Abstract 5590: Liquid biopsy testing in metastatic or advanced breast cancer patients during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Objective: During the COVID-19 pandemic, cancer patients had restricted access to standard of care tissue biopsy. Liquid biopsy assays using next generation sequencing technology provides a less invasive method for determining circulating tumour mutations (ctDNA) associated with targeted treatments or prognosis. As part of deploying technology to help cancer patients obtain molecular testing, a clinical program was initiated to offer liquid biopsy testing for Canadian patients with advanced or metastatic breast cancer. Methods: Blood was drawn in two 10 mL StreckTM DNA BCTs and sent to the CAP/CLIA/DAP accredited Imagia Canexia Health laboratory for testing using the clinically validated Follow ItTM liquid biopsy assay. Plasma was isolated using a double spin protocol and plasma cell-free DNA (cfDNA) extracted using an optimized Promega Maxwell RSC method. Extracted cfDNA was amplified using the multiplex amplicon-based hotspot 30 or 38 gene panel and sequenced. An in-house developed bioinformatics pipeline and reporting platform were used to identify pathogenic single nucleotide variants (SNVs), indels (insertions and deletions), and gene amplification. Included in the panel are genes associated with metastatic breast cancer: AKT1, BRAF, ERBB2, ESR1, KRAS, PIK3CA, TP53. Results: To identify biomarkers, 1214 metastatic or advanced breast cancer patient cfDNA samples were tested. There were 15 cases sent for repeat testing. We reported 48% of samples harboring pathogenic ctDNA mutations in TP53 (22%), PIK3CA (19%), ESR1 (18%), AKT1 (2%), ERBB2 (1.5%). Co-occurring variants were identified in samples with ESR1/PIK3CA as well as TP53/PIK3CA (both p-values <0.001). Interestingly, 29% of samples with mutated ESR1 harbored ≥ 2 ESR1 ctDNA mutations. In 56% of cases, previous molecular testing indicated the cancer subtype as hormone receptor (ER, PR) positive with/without HER2 negative status. In this specific subgroup, 49% harbored ctDNA mutations with 63% of those being PIK3CA and/or ESR1 mutations. Conclusions: A population of Canadian women with metastatic breast cancer were tested using a liquid biopsy gene panel during the COVID-19 pandemic for identification of biomarkers for targeted therapeutic options. Over 50% of the samples were identified as hormone positive, with greater than 60% harboring PIK3CA and ESR1 ctDNA mutations. Studies have shown that metastatic PIK3CA mutated ER-positive/HER2-negative tumors are predictive to respond to alpelisib therapy and have FDA and Health Canada approval. Additionally, ESR1 mutations are associated with acquired resistance to antiestrogen therapies, and interestingly we identified 29% of ESR1 mutated samples with multiple mutations possibly indicating resistance subclones. In future studies, longitudinal monitoring for presence of multiple targetable and resistance mutations could be utilized to predict or improve clinical management. Citation Format: Benjamin L. Furman, Ebru Baran, Sonal Brahmbhatt, Betty Chan, Ka Mun Nip, Adrian Kense, Brenda Murphy, Vincent Funari, David G. Huntsman, Ruth Miller, Melissa K. McConechy. Liquid biopsy testing in metastatic or advanced breast cancer patients during the COVID-19 pandemic. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5590.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".