Abstract A017: Characterizing intratumoral heterogeneity of <i>CCNE1</i> amplification in ovarian cancer using digital pathology
Bibliographic record
Abstract
Abstract Ovarian cancers with CCNE1 amplification (amp) are typically platinum resistant, constituting an area of unmet need. Advances in the field of synthetic lethality (SL) have identified Protein Kinase Membrane-associated Tyrosine/Threonine 1 (PKMYT1) as SL in tumors with CCNE1 amp. In preclinical studies, inhibition of PKMYT1 with the first-in-class, potent and selective inhibitor, lunresertib, causes premature mitotic entry and, in turn, cell death in CCNE1 amp cells. Lunresertib is in early clinical development as a single agent and in combination with the ATR inhibitor camonsertib (RP-3500), the Wee1 inhibitor Debio0123 (NCT04855656) or irinotecan (NCT05147350) in solid tumors harboring CCNE1 amp. A deeper understanding of CCNE1 amp as a predictive biomarker for PKMYT1 inhibition is critical to refine patient selection strategies and strengthen our understanding of the MoA. We previously reported substantial intratumoral CCNE1 copy number (CN) heterogeneity in gynecological tumors using fluorescent in situ hybridization (FISH) with manual enumeration. Here we aim to further characterize this heterogeneity in a cohort of human ovarian cancer tissues (n = 22) using a customized digital pathology algorithm, which enables unbiased whole tumor quantification of CCNE1 CN. Classifiers were developed to identify areas of autofluorescence, oversaturation, or indeterminate object morphology eliminating staining artifacts prior to CN analysis. An optimized FISH analysis module (Halo, Indica labs) was designed to detect and quantify CCNE1 and Chromosome 19 (Chr19) foci with single-cell resolution. To ensure foci quantification in individual nuclei, the analysis pipeline corrected for nuclear size, nuclear roundness and Chr19 ploidy. Amp calls, defined as mean CCNE1/Chr19 ratio ≥2, were concordant with manual and digital pathology in 91% (20/22) samples, and CCNE1/Chr19 ratio values were moderately correlated (Spearman ρ 0.575; p=0.005). Quantification of CCNE1/Chr19 ratio across an average of 10,000 cells/tissue revealed substantial differences in the percentage of CCNE1 amp cells across individual tumors (median: 67%, range: 9 - 95%). Spatial distribution analysis showed diverse amp patterns, including specimens with homogeneously distributed, low-level CCNE1 amp and tumors with localized areas of high CCNE1 CN. We calculated the median absolute deviation (MAD) of CCNE1/Chr19 ratio distribution per sample, a metric that represents variability independently of amp level. Our results demonstrate that MAD value correlates with CCNE1/Chr19 ratio. We are currently investigating the relationship between intratumoral CCNE1 amp heterogeneity and underlying genomic features, such as extrachromosomal DNA. Although CCNE1 amp is an early event in ovarian cancer, our data demonstrates the existence of a population of non-amplified cells within each tumor as well as remarkable intratumoral CN heterogeneity, particularly in tumors with higher amp levels. Future studies will explore the clinical implications of CCNE1 amp heterogeneity on lunresertib development. Citation Format: Adam Petrone, Isabel Soria-Bretones, Adrienne Johnson, Sunantha Sethuraman, Ian M Silverman, Jorge Reis-Filho, Gary Marshall, Artur Veloso, Elia Aguado-Fraile, Victoria Rimkunas. Characterizing intratumoral heterogeneity of CCNE1 amplification in ovarian cancer using digital pathology [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr A017.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".