A Retrospective Genomic Landscape of 661 Young Adult Glioblastomas Diagnosed Using 2016 WHO Guidelines for Central Nervous System Tumors.

The authors present a cohort of 661 young adult glioblastomas diagnosed using 2016 WHO World Health Organization Classification of Tumors of the Central Nervous System, utilizing comprehensive genomic profiling (CGP) to explore their genomic landscape and assess their relationship to currently defined disease entities. This analysis explored variants with evidence of pathogenic function, common copy number variants (CNVs), and several novel fusion events not described in literature. Tumor mutational burden (TMB) mutational signatures, anatomic location, and tumor recurrence are further explored. Using data collected from CGP, unsupervised machine-learning techniques were leveraged to identify 10 genomic classes in previously assigned young adult glioblastomas. The authors relate these molecular classes to current World Health Organization guidelines and reference current literature to give therapeutic and prognostic descriptions where possible.

Insights of Clinical Significance From 109 695 Solid Tumor Tissue-Based Comprehensive Genomic Profiles.

BACKGROUND: FoundationOneCDx is approved in the US and Japan as a companion diagnostic test to identify patients with cancer who may benefit from treatment with 30 drug therapies in the US and 23 in Japan. Tumor profiling with FoundationOneCDx also detects genomic findings with evidence of clinical significance that may inform clinical care decisions beyond companion diagnostic claims. This observational study reports the breadth and impact of clinical decision insights from FoundationOneCDx solid tumor profiles. MATERIALS AND METHODS: Consecutive test result reports for patients with solid tumor diagnoses (n = 109 695) were retrospectively analyzed for clinically significant predictive, prognostic, and diagnostic genomic alterations and signatures, determined in accordance with professional guidelines. Interventional clinical trials with targeted therapies or immune checkpoint inhibitors were matched to tumor profiles based on evidence that the genomic finding may be an actionable, investigational, or hypothetical target in the patient's tumor type. RESULTS: In 14 predefined cancer types (80.7% of analyzed solid tumors), predictive, prognostic, and diagnostic markers were reported in 47.6%, 13.2%, and 4.5% of samples, respectively, accounting for a total of 51.2% of tumor profiles. Pan-cancer predictive markers of tumor mutational burden (TMB) of 10 or more mutations per megabase, high microsatellite instability (MSI), or NTRK1/2/3 fusions were observed in 15.6%, 2.0%, and 0.1% of solid tumors, respectively. Most solid tumor profiles (89.2%) had genomic results that could theoretically inform decisions on the selection of immunotherapy and targeted therapy clinical trials. CONCLUSION: For this real-world population of patients with FoundationOneCDx solid tumor profiles in the routine course of clinical care, clinically significant findings were reported for approximately half of patients with genomic results.

Inaugural Results of the Individualized Screening Trial of Innovative Glioblastoma Therapy: A Phase II Platform Trial for Newly Diagnosed Glioblastoma Using Bayesian Adaptive Randomization.

PURPOSE: The Individualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT) is a phase II platform trial that uses response adaptive randomization and genomic profiling to efficiently identify novel therapies for phase III testing. Three initial experimental arms (abemaciclib [a cyclin-dependent kinase [CDK]4/6 inhibitor], neratinib [an epidermal growth factor receptor [EGFR]/human epidermal growth factor receptor 2 inhibitor], and CC-115 [a deoxyribonucleic acid-dependent protein kinase/mammalian target of rapamycin inhibitor]) were simultaneously evaluated against a common control arm. We report the results for each arm and examine the feasibility and conduct of the adaptive platform design. PATIENTS AND METHODS: Patients with newly diagnosed O6-methylguanine-DNA methyltransferase-unmethylated glioblastoma were eligible if they had tumor genotyping to identify prespecified biomarker subpopulations of dominant glioblastoma signaling pathways (EGFR, phosphatidylinositol 3-kinase, and CDK). Initial random assignment was 1:1:1:1 between control (radiation therapy and temozolomide) and the experimental arms. Subsequent Bayesian adaptive randomization was incorporated on the basis of biomarker-specific progression-free survival (PFS) data. The primary end point was overall survival (OS), and one-sided P values are reported. The trial is registered with ClinicalTrials.gov (identifier: NCT02977780). RESULTS: Two hundred thirty-seven patients were treated (71 control; 73 abemaciclib; 81 neratinib; 12 CC-115) in years 2017-2021. Abemaciclib and neratinib were well tolerated, but CC-115 was associated with ≥ grade 3 treatment-related toxicity in 58% of patients. PFS was significantly longer with abemaciclib (hazard ratio [HR], 0.72; 95% CI, 0.49 to 1.06; one-sided P = .046) and neratinib (HR, 0.72; 95% CI, 0.50 to 1.02; one-sided P = .033) relative to the control arm but there was no PFS benefit with CC-115 (one-sided P = .523). None of the experimental therapies demonstrated a significant OS benefit (P > .05). CONCLUSION: The INSIGhT design enabled efficient simultaneous testing of three experimental agents using a shared control arm and adaptive randomization. Two investigational arms had superior PFS compared with the control arm, but none demonstrated an OS benefit. The INSIGhT design may promote improved and more efficient therapeutic discovery in glioblastoma. New arms have been added to the trial.

Accessible Data Collections for Improved Decision Making in Neuro-Oncology Clinical Trials.

Drug development can be associated with slow timelines, particularly for rare or difficult-to-treat solid tumors such as glioblastoma. The use of external data in the design and analysis of trials has attracted significant interest because it has the potential to improve the efficiency and precision of drug development. A recurring challenge in the use of external data for clinical trials, however, is the difficulty in accessing high-quality patient-level data. Academic research groups generally do not have access to suitable datasets to effectively leverage external data for planning and analyses of new clinical trials. Given the need for resources to enable investigators to benefit from existing data assets, we have developed the Glioblastoma External (GBM-X) Data Platform which will allow investigators in neuro-oncology to leverage our data collection and obtain analyses. GBM-X strives to provide an uncomplicated process to use external data, contextualize single-arm trials, and improve inference on treatment effects early in drug development. The platform is designed to welcome interested collaborators and integrate new data into the platform, with the expectation that the data collection can continue to grow and remain updated. With such features, GBM-X is designed to help to accelerate evaluation of therapies, to grow with collaborations, and to serve as a model to improve drug discovery for rare and difficult-to-treat tumors in oncology.

ALK Amplification and Rearrangements Are Recurrent Targetable Events in Congenital and Adult Glioblastoma.

PURPOSE: Anaplastic lymphoma kinase (ALK) aberrations have been identified in pediatric-type infant gliomas, but their occurrence across age groups, functional effects, and treatment response has not been broadly established. EXPERIMENTAL DESIGN: We performed a comprehensive analysis of ALK expression and genomic aberrations in both newly generated and retrospective data from 371 glioblastomas (156 adult, 205 infant/pediatric, and 10 congenital) with in vitro and in vivo validation of aberrations. RESULTS: ALK aberrations at the protein or genomic level were detected in 12% of gliomas (45/371) in a wide age range (0-80 years). Recurrent as well as novel ALK fusions (LRRFIP1-ALK, DCTN1-ALK, PRKD3-ALK) were present in 50% (5/10) of congenital/infant, 1.4% (3/205) of pediatric, and 1.9% (3/156) of adult GBMs. ALK fusions were present as the only candidate driver in congenital/infant GBMs and were sometimes focally amplified. In contrast, adult ALK fusions co-occurred with other oncogenic drivers. No activating ALK mutations were identified in any age group. Novel and recurrent ALK rearrangements promoted STAT3 and ERK1/2 pathways and transformation in vitro and in vivo. ALK-fused GBM cellular and mouse models were responsive to ALK inhibitors, including in patient cells derived from a congenital GBM. Relevant to the treatment of infant gliomas, we showed that ALK protein appears minimally expressed in the forebrain at perinatal stages, and no gross effects on perinatal brain development were seen in pregnant mice treated with the ALK inhibitor ceritinib. CONCLUSIONS: These findings support use of brain-penetrant ALK inhibitors in clinical trials across infant, pediatric, and adult GBMs. See related commentary by Mack and Bertrand, p. 2567.

Validation of Predictive Analyses for Interim Decisions in Clinical Trials.

PURPOSE: Adaptive clinical trials use algorithms to predict, during the study, patient outcomes and final study results. These predictions trigger interim decisions, such as early discontinuation of the trial, and can change the course of the study. Poor selection of the Prediction Analyses and Interim Decisions (PAID) plan in an adaptive clinical trial can have negative consequences, including the risk of exposing patients to ineffective or toxic treatments. METHODS: We present an approach that leverages data sets from completed trials to evaluate and compare candidate PAIDs using interpretable validation metrics. The goal is to determine whether and how to incorporate predictions into major interim decisions in a clinical trial. Candidate PAIDs can differ in several aspects, such as the prediction models used, timing of interim analyses, and potential use of external data sets. To illustrate our approach, we considered a randomized clinical trial in glioblastoma. The study design includes interim futility analyses on the basis of the predictive probability that the final analysis, at the completion of the study, will provide significant evidence of treatment effects. We examined various PAIDs with different levels of complexity to investigate if the use of biomarkers, external data, or novel algorithms improved interim decisions in the glioblastoma clinical trial. RESULTS: Validation analyses on the basis of completed trials and electronic health records support the selection of algorithms, predictive models, and other aspects of PAIDs for use in adaptive clinical trials. By contrast, PAID evaluations on the basis of arbitrarily defined ad hoc simulation scenarios, which are not tailored to previous clinical data and experience, tend to overvalue complex prediction procedures and produce poor estimates of trial operating characteristics such as power and the number of enrolled patients. CONCLUSION: Validation analyses on the basis of completed trials and real world data support the selection of predictive models, interim analysis rules, and other aspects of PAIDs in future clinical trials.

Phase I study of a novel glioblastoma radiation therapy schedule exploiting cell-state plasticity.

BACKGROUND: Glioblastomas comprise heterogeneous cell populations with dynamic, bidirectional plasticity between treatment-resistant stem-like and treatment-sensitive differentiated states, with treatment influencing this process. However, current treatment protocols do not account for this plasticity. Previously, we generated a mathematical model based on preclinical experiments to describe this process and optimize a radiation therapy fractionation schedule that substantially increased survival relative to standard fractionation in a murine glioblastoma model. METHODS: We developed statistical models to predict the survival benefit of interventions to glioblastoma patients based on the corresponding survival benefit in the mouse model used in our preclinical study. We applied our mathematical model of glioblastoma radiation response to optimize a radiation therapy fractionation schedule for patients undergoing re-irradiation for glioblastoma and developed a first-in-human trial (NCT03557372) to assess the feasibility and safety of administering our schedule. RESULTS: Our statistical modeling predicted that the hazard ratio when comparing our novel radiation schedule with a standard schedule would be 0.74. Our mathematical modeling suggested that a practical, near-optimal schedule for re-irradiation of recurrent glioblastoma patients was 3.96 Gy × 7 (1 fraction/day) followed by 1.0 Gy × 9 (3 fractions/day). Our optimized schedule was successfully administered to 14/14 (100%) patients. CONCLUSIONS: A novel radiation therapy schedule based on mathematical modeling of cell-state plasticity is feasible and safe to administer to glioblastoma patients.

A Common Cell of Origin for Inflammatory Myofibroblastic Tumor and Lung Adenocarcinoma with ALK rearrangement.

This case signifies the importance of obtaining tumor comprehensive genomic profiling (CGP) as it has utility in cancer type classification and helping in diagnosing recurrence/metastasis or separately occurring primary tumors. CGP can also help guiding treatment as in this case separately occurring Inflammatory Myofibroblastic Tumor had ALK fusion and responded to crizotinib. As treatment progresses, new biopsies should be obtained and CGP used to evaluate for appearance of any new genomic alterations, in order to guide further therapy.

The design and evaluation of hybrid controlled trials that leverage external data and randomization.

Patient-level data from completed clinical studies or electronic health records can be used in the design and analysis of clinical trials. However, these external data can bias the evaluation of the experimental treatment when the statistical design does not appropriately account for potential confounders. In this work, we introduce a hybrid clinical trial design that combines the use of external control datasets and randomization to experimental and control arms, with the aim of producing efficient inference on the experimental treatment effects. Our analysis of the hybrid trial design includes scenarios where the distributions of measured and unmeasured prognostic patient characteristics differ across studies. Using simulations and datasets from clinical studies in extensive-stage small cell lung cancer and glioblastoma, we illustrate the potential advantages of hybrid trial designs compared to externally controlled trials and randomized trial designs.

Comparative Effectiveness of Immune Checkpoint Inhibitors vs Chemotherapy by Tumor Mutational Burden in Metastatic Castration-Resistant Prostate Cancer.

Importance: The most useful biomarkers for clinical decision-making identify patients likely to have improved outcomes with one treatment vs another. Objective: To evaluate treatment class-specific outcomes of patients receiving immune checkpoint inhibitor (ICI) vs taxane chemotherapy by tumor mutational burden (TMB). Design, Setting, and Participants: This comparative effectiveness analysis of clinical variables and outcomes used prospectively defined biomarker-stratified genomic data from a deidentified clinicogenomic database. Data included men with previously treated metastatic castration-resistant prostate cancer (mCRPC) receiving ICI or single-agent taxane chemotherapy from January 2011 to April 2021 at approximately 280 US academic or community-based cancer clinics (approximately 800 sites of care). Data were analyzed from July to August 2021. Exposures: Single-agent ICI or single-agent taxanes. Treatments were assigned at discretion of physician and patient without randomization. Imbalances of known factors between treatment groups were adjusted with propensity weighting. Main Outcomes and Measures: Prostate-specific antigen (PSA) response, time to next therapy (TTNT), and overall survival (OS). Results: A total of 741 men (median [IQR], 70 [64-76] years) with mCRPC received comprehensive genomic profiling and were treated with ICI or single-agent taxane therapy. At baseline, the median (IQR) PSA level was 79.4 (19.0-254) ng/mL, 108 men (18.8%) had Eastern Cooperative Oncology Group Performance Status scores of 2 or greater, and 644 men (86.9%) had received prior systemic treatments for mCRPC. A total of 45 patients (6.1%) received ICI therapy and 696 patients (93.9%) received taxane therapy. Among patients with TMB of fewer than 10 mutations per megabase (mt/Mb) receiving ICI, compared with those receiving taxanes, had worse TTNT (median [IQR], 2.4 [1.1-3.2] months vs 4.1 [2.2-6.3] months; hazard ratio [HR], 2.65; 95% CI, 1.78-3.95; P < .001). In contrast, for patients with TMB of 10 mt/Mb or greater, use of ICIs, compared with use taxanes, was associated with more favorable TTNT (median [IQR], 8.0 [3.4 to unknown] months vs 2.4 [2.4-7.3] months; HR, 0.37, 95% CI, 0.15-0.87; P = .02) and OS (median 19.9 [8.06 to unknown] months vs 4.2 [2.69 - 6.12] months; HR, 0.23; 95% CI, 0.10-0.57; P = .001). Among all 741 patients, 44 (5.9%) had TMB of 10 mt/Mb or greater, 22 (3.0%) had high microsatellite instability, and 20 (2.7%) had both. Treatment interactions with TMB of 10 mt/Mb or greater (TTNT: HR, 0.10; 95% CI, 0.32-0.31; P < .001; OS: HR, 0.25; 95% CI, 0.076-0.81; P = .02) were stronger than high microsatellite instability alone (TTNT: HR, 0.12; 95% CI, 0.03-0.51; P = .004; OS: HR, 0.38; 95% CI, 0.13-1.12; P = .08). Conclusions and Relevance: In this comparative effectiveness study, ICIs were more effective than taxanes in patients with mCRPC when TMB was 10 mt/Mb or greater but not when TMB was fewer than 10 mt/Mb. The results add validity to the existing TMB cutoff of 10 mt/Mb for ICI use in later lines of therapy, and suggest that ICIs may be a viable alternative to taxane chemotherapy for patients with mCRPC with high TMB.

Cancer patient survival can be parametrized to improve trial precision and reveal time-dependent therapeutic effects.

Individual participant data (IPD) from oncology clinical trials is invaluable for identifying factors that influence trial success and failure, improving trial design and interpretation, and comparing pre-clinical studies to clinical outcomes. However, the IPD used to generate published survival curves are not generally publicly available. We impute survival IPD from ~500 arms of Phase 3 oncology trials (representing ~220,000 events) and find that they are well fit by a two-parameter Weibull distribution. Use of Weibull functions with overall survival significantly increases the precision of small arms typical of early phase trials: analysis of a 50-patient trial arm using parametric forms is as precise as traditional, non-parametric analysis of a 90-patient arm. We also show that frequent deviations from the Cox proportional hazards assumption, particularly in trials of immune checkpoint inhibitors, arise from time-dependent therapeutic effects. Trial duration therefore has an underappreciated impact on the likelihood of success.

HPV51-associated Leiomyosarcoma: A Novel Class of TP53/RB1-Wildtype Tumor With Predilection for the Female Lower Reproductive Tract.

Inactivating mutations in tumor suppressor genes TP53 and RB1 are considered central drivers in leiomyosarcomas (LMSs). In high-risk human papillomavirus (HPV)-related tumors, a similar functional outcome is achieved through oncoproteins E6 and E7, which inactivate the p53 and RB1 proteins, respectively. Here, we hypothesized that HPV infection could provide an alternative mechanism for tumorigenesis in a subset of TP53/RB1-wildtype LMS. We evaluated tumor samples from 2585 consecutive unique patients carrying a diagnosis of gynecologic or soft tissue LMS. Tumor DNA and available RNA were analyzed by hybrid-capture-based next-generation sequencing/comprehensive genomic profiling of 406 genes and transcripts (FoundationOneHeme). Of the initial 2585 cases, we excluded 16 based on the presence of molecular alterations that are considered defining for sarcomas other than LMS. In the remaining 2569 cases, we searched for LMS that were TP53/RB1-wildtype (n=486 of 2569; 18.9%). We also searched LMS tumors for HPV sequences that we then classified into genotypes by de novo assembly of nonhuman sequencing reads followed by alignment to the RefSeq database. Among TP53/RB1-wildtype LMS, we identified 18 unique cases harboring HPV sequences. Surprisingly, most (n=11) were HPV51-positive, and these 11 represented all HPV51-positive tumors in our entire LMS database (n=11 of 2569; 0.4%). The absence of genomic alterations in TP53 or RB1 in HPV51-positive LMS represented a marked difference from HPV51-negative LMS (n=2558; 0% vs. 72% [P<0.00001], 0% vs. 53% [P=0.0002]). In addition, compared with HPV51-negative LMS, HPV51-positive LMS were significantly enriched for genomic alterations in ATRX (55% vs. 24%, P=0.027) and TSC1 (18% vs. 0.6%, P=0.0047). All HPV51-positive LMS were in women; median age was 54 years at surgery (range: 23 to 74 y). All known primary sites were from the gynecologic tract or adjacent anogenital area, including 5 cases of vaginal primary site. Histology was heterogeneous, with evaluable cases showing predominant epithelioid (n=5) and spindle (n=5) morphology. In situ hybridization confirmed the presence of high-risk HPV E6/E7 mRNA in tumor cells in three of three evaluable cases harboring HPV51 genomic sequences. Overall, in our pan-LMS analysis, HPV reads were identified in a subset of TP53/RB1-wildtype LMS. For all HPV51-associated LMS, the striking absence of any detectable TP53 or RB1 mutations and predilection for the female lower reproductive tract supports our hypothesis that high-risk HPV can be an alternative tumorigenic mechanism in this distinct class of LMS.

A molecularly integrated grade for meningioma.

BACKGROUND: Meningiomas are the most common primary intracranial tumor in adults. Clinical care is currently guided by the World Health Organization (WHO) grade assigned to meningiomas, a 3-tiered grading system based on histopathology features, as well as extent of surgical resection. Clinical behavior, however, often fails to conform to the WHO grade. Additional prognostic information is needed to optimize patient management. METHODS: We evaluated whether chromosomal copy-number data improved prediction of time-to-recurrence for patients with meningioma who were treated with surgery, relative to the WHO schema. The models were developed using Cox proportional hazards, random survival forest, and gradient boosting in a discovery cohort of 527 meningioma patients and validated in 2 independent cohorts of 172 meningioma patients characterized by orthogonal genomic platforms. RESULTS: We developed a 3-tiered grading scheme (Integrated Grades 1-3), which incorporated mitotic count and loss of chromosome 1p, 3p, 4, 6, 10, 14q, 18, 19, or CDKN2A. 32% of meningiomas reclassified to either a lower-risk or higher-risk Integrated Grade compared to their assigned WHO grade. The Integrated Grade more accurately identified meningioma patients at risk for recurrence, relative to the WHO grade, as determined by time-dependent area under the curve, average precision, and the Brier score. CONCLUSION: We propose a molecularly integrated grading scheme for meningiomas that significantly improves upon the current WHO grading system in prediction of progression-free survival. This framework can be broadly adopted by clinicians with relative ease using widely available genomic technologies and presents an advance in the care of meningioma patients.

The use of external control data for predictions and futility interim analyses in clinical trials.

BACKGROUND: External control (EC) data from completed clinical trials and electronic health records can be valuable for the design and analysis of future clinical trials. We discuss the use of EC data for early stopping decisions in randomized clinical trials (RCTs). METHODS: We specify interim analyses (IAs) approaches for RCTs, which allow investigators to integrate external data into early futility stopping decisions. IAs utilize predictions based on early data from the RCT, possibly combined with external data. These predictions at IAs express the probability that the trial will generate significant evidence of positive treatment effects. The trial is discontinued if this predictive probability becomes smaller than a prespecified threshold. We quantify efficiency gains and risks associated with the integration of external data into interim decisions. We then analyze a collection of glioblastoma (GBM) data sets, to investigate if the balance of efficiency gains and risks justify the integration of external data into the IAs of future GBM RCTs. RESULTS: Our analyses illustrate the importance of accounting for potential differences between the distributions of prognostic variables in the RCT and in the external data to effectively leverage external data for interim decisions. Using GBM data sets, we estimate that the integration of external data increases the probability of early stopping of ineffective experimental treatments by up to 25% compared to IAs that do not leverage external data. Additionally, we observe a reduction of the probability of early discontinuation for effective experimental treatments, which improves the RCT power. CONCLUSION: Leveraging external data for IAs in RCTs can support early stopping decisions and reduce the number of enrolled patients when the experimental treatment is ineffective.

Leveraging external data in the design and analysis of clinical trials in neuro-oncology.

Integration of external control data, with patient-level information, in clinical trials has the potential to accelerate the development of new treatments in neuro-oncology by contextualising single-arm studies and improving decision making (eg, early stopping decisions). Based on a series of presentations at the 2020 Clinical Trials Think Tank hosted by the Society of Neuro-Oncology, we provide an overview on the use of external control data representative of the standard of care in the design and analysis of clinical trials. High-quality patient-level records, rigorous methods, and validation analyses are necessary to effectively leverage external data. We review study designs, statistical methods, risks, and potential distortions in using external data from completed trials and real-world data, as well as data sources, data sharing models, ongoing work, and applications in glioblastoma.

Genomic Profiling of Combined Hepatocellular Cholangiocarcinoma Reveals Genomics Similar to Either Hepatocellular Carcinoma or Cholangiocarcinoma.

PURPOSE: Combined hepatocellular cholangiocarcinoma (cHCC-CCA) is a rare, aggressive primary liver carcinoma, with morphologic features of both hepatocellular carcinomas (HCC) and liver cholangiocarcinomas (CCA). METHODS: The genomic profiles of 4,975 CCA, 1,470 HCC, and 73 cHCC-CCA cases arising from comprehensive genomic profiling in the course of clinical care were reviewed for genomic alterations (GA), tumor mutational burden, microsatellite instability status, genomic loss of heterozygosity, chromosomal aneuploidy, genomic ancestry, and hepatitis B virus status. RESULTS: In cHCC-CCA, GA were most common in TP53 (65.8%), TERT (49.3%), and PTEN (9.6%), and 24.6% cHCC-CCA harbored potentially targetable GA. Other GA were predominantly associated with either HCC or CCA, including, but not limited to, TERT, FGFR2, IDH1, and presence of hepatitis B virus. On the basis of these features, a machine learning (ML) model was trained to classify a cHCC-CCA case as CCA-like or HCC-like. Of cHCC-CCA cases, 16% (12/73) were ML-classified as CCA-like and 58% (42/73) cHCC-CCA were ML-classified as HCC-like. The ML model classified more than 70% of cHCC-CCA as CCA-like or HCC-like on the basis of genomic profiles, without additional clinico-pathologic input. CONCLUSION: These findings demonstrate the use of ML for classification as based on a targeted exome panel used during routine clinical care. Classification of cHCC-CCA by genomic features alone creates insights into the biology of the disease and warrants further investigation for relevance to clinical care.

Characterization of Non-Small-Cell Lung Cancers With MET Exon 14 Skipping Alterations Detected in Tissue or Liquid: Clinicogenomics and Real-World Treatment Patterns.

PURPOSE: MET exon 14 (METex14) skipping alterations are oncogenic drivers in non-small-cell lung cancer (NSCLC). We present a comprehensive overview of METex14 samples from 1,592 patients with NSCLC, associated clinicogenomic characteristics, potential mechanisms of acquired resistance, treatment patterns, and outcomes to MET inhibitors. METHODS: Hybrid capture-based comprehensive genomic profiling (CGP) was performed on samples from 69,219 patients with NSCLC. For treatment patterns and outcomes analysis, patients with advanced METex14-altered NSCLC were selected from the Flatiron Health-Foundation Medicine clinicogenomic database, a nationwide deidentified electronic health record-derived database linked to Foundation Medicine CGP for patients treated between January 2011 and March 2020. RESULTS: A total of 1,592 patients with NSCLC (2.3%) were identified with 1,599 METex14 alterations spanning multiple functional sites (1,458 of 60,244 tissue samples and 134 of 8,975 liquid samples). Low tumor mutational burden and high programmed death ligand 1 expression were enriched in METex14-altered samples. MDM2, CDK4, and MET coamplifications and TP53 mutations were present in 34%, 19%, 11%, and 42% of tissue samples, respectively. Comparing tissue and liquid cohorts, coalteration frequency and acquired resistance mechanisms, including multiple MET mutations, EGFR, ERBB2, KRAS, and PI3K pathway alterations, were generally similar. Positive percent agreement with the tissue was 100% for METex14 pairs collected within 1 year (n = 7). Treatment patterns showed increasing adoption of MET inhibitors in METex14-altered NSCLC after receipt of CGP results; the real-world response rate to MET inhibitors was 45%, and time to treatment discontinuation was 4.4 months. CONCLUSION: Diverse METex14 alterations were present in 2%-3% of NSCLC cases. Tissue and liquid comparisons showed high concordance and similar coalteration profiles. Characterizing common co-occurring alterations and immunotherapy biomarkers, including those present before or acquired after treatment, may be critical for predicting responses to MET inhibitors and informing rational combination strategies.

Genomic profiling of solid tumors harboring BRD4-NUT and response to immune checkpoint inhibitors.

BACKGROUND: The translocation t(15:19) produces the oncogenic BRD4-NUT fusion which is pathognomonic for NUT carcinoma (NC), which is a rare, but extremely aggressive solid tumor. Comprehensive genomic profiling (CGP) by hybrid-capture based next generation sequencing of 186+ genes of a cohort of advanced cancer cases with a variety of initial diagnoses harboring BRD4-NUT may shed further insight into the biology of these tumors and possible options for targeted treatment. CASE PRESENTATION: Thirty-one solid tumor cases harboring a BRD4-NUT translocation are described, with only 16% initially diagnosed as NC and the remainder carrying other diagnoses, most commonly NSCLCNOS (22%) and lung squamous cell carcinoma (NSCLC-SCC) (16%). The cohort was all microsatellite stable and harbored a low Tumor Mutational Burden (TMB, mean 1.7 mut/mb, range 0-4). In two index cases, patients treated with immune checkpoint inhibitors (ICPI) had unexpected partial or better responses of varying duration. Notably, four cases - including the two index cases - were negative for PD-L1 expression. Neo-antigen prediction for BRD4-NUT and then affinity modeling of the peptide-MHC (pMHC) complex for an assessable index case predicted very high affinity binding, both on a ranked (99.9%) and absolute (33 nM) basis. CONCLUSIONS: CGP identifies BRD4-NUT fusions in advanced solid tumors which carry a broad range of initial diagnoses and which should be re-diagnosed as NC per guidelines. A hypothesized mechanism underlying responses to ICPI in the low TMB, PD-L1 negative index cases is the predicted high affinity of the BRD4-NUT fusion peptide to MHC complexes. Further study of pMHC affinity and response to immune checkpoint inhibitors in patients with NC harboring BRD4-NUT is needed to validate this therapeutic hypothesis.

Real-world association of HER2/ERBB2 concordance with trastuzumab clinical benefit in advanced esophagogastric cancer.

Aim: To assess concordance between HER2 status measured by traditional methods and ERBB2 amplification measured by next-generation sequencing and its association with first-line trastuzumab clinical benefit in patients with advanced esophagogastric cancer. Methods: Retrospective analysis of HER2/ERBB2 concordance using a deidentified USA-based clinicogenomic database. Clinical outcomes were assessed for patients with HER2+ advanced esophagogastric cancer who received first-line trastuzumab. Results: Overall HER2/ERBB2 concordance was 87.5%. Among patients who received first-line trastuzumab, concordant HER2/ERBB2 was associated with longer time to treatment discontinuation (adjusted hazard ratio [aHR]: 0.63; 95% CI: 0.43-0.90) and overall survival (aHR: 0.51; 95% CI: 0.33-0.79). ERBB2 copy number ≥25 (median) was associated with longer time to treatment discontinuation (aHR: 0.56; 95% CI: 0.35-0.88) and overall survival (aHR: 0.52; 95% CI: 0.30-0.91). Conclusion: HER2/ERBB2 concordance and higher ERBB2 copy number predicted clinical benefit from trastuzumab.

Lay abstract Trastuzumab is a drug that has been shown to prolong survival in some patients with advanced esophagogastric cancer whose tumor expresses a protein biomarker called HER2. There are different methods for assessing whether a patient's tumor expresses HER2, including but not limited to traditional methods such as immunohistochemistry and in situ hybridization and novel methods such as next-generation sequencing, which detects alterations in the gene (ERBB2) that encodes the HER2 protein. In our study, we assessed concordance between HER2 status (HER2-positive or HER2-negative) measured by traditional methods and ERBB2 amplification measured by next-generation sequencing, to determine whether there was an association between concordance and clinical benefit in patients with advanced esophagogastric cancer treated with trastuzumab. Our results suggest that, when HER2 positivity is detected through traditional methods, both ERBB2 concordance (i.e., agreement that a patient's tumor had the biomarker) and a higher ERBB2 copy number (the amount of the ERBB2 gene expressed by the tumor) were associated with longer time to treatment discontinuation and overall survival in patients with advanced esophagogastric cancer treated with first-line trastuzumab.