In Press
Yaghjyan L, Heng YJ, Baker GM, Bret-Mounet VC, Murphy D, Mahoney M, Mu Y, Rosner BA, Tamimi RM. Reliability of CD44, CD24, and ALDH1A1 immunohistochemical staining: pathologist assessment compared to quantitative image analysis. Front Med. In Press.
Asad S, Damicis A, Heng YJ, Kananen K, Collier KA, Adams EJ, Kensler KH, Baker GM, Wesolowski R, Sardesai S, et al. Association of Body Mass Index and Inflammatory Dietary Pattern with Breast Cancer Pathologic and Genomic Immunophenotype in the Nurses’ Health Study. Breast Can Res. 2022;24 (1) :78.
Wang L, Wang D, Sonzogni O, Ke S, Wang Q, Thavamani A, Batalini F, Stopka SA, Regan MS, Vandal S, et al. PARP-inhibition reprograms macrophages towards an anti-tumor phenotype. Cell Rep. 2022;41 (2) :111462.
Wang T, Heng YJ, Baker GM, Bret-Mounet VC, Quintana LM, Frueh L, Hankinson SE, Holmes MD, Chen WY, Willett WC, et al. Loss of PTEN Expression, PIK3CA Mutations, and Breast Cancer Survival in the Nurses’ Health Studies. Cancer Epidemiol Biomarkers Prev. 2022;31 (10) :1926-1934.
Baker GM, Bret-Mounet VC, Wang T, Veta M, Zheng H, Collins LC, Eliassen AH, Tamimi RM, Heng YJ. Immunohistochemistry scoring of breast tumor tissue microarrays: a comparison study across three software applications. J Path Inform. 2022;13 :100118. Publisher's Version
Liu Y, Gusev A, Heng YJ, Alexandrov LB, Kraft P. Somatic mutational profiles and germline polygenic risk scores in human cancer. Genome Med. 2022;14 (1) :14.
Heng YJ, Love S, Clague DeHart J, Fingeroth JD, Wulf GM. The association of infectious mononucleosis and invasive breast cancer in The Health of Women (HOW) Study. Breast Cancer. 2022;29 (4) :731-739.Abstract

Background. The link between Epstein-Barr Virus (EBV) and breast cancer (BC) remains unclear. Infectious mononucleosis (IM) is a clinical manifestation of delayed onset of EBV infection in early adulthood. We utilized the Health of Women (HOW) Study to understand the association between IM and BC risk.

Subjects and methods. The HOW Study was a web-based survey of BC risk factors with >40,000 participants who answered seven modules between 2012 and 2015; 3,654 women had IM between the ages of 10 and 22 years (16.8%) and 17,026 never developed IM (78.5%). Of these 20,680 women, 1,997 (9.7%) had Stages I-III BC and 13,515 (65.4%) were cancer-free. Multivariable binary logistic regression ascertained the association between IM and BC risk by controlling for ethnicity, family history, age at menarche, oral contraceptive use, tobacco use, birthplace, parity, age at first birth, body mass index, and breast biopsy. Secondary analyses stratified cancer cases into those who had BC at <50 or >=50 years old and by estrogen receptor (ER) subtype.

Results. Participants were mostly white, middle-aged women born in the United States or Canada. Women who had IM were less likely to develop BC than those who did not develop IM (adjusted odds ratio (OR)=0.83, 95% confidence interval (CI) 0.71-0.96). Findings were similar when stratifying women into <50 or >=50 years old at BC diagnosis (<50 years old, adjusted OR=0.82, 95% CI 0.67-0.998; >=50 years old, adjusted OR=0.83, 95% CI 0.69-1.00). Women who had IM were less likely to develop ER positive BC (adjusted OR=0.84, 95% CI 0.71-0.997); there was no association between IM and ER negative BC (adjusted OR=0.88, 95% CI 0.65-1.16).

Conclusion. In the HOW Study, women diagnosed with IM between the ages of 10 and 22 had lower breast cancer risk compared to women who never developed IM.

Zhou G, Holzman C, Heng YJ, Kibschull M, Lye SJ. Maternal blood EBF1-based microRNA transcripts as biomarkers for detecting risk of spontaneous preterm birth: a nested case-control study. J Matern Fetal Neonatal Med. 2022;35 (7) :1239-1247. Publisher's Version
Baker GM, Heng YJ. Transgender breast pathology. 2021. Publisher's Version
Roberts MR, Baker GM, Heng YJ, Pyle ME, Astone K, Rosner BA, Collins LC, Eliassen AH, Tamimi RM. Reliability of a computational platform as a surrogate for manually interpreted immunohistochemical markers in breast tumor tissue microarrays. Cancer Epi. 2021;74 :101999.
Yaghjyan L, Austin-Datta RJ, Oh H, Heng YJ, Vellal AD, SIRINUKUNWATTANA K, Baker GM, Collins LC, Murthy D, Rosner B, et al. Associations of reproductive breast cancer risk factors with breast tissue composition. Breast Can Res. 2021;23 (1) :70.
Heng YJ, Kensler KH, Baker GM, Collins LC, Schnitt SJ, Tamimi RM. TDLU involution and breast cancer risk--Reply Letter. Cancer Epidemiol Biomarkers Prev. 2021;30 :798.
Oh H, Yaghjyan L, Austin-Datta RJ, Heng YJ, Baker GM, SIRINUKUNWATTANA K, Vellal AD, Collins LC, Murthy D, Eliassen AH, et al. Early-life and adult adiposity, adult height, and benign breast tissue composition. Cancer Epidemiol Biomarkers Prev. 2021;30 :608-615.
Wetstein SC, Stathonikos N, Pluim JPW, Heng YJ, ter Hoeve ND, Vreuls CPH, van Diest PJ, Veta M. Deep Learning-Based Grading of Ductal Carcinoma In Situ in Breast Histopathology Images. Lab Invest. 2021;101 :525-533.
Wang J, Peng C, Askew C, Heng YJ, Baker GM, Rubadue CA, Glass K, Eliassen AH, Tamimi RM, Polyak K, et al. Early-life body adiposity and the breast tumor transcriptome. J Natl Cancer Inst. 2021;113 (6) :778-784.
Zhou G, Holzman C, Chen B, Wang P, Heng YJ, Kibschull M, Lye SJ, Kasten E. EBF1-correlated long non-coding RNA transcript levels in 3rd trimester maternal blood and risk of spontaneous preterm birth. Reprod Sci. 2021;28 :541-549.
Vellal AD, SIRINUKUNWATTANA K, Kensler KH, Baker GM, Stancu AL, Pyle ME, Collins LC, Schnitt SJ, Connolly JL, Veta M, et al. Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer. JNCI Cancer Spectr. 2021;5 (1) :pkaa119. Publisher's VersionAbstract
Background New biomarkers of risk may improve breast cancer risk prediction. We developed a computational pathology method to segment benign breast disease (BBD) whole slide images (WSIs) into epithelium, fibrous stroma, and fat. We applied our method to the BBD breast cancer nested case-control study within the Nurses’ Health Studies to assess whether computer-derived tissue composition or a morphometric signature was associated with subsequent risk of breast cancer. Methods Tissue segmentation and nuclei detection deep-learning networks were established and applied to 3795 WSIs from 293 cases who developed breast cancer and 1132 controls who did not. Percentages of each tissue region were calculated and 615 morphometric features were extracted. Elastic net regression was used to create a breast cancer morphometric signature. Associations between breast cancer risk factors and age-adjusted tissue composition among controls were assessed using analysis of covariance. Unconditional logistic regression, adjusting for the matching factors, BBD histological subtypes, parity, menopausal status, and BMI evaluated the relationship between tissue composition and breast cancer risk. Results Among controls, BBD subtypes, parity, and number of births were differentially associated with all three tissue regions (p<0.05); select regions were associated with childhood body size, BMI, age of menarche, and menopausal status (p<0.05). Higher proportion of epithelial tissue was associated with increased breast cancer risk (OR=1.39, 95% CI 0.91-2.14 comparing highest and lowest quartiles; p-trend<0.05). No morphometric signature was associated with breast cancer. Conclusion The amount of epithelial tissue may be incorporated into risk assessment models to improve breast cancer risk prediction.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThis work was supported by the National Institute of Health/National Cancer Institute R21CA187642 (RMT), R01CA175080 (RMT), R01CA240341 (RMT, YJH), UM1CA186107 (AHE), and U01 CA176726 (AHE), Susan G. Komen for the Cure IIR13264020 (RMT), Breast Cancer Research Foundation 17-174, the Klarman Family Foundation (YJH), BIDMC High School Summer Research Program (ADV).Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The study protocol was approved by the institutional review boards of the Brigham and Women’s Hospital and Harvard T.H. Chan School of Public Health, and those of participating registries as required.All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesThe data that support the findings of this study are available from the Nurses’ Health Studies, however they are not publicly available. Investigators interested in using the data can request access, and feasibility will be discussed at an investigators meeting. Limits are not placed on scientific questions or methods, and there is no requirement for co-authorship. Additional data sharing information and policy details can be accessed at The source code is available on GitHub
Baker GM, Guzman-Arocho YD, Bret-Mounet VC, Torous VF, Schnitt SJ, Tobias AM, Bartlett RA, Fein-Zachary VJ, Collins LC, Wulf GM, et al. Testosterone therapy and breast histopathological features in transgender individuals. Mod Pathol. 2021;34 :85-94.
Kensler KH, Liu EZF, Wetstein SC, Onken AM, Luffman CI, Baker GM, Collins LC, Schnitt SJ, Bret-Mounet VC, Veta M, et al. Automated Quantitative Measures of Terminal Duct Lobular Unit Involution and Breast Cancer Risk. Cancer Epidemiol Biomarkers Prev. 2020;29 (11) :2358–2368.
Peng C, DuPre N, VoPham T, Heng YJ, Baker GM, Rubadue CA, Glass K, Sonawane A, Zeleznik O, Kraft P, et al. Low dose environmental radon exposure and breast tumor gene expression. BMC Cancer. 2020;20 (1) :695. Publisher's VersionAbstract
The International Agency for Research on Cancer classified radon and its decay-products as Group-1-human-carcinogens, and with the current knowledge they are linked specifically to lung cancer. Biokinetic models predict that radon could deliver a carcinogenic dose to breast tissue. Our previous work suggested that low-dose radon was associated with estrogen-receptor (ER)-negative breast cancer risk. However, there is limited research to examine the role of radon in breast cancer biology at the tissue level. We aim to understand molecular pathways linking radon exposure with breast cancer biology using transcriptome-wide-gene-expression from breast tumor and normal-adjacent tissues.