Abstract 5358: Disparities between genomic alterations and clinical trial representation in precision oncology.

Abstract Background: Precision oncology has shifted from histology-based treatment toward molecularly driven frameworks, but it remains unclear whether clinical trial priorities align with the real-world genomic landscape. This study evaluated biomarker-driven solid tumor trials from 2010 to 2025 and compared trial representation with genomic prevalence in TCGA. Methods: Interventional solid tumor trials (2010-2025) were extracted from the AACT ClinicalTrials.gov database and processed in Python. Adult solid tumor studies were identified via structured filters, and 30 predefined biomarkers were detected through text-mining of titles, eligibility criteria, and interventions. Manual review removed false positives, and biomarker names were harmonized across AACT and TCGA. TMB-high was defined as ≥10 mutations per megabase, and MSI-high was defined as MSIsensor score ≥10. Trials were categorized by phase, sponsor type, and tissue specificity. Representation ratios (trial frequency ÷ TCGA genomic prevalence) were calculated, with ratios >1.5 classified as over-represented, 0.5-1.5 as balanced, and <0.5 as under-represented. TCGA overall survival (OS) analyses were performed for each biomarker. Results: Among 7,259 biomarker-driven solid tumor trials, 19.4% were tissue-agnostic, 54.5% were industry-sponsored, and 79.6% were early-phase (I/II). Of the 30 biomarkers analyzed, 6 (20%) were over-represented, 16 (53%) were balanced, and 8 (27%) were under-represented relative to their TCGA genomic prevalence. Over-represented biomarkers included EGFR (28.4% vs 8%), VEGFA (12.5% vs 2%), FGFR3 (6.0% vs 3%), and HER2 (12.4% vs 6%), reflecting prioritization of highly druggable oncogenic drivers. In contrast, under-represented biomarkers included TP53 (2.0% vs 37%), STK11 (0.5% vs 8%), KEAP1 (0.2% vs 4%), TMB-high (2.9% vs 12%), and CDKN2A (7.3% vs 17%), most of which were tumor suppressors without approved targeted therapies and have recognized immune relevance. Alterations in TP53, CDKN2A, and KEAP1 were associated with significantly shorter OS in TCGA (all p < 0.001), underscoring their prognostic significance despite limited clinical trial representation. Conclusions: Current oncology trials disproportionately focus on established, targetable oncogenes, while high-prevalence tumor suppressor alterations and immune-related biomarkers remain underrepresented. This mismatch highlights a translational gap between genomic prevalence and clinical trial design and underscores opportunities to broaden biomarker inclusion, expand tissue-agnostic frameworks, and integrate real-world genomic data to advance equitable precision oncology. Citation Format: Jingyao Zhang, Raoul Santiago, Kamila Bakirhan, Tenzin Tamdin. Disparities between genomic alterations and clinical trial representation in precision oncology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5358.

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Journal
Cancer Research
Published
2026-04-03
DOI
https://doi.org/10.1158/1538-7445.am2026-5358
Primary Topic
Cancer Genomics and Diagnostics
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article
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article

Abstract 5358: Disparities between genomic alterations and clinical trial representation in precision oncology.

J. Zhang, Tenzin Tamdin, Raoul Santiago, Kamila Bakirhan
Cancer Research
Cancer Genomics and Diagnostics
article

Abstract 5358: Disparities between genomic alterations and clinical trial representation in precision oncology.

J. Zhang, Tenzin Tamdin, Raoul Santiago, Kamila Bakirhan
article en

Abstract

Abstract Background: Precision oncology has shifted from histology-based treatment toward molecularly driven frameworks, but it remains unclear whether clinical trial priorities align with the real-world genomic landscape. This study evaluated biomarker-driven solid tumor trials from 2010 to 2025 and compared trial representation with genomic prevalence in TCGA. Methods: Interventional solid tumor trials (2010-2025) were extracted from the AACT ClinicalTrials.gov database and processed in Python. Adult solid tumor studies were identified via structured filters, and 30 predefined biomarkers were detected through text-mining of titles, eligibility criteria, and interventions. Manual review removed false positives, and biomarker names were harmonized across AACT and TCGA. TMB-high was defined as ≥10 mutations per megabase, and MSI-high was defined as MSIsensor score ≥10. Trials were categorized by phase, sponsor type, and tissue specificity. Representation ratios (trial frequency ÷ TCGA genomic prevalence) were calculated, with ratios >1.5 classified as over-represented, 0.5-1.5 as balanced, and <0.5 as under-represented. TCGA overall survival (OS) analyses were performed for each biomarker. Results: Among 7,259 biomarker-driven solid tumor trials, 19.4% were tissue-agnostic, 54.5% were industry-sponsored, and 79.6% were early-phase (I/II). Of the 30 biomarkers analyzed, 6 (20%) were over-represented, 16 (53%) were balanced, and 8 (27%) were under-represented relative to their TCGA genomic prevalence. Over-represented biomarkers included EGFR (28.4% vs 8%), VEGFA (12.5% vs 2%), FGFR3 (6.0% vs 3%), and HER2 (12.4% vs 6%), reflecting prioritization of highly druggable oncogenic drivers. In contrast, under-represented biomarkers included TP53 (2.0% vs 37%), STK11 (0.5% vs 8%), KEAP1 (0.2% vs 4%), TMB-high (2.9% vs 12%), and CDKN2A (7.3% vs 17%), most of which were tumor suppressors without approved targeted therapies and have recognized immune relevance. Alterations in TP53, CDKN2A, and KEAP1 were associated with significantly shorter OS in TCGA (all p < 0.001), underscoring their prognostic significance despite limited clinical trial representation. Conclusions: Current oncology trials disproportionately focus on established, targetable oncogenes, while high-prevalence tumor suppressor alterations and immune-related biomarkers remain underrepresented. This mismatch highlights a translational gap between genomic prevalence and clinical trial design and underscores opportunities to broaden biomarker inclusion, expand tissue-agnostic frameworks, and integrate real-world genomic data to advance equitable precision oncology. Citation Format: Jingyao Zhang, Raoul Santiago, Kamila Bakirhan, Tenzin Tamdin. Disparities between genomic alterations and clinical trial representation in precision oncology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5358.

Cancer ResearchVol. 86(7_Supplement)
Danbury Hospital (US), Université Laval (CA)
Openalex Percentile: Top 27%
Cancer Genomics and Diagnostics
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