An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics.

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.

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.

Selection of optimal quantile protein biomarkers based on cell-level immunohistochemistry data.

BACKGROUND: Protein biomarkers of cancer progression and response to therapy are increasingly important for improving personalized medicine. Advanced quantitative pathology platforms enable measurement of protein expression in tissues at the single-cell level. However, this rich quantitative cell-by-cell biomarker information is most often not exploited. Instead, it is reduced to a single mean across the cells of interest or converted into a simple proportion of binary biomarker-positive or -negative cells. RESULTS: We investigated the utility of retaining all quantitative information at the single-cell level by considering the values of the quantile function (inverse of the cumulative distribution function) estimated from a sample of cell signal intensity levels in a tumor tissue. An algorithm was developed for selecting optimal cutoffs for dichotomizing cell signal intensity distribution quantiles as predictors of continuous, categorical or survival outcomes. The proposed algorithm was used to select optimal quantile biomarkers of breast cancer progression based on cancer cells' cell signal intensity levels of nuclear protein Ki-67, Proliferating cell nuclear antigen, Programmed cell death 1 ligand 2, and Progesterone receptor. The performance of the resulting optimal quantile biomarkers was validated and compared to the standard cancer compartment mean signal intensity markers using an independent external validation cohort. For Ki-67, the optimal quantile biomarker was also compared to established biomarkers based on percentages of Ki67-positive cells. For proteins significantly associated with PFS in the external validation cohort, the optimal quantile biomarkers yielded either larger or similar effect size (hazard ratio for progression-free survival) as compared to cancer compartment mean signal intensity biomarkers. CONCLUSION: The optimal quantile protein biomarkers yield generally improved prognostic value as compared to the standard protein expression markers. The proposed methodology has a broad application to single-cell data from genomics, transcriptomics, proteomics, or metabolomics studies at the single cell level.

Quantile Index Biomarkers Based on Single-Cell Expression Data.

Current histocytometry methods enable single-cell quantification of biomolecules in tumor tissue sections by multiple detection technologies, including multiplex fluorescence-based immunohistochemistry or in situ hybridization. Quantitative pathology platforms can provide distributions of cellular signal intensity (CSI) levels of biomolecules across the entire cell populations of interest within the sampled tumor tissue. However, the heterogeneity of CSI levels is usually ignored, and the simple mean signal intensity value is considered a cancer biomarker. Here we consider the entire distribution of CSI expression levels of a given biomolecule in the cancer cell population as a predictor of clinical outcome. The proposed quantile index (QI) biomarker is defined as the weighted average of CSI distribution quantiles in individual tumors. The weight for each quantile is determined by fitting a functional regression model for a clinical outcome. That is, the weights are optimized so that the resulting QI has the highest power to predict a relevant clinical outcome. The proposed QI biomarkers were derived for proteins expressed in cancer cells of malignant breast tumors and demonstrated improved prognostic value compared with the standard mean signal intensity predictors. The R package Qindex implementing QI biomarkers has been developed. The proposed approach is not limited to immunohistochemistry data and can be based on any cell-level expressions of proteins or nucleic acids.

Development and validation of prognostic gene signature for basal-like breast cancer and high-grade serous ovarian cancer.

PURPOSE: Molecular similarities have been reported between basal-like breast cancer (BLBC) and high-grade serous ovarian cancer (HGSOC). To date, there have been no prognostic biomarkers that can provide risk stratification and inform treatment decisions for both BLBC and HGSOC. In this study, we developed a molecular signature for risk stratification in BLBC and further validated this signature in HGSOC. METHODS: RNA-seq data was downloaded from The Cancer Genome Atlas (TCGA) project for 190 BLBC and 314 HGSOC patients. Analyses of differentially expressed genes between recurrent vs. non-recurrent cases were performed using different bioinformatics methods. Gene Signature was established using weighted linear combination of gene expression levels. Their prognostic performance was evaluated using survival analysis based on progression-free interval (PFI) and disease-free interval (DFI). RESULTS: 63 genes were differentially expressed between 18 recurrent and 40 non-recurrent BLBC patients by two different methods. The recurrence index (RI) calculated from this 63-gene signature significantly stratified BLBC patients into two risk groups with 38 and 152 patients in the low-risk (RI-Low) and high-risk (RI-High) groups, respectively (p = 0.0004 and 0.0023 for PFI and DFI, respectively). Similar performance was obtained in the HGSOC cohort (p = 0.0131 and 0.004 for PFI and DFI, respectively). Multivariate Cox regression adjusting for age, grade, and stage showed that the 63-gene signature remained statistically significant in stratifying HGSOC patients (p = 0.0005). CONCLUSION: A gene signature was identified to predict recurrence in BLBC and HGSOC patients. With further validation, this signature may provide an additional prognostic tool for clinicians to better manage BLBC, many of which are triple-negative and HGSOC patients who are currently difficult to treat.

Comparative analysis of differentially abundant proteins quantified by LC-MS/MS between flash frozen and laser microdissected OCT-embedded breast tumor samples.

BACKGROUND: Proteomic studies are typically conducted using flash-frozen (FF) samples utilizing tandem mass spectrometry (MS). However, FF specimens are comprised of multiple cell types, making it difficult to ascertain the proteomic profiles of specific cells. Conversely, OCT-embedded (Optimal Cutting Temperature compound) specimens can undergo laser microdissection (LMD) to capture and study specific cell types separately from the cell mixture. In the current study, we compared proteomic data obtained from FF and OCT samples to determine if samples that are stored and processed differently produce comparable results. METHODS: Proteins were extracted from FF and OCT-embedded invasive breast tumors from 5 female patients. FF specimens were lysed via homogenization (FF/HOM) while OCT-embedded specimens underwent LMD to collect only tumor cells (OCT/LMD-T) or both tumor and stromal cells (OCT/LMD-TS) followed by incubation at 37 °C. Proteins were extracted using the illustra triplePrep kit and then trypsin-digested, TMT-labeled, and processed by two-dimensional liquid chromatography-tandem mass spectrometry (2D LC-MS/MS). Proteins were identified and quantified with Proteome Discoverer v1.4 and comparative analyses performed to identify proteins that were significantly differentially expressed amongst the different processing methods. RESULTS: Among the 4,950 proteins consistently quantified across all samples, 216 and 171 proteins were significantly differentially expressed (adjusted p-value < 0.05; |log2 FC|> 1) between FF/HOM vs. OCT/LMD-T and FF/HOM vs. OCT/LMD-TS, respectively, with most proteins being more highly abundant in the FF/HOM samples. PCA and unsupervised hierarchical clustering analysis with these 216 and 171 proteins were able to distinguish FF/HOM from OCT/LMD-T and OCT/LMD-TS samples, respectively. Similar analyses using significantly differentially enriched GO terms also discriminated FF/HOM from OCT/LMD samples. No significantly differentially expressed proteins were detected between the OCT/LMD-T and OCT/LMD-TS samples but trended differences were detected. CONCLUSIONS: The proteomic profiles of the OCT/LMD-TS samples were more similar to those from OCT/LMD-T samples than FF/HOM samples, suggesting a strong influence from the sample processing methods. These results indicate that in LC-MS/MS proteomic studies, FF/HOM samples exhibit different protein expression profiles from OCT/LMD samples and thus, results from these two different methods cannot be directly compared.

Neuronatin is a modifier of estrogen receptor-positive breast cancer incidence and outcome.

PURPOSE: Understanding the molecular mediators of breast cancer survival is critical for accurate disease prognosis and improving therapies. Here, we identified Neuronatin (NNAT) as a novel antiproliferative modifier of estrogen receptor-alpha (ER+) breast cancer. EXPERIMENTAL DESIGN: Genomic regions harboring breast cancer modifiers were identified by congenic mapping in a rat model of carcinogen-induced mammary cancer. Tumors from susceptible and resistant congenics were analyzed by RNAseq to identify candidate genes. Candidates were prioritized by correlation with outcome, using a consensus of three breast cancer patient cohorts. NNAT was transgenically expressed in ER+ breast cancer lines (T47D and ZR75), followed by transcriptomic and phenotypic characterization. RESULTS: We identified a region on rat chromosome 3 (142-178 Mb) that modified mammary tumor incidence. RNAseq of the mammary tumors narrowed the candidate list to three differentially expressed genes: NNAT, SLC35C2, and FAM210B. NNAT mRNA and protein also correlated with survival in human breast cancer patients. Quantitative immunohistochemistry of NNAT protein revealed an inverse correlation with survival in a univariate analysis of patients with invasive ER+ breast cancer (training cohort: n = 444, HR = 0.62, p = 0.031; validation cohort: n = 430, HR = 0.48, p = 0.004). NNAT also held up as an independent predictor of survival after multivariable adjustment (HR = 0.64, p = 0.038). NNAT significantly reduced proliferation and migration of ER+ breast cancer cells, which coincided with altered expression of multiple related pathways. CONCLUSIONS: Collectively, these data implicate NNAT as a novel mediator of cell proliferation and migration, which correlates with decreased tumorigenic potential and prolonged patient survival.

Validation of tumor protein marker quantification by two independent automated immunofluorescence image analysis platforms.

Protein marker levels in formalin-fixed, paraffin-embedded tissue sections traditionally have been assayed by chromogenic immunohistochemistry and evaluated visually by pathologists. Pathologist scoring of chromogen staining intensity is subjective and generates low-resolution ordinal or nominal data rather than continuous data. Emerging digital pathology platforms now allow quantification of chromogen or fluorescence signals by computer-assisted image analysis, providing continuous immunohistochemistry values. Fluorescence immunohistochemistry offers greater dynamic signal range than chromogen immunohistochemistry, and combined with image analysis holds the promise of enhanced sensitivity and analytic resolution, and consequently more robust quantification. However, commercial fluorescence scanners and image analysis software differ in features and capabilities, and claims of objective quantitative immunohistochemistry are difficult to validate as pathologist scoring is subjective and there is no accepted gold standard. Here we provide the first side-by-side validation of two technologically distinct commercial fluorescence immunohistochemistry analysis platforms. We document highly consistent results by (1) concordance analysis of fluorescence immunohistochemistry values and (2) agreement in outcome predictions both for objective, data-driven cutpoint dichotomization with Kaplan-Meier analyses or employment of continuous marker values to compute receiver-operating curves. The two platforms examined rely on distinct fluorescence immunohistochemistry imaging hardware, microscopy vs line scanning, and functionally distinct image analysis software. Fluorescence immunohistochemistry values for nuclear-localized and tyrosine-phosphorylated Stat5a/b computed by each platform on a cohort of 323 breast cancer cases revealed high concordance after linear calibration, a finding confirmed on an independent 382 case cohort, with concordance correlation coefficients >0.98. Data-driven optimal cutpoints for outcome prediction by either platform were reciprocally applicable to the data derived by the alternate platform, identifying patients with low Nuc-pYStat5 at ~3.5-fold increased risk of disease progression. Our analyses identified two highly concordant fluorescence immunohistochemistry platforms that may serve as benchmarks for testing of other platforms, and low interoperator variability supports the implementation of objective tumor marker quantification in pathology laboratories.

Prolactin-Stat5 signaling in breast cancer is potently disrupted by acidosis within the tumor microenvironment.

INTRODUCTION: Emerging evidence in estrogen receptor-positive breast cancer supports the notion that prolactin-Stat5 signaling promotes survival and maintenance of differentiated luminal cells, and loss of nuclear tyrosine phosphorylated Stat5 (Nuc-pYStat5) in clinical breast cancer is associated with increased risk of antiestrogen therapy failure. However, the molecular mechanisms underlying loss of Nuc-pYStat5 in breast cancer remain poorly defined. METHODS: We investigated whether moderate extracellular acidosis of pH 6.5 to 6.9 frequently observed in breast cancer inhibits prolactin-Stat5 signaling, using in vitro and in vivo experimental approaches combined with quantitative immunofluorescence protein analyses to interrogate archival breast cancer specimens. RESULTS: Moderate acidosis at pH 6.8 potently disrupted signaling by receptors for prolactin but not epidermal growth factor, oncostatin M, IGF1, FGF or growth hormone. In breast cancer specimens there was mutually exclusive expression of Nuc-pYStat5 and GLUT1, a glucose transporter upregulated in glycolysis-dependent carcinoma cells and an indirect marker of lactacidosis. Mutually exclusive expression of GLUT1 and Nuc-pYStat5 occurred globally or regionally within tumors, consistent with global or regional acidosis. All prolactin-induced signals and transcripts were suppressed by acidosis, and the acidosis effect was rapid and immediately reversible, supporting a mechanism of acidosis disruption of prolactin binding to receptor. T47D breast cancer xenotransplants in mice displayed variable acidosis (pH 6.5 to 6.9) and tumor regions with elevated GLUT1 displayed resistance to exogenous prolactin despite unaltered levels of prolactin receptors and Stat5. CONCLUSIONS: Moderate extracellular acidosis effectively blocks prolactin signaling in breast cancer. We propose that acidosis-induced prolactin resistance represents a previously unrecognized mechanism by which breast cancer cells may escape homeostatic control.

High PD-L2 Predicts Early Recurrence of ER-Positive Breast Cancer.

PURPOSE: T-cell-mediated cytotoxicity is suppressed when programmed cell death-1 (PD-1) is bound by PD-1 ligand-1 (PD-L1) or PD-L2. Although PD-1 inhibitors have been approved for triple-negative breast cancer, the lower response rates of 25%-30% in estrogen receptor-positive (ER+) breast cancer will require markers to identify likely responders. The focus of this study was to evaluate whether PD-L2, which has higher affinity than PD-L1 for PD-1, is a predictor of early recurrence in ER+ breast cancer. METHODS: PD-L2 protein levels in cancer cells and stromal cells of therapy-naive, localized or locoregional ER+ breast cancers were measured retrospectively by quantitative immunofluorescence histocytometry and correlated with progression-free survival (PFS) in the main study cohort (n = 684) and in an independent validation cohort (n = 273). All patients subsequently received standard-of-care adjuvant therapy without immune checkpoint inhibitors. RESULTS: Univariate analysis of the main cohort revealed that high PD-L2 expression in cancer cells was associated with shorter PFS (hazard ratio [HR], 1.8; 95% CI, 1.3 to 2.6; P = .001), which was validated in an independent cohort (HR, 2.3; 95% CI, 1.1 to 4.8; P = .026) and remained independently predictive after multivariable adjustment for common clinicopathological variables (HR, 2.0; 95% CI, 1.4 to 2.9; P < .001). Subanalysis of the ER+ breast cancer patients treated with adjuvant chemotherapy (n = 197) revealed that high PD-L2 levels in cancer cells associated with short PFS in univariate (HR, 2.5; 95% CI, 1.4 to 4.4; P = .003) and multivariable analyses (HR, 3.4; 95% CI, 1.9 to 6.2; P < .001). CONCLUSION: Up to one third of treatment-naive ER+ breast tumors expressed high PD-L2 levels, which independently predicted poor clinical outcome, with evidence of further elevated risk of progression in patients who received adjuvant chemotherapy. Collectively, these data warrant studies to gain a deeper understanding of PD-L2 in the progression of ER+ breast cancer and may provide rationale for immune checkpoint blockade for this patient group.

Low levels of Stat5a protein in breast cancer are associated with tumor progression and unfavorable clinical outcomes.

INTRODUCTION: Signal transducer and activator of transcripton-5a (Stat5a) and its close homologue, Stat5b, mediate key physiological effects of prolactin and growth hormone in mammary glands. In breast cancer, loss of nuclear localized and tyrosine phosphorylated Stat5a/b is associated with poor prognosis and increased risk of antiestrogen therapy failure. Here we quantify for the first time levels of Stat5a and Stat5b over breast cancer progression, and explore their potential association with clinical outcome. METHODS: Stat5a and Stat5b protein levels were quantified in situ in breast-cancer progression material. Stat5a and Stat5b transcript levels in breast cancer were correlated with clinical outcome in 936 patients. Stat5a protein was further quantified in four archival cohorts totaling 686 patients with clinical outcome data by using multivariate models. RESULTS: Protein levels of Stat5a but not Stat5b were reduced in primary breast cancer and lymph node metastases compared with normal epithelia. Low tumor levels of Stat5a but not Stat5b mRNA were associated with poor prognosis. Experimentally, only limited overlap between Stat5a- and Stat5b-modulated genes was found. In two cohorts of therapy-naïve, node-negative breast cancer patients, low nuclear Stat5a protein levels were an independent marker of poor prognosis. Multivariate analysis of two cohorts treated with antiestrogen monotherapy revealed that low nuclear Stat5a levels were associated with a more than fourfold risk of unfavorable outcome. CONCLUSIONS: Loss of Stat5a represents a new independent marker of poor prognosis in node-negative breast cancer and may be a predictor of response to antiestrogen therapy if validated in randomized clinical trials.

Spatial Metrics of Interaction between CD163-Positive Macrophages and Cancer Cells and Progression-Free Survival in Chemo-Treated Breast Cancer.

Tumor-associated macrophages (TAMs) promote progression of breast cancer and other solid malignancies via immunosuppressive, pro-angiogenic and pro-metastatic effects. Tumor-promoting TAMs tend to express M2-like macrophage markers, including CD163. Histopathological assessments suggest that the density of CD163-positive TAMs within the tumor microenvironment is associated with reduced efficacy of chemotherapy and unfavorable prognosis. However, previous analyses have required research-oriented pathologists to visually enumerate CD163+ TAMs, which is both laborious and subjective and hampers clinical implementation. Objective, operator-independent image analysis methods to quantify TAM-associated information are needed. In addition, since M2-like TAMs exert local effects on cancer cells through direct juxtacrine cell-to-cell interactions, paracrine signaling, and metabolic factors, we hypothesized that spatial metrics of adjacency of M2-like TAMs to breast cancer cells will have further information value. Immunofluorescence histo-cytometry of CD163+ TAMs was performed retrospectively on tumor microarrays of 443 cases of invasive breast cancer from patients who subsequently received adjuvant chemotherapy. An objective and automated algorithm was developed to phenotype CD163+ TAMs and calculate their density within the tumor stroma and derive several spatial metrics of interaction with cancer cells. Shorter progression-free survival was associated with a high density of CD163+ TAMs, shorter median cancer-to-CD163+ nearest neighbor distance, and a high number of either directly adjacent CD163+ TAMs (within juxtacrine proximity <12 µm to cancer cells) or communicating CD163+ TAMs (within paracrine communication distance <250 µm to cancer cells) after multivariable adjustment for clinical and pathological risk factors and correction for optimistic bias due to dichotomization.

Differential proteomic analysis of late-stage and recurrent breast cancer from formalin-fixed paraffin-embedded tissues.

The heterogeneity of breast cancer requires the discovery of more incisive molecular tools that better define disease progression and prognosis. Proteomic analysis of homogeneous tumor cell populations derived by laser microdissection from formalin-fixed, paraffin-embedded (FFPE) tissues has proven to be a robust strategy for conducting retrospective cancer biomarker investigations. We describe an MS-based analysis of laser microdissected cancerous epithelial cells derived from twenty-five breast cancer patients at defined clinical disease stages with the goal of identifying protein abundance characteristics indicative of disease progression and recurrence. Comparative analysis of stage 0 and stage III patients revealed 113 proteins that significantly differentiated these groups and included known factors associated with disease pathogenesis, such as CDH1 and CTNNB1, as well as those previously implicated in breast cancer, such as TSP-1. Similar analyses of patients presenting with stage II disease that did or did not exhibit recurrence two years postdiagnosis revealed 42 proteins that significantly differentiated these subgroups and included IRS-1 and PARK7. These data provide evidence supporting the utility of FFPE tissues for functional proteomic analyses and protein biomarker discovery and yielded protein candidates indicative of disease stage and recurrence in breast cancer that warrant further investigation for diagnostic utility and biological relevance.

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.

Malignant cell-specific pro-tumorigenic role of type I interferon receptor in breast cancers.

Within the microenvironment of solid tumors, stress associated with deficit of nutrients and oxygen as well as tumor-derived factors triggers the phosphorylation-dependent degradation of the IFNAR1 chain of type I interferon (IFN1) receptor and ensuing suppression of the IFN1 pathway. Here we sought to examine the importance of these events in malignant mammary cells. Expression of non-degradable IFNAR1S526A mutant in mouse mammary adenocarcinoma cells stimulated the IFN1 pathway yet did not affect growth of these cells in vitro or ability to form subcutaneous tumors in the syngeneic mice. Remarkably, these cells exhibited a notably accelerated growth when transplanted orthotopically into mammary glands. Importantly, in human patients with either ER+ or ER- breast cancers, high levels of IFNAR1 were associated with poor prognosis. We discuss the putative mechanisms underlying the pro-tumorigenic role of IFNAR1 in malignant breast cells.

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/ .

Loss of Nuclear Localized Parathyroid Hormone-Related Protein in Primary Breast Cancer Predicts Poor Clinical Outcome and Correlates with Suppressed Stat5 Signaling.

PURPOSE: Parathyroid hormone-related protein (PTHrP) is required for normal mammary gland development and biology. A PTHLH gene polymorphism is associated with breast cancer risk, and PTHrP promotes growth of osteolytic breast cancer bone metastases. Accordingly, current dogma holds that PTHrP is upregulated in malignant primary breast tumors, but solid evidence for this assumption is missing. EXPERIMENTAL DESIGN: We used quantitative IHC to measure PTHrP in normal and malignant breast epithelia, and correlated PTHrP levels in primary breast cancer with clinical outcome. RESULTS: PTHrP levels were markedly downregulated in malignant compared with normal breast epithelia. Moreover, low levels of nuclear localized PTHrP in cancer cells correlated with unfavorable clinical outcome in a test and a validation cohort of breast cancer treated at different institutions totaling nearly 800 cases. PTHrP mRNA levels in tumors of a third cohort of 737 patients corroborated this association, also after multivariable adjustment for standard clinicopathologic parameters. Breast cancer PTHrP levels correlated strongly with transcription factors Stat5a/b, which are established markers of favorable prognosis and key mediators of prolactin signaling. Prolactin stimulated PTHrP transcript and protein in breast cancer cell lines in vitro and in vivo, effects mediated by Stat5 through the P2 gene promoter, producing transcript AT6 encoding the PTHrP 1-173 isoform. Low levels of AT6, but not two alternative transcripts, correlated with poor clinical outcome. CONCLUSIONS: This study overturns the prevailing view that PTHrP is upregulated in primary breast cancers and identifies a direct prolactin-Stat5-PTHrP axis that is progressively lost in more aggressive tumors.

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.

Procedure for immunocytochemical detection of P16INK4A antigen in thin-layer, liquid-based specimens.

OBJECTIVE: To develop a procedure for the immunocytochemical detection of P16INK4A in ThinPrep specimens. STUDY DESIGN: Archived ThinPrep, liquid-based cervical/endocervical cytology specimens (Cytyc Corp., Boxborough, Massachusetts, U.S.A.) diagnosed as LSIL, HSIL and WNL were resampled and fixed in 95% ethanol for at least three days. Rehydration and endogenous peroxidase blocking of both ThinPreps and formalin-fixed, paraffin-embedded tissues were accomplished on a Leica Autostainer (Leica, Deerfield, Illinois, U.S.A.). Microwave antigen retrieval with CitraPlus (Biogenex, San Ramon, California, U.S.A.) was performed using a Panasonic microwave oven (Matsushita Cooking Appliances, Franklin Park, Illinois, U.S.A.) on the high setting twice for five minutes each. After cooling for 20 minutes and undergoing a buffer rinse, the slides were placed in a Dako autostainer (Dako-USA, Carpinteria, California, U.S.A.). The P16INK4A primary antibody, clone E6H4 (MTM Laboratories, Heidelberg, Germany) was diluted 1:200 in antibody diluent buffer. Detection was accomplished with a mouse non-avidin-biotin EnVision+ polymer (Dako). The expression of P16INK4A in ThinPreps and corresponding biopsies were scored by two pathologists. A ThinPrep case was scored as positive if it contained > 10 abnormal cells with nuclear and cytoplasmic immunocytochemical staining. Corresponding biopsies were scored as exhibiting negative, sporadic, focal or diffuse staining, as described by Klaes et al, Overexpression of P16INK4A as specific marker for dysplastic and neoplastic epithelial cells of the cervix uteri (Int J Cancer 2001;92:276-284). RESULTS: The P16INK4A antibody assay was positive in 14 of 19 (73.68%) LSIL ThinPrep cases and in 25 of 26 (96.15%) HSIL ThinPrep cases. Thirty-eight of the 39 (97.44%) biopsies corresponding to the positively stained ThinPreps also were positive, with a staining score of at least focal positivity in the dysplastic regions. The P16INK4A antibody assay was negative in 5 of 19 (26.32%) LSIL ThinPrep cases and negative in 1 of 26 (3.85%) HSIL ThinPrep cases. The six biopsies corresponding to the negative ThinPreps were similarly negative. The two cytologic specimens diagnosed as WNL were negative for P16INK4A, as were two tissue control cases with benign diagnoses. Nondysplastic squamous epithelium, identified in 17 biopsy cases, did not stain, nor did nondysplastic squamous cells identified in ThinPrep cases. Sporadic staining of bacteria, inflammatory cells and occasional endocervical glandular cells was identified. CONCLUSION: P16INK4A expression in ThinPrep specimens correlates with tissue expression of P16INK4A, as implemented in the above protocol. P16INK4A may thus serve as a surrogate marker in gynecologic cytology for high-risk HPV infection and for the development of cervical neoplasia.