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Background: Our objective was to characterize cancer immunity marker expression in gynecologic cancers and compare immune landscapes between gynecologic tumor subtypes and with non-gynecologic solid tumors. Methods: RNA expression levels of 51 cancer-immunity markers were analyzed in patients with gynecologic cancers vs. non-gynecologic cancers, and normalized to a reference population of 735 control cancers, ranked from 0-100, and categorized as low (0-24), moderate (25-74), or high (75-100) percentile rank. Results: Of the 72 patients studied, 43 (60%) had ovarian, 24 (33%) uterine, and 5 (7%) cervical cancer. No two immune profiles were identical according to expression rank (0-100) or rank level (low, moderate, or high). Patients with cervical cancer had significantly higher expression level ranks of immune activating, pro-inflammatory, tumor infiltrating lymphocyte markers and checkpoints than patients with uterine or ovarian cancer (p1% versus 0% had significantly higher expression levels of pro-inflammatory markers (58 vs. 49%, p=0.0004). Compared to patients with non-gynecologic cancers, more patients with gynecologic cancers express high levels of IDO-1 (44 vs. 13%, pConclusions: Patients with gynecologic cancers have complex and heterogeneous immune landscapes that are distinct from patient to patient and from other solid tumors. High levels of IDO1 and LAG3 suggest that clinical trials with IDO1 inhibitors or LAG3 inhibitors, respectively, may be warranted in gynecologic cancers.

Background Contemporary to the rapidly evolving landscape of cancer immunotherapy is the equally changing understanding of immune tumor microenvironments (TMEs) which is crucial to the success of these therapies. Their reliance on a robust host immune response necessitates clinical grade measurements of immune TMEs at diagnosis. In this study, we describe a stable tumor immunogenic profile describing immune TMEs in multiple tumor types with ability to predict clinical benefit from immune checkpoint inhibitors (ICIs). Methods A tumor immunogenic signature (TIGS) was derived from targeted RNA-sequencing (RNA-seq) and gene expression analysis of 1323 clinical solid tumor cases spanning 35 histologies using unsupervised analysis. TIGS correlation with ICI response and survival was assessed in a retrospective cohort of NSCLC, melanoma and RCC tumor blocks, alone and combined with TMB, PD-L1 IHC and cell proliferation biomarkers. Results Unsupervised clustering of RNA-seq profiles uncovered a 161 gene signature where T cell and B cell activation, IFNg, chemokine, cytokine and interleukin pathways are over-represented. Mean expression of these genes produced three distinct TIGS score categories: strong (n = 384/1323; 29.02%), moderate (n = 354/1323; 26.76%), and weak (n = 585/1323; 44.22%). Strong TIGS tumors presented an improved ICI response rate of 37% (30/81); with highest response rate advantage occurring in NSCLC (ORR = 36.6%; 16/44; p = 0.051). Similarly, overall survival for strong TIGS tumors trended upward (median = 25 months; p = 0.19). Integrating the TIGS score categories with neoplastic influence quantified via cell proliferation showed highly proliferative and strong TIGS tumors correlate with significantly higher ICI ORR than poorly proliferative and weak TIGS tumors [14.28%; p = 0.0006]. Importantly, we noted that strong TIGS and highly [median = not achieved; p = 0.025] or moderately [median = 16.2 months; p = 0.025] proliferative tumors had significantly better survival compared to weak TIGS, highly proliferative tumors [median = 7.03 months]. Importantly, TIGS discriminates subpopulations of potential ICI responders that were considered negative for response by TMB and PD-L1. Conclusions TIGS is a comprehensive and informative measurement of immune TME that effectively characterizes host immune response to ICIs in multiple tumors. The results indicate that when combined with PD-L1, TMB and cell proliferation, TIGS provides greater context of both immune and neoplastic influences on the TME for implementation into clinical practice.

Strategies for unlocking immunosuppression in the tumor microenvironment have been investigated to overcome resistance to first-generation immune checkpoint blockade with anti- programmed cell death protein 1 (PD-1)/ programmed death-ligand 1 (PD-L1) and anti-cytotoxic T-lymphocyte associated protein 4 (CTLA-4) agents. Indoleamine 2,3-dioxygenase (IDO) 1, an enzyme catabolizing tryptophan to kynurenine, creates an immunosuppressive environment in preclinical studies. Early phase clinical trials investigating inhibition of IDO1, especially together with checkpoint blockade, provided promising results. Unfortunately, the phase 3 trial of the IDO1 inhibitor epacadostat combined with the PD-1 inhibitor pembrolizumab did not show clinical benefit when compared with pembrolizumab monotherapy in patients with advanced malignant melanoma, which dampened enthusiasm for IDO inhibitors. Even so, several molecules, such as the aryl hydrocarbon receptor and tryptophan 2,3-dioxygenase, were reported as additional potential targets for the modulation of the tryptophan pathway, which might enhance clinical effectiveness. Furthermore, the combination of IDO pathway blockade with agents inhibiting other signals, such as those generated by PIK3CA mutations that may accompany IDO1 upregulation, may be a novel way to enhance activity. Importantly, IDO1 expression level varies by tumor type and among patients with the same tumor type, suggesting that patient selection based on expression levels of IDO1 may be warranted in clinical trials.

BackgroundImmune checkpoint inhibitor-based therapies have achieved impressive success in the treatment of several cancer types. Predictive immune biomarkers, including PD-L1, MSI and TMB are well established as surrogate markers for immune evasion and tumor-specific neoantigens across many tumors. Positive detection across cancer types varies, but overall ~50% of patients test negative for these primary immune markers.1 In this study, we investigated the prevalence of secondary immune biomarkers outside of PD-L1, TMB and MSI.MethodsComprehensive genomic and immune profiling, including PD-L1 IHC, TMB, MSI and gene expression of 395 immune related genes was performed on 6078 FFPE tumors representing 34 cancer types, predominantly composed of lung cancer (36.7%), colorectal cancer (11.9%) and breast cancer (8.5%). Expression levels by RNA-seq of 36 genes targeted by immunotherapies in solid tumor clinical trials, identified as secondary immune biomarkers, were ranked against a reference population. Genes with a rank value ≥75th percentile were considered high and values were associated with PD-L1 (positive ≥1%), MSI (MSI-H or MSS) and TMB (high ≥10 Mut/Mb) status. Additionally, secondary immune biomarker status was segmented by tumor type and cancer immune cycle roles.ResultsIn total, 41.0% of cases were PD-L1+, 6.4% TMB+, and 0.1% MSI-H. 12.6% of cases were positive for >2 of these markers while 39.9% were triple negative (PD-L1-/TMB-/MSS). Of the PD-L1-/TMB-/MSS cases, 89.1% were high for at least one secondary immune biomarker, with 69.3% having ≥3 markers. PD-L1-/TMB-/MSS tumor types with ≥50% prevalence of high secondary immune biomarkers included brain, prostate, kidney, sarcoma, gallbladder, breast, colorectal, and liver cancer. High expression of cancer testis antigen secondary immune biomarkers (e.g., NY-ESO-1, LAGE-1A, MAGE-A4) was most commonly observed in bladder, ovarian, sarcoma, liver, and prostate cancer (≥15%). Tumors demonstrating T-cell priming (e.g., CD40, OX40, CD137), trafficking (e.g., TGFB1, TLR9, TNF) and/or recognition (e.g., CTLA4, LAG3, TIGIT) secondary immune biomarkers were most represented by kidney, gallbladder, and sarcoma (≥40%), with melanoma, esophageal, head & neck, cervical, stomach, and lung cancer least represented (≥15%).ConclusionsOur studies show comprehensive tumor profiling that includes gene expression can detect secondary immune biomarkers targeted by investigational therapies in ~90% of PD-L1-/TMB-/MSS cases. While genomic profiling could also provide therapeutic choices for a percentage of these patients, detection of secondary immune biomarkers by RNA-seq provides additional options for patients without a clear therapeutic path as determined by PD-L1 testing and genomic profiling alone.ReferenceHuang R S P, Haberberger J, Severson E, et al. A pan-cancer analysis of PD-L1 immunohistochemistry and gene amplification, tumor mutation burden and microsatellite instability in 48,782 cases. Mod Pathol 2021;34: 252–263.

We describe the extent to which comprehensive genomic profiling (CGP) results were used by oncologists to guide targeted therapy selection in a cohort of solid tumor patients tested as part of standard care at Roswell Park Comprehensive Cancer Center June 2016–June 2017, with adequate follow up through September 2018 (n = 620). Overall, 28.4% of CGP tests advised physicians about targeted therapy use supported by companion diagnostic or practice guideline evidence. Post-test targeted therapy uptake was highest for patients in active treatment at the time of order (86% versus 76% of treatment naïve patients), but also took longer to initiate (median 50 days versus 7 days for treatment naïve patients), with few patients (2.6%) receiving targeted agents prior to testing. 100% of patients with resistance variants did not receive targeted agents. Treatment naïve patients received immunotherapy as the most common alternative. When targeted therapy given off-label or in a trial was the best CGP option, (7%) of patients received it. Our data illustrate the appropriate and heterogeneous use of CGP by oncologists as a longitudinal treatment decision tool based on patient history and treatment needs, and that some patients may benefit from testing prior to initiation of other standard treatments.

Background We performed profiling of the immune microenvironment of castration-resistant (CRPC) and castration-sensitive (CSPC) prostate cancer (PC) in order to identify novel targets for immunotherapy. Methods PD-L1 and CD3/CD8 immunohistochemistry, PD-L1/2 fluorescent in situ hybridization, tumor mutation burden, microsatellite instability, and RNA-seq of 395 immune-related genes were performed in 19 CRPC and CSPC. Targeted genomic sequencing and fusion analysis were performed in 17 of these specimens. Results CD276, PVR, and NECTIN2 were highly expressed in PC. Comparison of CRPC versus CSPC and primary versus metastatic tissue revealed the differential expression of immunostimulatory, immunosuppressive, and epithelial-to-mesenchymal transition (EMT)-related genes. Unsupervised clustering of differentially expressed genes yielded two final clusters best segregated by CRPC and CSPC status. Conclusion CD276 and the alternative checkpoint inhibition PVR/NECTIN2/CD226/TIGIT pathway emerged as relevant to PC checkpoint inhibition target development.

Background Immunotherapeutic approaches for pancreatic ductal adenocarcinoma (PDAC) are less successful as compared to many other tumor types. In this study, comprehensive immune profiling was performed in order to identify novel, potentially actionable targets for immunotherapy. Methods Formalin-fixed paraffin embedded (FFPE) specimens from 68 patients were evaluated for expression of 395 immune-related markers (RNA-seq), mutational burden by complete exon sequencing of 409 genes, PD-L1 expression by immunohistochemistry (IHC), pattern of tumor infiltrating lymphocytes (TILs) infiltration by CD8 IHC, and PD-L1/L2 copy number by fluorescent in situ hybridization (FISH). Results The seven classes of actionable genes capturing myeloid immunosuppression, metabolic immunosuppression, alternative checkpoint blockade, CTLA-4 immune checkpoint, immune infiltrate, and programmed cell death 1 (PD-1) axis immune checkpoint, discerned 5 unique clinically relevant immunosuppression expression profiles (from most to least common): (I) combined myeloid and metabolic immunosuppression [affecting 25 of 68 patients (36.8%)], (II) multiple immunosuppressive mechanisms (29.4%), (III) PD-L1 positive (20.6%), (IV) highly inflamed PD-L1 negative (10.3%); and (V) immune desert (2.9%). The Wilcoxon rank-sum test was used to compare the PDAC cohort with a comparison cohort (n=1,416 patients) for the mean expressions of the 409 genes evaluated. Multiple genes including TIM3, VISTA, CCL2, CCR2, TGFB1, CD73, and CD39 had significantly higher mean expression versus the comparison cohort, while three genes (LAG3, GITR, CD38) had significantly lower mean expression. Conclusions This study demonstrates that a clinically relevant unique profile of immune markers can be identified in PDAC and be used as a roadmap for personalized immunotherapeutic decision-making strategies.

Timely and accurate identification of molecular alterations in solid tumors is essential for proper management of patients with advanced cancers. This has created a need for rapid, scalable comprehensive genomic profiling (CGP) systems that detect an increasing number of therapeutically-relevant variant types and molecular signatures. In this study, we assessed the analytical performance of the TruSight Oncology 500 High-Throughput assay for detection of somatic alterations from formalin-fixed paraffin-embedded tissue specimens. In parallel, we developed supporting software and automated sample preparation systems designed to process up to 70 clinical samples in a single NovaSeq 6000TM sequencing run with a turnaround time of 99% overall accuracy and precision. Our results demonstrate that the high-throughput CGP assay is a reliable method for accurate detection of molecular alterations in support of precision therapeutics in oncology. The supporting systems and scalable workflow allow for efficient interpretation and prompt reporting of hundreds of patient cancer genomes per week with excellent analytical performance.

Purpose of Study: Cancer testis antigens (CTA) are highly immunogenic genes with the ability to cause cancer-specific immune responses when expressed. Their tumor cell-specific expression makes them a key target of natural T cell response, cancer vaccines, immune checkpoint blockade (ICB), and cell-based immunotherapies in a wide range of tumor types. In this study, we assess the pan-cancer distribution and ICB survival association of CTA burden (CTAB) in real-world solid tumors. Procedure: Three tumor sample cohorts were studied: 1) a pan-cancer discovery cohort to develop a low- and high-CTAB cutoff (n=5450, 39 tumor types), 2) a TCGA cohort (n=19923, 32 tumor types) used to validate the classifier based on CTAB distribution and serve as a non-ICB-treated population, and 3) an ICB-treated retrospective cohort to validate the classification on overall survival (OS) (n=242, 3 tumor types). The expression levels of 17 CTA were measured using targeted RNA-Seq of FFPE tumor samples and then ranked against a pan-cancer reference population. CTAB was calculated for each sample, cohort and tumor type as the sum of the 17 CTA gene expression ranks. The discovery cohort median CTAB of 171 was used to classify all three cohorts into high- and low-CTAB groups. OS analysis was performed on the TCGA and ICB-treated cohorts using a CoxPH regression model to determine the Hazard Ratio (HR). Results: The three cohorts demonstrated overlapping single-peak, left-skewed CTAB distribution curves centered at CTAB values between 170 (discovery cohort) and 256 (retrospective cohort). When grouping by tumor types and ordering by median CTAB, the CTAB distributions for tumor types within all three cohorts were comparable. CoxPH regression analysis revealed an association between the CTAB threshold classifier and OS in both the ICB-treated retrospective and non-ICB TCGA cohorts. However, the direction of this association differed between the two cohorts, with high-CTAB samples having better survival (HR=0.936, p=0.076) in the ICB-treated retrospective cohort and worse survival (HR: 1.007, p=0.084) in the non-ICB-treated cohort. Conclusion: Our studies show that the CTAB distribution was maintained across the discovery and TCGA cohorts and a wide range of tumor types, supporting that the CTAB classifier is valid and histology agnostic. Additionally, when evaluating the ICB and non-ICB-treated cohorts, CTAB demonstrated the ability to predict OS, pointing to the utility of ICB in supporting CTA-specific natural immune response. However, further studies are necessary to verify these mechanisms of response to ICB as well as cancer vaccines and cell-based immunotherapies. Additional validation is needed to establish the predictive utility of CTAB alone and in combination with other immune oncology biomarkers for resistance or response. Citation Format: R.J. Seager, Erik Van Roey, Shuang Gao, Blake Burgher, Paul DePietro, Mary Nesline, Roger Klein, Shengle Zhang, Jeffrey M. Conroy, Sarabjot Pabla. Cancer testis antigen burden: Pan-cancer distribution and survival implications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5137.

PURPOSE: The immunohistochemistry companion diagnostic test for pembrolizumab (IHC 22C3 pharmDx) lacks sensitivity, challenging immunotherapy selection for NSCLC patients with lower levels of expression. Unlike IHC 22C3, which restricts assessment of PD-L1 expression to viable tumor cells as a tumor proportion score (% TPS), mRNA next generation sequencing (RNA-seq) measures PD-L1 expression in the tumor microenvironment for both tumor and inflammatory background cells. RNA-seq previously demonstrated concordance with IHC and may be a robust alternative testing method for multiple tumor types. Here, we sought to optimize PD-L1 RNA-seq cutoff values in NSCLC to improve clinical sensitivity. PROCEDURE: NSCLC patients included in the study (n=3,465) were tested for PD-L1 expression by IHC 22C3 and clinically validated RNA-seq, measured as % rank (0-100) relative to a reference population based on normalized reads per million (nRPM). Patients were divided into an RNA-seq cut-off discovery cohort (n=3,168), and a test cohort pembrolizumab treated patients. Principal components analysis (PCA) was used to classify patients based on test results and explore cut-off values in the discovery cohort. Kaplan Meier curves and a Cox proportional hazards regression models assessed overall survival (OS) hazard ratios (HR) for RNA-seq versus standard of care IHC cut-offs in the test cohort. RESULTS: Unsupervised PCA clustering identified three distinct PD-L1 groups separated by combinations of significant over- and under-representation of RNA-seq and IHC result measures from prior testing. The groups were labeled as “low” (rank ≤40), “moderate” (rank 41-73), and “high” (rank ≥74), based on the median RNA-seq rank for each group (+/- 1SD for low and high). Both the low and moderate groups were overrepresented by patients in the PD-L1 IHC low and negative groups. The moderate group was overrepresented by patients with moderately high PD-L1 RNA-seq ranks (median=70), while the low group was overrepresented by patients that were not PD-L1 high by RNA-seq. The high group was overrepresented by patients high for PD-L1 by both IHC and RNA-seq. OS HRs were better for RNA-seq high versus moderate (HR=0.05, CI 0.00-0.63, p=.02), and RNA-seq high versus low (HR=0.16, CI 0.03-0.86, p=.03) groups compared to standard of care IHC 22C3 high versus low groups, (HR=0.21, CI 0.04-1.07, p=.06). Findings were non-significant for the RNA-seq moderate versus low groups, likely due to the limited and disproportionately high number of patients with poor performance status in these groups. CONCLUSIONS: PD-L1 expression by RNA-seq demonstrated improved clinical sensitivity in predicting OS versus standard of care PD-LI IHC in a pembrolizumab treated NSCLC patient cohort. Additional studies are needed to further define cut-offs in the context of performance status, and better understand immune escape mechanisms in the moderate group. Citation Format: Mary K. Nesline, Sarabjot Pabla, Yong Hee Lee, Paul DePietro, Amy Early, Roger Klein, Shengle Zhang, Jeffrey Conroy. PD-L1 expression by RNA-sequencing and survival from pembrolizumab in non-small cell lung cancer (NSCLC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1259.

2561 Background: Lymphocyte Activation Gene 3 (LAG3) or CD223 is an immune checkpoint that can be found on various T cells: CD4+, CD8+, regulatory T cells (Tregs), natural killer T cells, natural killer cells, and plasmacytoid dendritic cells. The expression of LAG3 molecule acts to increase T-cell exhaustion, leading to decreased tumor killing as well as an increase in immune suppressive cytokine release. Many clinical trials of LAG3 inhibitors have had modest effects, but recent data suggests that the LAG3 antibody relatlimab together with nivolumab (anti-PD1) provided greater benefit than nivolumab alone in patients with melanoma. Methods: The RNA expression levels of 397 genes in various types of solid tumors from 514 patients seen at the UCSD Moores Cancer Center were analyzed at a CLIA-licensed laboratory, OmniSeq (https://www.omniseq.com/). Following removal of germline variants, synonymous variants, indels and SNVs with < 5% VAF, TMB is reported as mutations/megabase. Transcript abundance was normalized to internal housekeeping gene profiles and ranked (0-100 percentile) in a standardized manner to a reference population of 735 tumors spanning 35 histologies. Odds ratio for high LAG3 expression was calculated and Bonferroni corrected for multiple genes and cancer histologies with > 40 samples. Results: A total of 116 (22.6%) tumors had high LAG3 (≥75) across 32 different histologies. Cancers with the highest proportion of LAG3 were neuroendocrine (47%), uterine (43%), sarcoma (33%), breast (31%), ovarian (30%), pancreatic (24%), lung (20%), stomach (16%), and colorectal (15%). There was significant association for high LAG3 with high PD-L1 (adj P < 0.001), high PD-1 (adj P < 0.0014), high PD-L2 (adj P < 0.0014), high CTLA-4 (adj P < 0.0014), TMB ≥10 mt/mb (adj P = 0.0504). There was no significant association between histologies colorectal (adj P = 0.1834), breast (adj P = NS), ovarian (adj P = NS), pancreatic (adj P = NS), or gender (adj P = 0.272). Conclusions: High LAG3 was found in almost a quarter of tumor samples and significantly associated with other immune checkpoints with FDA-approved drugs. Ongoing studies combining LAG3 inhibitors and specific immune checkpoint inhibitors may yield more clinical benefit if individualized immunomic transcript interrogation is undertaken, rather than population-based approaches without employment of rationally combined agents matched to each patient’s cancer.

2623 Background: Immune checkpoint inhibitors (ICIs) have emerged as effective treatments in non-small cell lung cancer (NSCLC). While the clinical utility of single agent ICI or in combination with chemotherapy has been well established, there remains an unmet need for the development of biomarkers that can better predict response. To address this need, we developed and applied a combination genomic and immune biomarker strategy to ICI-treated NSCLC patients which identified distinct patient subgroups with differential benefit among single agent or combination ICI treatment strategies. Methods: A discovery cohort (DC) of 5450 tumors across 37 histologies were evaluated by comprehensive genomic and immune profiling of the tumor immune microenvironment. Individual and combination biomarker assessment included PD-L1 IHC, TMB, tumor inflammation (TIGS), cell proliferation (CP) and cancer testis antigen burden (CTAB). From this cohort, combinations of molecular and immune biomarkers were identified and applied to a retrospective cohort (RC) of 225 metastatic NSCLC patients treated with pembrolizumab + chemo or pembrolizumab alone to correlate with response. Comparison of objective response rates (ORR) was performed using Chi-square test. Kaplan-Meir analysis was performed to test for differences in overall survival (OS) and 1-year OS. Results: Unsupervised analysis of the DC revealed four distinct biomarker combination groups that describe underlying tumor immunobiology: tumor dominant (CTAB, TMB, CP High), proliferative (CP High), inflamed (TIGS High), and checkpoint (PDL1, TIGS and TMB High). Application of these biomarker groups to the RC demonstrated significant differences in response to ICI regimens between groups (p = 0.04). Patients in the proliferative group (35.1%, 79/225; median PD-L1 = 20% TPS) treated with single agent pembrolizumab showed a significantly higher ORR (59%; 16/27) compared to pembrolizumab + chemo (27%; 14/52; p = 0.005), significantly improved 1-yr OS (p = 0.03), and trend towards better OS (p = 0.14). Importantly, patients in the inflamed group (16%, 36/225; median PD-L1 = 1% TPS), suggested that pembrolizumab + chemo (ORR 26.1%; 6/23) was not associated with ORR compared to pembrolizumab (ORR 31%; 4/13, p = 0.76), or OS (p = 0.37) and 1-yr OS (p = 0.57). Conclusions: Comprehensive genomic and immune profiling may identify PD-L1 low NSCLC patients who benefit from single agent pembrolizumab. PD-L1 low NSCLC patients with a proliferative phenotype may benefit from single agent pembrolizumab, whereas PD-L1 low cases with an inflamed phenotype may benefit from both single agent and combination pembrolizumab. Although further clinical validation of these predictive biomarker combinations is required, this data-driven approach demonstrates the potential to provide treatment decision support when selecting an ICI therapeutic strategy in lung cancer.

e21167 Background: CGIP analyzes FFPE tumor tissue by DNA/RNA sequencing for SNVs, indels, copy gain/loss, fusions, splice variants, MSI and TMB, along with PD-L1 IHC. A purported advantage of CGIP in NSCLC is the ability to identify targeted and immunotherapy biomarkers to inform clinical management. However, the extent to which CGIP supports treatment decisions and benefits NSCLC patients in various treatment settings is limited. Methods: A retrospective analysis of OmniSeq CGIP results (June 2017-March 2019) and real-world clinical data (through March 2020) for NSCLC patients (n = 300) was performed to evaluate treatment strategies at Roswell Park Comprehensive Cancer Center. Patient targeted and immunotherapies following CGIP were classified as “matched” to biomarker results (established or potentially clinically significant) at the indication level (single or multi-marker results, histology, treatment line) based on AMP/ASCO/CAP guidance for strength of biomarker clinical evidence. We estimated overall survival (OS) from CGIP report date for patients who first received either matched therapy or chemotherapy (and no subsequent matched therapy), and assessed the predictive value of matched therapy for OS in the first or subsequent line setting, adjusting for clinicopathologic covariates. Results: Most CGIP tested patients were female (55%), stage IIIB/IV (89%), ECOG < 2 (83%), non-squamous (86%), treatment naïve (62%), ever smokers (88%). 74% (228) of patients were treated post-CGIP, with 71% receiving at least one matched therapy. Matched therapies received in the frontline setting were supported by the highest (Tier 1A) category of evidence more often than subsequent line therapies (97% vs. 68%). 90% of patients with oncogenic driver mutations received targeted agents (17% of total) and 57% received matched immunotherapy. In the frontline setting, compared to chemotherapy, OS was highest for patients who first received matched targeted therapy (median = 23.4 mo; HR 0.26; p = .004; 95% CI 0.13-0.68) vs matched immunotherapy (median = 17.9 mo; HR 0.38; p = .001; 95% CI 0.21-0.69). Subsequent line, OS was also highest for patients who first received matched targeted therapy (median not est., mean = 27.5 mo; HR 0.20; p = .063; 95% CI 0.04-1.09) vs matched immunotherapy (median = 17.4 mo; HR 0.20; 95% CI 0.04-1.09), however, these differences were non-significant. Conclusions: CGIP supports evidence-based clinical decision making for NSCLC in the first and subsequent line settings and leads to improved survival for patients who receive matched targeted or immunotherapy compared to chemotherapy. Better predictive markers are needed to identify NSCLC patients who are more likely to respond to immunotherapies. Heterogeneity of patient biomarker profiles and treatment strategies over time in real world practice are a challenge to assessing CGIP efficacy.

2555 Background: Although immune checkpoint blockade (ICB) has revolutionized cancer treatment, not all patients with cancer benefit from ICB. One possible explanation for poor responders/resistance is the variable expression level of the target molecules (e.g., PD-1 and PD-L1) in the tumor microenvironment. There are recent or ongoing trials targeting variable pathways for immune evasion (e.g., LAG3 or IDO1). It is therefore of interest to know the expression levels related to variable immune checkpoints so that clinical trials can focus on the patients who can benefit from the cognate treatment. Methods: Overall, 514 patients with various solid tumors seen at the University of San Diego, Moores Center for Personalized Cancer Therapy were analyzed. The expression levels of checkpoint markers (ADORA2A, BTLA, CD276, CTLA4, IDO1, IDO2, LAG3, NOS2, PD-1, PD-L1, PD-L2, PVR, TIGIT, TIM3, VISTA, and VTCN) in the tumor samples were measured through RNA sequencing and normalized to internal housekeeping gene profiles, and ranked from 0 to 100 percentile based on a reference population. The expressions of each checkpoint marker were correlated with cancer types, microsatellite instability (MSI), tumor mutational burden (TMB), and programmed death-ligand 1 (PD-L1) status on immunohistochemistry. Results: In this cohort, 60% were female, median age of 60, and included 30 different tumor types, with colorectal cancer being the most common (27%). The rank values of all checkpoint markers were distributed broadly from 0 to 99 or 100. CD276 and NOS2 had the highest (68th percentile) and lowest (13.5 percentile) median rank values, respectively. When rank values were categorized to “Low” (0-24), “Intermediate” (25-74), and “High” (75-100), 41.6% of patients showed high expression of CD276 while only 13% showed high expression of PD-L1. Each patient had a distinctive protfolio of the categorical expression levels of 16 checkpoint markers. Several checkpoint markers, especially NOS2, showed a significant correlation with cancer type. (median rank values in colorectal, stomach, pancreatic, and breast cancer were 79, 76, 5 and 0 respectively, p < 0.001) Five markers (IDO1, LAG3, PD-1, PD-L1, and TIGIT) showed significant correlation with MSI, while seven markers (CTLA4, IDO1, LAG3, PD-1, PD-L1, PD-L2, and TIGIT) were significantly associated with positive PD-L1 status. However, no significant association was seen based on TMB or tissue-specific grouping of patients. Conclusions: The expression of immune checkpoint markers varies from patient to patient, though transcript expression of several markers correlates with cancer type, MSI, and PD-L1 status. Clinical trials with patient selection based on the expression level of checkpoint markers matched to the corresponding ICB drug are warranted.

Background PD-L1 immunohistochemistry (IHC) testing is suboptimal for predicting patient clinical benefit for checkpoint inhibition, while PD-L1 liquid biopsy is not clinically validated and lacks sensitivity, underscoring the need to include PD-L1 testing in more robust, tissue-efficient, comprehensive, scalable next generation sequencing (NGS) tests. Methods To assess comparability and efficacy of PD-L1 testing by NGS with IHC, we identified NSCLC patients treated by first-line pembrolizumab alone (n=54) or pembrolizumab + chemotherapy (n=49) whose tumors underwent companion diagnostic PD-L1 testing by IHC antibody 22C3 testing (high≥50%; low=1–49%, or negative=0% tissue proportion score), and also by RNA-seq, as part of a comprehensive immune profiling panel. PD-L1 expression by RNA-seq, was measured as a percentile rank, with ≥75 considered ‘high’, and Results More than 75% of IHC high cases were classified as high by RNA-Seq for both treatment groups (p Conclusions PD-L1 status by RNA-seq and IHC appear to be comparable. Unlike PD-L1 IHC however, PD-L1 RNA-seq high status versus not high status is associated with greater survival benefit, indicating PD-L1 by NGS may have utility for pembrolizumab selection.

Background Pembrolizumab, with or without chemotherapy, is NCCN guideline-recommended treatment for NSCLC cancer patients depending on tumor PD-L1 status by IHC. PD-L1 IHC provides guidance for treatment selection for response, but does not accurately predict survival benefit from pembrolizumab. Emerging evidence suggests TMB and other genomic markers (KRAS, STK11, TP53 mutations), may have clinical utility for predicting survival benefit. Methods We identified a cohort of metastatic EGFR/ALK wild type NSCLC patients (n=116) whose tumors underwent comprehensive profiling (June 2017-March 2019) for genomic variants, TMB and PD-L1 IHC 22C3 prior to selection of pembrolizumab (n=43), pembrolizumab + chemotherapy (n=41), or chemotherapy only (excluding subsequent targeted therapy or immunotherapy) (n=32) at Roswell Park Comprehensive Cancer Center, with at least one year of follow up. TMB was assessed using a 1.75 Mb capture of 409 oncogenes with full exon coverage (DNA-Seq), with ‘high’ TMB interpreted as ≥10 mutations/Mb. Electronic pharmacy records were curated to create pre and post-test treatment histories for each patient. Cox regression analysis evaluated OS with pembrolizumab monotherapy or pembrolizumab + chemotherapy vs chemotherapy only, adjusting for covariates including oncogenic driver mutations, TMB, PD-L1 IHC demographics, clinicopathologic characteristics, prior treatment, and performance status. Using the same model, we then assessed overall survival for each treatment group by TMB, KRAS, STK11, and TP53 mutant status. Results Overall, 47% of tumors were PD-L1 high, 47% TMB high, 34% KRAS mutant (codons 12, 13, 60, 61), 52% TP53 mutant and 16% STK11 mutant. As expected, pembrolizumab with or without chemotherapy significantly improved overall survival (OS) compared to chemotherapy alone; with TMB, smoking, and ECOG status identified as significant covariates. PD-L1 IHC status was not associated with OS for any treatment. TMB high status was significant for OS benefit with pembrolizumab either as monotherapy [HR=0.02; CI=0.01–0.40; p=0.01] or in combination with chemotherapy [HR=0.20; CI=0.04–0.95; p=0.04]. KRAS mutant status was independently significant for OS benefit from pembrolizumab + chemotherapy [HR=0.01; CI=0.01–0.79; p=0.04] but not for pembrolizumab monotherapy or chemotherapy alone. Among patients who received pembrolizumab monotherapy, there was a trend toward increased risk of death in those with STK11 mutations [HR=17.54; CI=0.35–1,000; p=0.15], whereas TP53 mutant status trended toward survival benefit [HR=0.18; CI=0.02–1.53; p=0.11]. Conclusions Data comparing pembrolizumab treatments with chemotherapy and independent marker associations suggest TMB has predictive power for determining overall survival benefit from pembrolizumab, while KRAS, STK11, and TP53 mutational status demonstrated potential prognostic relevance for NSCLC.

BackgroundWhen expressed in cancer cells, cancer testis antigens (CTAs) are highly immunogenic and have the capacity to elicit cancer-specific immune responses in diverse malignancies. With their expression limited to tumor cells, CTAs have become a prime target of natural T cell response, immune cell-based therapy, and cancer vaccines. In this study, we investigated CTA burden in real-world clinical tumors spanning multiple histologies, revealing a novel prognostic gene expression-based biomarker.MethodsTargeted RNA-seq was performed on 5450 FFPE tumors representing 39 histologic types, predominantly composed of lung cancer (40.4%) followed by colorectal cancer (10.6%) and breast cancer (8.6%). Using an amplicon-based NGS approach, expression levels of 17 CTA genes were ranked against a reference population. Cancer Testis Antigen Burden (CTAB) was calculated as the sum of the gene expression rank for each CTA gene. The median CTAB of ≥171 was used as cutoff for CTAB High versus Low classification. We estimated Pearson’s correlation for all CTA genes to discover co-expression patterns between CTAs and histologies. Overall survival (OS) analysis was performed using CoxPh regression model whereas response analysis was performed using logistic regression model with p-values reported.ResultsWithin the tumor samples, CTAB values ranged from 0–1700, with kidney cancer demonstrating overall lowest mean CTAB (110) and melanoma the highest (550). NSCLC had an average CTAB of 283. In an immune checkpoint blockade treated retrospective cohort of 110 NSCLC patients, High CTAB showed better OS compared to Low CTA (HR: 0.55, p=0.07). Additionally, when combined with tumor inflammation and cell proliferation biomarkers, highly inflamed but poorly proliferative tumors with High CTAB had improved OS (HR: 0.27, p=0.05). No significant association with response was detectedConclusionsOur studies show that co-expression of multiple CTA genes occurs in many tumor types and can be reliably detected using a targeted RNA-seq approach. Utilization of this co-expression pattern to calculate CTAB reveals tumor-type associated signatures, which in a small NSCLC cohort is associated with the overall survival. The findings suggest that these immunogenic antigens expose the tumor cells to natural or immunotherapy augmented cell-based immune response, and that CTAB is a potential predictive marker for therapeutic response to checkpoint inhibitors. Further studies are needed to establish the predictive value in other tumor types, as well as the role of CTAB in immune cell therapies and vaccinations.

BackgroundCancer of unknown primary (CUP) is a rare tumor type accounting for 2% of solid cancers. In the subset of CUP cases where tumor of origin is posited and treated as such, no clear clinical benefit has been demonstrated. Furthermore, CUP patients treated by empiric platinum-based regimens have low response and survival rates of approximately 20%.1 2 Support of tissue-agnostic marker-directed immunotherapy is growing because it targets the immune system rather than the tumor, with some efficacy evidence emerging for CUP.3 Identifying new targets for immunotherapeutic opportunities in this heterogeneous and difficult to treat patient group is a critical unmet need.MethodsComprehensive genomic and immune marker profiling by NGS4 was performed on FFPE tissue for CUP tumors (n=298) as indicated by physicians’ test orders from >100 clinical practice sites. Histology was verified by a molecular pathologist as part of pre-analytic test quality control, with cases representing tumors with adenocarcinoma (58%), carcinoma (26%), squamous (10%), and neuroendocrine (6%) histologic features. RNA-expression levels of immune genes that are current targets in non-CUP immunotherapy clinical trials (n=36) were ranked against a reference population (≥75th percentile=high), and described by histologic type, along with PD-L1 IHC (22C3) expression, tumor mutational burden (TMB) and genomic variants.Results90% of all CUP tumors had at least 1 highly expressed immune gene target in active immunotherapy trials, with the most frequent being TGFB1 (47%) and CCL2 (39%). 55% of CUP tumors were PD-L1 IHC 22C3 positive (>=1% TPS), and 21% had high TMB (>=10 mut/Mb) in CUP tumors with neuroendocrine (32%), carcinoma (30%), squamous cell (21%), and adenocarcinoma (17%) histologic features. Overall, 26% of CUP patient tumors, mostly adenocarcinomas (28%) and carcinomas (27%), harbored genomic variants (n=77) with FDA approved targeted therapies in other tumor types. The most frequently immunogenic CUP tumors were carcinomas, showing high RNA-seq expression of 26/36 genes in at least 20% of patients, most represented by CD20, CD27, TLR8, and PD-L1. High expression of CD40, CSF1R, TIM3, and VISTA was most common in adenocarcinomas. Squamous cell carcinomas were relatively immunogenic, with frequent high expression of 17/36 immune genes, uniquely including MAGEA4. Neuroendocrine tumors were the least immunogenic, with frequent high expression in only 4/36 genes, including ADORA2A (42%) and MAGEA1 (37%).ConclusionsCUP tumors diversely express both standard marker and novel immunotherapeutic targets based on histology and may benefit from selective access to clinical trials for these therapies.ReferencesNational Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) Occult Primary (Cancer of Unknown Primary [CUP]), Version 2.2021. Fort Washington, Pennsylvania: National Comprehensive Cancer Network; 2021. https://www.nccn.org/professionals/physician_gls/pdf/occult.pdf.Laprovitera N, Riefolo M, Ambrosini E, Klec C, Pichler M, Ferracin M. Cancer of unknown primary: Challenges and progress in clinical management. Cancers (Basel) 2021;13(3):1–30. doi:10.3390/cancers130304513.Naing A, Meric-Bernstam F, Stephen B, et al. Phase 2 study of pembrolizumab in patients with advanced rare cancers. J Immunother Cancer 2020;8(1):e000347. doi:10.1136/jitc-2019-0003474.Conroy JM, Pabla S, Glenn ST, et al. Analytical validation of a next-generation sequencing assay to monitor immune responses in solid tumors. J Mol Diagnostics 2018;20(1):95–109. doi:10.1016/j.jmoldx.2017.10.001

Motivation Methods to assess the quality of protein structure models are needed for user applications. To aid with the selection of structure models and further inform the development of structure prediction techniques, we describe the ResiRole method for the assessment of the quality of structure models. Results Structure prediction techniques are ranked according to the results of round-robin, head-to-head comparisons using difference scores. Each difference score was defined as the absolute value of the cumulative probability for a functional site prediction made with the FEATURE program for the reference structure minus that for the structure model. Overall, the difference scores correlate well with other model quality metrics; and based on benchmarking studies with NaïveBLAST, they are found to detect additional local structural similarities between the structure models and reference structures. Availabilityand implementation Automated analyses of models addressed in CAMEO are available via the ResiRole server, URL http://protein.som.geisinger.edu/ResiRole/. Interactive analyses with user-provided models and reference structures are also enabled. Code is available at github.com/wamclaughlin/ResiRole. Supplementary information Supplementary data are available at Bioinformatics online.

Introduction Metformin, a common medication used in the treatment of diabetes mellitus is known to have anticancer effects. We hypothesized that the salutary effect of metformin on the survival of patients with stage I NSCLC is influenced by body mass index (BMI). Methods Patients undergoing lobectomy for stage I NSCLC without neoadjuvant therapy were included. Univariate and multivariate survival analyses to examine the association between metformin use and overall survival (OS), disease-specific survival (DSS), and recurrence-free survival were performed, stratified by BMI (>25 kg/m2 and ≤25 kg/m2). Expression of immune checkpoints in patients on metformin and not was performed in a separate cohort of 205 patients with advanced disease. Results Four hundred thirty-four stage I patients (including 74 metformin users) were deemed eligible for analysis. Univariate and multivariate analysis revealed an association between metformin use and OS (hazard ratio [HR] = 0.52; p = 0.04) as well as DSS (HR = 0.21; p = 0.04) but not recurrence-free survival (HR = 0.67; p = 0.33) in high-BMI patients only. In a separate cohort of 205 patients with tumors of all stages (including 35 metformin users), downregulation of immune checkpoint gene expression (programmed cell death 1, cytotoxic T-lymphocyte associated protein 4, B and T lymphocyte associated, CD27 molecule, lymphocyte activating 3, and inducible T cell costimulator) in metformin users was seen only in high-BMI patients, with upregulation of these genes seen in low-BMI patients with metformin use. Conclusions Metformin use may be associated with better OS and DSS only in high-BMI patients. This hypothesis is supported by gene expression data of immune checkpoint genes in metformin users using a separate cohort of advanced-stage tumors. Further studies examining the interaction of BMI with metformin in NSCLC are worthwhile.

Background Regulatory approval of next generation sequencing (NGS) by the FDA is advancing the use of genomic-based precision medicine for the therapeutic management of cancer as standard care. Recent FDA guidance for the classification of genomic variants based on clinical evidence to aid clinicians in understanding the actionability of identified variants provided by comprehensive NGS panels has also been set forth. In this retrospective analysis, we interpreted and applied the FDA variant classification guidance to comprehensive NGS testing performed for advanced cancer patients and assessed oncologist agreement with NGS test treatment recommendations. Methods NGS comprehensive genomic profiling was performed in a CLIA certified lab (657 completed tests for 646 patients treated at Roswell Park Comprehensive Cancer Center) between June 2016 and June 2017. Physician treatment recommendations made within 120 days post-test were gathered from tested patients’ medical records and classified as targeted therapy, precision medicine clinical trial, immunotherapy, hormonal therapy, chemotherapy/radiation, surgery, transplant, or non-therapeutic (hospice, surveillance, or palliative care). Agreement between NGS test report targeted therapy recommendations based on the FDA variant classification and physician targeted therapy treatment recommendations were evaluated. Results Excluding variants contraindicating targeted therapy (i.e., KRAS or NRAS mutations), at least one variant with FDA level 1 companion diagnostic supporting evidence as the most actionable was identified in 14% of tests, with physicians most frequently recommending targeted therapy (48%) for patients with these results. This stands in contrast to physicians recommending targeted therapy based on test results with FDA level 2 (practice guideline) or FDA level 3 (clinical trial or off label) evidence as the most actionable result (11 and 4%, respectively). Conclusions We found an appropriate “dose-response” relationship between the strength of clinical evidence supporting biomarker-directed targeted therapy based on application of FDA guidance for NGS test variant classification, and subsequent treatment recommendations made by treating physicians. In view of recent changes at FDA, it is paramount to define regulatory grounds and medical policy coverage for NGS testing based on this guidance.

8 Background: We performed microsatellite instability testing and expression immune profiling of endometrioid and serous carcinomas to identify immunotherapeutic targets and histologic attribute correlates. Methods: Microsatellite instability and RNA-seq of 395 immune-related genes were performed in 37 endometrioid and 53 serous carcinomas. Each gene was ranked against a reference population and subsequently compared to a separate clinical database reference of diverse neoplasms to perform Wilcoxon ranked sum test comparisons. Benjamin-Hochberg corrected p-values are reported. Results: VTCN1, IDO1, LAG3, and components of the mTOR/AKT1 pathway were overexpressed in both endometrioid and serous carcinomas compared to the clinical database population. CD276 and NT5E were uniquely overexpressed in the endometrioid set (Table). In the endometrioid set ADGRE5 was overexpressed in endometrioid primaries when compared to their metastatic counterparts. Serous metastases relatively overexpressed HLA-DPA1 compared to their primary counterparts. Ten of 26 endometrioid cancers with available microsatellite instability status were microsatellite unstable. All serous carcinomas were microsatellite stable. There was no correlation between microsatellite instability status and expression of any gene in either endometrioid or serous cancers. Conclusions: We identified high RNA-expression of VTCN1, IDO1, CD276, and LAG3 in endometrioid and serous carcinomas while NT5E (ADORA2A ligand) was found to be uniquely upregulated in endometrioid carcinomas. These markers may play a role in immune suppression in gynecological cancers and are potential immunotherapeutic targets. The upregulation of ADGRE5 in primary endometrioid cancers and upregulation of HLA-DPA1 in metastatic serous cancers suggests immunologic differences which may be relevant to the formation of metastatic disease.

e15024Background: Immunotherapy for PD-1 axis driven tumors, defined as tumors with high expression of PD-1, PD-L1, or PD-L2, are generally considered more responsive to checkpoint inhibitors than ...

"The Structural Biology Knowledgebase (SBKB, http://sbkb.org) was established as a data aggregator to facilitate research design and analysis for a wide variety of biological systems. It serves as a single resource that integrates structure, sequence, and functional annotations plus technical information regarding protein production and structure determination. Researchers can search the SBKB by sequence, PDB ID or UniProt accession code, and receive an up-to-the-minute list of matching 3D experimental structures from the Protein Data Bank, pre-built theoretical models from the Protein Model Portal, annotations from 100+ open biological resources, structural genomics target histories and protocols from TargetTrack, and ready-to-use DNA clones from DNASU. It also possible to find structures according to functional relevance (KB-Rank tool), or find related technologies and publications from the PSI Technology and Publications Portals, respectively. Interactive tools such as real-time theoretical modeling and biophysical parameter prediction also enhance understanding of proteins that are not yet well characterized. Experimentally-focused ""hubs"" collect links to helpful tools and resources for the areas of Structural Targets; Structure, Sequence and Function; Homology Models, Methods and Technologies, and Membrane Proteins. In partnership with the Nature Publishing Group, latest research highlights and articles on specific biological systems are written monthly to share the impact of structural biology. This presentation will demonstrate how the SBKB turns data into knowledge and enables further research. SBKB is funded by a grant from the National Institute of General Medical Sciences of the National Institutes of Health (U01 GM093324)."

Background Protein Structure Initiative:Biology (PSI:Biology) is the third phase of PSI where protein structures are determined in high-throughput to characterize their biological functions. The transition to the third phase entailed the formation of PSI:Biology Partnerships which are composed of structural genomics centers and biomedical science laboratories. We present a method to examine the impact of protein structures determined under the auspices of PSI:Biology by measuring their rates of annotations. The mean numbers of annotations per structure and per residue are examined. These are designed to provide measures of the amount of structure to function connections that can be leveraged from each structure. Results One result is that PSI:Biology structures are found to have a higher rate of annotations than structures determined during the first two phases of PSI. A second result is that the subset of PSI:Biology structures determined through PSI:Biology Partnerships have a higher rate of annotations than those determined exclusive of those partnerships. Both results hold when the annotation rates are examined either at the level of the entire protein or for annotations that are known to fall at specific residues within the portion of the protein that has a determined structure. Conclusions We conclude that PSI:Biology determines structures that are estimated to have a higher degree of biomedical interest than those determined during the first two phases of PSI based on a broad array of biomedical annotations. For the PSI:Biology Partnerships, we see that there is an associated added value that represents part of the progress toward the goals of PSI:Biology. We interpret the added value to mean that team-based structural biology projects that utilize the expertise and technologies of structural genomics centers together with biological laboratories in the community are conducted in a synergistic manner. We show that the annotation rates can be used in conjunction with established metrics, i.e. the numbers of structures and impact of publication records, to monitor the progress of PSI:Biology towards its goals of examining structure to function connections of high biomedical relevance. The metric provides an objective means to quantify the overall impact of PSI:Biology as it uses biomedical annotations from external sources.