Publications by Year: 2017

Granter SR, Beck AH, Papke DJ. AlphaGo, Deep Learning, and the Future of the Human Microscopist. Arch Pathol Lab Med 2017;141(5):619-621.
Granter SR, Beck AH, Papke DJ. Straw Men, Deep Learning, and the Future of the Human Microscopist: Response to "Artificial Intelligence and the Pathologist: Future Frenemies?". Arch Pathol Lab Med 2017;141(5):624.
Casanova R, Xia D, Rulle U, Nanni P, Grossmann J, Vrugt B, Wettstein R, Ballester R, Astolfo A, Weder W, Moch H, Stampanoni M, Beck AH, Soltermann A. Morphoproteomic characterization of lung squamous cell carcinoma fragmentation, a histological marker of increased tumor invasiveness. Cancer Res 2017;Abstract
Accurate stratification of tumors is imperative for adequate cancer management. In addition to staging, morphological subtyping allows stratification of patients into additional prognostic groups. In this study, we used an image-based computational method on pan-cytokeratin immunohistochemical (IHC) stainings to quantify tumor fragmentation (TF), a measure of tumor invasiveness of lung squamous cell carcinoma (LSCC). In two independent clinical cohorts from tissue microarrays (TMA: n=208 patients) and whole sections (WS: n=99 patients), TF was associated with poor prognosis and increased risk of blood vessel infiltration. A third cohort from the cancer genome atlas (TCGA: n=335 patients) confirmed the poor prognostic value of TF using a similar human-based score on haematoxylin-eosin (H&E) staining. Integration of RNA-seq data from TCGA and LC-MS/MS proteomics from WS revealed an upregulation of extracellular matrix remodeling and focal adhesion processes in tumors with high TF, supporting their increased invasive potential. This proposed histologic parameter is an independent and unfavorable prognostic marker that could be established as a new grading parameter for LSCC.
Bejnordi BE, Linz J, Glass B, Mullooly M, Gierach GL, Sherman ME, Karssemeijer N, van der Laak J, Beck AH. Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images. arXiv preprint arXiv:1702.05803 2017;
Beca F, Kensler K, Glass B, Schnitt SJ, Tamimi RM, Beck AH. EZH2 protein expression in normal breast epithelium and risk of breast cancer: results from the Nurses' Health Studies. Breast Cancer Res 2017;19(1):21.Abstract
BACKGROUND: Enhancer of zeste homolog 2 (EZH2) is a polycomb-group protein that is involved in stem cell renewal and carcinogenesis. In breast cancer, increased EZH2 expression is associated with aggressiveness and has been suggested to identify normal breast epithelium at increased risk of breast cancer development. However, the association between EZH2 expression in benign breast tissue and breast cancer risk has not previously been evaluated in a large prospective cohort. METHODS: We examined the association between EZH2 protein expression and subsequent breast cancer risk using logistic regression in a nested case-control study of benign breast disease (BBD) and breast cancer within the Nurses' Health Studies. EZH2 immunohistochemical expression in normal breast epithelium and stroma was evaluated by computational image analysis and its association with breast cancer risk was analyzed after adjusting for matching factors between cases and controls, the concomitant BBD diagnosis, and the Ki67 proliferation index. RESULTS: Women with a breast biopsy in which more than 20% of normal epithelial cells expressed EZH2 had a significantly increased risk of developing breast cancer (odds ratio (OR) 2.95, 95% confidence interval (CI) 1.11-7.84) compared to women with less than 10% EZH2 epithelial expression. The risk of developing breast cancer increased for each 5% increase in EZH2 expression (OR 1.22, 95% CI 1.02-1.46, p value 0.026). Additionally, women with high EZH2 expression and low estrogen receptor (ER) expression had a 4-fold higher risk of breast cancer compared to women with low EZH2 and low ER expression (OR 4.02, 95% CI 1.29-12.59). CONCLUSIONS: These results provide further evidence that EZH2 expression in the normal breast epithelium is independently associated with breast cancer risk and might be used to assist in risk stratification for women with benign breast biopsies.
Irshad H, Oh E-Y, Schmolze D, Quintana LM, Collins L, Tamimi RM, Beck AH. Crowdsourcing scoring of immunohistochemistry images: Evaluating Performance of the Crowd and an Automated Computational Method. Sci Rep 2017;7:43286.Abstract
The assessment of protein expression in immunohistochemistry (IHC) images provides important diagnostic, prognostic and predictive information for guiding cancer diagnosis and therapy. Manual scoring of IHC images represents a logistical challenge, as the process is labor intensive and time consuming. Since the last decade, computational methods have been developed to enable the application of quantitative methods for the analysis and interpretation of protein expression in IHC images. These methods have not yet replaced manual scoring for the assessment of IHC in the majority of diagnostic laboratories and in many large-scale research studies. An alternative approach is crowdsourcing the quantification of IHC images to an undefined crowd. The aim of this study is to quantify IHC images for labeling of ER status with two different crowdsourcing approaches, image-labeling and nuclei-labeling, and compare their performance with automated methods. Crowdsourcing- derived scores obtained greater concordance with the pathologist interpretations for both image-labeling and nuclei-labeling tasks (83% and 87%), as compared to the pathologist concordance achieved by the automated method (81%) on 5,338 TMA images from 1,853 breast cancer patients. This analysis shows that crowdsourcing the scoring of protein expression in IHC images is a promising new approach for large scale cancer molecular pathology studies.
Sangoi AR, Kshirsagar M, Roma AA, Horvai AE, Chivukula M, Ellenson LH, Fadare O, Folkins AK, Garg K, Hanley K, Longacre TA, Haas J, McCluggage GW, McKenney JK, Nucci MR, Oliva E, Park KJ, Parkash V, Quick CM, Rabban JT, Rutgers JKL, Soslow R, Vang R, Yemelyanova A, Zaloudek C, Beck AH. Interobserver Reproducibility Among Gynecologic Pathologists in Diagnosing Heterologous Osteosarcomatous Component in Gynecologic Tract Carcinosarcomas. Int J Gynecol Pathol 2017;Abstract
Distinguishing hyalinized stroma from osteoid production by a heterologous osteosarcomatous component can be challenging in gynecologic tract carcinosarcomas. As heterologous components in a carcinosarcoma may have prognostic and therapeutic implications, it is important that these are recognized. This study examines interobserver reproducibility among gynecologic pathologists in the diagnosis of osteosarcomatous components, and its correlation with expression of the novel antibody SATB2 (marker of osteoblastic differentiation) in these osteosarcomatous foci. Digital H&E images from 20 gynecologic tract carcinosarcomas were reviewed by 22 gynecologic pathologists with a request to determine the presence or absence of an osteosarcomatous component. The 20 preselected cases included areas of classic heterologous osteosarcoma (malignant cells producing osteoid; n=10) and osteosarcoma mimics (malignant cells with admixed nonosteoid matrix; n=10). Interobserver agreement was evaluated and SATB2 scored on all 20 cases and compared with the original diagnoses. Moderate agreement (Fleiss' κ=0.483) was identified for the 22 raters scoring the 20 cases with a median sensitivity of 7/10 and a median specificity of 9/10 for the diagnosis of osteosarcoma. SATB2 showed 100% sensitivity (10/10) and 60% (6/10) specificity in discriminating classic osteosarcoma from osteosarcoma mimics. Utilizing negative SATB2 as a surrogate marker to exclude osteosarcoma, 73% (16/22) of the reviewers would have downgraded at least 1 case to not contain an osteosarcomatous component (range, 1-6 cases, median 1 case). Gynecologic pathologists demonstrate only a moderate level of agreement in the diagnosis of heterologous osteosarcoma based on morphologic grounds. In such instances, a negative SATB2 staining may assist in increasing accuracy in the diagnosis of an osteosarcomatous component.
Quiroz-Zárate A, Harshfield BJ, Hu R, Knoblauch N, Beck AH, Hankinson SE, Carey V, Tamimi RM, Hunter DJ, Quackenbush J, Hazra A. Expression Quantitative Trait loci (QTL) in tumor adjacent normal breast tissue and breast tumor tissue. PLoS One 2017;12(2):e0170181.Abstract
We investigate 71 single nucleotide polymorphisms (SNPs) identified in meta-analytic studies of genome-wide association studies (GWAS) of breast cancer, the majority of which are located in intergenic or intronic regions. To explore regulatory impacts of these variants we conducted expression quantitative loci (eQTL) analyses on tissue samples from 376 invasive postmenopausal breast cancer cases in the Nurses' Health Study (NHS) diagnosed from 1990-2004. Expression analysis was conducted on all formalin-fixed paraffin-embedded (FFPE) tissue samples (and on 264 adjacent normal samples) using the Affymetrix Human Transcriptome Array. Significance and ranking of associations between tumor receptor status and expression variation was preserved between NHS FFPE and TCGA fresh-frozen sample sets (Spearman r = 0.85, p<10^-10 for 17 of the 21 Oncotype DX recurrence signature genes). At an FDR threshold of 10%, we identified 27 trans-eQTLs associated with expression variation in 217 distinct genes. SNP-gene associations can be explored using an open-source interactive browser distributed in a Bioconductor package. Using a new a procedure for testing hypotheses relating SNP content to expression patterns in gene sets, defined as molecular function pathways, we find that loci on 6q14 and 6q25 affect various gene sets and molecular pathways (FDR < 10%). Although the ultimate biological interpretation of the GWAS-identified variants remains to be uncovered, this study validates the utility of expression analysis of this FFPE expression set for more detailed integrative analyses.
Devore EE, Warner ET, Eliassen HA, Brown SB, Beck AH, Hankinson SE, Schernhammer ES. Urinary Melatonin in Relation to Postmenopausal Breast Cancer Risk According to Melatonin 1 Receptor Status. Cancer Epidemiol Biomarkers Prev 2017;26(3):413-419.Abstract
Background: Urinary melatonin levels have been associated with a reduced risk of breast cancer in postmenopausal women, but this association might vary according to tumor melatonin 1 receptor (MT1R) expression.Methods: We conducted a nested case-control study among 1,354 postmenopausal women in the Nurses' Health Study, who were cancer free when they provided first-morning spot urine samples in 2000 to 2002; urine samples were assayed for 6-sulfatoxymelatonin (aMT6s, a major metabolite of melatonin). Five-hundred fifty-five of these women developed breast cancer before May 31, 2012, and were matched to 799 control subjects. In a subset of cases, immunohistochemistry was used to determine MT1R status of tumor tissue. We used multivariable-adjusted conditional logistic regression to estimate the relative risk (RR) of breast cancer [with 95% confidence intervals (CI)] across quartiles of creatinine-standardized urinary aMT6s level, including by MT1R subtype.Results: Higher urinary melatonin levels were suggestively associated with a lower overall risk of breast cancer (multivariable-adjusted RR = 0.78; 95% CI = 0.61-0.99, comparing quartile 4 vs. quartile 1; Ptrend = 0.08); this association was similar for invasive vs. in situ tumors (Pheterogeneity = 0.12). There was no evidence that associations differed according to MT1R status of the tumor (e.g., Pheterogeneity for overall breast cancer = 0.88).Conclusions: Higher urinary melatonin levels were associated with reduced breast cancer risk in this cohort of postmenopausal women, and the association was not modified by MT1R subtype.Impact: Urinary melatonin levels appear to predict the risk of breast cancer in postmenopausal women. However, future research should evaluate these associations with longer-term follow-up and among premenopausal women. Cancer Epidemiol Biomarkers Prev; 26(3); 413-9. ©2016 AACR.
Henry WS, Laszewski T, Tsang T, Beca F, Beck AH, McAllister SS, Toker A. Aspirin Suppresses Growth in PI3K-Mutant Breast Cancer by Activating AMPK and Inhibiting mTORC1 Signaling. Cancer Res 2017;77(3):790-801.Abstract
Despite the high incidence of oncogenic mutations in PIK3CA, the gene encoding the catalytic subunit of PI3K, PI3K inhibitors have yielded little clinical benefit for breast cancer patients. Recent epidemiologic studies have suggested a therapeutic benefit from aspirin intake in cancers harboring oncogenic PIK3CA Here, we show that mutant PIK3CA-expressing breast cancer cells have greater sensitivity to aspirin-mediated growth suppression than their wild-type counterparts. Aspirin decreased viability and anchorage-independent growth of mutant PIK3CA breast cancer cells independently of its effects on COX-2 and NF-κB. We ascribed the effects of aspirin to AMP-activated protein kinase (AMPK) activation, mTORC1 inhibition, and autophagy induction. In vivo, oncogenic PIK3CA-driven mouse mammary tumors treated daily with aspirin resulted in decreased tumor growth kinetics, whereas combination therapy of aspirin and a PI3K inhibitor further attenuated tumor growth. Our study supports the evaluation of aspirin and PI3K pathway inhibitors as a combination therapy for targeting breast cancer. Cancer Res; 77(3); 790-801. ©2016 AACR.
Heng YJ, Lester SC, Tse GMK, Factor RE, Allison KH, Collins LC, Chen Y-Y, Jensen KC, Johnson NB, Jeong JC, Punjabi R, Shin SJ, Singh K, Krings G, Eberhard DA, Tan PH, Korski K, Waldman FM, Gutman DA, Sanders M, Reis-Filho JS, Flanagan SR, Gendoo DM, Chen GM, Haibe-Kains B, Ciriello G, Hoadley KA, Perou CM, Beck AH. The molecular basis of breast cancer pathological phenotypes. J Pathol 2017;241(3):375-391.Abstract
The histopathological evaluation of morphological features in breast tumours provides prognostic information to guide therapy. Adjunct molecular analyses provide further diagnostic, prognostic and predictive information. However, there is limited knowledge of the molecular basis of morphological phenotypes in invasive breast cancer. This study integrated genomic, transcriptomic and protein data to provide a comprehensive molecular profiling of morphological features in breast cancer. Fifteen pathologists assessed 850 invasive breast cancer cases from The Cancer Genome Atlas (TCGA). Morphological features were significantly associated with genomic alteration, DNA methylation subtype, PAM50 and microRNA subtypes, proliferation scores, gene expression and/or reverse-phase protein assay subtype. Marked nuclear pleomorphism, necrosis, inflammation and a high mitotic count were associated with the basal-like subtype, and had a similar molecular basis. Omics-based signatures were constructed to predict morphological features. The association of morphology transcriptome signatures with overall survival in oestrogen receptor (ER)-positive and ER-negative breast cancer was first assessed by use of the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset; signatures that remained prognostic in the METABRIC multivariate analysis were further evaluated in five additional datasets. The transcriptomic signature of poorly differentiated epithelial tubules was prognostic in ER-positive breast cancer. No signature was prognostic in ER-negative breast cancer. This study provided new insights into the molecular basis of breast cancer morphological phenotypes. The integration of morphological with molecular data has the potential to refine breast cancer classification, predict response to therapy, enhance our understanding of breast cancer biology, and improve clinical management. This work is publicly accessible at Copyright © 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
Campbell PT, Rebbeck TR, Nishihara R, Beck AH, Begg CB, Bogdanov AA, Cao Y, Coleman HG, Freeman GJ, Heng YJ, Huttenhower C, Irizarry RA, Kip SN, Michor F, Nevo D, Peters U, Phipps AI, Poole EM, Qian ZR, Quackenbush J, Robins H, Rogan PK, Slattery ML, Smith-Warner SA, Song M, VanderWeele TJ, Xia D, Zabor EC, Zhang X, Wang M, Ogino S. Proceedings of the third international molecular pathological epidemiology (MPE) meeting. Cancer Causes Control 2017;28(2):167-176.Abstract
Molecular pathological epidemiology (MPE) is a transdisciplinary and relatively new scientific discipline that integrates theory, methods, and resources from epidemiology, pathology, biostatistics, bioinformatics, and computational biology. The underlying objective of MPE research is to better understand the etiology and progression of complex and heterogeneous human diseases with the goal of informing prevention and treatment efforts in population health and clinical medicine. Although MPE research has been commonly applied to investigating breast, lung, and colorectal cancers, its methodology can be used to study most diseases. Recent successes in MPE studies include: (1) the development of new statistical methods to address etiologic heterogeneity; (2) the enhancement of causal inference; (3) the identification of previously unknown exposure-subtype disease associations; and (4) better understanding of the role of lifestyle/behavioral factors on modifying prognosis according to disease subtype. Central challenges to MPE include the relative lack of transdisciplinary experts, educational programs, and forums to discuss issues related to the advancement of the field. To address these challenges, highlight recent successes in the field, and identify new opportunities, a series of MPE meetings have been held at the Dana-Farber Cancer Institute in Boston, MA. Herein, we share the proceedings of the Third International MPE Meeting, held in May 2016 and attended by 150 scientists from 17 countries. Special topics included integration of MPE with immunology and health disparity research. This meeting series will continue to provide an impetus to foster further transdisciplinary integration of divergent scientific fields.
Ahern TP, Beck AH, Rosner BA, Glass B, Frieling G, Collins LC, Tamimi RM. Continuous measurement of breast tumour hormone receptor expression: a comparison of two computational pathology platforms. J Clin Pathol 2017;70(5):428-434.Abstract
AIMS: Computational pathology platforms incorporate digital microscopy with sophisticated image analysis to permit rapid, continuous measurement of protein expression. We compared two computational pathology platforms on their measurement of breast tumour oestrogen receptor (ER) and progesterone receptor (PR) expression. METHODS: Breast tumour microarrays from the Nurses' Health Study were stained for ER (n=592) and PR (n=187). One expert pathologist scored cases as positive if ≥1% of tumour nuclei exhibited stain. ER and PR were then measured with the Definiens Tissue Studio (automated) and Aperio Digital Pathology (user-supervised) platforms. Platform-specific measurements were compared using boxplots, scatter plots and correlation statistics. Classification of ER and PR positivity by platform-specific measurements was evaluated with areas under receiver operating characteristic curves (AUC) from univariable logistic regression models, using expert pathologist classification as the standard. RESULTS: Both platforms showed considerable overlap in continuous measurements of ER and PR between positive and negative groups classified by expert pathologist. Platform-specific measurements were strongly and positively correlated with one another (r≥0.77). The user-supervised Aperio workflow performed slightly better than the automated Definiens workflow at classifying ER positivity (AUCAperio=0.97; AUCDefiniens=0.90; difference=0.07, 95% CI 0.05 to 0.09) and PR positivity (AUCAperio=0.94; AUCDefiniens=0.87; difference=0.07, 95% CI 0.03 to 0.12). CONCLUSIONS: Paired hormone receptor expression measurements from two different computational pathology platforms agreed well with one another. The user-supervised workflow yielded better classification accuracy than the automated workflow. Appropriately validated computational pathology algorithms enrich molecular epidemiology studies with continuous protein expression data and may accelerate tumour biomarker discovery.