ImportanceThere are few studies assessing the association of tumor mutational burden (TMB) and clinical outcomes in a large cohort of patients with diverse advanced cancers.ObjectiveTo clinically validate a TMB biomarker from a next-generation sequencing targeted gene panel assay.Design, Setting, and ParticipantsA prespecified cohort study using the deidentified clinicogenomic Tempus database of patients sequenced between 2018 and 2022, which contained retrospective, observational data originating from 300 cancer sites including 199 community sites and 101 academic sites. Patients with advanced solid tumors across 8 cancer types and more than 20 histologies, sequenced with Tempus xT who were treated with immune checkpoint inhibitors (ICIs) in the first-line or second-line setting were included. Data were analyzed from September 2018 to August 2022.ExposureTreatment with US Food and Drug Administration (FDA)–approved antiprogrammed cell death-1/programmed cell death-ligand 1 (PD-1/PD-L1) ICI and/or in combination with a cytotoxic T-lymphocyte-associated protein-4 ICI.Main Outcomes and MeasuresThe primary outcome was the association of tumor mutational burden (TMB) binary category (high [≥10 mut/mb] vs low) with overall survival (OS) in patients treated with ICIs. Secondary outcomes were progression-free survival (PFS), and time to progression (TTP).ResultsIn the evaluable cohort of 674 patients, the median (IQR) age was 69.4 (28.6-89.8) years, 271 patients (40.2%) were female, and 435 patients (64.5%) were White. The most common advanced cancers were non–small cell lung cancer (330 patients [49.0%]), followed by bladder cancer (148 patients [22.0%]), and head and neck squamous cell carcinoma (96 patients [14.8%]). Median (IQR) follow-up was 7.2 (3.2-14.1) months. High TMB (TMB-H) cancers (206 patients [30.6%]) were significantly associated with longer OS than low TMB (TMB-L) cancers (hazard ratio [HR], 0.72; upper confidence bound [UCB], 0.91; P = .01). In a prospective subset of 403 patients treated with ICIs after TMB testing, TMB-H cancers (135 patients [33.5%]) were significantly associated with longer OS (HR, 0.61; UCB, 0.84; P = .005), PFS (HR, 0.62; UCB, 0.82; P = .003), and TTP (HR, 0.67; UCB, 0.92; P = .02) than TMB-L cancers. An overall survival benefit was seen regardless of the type of ICI used (pembrolizumab, 339 patients; HR, 0.67; UCB, 0.94; P = .03), other ICIs (64 patients; HR, 0.37; UCB, 0.85; P = .03), and after adjusting for PD-L1 and microsatellite stability status (403 patients; HR = 0.67; UCB, 0.92; P = .02).Conclusions and RelevanceIn this cohort study of patients with advanced solid tumors treated with ICIs in diverse clinics, TMB-H cancers were significantly associated with improved clinical outcomes compared with TMB-L cancers.
3077 Background: While targeted DNA-seq can detect clinically actionable fusions in tumor tissue samples, technical and analytical challenges may give rise to false negatives. RNA-based, whole-exome sequencing provides a complementary method for fusion detection, and may improve the identification of actionable variants. In this study, we quantify this benefit using a large, real-world clinical dataset to assess actionable fusions detected from RNA in conjunction with DNA profiling. Methods: Using the Tempus Research Database, we retrospectively analyzed a de-identified dataset of ̃80K samples (77.4K patients) profiled with the Tempus xT assay (both DNA-seq with fusion detection in 21 genes and whole exome capture RNA-seq). Only patients that had successful RNA- and DNA-seq were included. Fusions were detected using the Tempus bioinformatic and clinical workflow. Candidate fusions were filtered based on read support thresholds, fusion annotation ( i.e., breakpoints, reading frame, conserved domains), and manual review. OncoKB was used to select fusion alterations in levels 1 and 2 and to identify those indication-matched to targeted therapies. Results: We identified 2118 level 1 and 2 fusion events across 1945 patients across 20 different cancer types. Most fusions were observed in non-small cell lung cancer (NSCLC) (25%) and biliary cancer (9%) samples. Of the 2118 fusion events, 29.1% (616) were detected only through RNA-seq while 4.8% (101) of the events were identifiable only through DNA-seq. Notably, 69.4% of fusions in low-grade glioma and 58.2% in sarcomas were detected only by RNA-seq. When evaluating specific gene fusion events, RNA-seq consistently improved the detection of fusions compared to DNA-seq alone (Table) across all cancer types. A total of 1106 fusions were classified as targetable by OncoKB indication-matched therapies with 19% (214) of these identifiable through RNA-seq alone, 5% (54) by DNA-seq alone, and 76% (838) identifiable through RNA- and DNA-seq. Overall, fusions identified through RNA-seq alone led to a 24% increase in the number of patients who were eligible to receive matched therapies (214 / 892). This included imatinib for patients with CML/BLCL (69.8%), crizotinib for NSCLC (40.3%) and entrectinib for NTRK and ROS1 fusions (32.5%). Conclusions: The addition of RNA-seq to DNA-seq significantly increased the detection of fusion events and ability to match patients to targeted therapies. Results support consideration of combined RNA-DNA-seq for standard-of-care fusion calling. [Table: see text]
Background: Recent groundbreaking work has shown that patients with lower levels of HER2-expression (HER2-low) may benefit from treatment with trastuzumab deruxtecan—an HER2 antibody-drug conjugate FDA-approved for treatment for HER2-positive (HER2+) patients and thus can represent a new molecular subtype. In fact, this HER2-low patient population is enriched with luminal disease but is clinically heterogeneous and outcomes have therefore not been extensively characterized due to the lack of annotated multimodal real-world data (RWD). We used Tempus RWD to identify unique HER2-low subtypes using RNA sequencing and compare outcomes across subtypes. Methods: We retrospectively analyzed 1,545 breast cancer samples from the Tempus database tested with the Tempus xT assay that includes whole-exome capture RNA-seq. Only tumors with known HER2 status determined via immunohistochemistry [IHC] and/or FISH were included. A HER2 RNA gene signature was developed by comparing HER2- (n=464, IHC 0+) and HER2+ (n=161, IHC 3+ or IHC 2+ and FISH+) patients—controlling for HR status—to identify genes associated with HER2 over-expression. This HER2 signature was used to stratify independent HER2-low samples (n=920, determined by IHC 1+ or IHC2+ and FISH-) via hierarchical clustering. Treatment use in this cohort was not assessed. Clusters were subsequently assessed according to clinical, demographic, and molecular factors including PAM50 molecular subtype classification of the RNA signatures. Real-world progression-free survival (rwPFS) was evaluated based on progression and death captured through manual expert abstraction for a subset of stage 4 patients (n=336) and estimated via Kaplan-Meier analysis. Results: HER2-low patients were clustered according to our HER2 expression signature identifying three distinct molecular clusters (Table 1). Stage and demographic distributions (race, ethnicity, age) were similar across clusters. Of note, cluster 3 (n=186, 20% of the HER2-low population) was significantly enriched in hormone receptor negative (HR–) patients (p< 1e-5) and had lower ERBB2 RNA expression (p< 1e-5). Interestingly, molecular characterization using PAM50 demonstrated that cluster 3 was predominantly a basal-like subtype, whereas luminal-like samples dominated cluster 1 and 2, and cluster 2 had the largest composition of HER2-like samples (Table 1). Strikingly, cluster 3 stage 4 patients (n=57) had a median rwPFS that was significantly shorter (>12 months) than cluster 1 and 2 stage 4 patients (n=279, HR>1.66, p< 2e-2, Table 1). Conclusions: Tempus multimodal RWD reveals that HER2-low breast cancers are comprised of distinct molecular subtypes. In a preliminary analysis, a cluster of HER2-low, predominantly basal-like patients demonstrated dramatically worse outcomes than other clusters. These data further emphasize the importance of using RNA expression to fully characterize clinically relevant subpopulations. Further prospective studies are urgently needed to assess treatment response in this heterogenous emerging HER2-low distinct population. Citation Format: Talal Ahmed, Daniel Stover, Adam Hockenberry, Matthew Mackay, Halla Nimeiri, James Chen, Rotem Ben-Shachar, Massimo Cristofanilli. HER2-08 Molecular characterization of HER-2 low patients identifies basal-enriched subset with poor clinical outcomes in real-world data [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr HER2-08.
3123 Background: Recent evidence has suggested that some patients with non-small cell lung cancer (NSCLC) harbor a HRD signature that represents a distinct genomic subtype that could be targeted by PARP inhibitors (PARPi). However, there is little data on HRD prevalence in NSCLC or its genomic associations. Here, we evaluated the co-occurrence of driver mutations and established immune biomarkers with an RNA-based HRD signature in a large, real-world NSCLC cohort. Methods: We analyzed data from 5119 NSCLC patients that underwent sequencing via the Tempus xT test (DNA-seq of 648 genes; RNA-seq with whole exome capture). HRD status was predicted by the Tempus HRD-RNA test, a pan-cancer logistic regression classifier that uses an RNA gene expression signature optimized to distinguish between BRCA-biallelic loss and homologous recombination repair (HRR)-WT samples (Leibowitz et al, 2022). Cohort samples were excluded from model training. All comparisons were tested via chi-squared or Fisher’s exact tests. Results: An RNA-derived signature of HRD (HRD-RNA+) was observed in 3.53% (n=181/5119) of patients. HRD-RNA+ prevalence was higher in squamous cell carcinoma (84/1331, 6.3%) relative to adenocarcinoma (68/3015, 2.3%; p < 0.001). The prevalence of select alterations by HRD-RNA status are shown in Table. Alterations in BRCA1/2 and HRR genes (inclusive of BRCA1/2) were enriched in HRD-RNA+ vs. HRD-RNA- cases (8.8% vs. 2.5%, p < 0.001; 22% vs. 15%, p = 0.008 respectively). Notably, 141 (78%) HRD-RNA+ patients had no alterations in HRR genes. Of all NCCN targetable driver mutations assessed, KRAS G12C and ALK fusions were the only targetable drivers with significantly different prevalence in HRD-RNA+ vs. HRD-RNA- patients. Across the entire cohort, NCCN driver mutations were depleted in HRD RNA+ patients (18% in HRD-RNA+ vs. 30% in HRD-RNA-, p < 0.001). Immune biomarkers (TMB, PD-L1) did not vary by HRD-RNA status. Conclusions: Compared to HRD-RNA- NSCLC, HRD-RNA+ NSCLC represents a unique, molecularly defined subset that has a decreased prevalence of NCCN-driver mutations and is not enriched for TMB-H or PD-L1 expression. Further, this signature increases the number of patients classified as HRD-RNA+ compared to HRR gene alterations alone. Functional characterization (e.g. RAD51 foci immunofluorescence assay) and clinical benefit of targeted HRD therapies such as PARPi should be explored in this HRD-RNA+ population. [Table: see text]
6610 Background: Next-generation sequencing-based molecular diagnostic classifiers can help identify the tissue of origin for CUP, and enable the selection of site-specific therapies (as opposed to empiric chemotherapy) for this vulnerable population with high unmet need. NCCN guidelines do not endorse the use of tissue of origin classifiers as standard of care for CUP patients due to limited clinical evidence. Here, we linked results of a commercial molecular diagnostic classifier with claims data to understand how this test impacted patient care. Methods: We assessed de-identified claims data from the Komodo Healthcare Map (a database including provider visits, laboratory tests, procedures, imaging, and prescriptions) linked to the Tempus Tumor Origin (TO) test—a machine learning classifier that uses RNA-seq data to classify tumors into one of 68 histological subtypes. Eligible patients had pathologist-confirmed CUP and were classified as one of 9 subtypes (each having n>10). Impact was determined by identifying either one or more new diagnostic codes or new subtype-related medication claims following TO testing. Results: We analyzed data from 490 patients: 483 for the diagnosis analysis and 213 for the medication analysis (206 patients appear in both, due to differences in timing of diagnosis vs. medication claims). We found that post-TO testing, 49.9% (n=241) of patients had a diagnostic code change, 63.8% (n=136) had a treatment change, and 41% (n=85) had both. In total, 59.6% (n=292) of patients were impacted by the use of this classifier, with variation according to predicted subtype (Table). Cancer types with specific treatment options were more likely to have changes in diagnostic code or treatment. For CUP predicted as lung adenocarcinomas, 85% of the cases had a new subtype-aligned medication, and 78% were specifically placed on immunotherapy (IO) compared with 24% of overall patients. Conclusions: Using real-world claims data, we show that molecular diagnostic testing utilizing Tempus TO impacted care for a majority of CUP patients. This cohort will be followed to identify how TO testing decisions impact outcomes. [Table: see text]
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