Targeted gene panel sequencing of liquid and tissue biopsies reveals actionable genomic alterations in Ghanaian metastatic breast cancer cases
Bibliographic record
Abstract
• Liquid biopsy can detect breast cancer mutations associated with African ancestry. • Liquid biopsy detects a diversity of potentially actionable genomic alterations associated with breast cancer and other cancers. • Paired liquid biopsy and tumour tissue genomic alterations are comparable. • More effort is needed to optimize liquid biopsy assays to capture novel African ancestry-specific variants. Breast cancer is a major cause of cancer-related mortality among African women. The adoption of molecular genomic technologies in the management of cancer cases is limited in Africa. To provide much-needed insights on the feasibility and utility of such precision medicine paradigms in Africa, we conducted a prospective, non-interventional study involving combined tissue and plasma Next-generation sequencing (NGS)-based testing in cancer patients in Ghana. We recruited 20 newly diagnosed, histologically confirmed, treatment-naïve women with metastatic breast cancer at the Cape Coast Teaching Hospital in Ghana. Tissue (NGS) and cell-free DNA (cfDNA) liquid biopsy analysis were ordered on all 20 patients. All 20/20 (100 %) liquid biopsy samples were acceptable for analysis, whereas only 6/20 (30 %) passed quality control for tissue NGS testing. Liquid biopsy detected 42 cfDNA mutations in 17/20 patients. Of the 17 patients, 3 (17.6 %) had mutations previously associated with African ancestry, including BRCA1 p.K719E, ARAF p.S262I and GATA3 p.G125dup. Eight potentially actionable alterations specific to breast cancer were found in 6/17 (35.3 %) liquid biopsy samples, while potentially actionable mutations non-specific to breast cancer were detected in 12/17 (70.6 %). Tissue biopsy analysis detected mutations in all 6 patients tested, with 3/6 (50 %) patients presenting potentially actionable mutations relevant to breast cancer. Liquid biopsy detected multiple additional actionable variants in Ghanaian women with breast cancer. Plasma cfDNA analysis featured fewer variations in sample preparation which is a key consideration in resource-limited settings. Liquid biopsy presents a great opportunity to improve cancer care in Africa.
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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 | 0.001 |
| 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".