Abstract B02: DNA repair landscape in High Grade Ovarian Cancer (HGOC) and evolution with neo-adjuvant chemotherapy
Bibliographic record
Abstract
Abstract Background: HGOC is frequently diagnosed at an advanced stage where NeoAdjuvant Chemotherapy (NACT) provides an essential component of the treatment strategy. HGOC are initially very chemosensitive, putatively due to DNA repair defects. While BRCA mutations explain impaired homologous recombination in 12% to 15% of HGOC, loss of other DNA repair proteins may also be relevant. Unfortunately despite initial response rates of 80%, most invariably relapse. Chemo-resistance mechanisms are poorly understood and previous studies have only focused on the characterization at diagnosis. However given their chemosensitivity, profiling the post-NACT tumor (cleared of sensitive cells and enriched for chemoresistant clones) may be more relevant to uncover mechanisms of platinum resistance. We aimed to evaluate the evaluate the expression of key DNA repair biomarkers in a large series of HGOC tumors obtained at baseline, Post-NACT and at relapse. Methods: TMA was constructed using sequential FFPE tumor samples of high-grade ovarian cancer collected at diagnosis (pre-NACT) (N=143), post-NACT (N=139) and at relapse (N=36) in 170 patients, encompassing 115 patients with sequential tumor samples. Expression of 53BP1, PAR, PARP-1 and ATM were scored by immunohistochemistry using an H-score (0-300) with biomarker negative defined as H-score =< 10. Markers were analysed with a Cox model to predict overall survival (OS) and progression free survival (PFS). Results: At diagnosis, a significant proportion of tumors showed loss of DNA repair proteins: 61% were PAR-negative, and 24%, 15% and 3% negative for ATM, 53BP1 and PARP-1 respectively and a fifth of tumors showed concomitant loss in 2 or more DNA proteins. There was an overall decrease in DNA repair protein expression with NACT, which was significant for PAR (pre-NACT vs post-NACT H-score= 45 vs 19, p=0.0025, N=201) and PARP-1(H-score=208 vs 190; p=0.0149, N=205). In addition paired samples analysis of matched pre- and post-NACT samples showed complete loss of biomarker expression after treatment in a subset (24%, 16%, 6% and 3% for PAR, ATM, TP53BP1 and PARP-1). When comparing post-NACT to relapsed samples, PAR expression increased significantly (mean H-score=19 vs 54, p=0,025). The prognostic value of loss of each DNA repair at diagnosis and after NACT was evaluated. At diagnosis, 53BP1-negative tumors were associated with a significantly worst PFS (HR=3.484; p: 0.0025). Post-NACT, ATM-negative tumors had a significantly worst PFS (HR=1.690; p: 0.0374). As PAR, TP53BP1 and ATM were shown not to be correlated in individual tumors (Pearson correlation), the impact of combined biomarker loss on PFS was evaluated. At diagnosis, double TP53BP1/PAR-negative tumors predicted shorter PFS (PFS: median=17.65 vs 23.23 mths, p:0.0372, HR=0.2237, N=58), while post-NACT double ATM/PAR-negativity was significantly predictive of poor outcome in terms of PFS and OS (PFS: median=17.35 vs 23.77 mths, p:0.0198, HR=0.4759; OS: median=35 vs 50 mths, p:0.0120, HR=0.3602; N=80). Conclusions: We present the first study of change in DNA repair protein expression with NACT. At diagnosis, HGOC is associated with loss of key DNA repair proteins in a significant proportion of patients and NACT can induce significant loss of DNA repair proteins. Combined loss of DNA repair proteins was significantly predictive of survival. Work in ongoing to extend this analysis to a greater panel of DNA repair biomarkers. Citation Format: Aurelie Auguste, Soizick Mesnage, Audrey Le Formal, Elena Cojocaru, Francoise Drusch, Julien Adam, Sebastien Gouy, Enrica Bentivegna, Catherine Lhomme, Patricia Pautier, Catherine Genestie, Alexandra Leary. DNA repair landscape in High Grade Ovarian Cancer (HGOC) and evolution with neo-adjuvant chemotherapy [abstract]. In: Proceedings of the AACR Special Conference on DNA Repair: Tumor Development and Therapeutic Response; 2016 Nov 2-5; Montreal, QC, Canada. Philadelphia (PA): AACR; Mol Cancer Res 2017;15(4_Suppl):Abstract nr B02.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 teacher head, 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".