Abstract 538: Cell-free whole genome sequencing for the detection of cancer in patients with germline <i>BRCA1/2</i> mutations
Bibliographic record
Abstract
Abstract Background: Hereditary breast and ovarian cancer syndrome (HBOC) is caused by a germline likely/pathogenic variant in BRCA1 or BRCA2, leading to increased risk of developing breast, ovarian, prostate, and pancreatic cancers. While HBOC patients often undergo regular surveillance including breast imaging, other cancers have no effective screening or biomarkers. One emerging technology is the analysis of circulating tumor DNA (ctDNA), fragments of DNA that are shed from the tumor into the bloodstream. In sporadic cancer, several studies have demonstrated the advantages and sensitivity of cell-free whole genome sequencing (cfWGS) compared to targeted panel sequencing. However, the use of cfWGS for cancer detection in HBOC has not been explored. Methods: Matched tumor, germline, and plasma were collected from 5 HBOC patients (3 breast, 2 ovarian) as part of the Canada-wide CHARM consortium (https://charmconsortium.ca). Tumor (40x), germline (20x), and plasma (20x) underwent whole genome sequencing (WGS) and subsequent variant calling pipeline using an ensemble of 6 variant callers. Insertions, deletions, and structural variants were not included in downstream analyses. Plasma tumor fraction prediction and copy number alterations were performed using ichorCNA. Detection of tumor associated mutations in plasma was compared to MRDetect software. Results: Variant calling identified an average of 11,170 tumor (7,649-16,706) and 1,215 (544-3,205) plasma SNVs per patient. Somatic BRCA1/2 mutations were detected in 0/5; whereas deletion of the intact BRCA1/2 allele was identified in 5/5 patients. An average of 24.6% (8.3%-75.9%) of plasma mutations where shared with SNVs identified in matched tumor. Targeted panel deep-sequencing (20,000X) was only able to detect mutations in TP53 in 3/5 cases (0.8% - 8.6%). Plasma derived copy number alterations correlated well with tumor derived copy number alterations in 3/5 cases and ichorCNA predicted tumor fractions ranged from 0.038 to 0.196. Conclusions: Traditionally, targeted panel sequencing has been used to identify tumor associated mutations in plasma. While this technique is sensitive, major disadvantages include the narrow breadth of the captured regions and the inability to detect deletion events such as those that occur in BRCA1/2. Here we show that plasma WGS is effective for detecting tumor associated mutations in HBOC and is more sensitive than targeted panel sequencing. We are currently expanding our cohort to an additional 20 matched tumor pairs, including patients with pre-diagnosis plasma timepoints. Citation Format: Derek Wong, Adriana Aguilar-Mahecha, Josiane Lafleur, Clarissa Chan, Kirsten Farncombe, Maia Norman, Leslie Oldfield, Prabhjit Basra, Stephanie Pederson, Johanna Wellum, Stephenie Prokopec, Mark Basik, Raymond H. Kim, Trevor Pugh. Cell-free whole genome sequencing for the detection of cancer in patients with germline BRCA1/2 mutations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 538.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".