Next Generation Sequencing of Prostate Cancer from a Patient Identifies a Deficiency of Methylthioadenosine Phosphorylase, an Exploitable Tumor Target
Bibliographic record
Abstract
Castrate-resistant prostate cancer (CRPC) and neuroendocrine carcinoma of the prostate are invariably fatal diseases for which only palliative therapies exist. As part of a prostate tumor sequencing program, a patient tumor was analyzed using Illumina genome sequencing and a matched renal capsule tumor xenograft was generated. Both tumor and xenograft had a homozygous 9p21 deletion spanning the MTAP, CDKN2, and ARF genes. It is rare for this deletion to occur in primary prostate tumors, yet approximately 10% express decreased levels of methylthioadenosine phosphorylase (MTAP) mRNA. Decreased MTAP expression is a prognosticator for poor outcome. Moreover, it seems that this deletion is more common in CRPC than in primary prostate cancer. We show for the first time that treatment with methylthioadenosine and high dose 6-thioguanine causes marked inhibition of a patient-derived neuroendocrine xenograft growth while protecting the host from 6-thioguanine toxicity. This therapeutic approach can be applied to other MTAP-deficient human cancers as deletion or hypermethylation of the MTAP gene occurs in a broad spectrum of tumors at high frequency. The combination of genome sequencing and patient-derived xenografts can identify candidate therapeutic agents and evaluate them for personalized oncology.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".