Small cell lung cancer molecular subtype and application thereof

Abstract

The invention relates to a small cell lung cancer molecular subtype and application thereof. Based on data of transcriptomics, proteomics and phosphorylation, unsupervised clustering analysis is carried out by using NMF, four subtypes of SCLC are found, analysis is carried out in combination with molecular characteristics of multiple omics subtypes and in-vivo experiments, an accurate treatment scheme of the SCLC is further disclosed, the treatment specificity of the subtypes of the SCLC is clarified, and the treatment effect of the subtypes of the SCLC is improved. According to the present invention, the defects of the current SCLC molecular typing treatment are made up, more treatment targets are provided for the SCLC treatment so as to achieve the precise treatment, more hopes are provided for the health of patients, and the good application prospects are provided.

Description

Technical Field

[0001] The present invention relates to the technical field of molecular biology, in particular to molecular typing of small cell lung cancer and its application. Background Art

[0002] Based on pathological characteristics, lung cancer can be divided into non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). SCLC accounts for about 15%-20% of lung cancer and has the characteristics of high malignancy, strong invasiveness, and rapid disease progression, which leads to extremely poor prognosis for patients.

[0003] Unlike NSCLC, which can choose different targeted therapies based on gene mutations, SCLC was previously considered a single type of homogeneous disease. Treatment options are generally selected based on the stage and recurrence of the disease, and there is a lack of effective molecular typing to guide precision treatment. Subsequently, some scholars have found that there is heterogeneity between SCLC tumors, so SCLC is divided into classical / mutant types. As the research deepens, scholars have divided it into neuroendocrine (neuroendocrine, NE) / non-neuroendocrine (non-neuroendocrine, non-NE) based on the neuroendocrine status characteristics of tumor cells. In recent years, scholars have further combined the transcriptome data of human SCLC tumor tissues, cell lines, and mouse models, and divided SCLC into subtypes such as ASCL1, NEUROD1, POU2F3, and YAP1 according to the differences in the expression of transcription factors. It is speculated that patients with SCLC-ASCL1 may benefit from treatment with BCL2, CREBBP, DLL3, and LSD1 inhibitors, patients with SCLC-NEUROD1 may benefit from treatment with arginine antagonists, AURK-A / B, CHK1, IMPDH, and LSD1 inhibitors, patients with SCLC-POU2F3 may benefit from treatment with arginine antagonists, AURK-A / B, CHK1, IMPDH, and IGF-R1 inhibitors, and patients with SCLC-YAP1 may benefit from arginine antagonists, AURK-A / B, CHK1, IMPDH inhibitors, and immunotherapy, but the above classification lacks relevant verification. As the research deepened, Gay and other scholars further used SCLC tumor tissue and IMpower133 clinical trial data to divide SCLC into ASCL1, NEUROD1, POU2F3, and Inflamed subtypes, and found that SCLC-Inflamed patients benefited from immunotherapy. Although the above molecular classification provides some ideas for the precise treatment of SCLC, the molecular classification based on transcriptome data has some shortcomings, mainly including: the data obtained from transcriptome sequencing cannot fully reflect the expression of genes at the proteomics level, which will lead to differences in molecular classification; in addition, transcriptomics data cannot reflect the expression of genes at the protein phosphorylation level, which results in the lack of important information in molecular classification.

[0004] In order to solve the above problems and further clarify the precise treatment plan for SCLC, it is necessary to further clarify the molecular classification of SCLC. However, there are currently no reports on the molecular classification of small cell lung cancer and its application as in the present invention. Summary of the invention

[0005] The purpose of the present invention is to provide a molecular classification of small cell lung cancer and its application in view of the deficiencies in the prior art.

[0006] In one aspect, the present invention provides the use of small cell lung cancer molecular typing in the preparation of a prognosis product for small cell lung cancer patients.

[0007] As a preferred example, prognosis includes classification of small cell lung cancer, prediction of drug effectiveness, prediction of treatment effectiveness, and evaluation of the patient's immune cell infiltration score.

[0008] More preferably, the molecular classification of small cell lung cancer includes the following 4 subtypes:

[0009] NMF1 isoforms: enriched in cell cycle, DNA damage, chromatin organization, and epigenetic regulation pathways;

[0010] NMF2 subtype: fewer specific up-regulated proteins or phosphorylation sites, but DLL3 expression was the highest;

[0011] NMF3 subtype: enriched in extracellular matrix receptor interactions, extracellular matrix organization and focal adhesion pathways, and increased signaling activity of RTK and MAPK signaling pathways;

[0012] NMF4 subtype: enriched for high expression of MYC targets, RNA metabolism, translation and ribosome-related pathways, and high correlation between CIN, stemness score and mRNA-protein of tumor samples.

[0013] More preferably, the treatment plan is a molecular typing-specific treatment, and the specific treatment plan is as follows:

[0014] NMF1 subtype: first-line chemotherapy based on etoposide and cisplatin (E / P);

[0015] NMF2 subtype: Targeting DLL3 for treatment;

[0016] NMF3 subtype: RTK targeted therapy;

[0017] NMF4 isoforms: targeting AURKA / B for therapy.

[0018] More preferably, in the immune cell infiltration assessment of patients with small cell lung cancer, the NMF3 subtype and NMF4 subtype scores are higher.

[0019] In a second aspect, the present invention provides a kit for molecular typing of small cell lung cancer, the kit comprising a reagent for detecting the expression level of any of the above-mentioned molecular typings of small cell lung cancer.

[0020] In a third aspect, the present invention provides use of a reagent for detecting molecular typing of small cell lung cancer in preparing a small cell lung cancer molecular typing detection kit.

[0021] In a fourth aspect, the present invention provides a pharmaceutical composition for treating small cell lung cancer, the drug comprising one or more of an ATR inhibitor, a TOP1 inhibitor, a DLL3 inhibitor, a RTK inhibitor or an AURKA / B inhibitor.

[0022] In a fourth aspect, the present invention provides use of the above-mentioned pharmaceutical composition in the preparation of a drug for treating small cell lung cancer.

[0023] The present invention has the advantages that:

[0024] Based on transcriptomics, proteomics and phosphoproteomics data, unsupervised cluster analysis was performed using NMF, and it was found that there are four subtypes of SCLC. The molecular characteristics of multi-omics subtypes and in vivo experiments were combined to further reveal the precise treatment plan for SCLC, clarify the treatment specificity of SCLC subtypes, make up for the shortcomings of the current SCLC molecular classification treatment, and provide more therapeutic targets for the treatment of SCLC to achieve precise treatment and bring more hope for patients' health. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Attached Figure 1 (A) NMF of SCLC tumor samples. Heatmaps show protein and phosphorylation features of each subtype, and representative pathways are annotated. (B) Box plots show comparisons of tumor mutation burden (TMB), chromosomal instability (CIN), stemness score, and mRNA-protein correlations among multi-omics subtypes. (C) Heatmaps of relative ANPY mRNA expression, neuroendocrine markers, thyroid transcription factor 1 (TTF-1 / NKX2-1), and proliferation index marker Ki-67 / MKI67 protein expression among multi-omics subtypes. (D) Box plots of ANPY expression comparison among multi-omics subtypes. (E) Heatmaps show expression levels of immune-related features in multi-omics subtypes. Within each subtype, samples were ranked by increasing ESTIMATE immune scores. (F) Box plots show comparisons of ESTIMATE immune scores among multi-omics subtypes.

[0026] Attached Figure 2Exploration of specific treatments for each SCLC subtype. (A) Proliferation index, E2F activity, and replication activity inferred from proteomic data of multi-omics subtypes, and response scores showing the efficacy of ATR inhibitors combined with TOP1 inhibitors. (B) Box plots showing the comparison of DLL3 protein expression and REST mRNA expression between multi-omics subtypes. (C) Experimental flow of multi-omics analysis of tumor tissues in xenograft mouse models. (D) Comparison of ATRi and TOP1i response scores in George study data, SCLC cell lines, and SCLCPDX / CDX models. (E) Tumor growth in H69 tumor-bearing mice treated with E / P (etoposide cisplatin) or control. Tumor volume was monitored every other day with a caliper (n = 5 per group). (F) Comparison of DLL3 protein expression in different subtypes in George study data. (G) Tumor growth in SC224 tumor-bearing mice treated with anlotinib or control. Tumor volume was monitored every other day (n = 5 per group). (H) Statistical analysis of tumor weight (left) and mouse weight (right) of the SC224 PDX model. (I) Tumor growth of DMS114 tumor-bearing mice treated with anlotinib or control. Tumor volume was monitored every other day (n = 5 mice per group). (J) Statistical analysis of tumor weight (left) and mouse weight (right) of the DMS14 CDX model. (K) Tumor growth of H446 tumor-bearing mice treated with alisertib or control. Tumor volume was monitored every other day (n = 5 mice per group). (L) Statistical analysis of tumor weight (left) and mouse weight (right) of the H446 CDX model. (M) H446 CDX model tumors treated with alisertib or control. (N) Tumor growth of SC234 tumor-bearing mice treated with anlotinib, alisertib, or control. Tumor volume was monitored every other day (n = 5 mice per group). (O) Statistical analysis of tumor weight (left) and mouse weight (right) of the SC234 PDX model. (P) Tumors in the SC234 PDX model were treated with anlotinib, alisertib, or vehicle.

[0027] Attached Figure 3(A) Tumor growth in SC022-bearing mice treated with anlotinib or control. Tumor volume was monitored every other day (n = 5 per group). (B) Statistical analysis of tumor weight (left) and mouse weight (right) in SC022 PDX models. (C) Tumors in SC022 PDX models treated with anlotinib or control. (DF) Relative viability of H146 (D), H82 (E), and H446 (F) cells after 72 h of treatment with increasing concentrations of the AURK inhibitors alisertib, barasertib, or AMG-900 (n = 3 replicates). (G) Tumor growth in SC222-bearing mice treated with E / P, alisertib, or control. Tumor volume was monitored every other day (n = 5 per group). (H) Statistical analysis of tumor weight (left) and mouse weight (right) in SC222 PDX models. (I) Tumors in SC222 PDX models treated with E / P, alisertib, or control. (J) Effects of anlotinib, alisertib or control on tumor growth in H146 tumor-bearing mice. Tumor volume was monitored every other day (n=5 per group). (K) Statistical analysis of tumor weight (left) and mouse weight (right) in the H146CDX model. (L) H146CDX model tumors treated with anlotinib, alisertib or vehicle. DETAILED DESCRIPTION

[0028] Below in conjunction with specific embodiment, further elaborate the present invention.It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention.In addition, it should be understood that after reading the content of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope limited by the appended claims of the present application.If not specifically indicated, the technical means used in the embodiment are conventional means well known to those skilled in the art and commercially available common instruments and reagents, and references such as " Molecular Cloning Experiment Guide (3rd Edition) " (Science Press), " Microbiology Experiment (4th Edition) " (Higher Education Press) and the manufacturer's instructions of corresponding instruments and reagents can be found.

[0029] Example 1 Multi-omics classification of SCLC and corresponding biological characteristics

[0030] 1 Experimental Materials

[0031] All SCLC tumors and normal adjacent tissues (NATs) for this study were collected at Shanghai Pulmonary Hospital (Tongji University, Shanghai, China) from April 2012 to June 2019. This study was approved by the Institutional Review Board of Shanghai Pulmonary Hospital, and informed consent was obtained from all participating patients. All patients underwent surgical resection and had not received previous chemotherapy, radiotherapy, or immunotherapy.

[0032] 2 Experimental methods

[0033] 2.1 RNA sequencing

[0034] Total RNA was extracted and purified from fresh frozen tissues using TRIzol reagent (Invitrogen, CA, USA). RNA Assay Kit, 2.0 Flurometer (Life Technologies, CA, USA) and The RNA concentration and purity of all RNA analytes were measured in a spectrophotometer (IMPLEN, CA, USA). RNA integrity was assessed using the RNA Nano 6000 AssayKit of the Bioanalyzer 2100 system (Agilent Technologies, CA, USA). Samples with an RNA integrity number (RIN) > 6.0 were considered high-quality samples and were used to construct transcriptome libraries. UltraTM RNA library Prep Kit for Total RNA-seq libraries were constructed for RNA samples using the Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA) kit (NEB, MA, USA), and index codes were added to the attribute sequence of each sample. Quality control was performed at each step, and the libraries were quantified using the Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA). Paired-end libraries were then sequenced on the Illumina Novaseq 6000 platform (Illumina, CA, USA) to generate 150 bp paired-end reads. A total of 107 paired tumor and NAT samples passed RNA quality control and were used for RNA sequencing. All transcriptome sequencing was completed by Tianjin Novogene.

[0035] 2.2 RNA quantification

[0036] After removing adapters and low-quality reads, RNA-seq cleaned sequence data were aligned to the human reference sequence hg38 using STAR (version 2.7.6a)106. Gene expression in the resulting BAM files was analyzed using Salmon (version 1.3.0)107 against the transcriptome reference GENCODE (version 22, https: / / www.gencodegenes.org). The resulting transcripts per million (TPM) values ​​for protein-coding genes were then log2 transformed, and redundant gene symbol data rows were summarized by calculating the mean expression value.

[0037] 2.3 Protein and protein phosphorylation sequencing

[0038] 2.3.1 Protein extraction and trypsin digestion

[0039] About 25-100 mg of each SCLC tumor or NATs was frozen and crushed and resuspended in 500 μL lysis buffer (4% SDS, 0.1M Tris-HCl, pH 7.6) and sonicated for 2 min (on and off 5s) at 20% amplitude using an ultrasonic homogenizer (JY92-IIDN, Ningbo Scientz Biotechnology Co., Ltd.). Then denatured and reduced at 95°C for 5 minutes. The lysate was centrifuged at 12,000g for 10 minutes to remove insoluble debris, and the protein concentration of the clarified lysate was determined by BCA assay (Beyotime Biotechnology). The protein lysate was reduced with 0.1M dithiothreitol (Sigma) at 56°C for 30 minutes, and then an equal amount of protein from each sample was digested by the filter-assisted sample preparation (FASP) procedure. Briefly, proteins were mixed with 200 μL UA buffer (8 M urea, 0.1 M Tris-HCl, pH 8.5) in a 10 kDa centrifugal filter tube (Pall Corporation) and centrifuged at 12,000 g at 22 °C. Under the same centrifugation conditions, the filter was washed twice with 200 μL UA buffer, alkylated with 50 mM iodoacetamide (Sigma) in 200 μL UA buffer for 30 min, washed again with 100 μL UA buffer for 3 times at room temperature, and finally washed 3 times with 100 μL 50 mM triethylammonium bicarbonate (TEAB, Sigma). Subsequently, proteolytic cleavage was performed with sequencing grade modified trypsin (Promega) in 50 mM TEAB at an enzyme-substrate ratio of 1:50 at 37 °C for 18 h. The digestion enzyme was collected by centrifugation, and the filter device was rinsed with 50 μL 50 mM TEAB and centrifuged again. The concentration of tryptophan peptides was determined by the BCA method, and 400 μg of peptides were dried in a Speed-Vac apparatus (Thermo Fisher Scientific) for each sample.

[0040] 2.3.2 Preparation of reference tissue samples

[0041] Proteomic and phosphoproteomic analysis of SCLC samples was performed using TMT 11-plex experiments. To facilitate quantitative comparisons between all samples in each TMT batch experiment, each TMT set contains an internal reference mixed sample. Therefore, we randomly selected 40 pairs of tumors and NATs and mixed them with equal amounts of protein to form the internal reference represented in this study. The resulting standard material was divided into 1.2 mg aliquots and digested using the same FASP method.

[0042] 2.3.3 TMT 11-plex labeling of peptides

[0043] 112 tumor samples with paired NATs were distributed in 23 sets of TMT 11-plex experiments, with 10 individual samples occupying the first 10 channels in each set and the 11th channel reserved for the internal reference sample. In each TMT experiment, 5 tumor samples were labeled with 126, 127C, 128C, 129C, and 130C, respectively; paired NAT samples were labeled with 127N, 128N, 129N, 130N, and 131N, respectively; the mixed sample was labeled with the 131C channel as the internal reference. In each TMT labeling experiment, the dried peptide (400 μg) of each sample was dissolved in 200 μL 100 mM TEAB, and 2 sets of TMT reagent (0.8 mg) dissolved in 82 μL (41 μL 2) anhydrous acetonitrile were added. After incubation at room temperature for 1 h, 16 μL 5% hydroxylamine was added to quench the labeling reaction at room temperature for 15 min. The labeled peptides were pooled, dried by speedvac, and subsequently desalted on a reverse phase tC18 SepPak column (Waters). The same batch of TMT reagent was used for all samples (TC261829 for TMT 10-plex and TF266762 for TMT 11-131C).

[0044] 2.3.4 Separation of peptides by high pH reversed phase liquid chromatography

[0045] In order to reduce the complexity of the sample and increase the depth of protein identification, high pH reverse phase liquid chromatography (RPLC) was used for peptide separation. For each group of TMT, a 4.6mm 250mm Waters XBridge BEH300 C18 column and 3.5μm beads (Waters) were used to separate about 4.4mg of TMT 11-plex labeled peptides. Agilent1100 high performance liquid chromatograph was used with solvent A (10mM ammonium formate, pH10) and solvent B (90% ACN, 10mM ammonium formate, pH10) as linear increasing concentrations, flow rate 0.7mL / min, and high pH reverse phase liquid chromatography was used to separate peptides. The separation gradient for 110 min was set as follows: 2 min 1% ~ 5% B; 5% ~ 25% B, 35 min; 25% ~ 40% B, 43 min; 40% ~ 55% B, 6 min; 55% ~ 95% B, 3 min; 95% B soak for 4 min; 95% ~ 1% B, 1 min; from 3 min to 99 min, 96 fragments were separated and collected per minute, and these fragments were combined into 24 fragments by a step-by-step cascade strategy. 5% of each of the 24 components was allocated and dried in Speed-Vac for global proteome analysis. The remaining 95% of the sample was then used for phosphopeptide enrichment as described below.

[0046] 2.3.5 Fe-IMAC enrichment of phosphopeptides

[0047] Phosphopeptide enrichment was performed using a high-selectivity Fe-NTA kit (Thermo Fisher Scientific) according to the manufacturer's instructions. Briefly, isolated peptides were reconstituted in 200 μL 80% ACN / 0.1% trifluoroacetic acid (TFA) and incubated with 50 μL Fe3-NTA agarose beads at room temperature for 20 min. Then, the mixture was transferred to a filter head (oxygen, TF-200-LRS) and the clear peptide flow-through without bound peptides was collected by centrifugation. After three consecutive washes with 200 μL 80% ACN / 0.1% TFA and 200 μL H2O, the mixture was washed twice with 200 μL 50% ACN / 5% NH3·H2O and dried using a quick vacuum. All the above centrifugation steps were performed at 50 g at room temperature.

[0048] 2.3.6 Preparation of reference samples

[0049] Longitudinal quality control of mass spectrometry performance was verified by periodic analysis of the entire process of repeated benchmark samples. Benchmark samples were prepared from 5 pairs of HCC tissues and their adjacent non-tumor liver tissues as previously reported.87 After protein extraction, trypsin digestion, TMT 10-plex labeling (tumor tissues were labeled with 126, 127C, 128C, 129C, and 130C; adjacent non-tumor liver tissues were labeled with 127N, 128N, 129N, 130N, and 131), peptide separation was performed as described above, and 1 μg of each fraction was diluted to 100 ng.

[0050] 2.3.7 Mass spectrometry analysis

[0051] For proteomic analysis, the separated peptides were resuspended in 2% ACN / 0.1% formic acid (FA), and about 1 μg of each fraction was separated on a nanoflow Easy nLC 1200 UHPLC system (Thermo Fisher Scientific) using an in-house packed 20 cm diameter 75 μm C18 column (1.9 μm resil-pur C18-aq micro beads, Dr. Maisch GmbH, Germany). The column was heated to 50°C using a homemade tower heater. The flow rate was set to 300 nL / min. Buffers A and B were 0.1% FA in H2O and 0.1% FA in 80% acetonitrile, respectively. The 120-min separation gradient was set as follows: 2% to 5% B for 1 min; 5% to 35% B, 93 min; 35-50% B, 15 min; 50% to 100% B, 3 min; the samples were analyzed using a QExactive HF mass spectrometer (Thermo Fisher Scientific) equipped with a nanoflow ionization source. Data-dependent acquisition was performed using Xcalibur software (version 4.0.27.19) in positive ion mode at a spray voltage of 2,300 V. The resolution of the MS1 spectrum was 120,000 @ m / z 200, the AGC target was 3e6, the maximum injection time (IT) was 50 ms, and the mass range was 350 to 1700 m / z. The data-dependent mode cycle was set to trigger MS2 scans of up to the top 20 most abundant precursors per cycle, with an MS2 resolution of 60,000 @ m / z 200, an AGC target of 1e5, a maximum injection time of 120 ms, an isolation window of 1.0 m / z, an HCD (high collision dissociation) collision energy of 32, and a fixed first mass of 105.0 m / z. The dynamic exclusion time was set to 40 s, and precursor ions 1, 7, 8, and >8 were excluded from MS2 analysis. A 90-min gradient was used to analyze 24 benchmark scores as follows: 2%-6% B in 1 min; 6%-32% B for 64 min; 32-50% B for 13 min; 50%-70% B for 3 min; 70%-100% B for 1 min, and 100% B for 8 min. The MS parameter settings were the same as for the SCLC proteomics samples.

[0052] For phosphoproteomic analysis, the enriched phosphopeptides were resuspended in 2% ACN / 0.1% formic acid, and half of each fraction was analyzed using the same instrument and mass spectrometry parameters as for global SCLC proteomics analysis, except for a 70-min LC gradient (2%-5% B in 1 min; 5-32% B, 48 min; 32%-45% B in 10 min; 45-100% B in 3 min, 100% B in 8 min). The 24 benchmark fractions for monitoring SCLC phosphoproteomic analysis were analyzed using a 70-min gradient as follows: 5%-8% B in 4 min; 8%-35% B, 46 min; 35%-50% B in 10 min; 50%-100% B in 2 min, 100% B in 8 min. The parameters for mass spectrometry were set the same as above.

[0053] 3. Proteomics and phosphoproteomics data processing

[0054] 3.1 Database retrieval of MS data

[0055] All MS raw files were searched against the human Swiss-Prot database (downloaded in August 2018) containing 20,431 sequences using MaxQuant (version 1.6.5.0)108. MS2 reporter ion quantification based on TMT 11-plex (SCLC tumor and NAT samples) and TMT 10-plex (benchmark and PDX / CDX samples) was selected, and the reporter mass tolerance was set to 0.003 Da. The purity of the TMT labeling channel was corrected according to the kit LOT number. The PIF (precursor intensity fraction) filter value was set to 0.5. The enzyme specificity was set to trypsin, and a maximum of two missed cleavages were allowed. Carbamidomethylcysteine ​​was used as a fixed modifier. Oxidation of methionine, protein N-acetylation, lysine acetylation, and deamidation of asparagine and glutamine (NQ) were used as variable modifications. For phosphorylation data analysis, phospho(STY) was also selected as a variable modification. The tolerance for the first search and main search of peptides was set to 20 ppm and 4.5 ppm, respectively. A cutoff of 1% FDR was applied at the peptide, protein, and site levels. A minimum of 7 amino acids was required for peptide identification. For phospho-localization, a localization probability greater than 0.75 was considered valid phosphorylation.

[0056] 3.2 Data Normalization

[0057] In 23 sets of TMT 11-plex labeling experiments, protein and phosphoprotein abundance were measured for 224 samples, and 11,209 proteins (unique peptides greater than or equal to 2) and 62,881 credible phosphoproteins (localization probability greater than 0.75) at the gene level were identified. In each set of TMT experiments, the relative protein or phospho abundance was calculated using the reporter ion intensity, that is, the ratio of sample abundance to the internal reference sample. The relative abundance was then log2 transformed and normalized using the median centering method to obtain log TMT ratio values ​​for these proteins and phosphorylation sites centered at zero. In these data, 7,010 proteins and 7,388 phosphorylation sites were quantified in all samples. The DreamAIR software package was used to estimate missing values ​​using the DreamAI integrated algorithm (https: / / github.com / WangLab-MSSM / DreamAI). Only proteins and phosphorus with a missing rate of less than 50% were entered. After substitution, 9,559 proteins and 26,979 phosphorylation sites were used for downstream analysis.

[0058] 4Chromosomal instability index (CIN) analysis

[0059] The CIN score reflects the overall copy number alteration of the entire genome. Based on the SCNA fragment results, we summarized the chromosomal instability of each sample using a weighted sum method. Specifically, the absolute log2 ratios of all fragments within the chromosome were weighted by fragment length and summed to infer the instability score of the chromosome. Then, the instability scores of all 22 autosomes were added together to calculate the genome-wide chromosomal instability index.

[0060] 5. Scoring of replication stress and E2F activity

[0061] All scores were inferred by the single sample gene set enrichment analysis (ssGSEA) method of the GSVA package. The replication_stress and E2F_targets gene sets were used to calculate the replication stress score and E2F activity score using proteomics data, respectively.

[0062] 6 Unsupervised Multi-omics Clustering

[0063] Non-negative matrix factorization (NMF) was used to perform unsupervised clustering of tumor samples based on mRNA, protein, and phosphoprotein expression data. Briefly, we required all features to be quantified in more than 50% of the samples, and the remaining missing values ​​were imputed as described above. All data tables were then concatenated, and the features with the lowest standard deviation (bottom 5th percentile) across all samples were removed for subsequent analysis. Each row in the data matrix was further scaled and normalized so that all features from different data types were represented as z-scores. Since NMF requires a non-negative input matrix, we converted the z-scores in the data matrix into a non-negative matrix by: 1) creating a data matrix with all negative numbers zeroed; 2) creating another data matrix with all positive numbers zeroed and the signs of all negative numbers removed; 3) concatenating the two matrices, resulting in a data matrix that was twice as large as the original matrix but contained only positive values ​​and zeros, and was therefore suitable for NMF. The resulting matrix was then subjected to NMF analysis via the NMF R-package (version 0.23.0) and using the dark factorization method. The cluster range between k=28 was tested to determine the optimal factorization rank k (number of clusters). 50 iterations were set for each k. We calculated the correlation coefficient and selected the k with the largest correlation coefficient. After determining the optimal factorization rank k, the NMF analysis was repeated with 1000 iterations to obtain stability and robustness results.

[0064] 7 Comparative analysis of multi-omics and transcriptional subtypes

[0065] Transcriptional subtype signature genes were derived from a previously reported NMF-derived gene list. Transcriptional subtypes were defined based on mRNA expression (TPM values) of overlapping genes in our cohort by NMF analysis and compared with our multi-omics subtypes by Fisher’s exact test.

[0066] 8 Multi-omics subtype tag protein and phosphorylation site identification

[0067] For each multi-omics subtype, the signature proteins and phosphorylation sites were defined as the subtype signature proteins and phosphorylation sites with higher abundance in that subtype than in all other three subtypes (Student's t-test). The obtained subtype signature proteins and phosphorylation sites (decomposed into gene level) were used for GO and KEGG analysis.

[0068] 9 Immune subtype identification and downstream analysis

[0069] RNA-seq data were analyzed by xCell (https: / / xcell.ucsf.edu / ) to calculate the abundance of 64 different cell types in 107 SCLC tumors and paired NAT samples. Unsupervised clustering of these cell signatures was then performed using the NMFR package (version 0.23.0). We used 50 iterations, k = 3 was selected based on the maximum coherence coefficient, and the NMF analysis was subsequently repeated using 200 iterations. In addition, the overall immune score and stromal score for each sample were derived based on the RNA-seq data using the ESTIMATE algorithm. Protein-based immune signatures of MHC proteins, stimulatory and inhibitory immunomodulators were calculated as the mean of protein expression for each gene set defined previously. The immune cytolytic activity (CYT) score was obtained by calculating the mean of mRNA expression of two key cytolytic effectors, granzyme A (GZMA) and perforin (PRF1).

[0070] 10 dryness score

[0071] The degree of cancerous dedifferentiation was assessed using a stemness score. Specifically, pluripotent stem cell samples (ESC and iPSC) were downloaded and preprocessed from the Progenitor Cell Biology Consortium (PCBC) data. Then, a one-class logistic regression (OCLR) machine learning algorithm was used to build a prediction model on the PCBC dataset. The mRNA expression values ​​of 107 SCLC tumor samples and 107 NAT samples were applied to calculate the stemness index. The TCGAanalyze Stemness function derived from the TCGAbiolinks Rpackage was used and the stemSig parameter was set to PCBC_stemSig following the previously published workflow.

[0072] 3 Results

[0073] 112 pairs of SCLC patient samples (including tumors and paired normal samples) were collected, of which 107 pairs of samples successfully completed transcriptomics, proteomics and phosphorylation sequencing. Based on transcriptomics, proteomics and phosphorylation data, unsupervised cluster analysis using NMF was performed, and it was found that SCLC has 4 subtypes ( Figure 1 A). The NMF1 subtype was mainly enriched in cell cycle, DNA damage, chromatin organization, and epigenetic regulation pathways, indicating that this subgroup had abnormally increased proliferation. NMF2 subtype tumors had fewer specific upregulated proteins or phosphorylation sites, but the highest TMB ( Figure 1B). NMF3 subtype tumors were enriched for extracellular matrix (ECM) receptor interactions, extracellular matrix organization, and focal adhesion pathways at the protein level. Notably, protein phosphoproteomics data showed increased signaling activity of receptor tyrosine kinase (RTK) and MAPK signaling pathways in the NMF3 subtype compared with RNA-seq or proteomics data. Finally, the NMF4 subtype was enriched for high expression of MYC targets, RNA metabolism, translation, and ribosome-related pathways, as well as chromosomal instability (CIN), stemness scores, and high mRNA-protein correlations in tumor samples ( Figure 1 B).

[0074] Based on mRNA expression, the expression patterns of transcription factors ANPY (ASCL1, NEUROD1, POU2F3, YAP1) in the four subtypes were detected, and obvious separation was found ( Figure 1 C and 1D). NMF1 subtype tumors highly expressed ASCL1, NEUROD1, or both, similar to previous reports that many SCLC tumors co-express ASCL1 and NEUROD1. Tumors in NMF2 selectively expressed ASCL1, with the lowest expression of ASCL1 and NEUROD1 in the NMF3 subtype, while the lowest expression of ASCL1 and NEUROD1 was found in the NMF4 subtype. High expression of POU2F3 was almost confined to the NMF4 subtype, mutually exclusive with ASCL1 and NEUROD1. Consistent with this, the NMF4 subtype included all POU2F3-high-expressing tumors except for YAP1-high expression. In addition, YAP1 expression was very low compared with the other three transcription factors, although YAP1 expression was relatively high and widely distributed in the NMF3 and NMF4 subtypes.

[0075] Finally, based on xCELL immune cell infiltration and proteogenomics-derived immune signatures, the differences in immune signatures between multi-omics subtypes were explored. Figure 1 E), the results showed that the tumor immunity scores of NMF3 and NMF4 were relatively high ( Figure 1 F).

[0076] Example 2 Exploration of Specific Treatments for Each SCLC Subtype

[0077] 1. Evaluation of treatment efficacy

[0078] To predict the therapeutic response of ATR inhibitor (ATRi) combined with TOP1 inhibitor (TOP1i), ATRi and TOP1i response scores were inferred based on a previously described SCLC clinical trial set of 225 genes differentially expressed between responding and non-responding tumors. Using RNA-seq data, the scores were calculated by the median expression of upregulated genes and the median expression of downregulated genes.

[0079] 2 Drug response experiments

[0080] Cell line-based drug response experiments Cells were seeded in 96-well plates at 2500 cells per well and cultured for 24 h, followed by incubation for 72 h with different concentrations of anlotinib, alisertib, barasertib, and AMG-900. Cell viability was measured with 60 μL of Cell Titer Glo reagent (Promega), and luminescence was measured with Bio Tek Synergy NEO.

[0081] 3 Xenograft tumorigenesis assay

[0082] To establish human SCLC cell line xenografts, nude mice (BKCo.) were injected subcutaneously with 1×10 6 To establish SCLC PDX models, we collected bronchoscopic lung biopsies from 16 SCLC patients, embedded them in matrix, and injected them into the right flank of severe combined immunodeficient (SCID) mice.

[0083] Multi-omics analysis of tumor tissues in 4 xenograft mouse models

[0084] Tumor tissues from 16 patient-derived tumor xenografts (PDX) and 6 SCLC cell line-derived xenografts (CDX) mouse models were used for multi-omic analysis. The methods for RNA extraction and RNA sequencing were exactly the same as described above. For proteomic and phosphoproteomic analysis, approximately 20-50 mg of tumor tissue was resuspended in 300 μL SDT lysis buffer (4% SDS, 0.1 M Tris-HCl, 0.1 M DTT, pH 7.6), sonicated at 20% amplitude for 2 minutes (5 seconds on, 5 seconds off), denatured and reduced at 95 degrees for 5 minutes. The lysate was then centrifuged at 12,000 g for 10 minutes, and the supernatant protein concentration was determined using a tryptophan-based fluorescence quantification method146. 16 samples with higher protein concentrations were selected and mixed in equal amounts to form internal reference samples for PDX / CDX tmt proteomic analysis. Three sets of TMT 10-plex (Thermo Fisher Scientific) were used to label these single mouse tumor tissues and mixed samples. Trypsin digestion, TMT 10-plex labeling, high phRPLC peptide separation, and phosphopeptide enrichment methods were the same as for SCLC patient samples. For proteomics analysis, LC-MS / MS analysis used nanoflow EasynLC 1200 UHPLC (Thermo Fisher Scientific) coupled to QExactive HF-X (Thermo Fisher Scientific) at a flow rate of 300 nL / min (2%-5% B, 1 min; 5%-32% B, 94 min; 32%-45% B in 15 min; 45-65% B, 3 min; 65%-100% B in 1 min; 100% B in 6 min). The reversed-phase column and solvent A / B were the same as above. MS parameters were as follows: MS1 resolution 120,000, mass range 350–1700 m / z, AGC target 3e6, maximum IT 50 ms, charge state exclusion 1, 7, 8, greater than 8, dynamic exclusion 40 s, top 20 ions selected for MS2; MS2: resolution 45,000, HCD energy 32, isolation window 1.0 m / z, AGC target 1e5, maximum IT 120 ms. For phosphoproteomic analysis, LC-MS / MS analysis was performed using a nanoflow Easy nLC 1200 UHPLC (Thermo Fisher Scientific) coupled to a Q Exactive HF (Thermo Fisher Scientific) at a flow rate of 300 nL / min (2%–25% B, 70 min; 25%–32% B, 10 min; 32%–100% B in 2 min; 100% B in 8 min). The mass spectrometer parameters were set the same as above.

[0085] 5. Drug response experiments based on cell lines and xenograft models

[0086] Four PDX models (SC022, SC222, SC224, SC234) and four SCLC cell line xenografts (H69, DMS114, H446, H146) were used to evaluate specific compounds or drugs. 3 At the time of the study, the mice were randomly divided into different treatment groups. For the NMF1 SCLC model (SC234, H146), the mice were orally administered with Anlotinib (6 mg / kg) or Alisertib (30 mg / kg) for 14 consecutive days. For the NMF3 SCLC model (SC022, SC224, DMS114), the mice were orally administered with Anlotinib (6 mg / kg) for 14 consecutive days. For the nms4 SCLC model (SC222, H446), the mice were orally administered with Alisertib (30 mg / kg) for 14 consecutive days. To evaluate the effect of EP on the H69 (NMF1) and SC222 (NMF14) models, mice were intraperitoneally injected with cisplatin (DDP, 6 mg kg -1 d -1 intraperitoneal injection), and etoposide (VP16, 10 mg / kg -1 d -1 One week was considered as one cycle. Nude mice were generally given two or more cycles of EP treatment.

[0087] 6 Results

[0088] To further reveal the precise treatment plan for SCLC, the research team intends to combine the molecular characteristics of multi-omics subtypes and in vivo experiments for analysis. The results suggest that the NMF1 subtype has high proliferation rate, E2F activity and replication stress ( Figure 2 A), suggesting that drugs that exacerbate genomic instability may be ideal therapeutic options. Therefore, we predict that the NMF1 subtype may benefit from first-line chemotherapy based on etoposide and cisplatin (E / P). A recent study showed that the simultaneous use of ATR and TOP1 can lead to sustained tumor regression in SCLC, especially in patients with high replicative stress and high neuroendocrine differentiation. Using the differentially expressed genes between responding and non-responding tumors described in the above clinical trials, we observed that the NMF1 subtype had the highest response score ( Figure 2 A), which further strengthened our speculation. Notably, we observed that the mRNA expression of the inhibitory NOTCH ligand delta-like protein 3 (DLL3) was the highest in the nmf2 isoform, while the mRNA expression of the NOTCH transcriptional target REST was the lowest ( Figure 2 B) Since DLL3 is considered a promising target for SCLC and the expression level of DLL3 is strongly correlated with the therapeutic effect of DLL3-targeted drugs, we speculated that the NMF2 subtype may gain greater benefits from DLL3-targeted therapy.

[0089] Despite the lack of RTK mutations in SCLC, RTK activation inferred from proteomic phosphorylation data may provide evidence for precision therapy in specific SCLC populations. As mentioned above, our phosphoproteomic analysis showed that RTK signaling activities, including VEGFR and ERBB pathways, were significantly elevated in the NMF3 subtype ( Figure 1 A), therefore, treatment targeting RTKs may be effective for these tumors. In fact, anlotinib, a multi-targeted tyrosine kinase inhibitor (RTKi) with anti-angiogenic and anti-tumor activities, has been shown to improve progression-free survival (PFS) and OS in SCLC patients. Therefore, we speculate that targeting RTKs may be an encouraging treatment option for patients with the NMF3 subtype. In addition, high expression of MYC and enrichment of MYC-related pathways in the NMF4 subtype may predict the unique vulnerability of these tumors ( Figure 1 A). Although MYC protein is considered an undruggable target, previous studies have shown that MYC-high SCLC tumors may be sensitive to AURKA / B inhibitors. Therefore, we speculate that patients with NMF4 subtype may benefit more from AURKA / B inhibitors.

[0090] To further verify the accuracy of the above molecular classification-specific treatment regimen, we performed transcriptome sequencing, proteome sequencing, and protein phosphorylation sequencing on 16 SCLC patient-derived tumor xenografts (PDX) and 6 human SCLC cell line-derived xenografts (CDX) models. Figure 2 C) and matched the above multi-omics classification, resulting in 10 tumors consistent with NMF1, 3 tumors consistent with NMF2, 5 tumors consistent with NMF3, and 4 tumors consistent with NMF4. In addition, we validated these subtypes in two other datasets, including RNA-seq data from 81 surgically resected SCLC tumors5 and RNA-seq data from 67 representative SCLC cell lines. We found that the NMF1 subtype showed the highest ATR and TOP1 inhibition response scores in all three SCLC datasets ( Figure 2 D).

[0091] In vivo, NMF1 subtype tumor H69 benefited from E / P treatment ( Figure 2 E). Previous studies have shown that other NMF1 subtype tumors including SC234, SC007, SC185PDX models and H146CDX models are also sensitive to E / P treatment. In addition, George et al. confirmed DLL3 overexpression in the NMF2 subtype. SCLC dataset ( Figure 2 F), further suggesting that the NMF2 subtype may benefit from DLL3-targeted therapy.

[0092] For NMF3 subtype tumors, we tested the therapeutic effect of anlotinib, a multi-target RTK inhibitor, on SCLCPDX / CDX models of NMF3 (SC022, SC224, DMS114) and NMF1 (H146, SC234) subtypes. As expected, anlotinib treatment had a strong inhibitory effect on tumor growth in all three models of NMF3 subtypes ( Figure 2 G-2J and 3A-3C). At the same time, the body weight of mice treated with anlotinib did not decrease significantly ( Figure 2 H, 2J and Figure 3 B). For NMF4 subtype tumors, we tested the effects of AURK inhibitors in SCLC cell lines and PDX / CDX models. These in vitro results show that NMF4 subtype (H82, H446) cells are more responsive to AURK inhibitors (alisertib, barasertib, and AMG-900) compared to NMF1 subtype (H146) ( Figure 3 D-3F). Consistent with this, in vivo experimental results also showed that treatment with the AURK inhibitor alisertib significantly inhibited tumor growth in the SC222PDX model and the H446CDX model, and these treatments did not cause significant weight loss in mice ( Figure 2 K-2M and 3G-3I). In addition, alisertib treatment was superior to E / P treatment in the SC222 PDX model ( Figure 3 G-3I). In addition, anlotinib or alisertib treatment barely induced any tumor growth inhibition in the NMF1 isoform H146 or SC234 models ( Figure 2 N-2P and 3J-3L), indicating that there is treatment specificity for SCLC subtypes.

[0093] The above is only a preferred embodiment of the present invention. It should be pointed out that ordinary technicians in this technical field can make several improvements and supplements without departing from the method of the present invention. These improvements and supplements should also be regarded as the scope of protection of the present invention.

Claims

1. Application of small cell lung cancer molecular typing in the preparation of prognostic products for patients with small cell lung cancer.

2. The use according to claim 1, characterized in that: The prognosis includes classification of small cell lung cancer, prediction of drug effectiveness, prediction of treatment effectiveness, and evaluation of the patient's immune cell infiltration score.

3. The use according to claim 2, characterized in that: The molecular classification of small cell lung cancer includes the following 4 subtypes: NMF1 isoforms: enriched in cell cycle, DNA damage, chromatin organization, and epigenetic regulation pathways; NMF2 subtype: fewer specific up-regulated proteins or phosphorylation sites, but DLL3 expression was the highest; NMF3 subtype: enriched in extracellular matrix receptor interactions, extracellular matrix organization and focal adhesion pathways, and increased signaling activity of RTK and MAPK signaling pathways; NMF4 subtype: enriched for high expression of MYC targets, RNA metabolism, translation and ribosome-related pathways, and high correlation between CIN, stemness score and mRNA-protein of tumor samples.

4. The use according to claim 2, characterized in that: The treatment plan is a molecular typing-specific treatment, and the specific treatment plan is as follows: NMF1 subtype: first-line chemotherapy based on etoposide and cisplatin (E / P); NMF2 subtype: Targeting DLL3 for treatment; NMF3 subtype: RTK targeted therapy; NMF4 isoforms: targeting AURKA / B for therapy.

5. The use according to claim 2, characterized in that: In the patient's immune cell infiltration assessment, the NMF3 subtype and NMF4 subtype scores were higher.

6. A kit for molecular typing of small cell lung cancer, characterized in that: The kit comprises a reagent for detecting the expression level of the molecular typing of small cell lung cancer according to any one of claim 3.

7. Use of reagents for detecting molecular typing of small cell lung cancer in the preparation of a small cell lung cancer molecular typing detection kit.

8. A pharmaceutical composition for treating small cell lung cancer, characterized in that: The drug comprises one or more of an ATR inhibitor, a TOP1 inhibitor, a DLL3 inhibitor, a RTK inhibitor or an AURKA / B inhibitor.

9. Use of the pharmaceutical composition according to claim 8 in the preparation of a drug for treating small cell lung cancer.

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