Precision medicine program for whole-exome sequencing (WES) provides new insight on platinum sensitivity in advanced prostate cancer (PCa).
Bibliographic record
Abstract
158 Background: WES has provided insight into the genomic landscape of PCa. The next step for precision medicine requires prospective patient (pt) follow-up and clinical and functional validation of both common and low frequency mutations. We describe a precision medicine WES program and illustrate how this can be used to identify novel biomarkers associated with response. Methods: Metastatic PCa pts were prospectively enrolled. WES of metastatic biopsies or rapid autopsies and normal DNA were sequenced by Illumina HiSeq 2500. Results: Tumor-normal pairs from 71 pts with metastatic PCa (11 hormone naïve, 38 CRPC, 23 NEPC) were sequenced including 3 rapid autopsies, with a biopsy success rate of >95%, avg tumor purity 10-95%, avg coverage 85X. The spectrum of mutations across metastatic PCa and how serial biopsies and rapid autopsies were used to assess heterogeneity and clonal evolution will be presented. WES from a metastatic PCa pt (PM12) with exceptional response to platinum with complete remission of liver and lung metastases at 2 years follow-up, demonstrated a hypermutated genotype and hemizygous deletion of the DNA repair gene FANCA in both his primary and metastatic PCa. A loss of function germline variant was detected within the second allele, and only the mutated allele was expressed in his tumor. Genome editing of FANCA using CRISPR in PCa cell lines resulted in cisplatin hypersensitivity and a significant decrease in FANC complex formation. In patient derived xenograft (PDX) models, PM12’s PDX was significantly more sensitive to cisplatin compared to a control Pca PDX of similar morphology but lacking FANCA deletion. By screening larger cohorts, FANCA loss was detected by FISH in 16% of localized PCa (n= 69), 14% metastatic Pca (n= 29), and not detected in any benign prostate (n=69). Conclusions: Our study provides a proof of principle for developing a precision medicine approach to cancer care with the capability to identify potential biomarkers of response. Our findings suggest that a subset of PCa with FANCA loss may be particularly vulnerable to cytotoxic therapy and provides biologic rationale to help explain the exceptional response of pt PM12 to platinum chemotherapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".