Predictive Biomarker In Cancer Immunotherapy Research Articles

PD-L1 expression and microsatellite instability (MSI) in cancer of unknown primary site.

Cancer of unknown primary site (CUP) is a heterogeneous group of tumors for which the origin remains unknown. Clinical outcomes might be influenced by regulatory processes in its microenvironment. Microsatellite instability (MSI) is a predictive biomarker for cancer immunotherapy and its status, as well as co-occurrence with PD-L1 expression, is poorly evaluated. We aim to evaluate the expression of PD-L1 and the status of MSI in CUP and their possible associations with clinical-pathological features. The combined positive score (CPS) PD-L1 expression was evaluated by immunohistochemistry. MSI status was assessed using a hexa-plex marker panel by polymerase chain reaction followed by fragment analysis. Among the 166 cases, MSI analysis was conclusive in 120, with two cases being MSI positive (1.6%). PD-L1 expression was positive in 18.3% of 109 feasible cases. PD-L1 expression was significantly associated with non-visceral metastasis and a dominance of nodal metastasis. The median overall survival (mOS) was 3.7 (95% CI 1.6-5.8) months and patients who expressed PD-L1 achieved a better mOS compared to those who did not express PD-L1 (18.7 versus 3.0 months, p-value: < .001). ECOG-PS equal to or more than two and PD-L1 expression were independent prognostic factors in multivariate analysis (2.37 and 0.42, respectively). PD-L1 is expressed in a subset (1/5) of patients with CUP and associated with improved overall survival, while MSI is a rare event. There is a need to explore better the tumor microenvironment as well as the role of immunotherapy to change such a bad clinical outcome.

Abstract 4220: Combined light sheet and multispectral imaging facilitates the detection of tumor-associated tertiary lymphoid structures in the KP NSCLC model

Abstract Tertiary lymphoid structures (TLS) have recently emerged as a predictive biomarker in cancer immunotherapy. Their consideration in preclinical drug development is impeded by rare and transient appearance and random distribution, making them difficult to analyze with conventional sampling methods. We applied whole-organ tissue clearing and 3D staining of B and T cell clusters to comprehensively identify TLS in a KP GEMM of NSCLC using 3D Light Sheet Fluorescence Microscopy (3D LSFM). To further investigate cellular composition of TLS we performed 3D LSFM-educated sectioning of relevant ROIs and cyclic immunofluorescence of the same samples. 3D LSFM facilitated correlation of tumor features and location with T cell infiltration as well as the analysis of number, size and location of TLS. The majority of TLS were located in proximity to bronchi instead of intratumorally. LSFM-guided multiplex immunofluorescence revealed further details on cellular composition and maturation status of TLS. This combination of 3D and 2D imaging modalities offers a holistic approach for the identification of TLS in whole murine tumors or organs. The technology could help studying effects of immunotherapy on de novo formation or maturation of TLS. Citation Format: Nils O'Brien, Birte Appelt, Marie-Lena Moerbitz, Joerg P. Mueller, Frank Herting, Pablo Umaña, Claudio Murgia, Sara Colombetti, Thomas Pöschinger. Combined light sheet and multispectral imaging facilitates the detection of tumor-associated tertiary lymphoid structures in the KP NSCLC model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4220.

Preliminary exploration of the potential biological functions and prognosis values of RAB4B in pan-cancer combing with experimental validation in BLCA.

Ras-related protein Rab-4B (RAB4B), a small GTPase in the RAS superfamily, is involved in glucose homeostasis, synaptic homeostasis, adaptive immunity, and other processes. RAB4B has also been implicated in tumorigenesis, and its dysregulation has been linked to cancer in multiple organs. However, the potential role of RAB4B in human pan-cancers remains unknown, and whether RAB4B is a predictive biomarker for cancer immunotherapy is yet to be explored. In order to investigate the potential for RAB4B as a therapeutic agent in human pan-cancers and its predictive properties, firstly, relevant data were downloaded from pan-cancers databases. Using the RAB4B expression as a parameter, an analysis was performed. The next step was to investigate how RAB4B relates to overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). Moreover, we performed an analysis of the relationship between RAB4B and tumor mutational burden (TMB), microsatellite instability (MSI), and tumor microenvironment (TME). Besides, the tumor immune dysfunction and exclusion (TIDE) algorithm was applied to evaluate the efficacy of RAB4B as a potential biomarker for cancer immunotherapy. A final analysis was performed on RAB4B in relation to immune-related genes and pathways. Results reflected that RAB4B expression was different between tumors and normal tissues. RAB4B not only positively or negatively correlated with survival indicators, but also with clinical characteristics. In addition, RAB4B positively or negatively correlated with TMB, MSI, and TME. The TIDE algorithm revealed that RAB4B was positively or negatively associated with immune checkpoint blockade. The outcome of gene set enrichment analysis (GSEA) suggested that RAB4B positively regulated biological processes and immune cell-related pathways in most cancers. According to the research results, we come to the conclusion that RAB4B as a biomarker for tumor immunotherapy, RAB4B facilitates the prediction of pan-cancers prognosis.

Abstract B016: Developing diagnostic tools via mutational spectra analysis and multiomic plasma profiling from liquid biopsies to identify potential signatures in cancer in Trinidad and Tobago

Abstract Cancer, especially lung cancer is a leading cause of nosocomial deaths worldwide. Non-small cell lung cancer (NSCLC) has a five-year survival rate of only 24%. Early diagnoses markedly improves survival with personalized cancer therapies using non-invasive techniques for asymptomatic detection of mutational spectrum-related by genotyping NSCLC patients, even prior to symptoms. Analysis of conformational change in proteins due to mutations in oncogenes allow for mutation-specific treatment with specialized cancer drugs. In addition, tumor mutational burden (TMB) is a promising predictive biomarker for cancer immunotherapy as high TMB patients respond better to immune checkpoint inhibitors cancer drugs. Treatment planning for non-small cell lung cancer generally requires testing for EGFR, ALK, ROS1, BRAF, MET, RET and KRAS gene alterations or whole genome analysis to assess TMB. The Caribbean region remains disadvantaged by the lack of available genetic testing mainly due to accessibility. We propose the genotyping of 30 stage III-IV NSCLC patients via next-generation sequencing (NGS) to improve personalized therapies. Through the analysis of tumor-specific alterations amongst a complete mutation spectrum analysis, including single nucleotide variants (SNVs), insertions, deletions, copy number variations (CNVs), and methylation alterations, we would generate data to be used as an accurate form of cancer genotyping for diagnostic purposes. We intend to include these genes as biomarkers in a mutational spectrum analysis, to be collated into a predictive DNA assay to be used for the first time as a diagnostic/prognostic tool in a medically underserved community. We intend to also identify TMB-high patients without cost-prohibitive whole genome sequencing by focusing on DNA damage repair (DDR) genes. There is an increasing utilization of agnostic testing which involves the analysis of all potentially actionable genes across all tumor and cancer types. The invention of genomic-tailored specialized cancer treatments has resulted in a spectacular improvement in disease outcomes for a significant portion of cancer patients, which we hope to offer to improve clinical outcomes and prognosis in the racially diverse population of Trinidad and Tobago. Citation Format: Nicole Ramlachan, Samuel M. West. Developing diagnostic tools via mutational spectra analysis and multiomic plasma profiling from liquid biopsies to identify potential signatures in cancer in Trinidad and Tobago [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: DNA Damage Repair: From Basic Science to Future Clinical Application; 2024 Jan 9-11; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2024;84(1 Suppl):Abstract nr B016.

Plasma cell-free DNA as a sensitive biomarker for multi-cancer detection and immunotherapy outcomes prediction

BackgroundCell-free DNA (cfDNA) has shown promise in detecting various cancers, but the diagnostic performance of cfDNA end motifs for multiple cancer types requires verification. This study aimed to assess the utility of cfDNA end motifs for multi-cancer detection.MethodsThis study included 206 participants: 106 individuals with cancer, representing 20 cancer types, and 100 healthy individuals. The participants were divided into training and testing cohorts. All plasma cfDNA samples were profiled by whole-genome sequencing. A random forest model was constructed using cfDNA 4 bp-end-motif profiles to predict cancer in the training cohort, and its performance was evaluated in the testing cohort. Additionally, a separate random forest model was developed to predict immunotherapy responses.ResultsIn the training cohort, the model based on 4 bp-end-motif profiles achieved an AUC of 0.962 (95% CI 0.936–0.987). The AUC in the testing cohort was 0.983 (95% CI 0.960–1.000). The model also maintained excellent predictive ability in different tumor sub-cohorts, including lung cancer (AUC 0.918, 95% CI 0.862–0.974), gastrointestinal cancer (AUC 0.966, 95% CI 0.938–0.993), and other cancer cohort (AUC 0.859, 95% CI 0.776–0.942). Moreover, the model utilizing 4 bp-end-motif profiles exhibited sensitivity in identifying responders to immunotherapy (AUC 0.784, 95% CI 0.609–0.960).ConclusionThe model based on 4 bp-end-motif profiles demonstrates superior sensitivity in multi-cancer detection. Detection of 4 bp-end-motif profiles may serve as potential predictive biomarkers for cancer immunotherapy.

PTPRD/PTPRT mutation correlates to treatment outcomes of immunotherapy and immune landscape in pan-cancers.

PTPRD and PTPRT are phosphatases of the JAK-STAT pathway related to immunotherapy. However, the role and mechanism of PTPRD and PTPRT mutations in multiple cancers remains unclear. Clinical data and PTPRD/PTPRT mutation information from 12 cohorts were collected and classified as a discovery cohort and three validation cohorts. The association between PTPRD/PTPRT mutations and immunotherapeutic efficacy was analyzed. Then, the association between PTPRD/PTPRT mutation and immune profiles was analyzed using The Cancer Genome Atlas (TCGA) cohort. A total of 2,392 patients across 20 cancer types were included in this study. Our results showed that patients harboring PTPRD/PTPRT mutation, especially co-mutations, had a significantly elevated response rate to immunotherapy in multiple cancers. Patients with PTPRD/PTPRT mutation had a higher objective response rate (ORR) (P=0.002), longer overall survival (OS) (P=0.005) and progression-free survival (PFS) (P=0.038). Importantly, the above findings were further verified in validation cohorts. In addition, we found that the PTPRD/PTPRT co-mutations (co-mut) subgroup exhibited an immune-activated phenotype, the wild-type subgroup tended to have an immune-desert phenotype, and the uni-mutation (uni-mut) subgroup might have an immune-mixed phenotype. Our further analyses suggested that combining programmed cell death ligand 1 (PD-L1) expression and PTPRD/PTPRT mutation can be used to screen patients who may benefit from immunotherapy. PTPRD/PTPRT mutation could serve as a potential predictive biomarker for cancer immunotherapy.

Abstract 1018: Multiplex immunofluorescence and image analysis to investigate the role of the immune contexture and fibroblast activation for tumor cell budding in colorectal cancer

Abstract The advent of immune oncology had a significant impact on the stratification of cancer patients in the past few years. Furthermore, immune cell phenotyping of the tumor microenvironment is becoming a tool not only for the identification of novel predictive biomarkers for cancer immunotherapy, but also for prognostic markers that may help to understand several mechanisms like invasion and epithelial-mesenchymal transition (EMT). We demonstrate how multiplexed immunofluorescence (mIF) assays and digital image analysis helped to investigate tumor budding in colorectal cancer by evaluating the tumor microenvironment and activated fibroblasts. Akoya Bioscience’s Opal detection system and fluorophores were optimized on LEICA Bond RX for use with human normal tonsil control tissues and FFPE colorectal cancer specimens. The 6-plex mIF panel included CD4 (clone SP35), CD8 (clone C8/144B), CD68 (clone PG-M1), FoxP3 (clone SP97), PD-L1 (clone SP263) and pan-Cytokeratin (clones AE1/AE3). In addition, FAP-alpha single-plex IHC was used to investigate reactivity of the stroma surrounding tumor cell buds. Each single marker of the mIF panel was independently validated for specificity regarding stripping efficacy and epitope stability, and for accuracy compared to single plex bright field immunohistochemistry. Automated image analysis and cellular phenotyping followed a workflow of custom Visiopharm apps. The tissue was segmented into regions of interest, such as areas with and without tumor cell budding, and bud microenvironment. Additional parameters like MSI (Microsatellite Instability) status and TNM classification were also taken into consideration for data analysis. The applied mIF immune cell panel and automated image analysis workflow identified a broad number of different immune cell phenotypes, including rare double- or triple-positive cell subtypes, as well as their spatial relation to tumor cell buds within the activated stroma region. This revealed new insights into the complexity of the tumor microenvironment in colorectal cancer around tumor cell buds, suggesting a potential effect of both the immune system and the stromal cells on tumor cell invasion into the surrounding tissue. Citation Format: Dirk Zielinski, Rebekka Vogtmann, Anne Siemon, Christina Koppel, Christina Schipper, Anastasiia Tereshchenko, Suso Platero, Hans-Ulrich Schildhaus. Multiplex immunofluorescence and image analysis to investigate the role of the immune contexture and fibroblast activation for tumor cell budding in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1018.

Prognostic nutritional index of early post-pembrolizumab therapy predicts long-term survival in patients with advanced urothelial carcinoma.

Pembrolizumab has been widely used to treat advanced urothelial carcinoma that has progressed after first-line platinum-based chemotherapy. Because its clinical benefits are limited, biomarkers that can predict a good response to pembrolizumab are required. The prognostic nutritional index (PNI), calculated using the serum albumin level and peripheral lymphocyte count, has been evaluated as a predictive biomarker in cancer immunotherapy. The present study investigated the application of PNI as a predictive biomarker for pembrolizumab response in patients with advanced urothelial cancer. A retrospective study was conducted on 34 patients treated with pembrolizumab at Shiga University of Medical Science Hospital between January 2018 and July 2022. The posttreatment PNI (post-PNI) was calculated within 2 months of starting pembrolizumab. The present study investigated the association between post-PNI and objective response, overall survival (OS) and progression-free survival (PFS). The patient cohort was stratified into two categories, high and low post-PNI groups, with a cutoff value of post-PNI at 40. The higher post-PNI group demonstrated a better disease control rate than the lower post-PNI group (complete response + partial response + stable disease, 75 vs. 21%, P=0.004). Regarding median OS, the higher post-PNI group exhibited a significantly longer survival time than the lower post-PNI group (23.1 vs. 2.9 months, P<0.001). Similarly, the higher post-PNI group exhibited a significantly longer PFS than the lower post-PNI group (10.2 vs.1.9 months, P<0.001). Multivariate analysis showed that a higher post-PNI value was an independent predictor for OS (hazard ratio, 0.04; 95% confidence interval, 0.01-0.14; P<0.001) and PFS (hazard ratio, 0.12; 95% confidence interval, 0.04-0.35; P<0.001). The present study indicated that the post-PNI was a predictor of favorable clinical outcomes in patients treated with pembrolizumab for advanced urothelial carcinoma.

21P Peripheral pre-existing T cell immunity as predictive biomarker in cancer immunotherapy for NSCLC patients

Pre-existing immunity that describes the endogenous tumor-specific adaptive immunity, before treatment may represent a valuable novel predictive biomarker for ICI treatment. In this study we estimate the potential value of pre-existing cancer-antigen specific CD8+ T cells as circulating predictive biomarkers. Additionally, we evaluate the major differences of known immune cell phenotypes between Pre-existing positive (PreI+) and Pre-existing negative (PreI-) NSCLC patients in circulation.

SY01-5 Predictive biomarkers for cancer immunotherapy based on analysis of TILs

Effector T cells play an important role for killing abnormal cells such as cancer cells. On the other hand, regulatory T (Treg) cells are essential in immunosuppression to control over-activation of immune responses and maintain immune tolerance. These immune cells are guided from the circulation into local tissues, such as the tumor microenvironment (TME), to exert their functions by the chemokine networks. We clarified the mechanisms by which EGFR-mutated non-small cell lung cancers make non-inflamed TME, while promoting Treg infiltration via CCL22 production.

Automated tumor immunophenotyping and response to immunotherapy in non-small cell lung cancer using a spatial statistics approach.

2626 Background: Based on the clinical success of immuno-oncology therapeutics, tissue-based, biomarker analyses have shifted from a focus on tumor cell phenotypes to spatial and functional analyses of the tumor immune environment. Distribution and composition of immune infiltrates have shown prognostic or predictive value in various studies; however, standardized approaches to categorize tumors into “Desert”, “Excluded” or “Inflamed” immunophenotypes based on the density and pattern of immune infiltrates are missing. This categorization is typically based on visual inspection of a stained tissue section; it is labor-intensive and associated with poor inter-observer concordance. To eliminate these barriers we developed an automated approach that relies on a set of derived spatial features and named this analysis pipeline LATIS (metric Learning based Automated Tumor Immunophenotyping with Spatial statistics). Methods: We used two clinical trials, POPLAR (phase II; n=258) and OAK (phase III; n=623) that compared anti-PD-L1 treatment vs chemotherapy in patients with advanced non-small cell lung cancer to develop and validate this approach. LATIS utilizes scans of slides stained immunohistochemically for CD8 and cytokeratin (CK); images were tiled and classified according to CK status (positive/negative). First, CD8 positive and CK density was calculated for each tile. Twenty six spatial features were then extracted for each tiled image scan. We used a labeled (manual immunophenotype calls) POPLAR dataset to learn a metric that separates classes of labeled data best with respect of spatial features provided for each data point. Then, to co-embed OAK dataset with POPLAR dataset into the same space, we used that learned metric as a measure of distance between new unlabeled points (OAK data set). K-means clustering in the embedded space was used to identify clusters of data points followed by QFMatch to assign class labels (“Inflamed”, “Excluded”, “Desert”) to each data point. Results: LATIS successfully relates spatial features to both, manual reads and patient response to therapy. In OAK, anti-PD-L1 treated patients experienced longer median overall survival (OS) when tumors showed intra-epithelial CD8+ cells (Inflamed) compared to tumors with a stromal pattern of CD8+ cells (Excluded) or a low density of CD8+ cells (Desert): 17.6 months vs 10.3 (Excluded) vs 12.1 (Desert) [p=0.0061]. Median progression-free survival (PFS) was 3.2 months (Inflamed) vs 2.6 (Excluded) vs 1.4 (Desert) [p=0.00058]. OS and PFS were not significantly different for the three categories in chemotherapy only arm. Conclusions: We suggest that tumor immunophenotype categories generated in an automated fashion by LATIS can serve as predictive biomarker for cancer immunotherapy.

Pan-cancer analysis of YAP1 expression as a predictive biomarker for cancer immunotherapy.

2629 Background: High YAP1 expression correlates with the ‘T-cell inflamed’ expression phenotype in small cell lung cancer (SCLC), but its association with other biomarkers of immune checkpoint vulnerability and in tumor types beyond SCLC is not known. We examined whether YAP1 expression correlates with other established markers of immune checkpoint blockade (ICB) efficacy (PDL1 expression and TMB) in a tumor agnostic manner to determine clinical relevance. Methods: Next-generation sequencing of DNA (592 gene panel or whole exome) and RNA (whole transcriptome) was performed for patient samples (n = 57,134), representing 13 cancer types, submitted to a CLIA-certified laboratory (Caris Life Sciences, Phoenix, AZ). The ‘T-cell inflamed’ signature (TIS) score was calculated as an 18-gene weighted coefficient composite value (Cristescu, 2018). PDL1 expression was assessed by immunohistochemistry (IHC) with cancer type-specific antibodies and thresholds, and high tumor mutational burden was defined as ≥10 mut/Mb. Patients were stratified into subgroups based on median YAP1 expression (YAP1-High/YAP-Low) within each cancer type. Significance was tested by Chi-square, Fisher’s exact test, or Mann-Whitney U test. Results: YAP1-High tumors were associated with significantly increased TIS scores compared to YAP1-Low across all 13 cancer types examined, with the largest fold increase observed in SCLC (1.33-fold, p &lt; 0.0001), followed by pancreatic cancer (1.28-fold, p &lt; 0.0001), while the smallest occurred in melanoma (1.13-fold, p &lt; 0.0001). Spearman correlation strength (range 0.23-0.57) between YAP expression and TIS scores was consistent with increased TIS scores in YAP1-High samples. TMB-High rates were similar in YAP-High and YAP1-Low subgroups for most cancer types, with slightly lower rates in YAP1-High tumors observed for endometrial (23.0 vs 26.6%, p &lt; 0.001) and esophageal (7.0 vs 9.5%, p &lt; 0.05) cancers. YAP1 expression was not significantly increased in PDL1+ (IHC) tumors for most cancer types. However, significantly decreased YAP1 expression was associated with PDL1+ samples in RCC (Renal Cell Carcinoma) (0.91-fold change, P &lt; 0.05), MM (0.90-fold change, P &lt; 0.001), and ENCA (0.80-fold change, P &lt; 0.0001) Conclusions: Our analyses provide confirmation that YAP1 expression positively correlates with the ‘T-cell inflamed’ phenotype across many cancer types, including those with approvals for (ICB) therapy. YAP1 expression was independent of established markers of ICB response, including TMB and PDL1. Further analysis of YAP1 expression as an additional tumor agnostic predictive biomarker is warranted.

PD-L1 expression and microsatellite instability in tumors of unknown primary site.

2635 Background: Cancer of unknown primary site is a heterogeneous group of tumors for which the origin remains unknown, despite detailed investigation. Its clinical presentation occurs due to symptoms resulting from metastasis, which can be influenced not only by biological processes of the tumor, but also due to regulatory processes in the tumor microenvironment such as PD-L1. Microsatellite instability (MSI) is a predictive biomarker for cancer immunotherapy and its status as well as co-occurrence with PD-L1 expression is poorly evaluated. The aim of this study was to evaluate the expression of PD-L1 and the status of MSI in tumors of unknown primary site and their possible associations with clinical-pathological characteristics and clinical outcomes in patients with this type of tumor. Methods: The PD-L1 expression was evaluated using the immunohistochemistry technique and measured by CPS and TPS scores. MSI status was assessed using a hexa-plex marker panel by polymerase chain reaction followed by fragment analysis. Overall survival was analyzed using the Kaplan Meier method and comparisons of the curves using the log rank test. Results: Of the 121 conclusive cases for MSI status, only two cases (1,6%) were MSI-High. PD-L1 expression, assessed by both TPS and CPS, was positive in approximately 18% of 108 conclusive cases. PD-L1 expression is significantly less frequent in patients with a predominance of hepatic or bone metastasis and more frequent in patients with a predominance of nodal metastasis. The median overall survival was about 4 months, and the predominance of liver metastases was associated with a worse prognosis, while the use of chemotherapy and low ECOG-PS were associated with better survival. PD-L1 expression was associated with better overall survival. Conclusions: PD-L1 is expressed in a subset of patients with cancer of unknown primary site, while MSI is a rare event. It is necessary to better explore the microenvironment of this type of tumor as well as the role of immunotherapy to change such a dismal clinical outcome.

Loss of SMAD4 Is Associated With Poor Tumor Immunogenicity and Reduced PD-L1 Expression in Pancreatic Cancer.

Transforming Growth Factor β (TGFβ) is a key mediator of immune evasion in pancreatic ductal adenocarcinoma (PDAC), and the addition of TGFβ inhibitors in select immunotherapy regimens shows early promise. Though the TGFβ target SMAD4 is deleted in approximately 55% of PDAC tumors, the effects of SMAD4 loss on tumor immunity have yet to be fully explored. Using a combination of genomic databases and PDAC specimens, we found that tumors with loss of SMAD4 have a comparatively poor T-cell infiltrate. SMAD4 loss was also associated with a reduction in several chemokines with known roles in T-cell recruitment, which was recapitulated using knockdown of SMAD4 in PDAC cell lines. Accordingly, JURKAT T-cells were poorly attracted to conditioned media from PDAC cells with knockdown of SMAD4 and lost their ability to produce IFNγ. However, while exogenous TGFβ modestly reduced PD-L1 expression in SMAD4-intact cell lines, SMAD4 and PD-L1 positively correlated in human PDAC samples. PD-L1 status was closely related to tumor-infiltrating lymphocytes, particularly IFNγ-producing T-cells, which were more abundant in SMAD4-expressing tumors. Low concentrations of IFNγ upregulated PD-L1 in tumor cells in vitro, even when administered alongside high concentrations of TGFβ. Hence, while SMAD4 may have a modest inhibitory effect on PD-L1 in tumor cells, SMAD4 indirectly promotes PD-L1 expression in the pancreatic tumor microenvironment by enhancing T-cell infiltration and IFNγ biosynthesis. These data suggest that pancreatic cancers with loss of SMAD4 represent a poorly immunogenic disease subtype, and SMAD4 status warrants further exploration as a predictive biomarker for cancer immunotherapy.

DNA Damage Repair Gene Set as a Potential Biomarker for Stratifying Patients with High Tumor Mutational Burden

Simple SummaryImmunotherapy has been a promising therapeutic approach for cancer treatment in recent years. Although cancer immunotherapy has achieved remarkable success, treatment response is only observed in a small number of patients. As nonresponders need to endure high treatment costs and toxicities with little benefit from treatment, identifying potential predictive biomarkers is critical to optimize the benefits of immunotherapy in patients. The total number of mutations in the tumor genome is a useful biomarker. Patients with a large number of mutations tend to respond better to cancer immunotherapy. However, assessment of the total number of mutations may not be easy. In this study, we identified gene sets with only a small number of genes whose mutations serve as an indicator of the total number of mutations. These cancer-specific gene sets can be used as a cost-effective approach to stratify patients with a large number of mutations in clinical practice.Tumor mutational burden (TMB) is a promising predictive biomarker for cancer immunotherapy. Patients with a high TMB have better responses to immune checkpoint inhibitors. Currently, the gold standard for determining TMB is whole-exome sequencing (WES). However, high cost, long turnaround time, infrastructure requirements, and bioinformatics demands have prevented WES from being implemented in routine clinical practice. Panel-sequencing-based estimates of TMB have gradually replaced WES TMB; however, panel design biases could lead to overestimation of TMB. To stratify TMB-high patients better without sequencing all genes and avoid overestimating TMB, we focused on DNA damage repair (DDR) genes, in which dysfunction may increase somatic mutation rates. We extensively explored the association between the mutation status of DDR genes and TMB in different cancer types. By analyzing the mutation data from The Cancer Genome Atlas, which includes information for 33 different cancer types, we observed no single DDR gene/pathway in which mutation status was significantly associated with high TMB across all 33 cancer types. Therefore, a computational algorithm was proposed to identify a cancer-specific gene set as a surrogate for stratifying patients with high TMB in each cancer. We applied our algorithm to skin cutaneous melanoma and lung adenocarcinoma, demonstrating that the mutation status of the identified cancer-specific DDR gene sets, which included only 9 and 14 genes, respectively, was significantly associated with TMB. The cancer-specific DDR gene set can be used as a cost-effective approach to stratify patients with high TMB in clinical practice.

Peritumoral Immune Infiltrate as a Prognostic Biomarker in Thin Melanoma.

Thin melanomas are tumors less than 1 mm thick according to Breslow classification. Their prognosis is in most cases excellent. However, a small subset of these tumors relapses. These clinical findings emphasize the need of novel prognostic biomarkers to identify this subset of tumors. Characterization of tumor immune microenvironment (TIME) is currently investigated as a prognostic and predictive biomarker for cancer immunotherapy in several solid tumors including melanoma. Here, taking into account the limited availability of tumor tissues, by characterizing some of the characteristics of TIME such as number of infiltrating lymphocytes, HLA class I antigen and PD-L1 expression, we show that number of infiltrating CD8+ and FOXP3+ T cells as well as CD8+/FOXP3+ T cell ratio can represent a useful prognostic biomarker in thin melanoma. Although further investigations in a larger patient cohort are needed, these findings have potential clinical significance since they can be used to define subgroups of thin melanoma patients who have a worse prognosis and might need different treatment modalities.

CD226hiCD8+ T Cells Are a Prerequisite for Anti-TIGIT Immunotherapy

Clinical trials are evaluating the efficacy of anti-TIGIT for use as single-agent therapy or in combination with programmed death 1 (PD-1)/programmed death-ligand 1 blockade. How and whether a TIGIT blockade will synergize with immunotherapies is not clear. Here, we show that CD226loCD8+ T cells accumulate at the tumor site and have an exhausted phenotype with impaired functionality. In contrast, CD226hiCD8+ tumor-infiltrating T cells possess greater self-renewal capacity and responsiveness. Anti-TIGIT treatment selectively affects CD226hiCD8+ T cells by promoting CD226 phosphorylation at tyrosine 322. CD226 agonist antibody-mediated activation of CD226 augments the effect of TIGIT blockade on CD8+ T-cell responses. Finally, mFOLFIRINOX treatment, which increases CD226hiCD8+ T cells in patients with pancreatic ductal adenocarcinoma, potentiates the effects of TIGIT or PD-1 blockade. Our results implicate CD226 as a predictive biomarker for cancer immunotherapy and suggest that increasing numbers of CD226hiCD8+ T cells may improve responses to anti-TIGIT therapy.

Distribution of PD-L1 expression level across major tumor types.

e15176 Background: Programmed death-ligand (PD-L1) expression is an efficacy predictive biomarker in cancer immunotherapy. PD-1/PD-L1 axis can restore the host immunity against malignancies causing durable tumor remissions, and thus the level of PD-L1 protein expression is of critical clinical significance. However, the knowledge of PD-L1 expression level of patients with different malignancies is still inadequate understanding. Methods: Based on the percentage of positive tumor cells (TC value) identified by immunohistochemistry (IHC) method, the protein expression of PD-L1 was divided into three groups as g1 (TC &lt; 1%), g2 (1% ≤ TC &lt; 50%) and g3 (TC ≥ 50%). The expressions of PD-L1 from 1772 patients with malignancies across eight types including 133 patients with liver cancer, 35 patients with bile duct cancer, 94 patients with gastric cancer, 146 patients with colorectal cancer, 48 patients with pancreatic cancer, 76 patients with melanoma cancer, 33 patients with glioma and 1207 patients with lung cancer cases were made. The relationships between cancer types and PD-L1 expression were calculated via χ2 test using SPSS v22.0. Results: The ratios of PD-L1 expression in three groups (g1, g2 and g3, respectively) across eight cancer types were 67.7, 30.8 and 1.5% in liver cancer, 65.7, 28.6 and 5.7% in bile duct cancer, 77.7, 20.2 and 2.1% in gastric cancer, 81.5, 18.5 and 0% in colorectal cancer, 64.6, 31.2 and 4.2% in pancreatic cancer, 50.5, 35.7 and 13.8% in melanoma, 30.3, 63.1 and 6.6% in glioma and 55.9, 34.0, 10.1% in lung cancer, respectively. Significant correlation between the PD-L1 expression and cancer types were found (χ2 = 140.543, Cramer’s V coefficient = 0.199, p = 5.53×10−23). The expression of PD-L1 in patients with lung cancer tended to be distributed in g3, in patients with melanoma tended to be distributed in g2, and in patients with gastric cancer, glioma, and colorectal cancer tended to be distributed in g1. Conclusions: Our study suggested the protein expression of PD-L1 was significantly heterogeneous among various cancer types and offered new references for cancer-specific characters of PD-L1 expression. As a retrospective study with a relatively small population, the conclusions of this study needed to be verified with a larger sample.

CD226hiCD8+ T cells are a prerequisite for anti-TIGIT immunotherapy

Abstract Multiple clinical trials are evaluating the efficacy of anti-TIGIT antibodies for use as single-agent therapy or in combination with PD-1/PD-L1 blockade. However, TIGIT’s mechanism of action and whether a TIGIT blockade will synergize with current immunotherapies is not fully understood. Here we show that CD226loCD8+ T cells are accumulated at the tumor site and have an exhausted phenotype with an impaired functionality. In contrast, CD226hiCD8+ tumor-infiltrating T cells possess a higher self-renewal capacity and potent responsiveness. Anti-TIGIT antibody treatment selectively affects CD226hiCD8+ T cells by promoting CD226 phosphorylation at tyrosine 322. CD226 agonist antibody-mediated activation of CD226 augments the effect of TIGIT blockade on CD8+ T cell responses. Finally, mFOLFIRINOX treatment, which increases CD226hiCD8+ T cells in patients with pancreatic ductal adenocarcinoma, potentiates the effects of TIGIT or PD-1 blockade. Our results implicate CD226 as a predictive biomarker for cancer immunotherapy and suggest that increasing CD226hiCD8+ T cells may improve anti-TIGIT therapy.

Abstract PD5-02: Effective and globally reproducible digital pathologist training program on PD-L1 immunohistochemistry scoring on immune cells as a predictive biomarker for cancer immunotherapy in triple negative breast cancer

Abstract BACKGROUND: The VENTANA PD-L1 (SP142) Assay (SP142 assay) is the FDA-approved companion diagnostic for the combination of TECENTRIQ (atezolizumab) and nab-paclitaxel for the treatment of patients with unresectable, locally advanced, or metastatic triple-negative breast cancer (TNBC) whose tumors express PD-L1 - defined as PD-L1 stained tumor-infiltrating immune cells (IC) of any intensity covering ≥ 1 percent of the tumor area. Accurate and reproducible assessment of IC PD-L1 staining with the SP142 assay by pathologists is important to ensure the right treatment decisions for patients are made. Analytical studies have demonstrated variable agreement rates for PD-L1 IC scoring with the SP142 assay, ranging from low, moderate, strong and near-perfect agreement depending on tumor type, cut-off and prior training. The Roche International Pathologist Training Program has trained over 1000 pathologists globally for the SP142 assay in the indications of non-small cell lung cancer, urothelial carcinoma and TNBC. Here we present results from the TNBC training program. METHODS: The glass-slide based training program for pathologists participating in clinical trials leading to the approval of the SP142 assay was adapted to enable live training to be conducted using a novel digital training platform (Pathomation) which was customized for Roche with a rapid, user-friendly interface, facilitating monitoring of scores and reporting. Feedback and endorsement from a Pathologist Training Expert Committee led to a global Train-the Trainer program which enabled regional training sessions to be conducted using the digital platform. Training content was developed around the FDA-approved 1% IC cut-off and was conducted over a 1-day program. Didactic instruction was followed by case reviews and a final proficiency test consisting of 28 cases, with a passing score of ≥85%. RESULTS: Between 17th November, 2018 and 19th June 2019, 432 pathologists from 58 countries participated in the TNBC training program for the SP142 assay. This includes 1 PTEC, 10 Train-the Trainer Sessions and 24 Regional Training sessions. The passing rate for trainees was 99.1%. Overall percent agreement (OPA) was 98.2%, with positive percent agreement (PPA) 99.4% and negative percent agreement (NPA) 96.6%. CONCLUSION: This is the first formal PD-L1 training program for practicing pathologists to be developed for the assessment of PD-L1 on ICs in TNBC. The SP142 assay training utilizing a novel digital platform demonstrates robust, reproducible and excellent pathologist concordance scores in this real-world program. Citation Format: Eslie Dennis, Mark Kockx, Greg Harlow, Zhuangyu Cai, Ken Bloom, Ehab ElGabry. Effective and globally reproducible digital pathologist training program on PD-L1 immunohistochemistry scoring on immune cells as a predictive biomarker for cancer immunotherapy in triple negative breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD5-02.