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Anti-PD-1/PD-L1 agents have transformed the treatment landscape of advanced non-small cell lung cancer (NSCLC). To expand our understanding of the molecular features underlying response to checkpoint inhibitors in NSCLC, we describe here the first joint analysis of the Stand Up To Cancer-Mark Foundation cohort, a resource of whole exome and/or RNA sequencing from 393 patients with NSCLC treated with anti-PD-(L)1 therapy, along with matched clinical response annotation. We identify a number of associations between molecular features and outcome, including (1) favorable (for example, ATM altered) and unfavorable (for example, TERT amplified) genomic subgroups, (2) a prominent association between expression of inducible components of the immunoproteasome and response and (3) a dedifferentiated tumor-intrinsic subtype with enhanced response to checkpoint blockade. Taken together, results from this cohort demonstrate the complexity of biological determinants underlying immunotherapy outcomes and reinforce the discovery potential of integrative analysis within large, well-curated, cancer-specific cohorts.

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Prior data have variably implicated the inactivation of the mammalian SWItch/Sucrose Non-Fermentable (mSWI/SNF) complex with increased tumor sensitivity to immune checkpoint inhibitors (ICI). Herein, we examined the association between mSWI/SNF variants and clinical outcomes to ICIs. We correlated somatic loss-of-function (LOF) variants in a predefined set of mSWI/SNF genes (ARID1A, ARID1B, SMARCA4, SMARCB1, PBRM1, and ARID2) with clinical outcomes in patients with cancer treated with systemic ICIs. We identified 676 patients from Dana-Farber Cancer Institute (DFCI, Boston, MA) and 848 patients from a publicly available database from Memorial Sloan Kettering Cancer Center (MSKCC, New York, NY) who met the inclusion criteria. Multivariable analyses were conducted and adjusted for available baseline factors and tumor mutational burden. Median follow-up was 19.6 (17.6–22.0) months and 28.0 (25.0–29.0) months for the DFCI and MSKCC cohorts, respectively. Seven solid tumor subtypes were examined. In the DFCI cohort, LOF variants of mSWI/SNF did not predict improved overall survival (OS), time-to-treatment failure (TTF), or disease control rate. Only patients with renal cell carcinoma with mSWI/SNF LOF showed significantly improved OS and TTF with adjusted HRs (95% confidence interval) of 0.33 (0.16–0.7) and 0.49 (0.27–0.88), respectively, and this was mostly driven by PRBM1. In the MSKCC cohort, where only OS was captured, LOF mSWI/SNF did not correlate with improved outcomes across any tumor subtype. We did not find a consistent association between mSWI/SNF LOF variants and improved clinical outcomes to ICIs, suggesting that mSWI/SNF variants should not be considered as biomarkers of response to ICIs.

Supplementary Tables

While the introduction of immune checkpoint blockade (ICB) has dramatically improved clinical outcomes for patients with advanced melanoma, a significant proportion of patients develop resistance to therapy, and mechanisms of resistance are poorly elucidated in most cases. Further, while combination ICB has higher response rates and improved progression free survival compared to single agent therapy in the front line setting, there is significantly increased toxicity with combination ICB, and biomarkers to identify patients who would disproportionately benefit from combination therapy vs aPD-1 ICB are poorly characterized. To understand resistance mechanisms to single vs combination ICB therapy, we analyze whole-exome-sequencing (WES) of pre-treatment tumor and matched normals of 4 cohorts (n=140) of previously ICB-naïve aPD-1 ICB treated patients. We find that high intratumoral genomic heterogeneity and low ploidy identify patients with intrinsic resistance to aPD-1 ICB. Comparing to a melanoma cohort from a pre-targeted therapy and ICB time period (“untreated” cohort), we find that genomic heterogeneity specifically predicts response and survival in the ICB treated cohorts, but not in the untreated cohort, while ploidy is also prognostic of overall survival in the “untreated” (by targeted therapy or ICB) group. To establish clinically actionable predictions, we optimize a simple decision tree using genomic ploidy and heterogeneity to identify with high confidence (90% PPV) a subset of patients with intrinsic resistance to and significantly worse survival on aPD1 ICB treatment. We then validate this model in independent cohorts, and further show that a significant proportion of patients predicted to have intrinsic resistance to single agent aPD-1 ICB respond to combination ICB, which suggests that nominated patients may benefit disproportionately from combination ICB. We further show that the features and predictions of the model are independent of known clinical features and previously nominated molecular biomarkers. These findings highlight the clinical and biological importance of genomic heterogeneity and ploidy, and sets a concrete framework towards clinical actionability, broadly advancing precision medicine in oncology.

Objectives: Cancer patients have worse outcomes from the COVID-19 infection and greater need for ventilator support and elevated mortality rates than the general population. However, previous artificial intelligence (AI) studies focused on patients without cancer to develop diagnosis and severity prediction models. Little is known about how the AI models perform in cancer patients. In this study, we aim to develop a computational framework for COVID-19 diagnosis and severity prediction particularly in a cancer population and further compare it head-to-head to a general population. Methods: We have enrolled multi-center international cohorts with 531 CT scans from 502 general patients and 420 CT scans from 414 cancer patients. In particular, the habitat imaging pipeline was developed to quantify the complex infection patterns by partitioning the whole lung regions into phenotypically different subregions. Subsequently, various machine learning models nested with feature selection were built for COVID-19 detection and severity prediction. Results: These models showed almost perfect performance in COVID-19 infection diagnosis and predicting its severity during cross validation. Our analysis revealed that models built separately on the cancer population performed significantly better than those built on the general population and locked to test on the cancer population. This may be because of the significant difference among the habitat features across the two different cohorts. Conclusions: Taken together, our habitat imaging analysis as a proof-of-concept study has highlighted the unique radiologic features of cancer patients and demonstrated effectiveness of CT-based machine learning model in informing COVID-19 management in the cancer population.

Tumor molecular profiling of single gene-variant (‘first-order’) genomic alterations informs potential therapeutic approaches. Interactions between such first-order events and global molecular features (for example, mutational signatures) are increasingly associated with clinical outcomes, but these ‘second-order’ alterations are not yet accounted for in clinical interpretation algorithms and knowledge bases. We introduce the Molecular Oncology Almanac (MOAlmanac), a paired clinical interpretation algorithm and knowledge base to enable integrative interpretation of multimodal genomic data for point-of-care decision making and translational-hypothesis generation. We benchmarked MOAlmanac to a first-order interpretation method across multiple retrospective cohorts and observed an increased number of clinical hypotheses from evaluation of molecular features and profile-to-cell line matchmaking. When applied to a prospective precision oncology trial cohort, MOAlmanac nominated a median of two therapies per patient and identified therapeutic strategies administered in 47% of patients. Overall, we present an open-source computational method for integrative clinical interpretation of individualized molecular profiles. Van Allen and colleagues develop a data-integration framework with an underlying knowledge base supporting clinical decision making and also serving as a hypothesis-generating platform, which the authors benchmark and validate across several retrospective cohorts and a prospective precision oncology trial.

Purpose In patients with advanced non-small cell lung cancer (NSCLC) and oncogenic driver mutations treated with effective targeted therapy, a characteristic pattern of tumor volume dynamics with an initial regression, nadir, and subsequent regrowth is observed on serial computed tomography (CT) scans. We developed and validated a linear model to predict the tumor volume nadir in EGFR-mutant advanced NSCLC patients treated with EGFR tyrosine kinase inhibitors (TKI). Materials and methods Patients with EGFR-mutant advanced NSCLC treated with EGFR-TKI as their first EGFR-directed therapy were studied for CT tumor volume kinetics during therapy, using a previously validated CT tumor measurement technique. A linear regression model was built to predict tumor volume nadir in a training cohort of 34 patients, and then was validated in an independent cohort of 84 patients. Results The linear model for tumor nadir prediction was obtained in the training cohort of 34 patients, which utilizes the baseline tumor volume before initiating therapy (V0) to predict the volume decrease (mm3) when the nadir volume (Vp) was reached: V0-Vp=0.717×V0-1347 (P=2×10-16; R2=0.916). The model was tested in the validation cohort, resulting in the R2 value of 0.953, indicating that the prediction model generalizes well to another cohort of EGFR-mutant patients treated with EGFR-TKI. Clinical variables were not significant predictors of tumor volume nadir. Conclusion The linear model was built to predict the tumor volume nadir in EGFR-mutant advanced NSCLC patients treated with EGFR-TKIs, which provide an important metrics in treatment monitoring and therapeutic decisions at nadir such as additional local abrasive therapy.

Background: The benefit of chemotherapy combined with immunotherapy in EGFR-mutant lung adenocarcinoma (LUAD) patients whose tumor developed resistance to EGFR tyrosine kinase inhibitors (TKIs) is not thoroughly investigated. The goal of this retrospective cohort study is to assess the clinical efficiency of immunotherapy alone or in combination with chemotherapy in a real-world setting. Methods: This retrospective cohort study enrolled LUAD patients with EGFR sensitive mutations whose tumor had acquired resistance to EGFR TKIs and received systemic treatment with chemotherapy (chemo; n = 84), chemotherapy combined with immunotherapy (chemoIO; n = 30), chemotherapy plus bevacizumab with or without IO (withBev; n = 42), and IO monotherapy (IO-mono; n = 22). Clinical progression-free survival (PFS) and overall survival (OS) were evaluated. Associations of clinical characteristics with outcomes were assessed using univariable and multi-covariate Cox Proportional Hazards regression models. Results: A total of 178 patients (median age = 63.3; 57.9% females) with a median follow-up time of 42.0 (Interquartile range: 22.9–67.8) months were enrolled. There was no significant difference in PFS between chemoIO vs. chemo groups (5.3 vs. 4.8 months, p = 0.8). Compared to the chemo group, patients who received withBev therapy trended towards better PFS (6.1 months vs. 4.8; p = 0.3; HR 0.79; 95% CI: 0.52–1.20), while patients treated with IO-mono had inferior PFS (2.2 months; p = 0.001; HR 2.22; 95% CI: 1.37–3.59). Furthermore, PD-L1 level was not associated with PFS benefit in the chemoIO group. Patients with EGFR-mutant LUAD with high PD-L1 (≥50%) had shorter PFS (5.8 months) than non-EGFR/ALK LUAD patients who received chemoIO (12.8 months, p = 0.002; HR 0.22; 95% CI: 0.08–0.56) as first-line treatment. Chemotherapy-based therapy rendered similar benefit to patients with either EGFR exon19 deletion vs. L858R in the LUAD. Conclusions: This retrospective analysis revealed that immunotherapy provided limited additional benefit to chemotherapy in TKI-refractory EGFR-mutant LUAD. Chemotherapy alone or combined with bevacizumab remain good choices for patients with actionable EGFR mutations.

Metastatic castration-resistant prostate cancer is typically lethal, exhibiting intrinsic or acquired resistance to second-generation androgen-targeting therapies and minimal response to immune checkpoint inhibitors1. Cellular programs driving resistance in both cancer and immune cells remain poorly understood. We present single-cell transcriptomes from 14 patients with advanced prostate cancer, spanning all common metastatic sites. Irrespective of treatment exposure, adenocarcinoma cells pervasively coexpressed multiple androgen receptor isoforms, including truncated isoforms hypothesized to mediate resistance to androgen-targeting therapies2,3. Resistance to enzalutamide was associated with cancer cell–intrinsic epithelial–mesenchymal transition and transforming growth factor-β signaling. Small cell carcinoma cells exhibited divergent expression programs driven by transcriptional regulators promoting lineage plasticity and HOXB5, HOXB6 and NR1D2 (refs. 4–6). Additionally, a subset of patients had high expression of dysfunction markers on cytotoxic CD8+ T cells undergoing clonal expansion following enzalutamide treatment. Collectively, the transcriptional characterization of cancer and immune cells from human metastatic castration-resistant prostate cancer provides a basis for the development of therapeutic approaches complementing androgen signaling inhibition., Single-cell transcriptomic analysis of metastatic castration-resistant prostate cancer uncovers pervasive coexpression of androgen receptor isoforms and cancer cell–intrinsic and microenvironmental programs of treatment resistance

Prior data have variably implicated the inactivation of the mammalian SWItch/Sucrose Non-Fermentable (mSWI/SNF) complex with increased tumor sensitivity to immune checkpoint inhibitors (ICI). Herein, we examined the association between mSWI/SNF variants and clinical outcomes to ICIs. We correlated somatic loss-of-function (LOF) variants in a predefined set of mSWI/SNF genes (ARID1A, ARID1B, SMARCA4, SMARCB1, PBRM1, and ARID2) with clinical outcomes in patients with cancer treated with systemic ICIs. We identified 676 patients from Dana-Farber Cancer Institute (DFCI, Boston, MA) and 848 patients from a publicly available database from Memorial Sloan Kettering Cancer Center (MSKCC, New York, NY) who met the inclusion criteria. Multivariable analyses were conducted and adjusted for available baseline factors and tumor mutational burden. Median follow-up was 19.6 (17.6–22.0) months and 28.0 (25.0–29.0) months for the DFCI and MSKCC cohorts, respectively. Seven solid tumor subtypes were examined. In the DFCI cohort, LOF variants of mSWI/SNF did not predict improved overall survival (OS), time-to-treatment failure (TTF), or disease control rate. Only patients with renal cell carcinoma with mSWI/SNF LOF showed significantly improved OS and TTF with adjusted HRs (95% confidence interval) of 0.33 (0.16–0.7) and 0.49 (0.27–0.88), respectively, and this was mostly driven by PRBM1. In the MSKCC cohort, where only OS was captured, LOF mSWI/SNF did not correlate with improved outcomes across any tumor subtype. We did not find a consistent association between mSWI/SNF LOF variants and improved clinical outcomes to ICIs, suggesting that mSWI/SNF variants should not be considered as biomarkers of response to ICIs.

The identification of biomarkers plays a crucial role in personalized medicine, both in the clinical and research settings. However, the contrast between predictive and prognostic biomarkers can be challenging due to the overlap between the two. A prognostic biomarker predicts the future outcome of cancer, regardless of treatment, and a predictive biomarker predicts the effectiveness of a therapeutic intervention. Misclassifying a prognostic biomarker as predictive (or vice versa) can have serious financial and personal consequences for patients. To address this issue, various statistical and machine learning approaches have been developed. The aim of this study is to present an in-depth analysis of recent advancements, trends, challenges, and future prospects in biomarker identification. A systematic search was conducted using PubMed to identify relevant studies published between 2017 and 2023. The selected studies were analyzed to better understand the concept of biomarker identification, evaluate machine learning methods, assess the level of research activity, and highlight the application of these methods in cancer research and treatment. Furthermore, existing obstacles and concerns are discussed to identify prospective research areas. We believe that this review will serve as a valuable resource for researchers, providing insights into the methods and approaches used in biomarker discovery and identifying future research opportunities.

Background: Compared to other well-studied biomarkers, the relationship between overall tumor burden and immune-checkpoint inhibitor (ICI) efficacy is poorly understood. Methods: We identified patients with stage IV NSCLC without EGFR, ALK or ROS1 alterations treated on the LONESTAR protocol with nivolumab + ipilimumab with formal 3-month radiographic response evaluation. Response was annotated using RECIST 1.1. Manual segmentation of primary and measurable metastatic lesions on pre-treatment CT scans was performed, and tumor volume was calculated from these measurements. Tumor burden was defined as the involvement status, volume, or lesion counts within specific organs (lungs, pleura, lymph nodes, liver, adrenals, bone, and soft tissue) and across whole body. We explored the impact of baseline tumor burden on ICI response and its association with other clinical features. Results:152 patients were included; patients with progressive disease (PD; n=50) at 3 months were compared to all others (n=102). Overall disease volume correlated to tumor volume at the primary lesion (spearman rho=0.69), lymph node (rho=0.45), soft tissue (rho=0.23), lung metastases (rho=0.22), and pleura (rho=0.20); overall lesion counts correlated to lesion counts in lymph nodes (rho=0.61), lung metastases (rho=0.45), and bone (rho=0.16). On univariate analysis, overall tumor volume (P=2e-53) and lesion counts (P=2e-51) had the strongest association with PD, outperforming PD-L1 (P=0.02), ECOG performance status (PS, P=7e-16), and clinical variables including prior treatment and smoking status. PD correlated with higher disease burden in all individual organs except bone; adrenal metastases inversely correlated with PD. Volumetric features complemented PD-L1 and other clinical risk factors to predict PD; in a composite model (ClinVol) AUC=0.82, sensitivity=66%, specificity=93%, while PD-L1 alone yielded an AUC=0.62, sensitivity=94%, specificity=28%. Similar trends were observed for subgroup analyses stratified by brain metastasis status or smoking history, in which volumetric measures increased the power of PD-L1 to predict PD by a consistent AUC increase of ~0.2. Upon subgroup analysis stratified by PD-L1, volumetric models showed robust stratification of PD, with AUC of 0.97, 0.81, 0.81 respectively for PD-L1 Conclusion: Higher baseline tumor burden was associated with increased likelihood of disease progression on combination ICI therapy. ClinVol modeling improves predictive accuracy of 3-month progression. Citation Format: Muhammad Aminu, Natalie I. Vokes, Maliazurina B. Saad, Hui Li, Lingzhi Hong, Mohamed S. Mohamed, John Boom, Pingjun Chen, Mehmet Altan, Saumil Gandhi, Stephen Swisher, Mara B. Antonoff, Jenny V. Pozadzides, George Blumenschein Jr, Don L. Gibbons, Tina Cascone, Yasir Y. Elamin, Xiuning Le, Marcelo V. Negrao, Ferdinandos Skoulidis, Anne S. Tsao, Janet Tu, J.Jack Lee, Jianjun Zhang, John V. Heymach, Jia Wu. Tumor volumetric analysis to correlate disease burden with response to dual immune checkpoint blockade in metastatic NSCLC. [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 4393.

Background: Immune checkpoint inhibitors (ICIs) as monotherapy (ICI-mono) or with chemotherapy (ICI-chemo) are standard first-line treatment in NSCLC patients lacking targetable driver mutations. Biomarkers to identify patients at risk for early progression on ICI-mono or those who would maximally benefit from upfront ICI-chemo have not been defined. Methods: We queried the GEMINI database to identify metastatic NSCLC patients without targetable EGFR/ALK alterations who were treated with ICI-mono or ICI-chemo. Mutational profiling was performed on tissue or blood using targeted NGS. Outcome measures were defined as clinical progression free survival (PFS) or early progressive disease (PD) rate (defined as rate of 3-month progression), and their association with variables was assessed via Cox Proportional Hazards regression (PFS) or logistic regression (early PD). Predictive deep learning models were used to integrate clinicopathological factors and genomic profile. Results: 735 patients were included in this study, 269 treated with ICI-chemo and 466 with ICI-mono; 446 were treated in the first-line setting. TP53 was the most frequently altered gene (60%), followed by KRAS (37%), AR (21%), and STK11 (19%). In ICI-mono patients, alterations in STK11, ERBB2, ARID1A and CDK6 were associated with a higher likelihood of early PD; only STK11 was associated with early PD (29% vs 17%, P = 0.04) on ICI-chemo. In all patients, low PD-L1 expression and high disease burden (stage IVb and liver metastases) associated with early PD, but there were borderline significant treatment effects in favor of ICI-chemo in never smokers and patients with liver metastases and stage IVb. Shorter PFS was observed in the ICI-chemo group who had CDKN2A alterations vs wild type (median PFS: 5.1 vs 9.0 months; HR: 1.72; P = 0.01). A subgroup analysis of patients with CDKN2A alterations demonstrated preferentially worse outcomes in ICI-chemo compared to ICI-mono, with the best PFS achieved in the ICI-mono treated patients with CDKN2A point mutation. Integration of clinicogenomic features into a multivariate model with feature selection to predict early PD demonstrated a predictive performance of AUC 0.73 (vs PD-L1 alone, AUC 0.60) in the ICI-mono group, driven by liver metastases, stage IVb disease, PD-L1 expression, and STK11 alterations. These features were less predictive in ICI-chemo-treated patients, indicating a protective effect against early PD in these patients from combination chemoimmunotherapy. Conclusions: Low PD-L1, high disease burden, and STK11 alterations are markers of early PD on ICI-mono, and patients with these features may particularly benefit from upfront combination treatment with ICI-chemo to protect against early progression. Citation Format: Lingzhi Hong, Muhammad Aminu, Xuetao Lu, Maliazurina B. Saad, Pingjun Chen, Waree Rinsurongkawong, Amy Spelman, Yasir Y. Elamin, Marcelo V. Negrao, Ferdinandos Skoulidis, Carl M. Gay, Tina Cascone, Mara B. Antonoff, Boris Sepesi, Jeff Lewis, Don L. Gibbons, Ara A. Vaporciyan, Xiuning Le, J.Jack Lee, Sinchita Roy-Chowdhuri, Mark J. Routbort, John V. Heymach, Jia Wu, Jianjun Zhang, Natalie I. Vokes. Genomic and clinical predictors of early disease progression and chemoimmunotherapy benefit in advanced NSCLC [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 964.

SUMMARYAnti-PD-1/PD-L1 agents have transformed the treatment landscape of advanced non-small cell lung cancer (NSCLC). While our understanding of the biology underlying immune checkpoint blockade in NSCLC is still incomplete, studies to date have established predictive roles for PD-L1 tumor expression and tumor mutational burden (TMB). To expand our understanding of the molecular features underlying response to checkpoint inhibitors in NSCLC, we describe here the first joint analysis of the Stand Up 2 Cancer - Mark Foundation (SU2C-MARK) Cohort, a resource of whole exome and/or RNA sequencing from 393 patients with NSCLC treated with anti-PD-(L)1 therapy, along with matched clinical response annotation. We identify a number of associations between molecular features and outcome, including: 1) favorable (e.g., ATM altered), and unfavorable (e.g., TERT amplified) genomic subgroups, 2) distinct immune infiltration signatures associated with wound healing (unfavorable) and immune activation (favorable), and 3) a novel de-differentiated tumor-intrinsic subtype characterized by expression of endodermal lineage genes, 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.

Patients with EGFR-mutant NSCLC experience variable duration of benefit on EGFR tyrosine kinase inhibitors. The effect of concurrent genomic alterations on outcome has been incompletely described.In this retrospective study, targeted next-generation sequencing data were collected from patients with EGFR-mutant lung cancer treated at the Dana-Farber Cancer Institute. Clinical data were collected and correlated with somatic mutation data. Associations between TP53 mutation status, genomic features, and mutational processes were analyzed.A total of 269 patients were identified for inclusion in the cohort. Among 185 response-assessable patients with pretreatment specimens, TP53 alterations were the most common event associated with decreased first-line progression-free survival and decreased overall survival, along with DNMT3A, KEAP1, and ASXL1 alterations. Reduced progression-free survival on later-line osimertinib in 33 patients was associated with MET, APC, and ERBB4 alterations. Further investigation of the effect of TP53 alterations revealed an association with worse outcomes even in patients with good initial radiographic response, and faster acquisition of T790M and other resistance mechanisms. TP53-mutated tumors had higher mutational burdens and increased mutagenesis with exposure to therapy and tobacco. Cell cycle alterations were not independently predictive, but portended worse OS in conjunction with TP53 alterations.TP53 alterations associate with faster resistance evolution independent of mechanism in EGFR-mutant NSCLC and may cooperate with other genomic events to mediate acquisition of resistance mutations to EGFR tyrosine kinase inhibitors.

PURPOSE To investigate the association between tumor volume growth rate after the nadir and survival in patients with EGFR-mutant advanced non–small-cell lung cancer (NSCLC) treated with erlotinib. MATERIALS AND METHODS Seventy-one patients with EGFR-mutant advanced NSCLC treated with erlotinib were studied for computed tomography tumor volume kinetics during therapy. The tumor growth rate after nadir was obtained using a previously published analytic module for longitudinal volume tracking to study its relationship with overall survival (OS). RESULTS The median tumor volume for the cohort was 19,842 mm3 at baseline and 4,083 mm3 at nadir. The median time to nadir was 6.2 months. The tumor growth rate after nadir for logeV (the natural logarithm of tumor volume measured in mm3) was 0.11/mo on average for the cohort (SE: 0.014), which was very similar to the previously validated reference value of 0.12/mo to define slow and fast tumor growth. The OS of 48 patients with slow tumor growth (≤ 0.12/mo) was significantly longer compared with 23 patients with fast tumor growth (> 0.12/mo; median OS: 37.8 v 25.0 months; P = .0012). In Cox models, tumor growth rate was also associated with survival (regression coefficient: 3.9903; P = .0024; faster rate leads to increased hazards), after adjusting for time to nadir (regression coefficient: –0.0863; P = .0008; longer time to nadir leads to decreased hazards) and smoking history. CONCLUSION In patients with EGFR-mutant advanced NSCLC treated with erlotinib, slower tumor growth rates after nadir were associated with longer OS, providing a rationale for using tumor growth rates to guide precision therapy for lung cancer.

Non-small cell lung cancer (NSCLC) that presents with multiple lung tumors (MLTs) poses a challenge to accurate staging and prognosis. MLTs that arise as clonally related secondary metastases from a common primary are higher stage and often require adjuvant chemotherapy or may in fact be incurable stage IV lesions. Conversely, MLTs that represent distinct primaries have a better prognosis and may be overtreated if inappropriately classified as related secondaries. Historically, pathologic and radiographic criteria were used to distinguish between primary and secondary MLTs; however, the advent of genomic profiling has demonstrated limitations to these historic classification systems. In this review, we discuss the use of molecular profiling to distinguish between primary and secondary lung cancers, with a focus on the insights gleaned from whole exome sequencing (WES) analyses. While WES is not yet feasible in routine clinical practice, WES studies have helped elucidate the clonal relationship between primary and secondary lung cancers and provide important context for the application of targeted sequencing panel-based analyses.

Introduction STK11 and KEAP1 mutations (STK11 mutant [STK11MUT] and KEAP1MUT) are among the most often mutated genes in lung adenocarcinoma (LUAD). Although STK11MUT has been associated with resistance to programmed death-(ligand)1 (PD-[L]1) inhibition in KRASMUT LUAD, its impact on immunotherapy efficacy in KRAS wild-type (KRASWT) LUAD is currently unknown. Whether KEAP1MUT differentially affects outcomes to PD-(L)1 inhibition in KRASMUT and KRASWT LUAD is also unknown. Methods Clinicopathologic and genomic data were collected from September 2013 to September 2020 from patients with advanced LUAD at the Dana-Farber Cancer Institute/Massachusetts General Hospital cohort and the Memorial Sloan Kettering Cancer Center/MD Anderson Cancer Center cohort. Clinical outcomes to PD-(L)1 inhibition were analyzed according to KRAS, STK11, and KEAP1 mutation status in two independent cohorts. The Cancer Genome Atlas transcriptomic data were interrogated to identify differences in tumor gene expression and tumor immune cell subsets, respectively, according to KRAS/STK11 and KRAS/KEAP1 comutation status. Results In the combined cohort (Dana-Farber Cancer Institute/Massachusetts General Hospital + Memorial Sloan Kettering Cancer Center/MD Anderson Cancer Center) of 1261 patients (median age = 61 y [range: 22–92], 708 women [56.1%], 1065 smokers [84.4%]), KRAS mutations were detected in 536 cases (42.5%), and deleterious STK11 and KEAP1 mutations were found in 20.6% (260 of 1261) and 19.2% (231 of 1202) of assessable cases, respectively. In each independent cohort and in the combined cohort, STK11 and KEAP1 mutations were associated with significantly worse progression-free (STK11 hazard ratio [HR] = 2.04, p Conclusions STK11 and KEAP1 mutations confer worse outcomes to immunotherapy among patients with KRASMUT but not among KRASWT LUAD. Tumors harboring concurrent KRAS/STK11 and KRAS/KEAP1 mutations display distinct immune profiles in terms of gene expression and immune cell infiltration.

Translocation renal cell carcinoma (tRCC) is an aggressive and poorly-characterized subtype of kidney cancer driven by MiT/TFE gene fusions. Here, we define the landmarks of tRCC through an integrative analysis of 152 tRCC patients identified across multiple genomic, clinical trial, and retrospective cohorts. Most tRCCs harbor few somatic alterations apart from MiT/TFE fusions and homozygous deletions at chromosome 9p21.3 (19.2% of cases). Transcriptionally, tRCCs display a heightened NRF2-driven antioxidant response that is associated with resistance to many targeted therapies. Consistently, we find that outcomes for tRCC patients treated with vascular endothelial growth factor receptor inhibitors (VEGFR-TKI) are worse than those treated with immune checkpoint inhibition (ICI). Multiparametric immunofluorescence confirmed the presence of CD8 + tumor-infiltrating T cells compatible with a clinical benefit from ICI and revealed an exhaustion immunophenotype distinct from clear cell RCC. Our findings comprehensively define the clinical and molecular features of tRCC and may inspire new therapeutic hypotheses.

Sarcomatoid and rhabdoid (S/R) renal cell carcinoma (RCC) are highly aggressive tumors with limited molecular and clinical characterization. Emerging evidence suggests immune checkpoint inhibitors (ICI) are particularly effective for these tumors, although the biological basis for this property is largely unknown. Here, we evaluate multiple clinical trial and real-world cohorts of S/R RCC to characterize their molecular features, clinical outcomes, and immunologic characteristics. We find that S/R RCC tumors harbor distinctive molecular features that may account for their aggressive behavior, including BAP1 mutations, CDKN2A deletions, and increased expression of MYC transcriptional programs. We show that these tumors are highly responsive to ICI and that they exhibit an immune-inflamed phenotype characterized by immune activation, increased cytotoxic immune infiltration, upregulation of antigen presentation machinery genes, and PD-L1 expression. Our findings build on prior work and shed light on the molecular drivers of aggressivity and responsiveness to ICI of S/R RCC., Sarcomatoid and rhabdoid tumours are highly aggressive forms of renal cell carcinoma that are also responsive to immunotherapy. In this study, the authors perform a comprehensive molecular characterization of these tumours discovering an enrichment of specific alterations and an inflamed phenotype.

Immune checkpoint inhibitors (ICI) have improved outcomes for patients with advanced non-small cell lung cancer (NSCLC). However, only a minority of patients experience benefit, and biomarkers of response are needed to improve patient selection. Tumor mutational burden (TMB), defined as the number of somatic mutations in a tumor, has been identified as a potential predictive biomarker. Studies assessing TMB and outcome in NSCLC have demonstrated improved response rates and progression-free survival to ICIs in patients with higher TMB compared to lower TMB, and patients with high TMB have a higher likelihood of benefit to ICI compared to chemotherapy. However, TMB imperfectly stratifies responders from non-responders, and these data are heterogenous with respect to treatment context, drug, TMB assay, and TMB threshold. Importantly, many of these analyses are retrospective, and few prospective clinical trials implementing TMB as a biomarker have been completed. Additional limitations to the implementation of TMB include the need for adequate tissue to perform sequencing, and heterogeneity in TMB assays. Efforts to develop blood-based TMB assays and standardization procedures may help address these limitations. Finally, further research to understand why TMB associates with response, and how TMB interacts with other biomarkers including PD-L1 and interferon-gamma expression, may help improve its predictive power.

Despite molecular and clinical heterogeneity, small cell lung cancer (SCLC) is treated as a single entity with predictably poor results. Using tumor expression data and non-negative matrix factorization, we identify four SCLC subtypes defined largely by differential expression of transcription factors ASCL1, NEUROD1, and POU2F3 or low expression of all three transcription factor signatures accompanied by an Inflamed gene signature (SCLC-A, N, P, and I, respectively). SCLC-I experiences the greatest benefit from the addition of immunotherapy to chemotherapy, while the other subtypes each have distinct vulnerabilities, including to inhibitors of PARP, Aurora kinases, or BCL-2. Cisplatin treatment of SCLC-A patient-derived xenografts induces intratumoral shifts toward SCLC-I, supporting subtype switching as a mechanism of acquired platinum resistance. We propose that matching baseline tumor subtype to therapy, as well as manipulating subtype switching on therapy, may enhance depth and duration of response for SCLC patients. Gay et al. provide a classification for four subtypes of small cell lung cancer, each with unique molecular features and therapeutic vulnerabilities. An inflamed, mesenchymal subtype predicts benefit with the addition of immunotherapy to chemotherapy. Intratumoral switching between chemosensitive and chemoresistant subtypes accompanies therapeutic resistance.

Introductory paragraphThe clinical success of targeted cancer therapy is limited by drug resistance that renders cancers lethal in patients1-4. Human tumours can evolve therapy resistance by acquiringde novogenetic alterations and increased heterogeneity via mechanisms that remain incompletely understood1. Here, through parallel analysis of human clinical samples, tumour xenograft and cell line models and murine model systems, we uncover an unanticipated mechanism of therapy-induced adaptation that fuels the evolution of drug resistance. Targeted therapy directed against EGFR and ALK oncoproteins in lung cancer induced adaptations favoring apolipoprotein B mRNA-editing enzyme, catalytic polypeptide (APOBEC)-mediated genome mutagenesis. In human oncogenicEGFR-driven andALK-driven lung cancers and preclinical models, EGFR or ALK inhibitor treatment induced the expression and DNA mutagenic activity ofAPOBEC3Bvia therapy-mediated activation of NF-κB signaling. Moreover, targeted therapy also mediated downregulation of certain DNA repair enzymes such as UNG2, which normally counteracts APOBEC-catalyzed DNA deamination events. In mutantEGFR-driven lung cancer mouse models, APOBEC3B was detrimental to tumour initiation and yet advantageous to tumour progression during EGFR targeted therapy, consistent with TRACERx data demonstrating subclonal enrichment of APOBEC-mediated mutagenesis. This study reveals how cancers adapt and drive genetic diversity in response to targeted therapy and identifies APOBEC deaminases as future targets for eliciting more durable clinical benefit to targeted cancer therapy.

Background High tumor mutational burden (TMB) and PD-L1 expression are associated with improved clinical outcomes in patients (pts) with non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs). However, how TMB performs as a predictive biomarker to ICIs in different PD-L1 expression subgroups is not well characterized. Methods We collected clinicopathologic and genomic data from NSCLCs which underwent targeted NGS and TMB assessment at DFCI. An unbiased recursive partitioning (URP) algorithm was used to investigate an optimal TMB cut-off with respect to objective response rate (ORR) in the subset of pts treated with ICIs. This TMB cut-off was then validated in the prospective POPLAR/OAK cohort. Results Among 3560 NSCLCs identified, median TMB was significantly higher among current smokers compared to former (P 25%, suggesting that TMB differentially impacts response to immunotherapy among PD-L1 high versus low NSCLCs (figure 5). Conclusions The impact of TMB may vary across PD-L1 expression subgroups. Rational integration of TMB and PD-L1 expression may identify NSCLCs with the greatest likelihood of response or resistance to ICIs. Ethics Approval Clinicopathologic data were collected from patients with advanced NSCLC who had consented to a correlative research study (DF/HCC protocol #02-180).

Individual tumor molecular profiling is routinely used to detect single gene-variant (“first-order”) genomic alterations that may inform therapeutic actions -- for instance, a tumor with aBRAFp.V600E variant might be considered for RAF/MEK inhibitor therapy. Interactions between such first-order events (e.g., somatic-germline) and global molecular features (e.g. mutational signatures) are increasingly associated with clinical outcomes, but these “second order” alterations are not yet generally accounted for in clinical interpretation algorithms and knowledge bases. Here, we introduce the Molecular Oncology Almanac (MOAlmanac), a clinical interpretation algorithm paired with a novel underlying knowledge base to enable integrative interpretation of genomic and transcriptional cancer data for point-of-care treatment decision-making and translational hypothesis generation. We compared MOAlmanac to first-order interpretation methodology in multiple retrospective patient cohorts and observed that the inclusion of preclinical and inferential evidence as well as second-order molecular features increased the number of nominated clinical hypotheses. MOAlmanac also performed matchmaking between patient molecular profiles and cancer cell lines to further expand individualized clinical actionability. When applied to a prospective precision oncology trial cohort, MOAlmanac nominated a median of two therapies per patient and identified therapeutic strategies administered in 46% of patient profiles. Overall, we present a novel computational method to perform integrative clinical interpretation of individualized molecular profiles. MOAlmanc increases clinical actionability over conventional approaches by considering second-order molecular features and additional evidence sources, and is available as an open-source framework.

Summary Immune checkpoint blockade (ICB) results in durable disease control in a subset of patients with advanced renal cell carcinoma (RCC), but mechanisms driving resistance are poorly understood. We characterize the single-cell transcriptomes of cancer and immune cells from metastatic RCC patients before or after ICB exposure. In responders, subsets of cytotoxic T cells express higher levels of co-inhibitory receptors and effector molecules. Macrophages from treated biopsies shift toward pro-inflammatory states in response to an interferon-rich microenvironment but also upregulate immunosuppressive markers. In cancer cells, we identify bifurcation into two subpopulations differing in angiogenic signaling and upregulation of immunosuppressive programs after ICB. Expression signatures for cancer cell subpopulations and immune evasion are associated with PBRM1 mutation and survival in primary and ICB-treated advanced RCC. Our findings demonstrate that ICB remodels the RCC microenvironment and modifies the interplay between cancer and immune cell populations critical for understanding response and resistance to ICB., Graphical abstract, Highlights • Distinct CD8+ T cell phenotypes are enriched after immune checkpoint blockade (ICB) • Immune checkpoint ligands are upregulated in macrophages and tumor cells after ICB • Two cancer cell subpopulations are conserved across heterogeneous RCC tumors • Cancer cell programs drive distinct immune interactions and predict patient outcomes, Bi et al. dissect the cancer cell and immune microenvironmental transcriptional programs in immune checkpoint blockade-exposed metastatic renal cell carcinoma, revealing immune subpopulation reprogramming and interactions with distinct cancer cell populations in the context of clinical resistance.

Sarcomatoid and rhabdoid (S/R) renal cell carcinoma (RCC) are highly aggressive tumors with limited molecular and clinical characterization. Emerging evidence suggests immune checkpoint inhibitors (ICI) are particularly effective for these tumors1–3, although the biological basis for this property is largely unknown. Here, we evaluate multiple clinical trial and real-world cohorts of S/R RCC to characterize their molecular features, clinical outcomes, and immunologic characteristics. We find that S/R RCC tumors harbor distinctive molecular features that may account for their aggressive behavior, including BAP1 mutations, CDKN2A deletions, and increased expression of MYC transcriptional programs. We show that these tumors are highly responsive to ICI and that they exhibit an immune-inflamed phenotype characterized by immune activation, increased cytotoxic immune infiltration, upregulation of antigen presentation machinery genes, and PD-L1 expression. Our findings shed light on the molecular drivers of aggressivity and responsiveness to immune checkpoint inhibitors of S/R RCC tumors.

Background: Lung adenocarcinoma (LUAD) is a disease classified by molecular markers. In KRAS-mutant LUAD, STK11 and KEAP1 mutations are associated with decreased overall survival (OS), but predictors of metastasis have been poorly defined. In this study, we identify clinical and genomic predictors of metastatic KRAS-mutant LUAD. Methods: Patients with KRAS-mutant LUAD profiled by targeted next generation sequencing (OncoPanel) were included. Stage, histology, recurrence-free and overall survival were assessed. Clinical and genomic features between metastatic vs non-metastatic samples were compared. KRAS-mutant LUAD samples profiled using MSK-IMPACT in the AACR GENIE database were used to validate our findings. Continuous variables were compared using the Mann-Whitney U test and categorical variables were compared using the Fisher’s Exact test. Survival analysis was performed using the Cox Proportional Hazards model. WExT was used to identify co-occurring and mutually exclusive genetic alterations. Benjamini-Hochberg was used to correct for multiple comparisons. P-values < 0.05 and q-values < 0.10 were considered significant. Results: In the OncoPanel cohort (metastatic n=290; non-metastatic n=324), tumor mutational burden (TMB) (p = .001) and KEAP1 mutations (q = 0.05) were enriched in metastatic samples, while NFKBIA amplifications (q = 0.07) were enriched in non-metastatic samples. KEAP1/STK11 mutations significantly co-occurred (q < 1e-8). Compared to double wild-type samples: KEAP1/STK11 co-mutations were significantly enriched in metastatic samples (n = 72, p = 0.0002, OR 3.4); KEAP1-mutant samples trended towards enrichment in metastatic samples, (n = 21,p = 0.07, OR 2.47); STK11 mutations did not associate with stage (n = 53, p = 0.88, OR = 0.94). In multivariable survival analysis, metastasis (p < 0.005), KEAP1 mutation (p=0.01), and STK11 mutation (p=0.02) were associated with worse OS. In the MSK-IMPACT validation cohort (metastatic site n=417, primary site n = 781), KEAP1 was the only gene enriched in metastatic samples (q < 0.001) at q < 0.05. Compared to double wild type samples: KEAP/STK11 co-mutations (n=138, p < 0.0001, OR 2.1) and KEAP1 mutations (n=59, p = 0.04, OR 1.77) were enriched in metastatic samples; STK11-mutations did not associate with metastasis (n = 190, p = 0.34, OR 0.83). Other predictors of metastasis included Fraction Genome Altered (FGA) (p < 1e-5), TMB (p < 1e-5), and CDKN2A/B deletions (q < 0.003). Conclusion: While both KEAP1 and STK11 mutations are associated with decreased OS in KRAS-mutant LUAD, we find in two independent cohorts that only KEAP1 mutations and KEAP1/STK11 co-mutations, but not STK11 mutations, are associated with metastasis. We also found that FGA, TMB, CDKN2A/B deletions are strongly associated with metastasis. Further research is necessary to understand the influence of KEAP1 mutations, independent of and in-conjunction with STK11 mutations, on metastasis. Citation Format: Daniel Boiarsky, Christine A. Lydon, Emily Chambers, Pasi A. Janne, Mark M. Awad, Eliezer M. Van Allen, David Barbie, Natalie I. Vokes. Genomic correlates of Metastasis in KRAS mutant lung adenocarcinoma [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 2181.

9061 Background: Many clinicopathological and molecular features are associate with clinical benefit from immune checkpoint inhibitors (ICIs) for patients with non-small-cell lung cancer (NSCLC), yet none was exclusive underscoring the heterogeneity of lung cancers. As images may provide a holistic view of cancer, we attempted deep learning to chest CT scans to derive a predictor of response to ICIs and test its benefit relative to known clinicopathological factors. Methods: 928 stage IV, EGFR/ALK-negative NSCLC patients treated with ICIs alone or in combination (MD Anderson GEMINI Database) were divided into training (CTtr = 572), validation (CTva = 78), and testing (CTte = 278) cohorts, balancing the distribution of clinicopathological and radiological factors. Progression-free (PFS) and overall survival (OS) were defined as outcomes. We analyzed whole lung, including tumor and normal parenchyma of chest CT images ≤ 3 months prior to ICI treatment. An ensemble learning model (CT-deep-learning) to clustering patients into high vs low risk groups of PFS or OS was developed by fusing risk scores from four independent deep learning networks (supervised, unsupervised, and hybrid). This CT-deep-learning model was further evaluated in different clinicopathological subgroups. Finally, a composite model (CT-Clinic-path) was built by combining image model with clinicopathological factors. Antolini's concordance index (C-index) was used to assess model performance. Results: Median PFS and OS were shorter in the high-risk vs low-risk group as defined by CT-deep-learning: PFS (CTtr: 4.2 vs 9.6 mons; HR 1.96; 95% CI 1.62-2.38; P < 0.0001; CTva: 3.7 vs 10.2 mons; HR 2.32; 95% CI 1.32-4.07; P = 0.0025; CTte: 3.6 vs 9.1 mons; HR 1.89; 95% CI 1.39-2.56; P < 0.0001) and OS (CTtr: 16.0 vs 31.4 mons; HR 2.19; 95% CI 1.72-2.79; P < 0.0001; CTva: 12.7 vs 28.6 mons; HR 2.01; 95% CI 1.04-3.88; P = 0.035; CTte: 14.8 vs 32.0 mons; HR 1.84; 95% CI 1.31-2.60; P = 0.0004). CT-deep-learning outperformed clinicopathologic features known to associate with ICI benefit, such as histology, smoking status, PD-L1 expression, and remained to be an independent (P < 0.001) prognostic factor on multivariate analysis. Furthermore, integrating CT-deep-learning to clinicopathological variables improved prediction performance with a net reclassification up to 7% (Clinic-path model, C-indices 0.60 – 0.62 vs CT-clinic-path model, 0.64 - 0.65 for PFS; Clinic-path model 0.64 – 0.67 vs CT-clinic-path model 0.69 – 0.71 for OS). Conclusions: We have developed and validated a deep learning signature associated with PFS and OS in ICI-treated NSCLC patients, which appears to be independent of and superior to known clinicopathological biomarkers. If validated, this signature may strengthen the predictive value of clinicopathological factors and facilitate selecting appropriate patients for ICI-based therapies.

2554 Background: Certain immune-related adverse events (irAEs) that emerge with immune checkpoint blockade share clinical features of autoimmune conditions. Preexisting auto-reactive antibodies and their contribution to irAEs have not been well defined, and observations are limited. Methods: We longitudinally collected patient plasma samples from a clinical trial that combines immune checkpoint inhibitors, Ipilimumab, and Nivolumab (I+N) with subsequent radiation therapy (Lonestar, NCT03391869). Plasma samples were collected at baseline, after 12 weeks of I+N (induction), and at the time of Grade ≥ 2 pneumonitis (CTCAEv5.0). Auto reactive antibody profiles were analyzed using a fluorescence-based assay system that measures more than 130 antigens and is capable of assaying antibody reactivity for IgG and IgM fractions, including nuclear-cytosolic and tissue-specific antigens. Selected antibodies had a reportable result range, reference intervals, and reproducibility with quality controls. A paired t-test was used to compare the mean of longitudinally collected baseline and toxicity samples. An unpaired t-test was used to compare differences between groups. The False Discovery Rate was used to control the Type I error rate of multiple comparisons. Results: In the study cohort, G≥2 pneumonitis was observed in 11 patients out of 194 (5.6%). Serum was collected at baseline for all 11 patients, and 9 of the 11 patients had a serum sample collected at the time of pneumonitis event. Longitudinal serum samples (baseline and post-induction) collected from 32 patients without any irAEs were used as control. At baseline AChR3 and calmodulin antibodies were elevated in patients who developed pneumonitis, compared with baseline samples from controls (p≤0.05). At the time of pneumonitis IgM antibodies against AChR3, CXCL10, NSE, BAFF, CA242, Cytokeratin 19 were noted to be elevated in serum for pneumonitis cases compared with post induction samples from control (p≤0.005). Conclusions: We identified auto reactive antibodies associated with a higher risk of immunotherapy associated pneumonitis in patients treated with ipilimumab and nivolumab. These included auto reactive antibodies against proteins associated with lung injury (AChR3), lung inflammation (BAFF, CXCL10) and against alveolar epithelium (Cytokeratin 19). Future studies are warranted to determine if auto-reactive antibodies can be used as pre-treatment risk markers or to diagnose pneumonitis and may offer insights into to mechanisms that predispose toward pneumonitis.

9055 Background: The benefit of combination immune checkpoint inhibitor (ICI) with chemotherapy over ICI monotherapy in non-small cell lung cancer (NSCLC) remains underexplored. Methods: This retrospective cohort included patients with metastatic NSCLC from a single-institution database treated with ICI monotherapy or with chemotherapy between 1/2014-2/2020. Clinical progression-free survival (PFS) and overall survival (OS) were the primary outcomes. Propensity score adjustment for clinical and sociodemographic characteristics was used for analysis of first-line treatment outcomes. Results: A total of 1,139 patients (54% male; median age, 64.9) were included. Adenocarcinoma histology, smoking history, higher PD-L1 expression, and lower metastatic stage associated with improved PFS. However, PD-L1 expression and smoking associated with PFS only in adenocarcinoma (LUAD); squamous (LUSC) patients had shorter PFS independent of PD-L1 and smoking history (PD-L1 > 50% vs 1-49%: LUAD P < 0.001; LUSC P = 0.69; Former vs never smoker: LUAD P = 0.008; LUSC P = 0.89). In first-line patients (n = 680), treatment with ICI plus chemotherapy (ICI-chemo) associated with higher progression-free rates at 3 and 6 months compared with ICI-monotherapy (ICI-chemo vs ICI-mono: 3-month PFS, 85.2% vs 68.8%, P = 0.001; 6-month PFS, 66.4% vs 52.6%, P = 0.008). However, there was no difference overall in PFS or OS in either the full or propensity-matched cohort. Treatment with ICI and chemotherapy concurrently vs sequentially was associated with similar PFS (log-rank P = 0.12). Conclusions: In this real-world cohort, the addition of chemotherapy to ICIs may protect against early progression but does not influence long-term outcomes. Treatment with sequential vs concurrent ICI and chemotherapy produced similar outcomes. These findings suggest that combination therapy may maximally benefit patients at risk of early progression.

9049 Background: Primary resistance to immune checkpoint inhibitor (ICI) therapy remains a major challenge in clinical oncology. Here, we describe the clinical outcome of patients who experienced radiologic progression within 12 weeks of therapy with nivolumab and ipilimumab (I+N) for metastatic non-small cell lung cancer (mNSCLC). Methods: The LONESTAR study is an ongoing phase III study (NCT03391869). Study enrolls patients with immunotherapy naïve mNSCLC (prior chemotherapy is allowed). All patients receive I+N for 12 weeks and are randomized to experimental therapy vs. control arm if they did not have disease progression. Patients who experience radiologic progression per RECIST v1.1 are not randomized and removed from the study. Treatment beyond progression is allowed if they clinically benefit from the systemic therapy. We prospectively collected clinicopathologic and radiologic outcome data from patients who experienced radiologic progression within 12 weeks of I+N therapy and have not randomized to investigational therapy. We described the primary progression pattern. We collected subsequent treatment, radiologic, and toxicity data and calculated clinical outcomes, including progression-free survival (PFS) and overall survival (OS). Results: Of the 194 patients who received at least one dose of I+N therapy, 72 patients had clinical and/or radiologic progression at ≤ 12 weeks. Thirty-five (35; 48%) patients did not receive subsequent treatment, 21 (29%) patients received subsequent 2nd line systemic therapy, and 16 (22%) patients were continued on I+N beyond radiologic progression due to ongoing clinical benefit. Among patients treated with 2nd line therapy, 13 patients were treated with platinum doublet +/- anti-PD-(L)1, seven (7) patients were treated with single-agent chemotherapy +/- VEGF inhibitor, and one (1) patient was treated with targeted therapy. The PFS for the 2nd line therapy was 6.5 months (95%CI: 4.8, 8.9), and OS was 10.4 months (95%Cl: 6.6, 16.1). Among the 16 patients treated with I+N beyond progression, 13 had a mixed response to induction therapy, where primary progression was most frequently observed in mediastinal lymph nodes. LCT with radiotherapy was utilized with I+N in 10 patients. The median duration of post-progression treatment with I+N plus LCT was 8.7 months (95%Cl: 5.9, 22.3) and 5.6 months (95%Cl 4.4, 11.5) with I+N alone. The OS was 19.5 months (95% CI: 6.2,18.7). Conclusions: In this study cohort, primary resistance to I+N was observed in 37% of the patients, and in a subset of these patients treated with post-progression I+N, either alone or in combination with LCT, durable clinical benefit was observed. Further studies are warranted to identify which patients are most likely to benefit from post-progression I+N. Clinical trial information: NCT03391869.

Tumor mutational burden correlates with response to immune checkpoint blockade in multiple solid tumors, though in microsatellite stable tumors this association is weak and of limited clinical utility. Here, we uniformly analyzed whole exome sequencing (WES) of 249 tumors and matched normal tissue from patients with clinically annotated outcomes to immune checkpoint therapy, including radiographic response, across multiple cancer types to examine additional tumor genomic features that contribute to selective response. Our analyses identified genomic correlates of response beyond mutational burden, including somatic events in individual driver genes, certain global mutational signatures, and specific HLA-restricted neoantigens. However, these features were often inter-related, highlighting the complexity of identifying genetic driver events that generate an immunoresponsive tumor environment. This study lays a path forward in analyzing large clinical cohorts in an integrated and multifaceted manner to enhance our ability to discover clinically meaningful predictive features of response to immune checkpoint blockade.

Translocation renal cell carcinoma (tRCC) is a poorly-characterized subtype of kidney cancer driven by MiT/TFE gene fusions. Here, we define the landmarks of tRCC through an integrative analysis of 152 patients with tRCC identified across genomic, clinical trial, and retrospective cohorts. Most tRCCs harbor few somatic alterations apart from MiT/TFE fusions and homozygous deletions at chromosome 9p21.3 (19.2% of cases). Transcriptionally, tRCCs display a heightened NRF2-driven antioxidant response that is associated with resistance to targeted therapies. Consistently, we find that outcomes for patients with tRCC treated with vascular endothelial growth factor receptor inhibitors (VEGFR-TKI) are worse than those treated with immune checkpoint inhibition (ICI). Using multiparametric immunofluorescence, we find that the tumors are infiltrated with CD8(+) T cells, though the T cells harbor an exhaustion immunophenotype distinct from that of clear cell RCC. Our findings comprehensively define the clinical and molecular features of tRCC and may inspire new therapeutic hypotheses.

Metastatic castration resistant prostate cancer (mCRPC) is primarily treated with therapies that prevent transcriptional activity of the androgen receptor (AR), cause DNA damage, or prevent cell division. Clinical resistance to these therapies, including second-generation androgen-targeting compounds such as enzalutamide and abiraterone, is nearly universal. Other treatment modalities, including immune checkpoint inhibitors, have provided minimal benefit except in rare subsets of patients1,2. Both tumour intrinsic and extrinsic cellular programs contributing to therapeutic resistance remain areas of active investigation. Here we use full-length single-cell RNA-sequencing (scRNA-seq) to identify the transcriptional states of cancer and immune cells in the mCRPC microenvironment. Within cancer cells, we identified transcriptional patterns that mediate a significant proportion of inherited risk for prostate cancer, extensive heterogeneity inARsplicing within and between tumours, and vastly divergent regulatory programs between adenocarcinoma and small cell carcinoma. Moreover, upregulation of TGF-β signalling and epithelial-mesenchymal transition (EMT) were both associated with resistance to enzalutamide. We found that some lymph node metastases, but no bone metastases, were heavily infiltrated by dysfunctional CD8+T cells, including cells undergoing dramatic clonal expansion during enzalutamide treatment. Our findings suggest avenues for rational therapeutic approaches targeting both tumour-intrinsic and immunological pathways to combat resistance to current treatment options.

We performed harmonized molecular and clinical analysis on 1,048 melanomas and discovered markedly different global genomic properties among subtypes (BRAF, (N)RAS, NF1, triple wild-type (TWT)), subtype-specific preferences for secondary driver genes and active mutational processes previously unreported in melanoma. Secondary driver genes significantly enriched in specific subtypes reflected preferential dysregulation of additional pathways, such as induction of transforming growth factor-β signaling in BRAF melanomas and inactivation of the SWI/SNF complex in (N)RAS melanomas, and select co-mutation patterns coordinated selective response to immune checkpoint blockade. We also defined the mutational landscape of TWT melanomas and revealed enrichment of DNA-repair-defect signatures in this subtype, which were associated with transcriptional downregulation of key DNA-repair genes, and may revive previously discarded or currently unconsidered therapeutic modalities for genomically stratified melanoma patient subsets. Broadly, harmonized meta-analysis of melanoma whole exomes revealed distinct molecular drivers that may point to multiple opportunities for biological and therapeutic investigation.

PURPOSE Heterogeneity in tumor mutational burden (TMB) quantification across sequencing platforms limits the application and further study of this potential biomarker of response to immune checkpoint inhibitors (ICIs). We hypothesized that harmonization of TMB across platforms would enable integration of distinct clinical data sets to better characterize the association between TMB and ICI response. METHODS Cohorts of patients with non–small-cell lung cancer sequenced by 1 of 3 targeted panels or by whole-exome sequencing (WES) were compared (N = 7,297). TMB was calculated uniformly and compared across cohorts. TMB distributions were harmonized by applying a normal transformation followed by standardization to z scores. In subcohorts of patients treated with ICIs (Dana-Farber Cancer Institute n = 272; Memorial Sloan Kettering Cancer Center n = 227), the association between TMB and outcome was assessed. Durable clinical benefit (DCB) was defined as responsive/stable disease lasting ≥ 6 months. RESULTS TMB values were higher in the panel cohorts than in the WES cohort. Average mutation rates per gene were highly concordant across cohorts (Pearson’s correlation coefficients, 0.842-0.866). Subsetting the WES cohort by gene panels only partially reproduced the observed differences in TMB. Standardization of TMB into z scores harmonized TMB distributions and enabled integration of the ICI-treated subcohorts. Simulations indicated that cohorts > 900 are necessary for this approach. TMB did not associate with response in never-smokers or patients who harbor targetable driver alterations, although these analyses were underpowered. An increase of TMB thresholds increased DCB rate, but DCB rates within deciles varied. Receiver operating characteristic curves yielded an area under the curve of 0.614, with no natural inflection point. CONCLUSION The z score conversion harmonizes TMB values and enables integration of data sets derived from different sequencing panels. Clinical and biologic features may provide context to the clinical application of TMB and warrant additional study.

Background: Although high TMB correlates with improved outcomes to immune checkpoint inhibitors (ICI) in patients (pts) with non-small cell lung cancer (NSCLC), an optimal TMB cut-off to discriminate cancers most likely to respond to ICI has not been identified. Whether TMB impacts outcomes to ICI in different PD-L1 levels subgroups is also unclear. Methods: Unbiased recursive partitioning (URP) was used to identify an optimal TMB cut-off for objective response rate (ORR) in two independent cohorts (DFCI and MSKCC) of pts with NSCLC treated with ICI. TCGA was interrogated to find differences in tumor immune cell subsets according to the TMB cut-off identified. Multiplexed immunofluorescence (IP) was also performed on NSCLC samples. Results: In the DFCI (N=686) and MSKCC (N=672) cohorts, URP found an optimal cut-off of TMB for ORR at 19 mutations/megabase (mut/Mb), corresponding to the 90th percentile in each cohort. Median progression-free (PFS) and overall survival (OS) were significantly longer in NSCLCs with TMB ≥19 mut/Mb vs Conclusion: A very high TMB is associated with better outcomes to ICI and a distinct immunophenotype in NSCLC. Rational integration of TMB and PD-L1 expression may identify NSCLCs most likely to respond to ICI. CohortPD-L1 expressionPFS TMB ≥ vs Citation Format: Biagio Ricciuti, Kathryn C. Arbour, Navin R. Mahadevan, Joao V. Alessi, James Lindsay, Renato Umeton, Rileen Sinha, Amir Hoojghan, Natalie Vokes, Gonzalo Recondo, Giuseppe Lamberti, Andrew Polio1, Hira Rizvi, Giulia Leonardi, Andrew J. Plodkowski, Kristen Felt, Bijaya Sharma, Michael Y. Tolstorukov, Pasi A. Janne, Eliezer M. Van Allen, Lynette M. Sholl, Scott J. Rodig, Matthew D. Hellmann, Mark M. Awad. A very high tumor mutational burden (TMB) is associated with improved efficacy of PD-(L)1 inhibition across different PD-L1 expression subgroups and a distinct immunophenotype in NSCLC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 490.

Background Small cell lung cancer (SCLC) is an aggressive neuroendocrine malignancy with dismal survival outcomes and no established predictive biomarkers. The landmark randomized, phase III IMpower133 trial established the new frontline standard of care for extensive-stage SCLC (ES-SCLC) as etoposide/platinum (EP) plus immune checkpoint blockade (ICB) [anti-PD-L1; atezolizumab (atezo)] based on an overall survival (OS) benefit compared to EP plus placebo. However, this survival benefit is limited in unselected populations, emphasizing the need for predictive biomarkers. Preclinically, there is emerging evidence of transcriptional heterogeneity among SCLC tumors, but the impact on therapeutic benefit remains undefined. Using non-negative matrix factorization (NMF) analysis of gene expression data from 81 SCLC tumors samples, we previously identified four subtypes, including three defined largely by differential expression of the transcription factors ASCL1 (SCLC-A), NEUROD1 (SCLC-N), and POU2F3 (SCLC-P), and a novel, fourth subtype with low expression of all three transcription factor signatures. Method and Results Using transcriptional and proteomic data from patient tumors and tumor-derived models, we molecularly characterized each of the four identified subtypes. The previously undescribed fourth subtype, dubbed SCLC-Inflamed (SCLC-I) showed high expression of non-neuroendocrine transcription factors (e.g. REST) and markers of EMT. Most distinctly, relative to the “cold” immune microenvironment typical of SCLC tumors, SCLC-I tumors possess markedly higher expression of interferon-γ signatures and immune checkpoints, including CD274 (PD-L1). Furthermore, cell type deconvolution using CIBERSORTx identified significantly higher infiltration into SCLC-I tumors by multiple immune cell types including T-cells, NK cells, macrophages, and dendritic cells. We predicted SCLC-I might derive disproportionate benefit from ICB due to its inflamed features. To test this, we applied our NMF-derived gene signature to 276 treatment-naïve, ES-SCLC patient tumors from the IMpower133 trial to assign patient subtype. The distribution of subtypes was as follows: SCLC-A 51%, SCLC-N 23%, SCLC-I 18% and SCLC-P 7%. While there was a trend toward OS benefit with the addition of atezo in each subtype, the benefit was numerically greater in SCLC-I. Specifically, median OS (atezo vs placebo arm) in months (mo) was 18.2 mo vs 10.4 mo for SCLC-I tumors, while median OS for the other three subtypes ranged from 9.6-10.9 mo (atezo arm) and 6.0-10.6 mo (placebo arm). Conclusion Unbiased transcriptional analyses identify four subtypes with distinct tumor and immune features. While all subtypes experienced improved OS with addition of anti-PD-L1 to frontline EP, SCLC-I patients appear to experience the most durable benefit. Citation Format: Carl M. Gay, C. Allison Stewart, Lixia Diao, Barzin Y. Nabet, Junya Fujimoto, Luisa M. Solis, Wei Lu, Yuanxin Xi, Robert J. Cardnell, Natalie I. Vokes, Kavya Ramkumar, Stephen G. Swisher, Jack A. Roth, Bonnie S. Glisson, David S. Shames, Ignacio I. Wistuba, Jing Wang, John Minna, John V. Heymach, Lauren A. Byers. A novel, inflamed small cell lung cancer transcriptional subtype, SCLC-I, defines a subset of patients with distinct immunotherapy vulnerability [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 22.

Background: Objective response rates for patients with advanced head and neck squamous cell carcinoma (HNSCC) receiving PD-1/L1 immune checkpoint inhibitors (ICI) without chemotherapy remain under 25%. Some studies suggest that HNSCC patients with human papillomavirus (HPV)-associated disease and high tumor mutational burden (TMB) derive greater benefit, although these findings do not yet guide patient selection. We hypothesized that uniform genomic analysis of a larger cohort may help place the predictive value of TMB in a broader biological context and identify more robust predictors of response. Methods: 274 patients with biopsy-proven HNSCC treated with ICI at our institution from June 2014 to September 2018 were identified. Following targeted massively parallel sequencing, single nucleotide variant calling, and quality control measures, 127 samples were available for analysis. Clinical records were reviewed to determine radiographic response and overall survival. Responders included complete or partial responders by RECIST v1.1. Nonresponders included those with progressive or stable disease. We evaluated associations between genomic features and response using standard statistical methods. Results: TMB for the entire cohort was significantly higher among responders (p = 0.01, Fisher's); however, it had poor predictive power (AUROC = 0.68). Among the entire cohort, somatic mutations in NOTCH1 were enriched in responders (12/26 R, 17/101 NR, p = 0.003). After correcting for TMB, NOTCH1 remained associated with complete or partial response (p < 0.05, logistic regression). After adjusting for mutations in NOTCH1, no significant difference in TMB was observed between responders and nonresponders (p > 0.05, logistic regression). Among HPV-negative responders, somatic mutations in CDK12, a transcription-associated kinase whose loss is associated with genomic instability, neoantigen burden, and T cell infiltration, were enriched (3/26 R, 0/101 NR, p = 0.008, Fisher's). Conclusion: Through uniform genomic analysis of the largest ICI-treated HNSCC cohort to-date, we validated prior findings regarding the significance of TMB and NOTCH1 as correlates of response for ICI in HNSCC. We also show that TMB alone is insufficient as a predictor of response. We identify CDK12 as a variant with a mechanistic relationship to tumor immunology as a possible correlate of response in HPV-negative patients, which warrants further investigation. Citation Format: Nisarg A. Patel, Natalie I. Vokes, Haitham Elmarakeby, Glenn J. Hanna, Eliezer M. Van Allen. Genomic correlates of response to immune checkpoint inhibitors in advanced head and neck squamous cell carcinoma [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 5859.