In the year-end editorial, the PLOS Medicine editors ask 11 researchers and clinicians about the most relevant challenges, promising research, and important initiatives in their fields as we head into 2016.
Ciriello G, Gatza ML, Beck AH, Wilkerson MD, Rhie SK, Pastore A, Zhang H, McLellan M, Yau C, Kandoth C, Bowlby R, Shen H, Hayat S, Fieldhouse R, Lester SC, Tse GMK, Factor RE, Collins LC, Allison KH, Chen Y-Y, Jensen K, Johnson NB, Oesterreich S, Mills GB, Cherniack AD, Robertson G, Benz C, Sander C, Laird PW, Hoadley KA, King TA, Perou CM. Comprehensive Molecular Portraits of Invasive Lobular Breast Cancer. Cell 2015;163(2):506-19.Abstract
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.
In epidemiologic studies, alcohol consumption appears more strongly associated with risk of estrogen receptor (ER)-positive than ER-negative breast cancer. However, this association has not been assessed by other potentially relevant tumor markers, such as androgen receptor (AR) or insulin receptor (IR). In the prospective Nurses' Health Study cohort, we evaluated alcohol consumption and breast cancer risk by individual tumor marker expression (i.e., ER, progesterone receptor [PR], AR, and IR) while controlling for other markers and also assessed the joint effect of these receptors. During 26 years follow-up of 106,037 women, 2552 invasive breast cancers contributed to the analysis. When all four markers were considered simultaneously, no significant heterogeneity of the alcohol and breast cancer association was observed by any of the markers. However, each increment in one drink per day was associated with 10% (95% confidence interval [CI] = 4%, 15%) and 9% (95% CI = 4%, 15%) increased risk of AR-positive and ER-positive breast cancer, respectively, while no increased risk was observed among AR-negative or ER-negative tumors. The association was independent of PR and IR expression. Assessment of the joint expression of hormone receptors revealed a significantly increased risk among AR+/ER+/PR+ (hazard ratio [HR] per drink/day = 1.11, 95% CI = 1.06, 1.17) but not in other subgroups (e.g. , AR-/ER-/PR-: HR = 0.99; 95% CI = 0.88, 1.12). Our data suggest that the alcohol and breast cancer association may be more pronounced among ER-positive and/or AR-positive breast tumors. However, our data do not support an important role of IR in the association.
The ERG gene is fused to TMPRSS2 in approximately 50% of prostate cancers (PrCa), resulting in its overexpression. However, whether this is the sole mechanism underlying ERG elevation in PrCa is currently unclear. Here we report that ERG ubiquitination and degradation are governed by the Cullin 3-based ubiquitin ligase SPOP and that deficiency in this pathway leads to aberrant elevation of the ERG oncoprotein. Specifically, we find that truncated ERG (ΔERG), encoded by the ERG fusion gene, is stabilized by evading SPOP-mediated destruction, whereas prostate cancer-associated SPOP mutants are also deficient in promoting ERG ubiquitination. Furthermore, we show that the SPOP/ERG interaction is modulated by CKI-mediated phosphorylation. Importantly, we demonstrate that DNA damage drugs, topoisomerase inhibitors, can trigger CKI activation to restore the SPOP/ΔERG interaction and its consequent degradation. Therefore, SPOP functions as a tumor suppressor to negatively regulate the stability of the ERG oncoprotein in prostate cancer.
BACKGROUND: This study aimed to determine if associations of pre-diagnostic percent breast density, absolute dense area, and non-dense area with subsequent breast cancer risk differ by the tumour's molecular marker status.
METHODS: We included 1010 postmenopausal women with breast cancer and 2077 matched controls from the Nurses' Health Study (NHS) and the Nurses' Health Study II (NHS II) cohorts. Breast density was estimated from digitised film mammograms using computer-assisted thresholding techniques. Information on breast cancer risk factors was obtained prospectively from biennial questionnaires. Polychotomous logistic regression was used to assess associations of breast density measures with tumour subtypes by the status of selected tissue markers. All tests of statistical significance were two sided.
RESULTS: The association of percent density with breast cancer risk appeared to be stronger in ER- as compared with ER+ tumours, but the difference did not reach statistical significance (density ⩾50% vs <10% odds ratio (OR)=3.06, 95% confidence interval (CI) 2.17-4.32 for ER+; OR=4.61, 95% CI 2.36-9.03 for ER-, Pheterogeneity=0.08). Stronger positive associations were found for absolute dense area and CK5/6- and EGFR- as compared with respective marker-positive tumours (Pheterogeneity=0.002 and 0.001, respectively). Stronger inverse associations of non-dense area with breast cancer risk were found for ER- as compared with ER+ tumours (Pheterogeneity=0.0001) and for AR+, CK5/6+, and EGFR+ as compared with respective marker-negative tumours (Pheterogeneity=0.03, 0.005, and 0.009, respectively). The associations of density measures with breast cancer did not differ by progesterone receptor and human epidermal growth factor receptor 2 status.
CONCLUSIONS: Breast density influences the risk of breast cancer subtypes by potentially different mechanisms.
INTRODUCTION: Screening mammography has contributed to a significant increase in the diagnosis of ductal carcinoma in situ (DCIS), raising concerns about overdiagnosis and overtreatment. Building on prior observations from lineage evolution analysis, we examined whether measuring genomic features of DCIS would predict association with invasive breast carcinoma (IBC). The long-term goal is to enhance standard clinicopathologic measures of low- versus high-risk DCIS and to enable risk-appropriate treatment.
METHODS: We studied three common chromosomal copy number alterations (CNA) in IBC and designed fluorescence in situ hybridization-based assay to measure copy number at these loci in DCIS samples. Clinicopathologic data were extracted from the electronic medical records of Stanford Cancer Institute and linked to demographic data from the population-based California Cancer Registry; results were integrated with data from tissue microarrays of specimens containing DCIS that did not develop IBC versus DCIS with concurrent IBC. Multivariable logistic regression analysis was performed to describe associations of CNAs with these two groups of DCIS.
RESULTS: We examined 271 patients with DCIS (120 that did not develop IBC and 151 with concurrent IBC) for the presence of 1q, 8q24 and 11q13 copy number gains. Compared to DCIS-only patients, patients with concurrent IBC had higher frequencies of CNAs in their DCIS samples. On multivariable analysis with conventional clinicopathologic features, the copy number gains were significantly associated with concurrent IBC. The state of two of the three copy number gains in DCIS was associated with a risk of IBC that was 9.07 times that of no copy number gains, and the presence of gains at all three genomic loci in DCIS was associated with a more than 17-fold risk (P = 0.0013).
CONCLUSIONS: CNAs have the potential to improve the identification of high-risk DCIS, defined by presence of concurrent IBC. Expanding and validating this approach in both additional cross-sectional and longitudinal cohorts may enable improved risk stratification and risk-appropriate treatment in DCIS.
Sampling of formalin-fixed paraffin-embedded (FFPE) tissue blocks is a critical initial step in molecular pathology. Image-guided coring (IGC) is a new method for using digital pathology images to guide tissue block coring for molecular analyses. The goal of our study is to evaluate the use of IGC for both tissue-based and nucleic acid-based projects in molecular pathology. First, we used IGC to construct a tissue microarray (TMA); second, we used IGC for FFPE block sampling followed by RNA extraction; and third, we assessed the correlation between nuclear counts quantitated from the IGC images and RNA yields. We used IGC to construct a TMA containing 198 normal and breast cancer cores. Histopathologic analysis showed high accuracy for obtaining tumor and normal breast tissue. Next, we used IGC to obtain normal and tumor breast samples before RNA extraction. We selected a random subset of tumor and normal samples to perform computational image analysis to quantify nuclear density, and we built regression models to estimate RNA yields from nuclear count, age of the block, and core diameter. Number of nuclei and core diameter were the strongest predictors of RNA yields in both normal and tumor tissue. IGC is an effective method for sampling FFPE tissue blocks for TMA construction and nucleic acid extraction. We identify significant associations between quantitative nuclear counts obtained from IGC images and RNA yields, suggesting that the integration of computational image analysis with IGC may be an effective approach for tumor sampling in large-scale molecular studies.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially. http://creativecommons.org/licenses/by-nc-nd/4.0/.
BACKGROUND: Epithelial-stromal crosstalk plays a critical role in invasive breast cancer pathogenesis; however, little is known on a systems level about how epithelial-stromal interactions evolve during carcinogenesis.
RESULTS: We develop a framework for building genome-wide epithelial-stromal co-expression networks composed of pairwise co-expression relationships between mRNA levels of genes expressed in the epithelium and stroma across a population of patients. We apply this method to laser capture micro-dissection expression profiling datasets in the setting of breast carcinogenesis. Our analysis shows that epithelial-stromal co-expression networks undergo extensive rewiring during carcinogenesis, with the emergence of distinct network hubs in normal breast, and estrogen receptor-positive and estrogen receptor-negative invasive breast cancer, and the emergence of distinct patterns of functional network enrichment. In contrast to normal breast, the strongest epithelial-stromal co-expression relationships in invasive breast cancer mostly represent self-loops, in which the same gene is co-expressed in epithelial and stromal regions. We validate this observation using an independent laser capture micro-dissection dataset and confirm that self-loop interactions are significantly increased in cancer by performing computational image analysis of epithelial and stromal protein expression using images from the Human Protein Atlas.
CONCLUSIONS: Epithelial-stromal co-expression network analysis represents a new approach for systems-level analyses of spatially localized transcriptomic data. The analysis provides new biological insights into the rewiring of epithelial-stromal co-expression networks and the emergence of epithelial-stromal co-expression self-loops in breast cancer. The approach may facilitate the development of new diagnostics and therapeutics targeting epithelial-stromal interactions in cancer.
Resistance to cytotoxic chemotherapy drugs, including doxorubicin, is a significant obstacle to the effective treatment of breast cancer. Here, we have identified a mechanism by which the PI3K/Akt pathway mediates resistance to doxorubicin. In addition to inducing DNA damage, doxorubicin triggers sustained activation of Akt signaling in breast cancer cells. We show that Akt contributes to chemotherapy resistance such that PI3K or Akt inhibitors sensitize cells to doxorubicin. We identify MERIT40, a component of the BRCA1-A DNA damage repair complex, as an Akt substrate that is phosphorylated following doxorubicin treatment. MERIT40 phosphorylation facilitates assembly of the BRCA1-A complex in response to DNA damage and contributes to DNA repair and cell survival following doxorubicin treatment. Finally, MERIT40 phosphorylation in human breast cancers is associated with estrogen receptor positivity. Our findings suggest that combination therapy with PI3K or Akt inhibitors and doxorubicin may constitute a successful strategy for overcoming chemotherapy resistance.
Disease classification system increasingly incorporates information on pathogenic mechanisms to predict clinical outcomes and response to therapy and intervention. Technological advancements to interrogate omics (genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics, interactomics, etc.) provide widely open opportunities in population-based research. Molecular pathological epidemiology (MPE) represents integrative science of molecular pathology and epidemiology. This unified paradigm requires multidisciplinary collaboration between pathology, epidemiology, biostatistics, bioinformatics, and computational biology. Integration of these fields enables better understanding of etiologic heterogeneity, disease continuum, causal inference, and the impact of environment, diet, lifestyle, host factors (including genetics and immunity), and their interactions on disease evolution. Hence, the Second International MPE Meeting was held in Boston in December 2014, with aims to: (1) develop conceptual and practical frameworks; (2) cultivate and expand opportunities; (3) address challenges; and (4) initiate the effort of specifying guidelines for MPE. The meeting mainly consisted of presentations of method developments and recent data in various malignant neoplasms and tumors (breast, prostate, ovarian and colorectal cancers, renal cell carcinoma, lymphoma, and leukemia), followed by open discussion sessions on challenges and future plans. In particular, we recognized need for efforts to further develop statistical methodologies. This meeting provided an unprecedented opportunity for interdisciplinary collaboration, consistent with the purposes of the Big Data to Knowledge, Genetic Associations and Mechanisms in Oncology, and Precision Medicine Initiative of the US National Institute of Health. The MPE meeting series can help advance transdisciplinary population science and optimize training and education systems for twenty-first century medicine and public health.
PURPOSE: Leiomyosarcoma is a malignant neoplasm with smooth muscle differentiation. Little is known about its molecular heterogeneity and no targeted therapy currently exists for leiomyosarcoma. Recognition of different molecular subtypes is necessary to evaluate novel therapeutic options. In a previous study on 51 leiomyosarcomas, we identified three molecular subtypes in leiomyosarcoma. The current study was performed to determine whether the existence of these subtypes could be confirmed in independent cohorts.
EXPERIMENTAL DESIGN: Ninety-nine cases of leiomyosarcoma were expression profiled with 3'end RNA-Sequencing (3SEQ). Consensus clustering was conducted to determine the optimal number of subtypes.
RESULTS: We identified 3 leiomyosarcoma molecular subtypes and confirmed this finding by analyzing publically available data on 82 leiomyosarcoma from The Cancer Genome Atlas (TCGA). We identified two new formalin-fixed, paraffin-embedded tissue-compatible diagnostic immunohistochemical markers; LMOD1 for subtype I leiomyosarcoma and ARL4C for subtype II leiomyosarcoma. A leiomyosarcoma tissue microarray with known clinical outcome was used to show that subtype I leiomyosarcoma is associated with good outcome in extrauterine leiomyosarcoma while subtype II leiomyosarcoma is associated with poor prognosis in both uterine and extrauterine leiomyosarcoma. The leiomyosarcoma subtypes showed significant differences in expression levels for genes for which novel targeted therapies are being developed, suggesting that leiomyosarcoma subtypes may respond differentially to these targeted therapies.
CONCLUSIONS: We confirm the existence of 3 molecular subtypes in leiomyosarcoma using two independent datasets and show that the different molecular subtypes are associated with distinct clinical outcomes. The findings offer an opportunity for treating leiomyosarcoma in a subtype-specific targeted approach.
BACKGROUND: Obesity and physical activity have been hypothesized to affect breast cancer risk partly via the androgen signaling pathway. We conducted the first study to evaluate these associations by tumor androgen receptor (AR) status.
METHODS: Height, weight, and physical activity were assessed using questionnaires in the Nurses' Health Study. AR, estrogen receptor (ER), and progesterone receptor (PR) status were determined using immunohistochemistry on tumor tissue and medical/pathology reports.
RESULTS: A total of 1,701 AR(+) and 497 AR(-) cases were documented during 26 years of follow-up of 103,577 women. After adjusting for ER/PR status and other risk factors, the relative risks (RR) and 95% confidence intervals (95% CI) for every 5 kg/m(2) increase in body mass index (BMI) were 1.07 (1.01-1.13) for AR(+) and 1.16 (1.05-1.29) for AR(-) tumors (P-heterogeneity = 0.17). The RRs (95% CIs) per 5 hours of brisk walking/week were 0.87 (0.73-1.04) for AR(+) and 0.67 (0.45-0.99) for AR(-) tumors (P-heterogeneity = 0.22). Further, BMI, but not physical activity, associations differed significantly across ER/PR/AR subtypes (P-heterogeneity = 0.04 and 0.63, respectively). The RRs (95% CIs) for 5 kg/m(2) increase in BMI were 1.23 (1.04-1.45) for ER(+)PR(+)AR(-), 1.19 (1.01-1.39) for ER(-)PR(-)AR(-), 1.15 (1.08-1.23) for ER(+)PR(+)AR(+), and 0.88 (0.75-1.03) for ER(+)PR(-)AR(+) tumors.
CONCLUSIONS: Higher BMI was associated with an increased risk of both AR(+) and AR(-) breast tumors in postmenopausal women, whereas physical activity, including brisk walking, was associated with a reduced risk of both subtypes. In addition, a significant positive association was observed between higher BMI and ER(-)PR(-)AR(-) tumors.
IMPACT: The similar associations observed by AR status suggest that mechanisms other than androgen signaling underlie these two breast cancer risk factors.
NFAT transcription factors are key regulators of gene expression in immune cells. In addition, NFAT1-induced genes play diverse roles in mediating the progression of various solid tumors. Here we show that NFAT1 induces the expression of the IL8 gene by binding to its promoter and leading to IL8 secretion. Thapsigargin stimulation of breast cancer cells induces IL8 expression in an NFAT-dependent manner. Moreover, we show that NFAT1-mediated IL8 production promotes the migration of primary human neutrophils in vitro and also promotes neutrophil infiltration in tumor xenografts. Furthermore, expression of active NFAT1 effectively suppresses the growth of nascent and established tumors by a non cell-autonomous mechanism. Evaluation of breast tumor tissue reveals that while the levels of NFAT1 are similar in tumor cells and normal breast epithelium, cells in the tumor stroma express higher levels of NFAT1 compared to normal stroma. Elevated levels of NFAT1 also correlate with increased neutrophil infiltrate in breast tumors. These data point to a mechanism by which NFAT1 orchestrates the communication between breast cancer cells and host neutrophils during breast cancer progression.
Background:Mammographic density (MD) is a strong breast cancer risk factor. We previously reported associations of percent MD with larger and node-positive tumors across all ages, and estrogen receptor (ER)-negative status among women ages <55 years. To provide insight into these associations, we examined the components of percent MD (dense area (DA) and non-dense area (NDA) with breast cancer subtypes. Methods:Data were pooled from six studies including 4095 breast cancers and 8558 controls. DA and NDA were assessed from digitized film-screen mammograms and standardized across studies. Breast cancer odds by density phenotypes and age according to histopathological characteristics and receptor status were calculated using polytomous logistic regression. Results:DA was associated with increased breast cancer risk [odds ratios (OR) for quartiles: 0.65, 1.00(Ref), 1.22, 1.55; p-trend <0.001] and NDA was associated with decreased risk [ORs for quartiles: 1.39, 1.00(Ref), 0.88, 0.72; p-trend <0.001] across all ages and invasive tumor characteristics. There were significant trends in the magnitude of associations of both DA and NDA with breast cancer by increasing tumor size (p-trend<0.001) but no differences by nodal status. Among women <55 years, DA was more strongly associated with increased risk of ER+ vs. ER- tumors [p-heterogeneity (het) = 0.02] while NDA was more strongly associated with decreased risk of ER- vs. ER+ tumors [p-het = 0.03]. Conclusions:DA and NDA have differential associations with ER+ vs. ER- tumors that vary by age. Impact:DA and NDA are important to consider when developing age- and subtype-specific risk models.
Imaging plays a central role in the diagnosis of cancer and the evaluation of therapeutic efficacy in patients with cancer. Because macroscopic imaging is noninvasive and quantitative, the development of specialized instruments for small animals has spurred increasing utilization in preclinical cancer studies. Some small-animal imaging devices are miniaturized derivatives of clinical imaging modalities, including computed tomography, magnetic resonance imaging, positron-emission tomography, single-photon emission computed tomography, and ultrasonography. Optical imaging, including bioluminescence imaging and fluorescence imaging, has evolved from microscopic cellular imaging technologies. Here, we review how current imaging modalities are enabling high-resolution structural imaging with micrometer-scale spatial resolution, thus allowing for the quantification of tumor burden in genetically engineered and orthotopic models of cancer, where tumors develop within organs not typically accessible to measurements with calipers. Beyond measuring tumor size, imaging is increasingly being used to assess the activity of molecular pathways within tumors and to reveal the pharmacodynamic efficacy of targeted therapies. Each imaging technology has particular strengths and limitations, and we discuss how studies should be carefully designed to match the imaging approach to the primary experimental question.
The development of tools in computational pathology to assist physicians and biomedical scientists in the diagnosis of disease requires access to high-quality annotated images for algorithm learning and evaluation. Generating high-quality expert-derived annotations is time-consuming and expensive. We explore the use of crowdsourcing for rapidly obtaining annotations for two core tasks in com- putational pathology: nucleus detection and nucleus segmentation. We designed and implemented crowdsourcing experiments using the CrowdFlower platform, which provides access to a large set of labor channel partners that accesses and manages millions of contributors worldwide. We obtained annotations from four types of annotators and compared concordance across these groups. We obtained: crowdsourced annotations for nucleus detection and segmentation on a total of 810 images; annotations using automated methods on 810 images; annotations from research fellows for detection and segmentation on 477 and 455 images, respectively; and expert pathologist-derived annotations for detection and segmentation on 80 and 63 images, respectively. For the crowdsourced annotations, we evaluated performance across a range of contributor skill levels (1, 2, or 3). The crowdsourced annotations (4,860 images in total) were completed in only a fraction of the time and cost required for obtaining annotations using traditional methods. For the nucleus detection task, the research fellow-derived annotations showed the strongest concordance with the expert pathologist- derived annotations (F-M =93.68%), followed by the crowd-sourced contributor levels 1,2, and 3 and the automated method, which showed relatively similar performance (F-M = 87.84%, 88.49%, 87.26%, and 86.99%, respectively). For the nucleus segmentation task, the crowdsourced contributor level 3-derived annotations, research fellow-derived annotations, and automated method showed the strongest concordance with the expert pathologist-derived annotations (F-M = 66.41%, 65.93%, and 65.36%, respectively), followed by the contributor levels 2 and 1 (60.89% and 60.87%, respectively). When the research fellows were used as a gold-standard for the segmentation task, all three con- tributor levels of the crowdsourced annotations significantly outperformed the automated method (F-M = 62.21%, 62.47%, and 65.15% vs. 51.92%). Aggregating multiple annotations from the crowd to obtain a consensus annotation resulted in the strongest performance for the crowd-sourced segmentation. For both detection and segmentation, crowd-sourced performance is strongest with small images (400 × 400 pixels) and degrades significantly with the use of larger images (600 × 600 and 800 × 800 pixels). We conclude that crowdsourcing to non-experts can be used for large-scale labeling microtasks in computational pathology and offers a new approach for the rapid generation of labeled images for algorithm development and evaluation.