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Introduction: Breast cancer (BC) is the second most common cause of cancer death among women in the USA. Recent proteogenomic studies of human BC have identified many potential therapeutic biomarkers for the most common types of BC. Here, we strive to comprehensively understand the proteogenomic landscape of difficult-to-treat breast cancer (DTBC) tumors. Methods: 118 primary breast tumors, with a focus on DTBC patients, were selected from the IRB-approved protocol. The study cohort included 30 triple negative, 16 HER2+, 16 Luminal A, 39 Luminal B1, and 17 Luminal B2 selected by immunohistochemistry. Breast tumors were embedded in OCT (Optimum Cutting Temperature) and processed by laser microdissection (LMD) to remove non-tumorous tissue. DNA, RNA, and protein were simultaneously extracted from each tumor using the illustra triplePrep kit. Paired-end RNA sequencing and whole genome sequencing (WGS) were performed for 118 and 100 tumors, respectively, using the Illumina HiSeq platform. Quantitative global proteomics and phosphoproteomic analyses were performed on 113 and 50 tumors, respectively, using isobaric TMT 6-plex labeling with the “universal reference” strategy. Results: Unlike other BC cohorts, our WGS cohort had an almost equal number of Estrogen(ER)+ (55.6%) and ER- (44.4%) cases, which gave us a nominally higher percentage TP53 (57%) and lower PIK3CA (32%) non-silent somatic mutation. Similar to previous reports, GATA3 was mutated in 13% of only ER+ cases. Unlike a recent report on proteomics clustering of bulk-processed tumors, our data show that a stromal-enriched cluster was not mixed with all subtypes but a majority of Luminal A (LumA) subtype, probably because LMD excluded stromal components in other tumor subtypes. Consensus clustering of proteomics data separated intrinsic Her2 patients with outcome differences. Her2 patients associated with the Basal-enriched proteome cluster had better survival than those associated with the LumA-enriched proteome cluster. Previously reported marker PGK1, a predictor for poor survival in BC, was upregulated in Her2 tumors associated with the LumA-enriched proteome cluster. The clustering of phosphoproteomics data separated intrinsic Basal patients into groups with and without relapse. We identified 3 and 18 genes with upregulated kinase activities in the relapse-free and relapsed Basal patients, respectively. In conclusion, this study provides a high-quality proteogenomic resource for DTBC investigation and identifies potential molecular markers for predicting outcomes for patients with less responsive tumors. The views expressed in this abstract are solely of the authors and do not reflect the official policy of the Departments of Army/Navy/Air Force, DoD, USUHS, HJF. Mention of trade names, commercial products, or organizations does not imply endorsement by the U.S. Government. Citation Format: Praveen-Kumar Raj-Kumar, Tao Liu, Lori A. Sturtz, Albert J. Kovatich, Marina A. Gritsenko, Vladislav A. Petyuk, Brenda Deyarmin, Jianfang Liu, Anupama Praveen-Kumar, Jason E. McDermott, Anil K. Shukla, Ronald J. Moore, Matthew E. Monroe, Bobbie-Jo M. Webb-Robertson, Jeffrey A. Hooke, Leigh Fantacone-Campbell, Leonid Kvecher, Jennifer Kane, Jennifer Melley, Stella Somiari, Stephen C. Benz, Justin Golovato, Shahrooz Rabizadeh, Patrick Soon-Shiong, Richard D. Smith, Richard J. Mural, Karin D. Rodland, Craig D. Shriver, Zhaoyu Li, Hai Hu. Proteogenomics of laser microdissected difficult-to-treat breast cancers identifies Basal and Her2 patients associated with outcome differences [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 2172.

This abstract was not presented at the symposium.

Introduction: Breast tumors have 4 well-established intrinsic subtypes based on transcriptome profiling. However, clusters defined by proteomics are often in disagreement with those defined by transcriptomics. Here, we report the findings of proteogenomic profiling of 118 laser microdissected (LMD) breast tumors using RNA-Seq and mass-spectrometry (MS)-based proteomic technologies. Methods: Cases used in this study were drawn from the Clinical Breast Care Project, with patients consented using an IRB-approved protocol. A total of 118 primary breast tumors embedded in OCT were selected and processed by LMD. Total RNA and protein were extracted using the Illustra triplePrep kit. Paired-end RNA sequencing of 118 cases was performed using the Illumina HiSeq platform, and the reads were preprocessed using a PERL-based pipeline involving the preprocessing tool PRINSEQ, splice-aligner GSNAP and HTSeq for quantifying expression. Quantitative global proteomics analyses were performed on 113 cases using isobaric TMT 6-plex labeling with the “universal reference” strategy. MS data were acquired using a Q-Exactive instrument and analyzed using Proteome Discoverer with Byonic node. Sample-to-sample normalization was conducted to remove pipetting errors and ComBat was used to remove batch effect. K-means clustering was done using Bioconductor package Consensusclustering. Results: The number of preprocessed RNA sequencing reads for the 118 cases ranged from over 43 to 295 million. An average of 83% of reads was mapped, and 24,518 genes with a mean expression of ≥ 10 counts across 118 tumor samples were identified. The PAM50 algorithm was used for intrinsic subtyping, yielding 37 Basal-like, 16 HER2-enriched, 39 Luminal A and 26 Luminal B calls. Unsupervised clustering of 3,000 highly varying genes reflected 4 intrinsic subtypes. In the global proteomics data, 840 proteins were identified across all 113 cases. Unsupervised K-means consensus clustering on all 840 or just using the top 210 highly varying proteins indicated the optimal number of clusters to be 3. These 3 clusters were identified as Basal-enriched, Luminal A-enriched and Luminal B-enriched. HER2-enriched cases were distributed among these clusters. We did not observe a stromal-enriched cluster in this analysis of LMD-prepared samples that selected against stromal components of the tumor. Conclusion: Analysis of LMD breast tumors using proteogenomic technologies resulted in 3 clusters for proteome data: basal-enriched, luminal A-enriched and luminal B-enriched. Unlike a recent report on proteomics clustering using bulk processing of tumors, a stromal-enriched cluster was not observed in this analysis which excluded stromal components of the samples. The views expressed in this abstract are those of the author and do not reflect the official policy of the Department of Army/Navy/Air Force, Department of Defense, or U.S. Government. Citation Format: Praveen-Kumar Raj-Kumar, Tao Liu, Lori A. Sturtz, Albert J. Kovatich, Marina A. Gritsenko, Vladislav A. Petyuk, Brenda Deyarmin, Viswanadham Sridhara, James Craig, Jason E. McDermott, Anil K. Shukla, Ronald J. Moore, Matthew E. Monroe, Bobbie-Jo M. Webb-Robertson, Jeffrey A. Hooke, J.Leigh Fantacone-Campbell, Leonid Kvecher, Jianfang Liu, Jennifer Kane, Jennifer Melley, Stella Somiari, Stephen C. Benz, Justin Golovato, Shahrooz Rabizadeh, Patrick Soon-Shiong, Richard D. Smith, Richard J. Mural, Karin D. Rodland, Craig D. Shriver, Hai Hu. Integrated proteogenomic analysis of laser microdissected primary breast tumors define proteome clusters [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 284.

Background: Heterogeneity between different breast cancer tumors plays an important role in patients’ survival outcomes, and such tumor heterogeneity unveiled by transcriptome and proteome are often in disagreement. We hypothesize that alternative splicing of mRNA could explain heterogeneity within intrinsic breast cancer subtypes. Methods: Cases used in this study were drawn from the Clinical Breast Care Project where breast cancer patients were consented using an IRB-approved protocol. Fifty breast tumors were selected and processed by laser microdissection. RNA and protein were extracted from tissues using the Illustra triplePrep kit (GE Healthcare). Paired-end mRNA sequencing was performed using the Illumina HiSeq platform. Paired-end reads were preprocessed using PRINSEQ and splice-aligned to the genome using GSNAP software. Gene counts were measured using HTSeq while Exon counts used the DEXSeq. Differential expression was called at 10% false discovery rate. Proteome Discoverer with Byonic node was used for analyzing the quantitative global proteomics dataset, and we were able to quantitate 8600 proteins. All other analyses were performed using Perl and R. Results: The number of sequencing reads for the 50 cases ranged from 60 million to 410 million reads. After preprocessing, an average of 93% of reads was mapped, and 36,700 genes were identified among 50 tumor samples. PAM50 algorithm was used for intrinsic subtype calls, yielding 16 Basal, 8 Her2+, 19 Luminal A (LA) and 7 Luminal B tumors. Unsupervised clustering of the 8 Her2+ cases using the PAM50 genes and highly varying proteins resulted in different clustering patterns, with the latter clustering 6 of the 8 Her2+ cases with two distinct 3-case sub-clusters. Between these two sub-clusters, there were 10 differentially expressed (DEX) genes and 25 DEX proteins, but none of the 25 proteins were mapped to the 10 DEX genes. This motivated us to investigate the DEX exons, and we found 7,076 DEX exons between the Her2+ sub-clusters, and 9 of the 25 DEX proteins were matched to genes bearing DEX exons. For comparison, we performed the same analyses between similarly clustered Basal and LA sub-clusters. Even though the number of DEX genes between Basal (8) and LA (18) sub-clusters were comparable to that between the Her2+ sub-clusters, DEX exons were much lower for both subtypes (616 & 157), and none of the DEX proteins (3 & 7) mapped to DEX exons or genes for either subtype. Conclusions: Our findings imply that there is more proteomic-level heterogeneity in the Her2+ subtype than in Basal and LA subtypes, which could be due to alternative usage of exons (alternative splicing). If such heterogeneity is associated with patients’ survival outcomes then our results will further stress the importance of alternative splicing in breast cancer. The views expressed in this article are those of the author and do not reflect the official policy of the Department of Defense, or U.S. Government. Citation Format: Praveen Kumar Raj Kumar, Tao Liu, Lori A. Sturtz, Albert Kovatich, Marina A. Gritsenko, Vladislav A. Petyuk, Brenda Deyarmin, Viswanadham Sridhara, James Craig, Jason E. McDermott, Anil K. Shukla, Ronald J. Moore, Matthew E. Monroe, Bobbie-Jo M. Webb-Robertson, Jeffrey A. Hooke, Leigh Fantacone-Campbell, Leonid Kvecher, Jianfang Liu, Jennifer Kane, Jennifer Melley, Stella Somiari, Joji Iida, Stephen C. Benz, Justin Golovato, Shahrooz Rabizadeh, Patrick Soon-Shiong, Richard D. Smith, Richard J. Mural, Karin D. Rodland, Craig D. Shriver, Hai Hu. Differential exon usage in the HER2 subtype of breast cancer identified with RNA-seq and proteomic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3372. doi:10.1158/1538-7445.AM2017-3372

Introduction: Molecular characteristics of breast tumors play an important role in determining patients’ survival outcome. Here, we report preliminary findings of proteogenomic profiling of 50 breast tumors using RNA-Seq and mass-spectrometry (MS) based proteomic technologies. An additional 60 tumors are being analyzed, including WGS for all samples. We are also collecting patient survival outcome data. Methods: Cases used in this study were drawn from the Clinical Breast Care Project, where patients were consented using an IRB-approved protocol. A total of 50 breast tumors were selected and processed by laser capture microdissection (LCM). This cohort includes 36 Caucasian Americans (CA) and 8 African Americans (AA), and the age of the patients is 57 ± 13 years. Protein and RNA were extracted using the Illustra triplePrep kit, which isolates DNA from the same cells as well. Quantitative global proteomics and phosphoproteomics analyses were performed using isobaric TMT 6-plex labeling with the “universal reference” strategy and IMAC enrichment of phosphopeptides. Mass spectrometry data were acquired using a Q-Exactive instrument and analyzed using Proteome Discoverer with Byonic node. Phosphopeptide abundance was normalized to abundance measurements of the parent protein for all of the phosphorylation analyses. Phosphoproteomic data was also searched for the presence of O-GlcNAc modifications. RNA-Seq analyses were done on Illumina HiSeq and the data were analyzed using GSNAP. Results: There were 19 Luminal A, 7 Luminal B, 8 HER2-enriched, and 16 basal-like subtypes based on the PAM50 algorithm. In the global proteomics data, we were able to quantitate >8600 proteins. Unsupervised clustering on the highly varying proteins across the samples resulted in two primary clusters, with one being luminal-enriched. The other cluster contains a basal-like tumor sub-cluster and a sub-cluster of mixed subtypes. Differential protein expression analyses between the two primary clusters confirmed known markers (e.g., overexpression of KRT8/KRT18 in luminal-enriched cluster). The luminal-enriched cluster is primarily CA with post-menopausal status. A similar search of the phosphoproteomic data yielded quantitation of >12500 phosphopeptides. Unsupervised clustering of the phosphoproteins resulted in four primary groups, with one being basal-enriched and another being luminal-enriched. We also observed >50 overexpressed phosphopeptides. While some of these phosphosites have been previously reported (e.g., on RANBP2), other phosphosites appeared to be novel (e.g., on IRF2BP2). Conclusion: Analysis of LCM breast tumors using proteogenomic technologies resulted in basal- and luminal-enriched clusters, thus enabling us to study protein and phosphopeptide markers across multiple platforms. The views expressed in this article are those of the author and do not reflect the official policy of the Department of Defense, or U.S. Government. Citation Format: Viswanadham Sridhara, Tao Liu, Marina A. Gritsenko, Lori A. Sturtz, Albert J. Kovatich, Vladislav A. Petyuk, Brenda Deyarmin, Jason E. McDermott, Anil K. Shukla, Ronald J. Moore, Matthew E. Monroe, Bobbie-Jo M. Webb-Robertson, Jeffrey A. Hooke, Leigh Fantacone-Campbell, Praveen Kumar Raj Kumar, Leonid Kvecher, Jianfang Liu, Jennifer Kane, Jennifer Melley, Stella Somiari, Joji Iida, Stephen C. Benz, Justin Golovato, Shahrooz Rabizadeh, Patrick Soon-Shiong, Richard D. Smith, Richard J. Mural, Craig D. Shriver, Hai Hu, Karin D. Rodland. Integrated proteogenomic analysis of laser capture microdissected breast tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 213. doi:10.1158/1538-7445.AM2017-213