Abstract 3131: Tumour spatial heterogeneity in breast cancer and the impact on clinical management
Bibliographic record
Abstract
Abstract We recognize that many cancers are highly complex mixtures of sub-populations of cells, which are also influenced by their microenvironment. This heterogeneity explains in part, why the majority of current therapeutic approaches for cancer work best when multiple agents are combined. Therefore, in an era of targeted therapeutics it becomes critical to understand the complexity of tumors both at diagnosis and over the course of therapy, including measures of heterogeneity. Here we present genomic, transcriptomic and in situ proteomic profiling of a cohort of breast cancer (BCa) lumpectomies to reveal the extent of heterogeneity in pathologically defined unifocal and multifocal cancers. Integration of molecular profiling, phylogenetic analyses and radiomics has the potential to significantly improve BCa clinical management and stratification to targeted therapies that are already available in the clinic. In this study, lumpectomies with imaging data were processed as whole mounts with serial blocks reviewed. Tissue cores were taken from at least three different regions through the lumpectomy for nucleic acid extraction and tissue microarray (TMA) construction, focusing on morphologic/histological differences in addition to the spatial orientation of the sampled region within the lumpectomy. Targeted sequencing using the Oncomine Comprehensive Assay v3 (OCAv3); transcriptional profiling using the NanoString Breast Cancer 360 Panel; in situ profiling by multiplex fluorescence immunohistochemistry (MxIF) and Digital Spatial Profiling (see abstract Spears et al) were performed. From this cohort of 60 patients, we present a subset of patients demonstrating integration of the molecular profiling to reveal the phylogenetic relationship between the multiple samplings and the impact on clinical decision making in BCa. Briefly, we identified differences in the molecular subtypes between the different sample regions from the same unifocal cancer as well as differences in the predicted responses to anti-PDL1 therapy by transcriptional profiling; while targeted sequencing of driver mutations suggested the likelihood of an ancestral tumor cell giving rise to the lesions in pathologically defined multifocal cancers. However it was evident that genes and pathways found to be aberrant in these different lesions from the same cancer could impact the response to standard BCa treatment, or the selection of targeted therapies. In situ proteomics demonstrated differences in the expression of standard BCa markers ER, PgR, HER2 and Ki67 in addition to immune markers in the tumor and tumor microenvironment. While there are clinically validated transcriptional risk test available for BCa, we have demonstrated that transcriptomics or genomics alone is insufficient for a rational stratification of patients to currently available targeted therapies; therefore supporting the need for an integrative approach. Citation Format: Jane Bayani, Quang M. Trinh, Mary Anne Quintayo, Cheryl Crozier, Megan Hopkins, Jingping Qiao, Alison Cheung, James Mainprize, Quaid Morris, Melanie Spears, Martin Yaffe, Lincoln Stein, John M. Bartlett. Tumour spatial heterogeneity in breast cancer and the impact on clinical management [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3131.
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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) | 0.002 | 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 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".