Abstract 5622: Integrative cross-platform characterisation of mammographic screen-detected breast cancer
Bibliographic record
Abstract
Abstract Mammography screening is widely used for earlier detection of breast cancer and has been shown to contribute to reduction of mortality and morbidity. Most screen-detected breast cancers are early stage, hormonal receptor-positive, HER2-negative (HR+ HER2-) breast cancers. The majority of HR+ HER2- cancers are assigned to molecular intrinsic subtypes of Luminal A and Luminal B, which generally harbour low recurrence risk, with Luminal B cases being more invasive but still less aggressive than HER2-enriched or Basal-like subtypes. However, some of these Luminal cancers later recur (5+ years after diagnosis) often as advanced and/or metastatic disease. We studied a cohort of screen-detected breast cancers (42 cases) at Sunnybrook Health Sciences Centre (Toronto, ON Canada) with integrated cross-platform radiomics, molecular and proteomic analysis in an attempt to better characterise these cancers and their proclivity for late recurrence. Utilising a Nanostring 200-gene assay, the molecular subtypes (PAM50 and MammaTyper-like) and a range of recapitulated clinical prognostic scores (50-Gene, 70-Gene and 21-Gene Risk) were determined for these cancers. While a majority of cases (29 out of 42) were subtyped as Luminal A cancers by PAM50, a fraction (12 out of 29) of these were either subtyped as Luminal B/HER2+ based on MammaTyper-like results, or measured as intermediate to high risk of recurrence based on the 70-Gene or 21-Gene Risk classification. Although discordant results from multi-parametric assays are not uncommon, we postulate that here, discordance could suggest presence of heterogeneous cellular or molecular elements with invasive phenotype that could lead to aggressive transformations in the long run. Differential expression analysis between Luminal A cases with consistent subtyping and low risk prognostication and those with discordant subtyping or prognostication results revealed a panel of genes with significant changes in expression. Further analysis of RNA expression of these genes via Receiver Operating Characteristic (ROC) curve demonstrated that some genes showed high Area Under the Curve (AUC) scores in the classification between the two groups of Luminal A breast cancers, further supporting the existence of distinct molecular phenotypes. Targeted sequencing with Oncomine Comprehensive Assay V3 did not reveal particular mutational patterns between the two Luminal A groups. Nevertheless, radiomic analysis of mammographic images of the cancer, as well as single cell phenotyping of the tumor microenvironment with protein multiplexing will be incorporated to further characterise elements that are phenotypically different, and potentially identify mechanistic drivers contributing to late recurrence in Luminal A cancers. Citation Format: Alison M. Cheung, Dan Wang, Kela Liu, Yutaka Amemiya, Elzbieta Slodkowska, James G. Mainprize, Jane Bayani, Arun Seth, John Bartlett, Martin J. Yaffe. Integrative cross-platform characterisation of mammographic screen-detected breast cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5622.
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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 | 0.000 |
| 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) | 0.002 | 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".