Breast Cancer Mortality in Older and Younger Patients in California

BACKGROUND: Breast cancer in younger patients is reported to be more aggressive and associated with lower survival; however, factors associated with age-specific mortality differences have not been adequately assessed. METHODS: We used data from the population-based California Cancer Registry for 38,509 younger (18-49 years) and 121,573 older (50 years and older) women diagnosed with stage I to III breast cancer, 2005-2014. Multivariable Cox regression models were used to estimate breast cancer-specific mortality rate ratios (MRR) and 95% confidence intervals (CI), stratified by tumor subtype, guideline treatment, and care at an NCI-designated cancer center (NCICC). RESULTS: Older breast cancer patients at diagnosis experienced 17% higher disease-specific mortality than younger patients, after multivariable adjustment (MRR = 1.17; 95% CI, 1.11-1.23). Higher MRRs (95% CI) were observed for older versus younger patients with hormone receptor (HR)(+)/HER2(-) (1.24; 1.14-1.35) and HR(+)/HER2(+) (1.38; 1.17-1.62), but not for HR(-)/HER2(+) (HR = 0.94; 0.79-1.12) nor triple-negative breast cancers (1.01; 0.92-1.11). The higher mortality in older versus younger patients was diminished among patients who received guideline-concordant treatment (MRR = 1.06; 95% CI, 0.99-1.14) and reversed among those seen at an NCICC (MRR = 0.86; 95% CI, 0.73-1.01). CONCLUSIONS: Although younger women tend to be diagnosed with more aggressive breast cancers, adjusting for these aggressive features results in older patients having higher mortality than younger patients, with variations by age, tumor subtype, receipt of guideline treatment, and being cared for at an NCICC. IMPACT: Higher breast cancer mortality in older compared with younger women could partly be addressed by ensuring optimal treatment and comprehensive patient-centered care

Hypothesized role of pregnancy hormones on HER2+breast tumor development

Breast cancer incidence rates have declined among older but not younger women; the latter are more likely to be diagnosed with breast cancers carrying a poor prognosis. Epidemiological evidence supports an increase in breast cancer incidence following pregnancy with risk elevated as much as 10 years post-partum. We investigated the association between years since last full-term pregnancy at the time of diagnosis (10 years) and breast tumor subtype in a case series of premenopausal Hispanic women (n = 627). Participants were recruited in the United States, Mexico, and Spain. Cases with known estrogen receptor (ER), progesterone receptor (PR), and HER2 status, with one or more full-term pregnancies >/=1 year prior to diagnosis were eligible for this analysis. Cases were classified into three tumor subtypes according to hormone receptor (HR+ = ER+ and/or PR+; HR- = ER- and PR-) expression and HER2 status: HR+/HER2-, HER2+ (regardless of HR), and triple negative breast cancer. Case-only odds ratios (ORs) and 95 % confidence intervals (CIs) were calculated for HER2+ tumors in reference to HR+/HER2- tumors. Participants were pooled in a mixed-effects logistic regression model with years since pregnancy as a fixed effect and study site as a random effect. When compared to HR+/HER2- cases, women with HER2+ tumors were more likely be diagnosed in the post-partum period of 45 years) did not materially alter our results (OR = 1.78; 95 % CI, 1.08-2.93). These findings support the novel hypothesis that factors associated with the post-partum breast, possibly hormonal, are involved in the development of HER2+ tumors

Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, "select and shrink for summary statistics" (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs

Identification of six new susceptibility loci for invasive epithelial ovarian cancer.

Genome-wide association studies (GWAS) have identified 12 epithelial ovarian cancer (EOC) susceptibility alleles. The pattern of association at these loci is consistent in BRCA1 and BRCA2 mutation carriers who are at high risk of EOC. After imputation to 1000 Genomes Project data, we assessed associations of 11 million genetic variants with EOC risk from 15,437 cases unselected for family history and 30,845 controls and from 15,252 BRCA1 mutation carriers and 8,211 BRCA2 mutation carriers (3,096 with ovarian cancer), and we combined the results in a meta-analysis. This new study design yielded increased statistical power, leading to the discovery of six new EOC susceptibility loci. Variants at 1p36 (nearest gene, WNT4), 4q26 (SYNPO2), 9q34.2 (ABO) and 17q11.2 (ATAD5) were associated with EOC risk, and at 1p34.3 (RSPO1) and 6p22.1 (GPX6) variants were specifically associated with the serous EOC subtype, all with P < 5 × 10(-8). Incorporating these variants into risk assessment tools will improve clinical risk predictions for BRCA1 and BRCA2 mutation carriers.COGS project is funded through a European Commission's Seventh Framework Programme grant (agreement number 223175 ] HEALTH ]F2 ]2009 ]223175). The CIMBA data management and data analysis were supported by Cancer Research.UK grants 12292/A11174 and C1287/A10118. The Ovarian Cancer Association Consortium is supported by a grant from the Ovarian Cancer Research Fund thanks to donations by the family and friends of Kathryn Sladek Smith (PPD/RPCI.07). The scientific development and funding for this project were in part supported by the US National Cancer Institute GAME ]ON Post ]GWAS Initiative (U19 ]CA148112). This study made use of data generated by the Wellcome Trust Case Control consortium. Funding for the project was provided by the Wellcome Trust under award 076113. The results published here are in part based upon data generated by The Cancer Genome Atlas Pilot Project established by the National Cancer Institute and National Human Genome Research Institute (dbGap accession number phs000178.v8.p7). The cBio portal is developed and maintained by the Computational Biology Center at Memorial Sloan ] Kettering Cancer Center. SH is supported by an NHMRC Program Grant to GCT. Details of the funding of individual investigators and studies are provided in the Supplementary Note. This study made use of data generated by the Wellcome Trust Case Control consortium, funding for which was provided by the Wellcome Trust under award 076113. The results published here are, in part, based upon data generated by The Cancer Genome Atlas Pilot Project established by the National Cancerhttp://dx.doi.org/10.1038/ng.3185This is the Author Accepted Manuscript of 'Identification of six new susceptibility loci for invasive epithelial ovarian cancer' which was published in Nature Genetics 47, 164–171 (2015) © Nature Publishing Group - content may only be used for academic research

Polygenic Risk Modelling for Prediction of Epithelial Ovarian Cancer Risk

Funder: Funding details are provided in the Supplementary MaterialAbstractPolygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally-efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, “select and shrink for summary statistics” (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestry; 7,669 women of East Asian ancestry; 1,072 women of African ancestry, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestry. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38(95%CI:1.28–1.48,AUC:0.588) per unit standard deviation, in women of European ancestry; 1.14(95%CI:1.08–1.19,AUC:0.538) in women of East Asian ancestry; 1.38(95%CI:1.21-1.58,AUC:0.593) in women of African ancestry; hazard ratios of 1.37(95%CI:1.30–1.44,AUC:0.592) in BRCA1 pathogenic variant carriers and 1.51(95%CI:1.36-1.67,AUC:0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.</jats:p

A five-gene reverse transcription-PCR assay for pre-operative classification of breast fibroepithelial lesions

BACKGROUND: Breast fibroepithelial lesions are biphasic tumors and include fibroadenomas and phyllodes tumors. Preoperative distinction between fibroadenomas and phyllodes tumors is pivotal to clinical management. Fibroadenomas are clinically benign while phyllodes tumors are more unpredictable in biological behavior, with potential for recurrence. Differentiating the tumors may be challenging when they have overlapping clinical and histological features especially on core biopsies. Current molecular and immunohistochemical techniques have a limited role in the diagnosis of breast fibroepithelial lesions. We aimed to develop a practical molecular test to aid in distinguishing fibroadenomas from phyllodes tumors in the pre-operative setting. METHODS: We profiled the transcriptome of a training set of 48 formalin-fixed, paraffin-embedded fibroadenomas and phyllodes tumors and further designed 43 quantitative polymerase chain reaction (qPCR) assays to verify differentially expressed genes. Using machine learning to build predictive regression models, we selected a five-gene transcript set (ABCA8, APOD, CCL19, FN1, and PRAME) to discriminate between fibroadenomas and phyllodes tumors. We validated our assay in an independent cohort of 230 core biopsies obtained pre-operatively. RESULTS: Overall, the assay accurately classified 92.6 % of the samples (AUC = 0.948, 95 % CI 0.913–0.983, p = 2.51E-19), with a sensitivity of 82.9 % and specificity of 94.7 %. CONCLUSIONS: We provide a robust assay for classifying breast fibroepithelial lesions into fibroadenomas and phyllodes tumors, which could be a valuable tool in assisting pathologists in differential diagnosis of breast fibroepithelial lesions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13058-016-0692-6) contains supplementary material, which is available to authorized users