Application of Sequencing, Liquid Biopsies, and Patient-Derived Xenografts for Personalized Medicine in Melanoma
Bibliographic record
Abstract
UNLABELLED: Targeted therapies and immunotherapies have transformed melanoma care, extending median survival from ∼9 to over 25 months, but nevertheless most patients still die of their disease. The aim of precision medicine is to tailor care for individual patients and improve outcomes. To this end, we developed protocols to facilitate individualized treatment decisions for patients with advanced melanoma, analyzing 364 samples from 214 patients. Whole exome sequencing (WES) and targeted sequencing of circulating tumor DNA (ctDNA) allowed us to monitor responses to therapy and to identify and then follow mechanisms of resistance. WES of tumors revealed potential hypothesis-driven therapeutic strategies for BRAF wild-type and inhibitor-resistant BRAF-mutant tumors, which were then validated in patient-derived xenografts (PDX). We also developed circulating tumor cell-derived xenografts (CDX) as an alternative to PDXs when tumors were inaccessible or difficult to biopsy. Thus, we describe a powerful technology platform for precision medicine in patients with melanoma. SIGNIFICANCE: Although recent developments have revolutionized melanoma care, most patients still die of their disease. To improve melanoma outcomes further, we developed a powerful precision medicine platform to monitor patient responses and to identify and validate hypothesis-driven therapies for patients who do not respond, or who develop resistance to current treatments.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".