Biallelic loss of TP53, PTEN, and RB1 in association to aggressive clinical features and poor outcomes in metastatic castration-resistant prostate cancer (mCRPC).
Bibliographic record
Abstract
5055 Background: Deleterious alterations in tumor suppressor genes (TSGs) TP53, RB1, and PTEN are potential markers of small cell neuroendocrine prostate cancer (SCNP), and androgen receptor pathway inhibitor (ARPI) resistance. We examined the outcomes and clinical features of mCRPC patients (pts) harboring biallelic loss in 0, 1, 2 or all 3 TSGs. Methods: We identified 210 consecutive mCRPC pts providing ≥1 plasma cell-free DNA sample with ≥20% circulating tumor DNA fraction (ctDNA%) during their mCRPC disease course. ctDNA% ≥20% enabled sensitive characterization of biallelic TSG loss (including by homozygous deletions and mutation plus somatic loss-of-heterozygosity; LOH). Patient records were reviewed for baseline characteristics, SCNP histology, and presence of liver metastases. We investigated associations between TSG loss and the following clinical outcomes: PSA response (PSA decline ≥50% (PSA50 RR)), progression free survival (PFS) on 1L therapy, and overall survival (OS) from 1L therapy. Results: Median follow-up was 16.5 months (range: 0.4-112.4) and OS event rate was 95%. Median age at 1L mCRPC was 71 years (range: 48-98). Most pts were ECOG PS 0-1 (79%) and 13% had liver metastases. 91% received ARPI for 1L mCRPC and 7% received ARPI for castration-sensitive disease. TP53 was primarily inactivated by somatic mutation plus LOH (90%), whereas RB1 (71%) and PTEN (86%) were more commonly inactivated by homozygous deletions. Compared to pts without evidence of biallelic TSG loss, pts with loss of 3 TSGs were significantly enriched for de-novo M1 disease (86 vs. 60%, p=0.05) and liver metastases (28.5 vs. 6.8%, p<0.05). Ten pts (4.7%) had histologically confirmed SCNP and provided ctDNA at time of SCNP diagnosis. Of these, 7 (70%) had biallelic loss of ≥2 TSGs. For all pts receiving 1L therapy, loss of ≥1 TSG(s) was associated with decreased OS (HR: 1.86, 95% CI: 1.40-2.48, p<0.01) and PFS (HR:1.74, 95% CI 1.29-2.34, p<0.01) compared to pts with no biallelic TSG loss. Furthermore, a cumulative increase in the number of TSGs lost was associated with an incremental reduction in OS and PFS (Table). Conclusions: In a cohort enriched for poor prognosis (i.e. high ctDNA%), cumulative loss of TSGs is associated with aggressive disease features and poor clinical outcomes. These patients may benefit from alternative treatment intensification strategies. [Table: see text]
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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 | 0.001 |
| 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 | 0.000 |
| 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".