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Gene Expression Profiling as a Potential Tool for Precision Oncology in Non-Small Cell Lung Cancer.

ABSTRACT: Recent technological advances and the application of high-throughput mutation and transcriptome analyses have improved our understanding of cancer diseases, including non-small cell lung cancer. For instance, genomic profiling has allowed the identification of mutational events which can be treated with specific agents. However, detection of DNA alterations does not fully recapitulate the complexity of the disease and it does not allow selection of patients that benefit from chemo- or immunotherapy. In this context, transcriptional profiling has emerged as a promising tool for patient stratification and treatment guidance. For instance, transcriptional profiling has proven to be especially useful in the context of acquired resistance to targeted therapies and patients lacking targetable genomic alterations. Moreover, the comprehensive characterization of the expression level of the different pathways and genes involved in tumor progression is likely to better predict clinical benefit from different treatments than single biomarkers such as PD-L1 or tumor mutational burden in the case of immunotherapy. However, intrinsic technical and analytical limitations have hindered the use of these expression signatures in the clinical setting. In this review, we will focus on the data reported on molecular classification of non-small cell lung cancer and discuss the potential of transcriptional profiling as a predictor of survival and as a patient stratification tool to further personalize treatments.

SUBMITTER: Hijazo-Pechero S

PROVIDER: S-EPMC8507534 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Accurate histological classification and identification of fusion genes represent two cornerstones of clinical diagnostics in non-small cell lung cancer (NSCLC). Here, we present a NanoString gene expression platform and a novel platform-independent, single sample predictor (SSP) of NSCLC histology for combined, simultaneous, histological classification and fusion gene detection in minimal formalin fixed paraffin embedded (FFPE) tissue. The SSP was developed in 68 NSCLC tumors of adenocarcinoma (AC), squamous cell carcinoma (SqCC) and large-cell neuroendocrine carcinoma (LCNEC) histology, based on NanoString expression of 11 (CHGA, SYP, CD56, SFTPG, NAPSA, TTF-1, TP73L, KRT6A, KRT5, KRT40, KRT16) relevant genes for IHC-based NSCLC histology classification. The SSP was combined with a gene fusion detection module (analyzing ALK, RET, ROS1, MET, NRG1, and NTRK1) into a multicomponent NanoString assay. The histological SSP was validated in six cohorts varying in size (n = 11-199), tissue origin (early or advanced disease), histological composition (including undifferentiated cancer), and gene expression platform. Fusion gene detection revealed five EML4-ALK fusions, four KIF5B-RET fusions, two CD74-NRG1 fusion and three MET exon 14 skipping events among 131 tested cases. The histological SSP was successfully trained and tested in the development cohort (mean AUC = 0.96 in iterated test sets). The SSP proved successful in predicting histology of NSCLC tumors of well-defined subgroups and difficult undifferentiated morphology irrespective of gene expression data platform. Discrepancies between gene expression prediction and histologic diagnosis included cases with mixed histologies, true large cell carcinomas, or poorly differentiated adenocarcinomas with mucin expression. In summary, we present a proof-of-concept multicomponent assay for parallel histological classification and multiplexed fusion gene detection in archival tissue, including a novel platform-independent histological SSP classifier. The assay and SSP could serve as a promising complement in the routine evaluation of diagnostic lung cancer biopsies.

Project description:ObjectivesBiomarker testing is indispensable for the implementation of precision medicine (PM) in oncology. The aim of this study was to assess the value of biomarker testing from a holistic perspective based on the example of advanced non-small cell lung cancer (aNSCLC).Materials and methodsA partitioned survival model was populated with data from pivotal clinical trials of first-line treatments in aNSCLC. Three testing scenarios were considered; "no biomarker testing" encompassing chemotherapy treatment, "sequential testing" for EGFR and ALK encompassing treatment with targeted- or chemotherapy, and "multigene testing" covering EGFR, ALK, ROS1, BRAF, NTRK, MET, RET and encompassing treatment with targeted- or immuno(chemo)therapy. Analyses of health outcomes and costs were run for nine countries (Australia, Brazil, China, Germany, Japan, Poland, South Africa, Turkey, United States). A 1-year and 5-year time horizon was applied. Information on test accuracy was combined with country-specific information on epidemiology and unit costs.ResultsCompared to the no-testing scenario, survival improved and treatment-related adverse events decreased with increased testing. Five-year survival increased from 2% to 5-7% and to 13-19% with sequential testing and multigene testing, respectively. The highest survival gains were observed in East Asia due to a higher local prevalence of targetable mutations. Overall costs increased with increased testing in all countries. Although costs for testing and medicines increased, costs for treatment of adverse events and end-of-life care decreased throughout all years. Non-health care costs (sick leave and disability pension payments) decreased during the first year but increased over a 5-year horizon.ConclusionThe broad use of biomarker testing and PM in aNSCLC leads to more efficient treatment assignment and improves health outcomes for patients globally, in particular prolonged progression-free disease phase and overall survival. These health gains require investment in biomarker testing and medicines. While costs for testing and medicines would initially increase, cost decreases for other medical services and non-health care costs may partly offset the cost increases.

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Gene Expression Profiling as a Potential Tool for Precision Oncology in Non-Small Cell Lung Cancer.

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