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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.

Background Pancreatic neuroendocrine neoplasms (PNENs) are biologically and clinically heterogeneous neoplasms with variable patient outcomes. Our study aims to uncover the molecular factors that underlie the clinical and biological heterogeneity among PNENs for a better understanding and potential classification of this disease. Methods Formalin-fixed paraffin-embedded primary tumour specimens from 92 patients with PNEN were procured for the study and split into two cohorts for discovery and validation purposes. Next-generation sequencing profiled the exome and transcriptome, and quantitative mass-spectrometry profiled the global proteome of specimens. Non-negative matrix factorization was used to identify subgroups, followed by differential analysis to identify subgroup-specific features. To corroborate subgroup-specific RNA- and protein- level distinctions, activities of key cellular regulators were inferred from target gene expression or co-expression signatures. Results Unsupervised clustering analysis of transcriptome data identified four robust molecular subgroups that were substantiated by proteome analysis (p=0.0005) within the discovery cohort and validated with the validation cohort and an external cohort of PNENs from a previous microarray-based gene expression study. A proliferative subgroup was enriched with neuroendocrine carcinomas and specimens with >20% Ki67, concomitant with reduced survival probability (p=0.0024; logrank test) and higher mRNA expression and protein abundance of cell cycle-related genes. Increased mRNA expression of ARX or PDX1 (adjusted p Citation Format: Kevin C. Yang, Steve Kalloger, John Aird, Michael Lee, Christopher Rushton, Sandra E. Spencer Miko, Karen L. Mungall, Andrew J. Mungall, Shane Colborne, Ryan D. Morin, Jonathan M. Loree, Marco A. Marra, Daniel J. Renouf, Gregg B. Morin, David F. Schaeffer, Sharon M. Gorski. Proteotranscriptomic classification and characterization of pancreatic neuroendocrine neoplasms [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2020 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2020;80(22 Suppl):Abstract nr PR-009.