Abstract B129: Clinical implications of inter- and intra- prostatic heterogeneity.
Bibliographic record
Abstract
Abstract Men with localized prostate cancer vary widely in clinical outcome, with a 30-50% failure rate after primary treatment. There is thus significant interest in developing genomically refined prognostic groups. We sought to evaluate the extent of genetic heterogeneity, both between patients (inter-prostate) and within individual prostate glands (intra-prostate) to assess the impact of genetic heterogeneity on risk stratification within a tight clinical cohort. Copy number aberrations (CNAs) from 75 Gleason 7 patients were determined by OncoScan SNP microarrays. We measure the percentage of genome involved in a CNA, termed percent genome aberration (PGA), a proxy for genomic instability. Additionally, whole genome sequencing was applied to 10 intermediate-risk prostate tumours and matched blood, including multiple manually macro-dissected regions from 5 of the prostates (range 2 to 9). Somatic single nucleotide variants (SNVs) and genomic rearrangements (GR) were extracted from each patient. We find a high degree of inter-prostatic heterogeneity between the 75 Gleason 7 patients, with the number of CNAs per patient ranging from 0 to 929, corresponding to PGA 0 to 16.7%. Known prognostic markers can differentiate between patients at higher risk for biochemical recurrence, but only account for a fraction of the cohort. Notably, when these prognostic genes are examined within multiple regions of five independent tumours, they differ in copy number between cancerous regions of the same prostate. For example, TP53 is deleted in 1/2, 1/3, 4/9, 0/4, and 4/5 prostate regions. Indeed, phylogenetic analysis of geographically distinct regions revealed multi-clonal disease in two of the five patients; separate analyses based on SNVs, CNAs, and GRs all concluded that these patient have two genetically distinct cancers within their prostate. We demonstrate dramatic levels of inter- and intra- prostate genetic heterogeneity within pathologically identical or similar cancers. The observed intra-prostatic genomic heterogeneity, both in terms of multi-focal and multi-clonal disease, has critical implications for clinical management. Prognostic information obtained by biopsy may be inconsistent depending on the site of biopsy, and applying personalized medicine to prostate cancer will be challenging. This study highlights the need for further evaluation of how intra-prostatic heterogeneity is related to patient prognosis. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):B129. Citation Format: Emilie Lalonde, Paul C. Boutros, Michael Fraser, Richard de Borja, Nicholas J. Harding, Dominique Trudel, Alice Meng, Pablo H. Hennings-Yeomans, Andrew McPherson, Amin Zia, Jianxin Wang, Timothy Beck, Natalie S. Fox, Taryne Chong, Michelle Sam, Jeremy Johns, Lee Timms, Nicholas Buchner, Sohrab Shah, Cenk Sahinalp, Thomas J. Hudson, John D. McPherson, Theodorus van der Kwast, Robert G. Bristow. Clinical implications of inter- and intra- prostatic heterogeneity. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr B129.
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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 | 0.001 |
| 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) | 0.005 | 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".