Abstract 923: Biomarker use in oncology approvals in the US
Bibliographic record
Abstract
Abstract Introduction: Precision medicine through the use of biomarkers has revolutionized the way we understand and combat cancer. We are summarizing the current state and challenges of biomarker use in the context of cancer therapeutic products approved by the United States Food and Drug Administration (FDA). Methods: We curated a dataset of oncology therapeutic products approved by the FDA through the regular and accelerated pathways from 2011 to 2022, based on publicly available FDA database, approval letters and reviews, prescribing information, publications, and clinicaltrial.gov. Results: During the period of 2011 to 2022, the FDA granted 132 accelerated and 331 regular approvals for 80 and 144 oncology therapeutic products with 133 and 340 new indications, respectively. The percentage of approvals for a biomarker-defined population was higher in accelerated approvals in comparison to regular approvals (i.e., 51% vs. 39%). The five disease sites with the highest number of approved indications were leukemia, lung, non-Hodgkin lymphoma, breast, and skin. The vast majority of lung and breast cancer indications were approved in a biomarker-defined population through a range of different biomarkers. Overall, HER2-, EGFR-, BRAF-, HR-, and ALK-defined populations received the highest numbers of approvals. The most common biomarkers linked to the approval of a specific cancer type include Philadelphia chromosome for leukemia, EGFR/ALK for lung cancer, HER2/HR for breast cancer, BRAF for skin cancer, and BRCA for ovarian cancer. Multiple myeloma, kidney cancer, and Hodgkin lymphoma did not have any approval with a biomarker-defined population. Non-Hodgkin lymphoma had a large number of approvals, but the percentage approved for a biomarker-defined population was small. dMMR, MSI-H or dMMR, BRAF mutation, NTRK fusions, TMB H, and RET fusions are biomarkers linked to tissue-agnostic indications approved for solid tumors only through the accelerated pathway. Conclusion: To the best our knowledge, the analysis represents the most comprehensive overview of the current landscape on integration of biomarker in oncology therapeutic products approved by the FDA through both the accelerated and regular pathways. Our analysis shows that the FDA has increasingly approved indications based on biomarkers, particularly through the accelerated approval pathway. Biomarkers are reshaping cancer therapy under an evolving regulatory landscape with a growing emphasis on precision medicine that promises continued progress in personalized cancer care. Citation Format: Yannan Nancy Dou, Christian Grimstein, Jian Wang, Jacques Mascaro. Biomarker use in oncology approvals in the US [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 923.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".