Abstract 81: Studying Ghanian Cancer Genomes Using Cell-free DNA
Bibliographic record
Abstract
Abstract Purpose: Analysis of cell free DNA could provide a rapid and non-invasive approach to detect cancer and provide new molecular insights in many African countries where expert pathology is lacking. Hence, we tested whether whole-genome sequencing of cfDNA (WGS-cfDNA) could identify somatic alterations that drive breast cancer. Methods: We conducted a pilot on 15 Ghanaian women (median age 49.5 years) recruited as part of the Ghana Breast Health Study. cfDNA was extracted and subjected to WGS at 30x and 0.1x. ichorCNA software was used to predict copy number alterations and ctDNA fractions. Results: We found extensive amplification and deletion of multiple chromosomal regions including those with oncogenes and tumor suppressor genes associated with breast cancer. Similar copy number alterations for selected breast cancer genes were observed with 0.1x and 30x cfDNA-WGS with increasing concordance between the two instruments as the ctDNA fraction increases. We observed a high frequency (>50%) of copy number gain in 3/5 regions and potential target genes for the amplification (chr8p11-12 [ZNF703] n=8, 53.3%; chr8q24.2 [MYC] n=9, 60%; chr19q12 [CCNE1] n=9, 60%), which were in agreement to previous observations among African-American (AA) ancestry compared to European-American (EA) ancestry in TCGA datasets. Conclusion: Our data provided evidence that ctDNA-based genomic studies are possible and ctDNA analysis could be a tool for future molecular oncology studies in Africa for cancer etiology, surveillance and clinical trials. Citation Format: Samuel Ahuno, Anna-Lisa Doebley, Thomas Ahearn, Joel Yarney, Nicholas Titiloye, Nancy Hamel, Ernest Adjei, Joe-Nat Clegg-Lamptey, Lawrence Edusei, Baffour Awuah, Xiaoyu Song, Verne Vanderpuye, Mustapha Abubakar, Maire Duggan, Daniel Stover, Kofi Nyarko, John Bartlet, Francis Aitpillah, Daniel Ansong, Kevin Gardner, Anne Bowcock, Carlos Caldas, William Foulkes, Seth Wiafe, Wiafe-Addai, Montserrat Garcia-Closas, Alexander Kwarteng, Gavin Ha, Jonine Figueroa, Paz Polak, On Behalf Of Ghana Breast Health Study Team. Studying Ghanian Cancer Genomes Using Cell-free DNA [abstract]. In: Proceedings of the 9th Annual Symposium on Global Cancer Research; Global Cancer Research and Control: Looking Back and Charting a Path Forward; 2021 Mar 10-11. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2021;30(7 Suppl):Abstract nr 81.
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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) | 0.006 | 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".