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Updated: Jul 2, 2026
Ultra-Fast Amplicon-Based Next-Generation Sequencing in Non-Squamous Non-Small Cell Lung Cancer
Published on: September 8, 2023
Systematic identification of genomic nonresponse biomarkers to cancer therapies
Background:
The costs of cancer therapies are rising rapidly worldwide, with novel therapies such as targeted treatment and immunotherapies being major contributors, but their effectiveness can be low or uncertain due to limited postmarket surveillance. Reliable biomarkers to identify patients highly unlikely to respond to cancer therapies represent an increasingly important clinical and societal need, as they could prevent unnecessary treatments, reduce side effects, and alleviate pressure on health care systems.
Materials And Methods:
We developed a robust statistical framework for the identification of nonresponse biomarkers for systemic treatments and applied it to whole-genome and transcriptome sequencing data of cancer patients (N = 2594) with advanced disease.
Results:
Our approach identified known and potentially novel genomic and transcriptomic biomarkers of nonresponse, such as immune evasion driver events in skin melanoma patients treated with anti-programmed cell death protein 1 checkpoint inhibitors and KRAS G12 mutations in metastatic colorectal cancer patients treated with different chemotherapy regimens. Analytical power analysis revealed that for most treatments and/or cancer types, the cohort sizes remain underpowered.
Conclusions:
Systematic identification of nonresponse signals reveals multiple potential biomarkers that will require larger cohort sizes for prospective clinical implementation.
Insights
Researchers developed a statistical framework to identify cancer treatment nonresponse biomarkers using genomic and transcriptomic data. This approach identified potential biomarkers for melanoma and colorectal cancer, highlighting the need for larger studies.
Area of Science:
- Oncology
- Genomics
- Translational Medicine
Background:
- Rising costs of novel cancer therapies (targeted treatments, immunotherapies) necessitate improved patient selection.
- Limited postmarket surveillance for novel therapies leads to uncertain effectiveness.
- Biomarkers identifying non-responders are crucial to avoid ineffective treatments, reduce side effects, and optimize healthcare resources.
Purpose of the Study:
- To develop and apply a statistical framework for identifying nonresponse biomarkers in cancer patients.
- To analyze whole-genome and transcriptome sequencing data for potential nonresponse signals.
Main Methods:
- Developed a robust statistical framework for nonresponse biomarker identification.
- Applied the framework to a cohort of 2594 advanced cancer patients using whole-genome and transcriptome sequencing data.
Main Results:
- Identified known and novel genomic and transcriptomic biomarkers associated with nonresponse to systemic cancer treatments.
- Examples include immune evasion events in melanoma patients treated with anti-PD-1 inhibitors and KRAS mutations in metastatic colorectal cancer patients.
- Power analysis indicated that current cohort sizes are underpowered for most treatments and cancer types.
Conclusions:
- Systematic identification of nonresponse signals has revealed multiple potential biomarkers.
- Prospective clinical implementation of these biomarkers requires validation in larger patient cohorts.
