METHODS FOR PREDICTING THE SURVIVAL TIME OF PATIENTS SUFFERING FROM A MICROSATELLITE UNSTABLE CANCER

Abstract

The present invention relates to methods for predicting the survival time of patients suffering from a micro satellite unstable cancer. In particular, the present invention relates to a method for predicting the survival time of a patient suffering from a micro satellite unstable cancer comprising i) determining the expression level of at least one gene encoding for an immune checkpoint protein in a tumor tissue sample obtained from the patient, ii) comparing the expression level determined at step i) with a predetermined reference value and iii) concluding that the patient will have a long survival time when the level determined at step i) is lower than the predetermined reference value or concluding that the patient will have a short survival time when the level determined at step i) is higher than the predetermined reference value.

Claims

1. A method for treating a non-metastatic microsatellite unstable cancer in a patient in need thereof comprising the steps of: obtaining at least one non-metastatic microsatellite unstable cancer cell from the patient, measuring an expression level of at least one gene encoding an immune checkpoint protein in the at least one non-metastatic microsatellite unstable cancer cell, wherein the at least one gene includes VTCN1, comparing the expression level of the at least one gene encoding an immune checkpoint protein with a predetermined reference value, wherein the predetermined reference value is a corresponding expression level obtained from microsatellite stable cancer cells or non-tumoral cells, determining that the patient will respond to an immune checkpoint inhibitor when the expression level is higher than the predetermined reference value, or determining that the patient will not respond to an immune checkpoint inhibitor when the expression level is lower than the predetermined reference value, and treating the patient with the immune checkpoint inhibitor when the expression level is higher than the predetermined reference value, or treating the patient with chemotherapy when the expression level is lower than the predetermined reference value.

2. The method of claim 1, wherein the microsatellite unstable cancer is at Stage I, II, III, or IV as determined by the TNM classification.

3. The method of claim 1, wherein the microsatellite unstable cancer is microsatellite unstable colorectal cancer.

4. The method of claim 1, wherein the at least one gene further comprises at least one gene selected from the group consisting of IDO1, CD40, CD274, ICOS, TNFRSF9, TNFRSF18, LAG3, IL2RB, HAVCR2, TNFRSF4, CD276, CTLA4, PDCD1LG2, and PDCD1.

5. The method of claim 1, wherein the immune checkpoint inhibitor is at least one antibody selected from the group consisting of anti-CTLA4, anti-PD1, anti-PDL1, anti-TIM-3, anti-LAG3, anti-B7H3, anti-B7H4, anti-BTLA, and anti-B7H6.

6. A method for identifying and treating a patient who has a non-metastatic microsatellite unstable cancer with an immune checkpoint inhibitor treatment, comprising the steps of: obtaining a tumor tissue sample of the non-metastatic microsatellite unstable cancer from the patient, measuring in the tumor tissue sample an increased expression level of at least one gene encoding an immune checkpoint protein in the at least one non-metastatic microsatellite unstable cancer cell as compared to a predetermined reference value, wherein the at least one gene includes VTCN1 and wherein the predetermined reference value is a corresponding expression level obtained from microsatellite stable cancer cells or non-tumoral cells, and treating the patient with the immune checkpoint inhibitor.

7. The method of claim 6, wherein the microsatellite unstable cancer is at Stage I, II, III, or IV as determined by the TNM classification.

8. The method of claim 6, wherein the microsatellite unstable cancer is microsatellite unstable colorectal cancer.

9. The method of claim 6, wherein the at least one gene further comprises at least one gene selected from the group consisting of IDO1, CD40, CD274, ICOS, TNFRSF9, TNFRSF18, LAG3, IL2RB, HAVCR2, TNFRSF4, CD276, CTLA4, PDCD1LG2, and PDCD1.

10. The method of claim 6, wherein the immune checkpoint inhibitor is at least one antibody selected from the group consisting of anti-CTLA4, anti-PD1, anti-PDL1, anti-TIM-3, anti-LAG3, anti-B7H3, anti-B7H4, anti-BTLA, and anti-B7H6.

11. A method for identifying and treating a patient suffering from an exhausted T cell response to a non-metastatic microsatellite cancer that predisposes the patient to having a relapse of the microsatellite unstable cancer, comprising the steps of: obtaining a tumor core sample of the non-metastatic microsatellite unstable cancer from the patient, detecting infiltration by lymphoid and myeloid cells in the tumor core sample, measuring in the tumor core sample an expression level of at least one gene encoding an immune checkpoint protein in the at least one non-metastatic microsatellite unstable cancer cell, wherein the at least one gene includes VTCN1, comparing the expression level of the at least one gene measured to that of a predetermined reference value, wherein the predetermined reference value is a corresponding expression level obtained from microsatellite stable cancer cells or non-tumoral cells, determining that the patient will respond to an immune checkpoint inhibitor when the expression level is higher than the predetermined reference value, or determining that the patient will not respond to an immune checkpoint inhibitor when the expression level is lower than the predetermined reference value, and treating the patient with the immune checkpoint inhibitor in a quantity sufficient to stimulate a T cell immune response when the expression level is higher than the predetermined reference value, or treating the patient with chemotherapy when the expression level is lower than the predetermined reference value.

12. The method of claim 11, wherein the microsatellite unstable cancer is at Stage I, II, III, or IV as determined by the TNM classification.

13. The method of claim 11, wherein the microsatellite unstable cancer is microsatellite unstable colorectal cancer.

14. The method of claim 11, wherein the at least one gene further comprises at least one gene selected from the group consisting of IDO1, CD40, CD274, ICOS, TNFRSF9, TNFRSF18, LAG3, IL2RB, HAVCR2, TNFRSF4, CD276, CTLA4, PDCD1LG2, and PDCD1.

15. The method of claim 11, wherein the immune checkpoint inhibitor is at least one antibody selected from the group consisting of anti-CTLA4, anti-PD1, anti-PDL1, anti-TIM-3, anti-LAG3, anti-B7H3, anti-B7H4, anti-BTLA, and anti-B7H6.

Description

FIGURES

[0083] FIG. 1A-1E. Prognostic value of immune gene expression. A. Overall survival stratified by MSI/MSS status (left) and CMS subtypes (right). Curves of overall survival (OS) rate (y-axis) according to time from diagnosis (in years) (x-axis) were obtained by the method of Kaplan and Meier for both the CIT and TCGA series. Differences between survival distributions were assessed by the log-rank test using an end point of 5 years. B. Prognostic values of immune gene/metagene expression and of clinical factors in MSI tumors. Forest plot of overall survival (OS) hazard ratios (HR) estimated by combining independent univariate Cox analyses on the CIT and TCGA series, adjusted for TNM stage. HR, as well as related Wald test p-value and 95% confidence intervals (95% C.I.), are given for metagenes (which aggregates the gene expression values of a gene set related to the four immune categories (immune checkpoints (ICK), cytotoxic T lymphocytes (CTL), cytotoxicity, Th1 functional orientation), individual immune genes and clinical annotations. Diamonds represent the HR and horizontal bars the 95% C.I. Red indicates a HR >1 with p-value <0.1 (worse prognosis), blue a HR <1 with p-value <0.1 (better prognosis) and grey a HR with Wald test p-value ≤0.1. C Overall survival stratified by overexpression of immune checkpoint genes within MSI (left), MSS (middle) and both CRC (right). MSI tumors in the higher quartile of ICK metagene values, in CIT and TCGA series independently, were assigned ICK+ (n=55), and the other tumors ICK− (n=139). The minimal ICK metagene value within MSI ICK+ tumors was used to divide MSS tumors into ICK+ (n=26) and ICK− (n=765). Curves of overall survival (OS) rate (y-axis) according to time from diagnosis (in years) (x-axis) were obtained by the method of Kaplan and Meier for both the CIT and TCGA series. Differences between survival distributions were assessed by the log-rank test using an end point of 5 years. D Prognostic value of immune and Immunoscore® gene expression metagenes according to CRC subtypes. Heatmap of univariate Cox analysis p-values of immune and Immunoscore® metagenes, adjusted for TNM stage, colored by significance and HR sign (red for worse prognosis (HR >1) and blue for better prognosis (HR <1)). Analyses were performed independently on the CIT and TCGA series and further combined, within all, MSI, MSS and MSS subdivided according to CMS subtypes tumors. Cox analysis HR and p-values are indicated in each cell. n corresponds to the number of samples evaluated. IS-like v1 and v2 correspond to metagenes of genes used in the 2 main versions of the Immunoscore® from Galon and colleagues. E Bivariate Cox models of OS, combining the ICK metagene with other metagenes, in MSI tumors. Forest plot of OS HR estimated by bivariate Cox analysis of ICK, CTL, CY-TOX, Th1 and Immunoscore®-like metagene expression in the CIT series, adjusted by TNM stage. The expression of metagenes related to CTL/Th1/Cytotoxicity/Immunoscore® markers was associated with trends for better prognosis (Hazard Ratio (HR) <1), whereas the ICK metagene was associated with worse prognosis (HR >1) in all bivariate models. Strikingly, whatever the association with prognosis (negative/positive/none) of ICK and CTL/Th1/Cytotoxicity/Immunoscore®-related metagenes, a similar pattern was found within different CRC subgroups (MSI and MSS CMS). This suggests a substantial correlation between these signals within each CRC stratum.

[0084] FIG. 2A-2C. Prognostic value of immune checkpoints in an independent metastatic MSI CRC patient series A. Prognostic value of ICK gene expression in metastatic MSI tumors. Forest plot of hazard ratio (HR) estimated by univariate Cox analysis of overall survival (OS, left panel) and survival after relapse (SAR, right panel) on all ICK genes available in the NanoString data. Diamonds represent HR estimates and bars the related 95% confidence intervals. Red indicates a worse prognosis hazard ratio, blue a better prognosis and grey a HR Wald test p-value ≤0.1. B. Prognostic value of metagene expression in MSI metastatic tumors. Forest plot of HR estimated by univariate Cox analysis of survival after relapse for metagene expression. The ICK metagene aggregates the 3 most associated ICK genes in univariate analysis (HAVCR2, CD274 and LAG3). The other metagene aggregate genes available in the NanoString dataset for the corresponding category were: CTL=CD3D, CD3G, CD8A, PTPRC; CYTOX=GNLY, GZMK; Th1=IFNG. Metagene expression values were calculated by averaging the expression values obtained within the corresponding gene set. C. Bivariate Cox models of OS and SAR, combining the ICK metagene with other metagenes, in metastatic MSI tumors. Forest plot of SAR HR estimated by bivariate Cox analysis of ICK, CTL, CYTOX, Th1 and Immunoscore®-like metagene expression in the independent MSI metastatic series.

[0085] FIG. 3. Bivariate Cox models for analysing the impact of stimulatory/Inhibitory ICK expression on the survival of MSI CRC patients. In bivariate Cox models, the expression of stimulatory ICK metagene was not associated with bad prognosis, as expected, whereas this remains to be the case with the expression of inhibitory ICK metagene.

EXAMPLE

[0086] Materials and Methods

[0087] Immune Genes

[0088] Immune checkpoint and modulator genes were selected according to Llosa et al. (15) and a recent review (23). Markers for cytotoxic T lymphocytes, cytotoxicity and T helper1 were selected as described earlier (15, 24).

[0089] Cohort Data

[0090] Tissue samples from a large, multisite cohort of CRC patients were collected as part of the ‘Cartes d'Identité des Tumeurs’ (CIT) research program/network, including tumors with or without microsatellite instability (MSI or MSS respectively) and adjacent non-tumoral tissue samples (NT). Samples from 146 MSI, 444 MSS tumors and 56 NT were analyzed for gene expression profiling on Affymetrix U133 plus 2 chips as described earlier (25). Data were normalized using frozen RMA method (26) followed by a Combat normalization (27) to remove technical batch effects (SVA R package). For validation purposes, the CRC cohort from the TCGA consortium was used. Both datasets were centered for each gene by subtracting the median value of the non-tumoral sample. To obtain a summarized value for each immune gene category, a metagene value was computed by taking the median value of all genes in the category per sample.

[0091] A retrospective, additional multisite series of 28 stage 4 primary MSI CRC was analyzed as an independent study for gene expression using NanoString technology on a set of immune genes that included 14 of the 32 analyzed markers. All patients from this metastatic cohort (11 synchronous metastatic lesions, 17 metachronous metastatic lesions) received standard of care chemotherapy but did not benefit from ICK blockade. The Nanostring data set also includes a subset of the CIT cohort.

[0092] Associations between gene expression and survival were assessed by univariate and bivariate Cox proportional-hazards regression analyses using the R package survival.

[0093] Immune Genes

[0094] We investigated 32 immune markers classified into four gene groups: (i) immune checkpoints and modulators (n=19; CD40, CD274, ICOS, LAG3, IL2RB, HAVCR2, TNFRSF4/9/18, CD276, CTLA4, PDCD1LG2, VTCN1, PDCD1, BTLA, CD28, C10orf54, CD27, IDO1); (ii) cytotoxicity (n=6; GZMA/B/K/H, GLNY, PRF1); (iii) Th1 orientation (n=2; TBX21, IFNG); and (iv) cytotoxic lymphocytes (n=5; CD8A, CD3D/E/G, PTPRC) (for review, see (24)).

[0095] TCGA Cohort

[0096] For validation purposes, the CRC cohort from the TCGA consortium was used. Preprocessed gene expression RNA-seq data were downloaded at the Broad Institute TCGA Genome Data Analysis Center (2015): Firehose stddata_2015_06_01 run. Broad Institute of MIT and Harvard. doi:10.7908/C1251HBG. Data were combined and normalized according to TCGA RNA-seq pipeline using RSEM quantification. The dataset contained 86 MSI, 527 MSS and 51 NT samples.

[0097] Survival Analysis

[0098] Associations between gene expression and survival were assessed by univariate and bivariate Cox proportional-hazards regression analyses using the R package survival. All Cox models were stratified by TNM stage and, for the CIT cohort, by clinical centers. For the CIT and TCGA cohorts, overall survival was used as the end point and was defined as the time from surgery to death (any cause) of the patient, or to last contact. The delays were censored at 5 years. Survival annotations were available for 137 MSI and 439 MSS CRC patients in the CIT cohort, and for 57 MSI and 352 MSS CRC patients in the TCGA cohort.

[0099] Separate analyses were performed independently on both data sets. Results from these two series were combined using a meta-analysis approach from DerSimonian et al. (38) using the inverse variance method for pooling of survival data, implemented in the R package meta (function metagen). For the metastatic patient cohort, survival after relapse was used. This was defined as the time from metastasis diagnosis to death from any cause, or last contact with the patient.

[0100] Functional Analysis

[0101] An enrichment analysis was performed to evaluate pathways associated with overexpression of ICKs using MSigDB gene sets. Significant genes associated with ICK overexpression were selected by a moderated t-test between low and high ICK expression level in MSI tumors (the top and bottom 30 samples based on the ICK metagene). The top 100 to 500 significant genes were evaluated for gene set enrichments by hypergeometric tests. The median pvalue across gene selections was used to select significant gene sets. Only a selection amongst the significant gene sets, based on functional interest, was shown.

[0102] The abundance of immune cell populations was estimated using MCP-counter software (28).

[0103] Immunohistochemistry and ImmunoFluorescence Procedures

[0104] 20 FFPE tumor samples of MSI colon cancers were sliced in thin tissue sections of 4 μm.

[0105] For IHC, automated routine staining procedures were carried out for HE, PD-L1 (Ventana, SP142) and CD8 (Dako, M7103) using Ventana Benchmark. Relative quantification for PD-L1 staining was performed independently by two pathologists. Absolute CD8 quantification was carried out with Definiens Tissue Studio software. Briefly, after numeration using Nanozoomer 2.0HT and NDPscan software (both from Hamamatsu), slides for each sample were processed and analyzed in several areas that were manually defined by a pathologist. Screen captures were made with NDPview software (Hamamatsu). PD-L1 staining without nuclei counterstaining was also performed and merged with HE staining using Paint.net free software (dotPDN LLC).

[0106] For IF procedures, the same samples from MSI patients were stained using Ki67-Alexa-Fluor 488 labelled (BD Pharmingen, 558616, dilution 1/10 incubated overnight) and CD8 (Dako, M7103, dilution 1/100 during one hour) antibodies. Secondary goat-anti-mouse Alexa-Fluor 555 was also used (Life Technologies, A21422, dilution 1/500 during 30 minutes) for CD8 staining (see also Supplementary Materials and Methods). Slides were then mounted using DAPI-containing mounting medium (Sigma, F6057), kept at 4° C. and imaged the following day using spectral microscopy technology (Mantra Workstation, PerkinElmer) at X20 magnification. DAPI-only positive cells, Ki67-only positive cells, CD8-only positive cells and CD8/Ki67 double positive cells were phenotyped using a trainable algorithm from inForm software (PerkinElmer).

[0107] Results

[0108] Prognostic Value of Immune Genes and Metagenes in Function of CMS Classification of Colorectal Cancer

[0109] To test our working hypothesis, we evaluated the prognostic significance of ICKs, Th1, CTLs and cytotoxicity markers in the combined CIT (n=590 CRC, comprising 146 MSI and 444 MSS) and TCGA (n=613 CRC, comprising 86 MSI and 527 MSS) series. In both cohorts, MSS tumors were categorized into one of the four CMS of CRC (22). We investigated 32 immune markers classified into the above four gene groups (for review, see (24)). Four of the 19 immune checkpoints and modulators were not found significantly overexpressed in CRC as compared to non-tumor colonic mucosa (NT), and were subsequently removed. Further analyses were thus carried on the 28 remaining genes. Four metagenes were then built from the four gene groups, by aggregating the corresponding genes (median of log 2 expression fold changes, relative to NT). Finally, in order to obtain Immunoscore® surrogates, six Immunoscore®-like metagenes were built based on the expression of Immunoscore® related markers (Table 1).

TABLE-US-00002 TABLE 1 description of the six Immunoscore ®-like metagenes Name Genes involved Immunoscore ®-like VO CD3, CD8A Immunoscore ®-like V1 CD3, CD8A, PTPRC Immunoscore ®-like V2 CD8A, PTPRC, GZMB, MS4A1 Immunoscore ®-like V3 CD8A, PTPRC, GZMB, MS4A1, CD68 Immunoscore ®-like V4 CD3, CD8A, PTPRC, GZMB, MS4A1 Immunoscore ®-like V5 CD3, CD8A, PTPRC, GZMB, MS4A1, CD68

[0110] As a preliminary step, univariate Cox models of overall survival (OS) were used to analyze the prognostic values of MSI and CMS status after adjusting for stage and tumor series. As expected, these models showed an improved prognosis for patients with MSI CRC compared to those with MSS CRC, as well as significant prognostic value for the CMS classification (FIG. 1A). Univariate Cox models were then used to analyze the 28 immune markers mentioned above after adjusting for stage and series. Most ICK genes were individually associated with poorer prognosis in MSI CRC, as reflected by the ICK metagene (FIGS. 1B-D). This association remained significant in non-metastatic MSI CRC. No prognostic association was observed in patients with MSS CRC, either for individual ICK markers or for ICK metagene (FIGS. 1C-D). Subdividing MSS CRC by CMS did not change this result, except in CMS3, where ICK metagene was found associated to better prognosis (FIG. 1D). As expected, the expression of most Immunoscore®-like metagenes were associated with improved outcome of CRC patients (FIG. 1D). However, when the CMS and MSI/MSS status was taken into account, expression of these Immunoscore® surrogates was predictive of better prognosis only in MSS tumors from CMS2 and CMS3. In contrast, they had no prognostic relevance in CRC from CMS1 and CMS4 (FIG. 1D).

[0111] In univariate models, the overexpression of CTL/Th1/cytotoxicity/Immunoscore® markers and metagenes was also associated with adverse prognosis in MSI CRC (FIGS. 1B and 1D). We therefore hypothesized that high expression levels of ICKs in MSI CRC could counterbalance and mask the expected positive effect of CTL/Th1/cytotoxic cells or Immunoscore®-related cells on prognosis. Bivariate Cox models at the metagene level were consistent with this assumption (FIG. 1E).

[0112] All together, these data underline that ICKs and Immunoscore® biomarkers constitute independent prognostic factor for overall survival in MSI and MSS tumor, respectively.

[0113] Expression and Prognostic Value of Immune Checkpoints in an Independent Metastatic MSI CRC Patient Series

[0114] The CIT and TCGA series included mostly non-metastatic MSI CRC patients (n=220/232, 94.8%). Since ICK blockade was recently proposed as a promising new therapy for metastatic MSI CRC, we endeavored to further evaluate the prognostic relevance of ICK expression in an independent cohort of stage 4 MSI CRC. To do this, we analyzed the expression of 7 ICKs (CD274, PDCD1LG2, HAVCR2, LAG3, ICOS, CTLA4, PDCD1) using NanoString technology in a retrospective, multisite series comprised of 28 stage 4 primary MSI CRC treated with standard care.

[0115] As with non-metastatic MSI colon tumors, we observed significant association of PD-L1 (CD274), TIM-3 (HAVCR2) and LAG3 expression with worse OS and worse survival after re-lapse (SAR) (FIG. 2A). Again, the overexpression of metagenes corresponding to CTL/Th1/Cytotoxicity/Immunoscore® markers (CTL=CD3D, CD3G, CD8A, PTPRC; CYTOX=GNLY, GZMK; Th1=IFNG) tended to associate with adverse prognosis in univariate models (FIG. 2B). However, in bivariate Cox models, the expression of CTL/Th1/Cytotoxicity/Immunoscore®-related metagenes was associated with good prognosis (Hazard Ratio (HR) <1), as expected (FIG. 2C). Several ICK markers, in particular the druggable PD-L1 (CD274) and TIM-3 (HAVCR2) molecules (23), are therefore likely to constitute biomarkers for poor prognosis in both metastatic and non-metastatic MSI CRC patients.

[0116] Impact of Stimulatory/Inhibitory ICK Expression on the Survival of MSI CRC Patients

[0117] We performed bivariate Cox models for analysing the impact of stimulatory/Inhibitory ICK expression on the survival of MSI CRC patients (FIG. 3). In bivariate Cox models, the expression of stimulatory ICK metagene was not associated with bad prognosis, as expected, whereas this remains to be the case with the expression of inhibitory ICK metagene (FIG. 3).

[0118] Immune Checkpoint Gene Expression Distribution in Colorectal Cancer

[0119] We next investigated the level of variation in ICK expression amongst CRC tumors in order to further assess their relevance as prognostic and theranostic markers. ICK expression was analyzed in stage 1-4 CRC and in non-tumor colonic mucosa (NT) from our CIT cohort (590 CRC, 56 NT) and in the TCGA cohort (613 CRC, 51 NT). In both cohorts, the metagenes corresponding to ICKs, CTL, cytotoxicity and Th1 orientation were overexpressed in MSI and in MSS tumors belonging to CMS1 and CMS4 as compared to MSS CRC from CMS2 and CMS3. Variable expression of ICKs relative to NT was noted in all CMS subtypes in both cohorts. A high degree of heterogeneity was found in CMS1 tumors, particularly in MSI tumors where high to very high expression levels of ICKs was observed in a large proportion of cases.

[0120] Expression levels for all of the 28 immune markers were highly correlated in the MSI CRC from both cohorts. The present results highlight the extent of heterogeneity of MSI CRC with respect to immunity and to the overexpression of ICK molecules. This was observed regardless of MSI CRC origin (inherited or sporadic) or of other clinical or molecular parameters such as gender, tumor location, tumor stage, CMS, or KRAS/BRAF mutations. Considerable variation in the expression of ICK markers was also observed in the independent metastatic MSI CRC series evaluated by Nanostring.

[0121] Functional Relevance of Immune Checkpoint Expression in CRC

[0122] We next addressed the possible physiological relevance of ICK overexpression in CRC. Tumor infiltration by immune cells was quantified using MCP-counter software (28) in both the CIT and TCGA cohorts. A strong correlation was observed between ICK expression and infiltration by lymphoid (NK, T cells, cytotoxic cells) and myeloid cells. In contrast, B cells, fibroblasts, vessels and granulocytes were less associated with ICK expression or not at all. These results suggest that ICK expression occurs in response to an efficient in situ adaptive T cell immune response. Pathway enrichment analysis (hypergeometric tests) using MSigDB pathways was performed to compare the expression profiles of MSI tumors with low vs high ICK expression levels. Significant associations were observed between ICK expression and immune response gene sets, including positive activation of T cell response, negative regulation of T cell activation, T cell exhaustion, IL-10 response and chronic viral infection (29). Hence, we conclude there is a strong correlation between ICK expression and the presence of an exhausted T cell immune response in MSI CRC.

[0123] To further investigate the functional relevance of ICKs in MSI tumors, we studied 8 primary MSI tumors showing up-regulation of ICKs and 12 without. PD-L1 and CD8 expression were examined using immunohistochemistry (IHC). PD-L1 expression was observed only in the tumor bed, whereas CD8 was present both in the tumor core and in stromal areas. Moreover, PD-L1 expression correlated strongly with ICK expression, while CD8 infiltrates in both the tumor bed and in peritumoral stroma also correlated with PD-L1 IHC staining. Proliferation and functional activity of CD8 T cells were then determined using multi-parametric immunofluorescence microscopy. CD8 T cells that were close to or in contact with PD-L1-expressing tumors were less proliferative, as observed with Ki67 labeling. These results indicate that interactions between CD8 T cells and ICK ligands in MSI primary tumors can impede CD8 T cell function.

[0124] Discussion:

[0125] During cancer progression, tumor-infiltrating T cells have been shown to display increased, chronic expression of different antagonist ICKs including PD-1, LAG-3, and TIM-3, causing functional exhaustion and unresponsiveness of T cells (30). The exhausted CD8 T cells fail to proliferate in response to antigen and lack critical anticancer effector functions such as cytotoxicity and Interferon gamma cytokine secretion (31). These observations have provided the rationale to develop antibodies that target these regulatory molecules. So called checkpoint inhibitors could boost the anticancer immune response and the potential relevance of these inhibitors for the treatment of metastatic MSI CRC patients was highlighted in a recent publication (16). In the present study we showed that ICK overexpression represents a more accurate prognostic biomarker for MSI CRC patients treated with standard care than the classical assessment of T cell number by Immunoscore® (1). This may be explained by the presence of exhausted non-proliferative CD8 T cells in the core of these neoplasms. More generally, our data indicates that assessment of the prognostic significance of antitumor immunity in CRC needs to take into account ICK expression. This is particularly relevant for colon tumors displaying immunogenic profiles with both high Immunoscore® and ICK expression, such as in MSI tumors and probably a significant proportion of MSS CRC.

[0126] The current results were obtained using univariate Cox models for survival analysis and a transcriptome-based method to quantify both ICK and CTL/Th1/Cytotoxicity (Immunoscore®) markers in tumor and tumor-adjacent normal mucosa samples. We validated our method by building Immunoscore®-like surrogates that were associated with significantly improved survival of CRC patients. Nervertheless, under the same conditions, the CTL/Th1/Cytotoxicity and Immunoscore® markers were both associated with worse prognostic in MSI CRC from both the CIT and TCGA series. These results are potentially in conflict with a recent publication that observed significant association between Immunoscore®, as assessed by Immuno-histochemistry and Immunomorphometry, and improved outcome in a single series of 105 MSI CRC patients (17). Although the studies are not directly comparable, here we assessed three independent cohorts of CRC patients totaling more than 1,200 cases and including 260 MSI CRC. It does not include classical Immunoscore® evaluation by immunohistochemistry. However, we performed bivariate Cox analysis at the metagene level. This revealed that expression of metagenes related to CTL/Th1/Cytotoxicity and Immunoscore® markers was associated with trends for better prognosis in MSI CRC from both the CIT and TCGA series, whereas the ICK metagene was significantly associated with worse prognosis. In contrast with the earlier study that focused only on PD1/PDL1 couple (17), a more global assessment of ICK gene expression in the tumor core, as proposed in the present study, allows a more holistic view of the T cell immune response in CRC. The transcriptome based method reported here is easier to use in both research and clinical settings, and more amenable to standardization. Importantly, it can be also used to test publicly available clinical data sets, whereas this is not possible with Immunoscore® because of the need to assess primary tumor samples.

[0127] The development of monoclonal Antibodies that target checkpoints inhibitors is an exciting new development in cancer therapy. Recent clinical trials have demonstrated that antibodies targeting PD-1 or PD-L1 can induce major response in many types of cancers (32). The overall survival rate with more than 5 years follow up for stage 2 and 3 MSI CRC patients is approximately 70% without adjuvant chemotherapy and 75-90% with standard care adjuvant chemotherapy (33-35). However, the 5-year survival rate for stage 4 MSI CRC patients is less than 5% 33. We report here for the first time the prognostic significance of ICK overexpression in both metastatic and non-metastatic MSI CRC and in the absence of immunotherapy. These findings should help to better inform the prognosis of MSI CRC patients and to identify those who are at high risk of relapse. They may be useful for guiding future immunotherapy involving antibody blockade of ICKs in non-metastatic MSI CRC patients and to have predictive factors of immunotherapy efficacy for patients with metastatic disease.

[0128] To conclude, our results highlight the extent of heterogeneity of CRC with respect to immunity and the overexpression of ICK molecules in particular. They suggest that prediction of CRC patient outcomes through evaluation of immune components in the tumor microenvironment will likely be improved by the integration of ICK markers, the prognosis of colon tumors being determined by the CTL/ICK balance. More particularly, our results indicate that ICK expression impacts the prognosis of MSI tumors and that overexpression of these molecules impedes CD8 T cell function in MSI CRC, regardless of their CMS subgroup. To inform future immunotherapy involving antibody blockade of ICKs and resistance to these molecules in MSI CRC patients, additional studies on the molecular mechanisms underlying the immune reaction against MSI tumor cells are required. These mechanisms may depend on the number and type of MSI-driven mutational events that drive tumor progression and lead to the synthesis of aberrant, immunogenic peptides (36), thereby impacting the relation of tumor cells with their complex immune microenvironment including ICK expression and/or function. Identifying these somatic events and investigating their functional relevance with respect to quantitative and qualitative anti-tumoral immunity may improve the personalized treatment of MSI CRC patients with ICK inhibitors, in both metastatic and non-metastatic settings.

REFERENCES

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