Sarcomas of soft tissue and bone are rare neoplasms that can be separated into a large number of different diagnostic entities. Over the years, a number of diagnostic markers have been developed that aid pathologists in reaching the appropriate diagnoses. Many of these markers are sarcoma-specific proteins that can be detected by immunohistochemistry in formalin-fixed, paraffin-embedded (FFPE) sections. In addition, a wide range of molecular studies have been developed that can detect gene mutations, gene amplifications or chromosomal translocations in FFPE material. Until recently, most sequencing-based approaches relied on the availability of fresh frozen tissue. However, with the advent of next-generation sequencing technologies, FFPE material is increasingly being used as a tool to identify novel immunohistochemistry markers, gene mutations, and chromosomal translocations, and to develop diagnostic tests.
UNLABELLED: The PI3K-AKT signaling pathway regulates all phenotypes that contribute to progression of human cancers, including breast cancer. AKT mediates signal relay by phosphorylating numerous substrates, which are causally implicated in biologic responses such as cell growth, survival, metabolic reprogramming, migration, and invasion. Here a new AKT substrate is identified, the adherens junction protein Afadin, which is phosphorylated by AKT at Ser1718. Importantly, under conditions of physiologic IGF-1 signaling and oncogenic PI3K and AKT, Afadin is phosphorylated by all AKT isoforms, and this phosphorylation elicits a relocalization of Afadin from adherens junctions to the nucleus. Also, phosphorylation of Afadin increased breast cancer cell migration that was dependent on Ser1718 phosphorylation. Finally, nuclear localization of Afadin was observed in clinical breast cancer specimens, indicating that regulation of Afadin by the PI3K-AKT pathway has pathophysiologic significance.
IMPLICATIONS: Phosphorylation of the adhesion protein Afadin by AKT downstream of the PI3K pathway, leads to redistribution of Afadin and controls cancer cell migration.
Repressive histone tail modifications have been associated with molecular breast cancer subtypes. We investigated whether histone 3 lysine 9 trimethylation (H3K9me3) and histone 3 lysine 27 trimethylation (H3K27me3) were associated with tumor features and subtypes while adjusting for prospectively collected reproductive and lifestyle breast cancer risk factors. We have tissue microarray data with immunohistochemical marker information on 804 incident cases of invasive breast cancer diagnosed from 1976-2000 in the Nurses' Health Study. Tissue microarray sections were stained for global H3K9me3 and H3K27me3, and scored into four categories. Multivariate odds ratios (OR) and 95 % confidence intervals (CI) were calculated using logistic regression models for tumor features and subtypes, adjusting for breast cancer risk factors. While there were no significant associations between H3K9me3 and tumor features, H3K27me3 was significantly associated with lower grade tumors compared to high grade tumors in the multivariate model (OR = 1.95, 95 % CI 1.35-2.81, p = 0.0004). H3K27me3 was suggestively associated with estrogen receptor-positive (ER+) tumors (OR = 1.47, 95 % CI 0.97-2.23, p = 0.07). In subtype analyses, H3K27me3 was positively associated with the luminal A subtype compared to all other subtypes (OR = 1.42, 95 % CI 1.14-1.77, p = 0.002), and was inversely associated with HER2-type (OR = 0.58, 95 % CI 0.37-0.91, p = 0.02) and basal-like breast cancer (OR = 0.52, 95 % CI 0.36-0.76, p = 0.0006). In the largest immunohistochemical examination of H3K9me3 and H3K27me3 in breast cancer, we found that H3K27me3 positivity, but not H3K9me3, was associated with lower grade tumors and the luminal A subtype after adjusting for reproductive and lifestyle breast cancer risk factors.
PTEN dysfunction plays a crucial role in the pathogenesis of hereditary and sporadic cancers. Here, we show that PTEN homodimerizes and, in this active conformation, exerts lipid phosphatase activity on PtdIns(3,4,5)P3. We demonstrate that catalytically inactive cancer-associated PTEN mutants heterodimerize with wild-type PTEN and constrain its phosphatase activity in a dominant-negative manner. To study the consequences of homo- and heterodimerization of wild-type and mutant PTEN in vivo, we generated Pten knockin mice harboring two cancer-associated PTEN mutations (PtenC124S and PtenG129E). Heterozygous Pten(C124S/+) and Pten(G129E/+) cells and tissues exhibit increased sensitivity to PI3-K/Akt activation compared to wild-type and Pten(+/-) counterparts, whereas this difference is no longer apparent between Pten(C124S/-) and Pten(-/-) cells. Notably, Pten KI mice are more tumor prone and display features reminiscent of complete Pten loss. Our findings reveal that PTEN loss and PTEN mutations are not synonymous and define a working model for the function and regulation of PTEN.
Genes encoding components of the PI3K-AKT-mTOR signaling axis are frequently mutated in cancer, but few mutations have been characterized in MTOR, the gene encoding the mTOR kinase. Using publicly available tumor genome sequencing data, we generated a comprehensive catalog of mTOR pathway mutations in cancer, identifying 33 MTOR mutations that confer pathway hyperactivation. The mutations cluster in six distinct regions in the C-terminal half of mTOR and occur in multiple cancer types, with one cluster particularly prominent in kidney cancer. The activating mutations do not affect mTOR complex assembly, but a subset reduces binding to the mTOR inhibitor DEPTOR. mTOR complex 1 (mTORC1) signaling in cells expressing various activating mutations remains sensitive to pharmacologic mTOR inhibition, but is partially resistant to nutrient deprivation. Finally, cancer cell lines with hyperactivating MTOR mutations display heightened sensitivity to rapamycin both in culture and in vivo xenografts, suggesting that such mutations confer mTOR pathway dependency.
Large-scale pharmacogenomic high-throughput screening (HTS) studies hold great potential for generating robust genomic predictors of drug response. Two recent large-scale HTS studies have reported results of such screens, revealing several known and novel drug sensitivities and biomarkers. Subsequent evaluation, however, found only moderate interlaboratory concordance in the drug response phenotypes, possibly due to differences in the experimental protocols used in the two studies. This highlights the need for community-wide implementation of standardized assays for measuring drug response phenotypes so that the full potential of HTS is realized. We suggest that the path forward is to establish best practices and standardization of the critical steps in these assays through a collective effort to ensure that the data produced from large-scale screens would not only be of high intrastudy consistency, so that they could be replicated and compared successfully across multiple laboratories.
The term 'field effect' (also known as field defect, field cancerization, or field carcinogenesis) has been used to describe a field of cellular and molecular alteration, which predisposes to the development of neoplasms within that territory. We explore an expanded, integrative concept, 'etiologic field effect', which asserts that various etiologic factors (the exposome including dietary, lifestyle, environmental, microbial, hormonal, and genetic factors) and their interactions (the interactome) contribute to a tissue microenvironmental milieu that constitutes a 'field of susceptibility' to neoplasia initiation, evolution, and progression. Importantly, etiological fields predate the acquisition of molecular aberrations commonly considered to indicate presence of filed effect. Inspired by molecular pathological epidemiology (MPE) research, which examines the influence of etiologic factors on cellular and molecular alterations during disease course, an etiologically focused approach to field effect can: (1) broaden the horizons of our inquiry into cancer susceptibility and progression at molecular, cellular, and environmental levels, during all stages of tumor evolution; (2) embrace host-environment-tumor interactions (including gene-environment interactions) occurring in the tumor microenvironment; and, (3) help explain intriguing observations, such as shared molecular features between bilateral primary breast carcinomas, and between synchronous colorectal cancers, where similar molecular changes are absent from intervening normal colon. MPE research has identified a number of endogenous and environmental exposures which can influence not only molecular signatures in the genome, epigenome, transcriptome, proteome, metabolome and interactome, but also host immunity and tumor behavior. We anticipate that future technological advances will allow the development of in vivo biosensors capable of detecting and quantifying 'etiologic field effect' as abnormal network pathology patterns of cellular and microenvironmental responses to endogenous and exogenous exposures. Through an 'etiologic field effect' paradigm, and holistic systems pathology (systems biology) approaches to cancer biology, we can improve personalized prevention and treatment strategies for precision medicine.Modern Pathology advance online publication, 13 June 2014; doi:10.1038/modpathol.2014.81.
Lindström S, Thompson DJ, Paterson AD, Li J, Gierach GL, Scott C, Stone J, Douglas JA, dos-Santos-Silva I, Fernandez-Navarro P, Verghase J, Smith P, Brown J, Luben R, Wareham NJ, Loos RJF, Heit JA, Pankratz SV, Norman A, Goode EL, Cunningham JM, deAndrade M, Vierkant RA, Czene K, Fasching PA, Baglietto L, Southey MC, Giles GG, Shah KP, Chan H-P, Helvie MA, Beck AH, Knoblauch NW, Hazra A, Hunter DJ, Kraft P, Pollan M, Figueroa JD, Couch FJ, Hopper JL, Hall P, Easton DF, Boyd NF, Vachon CM, Tamimi RM. Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk. Nat Commun 2014;5:5303.Abstract
Mammographic density reflects the amount of stromal and epithelial tissues in relation to adipose tissue in the breast and is a strong risk factor for breast cancer. Here we report the results from meta-analysis of genome-wide association studies (GWAS) of three mammographic density phenotypes: dense area, non-dense area and percent density in up to 7,916 women in stage 1 and an additional 10,379 women in stage 2. We identify genome-wide significant (P<5 × 10(-8)) loci for dense area (AREG, ESR1, ZNF365, LSP1/TNNT3, IGF1, TMEM184B and SGSM3/MKL1), non-dense area (8p11.23) and percent density (PRDM6, 8p11.23 and TMEM184B). Four of these regions are known breast cancer susceptibility loci, and four additional regions were found to be associated with breast cancer (P<0.05) in a large meta-analysis. These results provide further evidence of a shared genetic basis between mammographic density and breast cancer and illustrate the power of studying intermediate quantitative phenotypes to identify putative disease-susceptibility loci.
Gene set enrichment analysis (GSEA) associates gene sets and phenotypes, its use is predicated on the choice of a pre-defined collection of sets. The defacto standard implementation of GSEA provides seven collections yet there are no guidelines for the choice of collections and the impact of such choice, if any, is unknown. Here we compare each of the standard gene set collections in the context of a large dataset of drug response in human cancer cell lines. We define and test a new collection based on gene co-expression in cancer cell lines to compare the performance of the standard collections to an externally derived cell line based collection. The results show that GSEA findings vary significantly depending on the collection chosen for analysis. Henceforth, collections should be carefully selected and reported in studies that leverage GSEA.
Stromal cells within the tumor microenvironment are essential for tumor progression and metastasis. Surprisingly little is known about the factors that drive the transcriptional reprogramming of stromal cells within tumors. We report that the transcriptional regulator heat shock factor 1 (HSF1) is frequently activated in cancer-associated fibroblasts (CAFs), where it is a potent enabler of malignancy. HSF1 drives a transcriptional program in CAFs that complements, yet is completely different from, the program it drives in adjacent cancer cells. This CAF program is uniquely structured to support malignancy in a non-cell-autonomous way. Two central stromal signaling molecules-TGF-β and SDF1-play a critical role. In early-stage breast and lung cancer, high stromal HSF1 activation is strongly associated with poor patient outcome. Thus, tumors co-opt the ancient survival functions of HSF1 to orchestrate malignancy in both cell-autonomous and non-cell-autonomous ways, with far-reaching therapeutic implications.
Triple-negative breast cancer (TNBC) is currently the only major breast tumor subtype without effective targeted therapy and, as a consequence, in general has a poor outcome. To identify new therapeutic targets in TNBC, we performed a short hairpin RNA (shRNA) screen for protein kinases commonly amplified and overexpressed in breast cancer. Using this approach, we identified AKT3 as a gene preferentially required for the growth of TNBCs. Downregulation of Akt3 significantly inhibits the growth of TNBC lines in three-dimensional (3D) spheroid cultures and in mouse xenograft models, whereas loss of Akt1 or Akt2 have more modest effects. Akt3 silencing markedly upregulates the p27 cell-cycle inhibitor and this is critical for the ability of Akt3 to inhibit spheroid growth. In contrast with Akt1, Akt3 silencing results in only a minor enhancement of migration and does not promote invasion. Depletion of Akt3 in TNBC sensitizes cells to the pan-Akt inhibitor GSK690693. These results imply that Akt3 has a specific function in TNBCs; thus, its therapeutic targeting may provide a new treatment option for this tumor subtype.
The categorization of intraductal proliferative lesions of the breast based on routine light microscopic examination of histopathologic sections is in many cases challenging, even for experienced pathologists. The development of computational tools to aid pathologists in the characterization of these lesions would have great diagnostic and clinical value. As a first step to address this issue, we evaluated the ability of computational image analysis to accurately classify DCIS and UDH and to stratify nuclear grade within DCIS. Using 116 breast biopsies diagnosed as DCIS or UDH from the Massachusetts General Hospital (MGH), we developed a computational method to extract 392 features corresponding to the mean and standard deviation in nuclear size and shape, intensity, and texture across 8 color channels. We used L1-regularized logistic regression to build classification models to discriminate DCIS from UDH. The top-performing model contained 22 active features and achieved an AUC of 0.95 in cross-validation on the MGH data-set. We applied this model to an external validation set of 51 breast biopsies diagnosed as DCIS or UDH from the Beth Israel Deaconess Medical Center, and the model achieved an AUC of 0.86. The top-performing model contained active features from all color-spaces and from the three classes of features (morphology, intensity, and texture), suggesting the value of each for prediction. We built models to stratify grade within DCIS and obtained strong performance for stratifying low nuclear grade vs. high nuclear grade DCIS (AUC = 0.98 in cross-validation) with only moderate performance for discriminating low nuclear grade vs. intermediate nuclear grade and intermediate nuclear grade vs. high nuclear grade DCIS (AUC = 0.83 and 0.69, respectively). These data show that computational pathology models can robustly discriminate benign from malignant intraductal proliferative lesions of the breast and may aid pathologists in the diagnosis and classification of these lesions.