Abstract 3468: Immunoproteasome expression and checkpoint blockade response in advanced non-small cell lung cancer

Responders to checkpoint blockade in Non Small Cell Lung Cancer (NSCLC) often feature an inflamed microenvironment prior to therapy. However, the complete set of molecular drivers connecting this histologic observation to enhanced tumor clearance remain enigmatic.

In updated analysis of the Stand Up 2 Cancer-Mark Foundation (SU2C-MARK) Cohort - a collection of 393 patients with whole exome and/or RNA sequencing along with matched checkpoint blockade response annotation - we identify a prominent predictive role for inducible components of the immunoproteasome, a non-canonical peptide processing complex upstream of antigen presentation. Notably, these subunits are enriched as predictors relative to interferon-inducible genes as well as proteasome components in general, and are consistently associated with objective response, progression-free survival and overall survival. Expression of Immunoproteasome subunits associates positively with TCR (but not BCR) burden, supporting a mechanistic model in which enhanced immunoproteasome processivity leads to superior T-cell recognition. Furthermore, although they are known to be targets of interferon gamma (IFNɣ), we demonstrate that their expression is better modeled via a combination of IFNɣ and tumor necrosis factor-α (TNFα) levels, suggesting they may act as integrators of multiple cytokine cascades.

Given the fact that the immunoproteasome can alter both antigen quantity as well as quality (including peptide cleavage site preference), the enhanced expression of this complex in the setting of checkpoint blockade response may have important implications for modeling of antigen presentation. These data also suggest novel strategies to enhance immune checkpoint blockade.

Citation Format: Vivek Naranbhai, Arvind Ravi, Matthew Hellmann, Monica Arniella, Mark Holton, Samuel Freeman, Chip Stewart, Ignaty Leshchiner, Jaegil Kim, Yo Akiyama, Aaron Griffin, Natalie Vokes, Mustafa Sakhi, Vashine Kamesan, Hira Rizvi, Biagio Ricciuti, Patrick Forde, Valsamo Anagnostou, Jonathan Riess, Don Gibbons, Nathan Pennell, Vamsidhar Velcheti, Subba Digumarthy, Mari Mino-Kenudson, Andrea Califano, John Heymach, Roy Herbst, Julie Brahmer, Kurt Schalper, Victor Velculescu, Brian Henick, Naiyer Rizvi, Pasi Janne, Mark Awad, Andrew Chow, Benjamin Greenbaum, Marta Luksza, Alice Shaw, Jedd Wolchok, Nir Hacohen, Gad Getz, Justin Gainor. Immunoproteasome expression and checkpoint blockade response in advanced non-small cell lung 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 3468.

Abstract 3580: Integrative genomics of checkpoint blockade response in advanced non-small cell lung cancer

The introduction of checkpoint blockade therapy, specifically anti-PD-1/PD-L1 agents, has transformed the treatment landscape of advanced Non-Small Cell Lung Cancer (NSCLC). While our understanding of the biology underlying immunotherapy in NSCLC is still incomplete, studies to date have established central roles for Tumor Mutation Burden (TMB) and PD-L1 Tumor Proportion Score (PDL1-TPS). In order to expand our understanding of the molecular features underlying response in NSCLC, we describe here the first joint analysis of the Stand Up 2 Cancer-Mark Foundation (SU2C-MARK) Cohort, a collection of 393 patients with whole exome and/or RNA sequencing along with matched checkpoint blockade response annotation. We identify a number of significant associations between molecular features and response, including: 1) favorable and unfavorable genomic subgroups; 2) distinct immune infiltration signatures associated with wound healing (unfavorable) and immune activated (favorable) microenvironments; and 3) a novel de-differentiated tumor-intrinsic subtype characterized by high TMB, immune activation, and enhanced response rate. Taken together, results from this cohort extend our understanding of NSCLC-specific predictors, providing a rich set of molecular and immunologic hypotheses with which to further our understanding of the biology of checkpoint blockade in NSCLC.

Citation Format: Arvind Ravi, Justin Gainor, Monica Arniella, Chip Stewart, Sam Freeman, Mark M. Awad, Patrick Forde, Valsamo Anagnostou, Brian Henick, Jonathan W. Riess, Don Gibbons, Nathan Pennell, Vamisdhar Velcheti, Ignaty Leshchiner, Jaegil Kim, Subba Digumarthy, Mari Mino-Kenudson, John Heymach, Natalie Vokes, Andrew Griffin, Biagio Ricciuti, Naiyer Rizvi, Roy Herbst, Victor Velculescu, Julie Brahmer, Kurt Schalper, Pasi Janne, Jedd Wolchok, Alice Shaw, Nir Hacohen, Gad Getz, Matthew D. Hellmann. Integrative genomics of checkpoint blockade response in advanced non-small cell lung cancer [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 3580.

Tumor-induced double positive T cells display distinct lineage commitment mechanisms and functions

Transcription factors ThPOK and Runx3 regulate the differentiation of "helper" CD4+ and "cytotoxic" CD8+ T cell lineages respectively, inducing single positive (SP) T cells that enter the periphery with the expression of either the CD4 or CD8 co-receptor. Despite the expectation that these cell fates are mutually exclusive and that mature CD4+CD8+ double positive (DP) T cells are present in healthy individuals and augmented in the context of disease, yet their molecular features and pathophysiologic role are disputed. Here, we show DP T cells in murine and human tumors as a heterogenous population originating from SP T cells which re-express the opposite co-receptor and acquire features of the opposite cell type's phenotype and function following TCR stimulation. We identified distinct clonally expanded DP T cells in human melanoma and lung cancer by scRNA sequencing and demonstrated their tumor reactivity in cytotoxicity assays. Our findings indicate that antigen stimulation induces SP T cells to differentiate into DP T cell subsets gaining in polyfunctional characteristics.

Clinical characteristics and molecular features of non-small cell lung cancers (NSCLCs) following disease progression on immune checkpoint inhibitors (ICIs)

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Background: ICIs are cornerstones of therapy for advanced NSCLC. Despite dramatic and sometimes durable responses to therapy, most patients (pts) either (i) do not respond to therapy (intrinsic resistance), or (ii) subsequently progress after initial clinical benefit (acquired resistance). Currently, insights into the molecular mechanisms of resistance to ICIs in NSCLC are lacking. Methods: To investigate clinical and molecular features of pts progressing on ICIs, we identified pts who underwent repeat tumor biopsies on and/or after disease progression on ICIs and were included in the Stand Up 2 Cancer (SU2C)/Mark Foundation multi-institutional cohort. Biopsy specimens underwent whole-exome sequencing (WES) and/or whole transcriptome sequencing (RNAseq). Results: We identified 37 pts who underwent a total of 47 repeat biopsies on or after ICIs. Six pts underwent multiple post-ICI biopsies (range 2-4). Twenty-five pts (68%) received PD-(L)1 inhibitor monotherapy, 6 (16%) received PD-(L)1 plus CTLA-4 inhibitors, and 6 (16%) received other PD-1 inhibitor-based combinations. Overall, the objective response rate was 46% among pts undergoing repeat biopsies (complete response 2 [5%], partial response 15 [41%], stable disease 14 [38%], progressive disease 5 [14%] and not evaluable 1 [3%]). Median progression-free survival (PFS) was 8.1 months. In total, pre-ICI biopsy specimens were available in 20 pts. WES and RNAseq were performed on 67 and 44 specimens, respectively. Median tumor mutation burden (TMB) in pre-ICI specimens was 5.0 mutations/Mb versus 4.9 mutations/Mb in post-ICI specimens ( p= 0.3, Mann-Whitney U test). Among 20 paired pre/post-ICI specimens, there was no significant difference in TMB (pre-treatment median 3.9 mutations/Mb; post-treatment median 4.3 mutations/Mb; p= 0.7, Wilcoxon signed-rank test). One pt with a complete response acquired a nonsense mutation in B2M, and one pt with a partial response acquired a nonsense mutation in JAK1. Among 10 paired pre/post-ICI specimens that underwent RNAseq, we observed significant decreases in granzyme B and perforin in post-ICI specimens ( p= 4×10-5 and p= 2×10-3, respectively, limma-voom analysis). Conclusions: Genomic alterations impairing antigen presentation (e.g., B2M) or immune activation (e.g., JAK1) may enable resistance to ICIs in a small subset of cases. However, the majority of repeat biopsies obtained from pts progressing on ICIs lacked clear genetic mediators of resistance, suggesting the presence of additional tumor-intrinsic and/or tumor-extrinsic factors underlying resistance to ICIs in NSCLC.

Abstract 6670: Combined signals from tumor and immune cells predict outcomes of checkpoint inhibition in melanoma

Cancer immunotherapy with checkpoint blockade has improved survival and outcomes in melanoma, but still a majority of patients do not respond. Both high tumor mutation burden (TMB) and high T cell infiltration have been associated with response, but integrative models based on DNA or RNA assays have not been comprehensively explored and validated. Focusing on melanomas from patients receiving checkpoint blockade, we generated new and aggregated existing datasets of whole exome sequencing (WES) (n = 189 total) and bulk RNA sequencing (n = 154 total) to derive genomic and transcriptomic factors that predict survival and response to immunotherapy in melanoma.

We quantified T and B cell infiltrates using rearranged T cell receptor (TCR) and immunoglobulin (Ig) sequences, respectively, from DNA or RNA sequencing. High levels of rearranged TCR reads or rearranged Ig reads in RNA-seq were associated with survival (P = 0.0046, P = 0.015) and response (P = 0.0034, P = 0.047). We created RNA-based metrics of T and B cell burden (TCBRNA or BCBRNA) by normalizing the number of rearranged TCR reads by the total number of mapped reads. When we analyzed WES data in patients for whom DNA and RNA were extracted from the same region, we found that the TCBDNA correlated with TCBRNA (rho = 0.73) and BCBDNA with BCBRNA (rho = 0.41), demonstrating that the level of lymphocyte infiltration can be estimated using rearranged TCR or Ig reads from tumor WES alone.

We found that TCBDNA and BCBDNA both associated with survival (P = 0.0023 and 0.0089). In a combined model, patients with high TMB and high TCB DNA survived longer (P = 2.4e-4, HR = 2.68) and had a higher response rate (Fisher P = 0.028). This combined model was superior to models with TMB or TCBDNA alone. Similarly, patients with high TMB and high BCBDNA had longer survival and higher response rates (log-rank P = 0.0029, HR = 2.64, Fisher P = 0.015). We reanalyzed stage III/IV melanomas from TCGA and found that the TMB high, TCBDNA high subgroup had increased survival (P = 0.007).

Next, clustering of tumor transcriptomes identified 5 tumor subtypes based on melanocyte differentiation, immune infiltration and keratin levels. These melanoma subtypes were associated with survival outcomes after immunotherapy (P = 0.019). We found that TBX3, a tumor-expressed transcription factor enriched in poorly differentiated melanomas, was over-expressed among non-responders within the immune-infiltrated subtype and among all patients (P = 3.9e-4, P = 8.7e-5). Patients whose tumors had high immune infiltrate and low expression of TBX3 had longer survival (P = 1.6e-5, HR = 3.39), however this subgroup did not have longer survival in an independent cohort (n = 73, P = 0.10, HR = 2.63). In conclusion, we demonstrate both RNA-based (immune infiltrate and tumor subtype) and DNA-based metrics (TMB/TCB or TMB/BCB) can be used as pre-treatment predictors of survival after checkpoint blockade in melanoma.

Citation Format: Samuel S. Freeman, Moshe Sade-Feldman, Jaegil Kim, Chip Stewart, Arvind Ravi, Monica Arniella, Keren Yizhak, Ignaty Leshchiner, Liudmila Elagina, Oliver Spiro, Dimitri Livitz, Daniel Rosebrock, François Aguet, Jian Carrot-Zhang, Anna Gonye, Gavin Ha, Ziao Lin, Jonathan H. Chen, Dennie T. Frederick, Michal Barzily-Rokni, Marc R. Hammond, Hans Vitzthum, Shauna M. Blackmon, Yunxin J. Jiao, Donald P. Lawrence, Lyn M. Duncan, Anat Stemmer-Rachamimov, Jennifer A. Wargo, Keith T. Flaherty, Genevieve M. Boland, Ryan J. Sullivan, Matthew Meyerson, Gad Getz, Nir Hacohen. Combined signals from tumor and immune cells predict outcomes of checkpoint inhibition in melanoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6670.

Abstract 5902: Integrative genomic analysis of checkpoint blockade in lung cancer: A multi-institution SU2C collaborative

PD-1/PD-L1 checkpoint blockade has been a landmark advance for many patients suffering from advanced non-small cell lung cancer (NSCLC). However, detailed biomarkers of response beyond tumor mutational burden (TMB) are still poorly understood. As part of the effort to elucidate these additional signatures, we describe our progress on the Stand Up to Cancer Lung (SU2C-Lung) cohort. Initial characterization of exomes recapitulates mutational and copy number profiles seen in The Cancer Genome Atlas (TCGA) project. To better define expression subtypes using RNA sequencing, we performed non-negative matrix factorization (NMF) across an aggregated set of publicly available NSCLC expression data (including adenocarcinoma, squamous cell carcinoma, and large cell neuroendocrine histologies), and demonstrate good concordance in the SU2C-Lung cohort between this expression-based classifier and clinically annotated histology. To gain further insight into how immune cell infiltrates vary across our cohort, we additionally tested two common deconvolution algorithms, EPIC and CIBERSORT. While these two methods agree for some prominent cell types, such as B cells and CD4 T cells, discrepancies in minor infiltrating components such as NK cells may suggest a limit to the inference of rare subpopulations from bulk sequencing data.Finally, we describe a novel approach for determining single-gene predictors of response. Using the method, which is based on comparison of top single transcriptional features identified from random bootstraps of the full cohort as compared to a set of background shuffles, we are able to show that we remain powered for discovery of RNA response biomarkers despite the typically burdensome toll of multiple hypothesis correction at genome wide scale.

Acknowledgment: Supported by Stand Up To Cancer-American Cancer Society Lung Cancer Dream Team Translational Research Grant SU2C-AACR-DT17-15.

Citation Format: Monica Arniella, Arvind Ravi, Chip Stewart, Sam Freeman, Mark Awad, Patrick Forde, Valsamo Anagnostou, Brian Henick, Jonathan Riess, Don Gibbons, Nathan Pennell, Vamsidhar Velcheti, Ignaty Leshchiner, Jaegil Kim, Subba Digumarthy, Mari Mino-Kenudson, John Heymach, Nir Hacohen, Naiyer Rizvi, Roy Herbst, Victor Velculescu, Julie Brahmer, Kurt Schalper, Pasi Jänne, Jedd Wolchok, Alice Shaw, Justin Gainor, Matthew Hellmann, Gad Getz. Integrative genomic analysis of checkpoint blockade in lung cancer: A multi-institution SU2C collaborative [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5902.

SMART Cancer Navigator: A Framework for Implementing ASCO Workshop Recommendations to Enable Precision Cancer Medicine

Purpose

Data standards and interoperability are critical for improving care for patients with cancer. Recent efforts by ASCO include the Data Standards and Interoperability Summit in 2016, which led to the Omics and Precision Oncology and Advancing Interoperability workshops. To facilitate improved patient care, several recommendations for data sharing and standardization were made to the community.

Methods

To address these recommendations, we developed SMART Cancer Navigator, a Web application that uses application programming interfaces to gather clinical and genomic data from 11 public knowledge bases ranging from basic to clinical content coverage; three (CIViC, ClinVar, and OncoKB) explicitly linked genomic variants to clinical factors such as prognosis and treatment selection. We illustrated the utility of this application by selecting one of the monthly case studies presented by the ASCO University Molecular Oncology Tumor Board: Ovarian Cancer (BRCA Mutation). We also performed analyses on information from the three clinico-genomic knowledge bases to corroborate previous work and illustrate the state of data sharing among publicly available resources.

Results

SMART Cancer Navigator aggregates and contextualizes data from 11 different knowledge bases and stores user queries in a lightweight Web application that can link into Fast Healthcare Interoperability Resources-enabled electronic health records. Potentially relevant clinical trials and/or approved treatments were identified for three mutations found in a hypothetical patient with advanced ovarian cancer. A comparison of the three clinico-genomic knowledge bases indicated substantial differences in coverage at the gene and variant levels.

Conclusion

SMART Cancer Navigator has immediate relevance to practicing oncologists and others. Additional knowledge bases can be added without undue effort. As a first step toward utility, we generalized and disseminated the resulting implementation (https://smart-cancer-navigator.github.io) and data sets.