In vitro method for predicting the response to immunotherapy treatment for hepatocellular carcinoma in subjects

Abstract

The present invention relates to an in vitro method for predicting the response to immunotherapy treatment of hepatocellular carcinoma in a subject, comprising the following steps: 1) A step of determining the energy metabolic state of a liver tissue sample from a subject, wherein the sample comprises pathological tissue and optionally non-pathological tissue, 2) A step of detecting the presence or absence of energy conversion resulting from a reduction in mitochondrial respiration in the pathological tissue by comparing it with a liver tissue sample of the subject or a non-pathological tissue from a standard non-pathological liver sample, Here: - If mitochondrial respiration is reduced in pathological tissue, the subject is less likely to respond to immunotherapy treatment, and - If mitochondrial respiration is not reduced in the pathological tissue, the subject is highly likely to respond to immunotherapy treatment.

Description

Technology Field

[0001] The present invention relates to an in vitro ( ex vivo It is about the method.

[0002] Therefore, the present invention has utility in the medical field, more specifically in the field of diagnosis, prognosis, and prediction of treatment response.

[0003] In the description below, references within square brackets ([ ]) refer to the list of references located at the end of this specification. Background Technology

[0004] The therapeutic arsenal for cancer is expanding day by day. However, for oncologists to advance toward the goal of precision medicine, it is essential to identify what treatments are truly beneficial for each patient. Some targeted therapies already benefit from companion testing. However, detecting mutations in targeted oncogenes or labeling immunotherapy targets with immunohistochemistry does not guarantee an objective response. Today, some cancers, including liver cancer, are not benefiting from any current tools for guiding treatment choices.

[0005] Liver cancer is a major health issue and the second leading cause of cancer death worldwide, with over 850,000 new cases occurring annually. In Western countries, the number of cases has been increasing over the past 20 years. Hepatocellular carcinoma (HCC) is the most common primary liver cancer. HCC occurs frequently in cirrhosis but is rare in healthy livers. While cirrhosis surveillance programs can detect a significant portion of early-stage HCC eligible for curative treatment (surgical resection, percutaneous destruction, liver transplantation), 50 to 70 percent of HCC cases are actually diagnosed at the terminal stage. Advanced HCC, which has an extremely poor prognosis, can only be treated with local therapy (arterial chemoembolization) or palliative systemic therapy.

[0006] The role of liver biopsy in the diagnosis and management of HCC has always been questioned. To date, the use of liver biopsy in HCC management has been limited due to the risk of complications such as tumor dissemination and hemorrhage, as well as the availability of effective, non-invasive diagnostic methods via imaging. However, it is now widely accepted that these risks are rare, manageable, and do not affect the course of the disease; therefore, these reasons should not be considered to avoid liver biopsy. Conversely, the risk of insufficient sampling is also highlighted in many studies. This is primarily due to the fact that genetic mutations or transcriptome profiles associated with HCC have not demonstrated any predictive value for currently proposed treatments. Indeed, liver tumor biopsy is undoubtedly undervalued and insufficiently utilized, especially considering that fixed biopsies contain valuable information capable of reversing current risk-benefit ratios.

[0007] Sorafenib was approved as the standard first-line therapy for advanced HCC in most countries, and until recently. Although sorafenib originally demonstrated a survival benefit despite a very low number of responders and significant side effects, it subsequently failed to demonstrate a superior survival benefit for advanced HCC in several Phase 3 clinical trials. Therefore, the market launch of a new drug with proven efficacy was a revolution in palliative care for HCC. Different Phase 3 studies demonstrated the non-inferiority of lenvatinib over sorafenib in overall survival for patients with advanced HCC, along with equivalent safety and tolerability profiles, thus justifying its use in first-line treatment. Depending on clinical recommendations, patients may also receive second-line treatment including cabozantinib, ramucirumab, nivolumab, or pembrolizumab. Furthermore, following the initial promising results of immunotherapy treatments (nivolumab, pembrolizumab), the combination of atezolizumab (anti-programmed apoptosis-ligand 1 (PDL1) antibody) and bevacizumab (anti-vascular endothelial growth factor (VEGF) antibody) has recently demonstrated a significant advantage in first-line treatment compared to standard treatment with sorafenib. This combination has effectively become the new standard first-line treatment (Gordan JD et al. ([1])). This enhanced treatment regimen has demonstrated better preserved liver function and an increase in median overall survival from 6 months to over 26 months in patients with advanced HCC, representing a substantial improvement. However, treatment strategies guiding the selection of first-line treatment and / or treatment sequences are not defined. Consequently, it is crucial to predict the response to these various treatments to improve efficiency.

[0008] Laser capture and mass spectrometry (MS) analysis have previously been combined to thoroughly analyze the proteomic profiles of tumors (Henriet E et al. ([2]), Dourthe C et al. ([3])). Compatible with formalin-fixed and paraffin-embedded tissue (FFPET) as well as very small amounts of material (diagnostic biopsies), this methodology has already enabled the identification of the biomarker ASS1, which is currently used in the diagnosis of sonic hedgehog hepatocellular adenoma associated with a high risk of bleeding (Henriet E et al. ([2]), Sala M et al. ([4])). Furthermore, it is now possible to perform proteomic profile matching in clinical routines using recently developed machine learning tools. In practice, comparing the proteomic profile of a tumor with profiles in a reference database can be used to make a diagnosis or predict malignant transformation (Dourthe C et al. ([3])).

[0009] However, there are no precision medicine tools available to predict the prognosis of each cancer. For example, aside from contraindications, there is no biological evidence available to guide treatment choices in hepatocellular carcinoma (HCC). The atezolizumab / bevacizumab immunotherapy combination is administered as a first-line priority because it exhibits the best objective response rate (20–30%). However, in the absence of prognostic tools for the treatment of advanced hepatocellular carcinoma, if first-line treatment fails, only 25% of patients have time to attempt second-line treatment.

[0010] Therefore, there is a need for a new tool to predict the response to treatment for hepatocellular carcinoma. The present invention satisfies this need and other needs.

[0011] After extensive research, the applicant has remarkably demonstrated that enrichment of specific biological pathways, particularly energy metabolism, is a highly discriminative and robust tool for predicting the response to HCC immunotherapy, specifically atezolizumab / bevacizumab immunotherapy.

[0012] Therefore, the present invention is based on using the tumor energy metabolic state as a novel tool to predict the response to immunotherapy in HCC.

[0013] One of the main advantages of the present invention is that it enables patients to be guided to potentially more suitable other treatments, such as redeploying multikinase inhibitors (lenvatinib or sorafenib) when the tumor exhibits an objective response profile.

[0014] Another advantage of the present invention is that it can be performed using a diagnostic biopsy performed prior to treatment. Therefore, since only a small amount of tissue is required for this analysis, the sample is not depleted and can be reused.

[0015] Another impressive advantage of this process is that the entire analysis can be completed within one week, remaining compatible with deadlines imposed by clinical practice. Furthermore, the proteomic data, once generated, remains a resource that can be reused for additional applications (e.g., prognosis, prediction of response to other treatments, search for new pharmacological targets).

[0016] Furthermore, the applicant has developed a response prediction approach based specifically on proteomic profiling to implement the prediction process of the present invention. Another advantage is that proteomic profiling combined with predictive signatures for therapeutic response enables the direct prescription of the most appropriate treatment for each patient without loss of opportunity, thereby increasing life expectancy in HCC.

[0017] Accordingly, in a first aspect, the present invention provides an in vitro method for predicting the response to immunotherapy treatment of hepatocellular carcinoma in a subject, comprising the following steps:

[0018] 1) A step of determining the energy metabolic state of a liver tissue sample from a subject, wherein the sample comprises pathological tissue and optionally non-pathological tissue,

[0019] 2) A step of detecting the presence or absence of an energetic shift resulting from a decrease in mitochondrial respiration in the pathological tissue by comparing it with a liver tissue sample of the subject or a non-pathological tissue from a standard non-pathological liver sample,

[0020] Here:

[0021] - If mitochondrial respiration is reduced in pathological tissue, the subject is less likely to respond to immunotherapy treatment, and

[0022] - If mitochondrial respiration is not reduced in the pathological tissue, the subject is highly likely to respond to immunotherapy treatment.

[0023] In this specification, “hepatocellular carcinoma” or HCC refers to any HCC regardless of its stage of development. For example, it may be early-stage HCC, intermediate-stage HCC, or advanced HCC, and may be resectable or unresectable HCC. The HCC may have already been treated with first-line therapy or may not have been treated.

[0024] As used in this specification, "subject" is a patient, animal, mammal, or human suffering from HCC.

[0025] According to the present invention, immunotherapy treatment for HCC may be any treatment for HCC performed by activating the immune system of the subject known in the art. This may be a treatment already approved by drug regulatory authorities in at least one country, or a treatment that is not approved and is under testing. The treatment may be a single molecule or a combination of at least two molecules. Immunotherapy treatment may be a treatment using a monoclonal antibody. Monoclonal antibodies for use in immunotherapy may be naked, i.e., unconjugated, or conjugated, i.e., with a chemotherapy drug or radioactive particle attached thereto. Additionally, monoclonal antibodies for cancer immunotherapy may be bispecific, i.e., designed to recognize and bind to two different proteins. For example, immunotherapy treatment may be selected from anti-PDL1 monotherapy such as nivolumab, combination therapy of atezolizumab and bevacizumab, combination therapy of anti-PDL1 and anti-VEGF, combination therapy of anti-PD1 and CTLA4, combination therapy of PDL1 and CTLA4, anti-VEGFR2 therapy such as ramucirumab, and anti-PD1 therapy such as pembrolizumab.

[0026] The liver tissue sample may be a physical tissue sample or an image of liver tissue from medical imaging. Thus, the liver tissue sample may be any sample obtainable by at least one technique selected from known techniques, e.g., macrodissection, laser microdissection of formalin-fixed paraffin-embedded or frozen tissue, biopsy, particularly fresh biopsy, and positron emission tomography. Any surface area of ​​the tissue sample may be used, provided that it contains pathological tissue and optionally non-pathological tissue.

[0027] "Pathological tissue" refers to liver tissue containing HCC cancer cells in this specification.

[0028] "Non-pathological tissue" refers to liver tissue that does not contain HCC cancer cells in this specification.

[0029] Advantageously, liver tissue samples from the subject include pathological and non-pathological tissues.

[0030] "Energy metabolism" in this specification refers to all or part of the biological pathways involved in the process of generating energy (i.e., ATP) from nutrients within a liver tissue sample of a subject. Energy metabolism generally includes the following pathways: mitochondrial respiration pathways including oxidative phosphorylation, the TCA cycle, and fatty acid beta oxidation. The energy metabolic state refers to the level of activation of all or part of these metabolic pathways. Accordingly, the energy metabolic state to be determined in the method of the present invention may include determining the mitochondrial respiration pathway including oxidative phosphorylation, the TCA cycle, and fatty acid beta oxidation, or determining at least one pathway selected from the oxidative phosphorylation pathway, the TCA cycle pathway, and the fatty acid oxidation pathway. Advantageously, the energy metabolic state includes oxidative phosphorylation and the TCA cycle, which are biological pathways.

[0031] In step 1) of the method, the determination of the energy metabolic state may be determined by any method known to those skilled in the art to measure metabolic activity within the tissue. This may be, for example, at least one method selected from proteome profiling matching, immunohistochemistry, Western blot, global or targeted mass spectrometry analysis, reverse transcription PCR, and positron emission tomography (PET). In this regard, if the liver tissue sample is a physical tissue sample, proteome profiling matching, immunohistochemistry, Western blot, global or targeted mass spectrometry analysis, and reverse transcription PCR may be used. If the liver tissue is a liver tissue image from medical imaging, PET may be used. This may be a medical image for measuring the energy metabolic state using a direct measurement of the expression level of metabolic enzymes, an indirect measurement of the expression level of metabolic enzymes through a transcriptome-level measurement, a measurement of mitochondrial mass using histological techniques such as immunohistochemistry or immunofluorescence, for example, or a labeled tracer.

[0032] In a preferred embodiment of the present invention, the energy metabolic state can be determined by examining the expression of proteins involved in energy metabolism. For example, determining the energy metabolic state can be performed by determining the abundance of proteins involved in energy metabolism in the liver tissue sample from the subject. This can be performed directly, i.e., by quantifying the proteins within the tissue, or indirectly, i.e., by measuring the mRNA of said proteins.

[0033] Determining protein abundance can be performed by any method known in the art. This may be relative protein abundance or quantified protein abundance.

[0034] When determining relative protein abundance, any method known in the art may be used. For example, this may be a mass spectrometry-based analysis of the relative protein abundance of proteins involved in energy metabolism between pathological and non-pathological tissues. More precisely, this may be the relative abundance of each protein in non-pathological tissue relative to the corresponding protein in pathological tissue, or vice versa.

[0035] When quantifying protein abundance, this may be performed by any method known to those skilled in the art, for example, by targeted quantification techniques. This may be, for example, a method combining liquid chromatography and tandem mass spectrometry (LC-MS / MS) in a discovery mode or target approach (e.g., multiple reaction monitoring (MRM), parallel reaction monitoring (PRM)), data-dependent acquisition (DDA), or data-independent acquisition (DIA).

[0036] The determination of the energy metabolic state is realized in the pathological tissue of the subject's liver tissue sample. If the liver tissue is intended to include non-pathological tissue as well, the determination can also be realized in the non-pathological tissue. Advantageously, in this case, the detection of energy conversion is realized by comparing the subject's pathological tissue with the non-pathological tissue.

[0037] In another embodiment, the comparison may be made between the pathological tissue of the subject and a standard non-pathological liver sample tissue used as a reference, for example, an extract of normal liver tissue from one patient or a mixture of several patients. Alternatively, the comparison may be made with a predetermined energy metabolic state of a standard, which can be obtained from a non-pathological liver tissue sample from a third party or from several non-pathological liver tissue samples to be statistically representative.

[0038] "Reduction in mitochondrial respiration" in this specification refers to a decrease in the activation of biological pathways involved in mitochondrial respiration, particularly in favor of the glycolysis pathway. Without being bound by any theory, this corresponds to an energy conversion also known as the Warburg effect, where pyruvate is converted to lactic acid at the expense of mitochondrial activity via glycolysis. In the context of energy conversion, it is a hypothesis that tumor hypoxia, low pH, inhibitory metabolites, and low nutrient availability can all prevent T cells from acquiring optimal effector function even if antibody targets are well expressed, thereby explaining why they do not respond to immunotherapy. The reduction may be any statistically significant reduction. Statistical analysis may be performed by any method known in the art for similar purposes, depending on the method used and the data set to be tested. This may be, for example, the Wilcoxon-Mann-Whitney U-test or t-test. A person skilled in the art may select one or more statistical analyses, taking into account their knowledge of this technical field.

[0039] In one embodiment of the present invention, the energy metabolic state refers to determining the mitochondrial respiration pathway as defined above, and additionally determining the said pathway, which should not be modified (i.e., should be stable), or may be increased in the case of a Warburg-type metabolic transition.

[0040] "Likely to respond" to immunotherapy treatment refers herein to a statistically significant level of tumor regression and / or reduction in alpha-fetoprotein (AFP) levels. This may be tumor regression according to RECIST criteria, where tumor regression is considered significant, for example, at 30% of tumor mass and 50% of AFP. Alternatively, this may be a reduction in tumor regression and / or AFP levels of at least 5%, or at least 10%, or at least 20%, or at least 30%, or at least 40%, or at least 50%, or at least 60%, or at least 70%, or at least 80%, or at least 90%, or even 100%, or any level or range in between, compared to the value in the case of not receiving immunotherapy treatment, for example, within the first 2 months after the initiation of treatment.

[0041] “Low likelihood of response” to immunotherapy treatment refers to other cases in this specification. This may be tumor progression according to RECIST criteria where tumor progression is considered significant at 20% tumor progression. Alternatively, this may be tumor progression similar to that of untreated cases, e.g., at least 5%, or at least 10%, or at least 20%, or at least 30%, or at least 40%, or more within 2 months.

[0042] In another aspect, the present invention provides a method for selecting treatment for a patient with HCC for immunotherapy, comprising the steps of performing an in vitro prediction method as defined above, and selecting a patient in whom no energy conversion is detected.

[0043] In another aspect, the present invention provides a method for treating an HCC patient comprising the following steps:

[0044] (i) a step of selecting patients for treatment in whom no energy conversion is detected, and

[0045] (ii) Step of administering an effective amount of immunotherapy treatment to the patient.

[0046] This invention is further explained by the following examples related to the attached drawings, which should not be interpreted as limiting. Brief explanation of the drawing

[0047] - Figure 1: Principal component analysis (PCA) of proteomic profiles showing dysregulated protein expression between tumor tissue and non-tumor (T / NT) tissue in patients who showed an objective response (ORuAB n=6) or progression (PuAB n=12) under atezolizumab / bevacizumab immunotherapy. The dataset was reduced to the signatures of 113 proteins showing significantly different expression between ORuAB (gray) patients and PuAB (black) patients. - Figure 2: Shows a bootstrap analysis of signatures distinguishing PuAB and ORuAB patients. Of the 113 proteins, 54 (48%) were significant in more than 80% of the resampling configurations. - Fig. 3: Shows the gene set enrichment analysis (GSEA) obtained from the Gene Ontology database (cellular components) using 113 proteins that were significantly differentially expressed between patients with objective response (ORuAB) and patients with progression (PuAB) under atezolizumab / bevacizumab combination therapy. - Figure 4: Shows an enrichment analysis of gene sets obtained from the Ingenuity Pathways database (IPA Qiagen, Canonical Pathways) using 113 proteins that were significantly differentially expressed between patients with objective response (ORuAB) and patients with progression (PuAB) under atezolizumab / bevacizumab combination therapy. - Fig. 5: Shows the ratio of tumor to non-tumor (T / NT) expression of proteins involved in oxidative phosphorylation in patients who showed progression (PuAB) or objective response (ORuAB) to atezolizumab / bevacizumab immunotherapy (ratios are shown as logarithms). - Fig. 6: Shows the ratio of tumor to non-tumor (T / NT) expression of proteins involved in the TCA cycle in patients who showed progression (PuAB) or objective response (ORuAB) to atezolizumab / bevacizumab immunotherapy (ratios are shown as logarithms). - Fig. 7: Shows the ratio of tumor to non-tumor (T / NT) expression of proteins involved in the corresponding action in patients who showed progression (PuAB) or objective response (ORuAB) to atezolizumab / bevacizumab immunotherapy (ratios are shown as logarithms). - Fig. 8: Study on the effect of inhibition of mitochondrial respiration on immune cell infiltration into spheroids in hepatocellular carcinoma cells cultured in vitro in 3D. (a) Relative baseline OCR of Huh7 spheroids 48 hours after IACS-010759 treatment, normalized against protein content and mean vehicle (mean baseline OCR in vehicle: 219.58 pmol / min). (b) Relative lactic acid production in the supernatant of Huh7 spheroids 48 hours after IACS-010759 treatment, normalized against protein content and mean vehicle (mean lactic acid concentration in vehicle: 1.60 mM). (c) Immunoinfiltration of PBMCs in Huh7 spheroids assessed by flow cytometry normalized to the mean vehicle (mean percentage of surviving PBMCs among total surviving cells in the vehicle: 32.13%). (d) Immunoinfiltration of PBMCs in Huh7 spheroids assessed by CD45 staining normalized to the mean vehicle (mean percentage of CD45-positive cells in the vehicle: 26.04%). - Fig. 9: Shows the proteomic profile of pre-treatment biopsy distinguishing responders and progressors under sorafenib. (a) Principal component analysis of the proteomic profile showing dysregulated protein expression between tumor tissue and non-tumor (T / NT) tissue of patients who showed objective response (n=9) or progression (n=15) under sorafenib. The dataset was reduced to the signatures of 87 proteins showing significantly different expression between progressors (orange) and responders (blue). (b) Comparison of proteins having expressions that distinguish responses (responders or progressers) to two treatments (atezolizumab / bevacizumab or sorafenib). Specific details for implementing the invention

[0048] Examples

[0049] Example 1: Prediction of Response to Immunotherapy in Hepatocellular Carcinoma Using Tumor Energy Metabolism Status

[0050] Materials and Methods

[0051] patient

[0052] Patients with advanced HCC treated with atezolizumab / bevacizumab were selected and divided into two groups based on their response to treatment. Response was determined based on radiological tumor evaluation and / or alpha-fetoprotein (AFP) levels assessed within the first two months after the start of treatment. In this example, an objective response corresponded to tumor regression of more than 30% and / or a reduction in AFP levels of more than 50%. Tumor progression of more than 20% corresponded to no response. For study inclusion and prior to the start of treatment, each patient had sufficient tumor and non-tumor tissue (>1 mm²) for subsequent proteomic analysis by MS. 2 I underwent a biopsy containing ).

[0053] Laser capture

[0054] Liver tissue sections from each patient were stained with hematoxylin-eosin-safran (HES) to determine regions of interest for histopathological examination and dissection. Subsequently, tumor (T) and non-tumor (NT) livers with the same surface area and homogeneous tissue composition (absence of fibrosis) were selected under the supervision of an anatomical pathologist. Pre-selected tumor and non-tumor tissues (1 mm²) from an adjacent series of hematoxylin-eosin-stained sections 2 ) was microdissected from 5 μm thick FFPE sections using a PALM type 4 (Zeiss) laser microdissecter. The same tissue region was cut into a series of sections and processed in the same manner to form three technical replicas.

[0055] Sample preparation for proteomic analysis

[0056] Fixation reversion and protein extraction from tissue sections, and protein gel digestion ( in gel Digestion), peptide extraction from the gel, and preparation of peptide samples for LC-MS / MS analysis were performed as previously described (Sala M et al. ([4])).

[0057] Mass spectrometry analysis

[0058] Online nano LC-MS / MS analysis was performed using the Ultimate 3000 RSLC Nano-UPHLC system (Thermo Scientific, USA) combined with the Nanospray Q-Exactive hybrid quadrupole-orbitrap mass spectrometer or the Nanospray Orbitrap Fusion™ Lumos™ Tribrid™ mass spectrometer (Thermo Scientific, USA) as previously described (Henriet E et al. ([2]), Dourthe C et al. ([3]), Sala M et al. ([4])).

[0059] Database search, MS result processing, and quantification

[0060] For protein identification, the UniProt Homo sapiens database (71,793 entries, reference proteome set, published December 2017) on the website (http: / / www.uniprot.org / ) was searched, and the Mascot 2.5 algorithm of Proteome Discoverer 2.5 software (Thermo Fisher Scientific Inc.) was used in batch mode. Two missed enzyme cleavages were allowed. Mass tolerances for MS and MS / MS were set to 10 ppm and 0.02 Da, respectively. Methionine oxidation and lysine acetylation were searched as dynamic modifications. Cysteine ​​carbamidomethylation was searched as a fixed modification. Raw LC-MS / MS data were imported into Proline Web for feature detection, alignment, and quantification. Protein identification was accepted only if at least two specific peptides had a pretty rank of 1 and the protein false discovery rate calculated using Mascot's "decoy" option was less than 1.0%. Label-free quantification of proteomic data using MS1-extracted ion chromatograms was performed using previously indicated parameters (Henriet E et al. ([2]), Dourthe C et al. ([3]), Sala M et al. ([4])). Normalization was performed using the median ratio. Inference for missing values ​​was applied to 5% background noise.

[0061] Bioinformatics Analysis

[0062] Differential protein expression between tumor and non-tumor tissue samples from each patient was analyzed by calculating the log ratio of the median normalized protein abundance for each tumor / non-tumor tissue sample pair.

[0063] Welch's t-test (not assuming equal population variances) was performed to identify potential biomarkers distinguishing objective response (ORuAB) and progression (PuAB) under sorafenib. Predictive signatures for 113 proteins were obtained by setting the significance threshold for the adjusted p-value to 0.05.

[0064] Principal component analysis was performed using FactoMineR and Factoextra, which are available in R, with this dataset. Bootstrap analysis was performed using database packages in R (base and stats packages). To identify pathways showing significant dysregulation between ORuAB and PuAB, gene set enrichment analysis was performed on 113 protein signatures against the Gene Ontology (GO) database (cellular components) and the Ingenuity Pathways (IPA, Qiagen) database (canonical pathways).

[0065] result

[0066] Using a tissue proteome profiling methodology, relative protein expression levels between tumor and non-tumor tissues were first compared for each patient; this generated the proteome profile of HCC (Dourthe C et al. ([3])). The analysis was performed on FFPE liver biopsies collected prior to the initiation of atezolizumab / bevacizumab combination therapy. The analysis strategy was designed to limit bias due to unavoidable variability. Meanwhile, three technical replicates were designed to limit technical variability in quantification. Furthermore, among other things, greater statistical power was achieved by reducing background noise due to inter-individual variability through the comparison of tumor-to-non-tumor protein expression ratios for each patient. This methodology enabled the acquisition of robust data even in relatively small cohorts (Henriet E et al. ([2]), Dourthe C et al. ([3])).

[0067] Distinct tumor proteomic profiles according to response to atezolizumab / bevacizumab combination therapy

[0068] To identify proteomic profiles capable of predicting ORuAB and PuAB, the inventors applied a corrected Wilcoxon t-test comparing the T / NT ratios of patients in the ORuAB and PuAB groups. Signatures of 113 proteins were generated by setting the significance threshold for the corrected p-value to 0.05. Patients in the ORuAB versus PuAB groups were well distinguished in the principal component analysis of differential protein expression profiles (Fig. 1).

[0069] The robustness of this signature was tested using a bootstrap method involving 10,000 resamplings. In fact, 54 out of 113 (48%) proteins demonstrated significant accuracy exceeding 80% (Fig. 2). This means that more than half of the identified response prediction signatures exhibited high reproducibility. However, this does not mean that the remaining proteins were unpredictable; rather, it means that their dysregulation was more variable within the ORuAB and PuAB groups.

[0070] From a functional perspective, as indicated by gene set enrichment analysis performed on the gene ontology database (GO, cellular components), at least 70% of the proteins within the signature were associated with mitochondrial function (Fig. 3). Consistently, as indicated by gene set enrichment analysis performed on the Ingenuity Pathways database (IPA, Qiagen, canonical pathways), the most enriched biological pathways in this signature were associated with mitochondrial energy metabolism functions (oxidative phosphorylation, TCA cycle, fatty acid beta oxidation) (Fig. 4).

[0071] When focusing on the T / NT ratio of each protein, it was found that in patients who progressed during immunotherapy (PuAB), the expression of mitochondrial respiration proteins (oxidative phosphorylation (Fig. 5), TCA cycle (Fig. 6)) within the tumor was significantly and generally reduced, whereas in patients who showed an objective response (ORuAB), the T / NT expression ratio exceeded 1 and was occasionally significantly overexpressed in the tumor. However, the expression of the corresponding agonist proteins did not change significantly in PuAB or ORuAB, and there was no difference between the two groups of patients (Fig. 7).

[0072] argument

[0073] Despite numerous studies already conducted, the lack of predictive markers for treatment response in advanced HCC remains a significant challenge. These predictive markers can change how patients are treated, particularly due to the availability of new therapies. To date, three distinct treatment options are available for the first-line treatment of advanced HCC: sorafenib, lenvatinib, and the more recent atezolizumab / bevacizumab combination. It is evident that patients will respond differently to these treatments. Other factors to consider include the side effects of these drugs and significant medical costs. Consequently, we must move toward precision medicine to determine the optimal treatment for each patient. All the information necessary for this response prediction strategy is actually available from fixed diagnostic biopsies.

[0074] By combining laser capture and MS, it was possible to access profiles of dysregulated protein expression from fixed biological tissue samples available in routine clinical practice. Indeed, one major advantage of the approach according to the present invention is its broad applicability, considering that needle biopsies are used in routine care, and its potential for predicting treatment response in HCC.

[0075] According to the results obtained by the inventors, oxidative metabolism was found to be reduced in tumors that did not respond to immunotherapy, which appears to have favored an increase in glycolytic flux without changes in the expression of glycolysis proteins.

[0076] To supply energy to the cell, glucose enters the cell via transporters. It is then metabolized into pyruvate through a series of enzymatic reactions.

[0077] In the context of effective oxidative phosphorylation, pyruvate enters the mitochondria and is converted to acetyl-CoA, which is broken down in the Krebs cycle to generate energy via the electron transport chain of the mitochondrial inner membrane.

[0078] When energy conversion (known as the Warburg effect) occurs, pyruvate is converted to lactic acid at the expense of mitochondrial activity and is released from the cell by monocarboxylate transporters (MCTs). The secreted lactic acid subsequently contributes to extracellular acidification (DePeaux K et al. ([5])).

[0079] As already mentioned in the literature (21), a high rate of glycolysis can cause local depletion of oxygen and glucose levels (hypoxic regions with high levels of lactic acid).

[0080] In addition, increased tumor proliferation is often associated with dysregulation of the vascular system (damaged and leaking blood vessels), which reduces blood flow. Immunosuppression is established, and cells cannot leave the tumor.

[0081] Anticancer immunotherapy strategies based on checkpoint inhibition require sustained effector T cell responses to induce a sustained clinical response. T cells must enter and thrive within the tumor microenvironment. However, in the context of metabolic turnover, tumor hypoxia, low pH, inhibitory metabolites, and low nutrient availability can all prevent T cells from acquiring optimal effector function, even if antibody targets are well expressed. This likely occurred in the HCC patients analyzed by the inventors who underwent immunotherapy. Since differences in energy metabolic status are highly distinct between the two groups of HCC patients—those who responded to immunotherapy and those who did not—it is possible to guide treatment using metabolic turnover detection alone.

[0082] Example 2: Verification of in vitro function in a 3D hepatocellular carcinoma model: Inhibition of oxidative phosphorylation regulates immune cell tumor infiltration

[0083] Exploratory proteomic data have shown the downregulation of oxidative phosphorylation proteins within the mitochondrial respiratory chain, which generate energy via the TCA cycle under physiological conditions. In tumor cells, a metabolic shift known as the Warburg effect occurs at the expense of the oxidative phosphorylation (Oxphos) pathway; this converts pyruvate to lactate via aerobic glycolysis even in the presence of oxygen, rapidly providing high energy for cancer proliferation (Vander Heiden MG et al. ([6])). Lactate is subsequently evacuated out of the cell by monocarboxylate transporters (MCTs), contributing to extracellular acidification and immunosuppression (Kao KC et al. ([7])).

[0084] Here, the decrease in mitochondrial respiration is highly likely to be compensated by an increase in glycolysis, which may be reflected as an increase in flux without being associated with a high increase in glycolytic enzyme expression. The inventors hypothesized that this metabolic reprogramming, proposed as a predictor of response to atezolizumab / bevacizumab treatment, is a resistance mechanism in HCC patients (DePeaux K et al. ([8])).

[0085] To test this hypothesis, the inventors developed a 3D model mimicking the metabolic transition of tumor cells (from oxidative metabolism to glycolysis) associated with an increase in lactate levels within the microenvironment that hinders the invasion of immune cells into tumors. In vivo ( in vivo The metabolic shift identified in ) was mimicked in spheroid models of HCC cell lines (Huh7, Hep3b) using a specific inhibitor of mitochondrial respiratory chain complex I named IACS-010759. This highly potent inhibitor was selected because it has demonstrated promising efficacy in animal models and has already been used in clinical trials (instead of other classic complex I inhibitors such as rotenone) (Yap TA et al. ([9])). The detailed plan of the experiment is shown in Figure 8. IACS-010759 activity was evaluated using OCD measurements in seahorses (Figure 8a). A dose of 5 nM resulted in a 50% reduction in baseline OCR and was selected for the next experiment.

[0086] Lactic acid production was evaluated in the supernatant of Huh7 spheroids using high-performance ion chromatography and conductivity assays. IACS-010759 treatment increased lactic acid production in a dose-dependent manner, increasing twofold at 5 nM (Fig. 8b).

[0087] Subsequently, the effect of treatment of HCC spheroids with IACS-010759 on immune infiltration was evaluated through co-culture of Huh7 spheroids and PBMCs. Immune infiltration quantification was performed by flow cytometry using the ratio of viable PBMCs to all viable cells. Treatment with IACS-010759 reduced immune infiltration within the spheroids by 60% (Fig. 8c). A control experiment was conducted to evaluate the effect of IACS-010759 on PBMC infiltration ability, and it showed that pretreatment of PBMCs with IACS-010759 did not affect PBMC infiltration within Huh7 spheroids.

[0088] These results were confirmed by visualizing and quantifying immune cell infiltration using immunohistochemistry with the immune marker CD45. Immune infiltration quantification was performed using the percentage of CD45-positive cells. Treatment with IACS-010759 reduced immune infiltration by 50% in Huh7 spheroids (Fig. 8d) and by 25% in Hep3b spheroids. For reference, necrotic cores were observed in Hep3b spheroids after treatment with IACS-010759, suggesting that the Hep3b cell line has higher sensitivity to IACS-010759 compared to the Huh7 cell line.

[0089] Example 3: The proteomic signature predicting the response to treatment is completely different between atezolizumab / bevacizumab and sorafenib

[0090] The inventors aimed to evaluate the specificity of abnormalities in energy metabolism protein regulation associated with atezolizumab / bevacizumab resistance in patients treated with sorafenib. 3,153 proteins per patient (including >2 specific peptides per protein) were identified and quantified, which allowed for the extraction of 87 proteins exhibiting significant differential dysregulation between responders and progressors. By performing PCA on this significant dataset, patients could also be distinguished based on their response to TKIs (tyrosine kinase inhibitors) (Fig. 9a). The heatmap of this dataset clearly illustrates distinct proteomic profiles between patients who actually demonstrated an objective response and progressors. Only four common proteins were found between the signatures, demonstrating the specificity of the proteomic profiling (Fig. 9b).

[0091] Biological pathway enrichment analysis was performed using GSEA, and no enrichment of energy metabolism pathways was found among the proteins significantly enriched in sorafenib treatment.

[0092] List of references

[0093]

Claims

Claim 1 1) a step of determining the energy metabolic state of a liver tissue sample from a subject, wherein the sample comprises pathological tissue and optionally non-pathological tissue; 2) a step of detecting the presence or absence of an energetic shift resulting from a decrease in mitochondrial respiration in the pathological tissue by comparing it with non-pathological tissue from the subject's liver tissue sample or a standard non-pathological liver sample, comprising an in vitro ( ex vivo ) as a method:- If mitochondrial respiration is reduced in pathological tissue, the subject is less likely to respond to immunotherapy treatment, and- In vitro method in which mitochondrial respiration is not reduced in pathological tissue, and the subject is likely to respond to immunotherapy treatment. Claim 2 In claim 1, the energy metabolic state comprises at least one pathway selected from the mitochondrial respiration pathway, the oxidative phosphorylation pathway, the TCA cycle pathway, and the fatty acid oxidation pathway, in an in vitro method. Claim 3 An in vitro method according to claim 1 or 2, wherein the energy metabolic state is determined by at least one method selected from proteome profiling matching, immunohistochemistry, Western blot, global or targeted mass spectrometry analysis, reverse transcription PCR, and positron emission tomography. Claim 4 An in vitro method according to any one of claims 1 to 3, wherein the step of determining the energy metabolic state is performed by determining the abundance of proteins involved in the energy metabolism of the liver tissue sample from the subject, directly or indirectly, for example, by measuring the mRNA of the protein or measuring the mitochondrial mass. Claim 5 In paragraph 4, the step of determining protein abundance is performed by a label-free proteomic analysis of the relative protein abundance of proteins involved in energy metabolism between pathological tissue and non-pathological tissue, in an in vitro method. Claim 6 In paragraph 5, the protein abundance is the relative abundance of each protein in a non-pathological tissue with respect to the corresponding protein in the pathological tissue, in an in vitro method. Claim 7 In paragraph 4, the step of determining protein abundance is performed by an in vitro method using a target quantification technique. Claim 8 An in vitro method according to any one of claims 1 to 7, wherein the immunotherapy treatment is selected from anti-PDL1 monotherapy, combination therapy of atezolizumab and bevacizumab, combination therapy of anti-PDL1 and anti-VEGF, combination therapy of anti-PD1 and CTLA4, combination therapy of PDL1 and CTLA4, anti-VEGFR2 therapy such as ramucirumab therapy, and anti-PD1 therapy such as pembrolizumab therapy. Claim 9 An in vitro method according to any one of claims 1 to 8, wherein the immunotherapy treatment is as defined in claim 8, and the protein involved in energy metabolism is at least one selected from mitochondrial proteins, the tricarboxylic acid cycle, oxidative metabolism, and fatty acid beta oxidation pathways. Claim 10 An in vitro method comprising the step of measuring a protein involved in the corresponding action (glycolysis) in any one of claims 1 to 9. Claim 11 An in vitro method according to any one of claims 1 to 10, wherein the hepatocellular carcinoma is a resectable or unresectable hepatocellular carcinoma. Claim 12 An in vitro method according to any one of claims 1 to 11, wherein the sample can be obtained by at least one technique selected from macrodissection, laser microdissection of formalin-fixed paraffin-embedded or frozen tissue, fresh biopsy, and positron emission tomography.

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