Summary Invasive lobular carcinoma (ILC) is the second most prevalent histologic subtype of invasive breast cancer. Here, we comprehensively profiled 817 breast tumors, including 127 ILC, 490 ductal (IDC), and 88 mixed IDC/ILC. Besides E-cadherin loss, the best known ILC genetic hallmark, we identified mutations targeting PTEN, TBX3 and FOXA1 as ILC enriched features. PTEN loss associated with increased AKT phosphorylation, which was highest in ILC among all breast cancer subtypes. Spatially clustered FOXA1 mutations correlated with increased FOXA1 expression and activity. Conversely, GATA3 mutations and high expression characterized Luminal A IDC, suggesting differential modulation of ER activity in ILC and IDC. Proliferation and immune-related signatures determined three ILC transcriptional subtypes associated with survival differences. Mixed IDC/ILC cases were molecularly classified as ILC-like and IDC-like revealing no true hybrid features. This multidimensional molecular atlas sheds new light on the genetic bases of ILC and provides potential clinical options.

Despite improvements in early detection and treatment, cancer remains a major cause of mortality. Death from cancer is largely due to metastasis, which results in spreading of tumor cells to other parts of the body. The metastatic process is poorly understood, is often unpredictable, and usually results in incurable disease. There are no therapies specifically designed to target metastases or to block the metastatic process. Development and pre-clinical testing of new cancer therapies is limited by the scarcity of in vivo models that authentically reproduce human tumor growth and metastatic progression. Here, we report development of novel models for breast tumor growth and metastasis, which exist in the form of transplantable tumors derived directly from patients. These tumor grafts not only represent the diversity of human breast cancer, but also maintain essential features of the original patients’ tumors, including histopathology, clinical markers, hormone responsiveness, and metastasis to specific sites. Genomic features, such as gene expression profiles and DNA copy number variants, are also well maintained between the original specimens and the tumor grafts. We found that co-engraftment of primary human mesenchymal stem cells with tumor grafts helps to maintain the phenotypic stability of the tumors, and increases tumor growth by promoting angiogenesis and reducing necrosis. Remarkably, tumor engraftment is also a prognostic indicator of disease outcome: newly diagnosed women whose primary breast tumor successfully engrafted in mouse mammary glands had significantly reduced survival compared to patients whose tumors did not engraft. Thus, orthotopic breast tumor grafting marks a first step toward personalized medicine by replicating the diversity of human breast cancer through patient-centric models for tumor growth, metastasis, drug efficacy, and prognosis.

Because of the association between aberrant nuclear structure and tumour grade, nuclear morphology is an indispensible criterion in the current pathological assessment of cancer. Components of the nuclear envelope environment have central roles in many aspects of cell function that affect tumour development and progression. As the roles of the nuclear envelope components, including nuclear pore complexes and nuclear lamina, are being deciphered in molecular detail there are opportunities to harness this knowledge for cancer therapeutics and biomarker development. In this Review, we summarize the progress that has been made in our understanding of the nuclear envelope and the implications of changes in this environment for cancer biology.

Metastatic breast cancer remains challenging to treat, and most patients ultimately progress on therapy. This acquired drug resistance is largely due to drug-refractory sub-populations (subclones) within heterogeneous tumors. Here, we track the genetic and phenotypic subclonal evolution of four breast cancers through years of treatment to better understand how breast cancers become drug-resistant. Recurrently appearing post-chemotherapy mutations are rare. However, bulk and single-cell RNA sequencing reveal acquisition of malignant phenotypes after treatment, including enhanced mesenchymal and growth factor signaling, which may promote drug resistance, and decreased antigen presentation and TNF-α signaling, which may enable immune system avoidance. Some of these phenotypes pre-exist in pre-treatment subclones that become dominant after chemotherapy, indicating selection for resistance phenotypes. Post-chemotherapy cancer cells are effectively treated with drugs targeting acquired phenotypes. These findings highlight cancer’s ability to evolve phenotypically and suggest a phenotype-targeted treatment strategy that adapts to cancer as it evolves.

Programmed Cell Death 4 (PDCD4) has been described as a tumor suppressor, with high expression correlating with better outcomes in a number of cancer types. Yet a substantial number of cancer patients with high PDCD4 in tumors have poor survival, suggesting that oncogenic pathways may inhibit or change PDCD4 function. Here, we explore the significance of PDCD4 in breast cancer and identify Protein Arginine Methyltransferase 5 (PRMT5) as a cofactor that radically alters PDCD4 function. Specifically, we find that co-expression of PDCD4 and PRMT5 in an orthotopic model of breast cancer causes accelerated tumor growth and that this growth phenotype is dependent on both the catalytic activity of PRMT5 and a site of methylation within the N-terminal region of PDCD4. In agreement with the xenograft model, elevated PDCD4 expression was found to correlate with worse outcome within the cohort of breast cancer patients whose tumors contain higher levels of PRMT5. These results reveal a new cofactor for PDCD4 that alters its tumor suppressor functions and point to the utility of PDCD4/PRMT5 status as both a prognostic biomarker and a potential target for chemotherapy.

Background Data are lacking to describe gene expression-based breast cancer intrinsic subtype patterns for population-based patient groups. Methods We studied a diverse cohort of women with breast cancer from the Life After Cancer Epidemiology (LACE) and Pathways studies. RNA was extracted from 1 mm punches from fixed tumor tissue. Quantitative reverse-transcriptase polymerase chain reaction (RT-qPCR) was conducted for the 50 genes that comprise the PAM50 intrinsic subtype classifier. Results In a subcohort of 1,319 women, the overall subtype distribution based on PAM50 was 53.1% Luminal A, 20.5% Luminal B, 13.0% HER2-enriched, 9.8% Basal-like, and 3.6% Normal-like. Among low-risk endocrine positive tumors (i.e. estrogen and progesterone receptor positive by immunohistochemistry, Her2 negative, and low histologic grade), only 76.5% were categorized as Luminal A by PAM50. Continuous-scale Luminal A, Luminal B, HER2-enriched, and Normal-like scores from PAM50 were mutually positively correlated; Basal-like score was inversely correlated with other subtypes. The proportion with non-Luminal A subtype decreased with older age at diagnosis, p trend < 0.0001. Compared with non-Hispanic whites, African-American women were more likely to have Basal-like tumors, age-adjusted odds ratio (OR) 4.4 (95% CI 2.3,8.4), whereas Asian and Pacific Islander women had reduced odds of Basal-like subtype, OR 0.5 (95% CI 0.3,0.9). Conclusions Our data indicate that over 50% of breast cancers treated in the community have Luminal A subtype. Gene expression-based classification shifted some tumors categorized as low risk by surrogate clinicopathological criteria to higher-risk subtypes. Impact Subtyping in a population-based cohort revealed distinct profiles by age and race.

There are several sources of bias that are unique to diagnostic accuracy studies. Because pathologists are both consumers and producers of such studies, it is important that they be aware of the risk of bias.

IntroductionThe distribution of histopathological features of invasive breast tumors in BRCA1 or BRCA2 germline mutation carriers differs from that of individuals with no known mutation. Histopathological features thus have utility for mutation prediction, including statistical modeling to assess pathogenicity of BRCA1 or BRCA2 variants of uncertain clinical significance. We analyzed large pathology datasets accrued by the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) and the Breast Cancer Association Consortium (BCAC) to reassess histopathological predictors of BRCA1 and BRCA2 mutation status, and provide robust likelihood ratio (LR) estimates for statistical modeling.MethodsSelection criteria for study/center inclusion were estrogen receptor (ER) status or grade data available for invasive breast cancer diagnosed younger than 70 years. The dataset included 4,477 BRCA1 mutation carriers, 2,565 BRCA2 mutation carriers, and 47,565 BCAC breast cancer cases. Country-stratified estimates of the likelihood of mutation status by histopathological markers were derived using a Mantel-Haenszel approach.ResultsER-positive phenotype negatively predicted BRCA1 mutation status, irrespective of grade (LRs from 0.08 to 0.90). ER-negative grade 3 histopathology was more predictive of positive BRCA1 mutation status in women 50 years or older (LR = 4.13 (3.70 to 4.62)) versus younger than 50 years (LR = 3.16 (2.96 to 3.37)). For BRCA2, ER-positive grade 3 phenotype modestly predicted positive mutation status irrespective of age (LR = 1.7-fold), whereas ER-negative grade 3 features modestly predicted positive mutation status at 50 years or older (LR = 1.54 (1.27 to 1.88)). Triple-negative tumor status was highly predictive of BRCA1 mutation status for women younger than 50 years (LR = 3.73 (3.43 to 4.05)) and 50 years or older (LR = 4.41 (3.86 to 5.04)), and modestly predictive of positive BRCA2 mutation status in women 50 years or older (LR = 1.79 (1.42 to 2.24)).ConclusionsThese results refine likelihood-ratio estimates for predicting BRCA1 and BRCA2 mutation status by using commonly measured histopathological features. Age at diagnosis is an important variable for most analyses, and grade is more informative than ER status for BRCA2 mutation carrier prediction. The estimates will improve BRCA1 and BRCA2 variant classification and inform patient mutation testing and clinical management.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-014-0474-y) contains supplementary material, which is available to authorized users.