Dataset Information

Real-world survival analysis by tumor mutational burden in non-small cell lung cancer: a multisite U.S. study

ABSTRACT: Background: Tumor mutational burden (TMB) is a potential biomarker to predict tumor response to immuno-oncology agents in patients with metastatic non-small cell lung cancer (NSCLC). Materials and Methods: A multi-site cohort study evaluated patients diagnosed with stage IV NSCLC between 2012 and 2019 who had received comprehensive genomic profiling (CGP) and any NSCLC-related treatment at 9 U.S. cancer centers. Baseline characteristics and clinical outcomes were compared between patients with TMB <10 and TMB ≥10. Results: Among the 667 patients with CGP results, most patients received CGP from Foundation Medicine (64%) or Caris (20%). Patients with TMB ≥10 (vs. TMB <10) were associated with a positive smoking history. TMB was associated with ALK (p = 0.01), EGFR (p < 0.01), and TP53 (p < 0.05) alterations. TMB >10 showed a significant association towards longer overall survival (OS) (HR: 0.43, 95% CI: 0.21–0.88, p = 0.02) and progression-free survival (PFS) (HR: 0.43, 95% CI: 0.21–0.85, p = 0.02) in patients treated with first-line immunotherapy and tested by Foundation Medicine or Caris at treatment initiation. Conclusions: TMB levels greater than or equal to 10 mut/Mb, when tested by Foundation Medicine or Caris at treatment initiation, were significantly associated with improved OS and PFS among patients treated with first-line immunotherapy-containing regimens. Additional prospective research is warranted to validate this biomarker along with PD-L1 expression.

SUBMITTER: Willis C

PROVIDER: S-EPMC8803368 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundLung cancer is the leading cause of cancer death worldwide. With the growing number of targeted therapies and the introduction of immuno-oncology (IO), personalized medicine has become standard of care in patients with metastatic disease. The development of predictive and prognostic biomarkers is of great importance. Mutational signatures harbor potential clinical value as predictors of therapy response in cancer. Here we set out to investigate particular mutational processes by assessing mutational signatures and associations with clinical features, tumor mutational burden (TMB) and targetable mutations.MethodsIn this retrospective study, we studied tumor DNA from patients with non-small cell lung cancer (NSCLC) irrespective of stage. The samples were sequenced using a 2 megabase (Mb) gene panel. On each sample TMB was determined and defined as the total number of single nucleotide mutations per Mb (mut/Mb) including non-synonymous mutations. Mutational signature profiling was performed on tumor samples in which at least 30 somatic single base substitutions (SBS) were detected.ResultsIn total 195 samples were sequenced. Median total TMB was 10.3 mut/Mb (range 0-109.3). Mutational signatures were evaluated in 76 tumor samples (39%; median TMB 15.2 mut/Mb). SBS signature 4 (SBS4), associated with tobacco smoking, was prominently present in 25 of 76 samples (33%). SBS2 and/or SBS13, both associated with activity of the AID/APOBEC family of cytidine deaminases, were observed in 11 of 76 samples (14%). SBS4 was significantly more present in early stages (I and II) versus advanced stages (III and IV; P = .005).ConclusionIn a large proportion of NSCLC patients tissue panel sequencing with a 2 Mb panel can be used to determine the mutational signatures. In general, mutational signature SBS4 was more often found in early versus advanced stages of NSCLC. Further studies are needed to determine the clinical utility of mutational signature analyses.

Project description:IntroductionReal-world evidence derived from electronic health records (EHRs) is increasingly recognized as a supplement to evidence generated from traditional clinical trials. In oncology, tumor-based Response Evaluation Criteria in Solid Tumors (RECIST) endpoints are standard clinical trial metrics. The best approach for collecting similar endpoints from EHRs remains unknown. We evaluated the feasibility of a RECIST-based methodology to assess EHR-derived real-world progression (rwP) and explored non-RECIST-based approaches.MethodsIn this retrospective study, cohorts were randomly selected from Flatiron Health's database of de-identified patient-level EHR data in advanced non-small cell lung cancer. A RECIST-based approach tested for feasibility (N = 26). Three non-RECIST approaches were tested for feasibility, reliability, and validity (N = 200): (1) radiology-anchored, (2) clinician-anchored, and (3) combined. Qualitative and quantitative methods were used.ResultsA RECIST-based approach was not feasible: cancer progression could be ascertained for 23% (6/26 patients). Radiology- and clinician-anchored approaches identified at least one rwP event for 87% (173/200 patients). rwP dates matched 90% of the time. In 72% of patients (124/173), the first clinician-anchored rwP event was accompanied by a downstream event (e.g., treatment change); the association was slightly lower for the radiology-anchored approach (67%; 121/180). Median overall survival (OS) was 17 months [95% confidence interval (CI) 14, 19]. Median real-world progression-free survival (rwPFS) was 5.5 months (95% CI 4.6, 6.3) and 4.9 months (95% CI 4.2, 5.6) for clinician-anchored and radiology-anchored approaches, respectively. Correlations between rwPFS and OS were similar across approaches (Spearman's rho 0.65-0.66). Abstractors preferred the clinician-anchored approach as it provided more comprehensive context.ConclusionsRECIST cannot adequately assess cancer progression in EHR-derived data because of missing data and lack of clarity in radiology reports. We found a clinician-anchored approach supported by radiology report data to be the optimal, and most practical, method for characterizing tumor-based endpoints from EHR-sourced data.FundingFlatiron Health Inc., which is an independent subsidiary of the Roche group.

Dataset Information

Real-world survival analysis by tumor mutational burden in non-small cell lung cancer: a multisite U.S. study

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets