Proteogenomic characterization of difficult-to-treat breast cancer with tumor cells enriched through laser microdissection.

BACKGROUND: Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of cancer death among women globally. Despite advances, there is considerable variation in clinical outcomes for patients with non-luminal A tumors, classified as difficult-to-treat breast cancers (DTBC). This study aims to delineate the proteogenomic landscape of DTBC tumors compared to luminal A (LumA) tumors. METHODS: We retrospectively collected a total of 117 untreated primary breast tumor specimens, focusing on DTBC subtypes. Breast tumors were processed by laser microdissection (LMD) to enrich tumor cells. DNA, RNA, and protein were simultaneously extracted from each tumor preparation, followed by whole genome sequencing, paired-end RNA sequencing, global proteomics and phosphoproteomics. Differential feature analysis, pathway analysis and survival analysis were performed to better understand DTBC and investigate biomarkers. RESULTS: We observed distinct variations in gene mutations, structural variations, and chromosomal alterations between DTBC and LumA breast tumors. DTBC tumors predominantly had more mutations in TP53, PLXNB3, Zinc finger genes, and fewer mutations in SDC2, CDH1, PIK3CA, SVIL, and PTEN. Notably, Cytoband 1q21, which contains numerous cell proliferation-related genes, was significantly amplified in the DTBC tumors. LMD successfully minimized stromal components and increased RNA-protein concordance, as evidenced by stromal score comparisons and proteomic analysis. Distinct DTBC and LumA-enriched clusters were observed by proteomic and phosphoproteomic clustering analysis, some with survival differences. Phosphoproteomics identified two distinct phosphoproteomic profiles for high relapse-risk and low relapse-risk basal-like tumors, involving several genes known to be associated with breast cancer oncogenesis and progression, including KIAA1522, DCK, FOXO3, MYO9B, ARID1A, EPRS, ZC3HAV1, and RBM14. Lastly, an integrated pathway analysis of multi-omics data highlighted a robust enrichment of proliferation pathways in DTBC tumors. CONCLUSIONS: This study provides an integrated proteogenomic characterization of DTBC vs LumA with tumor cells enriched through laser microdissection. We identified many common features of DTBC tumors and the phosphopeptides that could serve as potential biomarkers for high/low relapse-risk basal-like BC and possibly guide treatment selections.

PCA-PAM50 improves consistency between breast cancer intrinsic and clinical subtyping reclassifying a subset of luminal A tumors as luminal B.

The PAM50 classifier is widely used for breast tumor intrinsic subtyping based on gene expression. Clinical subtyping, however, is based on immunohistochemistry assays of 3-4 biomarkers. Subtype calls by these two methods do not completely match even on comparable subtypes. Nevertheless, the estrogen receptor (ER)-balanced subset for gene-centering in PAM50 subtyping, is selected based on clinical ER status. Here we present a new method called Principle Component Analysis-based iterative PAM50 subtyping (PCA-PAM50) to perform intrinsic subtyping in ER status unbalanced cohorts. This method leverages PCA and iterative PAM50 calls to derive the gene expression-based ER status and a subsequent ER-balanced subset for gene centering. Applying PCA-PAM50 to three different breast cancer study cohorts, we observed improved consistency (by 6-9.3%) between intrinsic and clinical subtyping for all three cohorts. Particularly, a more aggressive subset of luminal A (LA) tumors as evidenced by higher MKI67 gene expression and worse patient survival outcomes, were reclassified as luminal B (LB) increasing the LB subtype consistency with IHC by 25-49%. In conclusion, we show that PCA-PAM50 enhances the consistency of breast cancer intrinsic and clinical subtyping by reclassifying an aggressive subset of LA tumors into LB. PCA-PAM50 code is available at ftp://ftp.wriwindber.org/ .

Serum Biomarkers for Racial Disparities in Breast Cancer Progression.

African American (AA) women are often diagnosed with more aggressive breast cancers and have worse survival outcomes than their Caucasian American (CA) counterparts. However, a comprehensive understanding of this disparity remains unclear. In this study, we attempted to identify the race-specific non-invasive protein biomarkers that may particularly benefit interventions aimed at reducing the risk of recurrence and metastasis in breast cancers (BrCa). Our technical strategy has been to discover candidate protein biomarkers in patient sera using a high throughput antibody microarray platform. A total of 240 subjects were selected, composed of controls and all immunohistochemistry-based subtypes of breast cancer cases, subdivided by pre- and post-menopausal status and by race. A global Wilcoxon analysis comparing no-cancer controls and cancer patients identified Pyk2, SAPK/JNK, and phosphatase and tensin homolog as present in higher concentrations in cancer patient serum. A paired t-test revealed that c-kit and Rb are significantly over-represented in AA cancer serum when compared to CA cancer serum. Interestingly, VEGFR2, a protein linked to BrCa metastasis and poor prognosis, was significantly over-represented in AA cancer serum compared to AA controls; however, this was not found in CA cancer serum compared to CA controls, suggesting a possible explanation for the higher incidence of aggressive BrCa in AA versus CA patients. Through examining race-specific differences in the protein landscape of BrCa patient serum, the identified proteins could lay the groundwork for the development of an all-inclusive "liquid mammogram test."

Comparative Survival Analysis of Invasive Breast Cancer Patients Treated by a U.S. Military Medical Center and Matched Patients From the U.S. General Population.

OBJECTIVE: Many differences between U.S. military beneficiaries and the U.S. general population, including differences in health care access, are known factors affecting invasive breast cancer outcomes. Thus, comparing the two populations for any outcome differences and their contributing factors may provide insights to breast cancer prognosis. METHODS: Using a marginal Cox proportional hazards regression model, we compared disease-specific survival (DSS) and 5-year DSS rates between 418 patients from the Clinical Breast Care Project at the Walter Reed National Military Medical Center (CBCP-WR) and a set of 1:5 randomly matched patients from the Surveillance, Epidemiology, and End Results program. Patients were compared in the "demographic model" (adjusted by diagnosis year, age, and race) and the "overall model" (further adjusted by estrogen receptor, progesterone receptor, stage, and grade). RESULTS: In the "overall model," CBCP-WR patients were less likely overall to die from breast cancer (hazard ratio [HR] = 0.631, 95% confidence interval [CI] = 0.437-0.911; p = 0.014). This increase in survival was also significant in African American patients (HR = 0.524, 95% CI = 0.277-0.992; p = 0.047) and patients older than 50 (HR = 0.511, 95% CI = 0.306-0.854; p = 0.010). The advantage in 5-year DSS rate for CBCP-WR patients was 5.3% (93.1% vs. 87.8%; p < 0.001) in the "demographic model" and 3.4% (91.3% vs. 87.9%; p = 0.018) in the "overall model." CONCLUSION: CBCP-WR patients demonstrated significantly better DSS over matched SEER patients. Although a portion of the outcome disparity, i.e., 36% of the 5.3% DSS rate difference, could be explained by differences in tumor characteristics, the cause(s) behind the majority of the disparity has yet to be identified. Identification and further analysis of contributing factors to survival differences have the potential to improve clinical practice and outcomes for invasive breast cancer patients.

Positive Association of Fibroadenomatoid Change with HER2-Negative Invasive Breast Cancer: A Co-Occurrence Study.

BACKGROUND: Risk assessment of a benign breast disease/lesion (BBD) for invasive breast cancer (IBC) is typically done through a longitudinal study. For an infrequently-reported BBD, the shortage of occurrence data alone is a limiting factor to conducting such a study. Here we present an approach based on co-occurrence analysis, to help address this issue. We focus on fibroadenomatoid change (FAC), an under-studied BBD, as our preliminary analysis has suggested its previously unknown significant co-occurrence with IBC. METHODS: A cohort of 1667 female patients enrolled in the Clinical Breast Care Project was identified. A single experienced breast pathologist reviewed all pathology slides for each case and recorded all observed lesions, including FAC. Fibroadenoma (FA) was studied for comparison since FAC had been speculated to be an immature FA. FA and Fibrocystic Changes (FCC) were used for method validation since they have been comprehensively studied. Six common IBC and BBD risk/protective factors were also studied. Co-occurrence analyses were performed using logistic regression models. RESULTS: Common risk/protective factors were associated with FA, FCC, and IBC in ways consistent with the literature in general, and they were associated with FAC, FA, and FCC in distinct patterns. Age was associated with FAC in a bell-shape curve so that middle-aged women were more likely to have FAC. We report for the first time that FAC is positively associated with IBC with odds ratio (OR) depending on BMI (OR = 6.78, 95%CI = 3.43-13.42 at BMI<25 kg/m2; OR = 2.13, 95%CI = 1.20-3.80 at BMI>25 kg/m2). This association is only significant with HER2-negative IBC subtypes. CONCLUSIONS: We conclude that FAC is a candidate risk factor for HER2-negative IBCs, and it is a distinct disease from FA. Co-occurrence analysis can be used for initial assessment of the risk for IBC from a BBD, which is vital to the study of infrequently-reported BBDs.

QAIT: a quality assurance issue tracking tool to facilitate the improvement of clinical data quality.

In clinical and translational research as well as clinical trial projects, clinical data collection is prone to errors such as missing data, and misinterpretation or inconsistency of the data. A good quality assurance (QA) program can resolve many such errors though this requires efficient communications between the QA staff and data collectors. Managing such communications is critical to resolving QA problems but imposes a major challenge for a project involving multiple clinical and data processing sites. We have developed a QA issue tracking (QAIT) system to support clinical data QA in the Clinical Breast Care Project (CBCP). This web-based application provides centralized management of QA issues with role-based access privileges. It has greatly facilitated the QA process and enhanced the overall quality of the CBCP clinical data. As a stand-alone system, QAIT can supplement any other clinical data management systems and can be adapted to support other projects.

DW4TR: A Data Warehouse for Translational Research.

The linkage between the clinical and laboratory research domains is a key issue in translational research. Integration of clinicopathologic data alone is a major task given the number of data elements involved. For a translational research environment, it is critical to make these data usable at the point-of-need. Individual systems have been developed to meet the needs of particular projects though the need for a generalizable system has been recognized. Increased use of Electronic Medical Record data in translational research will demand generalizing the system for integrating clinical data to support the study of a broad range of human diseases. To ultimately satisfy these needs, we have developed a system to support multiple translational research projects. This system, the Data Warehouse for Translational Research (DW4TR), is based on a light-weight, patient-centric modularly-structured clinical data model and a specimen-centric molecular data model. The temporal relationships of the data are also part of the model. The data are accessed through an interface composed of an Aggregated Biomedical-Information Browser (ABB) and an Individual Subject Information Viewer (ISIV) which target general users. The system was developed to support a breast cancer translational research program and has been extended to support a gynecological disease program. Further extensions of the DW4TR are underway. We believe that the DW4TR will play an important role in translational research across multiple disease types.

Biomedical informatics: development of a comprehensive data warehouse for clinical and genomic breast cancer research.

The Windber Research Institute is an integrated high-throughput research center employing clinical, genomic and proteomic platforms to produce terabyte levels of data. We use biomedical informatics technologies to integrate all of these operations. This report includes information on a multi-year, multi-phase hybrid data warehouse project currently under development in the Institute. The purpose of the warehouse is to host the terabyte-level of internal experimentally generated data as well as data from public sources. We have previously reported on the phase I development, which integrated limited internal data sources and selected public databases. Currently, we are completing phase II development, which integrates our internal automated data sources and develops visualization tools to query across these data types. This paper summarizes our clinical and experimental operations, the data warehouse development, and the challenges we have faced. In phase III we plan to federate additional manual internal and public data sources and then to develop and adapt more data analysis and mining tools. We expect that the final implementation of the data warehouse will greatly facilitate biomedical informatics research.