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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.

The clinical development of cytokines as cancer therapeutics has been limited due to significant toxicities generally observed with systemic administration. This narrow therapeutic window, together with relatively modest efficacy, have made natural cytokines unattractive drug candidates. Immunocytokines represent a class of next generation cytokines designed to overcome the challenges associated with traditional cytokines. These agents seek to improve the therapeutic index of cytokines by using antibodies as vehicles for the targeted delivery of immunomodulatory agents within the local tumor microenvironment. Various molecular formats and cytokine payloads have been studied. In this review, we provide an overview of the rationale, preclinical support, and current clinical development strategies for immunocytokines.

The organization of immune cells in human tumors is not well understood. Immunogenic tumors harbor spatially-localized multicellular ‘immunity hubs’ defined by expression of the T cell-attracting chemokinesCXCL10/CXCL11and abundant T cells. Here, we examined immunity hubs in human pre-immunotherapy lung cancer specimens, and found that they were associated with beneficial responses to PD-1-blockade. Immunity hubs were enriched for many interferon-stimulated genes, T cells in multiple differentiation states, andCXCL9/10/11+ macrophages that preferentially interact with CD8 T cells. Critically, we discovered the stem-immunity hub, a subtype of immunity hub strongly associated with favorable PD-1-blockade outcomes, distinct from mature tertiary lymphoid structures, and enriched for stem-like TCF7+PD-1+ CD8 T cells and activatedCCR7+LAMP3+ dendritic cells, as well as chemokines that organize these cells. These results elucidate the spatial organization of the human intratumoral immune response and its relevance to patient immunotherapy outcomes.

Supplementary Table S1. ALK inhibitors in clinical development. Supplementary Table S2. Summary of clinical characteristics of ALK-positive patients undergoing biopsies at the time of ALK inhibitor resistance. Supplementary Table S3. Paired, post-crizotinib and post second-generation ALK inhibitor biopsies. Supplementary Table S4. Treatment course of ALK-positive patients undergoing post-ceritinib biopsies. Supplementary Table S5. Treatment course of ALK-positive patients undergoing post-alectinib biopsies. Supplementary Table S6. Treatment course of ALK-positive patients undergoing post-brigatinib biopsies. Supplementary Table S7. Resistant biopsies with {greater than or equal to}2 resistance mutations. Supplementary Table S8. Mutations in ceritinib-resistant biopsies. Supplementary Table S9. Mutations in alectinib-resistant biopsies. Supplementary Table S10. Mutations in brigatinib-resistant biopsies. Supplementary Table S11. Single-nucleotide variants identified in patient-derived cell lines. Supplementary Table S12. EMT in ceritinib-resistant biopsies. Supplementary Table S13. Cells seeded for survival assays. Supplementary Table S14. Cells seeded for growth assays.

suppl tables

PDF file 70K, This table contains the IC50 values of in vitro cell survival assay for crizotinib or ceritinib in engineered EML4-ALK mutant Ba/F3 cells corresponding to Fig 4A

BH3 mimetic drugs, which inhibit prosurvival BCL2 family proteins, have limited single-agent activity in solid tumor models. The potential of BH3 mimetics for these cancers may depend on their ability to potentiate the apoptotic response to chemotherapy and targeted therapies. Using a novel class of potent and selective MCL1 inhibitors, we demonstrate that concurrent MEK + MCL1 inhibition induces apoptosis and tumor regression in KRAS-mutant non–small cell lung cancer (NSCLC) models, which respond poorly to MEK inhibition alone. Susceptibility to BH3 mimetics that target either MCL1 or BCL-xL was determined by the differential binding of proapoptotic BCL2 proteins to MCL1 or BCL-xL, respectively. The efficacy of dual MEK + MCL1 blockade was augmented by prior transient exposure to BCL-xL inhibitors, which promotes the binding of proapoptotic BCL2 proteins to MCL1. This suggests a novel strategy for integrating BH3 mimetics that target different BCL2 family proteins for KRAS-mutant NSCLC.Significance:Defining the molecular basis for MCL1 versus BCL-xL dependency will be essential for effective prioritization of BH3 mimetic combination therapies in the clinic. We discover a novel strategy for integrating BCL-xL and MCL1 inhibitors to drive and subsequently exploit apoptotic dependencies of KRAS-mutant NSCLCs treated with MEK inhibitors.See related commentary by Leber et al., p. 1511.This article is highlighted in the In This Issue feature, p. 1494

Supplementary Methods include Enzyme Kinetic Assays and Computational Methods.

Mutant-selective KRASG12C inhibitors, such as MRTX849 (adagrasib) and AMG 510 (sotorasib), have demonstrated efficacy in KRASG12C-mutant cancers, including non–small cell lung cancer (NSCLC). However, mechanisms underlying clinical acquired resistance to KRASG12C inhibitors remain undetermined. To begin to define the mechanistic spectrum of acquired resistance, we describe a patient with KRASG12C NSCLC who developed polyclonal acquired resistance to MRTX849 with the emergence of 10 heterogeneous resistance alterations in serial cell-free DNA spanning four genes (KRAS, NRAS, BRAF, MAP2K1), all of which converge to reactivate RAS–MAPK signaling. Notably, a novel KRASY96D mutation affecting the switch-II pocket, to which MRTX849 and other inactive-state inhibitors bind, was identified that interferes with key protein–drug interactions and confers resistance to these inhibitors in engineered and patient-derived KRASG12C cancer models. Interestingly, a novel, functionally distinct tricomplex KRASG12C active-state inhibitor RM-018 retained the ability to bind and inhibit KRASG12C/Y96D and could overcome resistance.Significance:In one of the first reports of clinical acquired resistance to KRASG12C inhibitors, our data suggest polyclonal RAS–MAPK reactivation as a central resistance mechanism. We also identify a novel KRAS switch-II pocket mutation that impairs binding and drives resistance to inactive-state inhibitors but is surmountable by a functionally distinct KRASG12C inhibitor.See related commentary by Pinnelli and Trusolino, p. 1874.This article is highlighted in the In This Issue feature, p. 1861

Supplementary Figure S1. ALK G1202R versus ALK G1202del. Supplementary Figure S2. ALK G1202del in ALK-rearranged lung cancer. Supplementary Figure S3. Swimmer's plot - alectinib. Supplementary Figure S4. Evaluation of MGH075-2E in drug screen. Supplementary Figure S5. MGH067-1 harbors an ALK rearrangement. Supplementary Figure S6. MGH021-5A. Supplementary Figure S7. MGH051-2C and MGH084-1D. Supplementary Figure S8. MGH049-1A and MGH075-2E. Supplementary Figure S9. MGH034-2A.

PDF file 135K, Figure S1 contains data about Ceritinib specificity and potency on crizotinib na?ve EML4-ALK wild-type cells. Figure S2 contains the Cell survival curves of H3122 CR1, MGH021-4 and MGH045 cells following treatment with Crizotinib or ceritinib corresponding to Fig2A. Figure S3 contains the Cell survival curves of Ba/F3 cells following treatment with Crizotinib or ceritinib corresponding to Fig4A. Figure S4 contains tumor growth curves corresponding to the Development of crizotinib resistant H2228 xenograft tumor models used in Fig 5A-D

Supplementary Tables and Figures

PDF file 100K, Legends for the supplementary figures

Supplementary Data and Methods

Supplementary Figures include Figure S1-S9.

Real-world evidence (RWE), conclusions derived from analysis of patients not treated in clinical trials, is increasingly recognized as an opportunity for discovery, to reduce disparities, and to contribute to regulatory approval. Maximal value of RWE may be facilitated through machine-learning techniques to integrate and interrogate large and otherwise underutilized datasets. In cancer research, an ongoing challenge for RWE is the lack of reliable, reproducible, scalable assessment of treatment-specific outcomes. We hypothesized a deep-learning model could be trained to use radiology text reports to estimate gold-standard RECIST-defined outcomes. Using text reports from patients with non–small cell lung cancer treated with PD-1 blockade in a training cohort and two test cohorts, we developed a deep-learning model to accurately estimate best overall response and progression-free survival. Our model may be a tool to determine outcomes at scale, enabling analyses of large clinical databases.Significance:We developed and validated a deep-learning model trained on radiology text reports to estimate gold-standard objective response categories used in clinical trial assessments. This tool may facilitate analysis of large real-world oncology datasets using objective outcome metrics determined more reliably and at greater scale than currently possible.This article is highlighted in the In This Issue feature, p. 1

Supplementary Materials

Supplementary Figure 1-5, Supplementary Tables 1-3

Supplementary Figures

TEAEs and TRAEs occurring in pts with intracranial disease

Supplementary Tables 1-2

Supplementary Figure from Activity of Adagrasib (MRTX849) in Brain Metastases: Preclinical Models and Clinical Data from Patients with KRASG12C-Mutant Non–Small Cell Lung Cancer

Supplementary Table from ARRY-382 in Combination with Pembrolizumab in Patients with Advanced Solid Tumors: Results from a Phase 1b/2 Study

Supplementary Data from First-in-Human Phase I/II ICONIC Trial of the ICOS Agonist Vopratelimab Alone and with Nivolumab: ICOS-High CD4 T-Cell Populations and Predictors of Response

Lung adenocarcinoma (LUAD) is one of the most common cancer types and has various treatment options. Better biomarkers to predict therapeutic response are needed to guide choice of treatment modality and to improve precision medicine. Here, we used a consensus hierarchical clustering approach on 509 LUAD cases from The Cancer Genome Atlas to identify five robust LUAD expression subtypes. Genomic and proteomic data from patient samples and cell lines was then integrated to help define biomarkers of response to targeted therapies and immunotherapies. This approach defined subtypes with unique proteogenomic and dependency profiles. Subtype 4 (S4)–associated cell lines exhibited specific vulnerability to loss of CDK6 and CDK6-cyclin D3 complex gene (CCND3). Subtype 3 (S3) was characterized by dependency on CDK4, immune-related expression patterns, and altered MET signaling. Experimental validation showed that S3-associated cell lines responded to MET inhibitors, leading to increased expression of programmed death-ligand 1 (PD-L1). In an independent real-world patient dataset, patients with S3 tumors were enriched with responders to immune checkpoint blockade. Genomic features in S3 and S4 were further identified as biomarkers for enabling clinical diagnosis of these subtypes. Overall, our consensus hierarchical clustering approach identified robust tumor expression subtypes, and our subsequent integrative analysis of genomics, proteomics, and CRISPR screening data revealed subtype-specific biology and vulnerabilities. These LUAD expression subtypes and their biomarkers could help identify patients likely to respond to CDK4/6, MET, or PD-L1 inhibitors, potentially improving patient outcome.Significance:Integrative analysis of multiomic and drug dependency data uncovers robust lung adenocarcinoma expression subtypes with unique therapeutic vulnerabilities and subtype-specific biomarkers of response.

Purpose:Acquired resistance to next-generation ALK tyrosine kinase inhibitors (TKIs) is often driven by secondary ALK mutations. Here, we investigated utility of plasma genotyping for identifying ALK resistance mutations at relapse on next-generation ALK TKIs.Experimental Design:We analyzed 106 plasma specimens from 84 patients with advanced ALK-positive lung cancer treated with second- and third-generation ALK TKIs using a commercially available next-generation sequencing (NGS) platform (Guardant360). Tumor biopsies from TKI-resistant lesions underwent targeted NGS to identify ALK mutations.Results:By genotyping plasma, we detected an ALK mutation in 46 (66%) of 70 patients relapsing on a second-generation ALK TKI. When post-alectinib plasma and tumor specimens were compared, there was no difference in frequency of ALK mutations (67% vs. 63%), but plasma specimens were more likely to harbor ≥2 ALK mutations (24% vs. 2%, P = 0.004). Among 29 patients relapsing on lorlatinib, plasma genotyping detected an ALK mutation in 22 (76%), including 14 (48%) with ≥2 ALK mutations. The most frequent combinations of ALK mutations were G1202R/L1196M and D1203N/1171N. Detection of ≥2 ALK mutations was significantly more common in patients relapsing on lorlatinib compared with second-generation ALK TKIs (48% vs. 23%, P = 0.017). Among 15 patients who received lorlatinib after a second-generation TKI, serial plasma analysis demonstrated that eight (53%) acquired ≥1 new ALK mutations on lorlatinib.Conclusions:ALK resistance mutations increase with each successive generation of ALK TKI and may be underestimated by tumor genotyping. Sequential treatment with increasingly potent ALK TKIs may promote acquisition of ALK resistance mutations leading to treatment-refractory compound ALK mutations.

Figure Legends - Supplementary Data

A-C): Images of autopsy specimens from an ALK-positive NSCLC patient, demonstrating heterogeneous expression of PD-L1 in multiple sections examined (Diffuse PD-L1 expression in A-B and focal PD-L1 expression in C).

Supplementary Figure from High-Resolution Profiling of Lung Adenocarcinoma Identifies Expression Subtypes with Specific Biomarkers and Clinically Relevant Vulnerabilities

Supplementary Figure 1-5

Supplementary Table 1. Summary of Interval Systemic Anticancer Therapies Received by ALK-Positive Patients Between Crizotinib and Ceritinib

Purpose: PD-1 inhibitors are established agents in the management of non–small cell lung cancer (NSCLC); however, only a subset of patients derives clinical benefit. To determine the activity of PD-1/PD-L1 inhibitors within clinically relevant molecular subgroups, we retrospectively evaluated response patterns among EGFR-mutant, anaplastic lymphoma kinase (ALK)-positive, and EGFR wild-type/ALK-negative patients.Experimental Design: We identified 58 patients treated with PD-1/PD-L1 inhibitors. Objective response rates (ORR) were assessed using RECIST v1.1. PD-L1 expression and CD8+ tumor-infiltrating lymphocytes (TIL) were evaluated by IHC.Results: Objective responses were observed in 1 of 28 (3.6%) EGFR-mutant or ALK-positive patients versus 7 of 30 (23.3%) EGFR wild-type and ALK-negative/unknown patients (P = 0.053). The ORR among never- or light- (≤10 pack years) smokers was 4.2% versus 20.6% among heavy smokers (P = 0.123). In an independent cohort of advanced EGFR-mutant (N = 68) and ALK-positive (N = 27) patients, PD-L1 expression was observed in 24%/16%/11% and 63%/47%/26% of pre–tyrosine kinase inhibitor (TKI) biopsies using cutoffs of ≥1%, ≥5%, and ≥50% tumor cell staining, respectively. Among EGFR-mutant patients with paired, pre- and post-TKI–resistant biopsies (N = 57), PD-L1 expression levels changed after resistance in 16 (28%) patients. Concurrent PD-L1 expression (≥5%) and high levels of CD8+ TILs (grade ≥2) were observed in only 1 pretreatment (2.1%) and 5 resistant (11.6%) EGFR-mutant specimens and was not observed in any ALK-positive, pre- or post-TKI specimens.Conclusions: NSCLCs harboring EGFR mutations or ALK rearrangements are associated with low ORRs to PD-1/PD-L1 inhibitors. Low rates of concurrent PD-L1 expression and CD8+ TILs within the tumor microenvironment may underlie these clinical observations. Clin Cancer Res; 22(18); 4585–93. ©2016 AACR.See related commentary by Gettinger and Politi, p. 4539

Supplementary Figures

Supplementary Figure 1. Individual swimmer plots for each patient who underwent a post-crizotinib/pre-ceritinib biopsy

Clinicopathologic Features of EGFR-Mutant and ALK-Positive Patients Included in PD-L1 Expression Analysis

Purpose: The first-generation ALK tyrosine kinase inhibitor (TKI) crizotinib is a standard therapy for patients with ALK-rearranged non–small cell lung cancer (NSCLC). Several next-generation ALK-TKIs have entered the clinic and have shown promising activity in crizotinib-resistant patients. As patients still relapse even on these next-generation ALK-TKIs, we examined mechanisms of resistance to the next-generation ALK-TKI alectinib and potential strategies to overcome this resistance.Experimental Design: We established a cell line model of alectinib resistance, and analyzed a resistant tumor specimen from a patient who had relapsed on alectinib. We developed Ba/F3 models harboring alectinib-resistant ALK mutations and evaluated the potency of other next-generation ALK-TKIs in these models. We tested the antitumor activity of the next-generation ALK-TKI ceritinib in the patient with acquired resistance to alectinib. To elucidate structure–activity relationships of ALK mutations, we performed computational thermodynamic simulation with MP-CAFEE.Results: We identified a novel V1180L gatekeeper mutation from the cell line model and a second novel I1171T mutation from the patient who developed resistance to alectinib. Both ALK mutations conferred resistance to alectinib as well as to crizotinib, but were sensitive to ceritinib and other next-generation ALK-TKIs. Treatment of the patient with ceritinib led to a marked response. Thermodynamics simulation suggests that both mutations lead to distinct structural alterations that decrease the binding affinity with alectinib.Conclusions: We have identified two novel ALK mutations arising after alectinib exposure that are sensitive to other next-generation ALK-TKIs. The ability of ceritinib to overcome alectinib-resistance mutations suggests a potential role for sequential therapy with multiple next-generation ALK-TKIs. Clin Cancer Res; 20(22); 5686–96. ©2014 AACR.

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