Abstract P3-08-22: The mutational landscape of cancer driver genes in matched primary ductal carcinoma <i>in situ</i> and recurrent ductal carcinoma <i>in situ</i> or recurrent invasive cancers
Bibliographic record
Abstract
Abstract Due to breast screening, ductal carcinoma in situ (DCIS) accounts for approximately 25% of all newly diagnosed breast neoplasms. Believed to be a precursor to invasive carcinoma, a significant number of patients diagnosed with DCIS are effectively managed by surgery alone or in conjunction with radiotherapy and endocrine therapy. In general, there is an 8-11% relative risk for a subsequent invasive carcinoma over a period of 10 year, with 98% breast cancer-specific survival after 10 years of follow up. Although mastectomy, breast conserving surgery, and radiotherapy can reduce the risk of recurrence, there are ongoing lifetime consequences of treatment. Thus, there is a clinical need to identify those patients who are at risk of an invasive recurrence from those who might not recur or experience a subsequent DCIS recurrence. In this study, 60 patients with pure primary DCIS, treated with only with breast conserving therapy, across three different clinical outcome groups were examined: patients who did not experience a recurrence within 5 years (n=20); patients who experienced a recurrent DCIS within 5 years (n=20); and patients that recurred with an invasive cancer within 5 years (n=20). Pure primary DCIS lesions, as well as the matched DCIS recurrence or invasive recurrence, were macrodissected from formalin fixed paraffin embedded tissues and subjected to nucleic acid extraction. All samples were profiled using Thermo Fisher Scientific’s validated targeted sequencing panel, the Oncomine Comprehensive Assay v3.0 (OCAv3.0). This assay is comprised of common cancer driver genes shown to be prognostic and predictive to targeted therapies in use or in late-phase clinical trials, and is currently being used in the NCI-MATCH trial (NCT02465060). While the OCA panel and accompanying Oncomine Knowledgebase Reporter provides information regarding the targeted treatments linked to known actionable mutations, this study utilized all somatic mutations and copy number changes to reveal the genomic landscape of DCIS and their matched recurrences across these pan-cancer driver genes Amongst all primary DCIS samples across the three different clinical outcome groups, PIK3CA, was frequently found to be affected by SNV and Indels (32.8%) in addition to TP53 (26.2%), NF1 (21.3%), CREBBP (16.4%), ATM (14.8%), PALB2 (14.8%). DNA repair genes, including CHEK1, RAD50, RAD51B, MRE11A, BRCA1 and BRCA2, were found to be frequently subject to mutation in these primary DCIS samples ranging from 5%-15%. Similarly, copy number gains were frequently detected in HER2 (26.2%), CDK12 (18%), FGFR1 (4.9%), GNAS (4.9%), MYC, CCNE1, AR, RAD51C and RNF43 (1.6% each), and loses at H3F3A and KNSTRN (each 1.6%). Matched primary DCIS and their recurrent DCIS or invasive lesions exhibited similar changes with invasive cancers suggesting that in some cases, the primary DCIS gives rise to the invasive cancer. We will present the preliminary findings mapping the mutational and copy-number landscape of primary DCIS and matched recurrences to identify putative genomic changes defining these clinical outcome groups and to investigate the genomic progression of DCIS to invasive carcinoma. Citation Format: Jane Bayani, Quang M Trinh, Mary Anne Quintayo, Cheryl Crozier, Ilinca Lungu, Dan Dion, Joema Felipe-Lima, Giancarlo Pruneri, Jonas Bergh, Fredrik Warnberg, Giuseppe Viale, Lincoln D Stein, John MS Bartlett. The mutational landscape of cancer driver genes in matched primary ductal carcinoma in situ and recurrent ductal carcinoma in situ or recurrent invasive cancers [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-08-22.
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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) | 0.002 | 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".