Abstract S1-04: Exome sequencing of post-menopausal ER+ breast cancer (BC) treated pre-surgically with aromatase inhibitors (AIs) in the POETIC trial (CRUK/07/015)
Bibliographic record
Abstract
Abstract Aims 1. To determine the variability of mutational profiles and sub-clonality in core-cut biopsies from ER+ BC and the impact of 2-weeks’ AI therapy on these. 2. To identify mutations or patterns of mutations associated with poor anti-proliferative response to AI treatment. Background DNA alterations may lead to de novo and acquired resistance to medical therapies including AIs. Assessing this requires single time-point or sequential sampling usually with core-cuts but there is little information on their ability to represent mutational profiles or sub-clonal structure. We studied this in paired biopsies from ER+ BC primaries in 60 selected postmenopausal patients from the Peri-Operative Endocrine Therapy for Individualising Care (POETIC) trial (CRUK/07/015) before and after 2-weeks’ non-steroidal AI or no AI (randomised 2:1). Methods DNA was extracted from RNAlater-preserved diagnostic and surgical 14-gauge core-cut samples and peripheral blood from 20 no AI (Control) and 40 AI-treated patients (15 poor and 25 good Ki67-responders [PR and GR, respectively]). Patients with low ER+ BC or unsuppressed estradiol on treatment were not considered. Exome sequencing (Illumina HiSeq 2000) achieved >60% coverage across the exome at 15x depth. Variants were validated by re-sequencing (median >100x) together with 79 genes of interest curated from COSMIC and selected publications. Statistically significant genes (SMGs) were determined using MuSiC. Sub-clonality was analysed by SciClone. Results Good quality exomes were obtained on 102 samples including 44 pairs (control n=14; PR n=10; GR n=20). There were 5684 mutations (including 3616 missense and 1322 silent) affecting 3261 genes. SMGs in this series were PIK3CA (35.3%), TP53 (27.5%), CDH1 (13.8%), HEATR7B2 (8.8%), GATA3 (5.9%), CENPF (5.9%), MAP3K1 (5.9%), MAP2K4 (4.9%), HTR1A (2.9%) and C22orf23 (1%). PR had more mutations than GR (median 65 vs 36, p=0.04). More PR than GR were HER2+ (5/14 vs 1/24, p=0.019) and/or TP53-mutated (5/10 vs 3/20, p=0.08) but similar proportions were PIK3CA-mutated. The correlation of diagnostic vs surgical variant allele frequencies was strong for the control (r=0.75) and treated (r=0.89) groups (for treated GR r=0.83; for treated PR r=0.65). In the treated group there were fewer mutations at surgery vs diagnosis (p<0.026). PIK3CA and TP53 mutation status was identical between the paired samples in 41/44 and 40/44 cases; less frequently mutated genes showed lower concordance. SciClone plots to infer sub-clonality were possible in 37 pairs; for 8 pairs (22%) there was clear evidence of at least one sub-clone being present in only one core-cut sample. Conclusion This is the largest reported study of exome reproducibility in ER+ BC for mutation profiles based on core-cut biopsy. Multiple sub-clones are identifiable in ER+ primary BC. In c.20% tumours, a single core-cut does not allow inference of all sub-clonal populations, probably due to spatial heterogeneity. TP53 mutations but not PIK3CA mutations are associated with PR. Large numbers of BC will be needed to identify any associations of lower frequency mutations with resistance. A trend to fewer mutations after just 2 weeks AI needs confirmation. Citation Format: Pascal Gellert, Corrinne V Segal, Qiong Gao, Tiandao Li, Christopher A Miller, Elaine Mardis, Lesley-Ann Martin, Christopher Holcombe, Anthony Skene, Judith Bliss, John Robertson, Ian Smith, Mitch Dowsett, POETIC Trial Management Group and Trialists. Exome sequencing of post-menopausal ER+ breast cancer (BC) treated pre-surgically with aromatase inhibitors (AIs) in the POETIC trial (CRUK/07/015) [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr S1-04.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.011 | 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".