Abstract PS18-06: Proteomic analysis of breast cancer formalin-fixed paraffin-embedded clinical specimens identifies biologically-important subtypes with distinct clinical outcomes
Bibliographic record
Abstract
Abstract Background: Genomic classification of breast cancer has advanced breast cancer diagnosis and outcomes. However, extensive heterogeneity still exists beyond their DNA or RNA profiles. Newer classifications based on protein profiling are being developed to investigate the molecular oncology of breast cancers at the level where most drugs act. Using a recently-developed technology, we performed global proteomic profiling of 300 breast cancer specimens linked to outcome data. Methods: Sections of 75 samples from each PAM50 intrinsic subtype (Luminal A, Luminal B, Her2-enriched, Basal-like; n = 300) were macrodissected and analyzed using the Single-Pot Solid-Phase enhanced Sample Preparation Clinical Tissue Proteomics, a highly sensitive 11-sample multiplex massspectrometry protocol applicable to formalin-fixed, paraffin embedded (FFPE) specimens. This methodology enables comprehensive quantification of protein expression for classifier and biomarker discovery. Patients were diagnosed during 2008-2013 (n = 178, dataset I) and 1986-1992 (n = 122, dataset II). Results: In-depth proteomic analysis measured 9088 proteins in total, including 4214 proteins quantified in every sample. Consensus clustering of 174 evaluable cases in dataset I identified four distinct groups based on expression values for 1054 highly variant proteins. Cluster 3 (n = 47, mostly basal-like with HER2-Enriched) displayed the most favorable recurrence free survival (RFS) when compared to other clusters (HR = 0.22, 95%CI [0.08-0.63], p = 0.005). This cluster was enriched for immune related pathways including antigen processing and presentation and type I & II interferon signaling, and displayed high tumor infiltrating lymphocyte counts, characterizing this cluster as “immune hot”. In contrast, cluster 2 (n = 50, mostly basal-like) exhibited the poorest RFS (HR = 2.88, 95%CI [1.45-5.70], p = 0.002) and was enriched for proteins related to stromal and extracellular matrix with few immune related peptides. Cluster 1 (n = 34, luminal B and HER2-Enriched) was associated with lipid metabolism, whereas cluster 4 (n = 43, mostly HER2-Enriched with luminal A and luminal B) had a profile enriched for extracellular matrix, blood coagulation and complement activation. Conclusions: Global proteomic analysis on FFPE specimens can characterize the heterogeneity of breast cancer in a reliable and clinically-applicable high throughput manner. Our methodology identifies protein candidates that potentially serve as therapeutic targets and could be adapted to archived clinical specimens from other tumors. Citation Format: Karama Asleh, Gian Luca Negri, Sandra E. Spencer Miko, Shane Colborne, Christopher S. Hughes, Xiu Q. Wang, Dongxia Gao, C. Blake Gilks, Stephen K.L. Chia, Torsten O. Nielsen, Gregg B. Morin. Proteomic analysis of breast cancer formalin-fixed paraffin-embedded clinical specimens identifies biologically-important subtypes with distinct clinical outcomes [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS18-06.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".