As used herein, “healthy controls” or “healthy population” may refer to apopulation of subjects that does not suffer from advanced breast cancer, or to a population before appearance of the disease. In some embodiments, the quantile threshold for expression of the biomarker in a control population refers to a baseline level of the disclosed biomarkers of a healthy population or to a baseline level of the expression of the at least one biomarker before appearance of a disease of interest in a studied population (negative controls), or alternatively, for representative cohort of advanced breast cancer patients. Thus, in some embodiments, "positive” is meant a score that is higher (increased, elevated, overexpressed) than about 5% to 95%, specifically, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, of a sample of advanced breast cancer patients. Such score is thus within the range of the scores of advanced breast cancer patients. Still further, a "negative” score in some embodiments may be a reduced, low, non-existing or lack of expression of a biomarker in about 5% to 100% or more, specifically, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, when compared to the score of a healthy control, any other suitable control or any other predetermined standard. A representative number of control samples (either positive or negative controls) are obtained from 2 to 100 or more subjects as discussed, typically, 10, 20, 30, 40 or 50 subjects. A "positive” score should be in the score range of a control patient diagnosed with advanced breast cancer, or any other cut off value obtained for a population of patients having advanced breast cancer. As used in the present disclosure, a "positive score" (the TAN score and / or the Neu TME-PIC), refers, in some embodiments, to a calculated an / or measured result indicative of over-expression and / or enrichment of one or more biomarkers included in a signatory TAN and / or in Neu TME-PIC biomarkers, including elevated expression of one or more signature genes, relative to an appropriate reference value. In some embodiments, a positive score indicates that the tested sample exhibits an expression profile consistent with an advanced breast cancer as defined by the scoring system, in accordance with the thresholds and comparisons described herein.

[0158] In some embodiments, determination of a positive score is performed by comparing expression of one or more of the signature biomarkers in a tested sample to one or more of the following control samples: (a) a healthy tissue sample from the same subject (for example, an adjacent non-involved tissue, or a prior baseline sample); (b) a healthy tissue sample from a healthy subject; and / or (c) a predetermined control set comprising one or more samples from one or more control subjects, from which a "standard expression value" and / or an associated "reference score" is derived for each biomarker and / or for the combined signature.In some embodiments, the comparison comprises normalization of expression data (for example, relative to housekeeping genes or control reference genes and / or using standard normalization methods), followed by calculation of a composite score according to predefined weights, a defined aggregation function, and / or a scoring algorithm. In some embodiments, the reference score and / or a decision threshold or cutoff is predetermined based on the distribution of expression values in control samples, for example based on a mean, median, standard deviation, a selected percentile, or any combination thereof, such that a value above such threshold is classified as a positive score. In some embodiments, a "positive control" is a sample known or verified to exhibit increased expression of some or all of the signature biomarkers (for example, a sample from patients diagnosed with advanced breast cancer, or a sample previously classified as having a positive score according to clinical and / or research criteria). Conversely, a "negative control" is a sample known or verified not to exhibit such increased expression (for example, healthy tissue, or a sample previously classified as having a negative or neutral score). In some embodiments, a score for the tested sample is classified as positive when it exceeds the score of a negative control and / or a threshold derived therefrom, and optionally when it approximates or exceeds the score of a positive control according to a predetermined criterion.

[0159] For the avoidance of doubt, the terms "control samples" and / or "standard expression value" may refer to one or more samples, from the same subject and / or from different subjects, including samples measured on the same analytical platform or an equivalent platform, subject to any required normalization adjustments. A positive score may refer to over-expression of the TAN signature only, the Neu TME-PIC signature only, or a combination of both.

[0160] In some embodiments of the disclosed diagnostic and prognostic methods, the expression level of biomarker / s in step (a), is determined for at least one of: (i) the TAN-score biomarkers, that comprise: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and / or (ii) for the Neu TME-PIC-score biomarkers, that comprise: ARG1, CHMP4C, CEDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SEFN12E, TESC, UPP1 and VEGF; to obtain a TAN-score of (i), and / or a Neu TME-PIC-score of (ii), for the sample.

[0161] Thus, in some embodiments of the disclosed methods, step (a) involves determining the expression level of the TAN-score biomarkers, that comprise: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, to obtain a TAN-score for the sample.In some further embodiments, step (a) of the disclosed methods involves determining the expression level of the Neu TME-PIC-score biomarkers, that comprise: ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF, to obtain a Neu TME-PIC-score for the sample.

[0162] In yet some further embodiments, step (a) of the disclosed methods involves determining the expression level of the TAN-score biomarkers, that comprise: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, to obtain a TAN-score for the sample, and additionally, of the Neu TME-PIC-score biomarkers, that comprise: ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF, to obtain a Neu TME-PIC-score for the sample.

[0163] "Cystatin A (CSTA)" refers to a cysteine protease inhibitor primarily expressed in epithelial and immune cells, where it regulates proteolytic activity by inhibiting cathepsins and other cysteine proteases. In some embodiments, CSTA, as used herein refers to the human CSTA. In more specific embodiments, the CSTA protein comprises the amino acid sequence as denoted by SEQ ID NO: 4. In yet some further embodiments, the CSTA protein is encoded by the nucleic acid sequence as denoted by SEQ ID NO: 3.

[0164] "Cathelicidin Antimicrobial Peptide (CAMP)" refers to a host-defense peptide synthesized primarily by epithelial cells and immune cells, known for its broad-spectrum antimicrobial activity against bacteria, viruses, and fungi. In addition to its antimicrobial functions, CAMP modulates innate and adaptive immune responses, promotes wound healing, and influences inflammatory processes by interacting with host cell receptors and signaling pathways. In some embodiments, CAMP, as used herein refers to the human CAMP. In more specific embodiments, the CAMP protein comprises the amino acid sequence as denoted by SEQ ID NO: 6. In yet some further embodiments, the CAMP protein is encoded by the nucleic acid sequence as denoted by SEQ ID NO: 5.

[0165] "Cluster of Differentiation 177 ( CD177) " refers to a glycosylphosphatidylinositol (GPI)-anchored surface protein predominantly expressed on neutrophils and involved in cell adhesion, migration, and host defense. CD 177 mediates interactions with endothelial cells through its binding to platelet endothelial cell adhesion molecule- 1 (PECAM-1) and plays a role in neutrophil transmigration and inflammatory responses.

[0166] In some embodiments, CD 177, as used herein refers to the human CD 177. In more specific embodiments, the CD 177 protein comprises the amino acid sequence as denoted by SEQ ID NO:8. In yet some further embodiments, the CD 177 protein is encoded by the nucleic acid sequence as denoted by SEQ ID NO: 7.

[0167] " Glycogenin 1 (GYGl)" refers to a self-glucosylating enzyme that serves as the core protein for glycogen synthesis, initiating the formation of the glycogen molecule by catalyzing the attachment of glucose residues to itself. Predominantly expressed in skeletal and cardiac muscle, GYGl plays a critical role in energy storage and metabolic regulation. In some embodiments, GYGl, as used herein refers to the human GYGl. In more specific embodiments, the GYGl protein comprises the amino acid sequence as denoted by SEQ ID NO: 10. In yet some further embodiments, the GYGl protein is encoded by the nucleic acid sequence as denoted by SEQ ID NO: 9.

[0168] "Interferon-Induced Transmembrane Protein 1 (1F1TM1)" refers to a transmembrane protein upregulated in response to interferon signaling, playing a crucial role in the innate immune response by inhibiting the entry and replication of viruses within host cells. Beyond its antiviral activity, IFITM1 is implicated in cellular processes such as proliferation, migration, and adhesion, with aberrant expression linked to cancer progression and metastasis. In some embodiments, IFITM1, as used herein refers to the human IFITM1. In more specific embodiments, the IFITM1 protein comprises the amino acid sequence as denoted by SEQ ID NO: 12. In yet some further embodiments, the IFITM1 protein is encoded by the nucleic acid sequence as denoted by SEQ ID NO: 11.

[0169] "Lipocalin 2 (LCN2)", also known as neutrophil gelatinase-associated lipocalin (NGAL), refers to a smallsecreted protein involved in innate immunity, iron homeostasis, and cellular response to stress. It functions by binding siderophores to sequester iron, limiting bacterial growth, and modulating processes such as inflammation, apoptosis, and epithelial repair. In some embodiments, LCN2, as used herein refers to the human LCN2. In more specific embodiments, the LCN2 protein comprises the amino acid sequence as denoted by SEQ ID NO: 14. In yet some further embodiments, the LCN2 protein is encoded by the nucleic acid sequence as denoted by SEQ ID NO: 13.

[0170] "Prokineticin 2 (PROK2)" refers to a secreted peptide that acts as a ligand for prokineticin receptors, regulating diverse physiological processes such as circadian rhythm, angiogenesis, inflammation, and gastrointestinal motility. It exerts its effects by activating G-protein-coupled receptor signaling pathways, influencing cell migration, survival, and vascular remodeling. In some embodiments, PROK2, as used herein refers to the human PROK2. In more specific embodiments, the PROK2 protein comprises the amino acid sequence as denoted by SEQ ID NO:16. In yet some further embodiments, the PROK2 protein is encoded by the nucleic acid sequence as denoted by SEQ ID NO: 15.

[0171] " Resistin-Like Beta (RETNLB)", also known as RELM-P, refers to a secreted protein predominantly expressed in the gastrointestinal tract, where it plays a role in metabolic regulation, inflammation, and immune responses. It is implicated in modulating glucose metabolism, insulin resistance, and inflammatory pathways. In some embodiments, RETNLB, as used herein refers to the human RETNLB. In more specific embodiments, the RETNLB protein comprises the amino acid sequence as denoted by SEQ ID NO: 18. In yet some further embodiments, the RETNLB protein is encoded by the nucleic acid sequence as denoted by SEQ ID NO: 17.

[0172] "S100 Calcium-Binding Protein A6 (S100A6)", also known as calcyclin, refers to a member of the S100 family of calcium-binding proteins involved in cytoskeletal dynamics, cell proliferation, and apoptosis. In some embodiments, S100A6, as used herein refers to the human S100A6. In more specific embodiments, the S100A6 protein comprises the amino acid sequence as denoted by SEQ ID NO: 20. In yet some further embodiments, the S100A6 protein is encoded by the nucleic acid sequence as denoted by SEQ ID NO: 19.

[0173] "S100 Calcium-Binding Protein A8 (S100A8)", also called calprotectin or MRP8, refers to a pro-inflammatory protein expressed primarily by myeloid cells and involved in immune response, inflammation, and tissue remodeling. It forms heterodimers with S100A9. In some embodiments, S100A8, as used herein refers to the human S100A8. In more specific embodiments, the S100A8 protein comprises the amino acid sequence as denoted by SEQ ID NO: 22. In yet some further embodiments, the S100A8 protein is encoded by the nucleic acid sequence as denoted by SEQ ID NO: 21.

[0174] "Uridine Phosphorylase 1 (UPP1)" refers to an enzyme that catalyzes the reversible phosphorolysis of uridine into uracil and ribose- 1 -phosphate, playing a critical role in nucleotide metabolism and salvage pathways. In some embodiments, UPP1, as used herein refers to the human UPP1. In more specific embodiments, the UPP1 protein comprises the amino acid sequence as denoted by SEQ ID NO: 24. In yet some further embodiments, the UPP1 protein is encoded by the nucleic acid sequence as denoted by SEQ ID NO: 23.

[0175] "WAP Four-Disulfide Core Domain 21, Pseudogene (WFDC21P)" refers to a non-coding pseudogene belonging to the WAP four-disulfide core family, implicated in the regulation of immune responses and protease inhibition through its conserved sequence motifs. Although not protein-coding, WFDC21P is associated with regulatory functions in gene expression and is of interest in the study of immunity and cancer-related processes. In some embodiments, WFDC21P,as used herein refers to the human WFDC21P. In yet some further embodiments, the WFDC21P non-coding pseudogene comprises the nucleic acid sequence as denoted by SEQ ID NO: 25. "Arginase 1 (ARG1)" refers to a cytosolic enzyme that catalyzes the hydrolysis of arginine into ornithine and urea, playing a central role in the urea cycle and nitrogen metabolism. In the tumor microenvironment, ARG1 is expressed by myeloid-derived suppressor cells (MDSCs) and macrophages, where it suppresses T-cell responses by depleting arginine. In some embodiments, ARG1, as used herein refers to the human ARG1. In more specific embodiments, the ARG1 protein comprises the amino acid sequence as denoted by SEQ ID NO: 28. In yet some further embodiments, the ARG1 protein is encoded by the nucleic acid sequence as denoted by SEQ ID NO: 27.

[0176] "Charged Multivesicular Body Protein 4C (CHMP4C)" is a component of the ESCRT-III complex, which mediates membrane remodeling events such as multivesicular body formation, cytokinetic abscission, and viral budding. In some embodiments, CHMP4C, as used herein, refers to the human CHMP4C. In more specific embodiments, the CHMP4C protein comprises the amino acid sequence as denoted by SEQ ID NO: 30. In yet some further embodiments, the CHMP4C protein is encoded by the nucleic acid sequence as denoted by SEQ ID NO: 29.

[0177] "Claudin 4 (CLDN4)" refers to a tight junction protein involved in maintaining epithelial barrier integrity and regulating paracellular permeability. In some embodiments, CLDN4, as used herein refers to the human CLDN4. In more specific embodiments, the CLDN4 protein comprises the amino acid sequence as denoted by SEQ ID NO: 32. In yet some further embodiments, the CLDN4 protein is encoded by the nucleic acid sequence as denoted by SEQ ID NO: 31.

[0178] "Chromosome 16 Open Reading Frame 91 (C16ORF91)" is a poorly characterized proteincoding gene with potential roles in cellular homeostasis and oncogenic processes. In some embodiments, C16ORF91, as used herein refers to the human C16ORF91. In more specific embodiments, the C16ORF91 protein comprises the amino acid sequence as denoted by SEQ ID NO: 34. In yet some further embodiments, the C16ORF91 protein is encoded by the nucleic acid sequence as denoted by SEQ ID NO: 33.

[0179] "EPH Receptor A2 (EPHA2) " refers to a member of the Eph family of receptor tyrosine kinases that regulate cell adhesion, migration, and angiogenesis. In some embodiments, EPHA2, as used herein refers to human EPHA2. In more specific embodiments, the EPHA2 protein comprises the amino acid sequence as denoted by SEQ ID NO: 36. In yet some further embodiments, the EPHA2 protein is encoded by the nucleic acid sequence as denoted by SEQ ID NO: 35."Interleukin 18 Receptor Accessory Protein (IL18RAP)" refers to a co-receptor for interleukin-18 (IL- 18), facilitating IL- 18 -mediated pro-inflammatory and immune-activating signaling pathways. IL18RAP plays a critical role in innate and adaptive immunity and has been implicated in autoimmune diseases, cancer, and infectious diseases. In some embodiments, IL18RAP, as used herein refers to the human IL18RAP. In more specific embodiments, the IL18RAP protein comprises the amino acid sequence as denoted by SEQ ID NO: 38. In yet some further embodiments, the IL18RAP protein is encoded by the nucleic acid sequence as denoted by SEQ ID NO: 37.

[0180] "Prolactin-Induced Protein (PIP)" refers to a secreted glycoprotein expressed predominantly in exocrine tissues, where it is involved in immune modulation, antimicrobial defense, and regulation of hormonal signaling. In some embodiments, PIP, as used herein refers to the human PIP. In more specific embodiments, the PIP protein comprises the amino acid sequence as denoted by SEQ ID NO: 40. In yet some further embodiments, the PIP protein is encoded by the nucleic acid sequence as denoted by SEQ ID NO: 39.

[0181] "Radical S-Adenosyl Methionine Domain-Containing 2 (RSAD2)", also known as viperin, is an interferon-inducible protein that plays a critical role in antiviral defense by inhibiting viral replication and disrupting lipid metabolism. RSAD2 is also implicated in modulating immune responses and metabolic pathways. In some embodiments, RSAD2, as used herein refers to the human RSAD2. In more specific embodiments, the RSAD2 protein comprises the amino acid sequence as denoted by SEQ ID NO: 42. In yet some further embodiments, the RSAD2 protein is encoded by the nucleic acid sequence as denoted by SEQ ID NO: 41.

[0182] "Secreted and Transmembrane 1 (SECTM1)" refers to a type I transmembrane and secreted glycoprotein predominantly expressed in immune-related tissues, such as the thymus, and by activated monocytes and macrophages. It plays a critical role in modulating immune responses by interacting with CD7, a receptor on T cells and natural killer (NK) cells, thereby promoting their survival, activation, and cytokine production. In some embodiments, SECTM1, as used herein refers to the human SECTM1. In more specific embodiments, the SECTM1 protein comprises the amino acid sequence as denoted by SEQ ID NO: 44. In yet some further embodiments, the SECTM1 protein is encoded by the nucleic acid sequence as denoted by SEQ ID NO: 43.

[0183] "Schlafen Family Member 12-Like (SLFN12L)" refers to a member of the Schlafen protein family, which regulates cell growth, differentiation, and immune responses.

[0184] In some embodiments, SLFN12L, as used herein refers to the human SLFN12L. In more specific embodiments, the SLFN12L protein comprises the amino acid sequence as denoted by SEQ IDNO: 46. In yet some further embodiments, the SLFN12L protein is encoded by the nucleic acid sequence as denoted by SEQ ID NO: 45.

[0185] "Tescalcin (TESC)" is a calcium-binding protein involved in regulating intracellular signaling, pH homeostasis, and cell differentiation. In some embodiments, TESC, as used herein refers to the human TESC. In more specific embodiments, the TESC protein comprises the amino acid sequence as denoted by SEQ ID NO: 48. In yet some further embodiments, the TESC protein is encoded by the nucleic acid sequence as denoted by SEQ ID NO: 47.

[0186] "Vascular Endothelial Growth Factor (VEGF)" is a secreted glycoprotein that promotes angiogenesis by stimulating endothelial cell proliferation, migration, and survival. Overexpression of VEGF is a hallmark of cancer and other angiogenic diseases, and its inhibition is a central strategy in anti-angiogenic therapies. In some embodiments, VEGF, as used herein refers to the human VEGF. In more specific embodiments, the VEGF protein comprises the amino acid sequence as denoted by SEQ ID NO: 26. In yet some further embodiments, the VEGF protein is encoded by the nucleic acid sequence as denoted by SEQ ID NO: 49.

[0187] As indicated above, the disclosed methods may be applicable for the diagnosis, prognosis and detection of breast cancer. Breast cancer is commonly described using three interconnected classification systems: staging, grading, and type (histologic and molecular subtype). Staging explains how far the disease has spread in the body and is one of the strongest predictors of prognosis and treatment planning. Grading describes how abnormal and aggressive the cancer cells look under the microscope and reflects the biological behavior of the tumor. “Type” refers both to the tissue pattern seen on pathology (histology) and to biomarker-defined subtypes that predict response to targeted therapies. Staging is most often assigned using the TNM system, which evaluates the primary tumor (T), regional lymph node involvement (N), and distant metastasis (M). The “T” category reflects the size of the breast tumor and whether it has extended into the skin or chest wall; carcinoma in situ is categorized separately as Tis. The “N” category captures whether cancer is present in regional nodes, most commonly axillary lymph nodes, with higher N categories indicating more extensive nodal involvement or involvement of particular nodal basins. The “M” category indicates whether there is distant spread to organs such as bone, liver, lung, or brain; Ml signifies metastatic disease. These factors are then grouped into overall stage groupings from Stage 0 to Stage IV, where Stage 0 represents in situ disease such as ductal carcinoma in situ (DCIS), Stages I and II generally represent earlier invasive disease with absent or limited nodal involvement, Stage III represents locally advanced disease with more extensive nodal disease and / or direct extension to skin or chest wall, and Stage IV represents metastatic disease with distantspread. In some embodiments, the method of the present disclosure may be applicable for prognosing and / or detecting advanced breast cancer in a mammalian subject. "Advanced breast cancer" or "ABC" encompasses malignancies that have progressed beyond early localized stages and are classified as either locally advanced breast cancer (LABC) or metastatic breast cancer (MBC). LABC corresponds to stage III disease, characterized by extensive regional involvement, including large primary tumors (>5 cm), direct extension to the chest wall or skin, or significant regional lymph node involvement. MBC corresponds to stage IV disease, where cancer has metastasized to distant organs such as the bones, lungs, liver, or brain. Both subtypes represent clinical scenarios requiring systemic and often multimodal therapeutic approaches.

[0188] Stage III breast cancer (LABC) is further subdivided in some embodiments, into stages IIIA, IIIB, and IIIC, reflecting varying degrees of local and regional spread. Stage IIIA involves extensive regional lymph node involvement, with or without a large tumor, while stage IIIB is defined by tumor invasion into the chest wall or skin, including ulceration or satellite nodules. Stage IIIC represents the most advanced form of LABC, with cancer extending to supraclavicular, infraclavicular, or internal mammary lymph nodes. Despite being confined to regional structures, stage III disease poses significant risk for systemic dissemination.

[0189] Stage IV breast cancer (MBC) denotes the presence of distant metastases, where tumor cells have established secondary growths in organs remote from the primary site. The most common metastatic sites include the skeleton, lungs, liver, and / or the central nervous system. MBC is considered incurable with current therapeutic modalities, and treatment focuses on disease control, symptom management, and quality of life improvement. The molecular and genetic heterogeneity of stage IV disease, combined with its dynamic evolution under treatment pressure, complicates management strategies.

[0190] As opposed to stage IV, "early-stage breast cancer" is characterized by malignancies that remain localized to the breast or exhibit limited regional lymph node involvement. Early-stage breast cancer corresponds to stages I and II of the disease. These stages are associated with a high likelihood of curative outcomes when treated appropriately, typically through a combination of surgical intervention, radiation therapy, and systemic treatments where indicated. Classification into stage I or II is determined by tumor size, the extent of local invasion, and the degree of regional lymph node involvement, as defined by the TNM (Tumor, Node, Metastasis) staging system. Stage I breast cancer represents the earliest form of invasive disease, where malignant cells have breached the ducts or lobules of the breast tissue but remain confined to the local environment. Stage I is further divided into two categories. In stage IA, the tumor is 2 cm or smaller in diameter,with no evidence of regional lymph node involvement. In stage IB, small clusters of cancer cells measuring 0.2 mm to 2 mm are detected in the axillary lymph nodes, with either no tumor in the breast or a tumor measuring 2 cm or less. Patients diagnosed at this stage typically have an excellent prognosis, and treatment often includes breast-conserving surgery or mastectomy, supplemented by radiation therapy and, when necessary, adjuvant systemic therapies. Stage II breast cancer encompasses larger tumors or those with more significant regional lymph node involvement but without evidence of distant metastasis. In stage IIA, the tumor is larger than 2 cm but no more than 5 cm, with no regional lymph node involvement, or it is 2 cm or smaller but involves 1 to 3 axillary or internal mammary lymph nodes. Stage IIB includes tumors larger than 2 cm but no more than 5 cm, with regional lymph node involvement, or tumors larger than 5 cm without nodal spread.

[0191] In some embodiments advanced breast cancer, determined by the disclosed diagnostic methods, is classified as stages III and IV (i.e. advance stage of breast cancer). In yet some further embodiments, early breast cancer is classified as belonging to stages I and II. Still further, in some embodiments, the disclosed methods may be applicable for determining the prognosis of a subject, for example, poor or bad prognosis, to evaluate the survival of the subject and / or to determine and evaluate the disease-free period of the subject.

[0192] It should be understood that in practice, many systems also recognize a prognostic staging approach that incorporates biomarkers such as estrogen receptor (ER), progesterone receptor (PR), HER2 status, and tumor grade, because tumors with the same anatomic extent can behave differently and respond to different therapies depending on these biological features.

[0193] Grading refers to how closely the cancer resembles normal breast tissue and how actively it is dividing. For most invasive breast cancers, grading is reported using the Nottingham (Elston-Ellis) system, which scores tubule formation, nuclear pleomorphism, and mitotic rate. Each component is scored and combined into an overall grade, typically reported as Grade 1 (well differentiated), Grade 2 (moderately differentiated), or Grade 3 (poorly differentiated). Lower-grade tumors tend to grow more slowly and have a more favorable prognosis than higher-grade tumors, although grade is interpreted together with stage and biomarker status. For in situ lesions such as DCIS, grading is often expressed as low, intermediate, or high nuclear grade and may also note the presence of necrosis, which can correlate with recurrence risk and treatment decisions.

[0194] Still further, breast cancer “types” are described first at the histologic level as non-invasive (in situ) or invasive, and then further by specific patterns. Non-invasive disease includes DCIS, which is confined to the ducts and has not invaded surrounding tissue, and lobular carcinoma in situ(LCIS), which is often treated as a marker of increased risk and managed differently from DCIS. Paget disease of the nipple is a distinct presentation that is frequently associated with underlying DCIS or invasive carcinoma. Invasive cancers include invasive ductal carcinoma, now often termed invasive carcinoma of no special type (NST), which is the most common form, and invasive lobular carcinoma, which can be more frequently multifocal or bilateral and may be present differently on imaging. There are also less common “special types” with distinct clinical behavior, such as tubular carcinoma and mucinous carcinoma, which often have a more favorable prognosis, and metaplastic carcinoma, which is often more aggressive.

[0195] In addition to histology, breast cancers are classified by molecular or receptor subtype, which is central to current treatment decisions. Tumors are commonly described by ER and PR hormone receptor status and by HER2 status, determined by immunohistochemistry and, when needed, in situ hybridization. The major clinical subtypes are hormone receptor-positive / HER2-negative disease, HER2 -positive disease (regardless of hormone receptor status), and triple-negative breast cancer, which lacks ER, PR, and HER2 expression and often overlaps with “basal-like” gene expression profiles. These subtypes help predict response to endocrine therapy, HER2-targeted therapy, and chemotherapy, and they influence recurrence patterns and survival.

[0196] Pathology reports typically integrate these concepts by stating tumor size and local extension (T), nodal status (N), and whether metastatic disease is present (M), alongside the histologic type, tumor grade, and key biomarkers such as ER, PR, HER2, and sometimes Ki-67 as a proliferation indicator. Other features like lymphovascular invasion and margin status after surgery can further refine risk assessment and guide recommendations for local and systemic therapy.

[0197] It should be appreciated that the disclosed methods may be applicable to advanced breast cancer classified at any known, staging, typing, and / or grading systems, for example, any of the systems as discussed above.

[0198] For prognosing and / or detecting advanced breast cancer in a mammalian subject, the level of the at least one biomarker is determined. In some specific embodiments, the expression level of the discussed at least one biomarker is determined at stage (a) of the disclosed methods. It should be understood that the "level' s well as the "expression level" of the biomarker, as used herein, further encompasses the expression level that reflects the level of the transcription, as well as the levels of translation into a protein product. In some embodiments the level of expression as used herein further reflects the stability of the RNA transcript and / or the stability of the protein product. More specifically, the terms “level of expression” or “expression level” are used interchangeably and generally refer to a numerical representation of the amount (quantity) of nucleic acid product oran amino acid product or polypeptide or protein of the at least one biomarker in a biological sample. In yet some further embodiments, the “ZeveZ of expression” or “ expression level” refers to the numerical representation of the amount (quantity) of polynucleotide which may be gene in a biological sample. “Expression” generally refers to the process by which gene-encoded information is converted into the structures present and operating in the cell. For example, the expression may be measured in the nucleic acid level, for example using RNA sequencing, Real-Time Polymerase Chain Reaction, sometimes also referred to as RT-PCR or quantitative PCR (qPCR). The luminosity in case of RT-PCR, or any other tag is captured by a detector that converts the signal intensity into a numerical representation which is the expression value, in terms of biomarker or a gene. Therefore, according to the present disclosure, “expression” of a gene, specifically, any gene encoding any of the biomarkers of the disclosed methods may refer to transcription into a polynucleotide and translation into a polypeptide. Fragments of the transcribed polynucleotide, the translated protein, or the post-translationally modified protein shall also be regarded as expressed whether they originate from a transcript generated by alternative splicing or a degraded transcript, or from a post- translational processing of the protein, e.g., by proteolysis. Methods for determining the level of expression of the biomarkers of the present disclosure will be described in more detail herein after.

[0199] The expression level of the biomarkers, that may be biomarker proteins / genes (expression either at the nucleic acid, specifically, mRNA level or the protein level) of the disclosure is determined to obtain an expression value required for determining the indicted scores, specifically, the TAN score and / or the Neu TME-PIC score as disclosed above. The term "expression value” refers to the result of a calculation, that uses as an input the “level of expression” or "expression level” obtained experimentally.

[0200] Still further, in some embodiments, the expression level of the disclosed biomarker / s is determined at the RNA and / or at the protein level. Specifically, by determining the RNA amount, that as indicated herein reflects the level of transcription, and / or the stability of the transcript. Alternatively, or additionally, when determined at the protein level, the amount of the biomarker protein reflects the level of transcription, the level of translation and the stability of both, the RNA and / or the protein product.

[0201] In some embodiments, the expression level of the biomarkers of the disclosed diagnostic methods is determined by sequencing and / or by using at least one detecting molecule, each detecting molecule is specific for one of the disclosed biomarker / s.In some embodiments, the sequencing as used in the disclosed methods, comprises RNA sequencing.

[0202] In some embodiments, the RNA sequencing comprises sequencing of the total RNA, specifically, sequencing of the mRNA of the cell. In some other embodiments, the RNA sequencing as used in the disclosed methods comprises single cell RNA sequencing (scRNA-seq) and / or physically interacting cell sequencing (PIC-seq).

[0203] "RNA sequencing" or "RNA-seq" refers herein to a next-generation sequencing (NGS) technology used to analyze the complete transcriptome of a biological sample or a portion thereof (e.g. specific cell population within the biological sample) by converting RNA into complementary DNA (cDNA) and sequencing it. This technique provides comprehensive insights into RNA molecules, enabling the identification of coding and non-coding RNAs, quantification of gene expression levels, detection of alternative splicing events, and exploration of post-transcriptional modifications. The method can be applied at the bulk level for entire cell populations or tailored for higher resolution, such as single-cell analysis or specific subpopulations, to study biological complexity and heterogeneity in greater detail. RNA-seq encompasses a range of approaches, including bulk RNA-seq, single-cell RNA-seq, and PIC-seq, each tailored to specific research needs. Bulk RNA-seq captures average gene expression across entire cell populations, offering a global view of transcriptomic changes^ In contrast, as exemplified in the present disclosure, scRNA-seq and PIC-seq allow to dissect cellular and intercellular transcriptomic landscapes at a much finer scale, enabling a more detailed understanding of complex multicellular systems. More specifically, "single-cell RNA sequencing (scRNA-seq)” is a specialized RNA-seq approach designed to capture the transcriptome of individual cells, offering unprecedented resolution of cellular heterogeneity. It enables the profiling of cell-specific gene expression, allowing the identification of rare cell types, trace developmental lineages, and study dynamic cellstate changes in complex tissues or systems. This technology has been transformative in characterizing intricate environments such as the tumor microenvironment, where diverse cell populations interact to influence disease progression. The basic approach is to convert the messenger RNA (mRNA) contained in thousands to millions of single cells into barcoded complementary DNA (cDNA) molecules so that sequencing reads can be assigned back to their cell of origin. Most modern workflows also attach a unique molecular identifier (UMI) to each captured RNA molecule (or cDNA molecule) to help distinguish true biological molecules from PCR duplicates, improving quantitative accuracy. In practice, scRNA-seq is implemented through either droplet-based platforms (high throughput, typically 3’ or 5’ end counting) or plate-basedapproaches (lower throughput but often higher coverage per cell, sometimes enabling near full-length transcript profiling). A typical scRNA-seq experiment begins with sample acquisition and dissociation. Tissue is processed to generate a viable single-cell suspension (or, in some protocols, isolated nuclei for single-nucleus RNA-seq when intact cell dissociation is difficult). At this stage, enrichment or sorting (for example by FACS) are often performed to collect specific populations, remove debris and doublets, and ensure adequate viability. The goal is to obtain intact cells (or nuclei) with minimal stress-induced transcriptional artifacts and minimal contamination from ambient RNA. Sequencing is performed on a high-throughput platform, generating reads that encode (i) the cell barcode, (ii) the UMI, and (iii) the cDNA sequence corresponding to a gene. The computational pipeline then demultiplexes and aligns reads to a reference genome or transcriptome, collapses reads with the same cell barcode and UMI to count unique molecules, and produces a gene-by-cell “count matrix” that represents expression levels across cells. Downstream analysis typically includes quality control and filtering (for example removing low-quality cells with few detected genes, cells with high mitochondrial RNA proportions suggesting damage, and likely doublets), normalization and correction for technical effects, dimensionality reduction (such as PCA and UMAP / t-SNE), clustering to identify groups of similar expression profiles, and annotation of clusters into cell types or states using known marker genes. Further analyses may include differential expression between conditions, trajectory or pseudotime inference to model differentiation processes, and cell-cell communication analyses (for example ligand-receptor inference) to identify potential signaling interactions across cell populations.

[0204] "Physically interacting cell sequencing (PIC-seq)" builds on RNA-seq to study the transcriptomes of two or more cells that are physically engaged in direct interactions. This method isolates physically interacting cells, allowing the simultaneous sequencing of their individual transcriptomes to reveal both their independent molecular profiles and the pathways mediating their interaction. PIC-seq is particularly valuable for studying cell-cell communication in contexts such as immune response, tissue remodeling, and tumor-immune dynamics, offering insights into how specific interactions influence broader biological processes. By bridging cellular transcriptomes and intercellular communication, PIC-seq provides a unique perspective on the functional outcomes of physical cell interactions. In a typical PIC-seq workflow, the experiment begins with tissue processing that preserves cell-cell contacts as much as possible while still generating a suspension that contains both single cells and intact cell pairs. This often involves gentler dissociation conditions (relative to standard scRNA-seq) to avoid disrupting conjugates, and careful handling to limit artificial aggregation. The suspension is then stained with markersthat distinguish the relevant compartments (for example, an immune marker such as CD45 and an epithelial marker such as EpCAM) so that physically interacting heterotypic pairs can be identified by flow cytometry. The next step is enrichment of physically interacting cells by sorting. FACS is used to gate for events consistent with cell pairs (often by forward and side scatter characteristics and by double-positive staining patterns, such as CD45+EpCAM+ events when the biological expectation is that immune cells are CD45+ and epithelial cells are EpCAM+). Importantly, PIC-seq typically includes parallel sequencing of “background singlets,” meaning separately sorted single immune cells and single epithelial (or other) cells from the same samples, because these singlet profiles are later used as references for identifying partners and estimating what gene expression would look like in the absence of interaction-induced changes. After sorting, the enriched PIC fraction is processed through an scRNA-seq library preparation workflow (commonly droplet-based). Each PIC, although it contains two cells, is captured within a single droplet or well and receives a single cell barcode, so its sequencing output is one combined transcriptome representing the sum of both interacting cells’ RNAs. The singlet fractions are processed in parallel, producing standard single-cell expression profiles for each relevant cell type / state. Computational analysis is a defining component of PIC-seq. First, quality control is applied to remove likely technical artifacts. This includes filtering out events that look like singlets mislabeled as doublets, as well as excluding multiplets or low-quality captures. Next, the PIC-seq pipeline uses the singlet reference atlas to infer, for each PIC, the most likely identity of each partner (for example, which immune subtype and which epithelial / tumor subtype best explain the mixed expression). This is commonly done by comparing the PIC expression profile to expected mixtures of candidate singlet profiles and selecting the best-matching pair, thereby “annotating” each PIC as a specific interaction (for example, neutrophil-tumor cell, macrophage-ductal epithelial, etc.). A further key step is distinguishing baseline “additive” mixing from interaction-induced transcriptional programs. Because a PIC’s transcriptome is expected to be approximately the sum of two singlet transcriptomes, the analysis typically generates simulated or expected PIC profiles by computationally combining the relevant singlet backgrounds. The observed PIC expression is then compared to the simulated expectation, and genes that are consistently higher or lower than expected are interpreted as candidate interaction- or contact-induced gene programs. These can be evaluated globally (all PICs of a given interaction type) or stratified by partner subtypes (for example, tumor cell state interacting with a particular neutrophil state). Finally, results are commonly integrated with orthogonal validation approaches. The presence of physical contacts are often validated by microscopy (confocal imaging, ImageStream) and confirm keyinteraction-associated markers at the protein level by flow cytometry or immunofluorescence. Functional assays may then be used to test whether contact (as opposed to secreted factors alone) drives the phenotypes suggested by PIC-seq, for example by comparing direct co-culture with transwell-separated co-culture where cell-cell contact is prevented.

[0205] In some alternative or additional embodiments, the diagnostic methods of the present disclosure use at least one detecting molecule for determining the expression levels of the at least one biomarker / s disclosed herein. In some embodiments, the detecting molecules may be selected from amino acid-based detecting molecules and / or nucleic acid-based detecting molecules.

[0206] Accordingly, in some embodiments, for determining the expression level of the specified biomarkers the sample or any nucleic acid molecules or proteins thereof is contacted with at least one specific detecting molecules for each of the biomarkers. The term “ contacting” means to bring, put, incubates or mix together. As such, a first item is contacted with a second item when the two items are brought or put together, e.g., by touching them to each other or combining them. In the context of the present disclosure, the term "contacting" includes all measures or steps which allow interaction between the at least one of the detection molecules of at least one of the biomarkers, and optionally, for at least one suitable control reference mRNA / protein of the tested sample. The contacting is performed in a manner so that the at least one detecting molecule specific for at least one of the biomarkers for example (each specific for one biomarker), can interact with or bind to the at least one of the biomarkers, in the tested sample. The binding will preferably be non-covalent, reversible binding, e.g., binding via salt bridges, hydrogen bonds, hydrophobic interactions or a combination thereof.

[0207] In yet some further embodiments, the detecting molecules may be appropriate for determining the level or the expression level of the specific biomarker either at the nucleic acid level or at the protein level, as discussed above. More specifically, the term "detecting molecule" refers to a specific biomolecule or chemical entity used to identify, bind, or interact with a target molecule of interest in biological, chemical, or diagnostic assays. As indicated above, where detecting molecules are used to determine the expression level of at least one of the specified biomarkers, and each detecting molecule is specific to a single biomarker, the plurality of detecting molecules that may be provided and used in accordance with the present disclosure includes at least one detecting molecule corresponding to each biomarker assessed. Accordingly, when the expression level of only one biomarker is determined, at least one detecting molecule is used; and when the expression levels of all 24 biomarkers are determined, the present disclosure uses and provides at least 24 detecting molecules.In some embodiments, detecting molecules may be provided as a mixture, as a composition or as a kit, either soluble or attached or immobilized to a solid support. Thus, in some embodiments, the at least one detecting molecule may be provided as a mixture of detecting molecules, wherein each detecting molecule is specific for one biomarker. It should be appreciated however, that for each biomarker, one or several specific detecting molecules may be used and provided. In yet some further alternative embodiments, the detecting molecules may be provided separately for each biomarker, e.g., in specific tube, containers, slots, spots, wells, and the like. In further alternative embodiments, the detecting molecules may be attached or immobilized to a solid support, specifically, in recorded location.

[0208] In some embodiments, the disclosed diagnostic methods may use at least one nucleic acid-based detecting molecule for determining the level of expression of the biomarkers. More specifically, such nucleic acid -based detecting molecule / s may comprise at least one of: (a), at least one oligonucleotide, each oligonucleotide specifically hybridizes to a nucleic acid sequence of one of the at least one biomarker. Such oligonucleotides may be in some embodiments, primers and / or probes. Alternatively, or additionally, (b), the detecting molecules may be at least one nucleic acid aptamer / s, each aptamer is specific for one of the at least one biomarker / s.

[0209] As used herein, "nucleic acid molecules" or “nucleic acid sequence” are interchangeable with the term "polynucleotide(s)" and it generally refers to any polyribonucleotide or polydeoxyribonucleotide, which may be unmodified RNA or DNA or modified RNA or DNA or any combination thereof. "Nucleic acids" include, without limitation, single- and double- stranded nucleic acids. As used herein, the term "nucleic acid(s)" also includes DNAs or RNAs as described above that contain one or more modified bases. Thus, DNAs or RNAs with backbones modified for stability or for other reasons are "nucleic acids". The term "nucleic acid / s" as it is used herein embraces such chemically, enzymatically or metabolically modified forms of nucleic acids, as well as the chemical forms of DNA and RNA characteristic of viruses and cells, including for example, simple and complex cells. A "nucleic acid" or "nucleic acid sequence" may also include regions of single- or double- stranded RNA or DNA or any combinations.

[0210] More specifically, in some other embodiments, the nucleic acid detecting molecules may comprise at least one isolated oligonucleotide / s, each oligonucleotide specifically hybridizes to a nucleic acid sequence encoding one of said at least one biomarkers. In an optional embodiment, where the expression levels of the biomarkers of the disclosure are normalized, the method of the disclosure may use nucleic acid detecting molecules specific for a nucleic acid sequence encoding the control reference protein / s.As used herein, the term "oligonucleotide” is defined as a molecule comprised of two or more deoxyribonucleotides and / or ribonucleotides, and preferably more than three. Its exact size will depend upon many factors which in turn, depend upon the ultimate function and use of the oligonucleotide. The oligonucleotides may be from about 3 to about 1,000 nucleotides long. Although oligonucleotides of 5 to 100 nucleotides are useful in the disclosure, preferred oligonucleotides range from about 5 to about 15 bases in length, from about 5 to about 20 bases in length, from about 5 to about 25 bases in length, from about 5 to about 30 bases in length, from about 5 to about 40 bases in length or from about 5 to about 50 bases in length. More specifically, the detecting oligonucleotides molecule used by the composition, methods and kits of the present disclosure may comprise any one of 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50 bases in length. It should be further noted that the term “oligonucleotide” refers to a single stranded or double stranded oligomer or polymer of ribonucleic acid (RNA) or deoxyribonucleic acid (DNA) or mimetics thereof. This term includes oligonucleotides composed of naturally-occurring bases, sugars and covalent internucleoside linkages (e.g., backbone) as well as oligonucleotides having non-naturally-occurring portions which function similarly.

[0211] In some embodiments, nucleic acid based detecting molecules may involve the step of hybridization of the detecting molecule / s (e.g. the prob and / or primer specific for each of the biomarkers of the present disclosure) with nucleic acids (e.g. mRNA) of the examined sample. The term "hybridize” or "Hybridization" , as used herein is the process in which two complementary single- stranded DNA and / or RNA molecules bond together to form a doublestranded molecule. The bonding is dependent on the appropriate base-pairing across the two single- stranded molecules.

[0212] In yet some other alternative embodiments, the detection molecule may be at least one primer, at least one pair of primers, nucleotide probes and any combinations thereof. Thus, it should be further appreciated that the methods, as well as the compositions and kits of the disclosure may comprise, as an oligonucleotide-based detection molecule, both primers and probes.

[0213] The term, "primer", as used herein refers to an oligonucleotide, whether occurring naturally as in a purified restriction digest, or produced synthetically, which is capable of acting as a point of initiation of synthesis when placed under conditions in which synthesis of a primer extension product, which is complementary to a nucleic acid strand, is induced, i.e., in the presence of nucleotides and an inducing agent such as a DNA polymerase and at a suitable temperature and pH. The primer may be single- stranded or double- stranded and must be sufficiently long to primethe synthesis of the desired extension product in the presence of the inducing agent. The exact length of the primer will depend upon many factors, including temperature, source of primer and the method used. For example, for diagnostic applications, depending on the complexity of the target sequence, the oligonucleotide primer typically contains 10-30 or more nucleotides, although it may contain fewer nucleotides. More specifically, the primer used by the methods, as well as the compositions and kits of the disclosure may comprise 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 or 30 nucleotides or more. In certain embodiments, such primers may comprise 30, 40, 50, 60, 70, 80, 90, 100 nucleotides or more. In specific embodiments, the primers used by the method of the disclosure may have a stem and loop structure. The factors involved in determining the appropriate length of primer are known to one of ordinary skill in the art and information regarding them is readily available.

[0214] As used herein, the term "probe” means oligonucleotides and analogs thereof and refers to a range of chemical species that recognize polynucleotide target sequences through hydrogen bonding interactions with the nucleotide bases of the target sequences. The probe or the target sequences may be single- or double- stranded RNA or single- or double- stranded DNA or a combination of DNA and RNA bases. A probe may be 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 and up to 30 or more nucleotides in length as long as it is less than the full length of the target mRNA or any gene encoding said mRNA. Probes can include oligonucleotides modified so as to have a tag which is detectable by fluorescence, chemiluminescence and the like. The probe can also be modified so as to have both a detectable tag and a quencher molecule, for example TaqMan(R) and Molecular Beacon(R) probes.

[0215] The oligonucleotides and analogs thereof may be RNA or DNA, or analogs of RNA or DNA, commonly referred to as antisense oligomers or antisense oligonucleotides. Such RNA or DNA analogs comprise, but are not limited to, 2-'0-alkyl sugar modifications, methylphosphonate, phosphorothiate, phosphorodithioate, formacetal, 3-thioformacetal, sulfone, sulfamate, and nitroxide backbone modifications, and analogs, for example, LNA analogs, wherein the base moieties have been modified. In addition, analogs of oligomers may be polymers in which the sugar moiety has been modified or replaced by another suitable moiety, resulting in polymers which include, but are not limited to, morpholino analogs and peptide nucleic acid (PNA) analogs. Probes may also be mixtures of any of the oligonucleotide analog types together or in combination with native DNA or RNA. At the same time, the oligonucleotides and analogs thereof may be used alone or in combination with one or more additional oligonucleotides or analogs thereof.According to this option, the expression level may be determined using amplification assay. The term "amplification assay", with respect to nucleic acid sequences, refers to methods that increase the representation of a population of nucleic acid sequences in a sample. Nucleic acid amplification methods, such as PCR, isothermal methods, rolling circle methods, etc., are well known to the skilled artisan. More specifically, as used herein, the term "amplified" , when applied to a nucleic acid sequence, refers to a process whereby one or more copies of a particular nucleic acid sequence is generated from a template nucleic acid, preferably by the method of polymerase chain reaction.

[0216] "Polymerase chain reaction" or "PCR" refers to an in vitro method for amplifying a specific nucleic acid template sequence. The PCR reaction involves a repetitive series of temperature cycles and is typically performed in a volume of 50-100 microliter. The reaction mix comprises dNTPs (each of the four deoxynucleotides dATP, dCTP, dGTP, and dTTP), primers, buffers, DNA polymerase, and nucleic acid template. The PCR reaction comprises providing a set of polynucleotide primers wherein a first primer contains a sequence complementary to a region in one strand of the nucleic acid template sequence and primes the synthesis of a complementary DNA strand, and a second primer contains a sequence complementary to a region in a second strand of the target nucleic acid sequence and primes the synthesis of a complementary DNA strand, and amplifying the nucleic acid template sequence employing a nucleic acid polymerase as a template-dependent polymerizing agent under conditions which are permissive for PCR cycling steps of (i) annealing of primers required for amplification to a target nucleic acid sequence contained within the template sequence, (ii) extending the primers wherein the nucleic acid polymerase synthesizes a primer extension product. "A set of polynucleotide primers", "a set of PCR primers" or "pair of primers" can comprise two, three, four or more primers.

[0217] Real time nucleic acid amplification and detection methods are efficient for sequence identification and quantification of a target since no pre-hybridization amplification is required. Amplification and hybridization are combined in a single step and can be performed in a fully automated, large-scale, closed-tube format.

[0218] Methods that use hybridization-triggered fluorescent probes for real time PCR are based either on a quench-release fluorescence of a probe digested by DNA Polymerase (e.g., methods using TaqMan(R), MGB- TaqMan(R)), or on a hybridization- triggered fluorescence of intact probes (e.g., molecular beacons, and linear probes). In general, the probes are designed to hybridize to an internal region of a PCR product during annealing stage (also referred to as amplicon). For those methods utilizing TaqMan(R) and MGB-TaqMan(R) the 5'-exonuclease activity of theapproaching DNA Polymerase cleaves a probe between a fluorophore and a quencher, releasing fluorescence.

[0219] Thus, a "real time PCR" or “RT-PCT” assay provides dynamic fluorescence detection of amplified biomarker proteins of the present disclosure, or any control reference gene produced in a PCR amplification reaction. During PCR, the amplified products created using suitable primers hybridize to probe nucleic acids (TaqMan(R) probe, for example), which may be labeled according to some embodiments with both a reporter dye and a quencher dye. When these two dyes are in close proximity, i.e. both are present in an intact probe oligonucleotide, the fluorescence of the reporter dye is suppressed. However, a polymerase, such as AmpliTaq GoldTM, having 5'-3' nuclease activity can be provided in the PCR reaction. This enzyme cleaves the Anorogenic probe if it is bound specifically to the target nucleic acid sequences between the priming sites. The reporter dye and quencher dye are separated upon cleavage, permitting Auorescent detection of the reporter dye. Upon excitation by a laser provided, e.g., by a sequencing apparatus, the Auorescent signal produced by the reporter dye is detected and / or quantified. The increase in Auorescence is a direct consequence of amplification of target nucleic acids during PCR.

[0220] More particularly, QRT-PCR or "qPCR" (Quantitative RT-PCR), which is quantitative in nature, can also be performed to provide a quantitative measure of gene expression levels. In QRT-PCR reverse transcription and PCR can be performed in two steps, or reverse transcription combined with PCR can be performed. One of these techniques, for which there are commercially available kits such as TaqMan(R) (Perkin Elmer, Foster City, CA), is performed with a transcript-specific antisense probe. This probe is specific for the PCR product (e.g. a nucleic acid fragment derived from a gene) and is prepared with a quencher and Auorescent reporter probe attached to the 5' end of the oligonucleotide. Different Auorescent markers are attached to different reporters, allowing for measurement of at least two products in one reaction.

[0221] When Taq DNA polymerase is activated, it cleaves off the Auorescent reporters of the probe bound to the template by virtue of its 5-to-3' exonuclease activity. In the absence of the quenchers, the reporters now Auoresce. The color change in the reporters is proportional to the amount of each specific product and is measured by a Auorometer; therefore, the amount of each color is measured, and the PCR product is quantified. The PCR reactions can be performed in any solid support, for example, slides, microplates, 96 well plates, 384 well plates and the like so that samples derived from many individuals are processed and measured simultaneously. The TaqMan(R) system has the additional advantage of not requiring gel electrophoresis and allows for quantification when used with a standard curve.A second technique useful for detecting PCR products quantitatively without using an intercalating dye such as the commercially available QuantiTect SYBR Green PCR (Qiagen, Valencia California). RT-PCR is performed using SYBR green as a fluorescent label which is incorporated into the PCR product during the PCR stage and produces fluorescence proportional to the amount of PCR product.

[0222] Both TaqMan(R) and QuantiTect SYBR systems can be used subsequent to reverse transcription of RNA. Reverse transcription can either be performed in the same reaction mixture as the PCR step (one-step protocol) or reverse transcription can be performed first prior to amplification utilizing PCR (two-step protocol).

[0223] Additionally, other known systems to quantitatively measure mRNA expression products include Molecular Beacons(R) which uses a probe having a fluorescent molecule and a quencher molecule, the probe capable of forming a hairpin structure such that when in the hairpin form, the fluorescence molecule is quenched, and when hybridized, the fluorescence increases giving a quantitative measurement of gene expression.

[0224] According to this embodiment, the detecting molecule may be in the form of probe corresponding and thereby hybridizing to any region or at least one of the biomarkers or any reference control. More particularly, it is important to choose regions which will permit hybridization to the target nucleic acids. Factors such as the Tm of the oligonucleotide, the percent GC content, the degree of secondary structure and the length of nucleic acid are important factors.

[0225] It should be noted however that a standard Northern blot assay or dot blot can also be used to ascertain an RNA transcript size and the relative amounts of the biomarker proteins of the disclosure or any control gene product, in accordance with conventional Northern hybridization techniques known to those persons of ordinary skill in the art.

[0226] In yet some further alternative embodiments (b), the detecting molecules used by the disclosed methods may be at least one nucleic acid aptamer, each specific for the at least one of the biomarker proteins. As used herein the term "aptamer" or “specific aptamers” denotes single-stranded nucleic acid (DNA or RNA) molecules which specifically recognizes and binds to a target molecule. The aptamers according to the disclosure may fold into a defined tertiary structure and can bind a specific target molecule with high specificities and affinities. Aptamers are usually obtained by selection from a large random sequence library, using methods well known in the art, such as SELEX and / or Molinex. In various embodiments, aptamers may include single-stranded, partially single-stranded, partially double- stranded or double- stranded nucleic acid sequences; sequences comprising nucleotides, ribonucleotides, deoxyribonucleotides, nucleotide analogs,- 41 -modified nucleotides and nucleotides comprising backbone modifications, branch points and nonnucleotide residues, groups or bridges; synthetic RNA, DNA and chimeric nucleotides, hybrids, duplexes, heteroduplexes; and any ribonucleotide, deoxyribonucleotide or chimeric counterpart thereof and / or corresponding complementary sequence. In certain specific embodiments, aptamers used by the disclosure are composed of deoxyribonucleotides.

[0227] According to the present disclosure and as appreciated in the art, the recognition between the aptamer and the antigen is specific and may be detected by the appearance of a detectable signal by using a colorimetric sensor or a fluorimetric / lumination sensor, radioactive sensor, or any appropriate means.

[0228] The aptamers that may be used according to some aspects of the disclosure may be biotinylated. The aptamers may optionally include a chemically reactive group at the 3' and / or 5' termini. The term reactive group is used herein to denote any functional group comprising a group of atoms which is found in a molecule and is involved in chemical reactions. Some non-limiting examples for a reactive group include primary amines (NH2), thiol (SH), carboxy group (COOH), phosphates (PO4), Tosyl, and a photo-reactive group.

[0229] In yet some alternative or additional embodiments, the disclosed diagnostic methods may use amino acid-based detecting molecule / s for determining the expression level of the biomarker / s. In more specific embodiments, such amino-acid based detecting molecules may comprise at least one of: (a), at least one antibody, each antibody is specific for one of the biomarker / s and / or any fragment thereof. Alternatively, the detecting molecule may comprise (b), at least one protein or peptide aptamer / s, or any other affinity molecule, each aptamer is specific for one of the biomarker / s. Another alternative for detecting molecules useful in the present disclosure may be (c), at least one labeled or tagged biomarker of the at least one biomarker / s or any fragment / s, peptide / s or mixture / s thereof. In some embodiments, such detecting molecules may be useful for determining the expression level of the disclosed biomarkers using Mass spectrometry methods. Therefore, it should be understood that the present disclosure contemplates the use of amino acidbased molecules such as proteins or polypeptides as detecting molecules disclosed herein and would be known by a person skilled in the art to measure at least one biomarker. As used herein, the terms "protein” and "polypeptide” are used interchangeably to refer to a chain of amino acids linked together by peptide bonds. In a specific embodiment, a protein is composed of less than 200, less than 175, less than 150, less than 125, less than 100, less than 50, less than 45, less than 40, less than 35, less than 30, less than 25, less than 20, less than 15, less than 10, or less than 5 amino acids linked together by peptide bonds. In another embodiment, a protein is composed of atleast 200, at least 250, at least 300, at least 350, at least 400, at least 450, at least 500, at least 1000 or more amino acids linked together by peptide bonds. It should be noted that peptide bond as described herein is a covalent amid bond formed between two amino acid residues. In certain embodiments, amino-acid-based detecting molecules are used by the methods, compositions and kits of the present disclosure for the purpose of determining the expression level of at least one of the disclosed biomarkers, at the protein level. It should be noted, however, that the nature and type of the amino-acid-based detecting molecules depends on the procedure used for determining the expression level of the specific biomarker.

[0230] Techniques for detection and quantification known to persons skilled in the art (for example, Mass spectrometry (MS) or alternatively, different affinity-based methods, that require affinity amino acid molecules, for example detecting molecules such as antibodies that are adapted for immunological techniques such as Western Blotting, Immunoprecipitation, ELISAs, protein microarray analysis, Flow cytometry and the like), or aptamers, can be used to measure the level of protein products corresponding to the at least one biomarker.

[0231] In some embodiments, the detecting molecules may be affinity molecules such as antibodies (a), or aptamers (b). In some embodiments, the detecting molecules may be antibodies and the determination of the level of expression of the biomarkers of the present disclosure may be performed by any appropriate immuno assay.

[0232] It should be understood that each antibody specifically recognizes one biomarker protein. Using these antibodies, the level of expression of at least one of the biomarker may be determined by an immunoassay which may be an assay that includes but not limited to FACS, a Western blot, an ELISA, a RIA, a slot blot, a dot blot, immune-histochemical assay and a radio-imaging assay, as described in more detail herein after. It should be noted that such assays may be performed using microarray protein arrays.

[0233] More specifically, the term “antibody” as used in this disclosure includes whole antibody molecules as well as functional fragments thereof, such as Fab, F(ab')2, and Fv that are capable of binding with antigenic portions of the target polypeptide, i.e., at least one of the biomarker proteins. The antibody may be preferably monospecific, e.g., a monoclonal antibody, or antigenbinding fragment thereof. The term "monospecific antibody" refers to an antibody that displays a single binding specificity and affinity for a particular target, e.g., epitope. This term includes a "monoclonal antibody" or "monoclonal antibody composition", which, as used herein, refer to a preparation of antibodies or fragments thereof of single molecular composition.It should be recognized that the antibody can be a human antibody, a chimeric antibody, a recombinant antibody, a humanized antibody, a monoclonal antibody, or a polyclonal antibody. The antibody can be an intact immuno globulin, e.g., an IgA, IgG, IgE, IgD, IgM or subtypes thereof. The antibody can be conjugated to a labeling moiety as discussed herein after.

[0234] As noted above, the term "antibody" also encompasses antigen-binding fragments of an antibody. The term "antigen-binding fragment" of an antibody (or simply "antibody portion," or "fragment"), as used herein, may be defined as follows:

[0235] (1) Fab, the fragment which contains a monovalent antigen-binding fragment of an antibody molecule, can be produced by digestion of whole antibody with the enzyme papain to yield an intact light chain and a portion of one heavy chain;

[0236] (2) Fab', the fragment of an antibody molecule that can be obtained by treating whole antibody with pepsin, followed by reduction, to yield an intact light chain and a portion of the heavy chain; two Fab' fragments are obtained per antibody molecule;

[0237] (3) (Fab')2, the fragment of the antibody that can be obtained by treating whole antibody with the enzyme pepsin without subsequent reduction; F(ab')2 is a dimer of two Fab' fragments held together by two disulfide bonds;

[0238] (4) Fv, defined as a genetically engineered fragment containing the variable region of the light chain and the variable region of the heavy chain expressed as two chains; and

[0239] (5) Single chain antibody (“SCA”, or ScFv), a genetically engineered molecule containing the variable region of the light chain and the variable region of the heavy chain, linked by a suitable polypeptide linker as a genetically fused single chain molecule. Methods of generating such antibody fragments are well known in the art.

[0240] Purification of serum immunoglobulin antibodies (polyclonal antisera) or reactive portions thereof can be accomplished by a variety of methods known to those of skill in the art including, precipitation by ammonium sulfate or sodium sulfate followed by dialysis against saline, ion exchange chromatography, affinity or immuno-affinity chromatography as well as gel filtration, zone electrophoresis, etc.

[0241] Still further, the antibodies used by the present disclosure may optionally be covalently or non-covalently linked to a detectable label or tag. In addition, the label and can also refer to indirect labeling of the protein by reactivity with another reagent that is directly labeled. Examples of indirect labeling include detection of at least one of the biomarker protein / s of the disclosure using a fluorescently labeled secondary antibody. More specifically, detectable labels suitable for suchuse include any composition detectable by spectroscopic, photochemical, biochemical, immunochemical, electrical, optical or chemical means.

[0242] In some other embodiments, the detecting molecules are peptide aptamers specific for the at least one of the biomarkers. "Protein or peptide aptamers" as used herein refers to small peptides with a single variable loop region tied to a protein scaffold on both ends that binds to a specific molecular target (e.g. protein), and which are bind to their targets only with said variable loop region and usually with high specificity properties.

[0243] It should be appreciated that in certain embodiments, the biomarkers of the present disclosure (e.g., at least one of CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and / or at least one of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF) or any protein-fragments thereof may be also detected and quantified either by using a labeled version of the biomarker protein, or alternatively, without the need for detection molecule / s. Detection can be based on mass spectrometry (MS) approaches using non-targeted or targeted methods such as selected reaction monitoring (SRM) or parallel reaction monitoring (PRM). These analyses can be performed with or without a reference heavy standard and provide quantitative measure of the peptide / protein amount. The heavy reference can be a synthetic peptide, or a chemically labeled peptide / protein or metabolically labeled proteins. In the absence of a standard, the MS signal can provide the measure of peptide abundance.

[0244] Still further, in some embodiments, the amino-acid-based detecting molecules used by the methods, compositions and kits of the present disclosure may be recombinantly expressed or synthetically prepared. In further embodiments, the recombinantly or synthetically expressed and prepared detecting molecules may be labeled or tagged. It should be noted that in some embodiments, these detecting molecules may be isolated detecting molecules. As used herein, "Recombinant proteins" denotes proteins encoded by a recombinant DNA which is a genetically engineered DNA formed by laboratory methods of genetic recombination to bring together genetic material from multiple sources and thus creating variable sequences. Recombinant proteins may be produced mainly, but not limited, by molecular cloning, namely incorporating the recombinant DNA into a living cell (e.g., bacteria or yeast) and using its system to express the DNA into mRNA and protein thereof.

[0245] In some embodiments, the term "labeled" or "tagged" may refer to direct labeling of the protein via, e.g., coupling (i.e., physically linking) or incorporating of a detectable substance to the protein. Useful labels in the present disclosure may include but are not limited to include isotopes (e.g.13C,15N), or any other radiolabels (e.g.,3H,1251,35S,14C, or32P), magnetic beads (e.g. DYNABEADS), fluorescent dyes (e.g., fluorescein isothiocyanate, Texas red, rhodamine, green fluorescent protein, and the like), enzymes (e.g., horseradish peroxidase, alkaline phosphatase and others commonly used in an ELISA and competitive ELISA, histochemistry and other similar methods known in the art) and colorimetric labels such as colloidal gold or colored glass or plastic (e.g. polystyrene, polypropylene, latex, etc.) beads. In some embodiments, the protein may be tagged. Different tags may be also used, for example, His, myc, HA, GFP, ABP, GST, biotin and the like. "Tagged" as used herein may further include fusion or linking of the biomarker or any fragment or peptide thereof, that serves herein as a detecting molecule, a tag that in some embodiments may contain several amino acids or a peptide that may be recognized by affinity or immunologically, using specific antibodies.

[0246] In some other embodiments, the biomarker or any fragments or peptides thereof may be fluorescently labeled. In another embodiment, the biomarker or any fragments or peptides thereof may be isotope labeled. The term "recombinant isotope labeled" denotes a protein 'labeled' by replacing specific atoms by their isotope.

[0247] Means of detecting such labels are well known to those of skill in the art. Thus, for example, radiolabels may be detected using photographic film or scintillation counters, fluorescent markers may be detected using a photodetector to detect emitted illumination. Enzymatic labels are typically detected by providing the enzyme with a substrate and detecting the reaction product produced by the action of the enzyme on the substrate, and colorimetric labels are detected by simply visualizing the colored label.

[0248] More specifically, in certain embodiments the biomarker of the present disclosure or any fragment or peptide thereof, when recombinantly expressed and labeled or tagged, may be used as detecting molecules for determining the quantity or level of expression of the biomarker of the disclosure in the examined sample. The term "labeled form" as used herein includes an isotope labeled form. Specifically, the labeled form is a chemically or metabolically isotope labeled, and more specifically a metabolically isotope labeled form of the biomarker proteins of the disclosure. Optional "isotope labeled forms" of the biomarker or any fragments or peptides thereof in accordance with the present disclosure are variants of naturally occurring molecules, in whose structure one or more atoms have been substituted with atom(s) of the same element having a different atomic weight, although isotope labeled forms in which the isotope has been covalently linked either directly or via a linker, or wherein the isotope has been complexed to the biomarker proteins are likewise contemplated. In either case, the isotope may be stable isotope.A stable isotope as referred to herein, is a non-radioactive isotopic form of an element having identical numbers of protons and electrons, but having one or more additional neutron(s), which increase(s) the molecular weight of the element. Specifically, the stable isotopes may be selected from the group consisting of2H,13C,15N,170,180,33P,34S and combinations thereof. Particularly specific examples include13C and5N, and combinations thereof.

[0249] The labeling can be affected by means known in the art. A labeled reference biomarker (used as detecting molecules) can be synthesized using isotope labeled amino acids as precursor molecules or chemically modified. Modification and labeling can be done on whole proteins or their fragments.

[0250] Metabolic labeling may also be used to produce the labeled reference biomarkers. For example, cells can be grown on media containing isotope labeled precursor molecules, such as isotope labeled amino acids, that are incorporated into proteins or peptides, which are thereby metabolically labeled. The metabolic isotope labeling may be a stable isotope labeling with amino acids in cell culture (SILAC). If metabolic labeling is used, and the labeled form of the one or the plurality of reference biomarker protein / s is a SILAC labeled form of the reference biomarker protein / s, the standard mixture as defined above is also referred to as SUPER-SILAC mix.

[0251] It should be noted that the amino acid-based detecting molecule / s may comprise labeled or tagged biomarker or any fragment / s, peptide / s or mixture / s thereof. As used herein, "fragment / s, peptide / s or mixture / s thereof' refers to a portion of a larger molecule, typically a protein, that retains some or all of the biological activity or structural characteristics of the parent molecule. A "peptide” is a short chain of amino acids linked by peptide bonds, generally shorter than a protein. A "mixture thereof' indicates a combination of two or more such fragments and / or peptides. Still further, in some embodiments, the disclosed methods may use any biological sample. Specifically, any biological sample that contains at least one immune cells, specifically, neutrophils, and / or at least one tumor cell or any pair thereof. Accordingly, in some embodiments, a biological sample applicable in the disclosed diagnostic methods may comprise at least one of: a cell sample, a tissue sample and / or a body fluid sample of said subject.

[0252] A "biological sample" as used herein refers to a sample derived from a mammalian subject, wherein the sample comprises at least one neutrophil cell and optionally, at least one tumor cell originating from a tumor tissue, specifically, from a breast tumor tissue. In some embodiments, the biological sample comprises a cell sample of the subject. A "cell sample" refers herein to a cell or a collection of cells obtained from a mammalian subject, wherein the cells comprise at least one neutrophil cell. These cells can be isolated from tissues, organs, or bodily fluids and may representa homogeneous population (e.g., a specific cell line) or a heterogeneous mixture of different cell types. In some other embodiments, the biological sample comprises a tissue sample of the subject. A "tissue sample" refers herein to a portion of biological tissue removed from an organ of a mammalian subject, such as a breast tissue. Tissue samples are typically obtained through biopsy or surgical resection. In some embodiments, such tissue samples may be obtained from a primary tumor tissue. In yet some other alternative embodiments, the tissue sample may be obtained from a metastatic tissue. A tissue sample according to some embodiments, can be further a fractionated or preselected sample, if desired, preselected or fractionated to contain or be enriched for particular cell types. In some further embodiments, the biological sample comprises a body fluid sample of the subject. It should be noted that the body fluid sample may be derived from any organ or tissue cavity comprising at least one neutrophil cell and at least one tumor cell originating from a breast tumor (e.g., pleural effusion, breast milk, blood, serum or any neutrophils containing fraction thereof). In some embodiments, the sample is a breast tumor sample. In more specific embodiments, the sample is a primary breast tumor sample. Still further, in some embodiments, a blood sample of the diagnosed subject may be used for the disclosed methods, kits and compositions. More specifically, in some embodiments, a blood sample may be used as the biological fluid sample of the subject in the disclosed methods. Such blood sample is particularly applicable for determining the TAN-score by using the TAN-score biomarkers.

[0253] In some embodiments the tissue sample is a tumor sample, in more specific embodiments, a sample suitable for the disclosed diagnostic methods may be a primary tumor sample.

[0254] In some embodiments, the disclosed diagnostic methods are applicable for diagnosing and prognosing breast cancer, specifically, breast carcinoma, more specifically, advanced stages of breast carcinoma.

[0255] Still further, in some embodiments, breast carcinoma, as used herein, includes specific types of epithelial-derived breast cancers, such as: Ductal Carcinoma In Situ (DCIS), Invasive Ductal Carcinoma (IDC), Lobular Carcinoma In Situ (LCIS), Invasive Lobular Carcinoma (ILC).

[0256] "Breast carcinoma" refers to a malignant neoplasm originating from the epithelial cells of the breast tissue, characterized by uncontrolled cell growth and the potential for invasion and metastasis. As one of the most common cancers affecting women worldwide, breast carcinoma arises primarily in the ductal or lobular epithelium, which are the glandular structures responsible for producing and transporting milk. The disease is highly heterogeneous, encompassing a spectrum of subtypes with distinct histopathological, molecular, and clinical features, which influence its behavior and response to therapy.Among the various subtypes, ductal carcinoma and lobular carcinoma represent the most commonly observed forms, further classified into non-invasive and invasive categories. Ductal Carcinoma In Situ (DCIS) is a non-invasive subtype confined to the epithelial lining of the milk ducts, with no evidence of invasion into surrounding stromal tissue. While DCIS is not lifethreatening, it is considered a precursor lesion that can progress to invasive carcinoma if left untreated. Invasive Ductal Carcinoma (IDC), the most prevalent subtype of breast carcinoma, is characterized by malignant epithelial cells that have breached the ductal basement membrane and infiltrated adjacent tissues, with the potential for regional and distant metastases.

[0257] Similarly, Lobular Carcinoma In Situ (LCIS) is a non-invasive lesion arising from the lobules of the breast and is often considered a marker of increased risk for developing invasive breast cancer in either breast. Invasive Lobular Carcinoma (ILC), the second most common invasive subtype, is distinguished by its diffuse growth pattern and the absence of cohesive cell clusters, often due to the loss of E-cadherin expression. ILC poses unique diagnostic and therapeutic challenges, as its clinical presentation and imaging characteristics can differ from IDC, often leading to delayed detection.

[0258] Unless expressly indicated otherwise, each and every term defined herein in the context of any method aspect of the present disclosure shall be understood to have the same meaning and applicability wherever such term appears in the context of any other aspect, embodiment, or part of the present disclosure. Accordingly, all definitions provided herein apply consistently across the various aspects of the disclosure, except where a different meaning is explicitly stated.

[0259] For clarity and to avoid unnecessary repetition, each term is defined only once, and such single definition is intended to apply throughout the specification and claims, unless expressly stated otherwise.

[0260] Another aspect of the present disclosure relates to a diagnostic composition comprising a means for determining the expression level of at least one biomarker in at least one biological sample of a subject to obtain a TAN-score and / or a Neu TME-PIC-score for the sample. The biomarker / s comprise at least one of: (i), at least one TAN-score biomarker selected from: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and (ii), at least one Neu TME-PIC-score biomarker selected from:ARGl, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF. In some embodiments the disclosed composition is adapted for prognosing and / ordetecting and / or identifying and / or determining and / or staging advanced breast cancer in a mammalian subject.

[0261] As used herein, a "diagnostic composition" refers to a formulation, array or any other solid or on-solid support or arrangement or combination designed for use in diagnostic methods disclosed herein. The composition of the present disclosure comprises a means for determining the expression level of the signatory biomarkers. The term "means for determining the expression level of at least one biomarker" refers to any material, element, system, device, and / or software tools applicable for performing any method, technology, or assay required for quantifying the level of expression of a specific biomarker of the present disclosure in a biological sample. This may include reagents, detecting molecules, and other components necessary to perform the detection, identification, and / or quantification of the at least one biomarkers, required for determination, or staging of the advanced breast cancer disease. In some embodiments, it may also encompass specialized equipment required for techniques such as PCR, quantitative PCR, microarrays, RNA sequencing, immunohistochemistry, ELISA, or mass spectrometry, provided that these techniques are particularly directed at determining the expression level of at least one of the signatory biomarkers of the present disclosure.

[0262] In some embodiments, the expression level of at least at least six of the TAN-score biomarkers, is determined by the disclosed methods, compositions and kits of the present disclosure, specifically, at least six of CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, and / or the expression level of at least six of the Neu TME-PIC-score biomarkers is determined by the disclosed methods, specifically, at least six of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF. In some embodiments, the expression level of at least at least 6, 7, 8, 9, 10, 11, 12 of the TAN-score biomarkers, is determined by the disclosed methods, disclosed methods, compositions and kits of the present disclosure, specifically, at least 6, 7, 8, 9, 10, 11, 12 of CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, and / or the expression level of at least 6, 7, 8, 9, 10, 11, 12, 13, 14 of the Neu TME-PIC-score biomarkers is determined by the disclosed methods, specifically, at least 6, 7, 8, 9, 10, 11, 12, 13, 14, of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF. In more specific embodiments, the diagnostic compositions of the present disclosure biomarker / s comprise a means for determining the expression level of the following biomarkers. More specifically, for (i), TAN-score biomarkers comprising CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6,S100A8, UPP1 and WFDC21P; and / or for (ii) Neu TME-PIC-score biomarkers comprising ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

[0263] In some embodiments, the disclosed compositions comprise a means for determining the expression level of the following TAN-score biomarkers: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P. In some other embodiments, the disclosed compositions comprise a means for determining the expression level of the following Neu TME-PIC-score biomarkers: ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

[0264] In yet some further embodiments, the disclosed compositions comprise a means for determining the expression level of the following TAN-score biomarkers: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, and a means for determining the expression level of the following Neu TME-PIC-score biomarkers: ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

[0265] In some embodiments, the means for detecting the expression level of the at least one biomarker of the disclosed composition, may comprise a means for sequencing and / or at least one detecting molecule, each specific for one of the biomarker / s. In some embodiments, the disclosed diagnostic compositions comprise a means for sequencing, specifically, RNA sequencing.

[0266] In some alternative or additional embodiments, the disclosed diagnostic compositions comprise at least one detecting molecule, for example, is at least one amino acid-based detecting molecule / s and / or at least one nucleic acid-based detecting molecule / s. Each of the detecting molecules in the disclosed diagnostic composition is specific for one of the disclosed signatory biomarkers required for obtaining the TAN-score and / or the Neu TME-PIC-score. More specifically, in some embodiments, the compositions of the present disclosure (as well as kits as defined herein after), may comprise at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, and at least twelve detecting molecules, specific for at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, and at least twelve, of the TAN-score biomarkers as specified above, respectively. In yet some further embodiments, the compositions of the present disclosure may comprise at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen and at least fourteen detecting molecules, specific for atleast one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen and at least fourteen of the Neu TME-PIC-score biomarkers, respectively. In more specific embodiments, the compositions of the present disclosure may comprise at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty, at least twenty-one, at least twenty-two, at least twenty-three and at least twenty-four detecting molecules, specific for at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty, at least twenty-one, at least twenty-two, at least twenty-three and at least twenty-four, of the TAN-score biomarkers and of the Neu TME-PIC-score biomarkers, respectively. It should be understood that each of the detecting molecules is specific for only one of the biomarkers. However, in some embodiments, the disclosed compositions (as well as kits as defined herein after), may comprise more than one detecting molecules specific for each of the biomarkers, for example, the disclosed compositions and kits may comprise two or more, and up to 100 or more different detecting molecules, specific for one of the biomarkers, provided that each of the detection molecules is specific for only one of the biomarkers and / or the control references, as specified above in connection with the disclosed methods.

[0267] In some embodiments, the disclosed compositions (as well as the kits discussed herein after), may be provided in the form of an array or a mixture. As defined herein, a "detecting molecule array" refers to a plurality of detection molecules that may be nucleic acids based or protein based detecting molecules, optionally attached to a support where each of the detecting molecules is attached to a support in a unique pre- selected and defined region. For example, an array may contain different detecting molecules, such as specific antibodies, labeled or tagged proteins, peptides, aptamers, probes and / or primers or any combinations thereof. As indicated herein before, in case a combined detection of the biomarker expression level, the different detecting molecules specific for each biomarker may be spatially arranged in a predetermined and separated location in an array. For example, an array may be a plurality of vessels (test tubes), plates, micro-wells in a micro-plate, each containing different detecting molecules, specifically, aptamers, primers and antibodies, specific for each biomarker used by the present disclosure. An array may also be any solid support holding, in distinct regions (dots, lines, columns) different and known, predetermineddetecting molecules. As used herein, "solid support" is defined as any surface to which molecules may be attached through either covalent or non-covalent bonds. Thus, useful solid supports include solid and semi-solid matrixes, such as aero gels and hydro gels, resins, beads, biochips (including thin film coated biochips), micro fluidic chip, a silicon chip, multi-well plates (also referred to as microtiter plates or microplates), membranes, filters, conducting and no conducting metals, glass (including microscope slides) and magnetic supports. More specific examples of useful solid supports include silica gels, polymeric membranes, particles, derivative plastic films, glass beads, cotton, plastic beads, alumina gels, polysaccharides such as Sepharose, nylon, latex bead, magnetic bead, paramagnetic bead, super paramagnetic bead, starch and the like. This also includes, but is not limited to, microsphere particles such as Lumavidin™ or LS-beads, magnetic beads, charged paper, Langmuir-Blodgett films, functionalized glass, germanium, silicon, PTFE, polystyrene, gallium arsenide, gold, and silver. Any other material known in the art that is capable of having functional groups such as amino, carboxyl, thiol or hydroxyl incorporated on its surface, is also contemplated. This includes surfaces with any topology, including, but not limited to, spherical surfaces and grooved surfaces. It should be appreciated that any of the reagents, substances or ingredients included in any of the methods, compositions and kits of the present disclosure may be provided as reagents embedded, linked, connected, attached, placed or fused to any of the solid support materials described above. In certain embodiments, the detecting molecules used in the diagnostic compositions and kits of the present disclosure may be provided in a mixture. In some alternative embodiments, detecting molecules used herein may be provided as molecules that are not attached to any solid support. In some embodiments, the non-attached detecting molecules may be provided in separate containers, wells, tube vessels and the like. In some alternative embodiments, the attached or non-attached detecting molecules may be provided in a mixture that contains at least two detecting molecules specific for at least two biomarker / s of the present disclosure.

[0268] In yet some further embodiments, the diagnostic compositions of the present disclosure comprise at least one nucleic acid-based detecting molecule / s, specific for at least one of the indicated signatory biomarkers. Accordingly, such diagnostic compositions comprise at least one of: (a), at least one oligonucleotide, each oligonucleotide specifically hybridizes to a nucleic acid sequence of one of the at least one biomarker, either of the TAN-score biomarkers or the Neu TME-PIC-score biomarkers specified above. In some embodiments, such oligonucleotides may be primers and / or probes. In yet some further embodiments, the nucleic acid-based detecting molecules may be (b), at least one nucleic acid aptamer / s, each aptamer is specific for one of the at least onebiomarker, either of the TAN-score biomarkers or the Neu TME-PIC-score biomarkers specified above. It should be understood that the definition of the nucleic acid-based detecting molecules as disclosed above is applicable for the present aspect as well. In some embodiments, the diagnostic composition of the present disclosure comprises amino acid-based detecting molecule / s. More specifically, such amino acid-based detecting molecule / s comprise at least one of: (a), at least one antibody, each antibody is specific for one of the biomarker / s and / or any fragment thereof, either of the TAN-score biomarkers or the Neu TME-PIC-score biomarkers specified above; (b), at least one protein or peptide aptamer / s, each aptamer is specific for one of the biomarker / s, either of the TAN-score biomarkers or the Neu TME-PIC-score biomarkers specified above; and / or (c), at least one labeled or tagged biomarker of the at least one biomarker / s or any fragment / s, peptide / s or mixture / s thereof, either of the TAN-score biomarkers or the Neu TME-PIC-score biomarkers specified above. It should be understood that the definition of the amino acid-based detecting molecules as disclosed above is applicable for the present aspect as well.

[0269] The disclosed compositions are in some embodiments, configured for prognosing and / or detecting and / or identifying and / or determining and / or staging advanced breast cancer in a mammalian subject. In some embodiments, the diagnostic composition of the present disclosure is adapted for use, is configured for use, and is practically used in methods for prognosing and / or for detecting and / or identifying and / or determining and / or staging advanced breast cancer in a mammalian subject. In some embodiments, the disclosed compositions are applicable for and configured for use in the diagnostic methods as disclosed and defined by the present disclosure above. More specifically, the disclosed compositions are “Adapted for use” in any of the diagnostic and prognostic methods disclosed by the present disclosure. This generally means that the diagnostic compositions are suitable for, or capable of, performing a stated function or being used in the disclosed methods. It implies that the components of the composition have been designed, selected, arranged, or otherwise made appropriate for the recited use in the disclosed methods. In yet some further embodiments, the compositions and kits disclosed herein are “Configured for use” in the diagnostic and prognostic methods of the present disclosure, this typically conveys that the disclosed compositions and kits are set up, arranged, programmed, or constructed so that they perform (or will perform) the stated function of the disclosed methods when operated.

[0270] Another aspect of the present disclosure relates to a kit comprising: as component (a), a means for determining the expression level of at least one biomarker in at least one biological sample of a subject, to obtain a TAN-score and / or a Neu TME-PIC-score for the sample. In someembodiments, such means comprise a means and / or reagent / s for sequencing and / or at least one detecting molecule. Each of the detecting molecule / s is specific to one of the biomarker / s. More specifically, the at least one biomarker / s comprise at least one of: (i), at least one TAN-score biomarker selected from: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and / or (ii), at least one Neu TME-PIC- score biomarker selected from: ARG1, CHMP4C, CEDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SEFN12E, TESC, UPP1 and VEGF. The disclosed kits further comprise at least one of (b) and / or (c). More specifically, Component (b), of the disclosed kit may comprise software for determining the expression level of the biomarker / s in the sample based on a calculation of normalized RNA expression of the target genes compared to a similarly expressed set of control genes. Alternatively, or additionally, component (c), a predetermined standard TAN-score and / or a predetermined standard Neu TME-PIC- score; and / or component (d), at least one control sample. The disclosed kit is in some embodiments configured for prognosing and / or detecting and / or identifying and / or determining and / or staging advanced breast cancer in a mammalian subject. In more specific embodiments, the diagnostic kits of the present disclosure biomarker / s comprise means for determining the expression level of the following biomarkers. More specifically, for (i), TAN-score biomarkers comprising CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and / or for (ii) Neu TME-PIC-score biomarkers comprising ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

[0271] In some embodiments, the disclosed kits comprise means for determining the expression level of the following TAN-score biomarkers: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P. In some other embodiments, the disclosed kits comprise means for determining the expression level of the following Neu TME-PIC-score biomarkers: ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

[0272] In yet some further embodiments, the disclosed kits comprise a means for determining the expression level of the following TAN-score biomarkers: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, and a means for determining the expression level of the following Neu TME-PIC-score biomarkers: ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF. In some embodiments, the expression level of at least at least six of the TAN-score biomarkers, is determined by the disclosed methods, compositions and kits of the presentdisclosure, specifically, at least six of CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, and / or the expression level of at least six of the Neu TME-PIC-score biomarkers is determined by the disclosed methods, specifically, at least six of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

[0273] In some embodiments, the expression level of at least at least 6, 7, 8, 9, 10, 11, 12 of the TAN-score biomarkers, is determined by the disclosed methods, disclosed methods, compositions and kits of the present disclosure, specifically, at least 6, 7, 8, 9, 10, 11, 12 of CSTA, CAMP, CD 177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, and / or the expression level of at least 6, 7, 8, 9, 10, 11, 12, 13, 14 of the Neu TME-PIC-score biomarkers is determined by the disclosed methods, specifically, at least 6, 7, 8, 9, 10, 11, 12, 13, 14, of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

[0274] In some embodiments, the disclosed kits comprise at least one detecting molecule that may be at least one amino acid-based detecting molecule / s and / or nucleic acid-based detecting molecule / s. In more specific embodiments, the disclosed kits comprise nucleic acid-based detecting molecule / s. In more specific embodiments, such nucleic acid-based detecting molecule / s comprise at least one of: (a), at least one oligonucleotide, each oligonucleotide specifically hybridizes to a nucleic acid sequence of one of the at least one biomarker; and / or (b), at least one nucleic acid aptamer / s, each aptamer is specific for one of the at least one biomarker.

[0275] Still further, in some embodiments, the disclosed kits comprise amino acid-based detecting molecule / s. In more specific embodiments, such amino acid-based detecting molecule / s comprise at least one of: (a), at least one antibody, each antibody is specific for one of the biomarkers and / or any fragment thereof; (b), at least one protein or peptide aptamer / s, each aptamer is specific for one of the biomarker / s; and / or (c), at least one labeled or tagged biomarker selected from the biomarker / s or any fragment / s, peptide / s or mixture / s thereof.

[0276] Still further, in some embodiments, the disclosed kits may further comprise a means for dissociation of the biological sample, specifically, the primary tumor obtained from the Mammary gland. The means may include the appropriate medium (e.g., Dulbecco’s modified Eagle medium / F12 medium) supplemented with various enzymes that facilitate degradation of the tissue, e.g., collagenase IV, collagenase II, hyaluronidase I-S and DNase I. Various buffers (e.g., PBS supplemented with 0.2mM ethylene diamine tetra-acetic acid (EDTA), pH 8, and 0.5% BSA).In some embodiments, the disclosed kits are adapted for use in a method for prognosing and / or detecting and / or identifying and / or determining and / or staging advanced breast cancer in a mammalian subject. In yet some further embodiments, the disclosed kits are adapted for performing the diagnostic methods as defined by the present disclosure.

[0277] It should be appreciated that the components in the compositions and kits of the present disclosure may depend on the method of detection and are not limited to any method.

[0278] In some embodiments, the kit of the present disclosure may comprise regents specific for performing single-cell RNA sequencing (scRNA-seq). Accordingly, in some embodiments, such kit for performing scRNA-seq, may comprise: (a) reagents for dissociating or lysing cells or nuclei and stabilizing RNA; (b) reagents for single-cell or single-nucleus capture and barcoding, including a cell barcode component and, optionally, unique molecular identifiers (UMIs); (c) reagents for reverse transcription and cDNA synthesis; (d) reagents for cDNA amplification; (e) reagents for sequencing library preparation including adapters and index primers; (f) one or more buffers, wash solutions, and nuclease-free consumables suitable for handling RNA; and (g) instructions for use for generating barcoded single-cell or single-nucleus cDNA libraries for high-throughput sequencing. Optionally, the kit further comprises one or more of: (h) reagents for cell staining and / or viability assessment; (i) reagents for enrichment or depletion of defined cell populations (including antibodies or magnetic beads); (j) reagents for removal of ambient RNA and / or depletion of ribosomal RNA; (k) positive and / or negative control RNA or cells; and (1) software, a data processing pipeline, or access credentials thereto, for demultiplexing, alignment, generation of a gene-by-cell count matrix, quality control, clustering, and / or downstream analysis. In some embodiments, the kit of the present disclosure may comprise regents specific for performing physically interacting cell RNA sequencing (PIC-seq), comprising: (a) reagents and / or buffers for gentle tissue dissociation and preparation of a suspension comprising physically interacting cell pairs; (b) reagents for preserving and / or stabilizing RNA in said physically interacting cell pairs; (c) one or more labeling reagents for identifying distinct cell populations for PIC isolation, optionally comprising antibodies specific for an immune-cell marker and an epithelial-cell marker, and optionally further comprising viability dyes; (d) reagents and / or consumables for enriching and / or isolating physically interacting cell pairs, optionally adapted for fluorescence-activated cell sorting (FACS) and / or magnetic separation; (e) reagents for lysing sorted physically interacting cell pairs and for reverse transcription and cDNA synthesis; (f) reagents for barcoding and optionally unique molecular identifiers (UMIs) such that transcripts from each physically interacting cell pair are associated with a single barcode; (g) reagents forcDNA amplification; (h) reagents for sequencing library preparation including adapters and index primers; and (i) instructions for use for generating PIC-derived barcoded cDNA libraries for high-throughput sequencing, optionally together with generation of background singlet libraries from one or more corresponding cell populations. Optionally, the kit further comprises one or more of: (j) reagents for parallel isolation and sequencing of background singlets from immune cells and / or epithelial / tumor cells; (k) positive and / or negative control cells or RNA; (1) reagents for reducing artificial aggregation and / or for removing ambient RNA; (m) software, a data processing pipeline, or access credentials thereto, configured to (i) perform quality control to filter suspected singlets and multiplets, (ii) infer interacting partner identities for each PIC based on reference singlet profiles, and / or (iii) identify interaction-associated gene expression programs by comparing observed PIC expression to an expected expression derived from simulated PICs generated from singlet profiles.

[0279] Some embodiments of the present disclosure concern a kit that further comprises at least one reagent for conducting a nucleic acid amplification-based assay, for example, a Real- Time PCR, micro arrays, PCR, in situ Hybridization and Comparative Genomic Hybridization. In some embodiments, the polynucleotide-based detection molecules used by the disclosed methods, compositions and kits may be in the form of nucleic acid probes which can be spotted onto an array to measure RNA from the sample of a subject to be diagnosed. As defined herein, a "nucleic acid array" refers to a plurality of nucleic acids (or "nucleic acid members"), optionally attached to a support where each of the nucleic acid members is attached to a support in a unique preselected and defined region. These nucleic acid sequences are used herein as detecting nucleic acid molecules. In one embodiment, the nucleic acid member attached to the surface of the support is DNA. In a preferred embodiment, the nucleic acid member attached to the surface of the support is either cDNA or oligonucleotides. In another embodiment, the nucleic acid member attached to the surface of the support is cDNA synthesized by polymerase chain reaction (PCR). In another embodiment, a "nucleic acid array" refers to a plurality of unique nucleic acid detecting molecules attached to nitrocellulose or other membranes used in Southern and / or Northern blotting techniques. For oligonucleotide-based arrays, the selection of oligonucleotides corresponding to the gene of interest which are useful as probes is well understood in the art.

[0280] As indicated above, assay based on micro array or RT-PCR may involve attaching or spotting of the probes in a solid support. As used herein, the terms "attaching" and "spotting" refer to a process of depositing a nucleic acid onto a substrate to form a nucleic acid array such that the nucleic acid is stably bound to the substrate via covalent bonds, hydrogen bonds or ionic interactions. As usedherein, "stably associated" or "stably bound" refers to a nucleic acid that is stably bound to a solid substrate to form an array via covalent bonds, hydrogen bonds or ionic interactions such that the nucleic acid retains its unique pre-selected position relative to all other nucleic acids that are stably associated with an array, or to all other pre-selected regions on the solid substrate under conditions in which an array is typically analyzed (i.e., during one or more steps of hybridization, washes, and / or scanning, etc.).

[0281] In some embodiments, the kit of the invention further comprising at least one reagent for conducting an immunological assay selected from protein microarray analysis, ELISA, RIA, slot blot, dot blot, FACS, western blot, immunohistochemical assay, immunofluorescent assay and a radio-imaging assay. Accordingly, such kit may comprise antibodies, labeling material, in some embodiments reagents substrates and enzymes required to perform colorimetric or electrochemical reaction, optionally, secondary antibodies, filters, beads and any required solid support. In some embodiments, the kit of the invention may further comprise at least one reagent for conducting a mass spectrometry assay. Such reagents may include trypsin, buffers, filters and the like, for peptide purification.

[0282] In further embodiments, the kit of disclosed herein may further comprise at least one device, means or any reagent for obtaining a biological sample, from a subject, for example any cell, tissue or body fluid sample (needles, aspirators and the like).

[0283] A further aspect of the present disclosure relates to a prognostic method for predicting and assessing responsiveness of a mammalian subject having breast cancer, (in some embodiments, an advanced stage of breast cancer), to at least one therapeutic agent or a treatment regimen comprising the at least one therapeutic agent, and optionally for monitoring disease progression. More specifically, the prognostic methods comprising the steps of: specifically, in step (a), determining the expression level of at least one biomarker in at least one biological sample of the subject, to obtain a TAN-score and / or a Neu TME-PIC-score for the sample. In some embodiments, the at least one biomarker / s comprise at least one of: (i), at least one TAN-score biomarker selected from: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and / or (ii), at least one Neu TME-PIC-score biomarker selected from: ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF. In step (b), classifying the subject as: (i), a responder subject to the therapeutic agent or treatment regimen, if the TAN-score and / or the Neu TME-PIC-score obtained in step (a), for at least one sample obtained after the initiation of thetreatment, is negative with respect to a predetermined standard TAN-score and / or a predetermined standard Neu TME-PIC-score; or to a TAN-score and / or a Neu TME-PIC-score of at least one control sample. Alternatively, the subject is classified as (ii), a non-responder subject to the therapeutic agent or treatment regimen, if the TAN-score and / or the Neu TME-PIC-score obtained in step (a) for at least one sample obtained after the initiation of the treatment, is positive with respect to a predetermined standard TAN-score and / or a standard Neu TME-PIC-score; or to a TAN-score and / or a Neu TME-PIC-score of at least one control sample; thereby, predicting and assessing responsiveness of the subject to said therapeutic agent or treatment regimen.

[0284] In some embodiments, the expression level of at least at least six of the TAN-score biomarkers, is determined by the disclosed methods, compositions and kits of the present disclosure, specifically, at least six of CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, and / or the expression level of at least six of the Neu TME-PIC-score biomarkers is determined by the disclosed methods, specifically, at least six of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

[0285] In some embodiments, the expression level of at least at least 6, 7, 8, 9, 10, 11, 12 of the TAN-score biomarkers, is determined by the disclosed methods, disclosed methods, compositions and kits of the present disclosure, specifically, at least 6, 7, 8, 9, 10, 11, 12 of CSTA, CAMP, CD 177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, and / or the expression level of at least 6, 7, 8, 9, 10, 11, 12, 13, 14 of the Neu TME-PIC-score biomarkers is determined by the disclosed methods, specifically, at least 6, 7, 8, 9, 10, 11, 12, 13, 14, of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

[0286] As used herein, "predicting” refers to forecasting the likelihood or probability of a future event, outcome, or characteristic, such as the course of a disease or the response to a treatment.

[0287] "Assessing” refers to evaluating or judging the quality, significance, or value of something, often involving measurement or observation. "Responsiveness" refers to the degree to which a subject reacts positively to a particular therapeutic agent or treatment regimen, typically measured by an improvement in clinical parameters or a reduction in disease progression. A "therapeutic agent" refers to any substance or modality used to treat, prevent, or alleviate a disease or condition. A "treatment regimen" is a planned course of medical treatment, including the type, dosage, frequency, and duration of therapeutic agents or procedures.In the case of determining responsiveness based on the TAN-score and / or the Neu TME-PIC-score obtained in step (a), the responsiveness to the therapeutic agent or treatment regimen is determined. More specifically, in some embodiments, the subject may be classified as either a responder or a non-responder (drug-resistant disease). As used herein, a "responder" refers to a subject who exhibits a positive and clinically meaningful reaction to a therapeutic agent or treatment regimen, characterized by an improvement in disease symptoms, stabilization of the disease, or a reduction in tumor burden. A "non-responder" (also referred to as "drug-resistant disease") is a subject who shows little to no positive reaction, or even a negative reaction, to a therapeutic agent or treatment regimen, indicating that the treatment is ineffective.

[0288] In yet some further embodiments, the subject may be further sub-classified with respect to the expected degree, depth, extent, and / or duration of responsiveness. For example, a subject may be classified as a "poor responder" (showing minimal or no improvement), a "responder displaying mild response" (showing some improvement but not optimal), a "responder displaying a good response" (showing significant improvement), or a "responder displaying excellent response" (showing near-complete or complete resolution of disease symptoms), and the like.

[0289] It should be noted that "prognosis" is defined as a forecast of the future course of a disease or disorder, based on medical knowledge. This highlights one of the major advantages of the present disclosure, namely, the ability to assess responsiveness or drug-resistance and thereby predict the progression of the disease, based on the genetic signature disclosed by the present disclosure that provides the TAN-score and / or the Neu TME-PIC-score.

[0290] A "negative prognosis," as used herein, also referred to as "poor prognosis" or "bad prognosis," refers to patients who have a low likelihood of response to treatment, increased chances for relapse, reduced disease-free survival, reduced or no chance of cure, increased disease symptoms, reduced survival, and even death.

[0291] As indicated herein, the disclosed prognostic methods provide a powerful tool for early detection and / or prediction of relapse that is connected to advanced disease, and in some embodiments, also of reduced disease-free survival, reduced or no chance of cure, increased disease symptoms, nonresponsiveness at the time of relapse, reduced survival and even death.

[0292] The disclosed methods may in addition, or alternatively, provide a tool for monitoring disease progression in a subject (e.g. during the course of disease, the course of treatment, and even during the remission of the disease). "Monitoring disease progression" refers to the continuous or periodic evaluation of a disease's status over time to track its advancement, stability, or regression, often involving clinical, laboratory, and / or imaging assessments.Thus, in some embodiments, the disclosed prognostic approach may be further extended for monitoring a subject during the disease, and alternatively, or additionally, during the period of treatment or remission. Thus, in some further embodiments of the prognostic methods of the present disclosure, monitoring disease progression comprises at least one of predicting and determining disease relapse and assessing a remission interval. "Remission” is either the reduction or disappearance of the signs and symptoms of a disease. "Remission interval" is used to refer to the period during which this reduction occurs. Specifically, these terms relates to the reduction or disappearance of the signs and symptoms of a disease being treated with a therapeutic compound or to the period during which this reduction occurs. The term "relapse", as used herein, relates to the re-occurrence of a condition, disease or disorder that affected a person in the past. Specifically, the term relates to the re-occurrence of a disease being treated with an appropriate therapeutic agent.

[0293] As indicated above, in some embodiments, the prognostic methods discussed herein in the present aspect, may further provide a tool for monitoring disease progression. Thus, in some embodiments, the prognostic method disclosed herein may further comprise the steps of: (c), repeating step (a) of the discussed methods to determine the expression level of at least one biomarker in at least one more biological sample of the subject, to obtain a TAN-score and / or a Neu TME-PIC-score for the sample in at least one more temporally-separated sample of the subject, specifically, to a sample obtained from the subject in at least one additional time point. The next and (d), concerns predicting and / or determining disease relapse in the subject, if at least one temporally separated sample obtained after the initiation of the treatment regimen, or in a later period, displays at least one of: elevated TAN-score and / or a Neu TME-PIC-score, specifically, if at least one of these scores became more positive or less negative with respect to the scores determined for samples obtained in a previous time point.

[0294] As indicated above, in accordance with some embodiments of the present disclosure, in order to assess the patient condition, or monitor the disease progression, as well as responsiveness to a certain treatment, at least two “temporally-separated” test samples must be collected from the examined patient and compared thereafter, in order to determine if there is any change or difference in the TAN-score and / or a Neu TME-PIC-score between the samples. Such change may reflect a change in the responsiveness of the subject, or the disease progression. In practice, to detect a change having more accurate predictive value, at least two "temporally-separated" test samples and preferably more, must be collected from the patient.The TAN-score and / or a Neu TME-PIC-score are determined using the method disclosed herein, applied for each sample. As detailed above, the change in the TAN-score and / or the Neu TME-PIC-score is calculated by determining the change in the TAN-score and / or the Neu TME-PIC-score (that reflect the change in the expression level of the TAN-score biomarkers and / or the Neu TME-PIC-score biomarkers, respectively) between at least two samples obtained from the same patient in different time-points or time intervals. This period of time, also referred to as "time interval", or the difference between time points (wherein each time point is the time when a specific sample was collected) may be any period deemed appropriate by medical staff and modified as needed according to the specific requirements of the patient and the clinical state he or she may be in. For example, this interval may be at least one day, at least three days, at least one week, at least two weeks, at least three weeks, at least one month, at least two months, at least three months, at least four months, at least five months, at least six months, at least one year, or even more. The number of samples collected and used for evaluation and classification of the subject either as a responder or alternatively, as a drug resistant or as a subject that may experience relapse of the disease, may change according to the frequency with which they are collected. For example, the samples may be collected at least every day, every two days, every four days, every week, every two weeks, every three weeks, every month, every two months, every three months every four months, every 5 months, every 6 months, every 7 months, every 8 months, every 9 months, every 10 months, every 11 months, every year or even more. Furthermore, to assess the disease progression according to the present disclosure, it is understood that the change in TAN-score and / or the Neu TME-PIC-score, may be calculated as an average change over at least three samples taken in different time points, or the change may be calculated for every two samples collected at adjacent time points. It should be appreciated that the sample may be obtained from the monitored patient in the indicated time intervals for a period of several months or several years. More specifically, for a period of 1 year, for a period of 2 years, for a period of 3 years, for a period of 4 years, for a period of 5 years, for a period of 6 years, for a period of 7 years, for a period of 8 years, for a period of 9 years, for a period of 10 years, for a period of 11 years, for a period of 12 years, for a period of 13 years, for a period of 14 years, for a period of 15 years or more. In some embodiments of the disclosed diagnostic and prognostic methods, the expression level of biomarker / s in step (a), is determined for at least one of: (i) the TAN-score biomarkers, that comprise: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and / or (ii) for the Neu TME-PIC-score biomarkers, that comprise: ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L,TESC, UPP1 and VEGF; to obtain a TAN-score of (i), and / or a Neu TME-PIC-score of (ii), for the sample.

[0295] Thus, in some embodiments of the disclosed methods, step (a) involves determining the expression level of the TAN-score biomarkers, that comprise: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, to obtain a TAN-score for the sample.

[0296] In some further embodiments, step (a) of the disclosed methods involves determining the expression level of the Neu TME-PIC-score biomarkers, that comprise: ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF, to obtain a Neu TME-PIC-score for the sample.

[0297] In yet some further embodiments, step (a) of the disclosed methods involves determining the expression level of the TAN-score biomarkers, that comprise: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, to obtain a TAN-score for the sample, and additionally, of the Neu TME-PIC-score biomarkers, that comprise: ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF, to obtain a Neu TME-PIC-score for the sample.

[0298] In some embodiments, the disclosed methods may be applicable for determining personalized treatment regimen based on determining the responsiveness of the subject to a specific treatment agent and / or regimen.

[0299] Accordingly, in some further aspects thereof, the present disclosure provides a method for determining a personalized treatment regimen for a subject having advanced breast cancer.

[0300] "Personalized treatment" refers to a medical approach that tailors therapeutic strategies to the individual patient, based on their unique biological characteristics, such as genetic makeup, biomarker profiles, and predicted response to specific interventions. This approach aims to optimize treatment efficacy and minimize adverse effects by considering the individual variability among patients. In the context of the present disclosure, personalized treatment for advanced breast cancer would involve selecting therapies based on the patient's specific TAN-score and / or Neu TME-PIC-score, which indicate the aggressiveness of the cancer and potential responsiveness to certain therapeutic agents. This allows for a more targeted and effective treatment regimen compared to a one-size-fits-all approach. The method disclosed herein is personally adapted for each patient and may further provide a continuous and monitored treatment regimen. This method therefore combines diagnostic steps for determining the TAN-score and / or Neu TME-PIC-score of the treated subject, specifically, the levels of expression of the TAN-score biomarkers and / orNeu TME-PIC-score biomarkers of the treated and / or monitored subject. More specifically, in some embodiments, the method comprises the following steps. First in step (I), determining the expression level of at least one biomarker in at least one biological sample of the subject, to obtain a TAN-score and / or a Neu TME-PIC-score for the sample. In some embodiments, the at least one biomarker / s comprise at least one of: (i), at least one TAN-score biomarker selected from: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and / or (ii), at least one Neu TME-PIC-score biomarker selected from: ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF. The next step (II), involves selecting a treatment regimen determined as reducing the TAN-score and / or the Neu TME-PIC-score in the subject.

[0301] Still further, as discussed above, in some embodiments, the disclosed prognostic methods may be applicable for monitoring diseased subjects, either a subject treated or not treated, for predicting relapse of the disease.

[0302] Still further, in some embodiments, the disclosed methods may be applicable for predicting the survival of the subjects. Still further, in some embodiments, the disclosed prognostic methods may be applicable for evaluating the disease-free interval of the subject, that reflects the prognosis of the prognosed subjects. "Survival” refers to the length of time a patient remains alive after diagnosis or treatment of a disease. It is a critical endpoint in clinical trials and prognostic assessments, often expressed as "overall survival (OS)”, which is the percentage of people in a study or treatment group who are still alive for a certain period of time after diagnosis or treatment. Survival rates can be influenced by various factors, including the stage of cancer, the effectiveness of treatment, and the patient's general health and / or specific characteristics. "Disease-free interval" or Disease-free period” refers to the length of time after primary treatment for cancer during which a patient remains free of disease. This metric is particularly important in evaluating the effectiveness of adjuvant therapies and in assessing the risk of recurrence. A longer disease-free interval indicates a more successful treatment outcome and a lower likelihood of the cancer returning. It is often used as a primary endpoint in clinical trials for early-stage cancers to determine if a treatment prevents recurrence.

[0303] Another aspect of the present disclosure relates to a method for treating, preventing, inhibiting, reducing, eliminating, protecting or delaying the onset of breast cancer in a mammalian subject. The method comprising: (a), determining the expression level of at least one biomarker in at least one biological sample of the subject to obtain a TAN-score and / or a Neu TME-PIC-score for thesample. More specifically, the at least one biomarker / s comprise at least one of: (i), at least one TAN-score biomarker selected from: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and / or (ii), at least one Neu TME-PIC-score biomarker selected from: ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF. Next, in step (b), determining if at least one of the TAN-score and / or the Neu TME-PIC- score obtained in step (a), is positive or negative with respect to a predetermined standard TAN-score and / or a standard Neu TME-PIC-score; or to a TAN-score and / or a Neu TME-PIC-score of at least one control sample. It should be noted that a positive TAN-score and / or Neu TME-PIC-score of the sample, classifies the subject as displaying advanced stage of breast cancer. The next step (c), involves the therapeutic step of administering at least one therapeutic agent to a subject classified in step (b), as displaying advanced stage of breast cancer. In some embodiments, the expression level of at least at least six of the TAN-score biomarkers, is determined by the disclosed methods, compositions and kits of the present disclosure, specifically, at least six of CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, and / or the expression level of at least six of the Neu TME-PIC-score biomarkers is determined by the disclosed methods, specifically, at least six of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

[0304] In some embodiments, the expression level of at least at least 6, 7, 8, 9, 10, 11, 12 of the TAN-score biomarkers, is determined by the disclosed methods, disclosed methods, compositions and kits of the present disclosure, specifically, at least 6, 7, 8, 9, 10, 11, 12 of CSTA, CAMP, CD 177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, and / or the expression level of at least 6, 7, 8, 9, 10, 11, 12, 13, 14 of the Neu TME-PIC-score biomarkers is determined by the disclosed methods, specifically, at least 6, 7, 8, 9, 10, 11, 12, 13, 14, of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

[0305] In some embodiments of the disclosed therapeutic methods, the expression level of the at least one biomarker / s in step (a), is determined for at least one of: (i) the TAN-score biomarkers, that comprise: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and / or (ii) for the Neu TME-PIC-score biomarkers, that comprise: ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF; to obtain a TAN-score of (i), and / or a Neu TME-PIC-score of (ii), for the sample.Thus, in some embodiments of the disclosed therapeutic methods, step (a) involves determining the expression level of the TAN-score biomarkers, that comprise: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, to obtain a TAN-score for the sample.

[0306] In some further embodiments, step (a) of the disclosed therapeutic methods involves determining the expression level of the Neu TME-PIC-score biomarkers, that comprise: ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF, to obtain a Neu TME-PIC-score for the sample.

[0307] In yet some further embodiments, step (a) of the disclosed therapeutic methods, involves determining the expression level of the TAN-score biomarkers, that comprise: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, to obtain a TAN-score for the sample, and additionally, of the Neu TME-PIC-score biomarkers, that comprise: ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF, to obtain a Neu TME-PIC-score for the sample. In some embodiments, the therapeutic agent used in the disclosed therapeutic methods is a compound that reduces and / or specifically depletes neutrophils or at least one sub-population of neutrophils in the treated subject. As used herein, "reduce” refers to a decrease in the number, amount, or activity of something, such as neutrophils or a sub-population of neutrophils, for example, a reduction of about 10% to 100% is encompassed by the present disclosure, specifically, reduction of 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100% of the number or activity of neutrophils or a sub-population of neutrophils. The reduction can be partial or complete. "Depletes” or "depleting" refers to the act of significantly decreasing or exhausting the number or amount of something, often to a very low or undetectable level. In the context of neutrophils, it implies a substantial removal or elimination of these cells.

[0308] As used herein, "neutrophils" refers to a type of white blood cell, specifically a granulocyte, that plays a critical role in the innate immune system. Neutrophils are typically the first immune cells to arrive at sites of infection or inflammation and are essential for fighting bacterial and fungal pathogens through phagocytosis, degranulation, and the release of neutrophil extracellular traps (NETs). In the context of cancer, neutrophils can exhibit significant plasticity and heterogeneity, adopting various phenotypes that can either promote or suppress tumor growth depending on the microenvironment.- 1 - A "sub-population of neutrophils" refers to a distinct group of neutrophils that share specific phenotypic, functional, or transcriptional characteristics, differentiating them from the broader neutrophil population. These sub-populations often arise in response to particular microenvironmental cues, such as those found in tumors, and can have specialized roles. Examples include tumor-associated neutrophils (TANs), which can be further categorized into pro-tumorigenic (N2) or anti-tumorigenic (Nl) phenotypes, or other functionally distinct subsets like young TANs, TAN1, TAN2, MHC class II (MHC-II Neut) expressing neutrophils, and Ptgs2-expressing neutrophils (Ptgs2+ Neut), each contributing uniquely to disease progression or resolution.

[0309] In more specific embodiments, the disclosed therapeutic methods may use a therapeutic compound that decreases or deplete at least one sub-population of neutrophils in the subject, for example, a neutrophil sub-population comprising at least one of: young TANs, TAN1 and TAN2 and / or enriched in at least one of MHC class II (MHC-II Neut) and Ptgs2-expressing neutrophil (Ptgs2+ Neut).

[0310] The term "young tissue-associated neutrophils” or "young TANs" as used herein refers to a neutrophil state enriched in the TME of advanced breast cancer, more specifically of advanced breast carcinoma. In some embodiments, the young TANs express a gene program that includes Ngp, Camp and Ltf. In some further embodiments, the gene program expressed by young TANs includes also at least one of Cdl 77 and Mmp8.

[0311] The Young TAN subset shares their gene signature (Ngp, Camp, Ltf) mostly with blood neutrophils but less with the other mammary gland neutrophil states (MHC-II Neut, Ptgs2+Neut, TAN1, TAN2), suggesting that the young TANs might be early recruited cells into the TME.

[0312] "TAN1" and "TAN2" as used herein refers to two additional tumor-associated neutrophil states enriched in the TME of advanced breast cancer, more specifically of advanced breast carcinoma. TAN1 and TAN2 upregulates a unique TME gene program (e.g. Ifitml, Cxcl2, Ccrl2, Wfdcl7). A neutrophil state expressing the MHC class II genes (MHC-II Neut) as used herein refers to a neutrophil state in the TME that is enriched in early breast cancer, more specifically in early breast carcinoma. The MHC class II genes expressed by the MHC-II Neut includes for example Cd74, H2-Aa and H2-Aabl.

[0313] The term "Ptgs2- expressing neutrophil" or "Ptgs2+ Neut" refers herein to another neutrophil state enriched in the TME of early breast cancer, more specifically of early breast carcinoma. Genes associated with the Ptgs2+Neut state includes for example Cxcl2, Nfkbia, Ptgs2, and Ccrl2.Still further, in some alternative or additional embodiments, the disclosed therapeutic methods may use a therapeutic agent that interferes with the interaction between at least one ligand-receptor pair of neutrophils-tumor cells.

[0314] A "ligand-receptor” pair refers to a specific molecular interaction between a ligand, which is typically a signaling molecule, and its corresponding receptor, which is a protein typically located on the surface of a cell or within the cell. The ligand-receptor pairs related to the present disclosure can mediate the physical interaction between a neutrophil cell and a breast tumor cell. In some embodiments of the present disclosure, the ligand refers to a specific molecule expressed by neutrophils within the TME and the receptor refers to a receptor expressed by the tumor cell. In other embodiments, the ligand refers to a specific molecule expressed by the tumor cells and the receptor refers to a receptor expressed by neutrophils within the TME.

[0315] In more specific embodiments of the disclosed therapeutic methods, such at least one ligandreceptor pairs of neutrophils-tumor cells comprise at least of: App (amyloid beta precursor protein) and Fpr2 (formyl peptide receptor 2); Gnai2 (Guanine nucleotide-binding protein G(i), alpha-2 subunit) and Cxcr2 (C-X-C Motif Chemokine Receptor 2); Cdl4 (cluster of differentiation 14) and Itgbl (Integrin beta 1); Vasp (Vasodilator Stimulated Phosphoprotein) and Cxcr2 (C-X-C Motif Chemokine Receptor 2); Tnfsfl4 (tumor necrosis factor Superfamily Member 14) and Ltbr (lymphotoxin beta receptor); Jami (Junctional Adhesion Molecule-Like) and Cxadr (Coxsackievirus and adenovirus receptor); Sema4a (Semaphorin 4A) and Plxnb2 (Plexin B2); Lta_Ltb (Lymphotoxin Alpha_lymphotoxin beta) and Ltbr (lymphotoxin beta receptor); Ltb (lymphotoxin beta) and Tnfrsfla (tumour necrosis factor receptor superfamily 1A); Gnai2 (Guanine nucleotide-binding protein G(i), alpha-2 subunit) and Egfr (epidermal growth factor receptor); Ptpn6 (Tyro sine-protein phosphatase non-receptor type 6) and Egfr (epidermal growth factor receptor); Fllr (Fll receptor) and Itgal_Itgb2 (Integrin alpha L_Integrin Subunit Beta 2); Lamb2 (laminin subunit beta 2) and Cd44 (Cluster differentiation 44); Cdhl (Cadherin-1) and Igflr (insulin like growth factor 1 receptor); Cd55 (Complement decay-accelerating factor) and Adgre5 (Adhesion G Protein-Coupled Receptor E5); B2m (beta-2-microglobulin) and Tfrc (Transferrin Receptor); Mucl (Mucin 1) and Siglece (sialic acid binding Ig-like lectin E); Lgals3bp (Galectin 3 binding protein) and Cd33 (sialic acid binding Ig-like lectin 3); Anxa2 (Annexin A2) and Tlr2 (Toll-like receptor 2); Gnai2 (Guanine nucleotide-binding protein G(i), alpha-2 subunit) and Fprl (formyl peptide receptor 1); Adam 17 (ADAM Metallopeptidase Domain 17) and I16ra (interleukin 6 receptor, alpha); Tlnl (Talin-1) and Itgb3 (integrin, beta 3)..In some specific embodiments, a therapeutic agent useful in the present therapeutic methods may comprise an antibody specific for at least one of: the ligand and / or the receptor of the ligandreceptor pair.

[0316] The present disclosure further provides at least one therapeutic agent for use in a method for treating, preventing, inhibiting, reducing, eliminating, protecting or delaying the onset of breast cancer in a mammalian subject. In some embodiments, the method comprising: (a), determining the expression level of at least one biomarker in at least one biological sample of the subject to obtain a TAN-score and / or a Neu TME-PIC-score for the sample. More specifically, the at least one biomarker / s comprise at least one of: (i), at least one TAN-score biomarker selected from: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and / or (ii), at least one Neu TME-PIC-score biomarker selected from: ARG1, CHMP4C, CEDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SEFN12E, TESC, UPP1 and VEGF. Next, in step (b), determining if at least one of the TAN-score and / or the Neu TME-PIC- score obtained in step (a), is positive or negative with respect to a predetermined standard TAN-score and / or a standard Neu TME-PIC-score; or to a TAN-score and / or a Neu TME-PIC-score of at least one control sample. It should be noted that a positive TAN-score and / or Neu TME-PIC-score of the sample, classifies the subject as displaying advanced stage of breast cancer. The next step (c), involves the therapeutic step of administering said at least one therapeutic agent to a subject classified in step (b), as displaying advanced stage of breast cancer. In some embodiments, the therapeutic agent reduces the TAN-score and / or the Neu TME-PIC-score in the subject.

[0317] A further aspect of the present disclosure relates to a method for treating, preventing, inhibiting, reducing, eliminating, protecting or delaying the onset of breast cancer in a mammalian subject. The method comprises the steps of administrating to the subject at least one therapeutic agent that: (i) reduces and / or specifically depletes at least one sub-population of neutrophils in the subject; and / or (ii) interferes with the interaction between at least one ligand-receptor pairs of neutrophilstumor cells. In some embodiments, the sub-population of neutrophils comprises at least one of: young TANs, TAN1 and TAN2 and / or enriched in at least one of MHC class II (MHC-II Neut) and Ptgs2-expressing neutrophil (Ptgs2+ Neut).

[0318] In some embodiments, the therapeutic agent interferes with the interaction between at least one ligand-receptor pair of neutrophils-tumor cells.As used herein, "interferes with the interaction" refers to any action or mechanism that disrupts, blocks, inhibits, or otherwise prevents the normal binding or communication between two entities, such as a ligand and its receptor. In the context of the provided text, it specifically means that the therapeutic agent acts to hinder or stop the molecular recognition and binding events that would typically occur between a ligand on one cell (e.g., a neutrophil) and its corresponding receptor on another cell (e.g., a tumor cell). This interference can lead to a reduction or complete cessation of the downstream signaling and biological effects that would normally result from such an interaction.

[0319] In yet some further embodiments of the disclosed therapeutic methods, the at least one ligandreceptor pairs of neutrophils-tumor cells comprise at least of: App and Fpr2; Gnai2 and Cxcr2; Cdl4 and Itgbl; Vasp and Cxcr2; Tnfsfl4 and Ltbr; Jami and Cxadr; Sema4a and Plxnb2; Lta_Ltb and Ltbr; Ltb and Tnfrsfla; Gnai2 and Egfr; Ptpn6 and Egfr; Fllr and Itgal_Itgb2; Lamb2 and Cd44; Cdhl and Igflr; Cd55 and Adgre5; B2m and Tfrc; Mucl and Siglece; Lgals3bp and Cd33; Anxa2 and Tlr2; Gnai2 and Fprl; Adaml7 and I16ra; Tlnl and Itgb3.

[0320] Still further, in some embodiments of the disclosed methods, the therapeutic agent comprises an antibody specific for at least one of: the ligand and / or the receptor of the ligand-receptor pair disclosed herein above.

[0321] In some embodiments, the therapeutic methods of the present disclosure may be applicable for a subject diagnosed as displaying advanced breast cancer, specifically, by using a diagnostic method as defined by the present disclosure.

[0322] The present disclosure further provides at least one therapeutic agent that: (i) reduces and / or specifically depletes at least one sub -population of neutrophils in the subject; and / or (ii) interferes with the interaction between at least one ligand-receptor pairs of neutrophils-tumor cells; for use in a method for treating, preventing, inhibiting, reducing, eliminating, protecting or delaying the onset of breast cancer in a mammalian subject. In some embodiments, the sub-population of neutrophils comprises at least one of: young TANs, TAN1 and TAN2 and / or enriched in at least one of MHC class II (MHC-II Neut) and Ptgs2-expressing neutrophil (Ptgs2+ Neut).

[0323] It should be appreciated that the methods of the disclosure may be suitable for any mammalian subject. By "patient ", "mammalian subject" or "subject " it is meant any mammal that may be affected by the above-mentioned conditions, and to whom the treatment and diagnosis methods herein described is desired, including human, bovine, equine, canine, murine and feline subjects. Specifically, said subject is a human. Thus, in yet some further embodiments, the methods of the disclosure may be suitable for any mammalian female subject, specifically to any woman. In yet-some further embodiments, the methods and kits of the disclosure may be suitable for any woman aged between 12 years to 90 or older. In yet some specific embodiments, the methods and kits of the disclosure may be suitable for diagnosis of advanced breast cancer in any woman over 30, 35, 40, 45, 50, 55, 60, 65, 70 years old, or even older.

[0324] It is to be understood that the terms "treat”, “treating”, “treatment" or forms thereof, as used herein, mean preventing, ameliorating or delaying the onset of one or more clinical indications of disease activity in a subject having a pathologic disorder. Treatment refers to therapeutic treatment. Those in need of treatment are subjects having pathologic disorder. Specifically, providing a "preventive treatment" (to prevent) or a "prophylactic treatment" is acting in a protective manner, to defend against or prevent something, especially a condition or disease.

[0325] The term “treatment or prevention” as used herein, refers to the complete range of therapeutically positive effects of administrating to a subject including inhibition, reduction of, alleviation of, and relief from, a condition or disease, symptoms or undesired side effects. More specifically, treatment or prevention of relapse or recurrence of the disease, includes the prevention or postponement of development of the disease, prevention or postponement of development of symptoms and / or a reduction in the severity of such symptoms that will or are expected to develop. These further include ameliorating existing symptoms, preventing- additional symptoms and ameliorating or preventing the underlying metabolic causes of symptoms. It should be appreciated that the terms "inhibition", "moderation", “reduction”, "decrease" or "attenuation" as referred to herein, relate to the retardation, restraining or reduction of a process by any one of about 1% to 99.9%, specifically, about 1% to about 5%, about 5% to 10%, about 10% to 15%, about 15% to 20%, about 20% to 25%, about 25% to 30%, about 30% to 35%, about 35% to 40%, about 40% to 45%, about 45% to 50%, about 50% to 55%, about 55% to 60%, about 60% to 65%, about 65% to 70%, about 75% to 80%, about 80% to 85% about 85% to 90%, about 90% to 95%, about 95% to 99%, or about 99% to 99.9%, 100% or more.

[0326] With regards to the above, it is to be understood that, where provided, percentage values such as, for example, 10%, 50%, 120%, 500%, etc., are interchangeable with "fold change" values, i.e., 0.1, 0.5, 1.2, 5, etc., respectively.

[0327] The term "amelioration" as referred to herein, relates to a decrease in the symptoms, and improvement in a subject's condition brought about by the methods according to the present disclosure, wherein said improvement may be manifested in the forms of inhibition of pathologic processes associated with the breast cancer described herein, a significant reduction in their magnitude, or an improvement in a diseased subject physiological state.The term "inhibit" and all variations of this term is intended to encompass the restriction or prohibition of the progress and exacerbation of pathologic symptoms or a pathologic process progress, said pathologic process symptoms or process are associated with.

[0328] The term "eliminate" relates to the substantial eradication or removal of the pathologic symptoms and possibly pathologic etiology, optionally, according to the methods of the present disclosure described herein.

[0329] The terms "delay", "delaying the onset", "retard" and all variations thereof are intended to encompass the slowing of the progress and / or exacerbation of a disorder associated with the disorders and their symptoms slowing their progress, further exacerbation or development, so as to appear later than in the absence of the treatment according to the present disclosure.

[0330] It should be noted that the terms "disease", "disorder", "condition" and "illness", are equally used herein.

[0331] It should be appreciated that any of the methods described by the present disclosure may be applicable for treating and / or ameliorating any of the disorders disclosed herein or any condition associated therewith. It is understood that the interchangeably used terms "associated", “linked” and "related", when referring to pathologies herein, mean diseases, disorders, conditions, or any pathologies which at least one of: share causalities, co-exist at a higher than coincidental frequency, or where at least one disease, disorder condition or pathology causes the second disease, disorder, condition or pathology. More specifically, as used herein, “disease”, “disorder”, “condition”, “pathology” and the like, as they relate to a subject's health, are used interchangeably and have meanings ascribed to each and all of such terms.

[0332] A further aspect of the present disclosure relates to a screening method for identifying at least one therapeutic compound for the treatment of advanced breast cancer in a mammalian subject. The screening method comprising the steps of: (a), determining a TAN-score and / or a Neu TME-PIC-score of at least one sample contacted with a candidate compound, wherein: (i), a TAN-score is determined in a sample comprising a population of neutrophils, by determining the expression level of at least one biomarker selected from: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and / or (ii), a Neu TME-PIC-score is determined in a sample comprising neutrophil-tumor cell pairs, by determining the expression level of at least one biomarker selected from: ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF. The next step (b), involves determining that the candidate compound is a therapeutic compound for advancedbreast cancer if the TAN-score and / or the Neu TME-PIC-score determined in step (a) for the sample is negative with respect to a predetermined standard TAN-score and / or a predetermined standard Neu TME-PIC-score; or to a TAN-score and / or a Neu TME-PIC-score of at least one control sample; thereby identifying a therapeutic compound for advanced breast cancer.

[0333] As used herein, a "candidate compound" refers to a chemical substance, molecule, or biological agent that is being tested or screened for its potential therapeutic effects. The term "therapeutic compound" refers to a compound that has been identified or confirmed to possess a beneficial effect in treating, preventing, inhibiting, reducing, eliminating, protecting, or delaying the onset of a disease or condition, such as advanced breast cancer. In the context of the screening methods described, a candidate compound is deemed a therapeutic compound if it demonstrates the desired biological activity, such as negatively influencing the TAN-score and / or Neu TME-PIC-score, indicating its potential to combat advanced breast cancer.

[0334] In some embodiments, the candidate molecule is a therapeutic agent / drug. More specifically, a compound to be tested by the disclosed screening methods may be referred to as a test compound or a candidate compound. The candidate compounds may be any known compound used for a specific disorder, specifically, advanced breast cancer, or any unknown drug or compound that is screened herein based on its effect on the TAN-score and / or the Neu TME-PIC-score, and thus, as a candidate compound that may modulate the TAN-score and / or the Neu TME-PIC-score of a given subject. Any compound may be used as a test compound in various embodiments. In some embodiments a library of FDA approved compounds that can be used by humans may be used. Compound libraries are commercially available from a number of companies including but not limited to Maybridge Chemical Co. (Trevillet, Cornwall, UK), Comgenex (Princeton, NJ), Microsource (New Milford, CT), Aldrich (Milwaukee, WI), AKos Consulting and Solutions GmbH (Basel, Switzerland), Ambinter (Paris, France), Asinex (Moscow, Russia), Aurora (Graz, Austria), BioFocus DPI, Switzerland, Bionet (Camelford, UK), ChemBridge, (San Diego, CA), ChemDiv, (San Diego, CA), Chemical Block Lt, (Moscow, Russia), ChemStar (Moscow, Russia), Exclusive Chemistry, Ltd (Obninsk, Russia), Enamine (Kiev, Ukraine), Evotec (Hamburg, Germany), Indofine (Hillsborough, NJ), Interbio screen (Moscow, Russia), Interchim (Montlucon, France), Life Chemicals, Inc. (Orange, CT), Microchemistry Ltd. (Moscow, Russia), Otava, (Toronto, ON), PharmEx Ltd.(Moscow, Russia), Princeton Biomolecular (Monmouth Junction, NJ), Scientific Exchange (Center Ossipee, NH), Specs (Delft, Netherlands), TimTec (Newark, DE), Toronto Research Corp. (North York ON), UkrOrgSynthesis (Kiev, Ukraine), Vitas-M, (Moscow, Russia), Zelinsky Institute, (Moscow, Russia), and Bicoll (Shanghai, China).Combinatorial libraries are available and can be prepared. Libraries of natural compounds in the form of bacterial, fungal, plant and animal extracts are commercially available or can be readily prepared by methods well known in the art. Compounds isolated from natural sources, such as animals, bacteria, fungi, plant sources, and marine samples may be tested for the presence of potentially useful pharmaceutical compounds. It will be understood that the agents to be screened could also be derived or synthesized from chemical compositions or man-made compounds. In some embodiments a library useful in the present invention may comprise at least 10,000 compounds, at least 50,000 compounds, at least 100,000 compounds, at least 250,000 compounds, or more. In yet some further embodiments, the candidate compound may be at least one of a small molecule, aptamer, a peptide, a nucleic acid molecule and an immunological agent, and any combinations thereof. In some specific embodiments, the compound used by the screening methods of the present disclosure, that specifically reduces the TAN-score and / or the Neu TME-PlC-score in a subject, may be a small molecule. A "small molecule" as used herein, is an organic molecule that is less than about 2 kilodaltons (kDa) in mass. In some embodiments, the small molecule is less than about 1.5 kDa, or less than about 1 kDa. In some embodiments, the small molecule is less than about 800 daltons (Da), 600 Da, 500 Da, 400 Da, 300 Da, 200 Da, or 100 Da. Often, a small molecule has a mass of at least 50 Da. In some embodiments, a small molecule is non-poly meric. In some embodiments, a small molecule is not an amino acid. In some embodiments, a small molecule is not a nucleotide. In some embodiments, a small molecule is not a saccharide. In some embodiments, a small molecule contains multiple carbon-carbon bonds and can comprise one or more heteroatoms and / or one or more functional groups important for structural interaction with proteins (e.g., hydrogen bonding), e.g., an amine, carbonyl, hydroxyl, or carboxyl group, and in some embodiments at least two functional groups. Small molecules often comprise one or more cyclic carbon or heterocyclic structures and / or aromatic or polyaromatic structures, optionally substituted with one or more of the above functional groups.

[0335] In some embodiments, the candidate therapeutic compound is a well-known drug used for the treatment of advanced breast cancer. In such case, the method disclosed herein is used to evaluate if the particular drug is suitable and / or optimal for treating the particular subject, thereby providing a personalized therapeutic tool.

[0336] In some embodiments, determining a TAN-score and / or a Neu TME-PIC-score of at least one sample contacted with a candidate compound performed by step (a) of the disclosed screening methods may be done by: (i), determining a TAN-score in step (a) by determining the expression level of CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8,UPP1 and WFDC21P; and / or (ii), determining Neu TME-PIC-score in step (a), by determining the expression level of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF. In some embodiments, the disclosed screening methods involve (i), determining a TAN-score in step (a) by determining the expression level of CSTA, CAMP, CD177, GYG1, IFfTMl, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P. In yet some further embodiments, the disclosed screening methods involve (ii), determining Neu TME-PIC-score in step (a), by determining the expression level of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF. Alternatively, the disclosed screening methods may involve (i), determining a TAN-score in step (a) by determining the expression level of CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, and (ii), determining Neu TME-PIC-score in step (a), by determining the expression level of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

[0337] In some embodiments, the screening methods of the present disclosure are applicable for identifying a therapeutic compound for treating a subject that has advanced breast cancer.

[0338] Still further, in some embodiments of the disclosed screening methods, the sample used by the method and contacted with the candidate compound is a sample obtained from a tumor organoid. Specifically, breast tumor organoid. Accordingly, the disclosed screening and contacting of the cells in the samples with the candidate compound is performed in vitro. As used herein, a "tumor organoid" refers to a three-dimensional, in vitro culture system derived from primary tumor cells or cancer stem cells that self-organizes and recapitulates key architectural, cellular, and molecular features of the original tumor. These organoids are grown in a specialized matrix, often supplemented with growth factors, allowing them to mimic the complex microenvironment and heterogeneity of in vivo tumors more closely than traditional two-dimensional cell cultures. A "breast tumor organoid" refers to a tumor organoid derived from primary breast tumor cells or breast cancer stem cells.

[0339] Still further, in some additional or alternative embodiments, the disclosed screening methods may use samples obtained from a non-human mammalian model. More specifically, according to such embodiments, the candidate compound is contacted with the cells in vivo in the non-human animal, and isolated thereafter for further analysis and determination of the level of the disclosed biomarkers. In some embodiments, a suitable anima model may be the transgenic mammary-specific polyomavirus middle T antigen overexpression mouse model (MMTV-PyMT), as exemplified in the present disclosure.

[0340] In yet some further embodiments, promising candidate compounds that reduce the score defined by the screening methods, may be further evaluated for at least one of: inhibition of cell migration, inhibition of cell proliferation, inhibition of angiogenesis, specifically, as shown by the present disclosure.

[0341] As used herein, "inhibition of cell migration" refers to the process of reducing or preventing the movement of cells from one location to another. In the context of cancer, inhibiting cell migration is crucial for preventing metastasis, where cancer cells spread from the primary tumor to distant sites in the body. This can involve disrupting cellular mechanisms responsible for motility, such as actin polymerization, adhesion, or chemotaxis. Inhibition of migration caused by a candidate compound may be evaluated for example using the scratch assay as exemplified in Example 6.

[0342] "Inhibition of cell proliferation" refers to the process of reducing or preventing the growth and division of cells, by at least 10% to 100%, specifically, 50%-100%. In cancer treatment, this is a primary goal, as uncontrolled cell proliferation is a hallmark of tumor development. This can be achieved by targeting various stages of the cell cycle, DNA replication, or signaling pathways that promote cell growth. "Inhibition of angiogenesis" refers to the process of reducing or preventing the formation of new blood vessels from pre-existing ones. Angiogenesis is critical for tumor growth and metastasis, as tumors require a blood supply to deliver nutrients and oxygen and remove waste products. Inhibiting angiogenesis starves the tumor, thereby limiting its growth and spread. This can involve targeting pro-angiogenic factors like VEGF or their receptors, or disrupting the endothelial cells that form blood vessels (see Example 7).

[0343] It should be understood that in some embodiments, for each of the disclosed aspects disclosed by the present disclosure, the expression level of at least at least six of the TAN-score biomarkers, is determined by the disclosed methods, compositions and kits of the present disclosure, specifically, at least six of CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P, and / or the expression level of at least six of the Neu TME-PIC-score biomarkers is determined by the disclosed methods, specifically, at least six of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF. In some embodiments, the expression level of at least at least 6, 7, 8, 9, 10, 11, 12 of the TAN-score biomarkers, is determined by the disclosed methods, disclosed methods, compositions and kits of the present disclosure, specifically, at least 6, 7, 8, 9, 10, 11, 12 of CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1and WFDC21P, and / or the expression level of at least 6, 7, 8, 9, 10, 11, 12, 13, 14 of the Neu TME-PIC-score biomarkers is determined by the disclosed methods, specifically, at least 6, 7, 8, 9, 10, 11, 12, 13, 14, of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF. In yet some further embodiments, the expression level of all 12 of the TAN-score biomarkers, is determined by the disclosed methods, disclosed methods, compositions and kits of the present disclosure and / or the expression level of all 14 of the Neu TME-PIC-score biomarkers.

[0344] It should be appreciated that unless expressly stated otherwise, each definition of a term appearing in the claims that is provided in the context of one aspect (or embodiment) shall apply equally to the same term as used in the context of any other aspect (or embodiment) of the present disclosure. Such definitions are incorporated by reference across aspects, and are not repeated in each aspect solely to avoid unnecessary repetition.

[0345] The term "about" as used herein indicates values that may deviate up to 1%, more specifically 5%, more specifically 10%, more specifically 15%, and in some cases up to 20% higher or lower than the value referred to, the deviation range including integer values, and, if applicable, non-integer values as well, constituting a continuous range. Thus, as used herein the term "about" refers to ± 10 %.

[0346] The terms "comprises", "comprising", "includes", "including", "having" and their conjugates mean "including but not limited to". This term encompasses the terms "consisting of" and "consisting essentially of". The phrase "consisting essentially of" means that the composition or method may include additional ingredients and / or steps, and / or parts, but only if the additional ingredients and / or steps do not materially alter the basic and novel characteristics of the claimed composition or method. Throughout this specification and the Examples and claims which follow, unless the context requires otherwise, the word “comprise”, and variations such as “comprises” and “comprising”, will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps. It should be noted that various embodiments of this disclosure may be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the disclosure. Accordingly, the description of a range should be considered to have specifically disclosed all the possible sub ranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed sub ranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individualnumbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range. Whenever a numerical range is indicated herein, it is meant to include any cited numeral (fractional or integral) within the indicated range. The phrases "ranging / ranges between" a first indicate number and a second indicate number and "ranging / ranges from" a first indicate number "to" a second indicate number are used herein interchangeably and are meant to include the first and second indicated numbers and all the fractional and integral numerals there between. As used herein the term "method" refers to manners, means, techniques and procedures for accomplishing a given task including, but not limited to, those manners, means, techniques and procedures either known to, or readily developed from known manners, means, techniques and procedures by practitioners of the chemical, pharmacological, biological, biochemical and medical arts.

[0347] It is appreciated that certain features of the disclosure, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the disclosure, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable sub combination or as suitable in any other described embodiment of the disclosure. Certain features described in the context of various embodiments are not to be considered essential features of those embodiments, unless the embodiment is inoperative without those elements.

[0348] Various embodiments and aspects of the present disclosure as delineated hereinabove and as claimed in the claims section below find experimental support in the following examples.

[0349] Disclosed and described, it is to be understood that this disclosure is not limited to the particular examples, methods steps, and compositions disclosed herein as such methods steps and compositions may vary somewhat. It is also to be understood that the terminology used herein is used for the purpose of describing particular embodiments only and not intended to be limiting since the scope of the present disclosure will be limited only by the appended claims and equivalents thereof.

[0350] It must be noted that, as used in this specification and the appended claims, the singular forms “a”, “an” and “the” include plural referents unless the content clearly dictates otherwise.EXAMPLES

[0351] Experimental Procedures

[0352] Human patient samples

[0353] Fresh and formalin-fixed paraffin-embedded (FFPE) human breast tissue samples were obtained from surgical specimens of invasive ductal carcinoma (IDC) and adjacent normal tissue from patients undergoing resection at Tel Aviv Sourasky Medical Center Ichilov after obtaining informed consent in accordance with a protocol approved by the Institutional Review Board (IRB) of Tel Aviv Sourasky Medical Center Ichilov in collaboration with the Faculty of Medicine at Tel Aviv University.

[0354] Animals

[0355] All experimental animal work was performed in accordance with the guidelines and approval of the Animal Care and Use Committee of Tel Aviv University's Faculty of Medicine (TAU-MD-IL-2203-130-5). The transgenic mammary-specific polyomavirus middle T antigen overexpression mouse model (MMTV-PyMT) used in this study was kindly donated by Prof. Neta Erez’s laboratory at Tel Aviv University. FBV / n females were purchased from ENVIGO.

[0356] Mice were housed under specific -pathogen-free conditions at the Animal Breeding Center at Tel Aviv University. The MMTV-PyMT mouse colony was maintained by crossing MMTV-PyMT males and FBV / n females. Genotyping was performed by PCR of DNA extracted from the mouse ear sample using Phire Tissue Direct PCR Master Mix (F170S, Thermo Fisher Scientific) and the set of primers: GGAAGCAAGTACTTCACAAGGG and GGAAAGTCACTAGGAGCAGGG, as denoted by SEQ ID NO: 1 and 2, respectively.

[0357] Mammary gland dissociation

[0358] The fourth pair of mammary glands was collected from MMTV-PyMT oncogene carrier and noncarrier females along 6 developmental and cancerous time points: postnatal day 10, and weeks 3, 6, 8, 10 and 12 (Fig. 1A, Table 1). In order to achieve large enough cohorts, samples were pooled as follows: at lOd, 5-6 mice were pooled, at 3w 3 mice were pooled and at 6-12w 1-2 mice were pooled in each replicate. For parts of the analysis, ages were merged as development (lOd and 3w), youth / early carcinoma (6-8w) and adulthood / advanced carcinoma (10-12w). The lymph nodes were removed from the dissected mammary glands. Both healthy and cancerous mammary tissues were suspended in Dulbecco’s modified Eagle medium / F12 medium (01-170-1A, Biological Industries) supplemented with collagenase IV (1 mg / ml, Worthington LS004188), collagenase II (1 mg / ml, Worthington LS004176), hyaluronidase I-S (800 U / ml, Sigma H3506) and DNase I (1.2 U / ml, Sigma D5021-15KU), cut into small pieces, mechanically dissociated by pipetting usingdifferent sizes of needles and incubated at 37°C with agitation of 100 rpm for 30 minutes. Then, cells were washed with ice-cold sorting buffer (PBS supplemented with 0.2mM ethylene diamine tetra-acetic acid (EDTA), pH 8, and 0.5% BSA), filtered through a 100 pm cell strainer and centrifuged at 1500 rpm for 5 minutes at 4°C.

[0359] Flow cytometry and sorting

[0360] Flow cytometry was performed on the BD FACSAria III Cell sorter or BD FACSymphony S6, using a 100 pm nozzle, analyzed with BD FACSDIVA software (BD Biosciences) and FlowJo software. Cells suspended in ice-cold sorting buffer were incubated with anti-mouse CD 16 / 32 (BD Bioscience) to block Fc receptors before labeling with fluorescent antibodies against cell-surface epitopes. Cells were stained with DAPI (Biolegend) and then with the following antibodies: eFLuor450-conjugated TER- 119 (eBioscience), FITC-conjugated CD45 (Biolegend), APC-conjugated CD45 (Biolegend), PE-conjugated CD45 (Biolegend), APC / Cy? -conjugated EpCAM (, Biolegend), APC-conjugated EpCAM (Biolegend), FITC-conjugated F4 / 80 (Biolegend), FITC- conjugated Ly6G (Biolegend), PE-conjugated Ly6G (Biolegend), FITC-conjugated CD31 (Biolegend), PE-conjugated Pdgfr-a (Bioloegend), FITC-conjugated I-A / I-E (MHC II, Biolegend).

[0361] For FACS intracellular staining, Cyto-FastTM Fix / Perm Buffer Set (Biolegend, 426803) was used for intracellular staining of cells from dissociated tissues and in vitro assays. Briefly, cells were resuspended in Cyto-Fast™ fix / Perm buffer (IxlO6in 100 pL), incubated 20-30 min at RT, washed with 250 pL of lx Cyto-Fast™ Perm Wash solution and centrifuged at 1500 rpm, 5 min at 4°C. Cells were resuspended in anti-mouse Fc block in lx Cyto-Fast™ Perm Wash solution and incubated in ice for 10 min. Following, cells were stained with conjugated and primary antibodies mix prepared in lx Cyto-Fast™ Perm Wash solution and incubated for 20 minutes. Then, secondary antibodies were added and incubated for 20 minutes. Cells were centrifuged at 1500 rpm, 5 min at 4°C, resuspended in FACS buffer and read by flow cytometry. Besides surface markers described above, cells were stained with the following intracellular antibodies: AF647-conjugated LCN2 / NGAL (Novus biologicals), PE-conjugated KI67 (biolegend), PE-conjugated VEGFA (Abeam), primary antibody COX2 (PTGS2; R&D systems) and secondary antibody Cy3 donkey anti-goat (Jackson).

[0362] For sorting, dead cells (DAPF) and erythrocytes (TERH9+) were excluded. First, total immune cells (CD45+) and non-immune cells (CD45") were sorted from DAPI7TER119" gate (Live cells). For enrichment of immune-epithelial / cancer cell PICs, the inventors sorted the double positive population from live cells by using immune and epithelial markers (CD45+EpCAM+) and theirrespective singlet background, singlet immune (CD45+EpCAM') and epithelial cells (CD45' EpCAM1’)- For enrichment of neutrophil s-epithelial PICs, the Ly6G1‘EpCAM+population was gated from general immune-tumor cell PICs (CD45+EpCAM+). For enrichment of singlet neutrophils, Ly6G+cells were sorted from CD45+gate. Singlet EpCAM+cells used for analysis as tumor cell background were isolated from tumor carrier mice as described above. Singlets and PICs were sorted into 384-well cell capture plates. These plates contain 2pl of lysis solution along with barcoded poly (T) reverse-transcription primers for single-cell RNA-seq. As no-cell control for the data analysis, four wells were left empty. Immediately after sorting, each plate was spun down and stored at -80°C until further processing [Cohen, M. et al. Nat Cancer 3, 303-317 (2022)].

[0363] Visualization of sorted PICs by confocal microscopy

[0364] Cells were obtained from cancerous mammary glands following dead cell and erythrocyte exclusion. CD45+EpCAM+PICs and Ly6G+EpCAM+PICs were gated using the antibodies FITC-conjugated CD45, APC-conjugated EpCAM and PE-conjugated Ly6G, and then bulk sorted to a 1.5 ml tube containing 50pl of FACS sorting buffer. Sorted PICs were stained again using the same antibodies, fixed for 10 minutes with 4% paraformaldehyde (PFA), and then permeabilized by adding 0.1% Triton X-100 (Sigma-Aldrich) for 5 minutes on ice. For detection of cell nuclei, DAPI was added for 30 seconds. Cells were centrifuged at 1500 rpm for 5 minutes at 4°C., the pellet was suspended in the mounting solution (SlowFade, Thermo Fisher Scientific) and sealed with cover slips. Microscopic analysis was performed using Leica TCS SP8 HyD confocal microscope. Images were acquired and processed with ImageJ software.

[0365] Visualization of sorted PICs by ImageStream

[0366] For additional validation of Ly6G+CD45+EpCAM+PICs, dissociated cancerous mammary glands were stained using Ly6G, CD45 and EpCAM conjugated antibodies, suspended in ice-cold FACS sorting buffer and run in the ImageStream X Mark III flow cytometer. Images were analyzed with IDEAS software.

[0367] Immunofluorescence

[0368] Fresh mice mammary glands (MMTV-PyMT oncogene carrier and non-carrier females) and breast human samples (IDC, tumor and normal adjacent) were fixed in 4% PFA solution for 4 hours, and then transferred to 30% of sucrose solution for 2 days. Tissues were embedded in Optimal Cutting Temperature compound (OCT, Thermo Fisher Scientific), and 30pm sections were cut using Leica CM1950 Cryostat. Sections were first blocked with a blocking buffer solution (PBS, 5% BSA, 0.2% Triton) for 30 minutes at room temperature. Sections were washed with PBS once andincubated with conjugated antibodies overnight at 4°C. For human tissues only, the sections were washed once with PBS and incubated for 2 hours with corresponding secondary antibodies at RT. Then, sections were washed once with PBS, stained with DAPI for 10 minutes and washed again with PBS. Sections were mounted with Fluoromount-G (SouthernBiotech) and sealed with cover slips. Microscopic analysis was performed using a Leica TCS SP8 HyD confocal microscope, and images were acquired and processed by using ImageJ software.

[0369] For FFPE staining, human sections were baked at 37 °C overnight, de-paraffinized in xylene and rehydrated in decreasing concentrations of ethanol. Tissue sections were incubated in citrate buffer for antigen retrieval at 95 °C for 15 minutes and left at room temperature for 20 minutes. After three PBS washes, blocking buffer (10% donkey serum in PBST and 0.1% Triton X-100) was added for 2 hours at room temperature. After blocking, all primary antibodies were incubated at 4 °C overnight. After three PBS washes, corresponding secondary antibodies were used simultaneously for 2 hours at room temperature and washed again three times with PBS. Then, sections were washed once with PBS, stained with DAPI for 10 minutes and washed again with PBS. Sections were mounted with Fluoromount-G (Southern Biotech) and sealed with cover slips. For mouse staining, the same antibodies used for flow cytometry were used: FITC-conjugated CD45, APC-conjugated EpCAM, PE-conjugated Ly6G, FITC-conjugated CD31 and FITC-conjugated F4 / 80, FITC-conjugated I-A / I-E (MHC II, Biolegend), AF647-conjugated LCN2 / NGAL (Novus biologicals), PE-conjugated VEGFA (Abeam), primary antibody COX2 (PTGS2; R&D systems) and secondary antibody Cy3 donkey anti-goat (Jackson). For staining of human samples, the inventors used primary antibodies PE-conjugated anti-human EpCAM (Biolegend), APC-conjugated anti-human CD45 (Biolegend), rabbit anti-human S100A9 antibody (Abeam), rat anti-human CD31 (Biolegend) and secondary antibodies Alexa Fluor 647 goat antirabbit IgG H&L (Jackson) and Alexa fluor 488 goat anti-rat IgG H&L (Abeam).

[0370] Cell culture

[0371] Met-1 cells, a cancer cell line isolated from MMTV-PyMT mice [Borowsky, A. D. el al. Clin Exp Metastasis 22, 47-59 (2005)], were maintained in DMEM medium (Gibco, Catalog number: 41965039), supplemented with 10% FCS, 1% penicillin-streptomycin, and 1% L-glu at 37 °C in 5% CO2.

[0372] Preparation of breast tumor conditioned medium

[0373] To prepare breast tumor conditioned medium, the fourth pair of mammary glands was collected from 12w MMTV-PyMT’’ females and washed with PBS. Then, the breast tumors were cut transversely into 300-p.M-thick using a Sorvall TC-2 tissue chopper, as described previously[Camargo, S., Gofrit, O. N., Assis, A. & Mitrani, E. Cancers (Basel) 13, 2972 (2021)]. Tissue explants were washed three times with PBS with 2% penicillin (100 U / mL)-streptomycin (1 mg / mL), and then with supplemented RPMI medium. Breast tumor explants were seeded in 6- well-plate (20 fragments per well) in 1.5ml of supplemented RPMI and maintained at 37 °C in 5% CO2. The medium from the cultures (conditioned medium, CM) was collected every day for 4 days and kept at -20 °C.

[0374] Isolation of bone-marrow macrophages and neutrophils

[0375] Macrophages: Femur and tibia were collected from euthanized MMTV-PyMT’ female mouse. Bone marrow was extracted from bones by washing with cold PBS and kept in ice. Bone marrow suspension was centrifuged at 400 g, 5 min at 4°C. Then, the pellet was resuspended with red blood lysis buffer, incubated for 3 min in ice, wash with PBS and centrifuged 400g, 7min at 4°C. Bone marrow derived cells were seeded (5xl06cells in 100mm plate) in 8 ml of supplemented RPMI containing 50 ng / mL of M-CSF. At day 3 of culture, 50ng / mL of M-CSF was added again. Macrophages were harvested at day 7 and used for further experiments.

[0376] Neutrophils: Femur and tibia were collected from MMTV-PyMT' female mouse. Bone marrow was collected from bones by washing with supplemented RPMI with 2mM EDTA and centrifuged at 1400rpm, 7min at 4°C. Then, the pellet was incubated with red blood lysis buffer, washed with supplemented RPMI with 2mM EDTA and centrifuged 400g, 7min at 4°C. The pellet was resuspended in ImL of cold PBS, added on top of a density gradient composed by 3mL Histopaque-1077 (Sigma-Life Sciences, 10771) on top and 3mL Histopaque-1199 (Sigma-Life Sciences,! 1191) on the bottom and centrifuged at 2000rpm, 30 min at 25 °C without brake. Neutrophils were collected at the interface of the Histopaque-1077 and Histopaque-1199 layers, washed with supplemented RPMI and used for further experiments.

[0377] Isolation of peripheral blood neutrophils

[0378] Peripheral blood was collected by cardiac puncture from MMTV-PyMT+female mouse. Peripheral blood was diluted in PBS and layered on top of Histopaque-1077, centrifuge at 400g, 30min, no break and acceleration. Buffy coat was diluted 10 times with PBS, centrifuged at 400 g, 7min at 4°C and resuspended in FACS buffer. Peripheral blood neutrophils were stained and sorted (CD45+orCD45+Ly6G+).

[0379] Neutrophil migration assay

[0380] Bone marrow-derived macrophages were seeded with 500pL of breast tumor CM or with 500p L supplemented RPMI (2xl05cells / well in 24-well-plate) and incubated at 37°C in 5%CO2, for 24h. Both conditions contained lOng / ml of M-CSF. After incubation, CM and supplemented RPMIwere removed and cells were washed with serum-free RPMI. Then, 500|aL / well of serum-free RPMI with lOng / mL of M-CSF and with / without anti-CCL3 (R&D, AF-450-NA) were added. Bone marrow-derived neutrophils (2x 102in 200ul of serum free RPMI with lOng / mL of M-CSF) were placed at the upper chamber (5pm pores inserts, Corning) and incubated at 37°C in 5%CO2, for 3h. Migrated neutrophils were stained for CD45+Ly6G+and counted by flow cytometry (BD Symphony S6).

[0381] In vitro coculture assays

[0382] Met-1 cells stained with pH rodo (Invitrogen, P36600) were seeded (2.5xl05cells / well) in a 24-well plate in monoculture, in coculture with bone marrow-derived neutrophils (tumor cell: neutrophils, 1:1) or exposed to neutrophils placed in the upper chamber of a transwell membrane 0.4pm (Corning, C3413). After overnight incubation at 37°C in 5%CCk, supernatant was collected and cells were detached using 5% EDTA in PBS or using a scraper. Cells were stained for corresponding antibodies and analyzed by flow cytometry.

[0383] Scratch assay

[0384] Met-1 cells were seeded (5xl04cells / well) in a 96-well IncuCyte imageLock plate (Sartorius, BA-04855) in completed DMEM medium and incubated overnight at 37°C in 5%CO2. The scratch wound was done using IncuCyte woundmaker tool (Sartorius, BA-04858). After the scratch, cells were washed three times with PBS to remove floating cells. Then, scratched Met-1 cells were treated as follows: cocultured with bone marrow-derived neutrophils (tumor cell: neutrophils, 1:2), or cocultured with neutrophils pretreated with lOOnM of Cytochalasin D for 30min before being seeded [Petzold, T. et al. Immunity 55, 2285 (2022)]. Also, Met-1 cells were exposed to CM of neutrophils, collected as follows. Neutrophils were first exposed to breast tumor CM from breast tumor explants overnight, then neutrophils were washed and seeded in regular medium overnight, and this medium was then collected and used as neutrophil conditioned medium (neut CM). Monocultures of Met-1 cells were used as control. After 12h of incubation using Incucyte system (Incucyte® S3), images were analyzed using the IncuCyte software and ImageJ.

[0385] In vivo depletion of neutrophils

[0386] MMTV-PyMT+female mice at the age of 10.5 weeks old were treated daily for 9 days via intraperitoneal injection with 12.5 pg per mouse of rat anti-Ly6G antibody (BioXcell, clone 1A8, BE0075-1-5AC) in PBS or rat IgG2a isotype control (BioXcell, BE0089-25AC) as described previously [Nolan, E. et al. Nat Cancer 3, 173-187 (2022)]. The fourth pairs of mammary glands were collected from treated mice, dissociated, FACS analyzed and sorted as described previously.MARS-seq library preparation

[0387] The library preparation was conducted following the previously reported protocol [Keren-Shaul, H. et al. Nat Protoc 14, 1841-1862 (2019)]. In brief, mRNA from cells sorted into capture plates was barcoded and converted into cDNA. The resulting cDNA was pooled and amplified by T7 in vitro transcription. Then, the RNA was fragmented and converted into a sequencing-ready library by tagging the samples with pool barcodes and Illumina sequences during the ligation, reverse transcription and PCR steps. The library's quality was evaluated and concentration was assessed as described in the MARS-seq2.0 protocol [Keren-Shaul, H. et al. Nat Protoc 14, 1841-1862 (2019)]. Primer barcodes and genes were used according to MARS-seq2.0 protocol for both library preparations and qPCR validations.

[0388] Preprocessing of raw sequencing data

[0389] scRNA-seq libraries (pooled at equimolar concentration) were sequences on Illumina NextSeq 500 or NOVA-seq, and FASTQ files were generated from the raw BCL files using the bcl2fastq software provided by Illumina (v.2.20.0.422; n.d.). Sequences were mapped to the mouse genome (GRCm39) using STAR (v.2.7.1a) [Dobin, A. et al. Bioinformatics 29, 15 (2013)]. Reads with multiple mapping positions were excluded. Gene names were then assigned for exonic, genomic and mitochondrial regions using featureCounts (v.2.0.0) [Liao, Y., Smyth, G. K. & Shi, W. Bioinformatics 30, 923-930 (2014)], the resulting BAM file was processed using samtools (v.1.10) [Danecek, P. et al. Gigascience 10, 1-4 (2021)] and finally gene-cell UMI matrices were created using the UMLtools software package (v.1.1.2) [Smith, T., Heger, A. & Sudbery, I. Genome Res 27, 491-499 (2017)]. Using the Scanpy package (v.1.9.1), [Wolf, F. A., Angerer, P. & Theis, F. J. Genome Biol 19, 15 (2018)] these files were loaded to a unified data object.

[0390] MetaCell clustering of kinetics dataset (CD45‘, EPCAM+, CD45+)

[0391] A quality control was performed (QC) by excluding cells with <300 UMIs, >6000 UMIs or >30% mitochondrial and hemoglobin related genes from the analysis (Fig. 2B-2C). The Metacell-2 python package (v.0.8.0) [Ben-Kiki, O., Bercovich, A., Lifshitz, A. & Tanay, A. Genome Biol 23, 100 (2022)] was used to analyze all scRNA-seq data collected in this study and to derive MetaCell covers. Default parameters were used unless otherwise stated. Mitochondrial genes, hemoglobin genes and specific problematic genes taken from the MetaCell-2 vignette IN call. TmsblO, Tmsb4x) were removed from the UMI table. Gene features for MetaCell covers were selected using the parameters Tvm = 0.2, total UMI > 50 and more than three UMIs in at least three cells. The list of gene features used for MetaCell analysis was filtered from genes associated with cell cycle, immediate stress response and gene modules inducing strong batch-specific biases. To this end,the inventors first identified all genes with a correlation coefficient of at least 0.1 for one of the manually selected anchor genes Pena, Mki67, Top2a, Histlhld, Txn, Hspa5, 5_8S_rRNA, 7SK, Fos, Jun, Hsp90abl, Hspala, Hspalb, Tubala, Tubalb, Cenpa, Isgl5, Wars, Rnl8s-rs5, Malatl, Mir6236, AW112010, Tubalc, Tuba4a, Tubb2a and Tubb4b genes beginning with Mem, Smc, Gm, Rpl or Rps, and genes ending with Rik and immunoglobulin genes. The correlation matrix between these genes (filtering genes with low coverage and computing correlation using a downsampled UMI matrix) was then hierarchically clustered and gene clusters that contained unwanted genes were manually selected. MetaCell-2 with a target size of 75000 UMIs was used to construct the MetaCell covers. Outlier cells featuring gene expressions higher than three times the geometric mean in the metacells in at least one gene were discarded.

[0392] MetaCell clustering of the neutrophil dataset (CD45+, Ly6G+)

[0393] First, a QC was performed for all CD45+and Ly6G+cells by excluding cells with <100 UMIs, >6000 UMIs or >30% mitochondrial and hemoglobin genes. The Scanpy python package [Wolf, F. A., Angerer, P. & Theis, F. J. Genome Biol 19, 15 (2018)] was used to perform Leiden clustering as described in the tutorial with the default parameters, except for setting a target UMI count of 10,000, selecting the top 3000 highly variable genes and excluding the scaling step. Then, based on marker gene expression (Csf3r, Retnlg, S100a8), a neutrophil cluster containing 3,776 cells was identified (Fig.8C-8E). The inventors observed the density of neutrophil UMI sizes (Fig.2C, 8E) and decided to select the higher quality cells by increasing the minimum UMI count to 200, leaving them with 2,665 total neutrophils. Doublets containing more than 2 UMIs of non-neutrophil genes (Clqa, Clqb, Clqc, Itgax, CleclOa, Mrcl, Apoe, Tmeml76b, H2-DMa, Ly86, Cd3g, Cd3e, Cd3d, Trbc2, Trac, Trdc, Thyl, Cd8bl, Cd8a, Pecaml, Sparc, Den, Cavl, Collal, Col9al, Coll4al, Col3al, Col4al, Krtl8, Csn2, Csn3, Lalba, Epcam, Wfdcl8) were then manually excluded. Finally, only cells coming from replicates with at least 15 neutrophils that passed QC were retained.

[0394] Following this neutrophil identification and QC process, the inventors proceeded with the MetaCell pipeline as described above, with the following changes: the genes G0s2 and Actb were added to the unwanted anchor genes; gene parameters were changed to Tvm = 0.1, total UMI > 25 and at least one UMIs in at least three cells; target metacell size was set to 35,000; and outlier cell exclusion was defined by gene expression higher than five times the geometric mean in the metacells.Comparison of neutrophil gene expression patterns in tumor and blood

[0395] For this comparison, all CD45+and Ly6G+cells from 10-12w tumor samples from both the mammary gland and the peripheral blood samples were taken. A QC was performed by excluding cells with <100 UMIs, >6000 UMIs, >30% mitochondrial, >3 hemoglobin UMIs or >3 nonneutrophil genes listed above. The Scanpy python package [Wolf, F. A., Angerer, P. & Theis, F. J. Genome Biol 19, 15 (2018)] was used to perform Leiden clustering as described in the tutorial skipping the scaling step and setting the Leiden resolution to 0.1 to select only neutrophils, leaving 2,397 blood neutrophils and 932 tumor neutrophils. Following this, the decoupleR python package (1.6) [Badia-LMompel, P. et al. BioinfAdv 2, vbac016 (2022)] was used to create a pseudobulk sum of UMIs per replicate and ran DGE using PyDESeq2 (0.4.4) [Muzellec, B., Telenczuk, M., Cabeli, V. & Andreux, M. Bioinformatics 39, btad547 (2023)].

[0396] Comparison of neutrophil signatures across tumors

[0397] For the breast data, the same cohort of neutrophils mentioned above was used. For the PDAC data, a published scRNA-seq neutrophil dataset [Ref 12] was downloaded and only cells coming from blood or tumor samples were selected, and then pseudo-bulk analysis was performed as described in the breast. For the CRC data, a published bulk RNA-seq dataset [Bui, T. M. et al. J Clin Invest 134, el74545 (2024)] was downloaded. The three datasets were integrated, keeping only shared genes with at least 3 counts in each of the datasets. Then, for genes with over 100 total counts, the expression for tumor and blood in each tissue was summed, normalized to counts per 100,000 and a tumor / blood LFC for each tissue was calculated. A linear regression on the LFC values was ran comparing breast to PDAC and breast to CRC using the Scipy package (1.10.1; https: / / scipy.org / ).

[0398] Visualizations and analysis

[0399] For further analysis, the final gene-cell matrix and MetaCell cover was transferred to the R MetaCell package [Baran, Y. et al. Genome Biol 20, 206 (2019)]. The metacells were then thoroughly annotated based on their gene expression profiles by comparing the fold-change enrichment of gene programs in each metacell and projecting gene expression using built-in functions and the ComplexHeatmap package [Gu, Z., Eils, R. & Schlesner, M. Bioinformatics 32, 2847-2849 (2016)]. In this step, the inventors also manually identified and excluded doublet metacells by searching for co-expression of marker genes that are normally exclusive to different cell states. Other visualizations such as 2D projections, bar plots and scatter plots were created using the ggplot2 package (3.4.3, https: / / ggplot2.tidyverse.org).Identification of cellular niches

[0400] In order to identify cell states displaying a similar difference in abundance between time points and conditions, the inventors first calculated the fraction each cell state took up in its relevant FACS gate and calculated the mean fraction across biological replicates for each age and condition. Then, the inventors scaled these values for each cell state using the base R scale function to quantify relative changes in composition. Visualization, hierarchical clustering and separation to niches were performed using the pheatmap package (1.0.12; https: / / CRAN.R-project.org / package=pheatmap).

[0401] TCGA survival analysis

[0402] Using the TCGAbiolinks R package (v 2.29.6) [Silva, T. C. et al. FlOOORes 5, 1542 (2016)], the gene expression data and clinical information of patients were downloaded from the BRCA project. A counts-per-million normalization was then performed on the gene counts. Selected were 792 patients with stage I or II breast cancer and 265 patients with stage III or IV, as specified in the clinical information of the patients, according to the definitions of the AJCC.

[0403] Then, using the GSVA R package (v 1.42.0) [Hanzelmann, S., Castelo, R. & Guinney, J. BMC Bioinformatics 14, 7 (2013)] and gene lists generated as described below, each patient was scored based on her expression of the gene list. The Kaplan-Meier plot were created and the p-values were calculated by using the Survminer R package (v 0.4.9).

[0404] Gene list creation:

[0405] TAN gene score'. To best capture the signature of the TANs (defined here as TAN1 and TAN2), the inventors looked into both highly expressed and differentially expressed genes (DEGs), comparing TAN metacells to MHCII Neut and Ptgs2+Neut metacells. Using Seurat's FindMarkers method, the 20 significantly differentially expressed genes with the highest LFC were selected. Then, for each of the mentioned metacells, the inventors calculated the top 15 highly expressed genes based on the UMIs from the raw data, and selected genes that were unique to the TAN metacells. Finally, the two lists were merged to create the final TAN gene score.

[0406] NeuTME-PIC gene score. The inventors first ran PIC-seq gene analysis on all Neutrophil-tumor cell PICs from 10-12w PyMT+mice, as described below (Simulation-based observed / expected gene analysis). Then, for the neutrophil states most abundant in the physical crosstalk (TAN1, TAN2 and Ptgs2+Neut), the top 20 genes with the highest UFC and a p-value under 0.05 were selected. Finally, the gene list was filtered to only keep genes with a mean expression in PICs that was higher than the mean expression of all other populations.Both gene lists were manually converted from mouse gene to their human homologues using the MGI website [Blake, J. A. et al. Nucleic Acids Res 49, D981-D987 (2021)]. Gene scores of both lists were assigned to singlets and PICs using the AddModuleScore function from the Seurat package (v 5.0.1) [Hao, Y. et al. Nat Biotechnol I 42, 293-304 (2024)].

[0407] CD45+ / EpCAM+PIC-seq QC

[0408] The PIC-seq analytical pipeline [Giladi, A. et al. Nat Biotechnol 38, 629-637 (2020)] was used to analyze immune-epithelial PICs. For the background model, all epithelial and immune cells from the kinetics analysis were selected based on their annotation. When selecting feature genes for the analysis, genes containing Gm, Mir, -ps, Rpl, Rps, Jchain, Ftll, Hsp, Rnl8s-rs5, Actb, Tuba orRik were added to the list of filtered genes from the singlets analysis.

[0409] For quality control of PICs, PICs expressing >30% mitochondrial genes were first excluded. A minimum UMI threshold of 500 was selected by observing the distribution of total UMI counts across sequenced PIC events (Fig. 6A) and testing the performance of the PIC-seq pipeline on simulated PICs with various minimum UMI counts (Fig. 6B-6D).

[0410] PICs that appeared to actually be singlets, or whose gene expression profile was more similar to one subset of singlets than to a combination of both were also excluded. To this end, the inventors calculated a log-likelihood score as described in the PIC-seq pipeline, once with the inferred mixing factor (PIC score), once with mixing factor 0 and once with mixing factor 1 (singlet scores). The inventors calculated the difference between the PIC score and the stronger singlet score (PIC-singlet diff), performing these calculations on simulated PICs and on singlets, in order to select the PIC-singlet diff threshold that would retain the largest fraction of PICs while excluding the largest number of singlets (Fig. 6H). PICs with a diff under this threshold were excluded from later analysis (Fig. 6I-6J).

[0411] Neutrophil / EpCAM+PIC-seq QC

[0412] For the background singlet models, the epithelial cells from the kinetics analysis were merged with the neutrophil cells analyzed in the neutrophil analysis. All Ly6G+EpCAM+PICs were first integrated with the CD45+EpCAM+PICs that were identified as neutrophil-epithelial PICs. PICs expressing at least two counts of endothelial cell genes (Pecaml, Cdh5, Col4al, Col4a2, Cd36, Cavl, Podxl, Slprl, Plprb), hemoglobin genes, immunoglobulin genes or other non-neutrophil and non-epithelial genes (Clqa, Clqb, Clqc, Itgax, CleclOa, Mrcl, H2-DMa, Ly86, Cd3g, Cd3e, Cd3d, Trbc2, Trac, Trdc, Thyl, Cd8bl, Cd8a, Cd4, Tcf7, Gzma, Gzmb, Nkg7, Cdl9, Cd79a, Ms4aT) were manually excluded.Due to the significant difference in typical total UMI counts between neutrophils and epithelial cells (Fig.2C), the inventors modified the PIC simulation function to randomly select the mixing factor for the simulated PIC and multiply the UMI counts if necessary for the partners to reach the necessary total UMI count for their part in the simulated PIC.

[0413] Ligand-receptor analysis by LIANA

[0414] The Ligand-receptor ANalysis frAmework (LIANA; v 0.1.6) [Dimitrov, D. el al. Nat Commun 13, 3224 (2022)] was ran on an integrated dataset of singlets and PICs from advanced (10-12w) PyMT+samples. All non-neutrophil singlet populations were included, the 5 adult neutrophil states (MHCII Neut, Ptgs2+Neut, Young TAN, TAN1 and TAN2) merged and annotated as TANs and TAN-tumor cell PICs. Grouping these annotations in the focused dataset resulted in a cohort of 942 TAN-tumor cell PICs and 932 TANs. Annotations with fewer than 10 total cells in these time points were then filtered out. The inventors used the Consensus resource included in the package, converted to mouse homologues using the generate_homologs function with str_to_title used as the .missing_fun. The inventors added the interaction S100a8 / 9-Mcam and used expr_prop=0.05.

[0415] Simulation-based observed / expected gene analysis

[0416] For each PIC, the inventors deduced the most likely metacell partners and mixing factor using the PIC-seq pipeline as previously described. Using these parameters and the total UMI counts of each PIC, the inventors randomly selected singlets from the metacell partners and randomly sampled UMIs from each singlet according to the mixing factor (multiplying if necessary) until reaching the total UMI counts of the observed PIC. Repeating this process, 1,000 simulated PICs were created for each observed PIC in the dataset. Normalized mean expression values were calculated for each simulation, forming a distribution of 1,000 values for each gene. By comparing this distribution to the normalized mean expression of the observed PICs, a z-score was calculated and then used to calculate a p-value. This method allowed to analyze the gene expression of neutrophil-epithelial PICs and identify genes that were significantly upregulated or downregulated in the observed PICs when compared to the distribution of simulated values.

[0417] Transcrip tomic analysis of anti-Ly6G depletion

[0418] Using the Scanpy package [Wolf, F. A., Angerer, P. & Theis, F. J. Genome Biol 19, 15 (2018)] data coming from 12w PyMT+mice treated with anti-Ly6G (depletion) or a non-active isotype was analyzed. First, QC excluding cells with <300 UMIs, >6000 UMIs, >1 hemoglobin UMIs or >30% mitochondrial gene UMIs and excluding genes with <3 counts was performed. A Leiden clustering was then performed as described in the tutorial with the default parameters, except for setting a target UMI count of 10,000, excluding the scaling step and setting a target Leidenresolution of 0.3. The clusters were then annotated based on marker gene expression, and the tumor and endothelial clusters were further clustered to identify the relevant cells for comparisons. Finally, within each cluster, a T-test was run using Scanpy to identify differentially expressed genes between single cells derived from anti-Ly6G and isotype injected mammary glands.

[0419] GSEA analysis was performed on all genes of the tumor cells sorted by T-score using the GSEA software developed by UC San Diego and Broad Institute [Mootha, V. K. et al. Nat Genet 34, 267-273 (2003); Subramanian, A. et al. Proc Natl Acad Sci U S A 102, 15545-15550 (2005)] using the GO:BP gene sets database as a reference.

[0420] EXAMPLE 1

[0421] Multi-lineage cell states comprising the mammary gland tissue

[0422] To investigate cellular niches and intercellular crosstalk across development and cancer progression of the breast tissue, the inventors used the MMTV-PyMT transgenic mouse model, which mimics the different stages of human ductal breast cancer including hyperplasia, neoplasia, early and advanced carcinoma, and metastasis. This model displays immune infiltration, progressive loss of estrogen and progesterone receptors and despite the overexpression of ErbB2, it is widely used as a triple-negative breast cancer (TNBC) mouse model [Attalla, S., et al. Oncogene 40, 475-491 (2021); Aleckovic, M. et al. Mol Cancer Ther 22, 1304 (2023)]. The inventors first explored the processes inducing physiological development of the mammary gland, by purifying single cells from oncogene carrier (PyMT+) and non-carrier control (PyMT') tissues derived from the same litter, during the developmental stages: pre-puberty (10 days; lOd) and puberty (3 weeks; 3w). The inventors then proceeded to time points that represent tissuemaintenance in the young (6w and 8w) and adult (lOw and 12w) tissues of the PyMT' control littermates, but early carcinoma (6w and 8w) and advanced carcinoma (lOw and 12w) stages in the PyMT+mammary glands (Fig. 1A). The goal was to isolate and molecularly characterize a wide variety of immune and non-immune cells in order to thoroughly explore cellular heterogeneity, niche composition and intercellular communications driving development and cancer progression in the mammary gland.

[0423] The inventors single-cell sorted, sequenced and analyzed 16,778 CD45+immune and 12,783 CD45' non-immune cells (after quality control (QC), Experimental Procedures) from PyMT+and PyMT' mammary glands along the different developmental and cancerous time points. To gain further insight into cellular heterogeneity of the epithelial cell lineage, an additional 8,645 CD45' EpCAM+epithelial cells were enriched from both PyMT+and PyMT' tissues (Fig. IB, Fig. 2A-2C, Table 1). These immune, non-immune and epithelial cells were pooled and, through in-depth clustering and annotation based on expression of cell state hallmark genes (Fig. 2D-2G), a wide variety of cell types and states were identified from the immune and non-immune compartments (Fig. IB). In the lymphoid compartment, T cells, CD8+T cells, T reg, y5 NKT, NK, ILC2 and B cells were identified (Fig. IB, 1C, 2D). In the myeloid compartment, monocytes, MonMacs, perivascular macrophages (pMac), ductal macrophages (ductal macs), conventional dendritic cell type 1 (cDCl), cDC2, plasmacytoid DC (pDC), immuno-regulatory DC (mregDC), mast cells, basophils, and neutrophils were identified (Fig. IB, 1C, 2E).

[0424] From the non-immune cellular compartment, heterogeneous cell states related to vascular, epithelial and fibroblast lineages were identified. In the vascular niche, endothelial and endothelial Mcamhlcells, smooth muscle cells and pericytes were identified (Fig. IB, 1C, 2F). The fibroblast compartment was grouped into fibroblasts, fibroblasts Dpp4hl, and cancer-associated fibroblasts (CAFs). A unique gene program related to Schwann cells (SoxlO, Mbp, Mpz) was also identified in the mammary gland stromal compartment (Fig. IB, 2F). Epithelial cells were grouped into two progenitor states (prog.l and prog.2), alveolar cells, luminal hormone-sensing cells (HS), myoepithelial cells, basal cells, and myoepithelial-luminal bi-potent progenitors (Myo / Lum; Fig. IB, 1C, 2G). Among the alveolar epithelial cells, the inventors identified tumor cells based on their high gene expression of Saal, Saa2, and Car6 (Fig. IB, 1C, 2G). Projection of cells by FACS gate and tissue condition (PyMT+or PyMT' mammary glands) on the general 2D cellular map revealed tumor- specific immune, non-immune and epithelial cell composition (Fig. ID).

[0425] Quantification of cellular composition in the tumor (PyMT+) relative to the normal (PyMT') tissues revealed that ductal macrophages, CAFs, endothelial Mcamhlcells, prog.2, Myo / Lum and tumor cells were significantly more abundant in the tumorigenic mammary glands (Fig. IE).

[0426] EXAMPLE 2

[0427] Unique cellular niches are related to tissue developmental stages and cancer

[0428] The inventors further explored changes in cellular composition along the different time points in tumor (PyMT+) and normal (PyMT') mammary glands. Investigation of the broad changes in FACS data showed no significant difference in CD45+percentage between PyMT+and PyMT' mice, while the EpCAM+percentage was significantly higher in PyMT+samples from week 6 onwards, suggesting increased proliferation of epithelial cells (Fig. 3A). By comparing tissue composition at the single-cell level across compartments, significant differences in cellular dynamics along developmental stages and across conditions were observed (normal vs tumor; Fig.3B-3E). The inventors first focused on populations that were significantly over-represented in developmental stages with no relation to oncogene expression, suggesting a functional role in the maturation of the mammary gland. For instance, in the myeloid compartment, it was observed that the abundance of neutrophils is higher during early development when compared to later time points in both PyMT+and PyMT' mice (Fig. 4A, Fig. 3C). Moreover, Schwann cells from the stromal compartment exhibited a similar pattern of higher abundance in early development followed by a decline during tissue maturation (Fig. 3D, 3F). Then, comparisons of cellular composition between conditions (PyMT+vs PyMT' mice) highlighted cellular dynamics unique to the carcinogenic process. An expansion of ductal macrophages was identified since week 6 (Fig.

[0429] 4B, 3C), which increased further along tumor progression. In the stromal compartment, a similar trend of enrichment in CAFs, endothelial Mcamhlcells and pericytes was seen (Fig. 4C-4D, 3D, 3G). Interestingly, the epithelial compartment exhibited a distinct cellular composition since early developmental stages (lOd) in the PyMT+mammary glands, with the expansion of both Myo / Lum and tumor cell populations (Fig.3E). While the portion of Myo / Lum cells declined over time (Fig.

[0430] 3H), the portion of the tumor cells increased (Fig.4E). These results highlight that the physiology of the mammary gland and the process of breast cancer development are highly dynamic, accompanied by the formation of different cellular niches along developmental and pathological conditions.

[0431] Therefore, in order to group cells tending to appear together within the tissue and define the cellular niches related to specific developmental stages or disease phases, the inventors scaled cell abundances in PyMT+and PyMT' mammary glands across the sampled time points. This strategy enabled the inventors to identify similar trends in abundance across populations. Hierarchical clustering was then performed and three cellular niches with similar abundancy dynamics were identified which were defined as development, homeostasis and tumor niches (Fig. 4F, Experimental Procedures). In the developmental niche, the inventors observed many cell states that were most abundant in lOd and 3w samples, both in PyMT+and PyMT' mice (e.g., Schwann cells and neutrophils; Fig.4F, upper cluster). From 6w onward, the inventors identified a cellular niche established only in the mature normal PyMT' mammary glands, characterized by the appearance of specific cell states (e.g., alveolar epithelial cells, fibroblasts, and MonMacs; Fig.

[0432] 4F, middle cluster). Notably, a tumor niche that appeared specifically in mammary glands derived from adult PyMT+mice was identified by the inventors. This tumor niche was characterized by the accumulation of immune and non-immune cells such as pericytes, endothelium Mcamhl, CAFs, mregDC, ductal macs and the tumor cells themselves (Fig. 4F, lower cluster). By immuno-fluorescence (IF) staining, the inventors validated the accumulation and the spatial proximity of epithelial (EpCAM+) and immune cells (CD45+), specifically macrophages (CD45+F4 / 80+) around the duct of the murine tumorigenic PyMT+mammary glands (Fig. 4G, 31) and in the TME of human-invasive ductal carcinoma (IDC; Fig. 4H). This analysis, which defines cellular niches involved in different biological processes in the mammary gland, enabled the focus on the tumorspecific cellular niche, in the following experiments and analysis.

[0433] EXAMPLE 3

[0434] Neutrophils are enriched in physical crosstalk with breast tumor cells

[0435] Since the tumor- specific cellular niche was enriched with epithelial and immune cell states, the inventors aimed to explore the cellular composition and the molecular signature of physical crosstalk between these cell types. For that purpose, the inventors applied PIC-seq [Giladi, A. et al. Nat Biotechnol 38, 629-637 (2020)], a platform for sequencing and analyzing doublets of physically interacting cells (PICs). Using scRNA-seq data as background singlets, PIC-seq analysis allows to characterize the single cell composition of the PICs and highlight specific gene programs induced by the physical crosstalk [Giladi, A. et al. Nat Biotechnol 38, 629-637 (2020); Cohen, M. et al. Nat Cancer 3, 303-317 (2022)].

[0436] Based on the annotations described (Fig. IB, 2D-2G), all immune and epithelium / tumor cells were analyzed as background singlets, together with additional 8,158 CD45+EpCAM+immune-epithelial / tumor cell PICs isolated from the PyMT' and PyMT+mammary glands along developmental (lOd, 3w), youth / early carcinoma (6w, 8w from PyMT' or PyMT+mice), and adulthood / late carcinoma (lOw, 12w from PyMT' or PyMT+mice) time points (Fig.5A-5B, Table 1). Following filtering PICs suspected as singlets (Fig.6, Experimental Procedures), the inventors analyzed the 3,348 PICs that passed QC using the PIC-seq computational pipeline and found the most likely epithelial and immune partners interacting in each PIC based on the gene expression patterns of both singlets and PICs (Fig. 7A). By comparing EpCAM+and CD45+singlet composition to the epithelial and immune partners in the EpCAM+CD45+isolated PICs, cell states enriched in the physical crosstalk were observed. In the epithelial compartment, it was found that the appearance of cell states in PICs was directly correlated with their single-cell abundances in both normal and tumor tissues (Fig. 7B). Among the immune subsets, unique patterns of cell enrichment in PICs along stages and across normal and tumor tissues were found (Fig. 5C). For example, mregDC were enriched in CD45+EpCAM+PICs when compared to CD45+singletsduring development (Fig. 5C-5D), while ductal macs were a dominant population in tumor samples both in singlets and PICs (Fig. 5C, 5E).

[0437] Interestingly, a unique pattern of neutrophil enrichment in PICs was identified. Neutrophils appeared as singlets during development of the mammary gland (lOd and 3w) and their percentage was reduced during tissue maturation (Fig. 4A, 3C). However, neutrophils were significantly enriched in physical crosstalk with epithelial tumor cells in the PyMT+tissue during advanced carcinoma (Fig. 5C, 5F). This result was validated by quantifying the FACS staining for Ly6G+neutrophils in CD45+singlets and CD45+EpCAM+PICs (Fig. 5G), by observation of Ly6G+CD45+EpCAM+bulk sorted doublets in confocal microscopy (Fig. 5H), and by ImageStream analysis of representative 12w PyMT+mammary glands (Fig. 7C). Importantly, transcriptionally the inventors demonstrated that these PICs exhibited a joint gene expression profile of both neutrophils and epithelial cells (Fig. 51). Additionally, IF staining of murine mammary glands at the age of 12w revealed that Ly6G+CD45+neutrophils and EpCAM+epithelial cells were in physical contact at the TME of PyMT+tumor tissues (Fig. 5 J). Taken together, the inventors found a phenomenon of neutrophil re-appearance in the mammary gland during advanced carcinoma, which they were able to identify by focusing on the immune cells that are in physical contact with the tumor cells in the TME.

[0438] Table 1. Number of biological replicates, total cells and number of pooled mice per biological replicate for each age and condition.

[0439]

[0440]

[0441]

[0442] EXAMPLE 4

[0443] Neutrophil heterogeneity in the mammary gland TME

[0444] The unique neutrophil dynamics in the singlet and PIC populations along mammary gland development and cancer progression led the inventors to explore the plasticity of neutrophil gene programs in singlets and PICs. To that end, CD45+Ly6G+neutrophil singlets were first enriched (Fig. 8A) from developmental and carcinogenic time points (Table 1). To identify and isolate neutrophil singlets in the data, the CD45+Ly6G+and CD45+singlets were pooled and cells with high expression of neutrophil marker genes were selected (Csf3r, Retnlg, S100a8; Fig. 8B-8D).

[0445] The inventors then clustered the 1,759 passed-QC neutrophils (Fig.8E, Experimental Procedures) and identified seven main groups based on expression of distinct hallmark and functional gene programs (Fig. 9A, 8F-8G). The inventors identified two neutrophil states enriched during early developmental stages of both the PyMT+and PyMT’ mammary glands, annotated as a cystatin-high neutrophil state (Cy statin Neut; Cstdc5, Cstdc4 and Slfa2ll) and a young neutrophil state(Young Neut; Camp, Ngp, and Ltf) that also shared the cy statin-high marker genes (Fig. 9A, 9B up). Next, a neutrophil state expressing the MHC class II genes (MHC-II Neut; Cd74, H2-Aa, H2-Aabl), and a Ptgs2-expressing neutrophil state (Ptgs2+Neut) that exhibited also upregulation of a unique gene program including Nr4al, Nfkbid and Tnfaip3 genes were annotated (Fig. 9A). Both MHC-II and Ptgs2+singlet neutrophils appeared since early carcinoma and then declined at later time points in PyMT+mice (Fig. 9B middle). The remaining three states were enriched mostly during advanced carcinoma, therefore they were annotated as tumor associated neutrophils (TANs;

[0446] Fig. 9B down). While these states exhibited a common gene program of several genes (e.g. Lcn2, Wfdc21 , Anxal , Retnlg-, Fig.9A), TAN 1 and TAN2 upregulated a unique TME gene program (e.g. Ifitml, Cxcl2, Ccrl2, Wfdcl7-, Fig. 9A). The young TANs expressed a gene program associated with young neutrophils (e.g. Camp, Ngp, Cdl 77, Ltf, Mmp8). that was also seen to a lesser degree in TAN1 (Fig. 9A).

[0447] The unique neutrophil state dynamics along tumor progression (Fig. 9B) prompted the inventors to validate it by FACS quantification and protein staining (Fig. 9C-9D). For that purpose, the inventors stained for the surface marker MHC-II and for the intracellular proteins PTGS2 and ECN2 to mark the different neutrophil states (MHC-II Neut, Ptgs2+Neut, TANs, respectively). The protein validations confirmed that the percentage of CD45+Ey6G+neutrophils expressing MHC-II and PTGS2 was higher at early carcinoma (8w PyMT+) compared to advanced carcinoma (12w PyMT+), while TANs (ECN2hlghcells) were more abundant during advanced carcinoma (Fig.

[0448] 9C). The different neutrophil states were also identified by IF in the advanced carcinoma TME of PyMT+mammary glands (Fig. 9D).

[0449] Next, it was investigated whether the gene signature of TANs in the murine mammary gland TME is correlated with breast cancer patient survival using published clinical data. To that end, the inventors selected highly expressed and highly differentiated genes that separated the abundant TAN states (TAN1 and TAN2) from the other neutrophil states, and converted them to homologous human genes (Fig. 8H, Experimental Procedures). This integrated TAN gene score was used to analyze bulk RNA-sequencing data of patients with different types of breast cancer, mostly lobular and invasive ductal carcinomas, from The Cancer Genome Atlas (TCGA). Kaplan-Meier survival analyses revealed that breast cancer patients at stages III and IV with high expression of the TAN gene score (CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P) exhibited a significantly lower survival pattern (Fig. 9E). Importantly, this trend was not observed when analyzing patients from earlierstages I and II (Fig. 81). Thus, it was shown that the breast TAN gene score can serve as a biomarker to predict patient survival in advanced breast cancer stages.

[0450] To better understand whether the neutrophil transcriptomic profiles were unique to the mammary gland tissue, the inventors isolated and sequenced 2,397 neutrophils from the peripheral blood of PyMT+mice with advanced carcinoma and compared gene expression profiles (Fig. 9F, Experimental Procedures). Differential gene expression analysis between mammary gland and blood neutrophils revealed a tissue-specific gene signature that was significantly upregulated by the TME -residing neutrophils. In the tissue-specific neutrophil gene signature, the inventors found genes associated with the Ptgs2+Neut state (e.g. Cxcl2, Nfkbia, Ptgs2, Ccrl2) and genes with functional properties in the TME (e.g. Vegfa, Osm, Sppl ; Fig. 9F). Then, the inventors further compared the expression of selected genes in blood neutrophils and across the different mammary gland neutrophil states (Fig. 9G). Interestingly, blood neutrophils shared their gene signature mostly with the Young TAN subset (Ngp, Camp, Ltf), but less with the other mammary gland neutrophil states, suggesting that the young TANs might be early recruited cells into the TME (Fig. 9G).

[0451] Next, it was asked whether the TME -residing neutrophils derived from the mammary gland share a transcriptional program with neutrophils isolated from the TME of other primary tumors. To this end, the inventors reanalyzed published scRNA-seq data of murine pancreatic ductal adenocarcinoma (PDAC) [Ref 12] and bulk RNA-seq data of murine colorectal cancer (CRC) [Bui, T. M. et al. J Clin Invest 134, el74545 (2024)]. The differential gene expression between tissue and blood neutrophils was calculated in each cancer model and it was found that TME neutrophils share a specific transcriptional program across the tested primary tumors (e.g. Ptgs2, Cxcl2, Ccrl2, and III h) when compared to their blood counterparts (Fig.9H, 8J-8K, Experimental Procedures). Overall, these findings characterize neutrophil transcriptional heterogeneity in the mammary gland TME, and further suggest that there is a TME-related transcriptomic signature shared among TANs in different primary tumor sites.

[0452] EXAMPLE 5

[0453] Neutrophils interact with tumor cells, ductal macrophages and vasculature in the tumor niche

[0454] Due to the heterogeneity of neutrophil states and their enrichment in physical crosstalk, the potential intercellular crosstalk of neutrophil singlets and neutrophil-tumor cell PICs was examined. To allow analysis of PIC signaling, which provides a unique perspective oncommunication networks, the enriched CD45+Ly6G+EpCAM+neutrophil-tumor cell PICs was used (Fig. 5H) and a PIC-seq analysis was performed using the singlet neutrophils and singlet epithelial cells previously analyzed as the background model (Fig. 10A-10C, Experimental Procedures). PIC-seq composition analysis revealed no significant enrichment of neutrophil states or epithelial subsets in physical interactions compared to the single cells (Fig. 10D). The inventors focused on signaling activated in advanced carcinoma samples (10-12w PyMT+) between all abundant neutrophil states in the TME (MHCII Neut, Ptgs2+Neut, Young TAN, TAN1 and TAN2), neutrophil-tumor cell PICs and the cell states belonging to the tumor niche they identified (Fig. 4F). To this end, the inventors ran the Ligand-receptor ANalysis frAmework (LIANA) [Dimitrov, D. el al. Nat Commun 13, 3224 (2022)] that integrates several ligand-receptor (L-R) analysis methods to suggest potential intercellular crosstalk within the tumor niche (Experimental Procedures).

[0455] The signaling between singlet neutrophils and the tumor niche was first investigated (Fig. 10E-10F), focusing on incoming L-R pairs to the neutrophils (Fig. 10H). Interestingly, many of these interactions were related to cell adhesion (e.g., Cd34. Selplg-, Fig. 11A) and neutrophil recruitment signaling (e.g., Ccl4. Ccl3. Cxcl2; Fig. 11B-11C), highlighting the role of the tumor niche, especially ductal macs in neutrophil recruitment and infiltration into the breast TME. To further functionally examine the role of ductal macs in neutrophil recruitment, an ex vivo migration assay was performed. Conditioned medium (CM) containing secreted factors from the breast TME was collected following 24h of incubation of mammary gland explant cultures from 12w PyMT+mice (Fig. 11D up, Experimental Procedures). Bone marrow-derived macrophages pre-activated with CM (CM-MACs) or incubated in regular medium (RM), were used as chemoattractant agents for bone marrow-derived neutrophils that were seeded on top of a transwell membrane with RM (Fig. HD). The inventors found that pre-activated CM-MACs significantly enhanced neutrophil recruitment when compared to RM-MACs (Fig. HE). The inventors' transcriptomic data highlighted Ccl3, among others, as a neutrophil-recruiting molecule expressed by ductal macrophages (Fig. 11B). Therefore, the inventors blocked CCL3 signaling in their established ex vivo model (Fig. HD) and found a reduction in the number of neutrophils that migrated to the bottom chamber (Fig. HF, 101). This suggests that CM-MACs recruit neutrophils via CCL3 signaling axis in the TME.

[0456] The number of interactions between neutrophil-tumor cell PICs and other cell states belonging to the tumor niche was then quantified (Fig. 11G, 10G). Since the highest number of outgoing interactions from the neutrophil-tumor cell PICs were to the perivascular populations (endothelialMcamhland pericytes), the inventors further examined significant interactions within this crosstalk (Fig. 10J), and identified crosstalk driven by the pro-angiogenic ligands Vegfa and Osm [Zhu, M. et al. Oncol Rep 34, 129-138. (2015)] (Fig. 11H-11I). This led the inventors to validate the spatial proximity of Ly6G+EpCAM+ / S100A9+EpCAM+neutrophil-tumor cell PICs to CD31+endothelial cells in 12w PyMT+murine mammary glands and in the TME of IDC patients (Fig. 11J-11K).

[0457] Taken together, the inventors' analysis suggests complex secreted and physical crosstalk within the tumor niche, where neutrophils are recruited by ductal macrophages, interact physically with tumor cells and induce pro-angiogenic signaling with the perivascular niche.

[0458] Table 2 describes the pairs of ligand-receptor for interacting TAN-tumor cells

[0459] Table 2: Ligand-receptor pair for interacting TAN-tumor cells

[0460]

[0461]

[0462]

[0463] EXAMPLE 6

[0464] Neutrophil-tumor cell PICs express a unique pro-tumorigenic gene program

[0465] In order to fully understand the effect of the physical crosstalk on the molecular signature of the interacting neutrophils and tumor cells, a computational method was applied comparing the observed gene expression of the PICs to the expected expression based on simulated PICs created using the underlying background singlets (Experimental Procedures). First, by analyzing all neutrophil-tumor cell PICs from advanced carcinoma mice (10-12w PyMT+), the inventors found an exclusive gene program induced in these PICs when compared to the singlet-based simulations, including the upregulation of Vegfa, Anxa3 and Sox4, and the downregulation of the mature epithelial cell gene Lalba (Fig. 12A).

[0466] Since all five neutrophil states (MHC-II neut, Ptgs2+Neut, Young TAN, TAN1 and TAN2) were physically interacting with tumor cells in the PyMT+mammary glands during advanced carcinoma (Fig. 10D), it was next asked whether each neutrophil state induces an exclusive gene program in its corresponding PICs. The PIC-seq gene analysis was stratified to the five neutrophil states and the molecular programs induced by the interaction was analyzed. Interestingly, Ptgs2+Neut, TAN 1 and TAN2 PICs showed upregulation of genes related to metastasis, migration and invasion such as Rsad.2, Rock2 and Nup88 (Fig. 13A). To confirm that the physical interaction induces this functional program, an invasion scratch assay was performed mimicking the process of wound closure. Met-1 tumor cells (a breast cancer cell line derived from PyMT+mammary glands) were seeded in monocultures, monocultures supplemented with CM derived from tumor-activated neutrophil cultures, cocultures with neutrophils, or cocultures supplemented with neutrophils pretreated with Cytochalasin-D to inhibit actin polymerization and therefore the ability to form physical interactions [Petzold, T. et al. Immunity 55, 2285 (2022)] (Fig. 13B, Fig. 12B, Experimental Procedures). Importantly, in the coculture condition where tumor cells were able to physically interact with neutrophils, they showed a higher percentage of wound closure compared to all other conditions (Fig. 13B). Therefore, the inventors concluded that the physical interaction between neutrophils and tumor cells, and not the secreted factors, promotes tumor cell invasion properties.

[0467] The inventors also found that the physical interactions of MHC-II Neut, Ptsg2+Neut and TAN2 with tumor cells led to a significant downregulation of Lalba gene expression (Fig. 13C), a hallmark gene of differentiated mammary epithelial cells. Interestingly, this was associated withthe upregulation of proliferating genes such as Btf3 (in MHC-II and P...

Claims

CLAIMS:

1. A diagnostic method for prognosing and / or detecting advanced breast cancer in a mammalian subject, the method comprising:(a) determining the expression level of at least one biomarker in at least one biological sample of said subject to obtain a tumor associated neutrophil (TAN)-score and / or a neutrophil tumor microenvironment physically interacting cell (Neu TME-PIC)-score for said sample, wherein said at least one biomarker / s comprise at least one of:(i) at least one TAN-score biomarker selected from: Cystatin A (CSTA), Cathelicidin Antimicrobial Peptide (CAMP), Cluster of Differentiation 177 (CD177), Glycogenin 1 (GYG1), Interferon-Induced Transmembrane Protein 1 (IFITM1), Lipocalin 2 (LCN2), Prokineticin 2 (PR0K2), Resistin-Like Beta (RETNLB), S100 Calcium-Binding Protein A6 (S100A6), S100 Calcium-Binding Protein A8 (S100A8), Uridine Phosphorylase 1 (UPP1) and WAP Four-Disulfide Core Domain 21, Pseudogene (WFDC21P); and(ii) at least one Neu TME-PIC- score biomarker selected from: Arginase 1 (ARG1), Charged Multivesicular Body Protein 4C (CHMP4C), Claudin 4 (CLDN4), Chromosome 16 Open Reading Frame 91 (C16ORF91), EPH Receptor A2 (EPHA2), Interleukin 18 Receptor Accessory Protein (IL18RAP), Prolactin- Induced Protein (PIP), Prokineticin 2 (PR0K2), Radical S-Adenosyl Methionine Domain-Containing 2 (RSAD2), Secreted and Transmembrane 1 (SECTM1), Schlafen Family Member 12-Like (SLFN12L), Tescalcin (TESC), Uridine Phosphorylase 1 (UPP1) and Vascular Endothelial Growth Factor (VEGF);(b) determining if at least one of the TAN-score and / or the Neu TME-PIC- score obtained in step (a), is positive or negative with respect to a predetermined standard TAN- score and / or a standard Neu TME-PIC- score; or to a TAN-score and / or a Neu TME-PIC- score of at least one control sample;wherein a positive TAN-score and / or Neu TME-PIC-score of said sample, indicates that said subject has an advanced stage of breast cancer, and / or with poor prognosis.

2. The diagnostic method according to claim 1, wherein the expression level of biomarker / s in step (a), is determined for at least one of:(i) TAN-score biomarkers comprising CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and(ii) Neu TME-PIC-score biomarkers comprising ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF; to obtain a TAN-score of (i), and / or a Neu TME-PIC-score of (ii), for said sample.

3. The diagnostic method according to any one of claims 1 or 2, wherein the expression level of said biomarker / s is determined at the RNA or the protein level.

4. The diagnostic method according to any one of claims 1 to 3, wherein said biomarker expression level is determined by sequencing and / or by using at least one detecting molecule.

5. The diagnostic method according to claim 4, wherein said sequencing comprises RNA sequencing.

6. The diagnostic method according to claim 4, wherein said at least one detecting molecule is selected from amino acid-based detecting molecules and / or nucleic acid-based detecting molecules.

7. The diagnostic method according to claim 6, wherein said nucleic acid-based detecting molecule / s comprise at least one of:(a) at least one oligonucleotide, each oligonucleotide specifically hybridizes to a nucleic acid sequence of one of said at least one biomarker;(b) at least one nucleic acid aptamer / s, each aptamer is specific for one of said at least one biomarker / s.

8. The diagnostic method according to claim 6, wherein said amino acid-based detecting molecule / s comprise at least one of:(a) at least one antibody, each antibody is specific for one of said biomarker / s and / or any fragment thereof;(b) at least one protein or peptide aptamer / s, each aptamer is specific for one of said biomarker / s; and(c) at least one labeled or tagged biomarker of said at least one biomarker / s or any fragment / s, peptide / s or mixture / s thereof.

9. The diagnostic method according to any one of claims 1 to 8, wherein said biological sample comprises at least one of: a cell sample, a tissue sample and / or a body fluid sample of said subject.

10. The diagnostic method of any one of claims 1 to 9, wherein said breast cancer is a breast carcinoma.

11. A diagnostic composition comprising a means for determining the expression level of at least one biomarker in at least one biological sample of a subject to obtain a TAN-score and / or a Neu TME-PIC-score for said sample, wherein said biomarker / s comprise at least one of:(i) at least one TAN-score biomarker selected from: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and (ii) at least one Neu TME-PIC-score biomarker selected from: ARG1, CHMP4C, CEDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SEFN12E, TESC, UPP1 and VEGF.

12. The diagnostic composition according to claim 11, wherein said biomarker / s comprise at least one of:(i) TAN-score biomarkers comprising CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and (ii) Neu TME-PIC-score biomarkers comprising ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

13. The diagnostic composition according to any one of claims 11 and 12, wherein a means for detecting the expression level of said at least one biomarker comprise a means for sequencing and / or at least one detecting molecule, each specific for one of said biomarker / s.

14. The diagnostic composition according to claim 13, wherein said sequencing comprises RNA sequencing.

15. The diagnostic composition according to claim 13, wherein said at least one detecting molecule is at least one amino acid-based detecting molecule / s and / or nucleic acid-based detecting molecule / s.

16. The diagnostic composition according to claim 15, wherein said nucleic acid-based detecting molecule / s comprise at least one of:(a) at least one oligonucleotide, each oligonucleotide specifically hybridizes to a nucleic acid sequence of one of said at least one biomarker;(b) at least one nucleic acid aptamer / s, each aptamer is specific for one of said at least one biomarker.

17. The diagnostic composition according to claim 15, wherein said amino acid-based detecting molecule / s comprise at least one of:(a) at least one antibody, each antibody is specific for one of said biomarker / s and / or any fragment thereof;(b) at least one protein or peptide aptamer / s, each aptamer is specific for one of said biomarker / s; and(c) at least one labeled or tagged biomarker of said at least one biomarker / s or any fragment / s, peptide / s or mixture / s thereof.

18. The diagnostic composition according to any one of claims 11 to 17, wherein said composition is adapted for use in a method for prognosing and / or for detecting advanced breast cancer in a mammalian subject, and wherein said method is as defined in any one of claims 1 to 10.

19. A kit comprising:(a) means for determining the expression level of at least one biomarker in at least one biological sample of a subject, to obtain a TAN-score and / or a Neu TME-PIC-score for said sample, said means comprise means and / or reagent / s for sequencing and / or at least one detecting molecule, each detecting molecule is specific for one of said biomarker / s;wherein said at least one biomarker / s comprise at least one of:(i) at least one TAN-score biomarker selected from: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and(ii) at least one Neu TME-PIC- score biomarker selected from: ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF;the kit further comprising at least one of:(b) software for determining the expression level of the biomarker / s in the sample;(c) a predetermined standard TAN-score and / or a predetermined standard Neu TME-PIC-score; and(d) at least one control sample.

20. The kit according to claim 19, wherein said biomarker / s comprise at least one of:(i) TAN-score biomarkers comprising CSTA, CAMP, CD177, GYG1, IFITM1, ECN2, PROK2, RETNEB, S100A6, S100A8, UPP1 and WFDC21P; and(ii) Neu TME-PIC-score biomarkers comprising ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

21. The kit according to any one of claims 19 and 20, wherein said at least one detecting molecule is at least one amino acid-based detecting molecule / s and / or nucleic acid-based detecting molecule / s.

22. The kit according to claim 21, wherein said nucleic acid-based detecting molecule / s comprise at least one of:(a) at least one oligonucleotide, each oligonucleotide specifically hybridizes to a nucleic acid sequence of one of said at least one biomarker; and(b) at least one nucleic acid aptamer / s, each aptamer is specific for one of said at least one biomarker.

23. The kit according to claim 21, wherein said amino acid-based detecting molecule / s comprise at least one of:(a) at least one antibody, each antibody is specific for one of said biomarkers and / or any fragment thereof;(b) at least one protein or peptide aptamer / s, each aptamer is specific for one of said biomarker / s; and(c) at least one labeled or tagged biomarker selected from said biomarker / s or any fragment / s, peptide / s or mixture / s thereof.

24. The kit according to any one of claims 19 to 23, wherein said kit is adapted for use in a method for prognosing and / or detecting advanced breast cancer in a mammalian subject, and wherein said method is as defined in any one of claims 1 to 10.

25. A prognostic method for predicting and assessing responsiveness of a mammalian subject having breast cancer, to at least one therapeutic agent or a treatment regimen comprising said at least one therapeutic agent, and optionally for monitoring disease progression, the method comprising the steps of:(a) determining the expression level of at least one biomarker in at least one biological sample of said subject, to obtain a TAN-score and / or a Neu TME-PIC-score for said sample, wherein said at least one biomarker / s comprise at least one of:(i) at least one TAN-score biomarker selected from: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and (ii) at least one Neu TME-PIC-score biomarker selected from: ARG1, CHMP4C, CEDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SEFN12E, TESC, UPP1 and VEGF;(b) classifying the subject as:(i) a responder subject to said therapeutic agent or treatment regimen, if the TAN-score and / or the Neu TME-PIC-score obtained in step (a), for at least one sample obtained after the initiation of the treatment, is negative with respect to a predetermined standard TAN- score and / or a predetermined standard Neu TME-PIC-score; or to a TAN-score and / or a Neu TME-PIC-score of at least one control sample;(ii) a non-responder subject to said therapeutic agent or treatment regimen, if the TAN- score and / or the Neu TME-PIC-score obtained in step (a) for at least one sample obtained after the initiation of the treatment, is positive with respect to a predetermined standard TAN-score and / or a standard Neu TME-PIC-score; or to a TAN-score and / or a Neu TME- PIC-score of at least one control sample;thereby, predicting and assessing responsiveness of said subject to said therapeutic agent or treatment regimen.

26. The prognostic method according to claim 24, wherein the expression level of said biomarker / s in step (a), is determined for at least one of:(i) TAN-score biomarkers comprising CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and (ii) Neu TME-PIC-score biomarkers comprising ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF;to obtain a TAN-score of (i), and / or a Neu TME-PIC-score of (ii), for said at least one sample.

27. A method for treating, preventing, inhibiting, reducing, eliminating, protecting or delaying the onset of breast cancer in a mammalian subject, the method comprising:(a) determining the expression level of at least one biomarker in at least one biological sample of said subject to obtain a TAN-score and / or a Neu TME-PIC-score for said sample, wherein said at least one biomarker / s comprise at least one of:(i) at least one TAN-score biomarker selected from: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and (ii) at least one Neu TME-PIC-score biomarker selected from: ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF;(b) determining if at least one of the TAN-score and / or the Neu TME-PIC- score obtained in step (a) is positive or negative with respect to a predetermined standard TAN-score and / or a standard Neu TME-PIC-score; or to a TAN-score and / or a Neu TME-PIC-score of at least one control sample;wherein a positive TAN-score and / or Neu TME-PIC-score of said sample, classifies said subject as displaying advanced stage of breast cancer; and(c) administering at least one therapeutic agent to a subject classified in step (b), as displaying advanced stage of breast cancer.

28. The method according to claim 27, wherein said expression level of the at least one biomarker / s in step (a), is determined for at least one of:(i) TAN-score biomarkers comprising CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and(ii) Neu TME-PIC-score biomarkers comprising ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF; to obtain a TAN-score for (i), and / or a Neu TME-PIC-score of (ii), for said sample.

29. The method according to any one of claims 27 and 28, wherein said therapeutic agent reduces and / or specifically depletes neutrophils or at least one sub-population of neutrophils in said subject.

30. The method according to claim 29, wherein said sub-population of neutrophils comprises at least one of: young TANs, TAN1 and TAN2 and / or enriched in at least one of MHC class II (MHC-II Neut) and Ptgs2-expressing neutrophil (Ptgs2+ Neut).

31. The method according to any one of claims 27 to 30, wherein said therapeutic agent interferes with the interaction between at least one ligand-receptor pair of neutrophils-tumor cells.

32. The method according to claim 31, wherein said at least one ligand-receptor pairs of neutrophils-tumor cells comprise at least of: App (amyloid beta precursor protein) and Fpr2 (formyl peptide receptor 2); Gnai2 (Guanine nucleotide-binding protein G(i), alpha-2 subunit) and Cxcr2 (C-X-C Motif Chemokine Receptor 2); Cdl4 (cluster of differentiation 14) and Itgbl (Integrin beta 1); Vasp (Vasodilator Stimulated Phosphoprotein) and Cxcr2 (C-X-C Motif Chemokine Receptor 2); Tnfsfl4 (tumor necrosis factor Superfamily Member 14) and Ltbr (lymphotoxin beta receptor); Jami (Junctional Adhesion Molecule-Like) and Cxadr (Coxsackievirus and adenovirus receptor); Sema4a (Semaphorin 4A) and Plxnb2 (Plexin B2); Lta_Ltb (Lymphotoxin Alpha_lymphotoxin beta) and Ltbr (lymphotoxin beta receptor); Ltb (lymphotoxin beta) and Tnfrsfla (tumour necrosis factor receptor superfamily 1A); Gnai2 (Guanine nucleotide-binding protein G(i), alpha-2 subunit) and Egfr (epidermal growth factor receptor); Ptpn6 (Tyro sine-protein phosphatase non-receptor type 6) and Egfr (epidermal growth factor receptor); Fllr (Fll receptor) and Itgal_Itgb2 (Integrin alpha L_Integrin Subunit Beta 2); Lamb2 (laminin subunit beta 2) and Cd44 (Cluster differentiation 44); Cdhl (Cadherin-1) and Igflr (insulin like growth factor 1 receptor); Cd55 (Complement decay-accelerating factor) and Adgre5 (Adhesion G Protein-Coupled Receptor E5); B2m (beta-2-microglobulin) and Tfrc (Transferrin Receptor); Mucl (Mucin 1) and Siglece (sialic acid binding Ig-like lectin E); Lgals3bp (Galectin 3 binding protein) and Cd33 (sialic acid binding Ig-like lectin 3); Anxa2 (Annexin A2)and Tlr2 (Toll-like receptor 2); Gnai2 (Guanine nucleotide-binding protein G(i), alpha-2 subunit) and Fprl (formyl peptide receptor 1); Adam 17 (ADAM Metallopeptidase Domain 17) and I16ra (interleukin 6 receptor, alpha); Tlnl (Talin-1) and Itgb3 (integrin, beta 3).

33. The method according to any one of claims 27 to 32, wherein said therapeutic agent comprises an antibody specific for at least one of: the ligand and / or the receptor of said ligandreceptor pair.

34. A method for treating, preventing, inhibiting, reducing, eliminating, protecting or delaying the onset of breast cancer in a mammalian subject, the method comprising the steps of administrating to said subject at least one therapeutic agent that: (i) reduces and / or specifically depletes at least one sub-population of neutrophils in said subject; and / or (ii) interferes with the interaction between at least one ligand-receptor pairs of neutrophils-tumor cells, wherein said subpopulation of neutrophils comprises at least one of: young TANs, TAN1 and TAN2 and / or enriched in at least one of MHC class II (MHC-II Neut) and Ptgs2-expressing neutrophil (Ptgs2+ Neut).

35. The method according to claim 34, wherein said therapeutic agent interferes with the interaction between at least one ligand-receptor pair of neutrophils-tumor cells.

36. The method according to claim 35, wherein said at least one ligand-receptor pairs of neutrophils-tumor cells comprise at least of: App and Fpr2; Gnai2 and Cxcr2; Cd 14 and Itgbl; Vasp and Cxcr2; Tnfsfl4 and Ltbr; Jami and Cxadr; Sema4a and Plxnb2; Lta_Ltb and Ltbr; Ltb and Tnfrsfla; Gnai2 and Egfr; Ptpn6 and Egfr; Fllr and Itgal_Itgb2; Lamb2 and Cd44; Cdhl and Igflr; Cd55 and Adgre5; B2m and Tfrc; Mucl and Siglece; Lgals3bp and Cd33; Anxa2 and Tlr2; Gnai2 and Fprl; Adam 17 and I16ra; Tlnl and Itgb3.

37. The method according to any one of claims 34 to 36, wherein said therapeutic agent comprises an antibody specific for at least one of: the ligand and / or the receptor of said ligandreceptor pair.

38. The method according to any one of claims 34 to 37, wherein said subject is diagnosed as displaying advanced breast cancer by a diagnostic method as defined in any one of claims 1 to 10.

39. A screening method for identifying at least one therapeutic compound for the treatment of advanced breast cancer in a mammalian subject, the method comprising the steps of:(a) determining a TAN-score and / or a Neu TME-PIC-score of at least one sample contacted with a candidate compound, wherein:(i) a TAN-score is determined in a sample comprising a population of neutrophils, by determining the expression level of at least one biomarker selected from: CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PR0K2, RETNLB, S100A6, S100A8, UPP1 and WFDC21P; and / or(ii) a Neu TME-PIC-score is determined in a sample comprising neutrophil-tumor cell pairs, by determining the expression level of at least one biomarker selected from: ARG1, CHMP4C, CEDN4, C16ORF91, EPHA2, IL18RAP, PIP, PR0K2, RSAD2, SECTM1, SEFN12E, TESC, UPP1 and VEGF; and(b) determining that the candidate compound is a therapeutic compound for advanced breast cancer if the TAN-score and / or the Neu TME-PIC-score determined in step (a) for said sample is negative with respect to a predetermined standard TAN-score and / or a predetermined standard Neu TME-PIC-score; or to a TAN-score and / or a Neu TME-PIC-score of at least one control sample; thereby identifying a therapeutic compound for advanced breast cancer.

40. The screening method according to claim 39, wherein at least one of:(i) said TAN-score is determined in step (a) by determining the expression level of CSTA, CAMP, CD177, GYG1, IFITM1, LCN2, PROK2, RETNLB, SI 00A6, S100A8, UPP1 and WFDC21P; and / or(ii) said Neu TME-PIC-score is determined in step (a) by determining the expression level of ARG1, CHMP4C, CLDN4, C16ORF91, EPHA2, IL18RAP, PIP, PROK2, RSAD2, SECTM1, SLFN12L, TESC, UPP1 and VEGF.

41. The screening method according to any one of claims 39 to 40, wherein said subject is a human subject having advanced breast cancer.

42. The screening method according to claim 41, wherein said sample is obtained from a tumor organoid.

43. The screening method according to claim 41, wherein said sample is obtained from a nonhuman mammalian model.

44. The screening method according to any one of claims 39 to 43, wherein said candidate is further evaluated for at least one of: inhibition of cell migration, inhibition of cell proliferation, inhibition of angiogenesis.

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