Abstract CC06-01: Identifying therapeutic options for patients with advanced prostate cancer through genes in liquid biopsies
Bibliographic record
Abstract
Abstract Prostate cancer (PCa) is curable in most men but becomes lethal for patients with metastases at diagnosis and those who experience biochemical recurrence (BCR) after curative therapies. Androgen deprivation therapy is given upon BCR, but patients inevitably fail and become castration resistant (CRPC), dying from metastatic (m)CRPC despite receiving 2nd and 3rd line therapies (androgen receptor inhibitors/ARIs and/or taxanes). Drugs targeting other pathways have been tested in clinical trials. Few are implemented in practice due to low response rates, although some show a significant benefit in subsets of unselected patients. This clinical heterogeneity underscores the cellular and functional heterogeneity of malignant cells, highlighting the need for biomarkers of response to therapies. In line with the growing interest in using non-invasive liquid biopsies to monitor disease progression, we hypothesized that genes encoding proteins representative of prostate epithelial cell-subtypes may be detectable in blood as predictive biomarkers. Our objective was to identify such representative genes and test them in the blood of patients to determine whether they can stratify patients for optimal disease management. 14 genes pertaining to cell subtypes were chosen based on a thorough literature review. The panel was validated in transcriptomic datasets showing their predominant overexpression in metastases of mCRPC cases compared to primary tumours and benign prostate from diverse cohorts of patients. TaqMan assays were designed and optimized in serial dilutions of RNA from five prostate cancer cell lines. The panel was tested in the blood of healthy controls (n=9) and patients prior to prostatectomy (n=8), post-curative therapies (n=7), or mCRPC (n=19). In control men with no prostatic disease in their lifetime, we see low or no expression of most genes, with no correlation with age (29-71 years old). No association was seen between the proportions of different white blood cells and genes of interest expressed at varying levels in the blood of mCRPC patients. The threshold for overexpression in patients was defined as 2.58 standard deviation above the mean expression from controls (99.5% confidence interval). Phenotypic and functional diversity was seen in all categories of patients. Changes in genes patterns were significant in mCRPC cases based on current treatments (at time of blood draw) and the choice of initial curative therapy. For example, neuroendocrine genes were predominantly overexpressed in patients who underwent curative radiation therapy, whereas stem cell genes arose in cases under AR-Is at blood draw. In conclusion, we identified circulating genes that may be clinically meaningful to stratify and follow patients and predict response to therapies. Genes encoding drug targets may allow patient-tailored clinical trials for personalized treatments to impact on this unpredictable and lethal disease. Citation Format: Seta Derderian, Edouard Jarry, Arynne Santos, Mohanachary Amaravadi, Quentin Vesval, Lucie Hamel, Nathalie Cote, Marie Vanhuyse, Armen Aprikian, Simone Chevalier. Identifying therapeutic options for patients with advanced prostate cancer through genes in liquid biopsies [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr CC06-01.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".