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Introduction: Durvalumab improves survival when used as consolidation therapy after chemoradiation (CRT) in patients with stage III NSCLC. The optimal consolidation therapy for patients with EGFR-mutant (EGFRmut) stage III NSCLC remains unknown., Methods: In this multi-institutional, international retrospective analysis across 24 institutions, we evaluated outcomes in patients with stage III EGFRmut NSCLC treated with concurrent CRT followed by consolidation therapy with osimertinib, durvalumab, or observation between 2015 and 2022. Kaplan-Meier method was used to estimate real-world progression-free survival (rwPFS, primary end point) and overall survival (secondary end point). Treatment-related adverse events (trAEs) during consolidation treatment were defined using Common Terminology Criteria for Adverse Events version 5.0. Multivariable Cox regression analysis was used., Results: Of 136 patients with stage III EGFRmut NSCLC treated with definitive concurrent CRT, 56 received consolidation durvalumab, 33 received consolidation osimertinib, and 47 was on observation alone. Baseline characteristics were similar across the three cohorts. With a median follow-up of 46 months for the entire cohort, the median duration of treatment was not reached (NR) for osimertinib (interquartile range: NR-NR) and was 5.5 (interquartile range: 2.4-10.8) months with durvalumab. After adjusting for nodal status, stage III A/B/C, and age, patients treated with consolidation osimertinib had significantly longer 24-month rwPFS compared to those treated with durvalumab or in the observation cohorts (osimertinib: 86%, durvalumab: 30%, observation: 27%, p < 0.001 for both comparisons). There was no difference in rwPFS between the durvalumab and the observation cohorts. No significant difference in overall survival across the three cohorts was detected, likely due to the limited follow-up. Any-grade trAE occurred in 52% (2 [6.1%] grade ≥3) and 48% (10 [18%] grade ≥3) of patients treated with osimertinib and durvalumab, respectively. Of 45 patients who progressed on consolidation durvalumab, 37 (82%) subsequently received EGFR tyrosine kinase inhibitors. Of these, 14 (38%) patients developed trAEs including five patients with pneumonitis (14%; 2 [5.4%] grade ≥3) and five patients with diarrhea (14%; 1 [2.7%] grade ≥3)., Conclusions: This study suggests that among patients with stage III unresectable NSCLC with a sensitizing EGFR mutation, consolidation osimertinib was associated with a significantly longer rwPFS compared to durvalumab or observation. No unanticipated safety signals were observed with consolidation osimertinib., Competing Interests: Disclosure Dr. Nassar receives honoraria from OncLive, TEMPUS, and Korean Society for Medical Oncology and consulting fees from Guidepoint Global. Dr. Kim is on the advisory board for Amgen. Dr. Shepherd reports having stock and other ownership interests from Eli Lilly and AstraZeneca; receiving honoraria from AstraZeneca, Merck Serono, Takeda, and Daiichi Sankyo; having consulting/advisory role at AstraZeneca and Merck Serono; and receiving research funding from Eli Lilly (inst), Pfizer (inst), Bristol-Myers Squibb (inst), AstraZeneca/MedImmune (inst), and Roche Canada (inst). Dr. Gray reports receiving grants from AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb, Genentech, G1 Therapeutics, Ludwig Institute of Cancer Research, Merck & Co., Inc., Novartis, and Pfizer; receiving consulting fees from AbbVie, AstraZeneca, Blueprint Medicines, EMD Serono, Gilead Sciences, Inc., Janssen Scientific Affairs, LLC, Jazz Pharmaceuticals, Loxo Oncology Inc., Merck & Co., Inc., Novartis, OncoCyte Biotechnology, Spectrum ODAC, Takeda Pharmaceuticals, and Triptych Health Partners; and having leadership role as SWOG Lung Committee Chair, IASLC Board of Director member, and ASCO Education Committee Ex-Chair. Dr. Dilling reports receiving consulting fees from AstraZeneca and support for travel from NCCN. Dr. Neal reports receiving grants from Genentech/Roche, Merck, Novartis, Boehringer Ingelheim, Exelixis, Nektar Therapeutics, Takeda Pharmaceuticals, Adaptimmune, GlaxoSmithKline, Janssen, AbbVie, and Novocure; receiving consulting fees from AstraZeneca, Genentech/Roche, Exelixis, Takeda Pharmaceuticals, Eli Lilly and Company, Amgen, Iovance Biotherapeutics, Blueprint Pharmaceuticals, Regeneron Pharmaceuticals, Natera, Sanofi/Regeneron, D2G Oncology, Surface Oncology, Turning Point Therapeutics, Mirati Therapeutics, Gilead Sciences, AbbVie, Summit Therapeutics, Novartis, Novocure, Janssen Oncology, and AnHeart Therapeutics; and receiving honorarium from CME Matters, Clinical Care Options CME, Research to Practice CME, Medscape CME, Biomedical Learning Institute CME, MLI Peerview CME, Prime Oncology CME, Projects in Knowledge CME, Rockpointe CME, MJH Life Sciences CME, Medical Educator Consortium, and HMP Education. Dr. Wakelee reports receiving grants from Arrys Therapeutics Research, AstraZeneca/Medimmune, Bristol-Myers Squibb, Genentech/Roche, Merck, Helsinn, Seagen, and Xcovery; and serving on the advisory board of Mirati (compensated)—ended November 2022, IOBiotech (compensated)—October 2023, Merck (NOT compensated), and Genentech/Roche/Bristol-Myers Squibb/AstraZeneca (NOT compensated). Dr. S.H. Lin reports receiving grants from STCube Pharmaceuticals, Beyond Spring, and Nektar Therapeutics; consulting fees from XRAD Therapeutics; serving on the advisory board of AstraZeneca; having stock/stock options from Apple, Google, Amazon, Tesla, Meta, Rivian, and CreatvMicrotech; and having other support from SEEK Diagnostics (co-founder). Dr. Patil reports receiving grants from Gilead Research Scholars and LUNGevity Foundation Award; consulting fees from AstraZeneca, Boehringer Ingelheim, Bicara, Daiichi, Janssen, Jazz Pharmaceuticals, Mirati Therapeutics, Pfizer, Sanofi, Regeneron, Roche/Genentech, Takeda, Gilead Research Scholars, and LUNGevity Foundation Award; and honorarium from Janssen. Dr. Bar reports receiving grants from Merck Sharp & Dohme, AstraZeneca, Roche, Bristol-Myers Squibb, Takeda, AbbVie, Bayer, Boehringer Ingelheim, and Pfizer; receiving consulting fees from AstraZeneca, Merck Sharp & Dohme, Boehringer Ingelheim, Roche, Bristol-Myers Squibb, Takeda, AbbVie, Pfizer, Bayer, and VBL; and having leadership role from Lung Ambition Consortium, Israel Lung Cancer Group, and IASLC committee. Dr. Marron receives grants from Regeneron, Bristol-Myers Squibb, Merck, Boehringer Ingelheim, NCI, and Cancer Research Institute; and serving on the advisory board of AbbVie, Celldex, and Rockefeller University. Dr. Yu receives grants/contracts from AstraZeneca, Pfizer, Daiichi Cullinan Oncology, Janssen, Novartis, Blueprint med, Black Diamond, and Systimmune and consulting fees from AstraZeneca, Daiichi, Taiho, Takeda, Janssen, Amgen, AbbVie, Novocure, and Ipsen. Dr. Owen receives grants/contracts from Bristol-Myers Squibb, Merck, Palobiofarma, Genentech, Onc.AI, and Pfizer; and travel support from Amgen, AstraZeneca, and Genentech. Rocha receives grants/contracts from SEOM, ESMO Fellowship, and AECC; and travel support from Merck Sharp & Dohme, AstraZeneca, and Bristol-Myers Squibb. Dr. Arriola receives grants/contracts from AstraZeneca and Bristol-Myers Squibb; honorarium from Eli Lilly, AstraZeneca, Roche, Takeda, Merck Sharp & Dohme, Pfizer, Janssen, and Bristol-Myers Squibb; and travel support from AstraZeneca, Roche, and Pfizer. Dr. Cheng receives grants/contracts from AstraZeneca and Genentech; and serving on the advisory board of Janssen, G1 Therapeutics, and AstraZeneca. Dr. Parikh receives consulting fees from Jazz Pharmaceuticals, Guardant Health, and AstraZeneca; and honorarium from MJH Life Sciences. Dr. Harris receives grants/contract from NIH P30CA062203, the UC Irvine Comprehensive Cancer Center using UCI Anti-Cancer Challenge Grant, and ACS Seed Grant 129801-IRG-16-187-13-IRG; and serving on the advisory board of UC Irvine as DSMB member. Dr. Nagasaka receives consulting fees from Caris Life Sciences; honorarium/speaker fees from AstraZeneca, Daiichi, Novartis, EMD Serono self, Pfizer, Eli Lilly, Genentech, Regeneron, Takeda, Janssen, Blueprint, and Mirati; travel support from AnHeart Therapeutics; and stock/stock options from Mbrace Therapeutics. Dr. Valazquez receives grants/contracts from ASCO, American Association for Cancer Research (AACR), LUNGevity, NIA P30AG015272, NCI 5U54CA242646-03 Conquer Cancer and Niarchos Foundation, UCSF Clinical Practice Group, and AAMC; consulting fees from AstraZeneca, Merus, Novocure, and Cadence Communications & Research; and having stock/stock options from Corbus Pharmaceuticals. Dr. J.J. Lin receives grants/contracts from Hengrui Therapeutics, Turning Point Therapeutics, Neon Therapeutics, Relay Therapeutics, Bayer, Elevation Oncology, Roche, Linnaeus Therapeutics, Nuvalent, and Novartis; consulting fees from Genentech, C4 Therapeutics, Blueprint Medicines, Nuvalent, Bayer, Elevation Oncology, Novartis, Mirati Therapeutics, AnHeart Therapeutics, Takeda, CLaiM Therapeutics, Regeneron, Pfizer, Turning Point Therapeutics, Daiichi Sankyo, AstraZeneca, and Merus. Dr. Piotrowska receives grants/contracts from Novartis, Takeda, Spectrum, AstraZeneca, Tesaro/GlaxoSmithKline, Cullinan Oncology, Daiichi Sankyo, AbbVie, Janssen, and Blueprint; consulting fees from Taiho, Janssen, Takeda, AstraZeneca, Cullinan Oncology, C4 Therapeutics, Jazz Pharmaceuticals, and Blueprint; and honorarium from Daiichi Sankyo, Janssen, and Eli Lilly (honoraria for nonpromotional educational events). Dr. Reuss receives grants/contracts from Genentech/Roche, Verastem, Nuvalent, and LUNGevity Foundation; consulting fees from Genentech/Roche, Sanofi/Genzyme, Personalis, Guardant, AstraZeneca, Bristol-Myers Squibb, Arcus, AbbVie, Daiichi Sankyo, Catalym, Seagen, and Gilead; and honorarium from AstraZeneca and Merck. Dr. Baena Espinar receives consulting fees from AstraZeneca, Roche, and Merck Sharp & Dohme; honorarium from AstraZeneca, Roche, and Merck Sharp & Dohme; payment for expert testimony from Roche; and travel support from Roche and Janssen. Leal receives research funding to institution from Pfizer, Advaxis, and Bayer, outside the submitted work. Dr. Mak receives grants/contracts from ViewRay; consulting fees from AstraZeneca, Novartis, and Sio Capital Management; and other support from Founder of HealthAI. Dr. Naqash receives grants/contracts from SWOG Hope Foundation and FDA Broad Agency Contract; serving on the advisory board of JCO Precision Oncology; receiving travel support from SITC/American Association for Cancer Research (AACR)/Conquer Cancer Foundation/BinayTara Foundation and Foundation Med/Caris Life Sciences, and Jazz Pharmaceuticals; and funding to institution for trials from Loxo@Lilly, Surface Oncology, ADC Therapeutics, IGM Biosciences, EMD Serono, Aravive, Nikang Therapeutics, Inspirna, Exelexis, Revolution Medicine, Jacobio, Pionyr, Jazz Pharmaceuticals, NGM Biopharmaceuticals, Immunophotonics, and Selexine. Dr. Goldberg receives grants/contracts from AstraZeneca, Boehringer Ingelheim, and Mirati; and serving on the advisory board of AstraZeneca, Bristol-Myers Squibb, Amgen, Sanofi, Genzyme, Daiichi Sankyo, Regeneron, Takeda, Janssen, Summit, and Merck. The remaining authors declare no conflict of interest., (Copyright © 2024. Published by Elsevier Inc.)

Background: Patients with paraneoplastic syndromes (PNS) are excluded from clinical trials involving immune checkpoint inhibitors (ICIs) due to safety concerns. Moreover, real-world data on efficacy and safety is scarce., Methods: In this retrospective study, data were collected on patients with PNS and solid tumors receiving ICI between 2015 and 2022 at nine institutions. Patients were classified into: Cohort 1 (pre-existing PNS before ICI initiation), cohort 2 (PNS during ICI treatment), and cohort 3 (PNS after ICI discontinuation). Patients with metastatic non-small cell lung cancer (NSCLC) (mNSCLC) from cohort 1 were matched to patients who were PNS-free at each institution up to a 1:3 ratio for age, sex, type of ICI, use of concurrent chemotherapy, and number of lines of systemic therapy prior to ICI initiation. Kaplan-Meier method was used to assess overall survival (OS) and time-to-next treatment (TTNT)., Results: Among 109 patients with PNS treated with ICIs, median age at ICI initiation was 67 years (IQR: 58-74). The most represented cancer type was NSCLC (n=39, 36%). In cohort 1 (n=55), PNS exacerbations occurred in 16 (29%) patients with median time to exacerbation after ICI of 1.1 months (IQR: 0.7-3.3). Exacerbation or de novo PNS prompted temporary/permanent interruption of ICIs in 14 (13%) patients. For cohort 2 (n=16), median time between ICI initiation and de novo PNS was 1.2 months (IQR: 0.4-3.5). Treatment-related adverse events (trAEs) occurred in 43 (39%) patients. Grade ≥3 trAEs occurred in 18 (17%) patients. PNS-directed immunosuppressive therapy was required in 55 (50%) patients. We matched 18 patients with mNSCLC and PNS (cohort 1) to 40 without PNS, treated with ICIs. There was no significant difference in OS or TTNT between patients with mNSCLC with and without PNS, although a trend was seen towards worse outcomes in patients with PNS. TrAEs occurred in 6/18 (33%) and 14/40 (35%), respectively. Grade ≥3 trAEs occurred in 4 (22%) patients with PNS and 7 (18%) patients without PNS., Conclusions: Exacerbations of pre-existing PNS occurred in 29% of patients treated with ICIs and both exacerbations and de novo PNS occur early in the ICI course. TrAE from ICIs were similar between patients with and without PNS. Our data suggest that pre-existing PNS should not preclude consideration of ICI therapy although patients may not derive the same clinical benefit compared with patients without PNS., Competing Interests: Competing interests: AHN receives honoraria from OncLive, TEMPUS, and Korean Society for Medical Oncology. Consulting fees: Guidepoint Global. RRM: Consulting/Advisory Board – Aveo, AstraZeneca, Bayer, Bristol Myers Squibb, Blue Earth Diagnostics, Calithera, Caris, Denderon, Exelixis, Janssen, Merck, Myovant, Pfizer, Sanofi, SeaGen, Sorrento Therapeutics, Tempus. Institutional Research Funding – AstraZeneca, BMS, Exelixis, Artera, Oncternal, Bayer, Tempus. JAS is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (grant numbers R01 AR080659, R01 AR077607, P30 AR070253, and P30 AR072577), the R. Bruce and Joan M. Mickey Research Scholar Fund, and the Llura Gund Award funded by the Gordon and Llura Gund Foundation. JAS has received research support from Bristol Myers Squibb and performed consultancy for AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Gilead, Inova Diagnostics, Janssen, Optum, Pfizer, ReCor, and Sobi unrelated to this work. The funders had no role in the decision to publish or preparation of this manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of Harvard University, its affiliated academic health care centers, or the National Institutes of Health. AC received grants for consultancies/advisory boards: MSD, OncoC4, IQVIA, AstraZeneca, Access Infinity, Ardelis Health, Alpha Sight. Speaker fees: AstraZeneca, Eisai, Pierre-Fabre, MSD. Writing/Editorial activity: BMS. Travel support: Sanofi and MSD. ARN reports Funding to Institution for Trials he is PI on:Loxo@Lilly, Surface Oncology, ADC Therapeutics, IGM Biosciences, EMD Serono, Aravive, Nikang Therapeutics, Inspirna, Exelixis, Revolution Medicine, Jacobio, Pionyr, Jazz Pharmaceuticals, NGM Biopharmaceuticals. ARN receives Consultant Editor Compensation: JCO Precision Oncology. Consulting/Advisory Board: Foundation Med. ARN reports Travel Compensation from: SITC/ AACR/ Conquer Cancer Foundation, Jazz Pharmaceuticals, Binay Tara Foundation, Foundation Med., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Purpose: Non-small-cell lung cancer (NSCLC) with STK11 mut has inferior outcomes to immune checkpoint inhibitors (ICIs). Using multiomics, we evaluated whether a subtype of STK11 mut NSCLC with a uniquely inflamed tumor immune microenvironment (TIME) harboring TP53 comutations could have favorable outcomes to ICIs., Patients and Methods: NSCLC tumors (N = 16,896) were analyzed by next-generation sequencing (DNA-Seq/592 genes). A subset (n = 5,034) underwent gene expression profiling (RNA-Seq/whole transcriptome). Exome-level neoantigen load for STK11 mut NSCLC was obtained from published pan-immune analysis. Tumor immune cell content was obtained from transcriptome profiles using the microenvironment cell population (MCP) counter. ICI data from POPLAR/OAK (n = 34) and the study by Rizvi et al (n = 49) were used to model progression-free survival (PFS), and a separate ICI-treated cohort (n = 53) from Dana-Farber Cancer Institute (DFCI) was used to assess time to treatment failure (TTF) and tumor RECIST response for STK11 mut TP53 mut versus STK11 mut TP53 wt NSCLC., Results: Overall, 12.6% of NSCLC tumors had a STK11 mut with the proportions of tumor mutational burden (TMB)-high (≥10 mut/Mb), PD-L1 ≥50%, and microsatellite instability-high being 38.3%, 11.8%, and 0.72%, respectively. Unsupervised hierarchical clustering of STK11 mut (n = 463) for stimulator of interferon-gamma (STING) pathway genes identified a STING-high cluster, which was significantly enriched in TP53 mut NSCLC ( P < .01). Compared with STK11 mut TP53 wt , tumors with STK11 mut TP53 mut had higher CD8+T cells and natural killer cells ( P < .01), higher TMB ( P < .001) and neoantigen load ( P < .001), and increased expression of MYC and HIF-1A ( P < .01), along with higher expression ( P < .01) of glycolysis/glutamine metabolism genes. Meta-analysis of data from OAK/POPLAR and the study by Rizvi et al showed a trend toward improved PFS in patients with STK11 mut TP53 mut . In the DFCI cohort, compared with the STK11 mut TP53 wt cohort, the STK11 mut TP53 mut tumors had higher objective response rates (42.9% v 16.7%; P = .04) and also had longer TTF (14.5 v 4.5 months, P adj = .054) with ICI., Conclusion: STK11 mut NSCLC with TP53 comutation is a distinct subgroup with an immunologically active TIME and metabolic reprogramming. These properties should be exploited to guide patient selection for novel ICI-based combination approaches.

Purpose: Recent evidence has shown that higher tumor mutational burden strongly correlates with an increased risk of immune-related adverse events (irAEs). By using an integrated multiomics approach, we further studied the association between relevant tumor immune microenvironment (TIME) features and irAEs., Methods: Leveraging the US Food and Drug Administration Adverse Event Reporting System, we extracted cases of suspected irAEs to calculate the reporting odds ratios (RORs) of irAEs for cancers treated with immune checkpoint inhibitors (ICIs). TIME features for 32 cancer types were calculated on the basis of the cancer genomic atlas cohorts and indirectly correlated with each cancer's ROR for irAEs. A separate ICI-treated cohort of non-small-cell lung cancer (NSCLC) was used to evaluate the correlation between tissue-based immune markers (CD8 + , PD-1/L1+, FOXP3+, tumor-infiltrating lymphocytes [TILs]) and irAE occurrence., Results: The analysis of 32 cancers and 33 TIME features demonstrated a significant association between irAE RORs and the median number of base insertions and deletions (INDEL), neoantigens (r = 0.72), single-nucleotide variant neoantigens (r = 0.67), and CD8 + T-cell fraction (r = 0.51). A bivariate model using the median number of INDEL neoantigens and CD8 T-cell fraction had the highest accuracy in predicting RORs (adjusted r 2 = 0.52, P = .002). Immunoprofile assessment of 156 patients with NSCLC revealed a strong trend for higher baseline median CD8 + T cells within patients' tumors who experienced any grade irAEs. Using machine learning, an expanded ICI-treated NSCLC cohort (n = 378) further showed a treatment duration-independent association of an increased proportion of high TIL (>median) in patients with irAEs (59.7% v 44%, P = .005). This was confirmed by using the Fine-Gray competing risk approach, demonstrating higher baseline TIL density (>median) associated with a higher cumulative incidence of irAEs ( P = .028)., Conclusion: Our findings highlight a potential role for TIME features, specifically INDEL neoantigens and baseline-immune infiltration, in enabling optimal irAE risk stratification of patients.

Purpose: Compared with people living without HIV (PWOH), people living with HIV (PWH) and cancer have traditionally been excluded from immune checkpoint inhibitor (ICI) trials. Furthermore, there is a paucity of real-world data on the use of ICIs in PWH and cancer., Methods: This retrospective study included PWH treated with anti-PD-1- or anti-PD-L1-based therapies for advanced cancers. Kaplan-Meier method was used to estimate overall survival (OS) and progression-free survival (PFS). Objective response rates (ORRs) were measured per RECIST 1.1 or other tumor-specific criteria, whenever feasible. Restricted mean survival time (RMST) was used to compare OS and PFS between matched PWH and PWOH with metastatic NSCLC (mNSCLC)., Results: Among 390 PWH, median age was 58 years, 85% (n = 331) were males, 36% (n = 138) were Black; 70% (n = 274) received anti-PD-1/anti-PD-L1 monotherapy. Most common cancers were NSCLC (28%, n = 111), hepatocellular carcinoma ([HCC]; 11%, n = 44), and head and neck squamous cell carcinoma (HNSCC; 10%, n = 39). Seventy percent (152/216) had CD4+ T cell counts ≥200 cells/µL, and 94% (179/190) had HIV viral load <400 copies/mL. Twenty percent (79/390) had any grade immune-related adverse events (irAEs) and 7.7% (30/390) had grade ≥3 irAEs. ORRs were 69% (nonmelanoma skin cancer), 31% (NSCLC), 16% (HCC), and 11% (HNSCC). In the matched mNSCLC cohort (61 PWH v 110 PWOH), 20% (12/61) PWH and 22% (24/110) PWOH had irAEs. Adjusted 42-month RMST difference was -0.06 months (95% CI, -5.49 to 5.37; P = .98) for PFS and 2.23 months (95% CI, -4.02 to 8.48; P = .48) for OS., Conclusion: Among PWH, ICIs demonstrated differential activity across cancer types with no excess toxicity. Safety and activity of ICIs were similar between matched cohorts of PWH and PWOH with mNSCLC.

Background: We aim to implement an immune cell score model in routine clinical practice for resected non-small-cell lung cancer (NSCLC) patients (NCT03299478). Molecular and genomic features associated with immune phenotypes in NSCLC have not been explored in detail., Patients and Methods: We developed a machine learning (ML)-based model to classify tumors into one of three categories: inflamed, altered, and desert, based on the spatial distribution of CD8+ T cells in two prospective (n = 453; TNM-I trial) and retrospective (n = 481) stage I-IIIA NSCLC surgical cohorts. NanoString assays and targeted gene panel sequencing were used to evaluate the association of gene expression and mutations with immune phenotypes., Results: Among the total of 934 patients, 24.4% of tumors were classified as inflamed, 51.3% as altered, and 24.3% as desert. There were significant associations between ML-derived immune phenotypes and adaptive immunity gene expression signatures. We identified a strong association of the nuclear factor-κB pathway and CD8+ T-cell exclusion through a positive enrichment in the desert phenotype. KEAP1 [odds ratio (OR) 0.27, Q = 0.02] and STK11 (OR 0.39, Q = 0.04) were significantly co-mutated in non-inflamed lung adenocarcinoma (LUAD) compared to the inflamed phenotype. In the retrospective cohort, the inflamed phenotype was an independent prognostic factor for prolonged disease-specific survival and time to recurrence (hazard ratio 0.61, P = 0.01 and 0.65, P = 0.02, respectively)., Conclusions: ML-based immune phenotyping by spatial distribution of T cells in resected NSCLC is able to identify patients at greater risk of disease recurrence after surgical resection. LUADs with concurrent KEAP1 and STK11 mutations are enriched for altered and desert immune phenotypes., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Importance: Currently, predictive biomarkers for response to immune checkpoint inhibitor (ICI) therapy in lung cancer are limited. Identifying such biomarkers would be useful to refine patient selection and guide precision therapy., Objective: To develop a machine-learning (ML)-based tumor-infiltrating lymphocytes (TILs) scoring approach, and to evaluate TIL association with clinical outcomes in patients with advanced non-small cell lung cancer (NSCLC)., Design, Setting, and Participants: This multicenter retrospective discovery-validation cohort study included 685 ICI-treated patients with NSCLC with median follow-up of 38.1 and 43.3 months for the discovery (n = 446) and validation (n = 239) cohorts, respectively. Patients were treated between February 2014 and September 2021. We developed an ML automated method to count tumor, stroma, and TIL cells in whole-slide hematoxylin-eosin-stained images of NSCLC tumors. Tumor mutational burden (TMB) and programmed death ligand-1 (PD-L1) expression were assessed separately, and clinical response to ICI therapy was determined by medical record review. Data analysis was performed from June 2021 to April 2022., Exposures: All patients received anti-PD-(L)1 monotherapy., Main Outcomes and Measures: Objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) were determined by blinded medical record review. The area under curve (AUC) of TIL levels, TMB, and PD-L1 in predicting ICI response were calculated using ORR., Results: Overall, there were 248 (56%) women in the discovery cohort and 97 (41%) in the validation cohort. In a multivariable analysis, high TIL level (≥250 cells/mm2) was independently associated with ICI response in both the discovery (PFS: HR, 0.71; P = .006; OS: HR, 0.74; P = .03) and validation (PFS: HR = 0.80; P = .01; OS: HR = 0.75; P = .001) cohorts. Survival benefit was seen in both first- and subsequent-line ICI treatments in patients with NSCLC. In the discovery cohort, the combined models of TILs/PD-L1 or TMB/PD-L1 had additional specificity in differentiating ICI responders compared with PD-L1 alone. In the PD-L1 negative (<1%) subgroup, TIL levels had superior classification accuracy for ICI response (AUC = 0.77) compared with TMB (AUC = 0.65)., Conclusions and Relevance: In these cohorts, TIL levels were robustly and independently associated with response to ICI treatment. Patient TIL assessment is relatively easily incorporated into the workflow of pathology laboratories at minimal additional cost, and may enhance precision therapy.

The immune checkpoint inhibitor (ICI) pembrolizumab is US FDA approved for treatment of solid tumors with high tumor mutational burden (TMB-high; ≥10 variants/Mb). However, the extent to which TMB-high generalizes as an accurate biomarker in diverse patient populations is largely unknown. Using two clinical cohorts, we investigated the interplay between genetic ancestry, TMB, and tumor-only versus tumor-normal paired sequencing in solid tumors. TMB estimates from tumor-only panels substantially overclassified individuals into the clinically important TMB-high group due to germline contamination, and this bias was particularly pronounced in patients with Asian/African ancestry. Among patients with non-small cell lung cancer treated with ICIs, those misclassified as TMB-high from tumor-only panels did not associate with improved outcomes. TMB-high was significantly associated with improved outcomes only in European ancestries and merits validation in non-European ancestry populations. Ancestry-aware tumor-only TMB calibration and ancestry-diverse biomarker studies are critical to ensure that existing disparities are not exacerbated in precision medicine., Competing Interests: Declaration of interests G.S. reports the following disclosures: advisory boards of BMS, Genentech, EMD Serono, Merck, Sanofi, Seattle Genetics/Astellas, Astrazeneca, Exelixis, Janssen, Bicycle Therapeutics, Pfizer, Immunomedics/Gilead, Scholar Rock, and G1 Therapeutics; research support to Sanofi, Astrazeneca, Immunomedics/Gilead, QED, Predicine, and BMS; steering committee of studies of BMS, Bavarian Nordic, Seattle Genetics, QED, and G1 Therapeutics (all unpaid) and Astrazeneca, EMD Serono, Debiopharm (paid); data safety monitoring committee of Mereo; travel costs from BMS (2019) and Astrazeneca (2018); writing/editor fees from UpToDate and as editor of the Elsevier PracticeUpdate Bladder Cancer Center of Excellence; speaking fees from Physicians Education Resource (PER), Onclive, Research to Practice, and Medscape (all educational). M.G. receives research funding from Bristol-Myers Squibb, Merck, Servier, and Janssen. F.S.H. reports grants and other from Bristol-Myers Squibb; personal fees from Merck; personal fees from EMD Serono; grants, personal fees, and other from Novartis; personal fees from Surface; personal fees from Compass Therapeutics; personal fees from Apricity; personal fees from Aduro; personal fees from Sanofi; personal fees from Pionyr; personal fees from Torque; personal fees from Bicara; other from Pieris Pharmaceuticals; personal fees from Eisai; personal fees from Checkpoint Therapeutics; personal fees from Idera; personal fees from Genentech/Roche; personal fees from BioEntre; personal fees from Gossamer; personal fees from Phio; personal fees from Iovance; personal fees from Trillium; personal fees from Abcuro; personal fees from Catalym; personal fees from Immunocore; outside the submitted work. In addition, F.S.H. has the following patents: Methods for Treating MICA-Related Disorders (20100111973) with royalties paid; Tumor Antigens and Uses Thereof (7250291) issued; Angiopoiten-2 Biomarkers Predictive of Anti-immune Checkpoint Response (20170248603) pending; Compositions and Methods for Identification, Assessment, Prevention, and Treatment of Melanoma using PD-L1 Isoforms (20160340407) pending; Therapeutic Peptides (20160046716) pending; Therapeutic Peptides (20140004112) pending; Therapeutic Peptides (20170022275) pending; Therapeutic Peptides (20170008962) pending; Therapeutic Peptides (9402905) issued; Methods of Using Pembrolizumab and Trebananib pending; Vaccine Compositions and Methods for Restoring NKG2D Pathway Function against Cancers (10279021) issued; Antibodies That Bind to MHC Class I Polypeptide-Related Sequence A (10106611) issued; and Anti-galectin Antibody Biomarkers Predictive of Anti-immune Checkpoint and Anti-angiogenesis Responses. T.K.C. reports the following disclosures: research/advisory boards/consultancy/honoraria (institutional and personal, paid and unpaid) for or from AstraZeneca, Aveo, Bayer, Bristol Myers-Squibb, Eisai, EMD Serono, Exelixis, GlaxoSmithKline, IQVA, Ipsen, Kanaph, Lilly, Merck, Nikang, Novartis, Pfizer, Roche, Sanofi/Aventis, Takeda, and Tempest; travel, accommodations, expenses, medical writing in relation to consulting, advisory roles, or honoraria; stock options in Pionyr and Tempest; and other: UpToDate royalties, CME-related events (e.g., OncLIve, PVI, MJH Life Sciences) honoraria, NCI GU steering committee. T.K.C. also has patents filed, royalties, or other intellectual properties (no income as of this writing) related to biomarkers of immune checkpoint blockers and ctDNA. No speaker’s bureau. T.K.C. is supported in part by the Dana-Farber/Harvard Cancer Center Kidney SPORE and Program, the Kohlberg Chair at Harvard Medical School and the Trust Family, Michael Brigham, and Loker Pinard Funds for Kidney Cancer Research at DFCI., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: Genomic alterations in 8 genes are now the targets of FDA-approved therapeutics in non-small cell lung cancer (NSCLC), but their distribution according to genetic ancestry, sex, histology, and smoking is not well established., Methods: Using multi-institutional genetic testing data from GENIE, we characterize the distribution of targetable genomic alterations in 8 genes among 8675 patients with NSCLC (discovery cohort: DFCI, N = 3115; validation cohort: Duke, Memorial Sloan Kettering Cancer Center, Vanderbilt, N = 5560). For the discovery cohort, we impute genetic ancestry from tumor-only sequencing and identify differences in the frequency of targetable alterations across ancestral groups, smoking pack-years, and histologic subtypes., Results: We identified variation in the prevalence of KRAS G12C , sensitizing EGFR mutations, MET alterations, ALK, and ROS1 fusions according to the number of smoking pack-years. A novel method for computing continental (African, Asian, European) and Ashkenazi Jewish ancestries from panel sequencing enables quantitative analysis of the correlation between ancestry and mutation rates. This analysis identifies a correlation between Asian ancestry and EGFR mutations and an anti-correlation between Asian ancestry and KRAS G12C mutation. It uncovers 2.7-fold enrichment for MET exon 14 skipping mutations and amplifications in patients of Ashkenazi Jewish ancestry. Among never/light smokers, targetable alterations in LUAD are significantly enriched in those with Asian (80%) versus African (49%) and European (55%) ancestry. Finally, we show that 5% of patients with squamous cell carcinoma (LUSC) and 17% of patients with large cell carcinoma (LCLC) harbor targetable alterations., Conclusions: Among patients with NSCLC, there was significant variability in the prevalence of targetable genomic alterations according to genetic ancestry, histology, and smoking. Patients with LUSC and LCLC have 5% rates of targetable alterations supporting consideration for sequencing in those subtypes., (© 2022. The Author(s).)