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In cancer, the primary tumour’s organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA., Some cancer patients first present with metastases where the location of the primary is unidentified; these are difficult to treat. In this study, using machine learning, the authors develop a method to determine the tissue of origin of a cancer based on whole sequencing data.

The brain is composed of hundreds of different neuronal subtypes, which largely retain their identity throughout the lifespan of the organism. The mechanisms governing this stability are not fully understood, partly due to the diversity and limited size of clinically relevant neuronal populations, which constitute a technical challenge for analysis. Here, using a strategy that allows for ChIP-seq combined with RNA-seq in small neuronal populations in vivo, we present a comparative analysis of permissive and repressive histone modifications in adult midbrain dopaminergic neurons, raphe nuclei serotonergic neurons, and embryonic neural progenitors. Furthermore, we utilize the map generated by our analysis to show that the transcriptional response of midbrain dopaminergic neurons following 6-OHDA or methamphetamine injection is characterized by increased expression of genes with promoters dually marked by H3K4me3/H3K27me3. Our study provides an in vivo genome-wide analysis of permissive/repressive histone modifications coupled to gene expression in these rare neuronal subtypes. Correction in: NATURE COMMUNICATIONS, Volume: 9, Article Number: 4639, DOI: 10.1038/s41467-018-07154-5

Transcriptional profiling of lung adenocarcinomas has identified numerous gene expression phenotype (GEP) and risk prediction (RP) signatures associated with patient outcome. However, classification agreement between signatures, underlying transcriptional programs, and independent signature validation are less studied. We classified 2395 transcriptional adenocarcinoma profiles, assembled from 17 public cohorts, using 11 GEP and seven RP signatures, finding that 16 signatures were associated with patient survival in the total cohort and in multiple individual cohorts. For significant signatures, total cohort hazard ratios were ~2 in univariate analyses (mean=1.95, range=1.4-2.6). Strong classification agreement between signatures was observed, especially for predicted low-risk patients by adenocarcinoma-derived signatures. Expression of proliferation-related genes correlated strongly with GEP subtype classifications and RP scores, driving the gene signature association with prognosis. A three-group consensus definition of samples across 10 GEP classifiers demonstrated aggregation of samples with specific smoking patterns, gender, and EGFR/KRAS mutations, while survival differences were only significant when patients were divided into low- or high-risk. In summary, our study demonstrates a consensus between GEPs and RPs in lung adenocarcinoma through a common underlying transcriptional program. This consensus generalizes reported problems with current signatures in a clinical context, stressing development of new adenocarcinoma-specific single sample predictors for clinical use.

Cancer-associated fibroblasts (CAFs) are a major constituent of the tumor microenvironment, although their origin and roles in shaping disease initiation, progression and treatment response remain unclear due to significant heterogeneity. Here, following a negative selection strategy combined with single-cell RNA sequencing of 768 transcriptomes of mesenchymal cells from a genetically engineered mouse model of breast cancer, we define three distinct subpopulations of CAFs. Validation at the transcriptional and protein level in several experimental models of cancer and human tumors reveal spatial separation of the CAF subclasses attributable to different origins, including the peri-vascular niche, the mammary fat pad and the transformed epithelium. Gene profiles for each CAF subtype correlate to distinctive functional programs and hold independent prognostic capability in clinical cohorts by association to metastatic disease. In conclusion, the improved resolution of the widely defined CAF population opens the possibility for biomarker-driven development of drugs for precision targeting of CAFs., Cancer-associated fibroblasts (CAFs) are an important component of the breast tumour microenvironment. Here, single-cell RNA sequencing of CAFs from a mouse model of breast cancer defines three transcriptomically distinct subpopulations with putatively different functions.

Global loss of DNA methylation and CpG island (CGI) hypermethylation are key epigenomic aberrations in cancer. Global loss manifests itself in partially methylated domains (PMDs) which extend up to megabases. However, the distribution of PMDs within and between tumor types, and their effects on key functional genomic elements including CGIs are poorly defined. We comprehensively show that loss of methylation in PMDs occurs in a large fraction of the genome and represents the prime source of DNA methylation variation. PMDs are hypervariable in methylation level, size and distribution, and display elevated mutation rates. They impose intermediate DNA methylation levels incognizant of functional genomic elements including CGIs, underpinning a CGI methylator phenotype (CIMP). Repression effects on tumor suppressor genes are negligible as they are generally excluded from PMDs. The genomic distribution of PMDs reports tissue-of-origin and may represent tissue-specific silent regions which tolerate instability at the epigenetic, transcriptomic and genetic level., In cancer, global DNA methylation loss and CpG island hypermethylation are commonly observed. Here, in breast cancer the authors find that hyper-variability of partially methylated domains is the prime source of DNA methylation variation and that these domains fuel CpG island hypermethylation.

Purpose: Primary lung adenocarcinoma remains a deadly disease. Gene-expression phenotypes (GEPs) in adenocarcinoma have potential to provide clinically relevant disease stratification for improved prognosis and treatment prediction, given appropriate clinical and methodologic validation. Experimental Design: 2,395 transcriptional adenocarcinoma profiles were assembled from 17 public cohorts and classified by a nearest centroid GEP classifier into three subtypes: terminal respiratory unit (TRU), proximal-proliferative, and proximal-inflammatory, and additionally scored by five transcriptional metagenes representing different biologic processes, including proliferation. Prognostic- and chemotherapy-predictive associations of the subtypes were analyzed by univariate and multivariate analysis using overall survival or distant metastasis-free survival as endpoints. Results: Overall, GEPs were associated with patient outcome in both univariate and multivariate analyses, although not in all individual cohorts. The prognostically relevant division was between TRU- and non-TRU–classified cases, with expression of proliferation-associated genes as a key prognostic component. In contrast, GEP classification was not predictive of adjuvant chemotherapy response. GEP classification showed stability to random perturbations of genes or samples and alterations to classification procedures (typically 20% of cases switching subtype) was observed when removing larger or entire fractions of a single subtype, due to gene-centering shifts not addressable by the classifier. Conclusions: In a large-scale evaluation, we show that GEPs add prognostic value to standard clinicopathologic variables in lung adenocarcinoma. Subject to classifier refinement and confirmation in prospective cohorts, GEPs have potential to affect the prognostication of adenocarcinoma patients through a molecularly driven disease stratification. Clin Cancer Res; 22(1); 218–29. ©2015 AACR.

Melanoma is currently divided on a genetic level according to mutational status. However, this classification does not optimally predict prognosis. In prior studies, we have defined gene expression phenotypes (high-immune, pigmentation, proliferative and normal-like), which are predictive of survival outcome as well as informative of biology. Herein, we employed a population-based metastatic melanoma cohort and external cohorts to determine the prognostic and predictive significance of the gene expression phenotypes. We performed expression profiling on 214 cutaneous melanoma tumors and found an increased risk of developing distant metastases in the pigmentation (HR, 1.9; 95% CI, 1.05-3.28; P=0.03) and proliferative (HR, 2.8; 95% CI, 1.43-5.57; P=0.003) groups as compared to the high-immune response group. Further genetic characterization of melanomas using targeted deep-sequencing revealed similar mutational patterns across these phenotypes. We also used publicly available expression profiling data from melanoma patients treated with targeted or vaccine therapy in order to determine if our signatures predicted therapeutic response. In patients receiving targeted therapy, melanomas resistant to targeted therapy were enriched in the MITF-low proliferative subtype as compared to pre-treatment biopsies (P=0.02). In summary, the melanoma gene expression phenotypes are highly predictive of survival outcome and can further help to discriminate patients responding to targeted therapy.

Amplification of 8p12-p11 is relatively common in breast cancer and several genes within the region have been suggested to affect breast tumor progression. The aim of the study was to map the amplified 8p12-p11 region in a large set of breast tumors in an effort to identify the genetic driver and to explore its impact on tumor progression and prognosis. Copy number alterations (CNAs) were mapped in 359 tumors, and gene expression data from 577 tumors (359 tumors included) were correlated with CNA, clinical–pathological factors, and protein expression (39 tumors). 8p12-p11 was amplified in 11.4% of tumors. The smallest region of amplification harbored one full-length gene, ZNF703. ZNF703 mRNA expression was significantly higher in estrogen receptor (ER)-positive than ER-negative tumors (P = 2 × 10−16), a reflection of high expression in luminal tumors. Forty-eight percent of tumors with ZNF703 amplification were luminal B tumors in which the best correlation between DNA copy number and mRNA was seen (P = 1.2 × 10−7) as well as correlation between mRNA and protein expression (P = 0.02). High ZNF703 mRNA correlated with poor survival in patients with ER-positive luminal B tumors (log rank P = 0.04). Furthermore, high ZNF703 mRNA expression correlated with poor outcome in patients with ZNF703 copy number neutral, ER-positive, luminal B tumors (log rank P = 0.004). The results support ZNF703 as the driver gene of the 8p12 amplification and suggest that independent of amplification, high expression of the gene affects prognosis in luminal B tumors. Our mapping of 8p12-p11 and analyses of ZNF703 mRNA and protein expression in breast tumors support ZNF703 as an oncogene in luminal B tumors. High ZNF703 expression, independent of the amplification, correlated with worse prognosis for the breast cancer patients with ER-positive luminal tumors, particularly of the luminal B subtype.

Hypoxia, or low oxygen tension, is a major regulator of tumor development and aggressiveness. However, how cancer cells adapt to hypoxia and communicate with their surrounding microenvironment during tumor development remain important questions. Here, we show that secreted vesicles with exosome characteristics mediate hypoxia-dependent intercellular signaling of the highly malignant brain tumor glioblastoma multiforme (GBM). In vitro hypoxia experiments with glioma cells and studies with patient materials reveal the enrichment in exosomes of hypoxia-regulated mRNAs and proteins (e.g., matrix metalloproteinases, IL-8, PDGFs, caveolin 1, and lysyl oxidase), several of which were associated with poor glioma patient prognosis. We show that exosomes derived from GBM cells grown at hypoxic compared with normoxic conditions are potent inducers of angiogenesis ex vivo and in vitro through phenotypic modulation of endothelial cells. Interestingly, endothelial cells were programmed by GBM cell-derived hypoxic exosomes to secrete several potent growth factors and cytokines and to stimulate pericyte PI3K/AKT signaling activation and migration. Moreover, exosomes derived from hypoxic compared with normoxic conditions showed increased autocrine, promigratory activation of GBM cells. These findings were correlated with significantly enhanced induction by hypoxic compared with normoxic exosomes of tumor vascularization, pericyte vessel coverage, GBM cell proliferation, as well as decreased tumor hypoxia in a mouse xenograft model. We conclude that the proteome and mRNA profiles of exosome vesicles closely reflect the oxygenation status of donor glioma cells and patient tumors, and that the exosomal pathway constitutes a potentially targetable driver of hypoxia-dependent intercellular signaling during tumor development.

Polycomb repressive complex 2 (PRC2) and its core member enhancer of zeste homolog 2 (EZH2) mediate the epigenetic gene silencing mark: trimethylation of lysine 27 on histone 3 (H3K27me3). H3K27me3 is characteristic of the chromatin at genes involved in developmental regulation in undifferentiated cells. Overexpression of EZH2 has been found in several cancer types such as breast, prostate, melanoma and bladder cancer. Moreover, overexpression is associated with highly proliferative and aggressive types of breast and prostate tumors. We have analyzed the abundance of EZH2 and H3K27me3 using immunohistochemistry in two large and well-characterized breast tumor data sets encompassing more than 400 tumors. The results have been analyzed in relation to the molecular subtypes of breast tumors (basal-like, luminal A, luminal B, HER2-enriched and normal-like), as well as in subtypes defined by clinical markers (triple negative, ER+/HER2-/Ki67low, ER+/HER2-/Ki67high and HER2+), and were validated in representative breast cancer cell lines by western blot. We found significantly different expression of both EZH2 and H3K27me3 across all subtypes with high abundance of EZH2 in basal-like, triple negative and HER2-enriched tumors, and high H3K27me3 in luminal A, HER2-enriched and normal-like tumors. Intriguingly, the two markers show an inverse correlation, particularly for the basal-like and triple negative tumors. Consequently, high expression of EZH2 was associated with poor distant disease-free survival whereas high expression of H3K27me3 was associated with better survival. Additionally, none of 182 breast tumors was found to carry a previously described EZH2 mutation affecting Tyr641. Our observation that increased expression of EZH2 does not necessarily correlate with increased abundance of H3K27me3 supports the idea that EZH2 can have effects beyond epigenetic silencing of target genes in breast cancer.

Male breast cancer (MBC) is extremely rare and poorly characterized on the molecular level. Using high-resolution genomic data, we aimed to characterize MBC by genomic imbalances and to compare it with female breast cancer (FBC), and further to investigate whether the genomic profiles hold any prognostic information. Fifty-six fresh frozen MBC tumors were analyzed using high-resolution tiling BAC arrays. Significant regions in common between cases were assessed using Genomic Identification of Significant Targets in Cancer (GISTIC) analysis. A publicly available genomic data set of 359 FBC tumors was used for reference purposes. The data revealed a broad pattern of aberrations, confirming that MBC is a heterogeneous tumor type. Genomic gains were more common in MBC than in FBC and often involved whole chromosome arms, while losses of genomic material were less frequent. The most common aberrations were similar between the genders, but high-level amplifications were more common in FBC. We identified two genomic subgroups among MBCs; male-complex and male-simple. The male-complex subgroup displayed striking similarities with the previously reported luminal-complex FBC subgroup, while the male-simple subgroup seems to represent a new subgroup of breast cancer occurring only in men. There are many similarities between FBC and MBC with respect to genomic imbalances, but there are also distinct differences as revealed by high-resolution genomic profiling. MBC can be divided into two comprehensive genomic subgroups, which may be of prognostic value. The male-simple subgroup appears notably different from any genomic subgroup so far defined in FBC.

Purpose Human epidermal growth factor receptor 2 (HER2) gene amplification or protein overexpression (HER2 positivity) defines a clinically challenging subgroup of patients with breast cancer (BC) with variable prognosis and response to therapy. We aimed to investigate the heterogeneous biologic appearance and clinical behavior of HER2-positive tumors using molecular profiling. Patients and Methods Hierarchical clustering of gene expression data from 58 HER2-amplified tumors of various stage, histologic grade, and estrogen receptor (ER) status was used to construct a HER2-derived prognostic predictor that was further evaluated in several large independent BC data sets. Results Unsupervised analysis identified three subtypes of HER2-positive tumors with mixed stage, histologic grade, and ER status. One subtype had a significantly worse clinical outcome. A prognostic predictor was created based on differentially expressed genes between the subtype with worse outcome and the other subtypes. The predictor was able to define patient groups with better and worse outcome in HER2-positive BC across multiple independent BC data sets and identify a sizable HER2-positive group with long disease-free survival and low mortality. Significant correlation to prognosis was also observed in basal-like, ER-negative, lymph node–positive, and high-grade tumors, irrespective of HER2 status. The predictor included genes associated with immune response, tumor invasion, and metastasis. Conclusion The HER2-derived prognostic predictor provides further insight into the heterogeneous biology of HER2-positive tumors and may become useful for improved selection of patients who need additional treatment with new drugs targeting the HER2 pathway.

Item does not contain fulltext We analysed whole-genome sequences of 560 breast cancers to advance understanding of the driver mutations conferring clonal advantage and the mutational processes generating somatic mutations. We found that 93 protein-coding cancer genes carried probable driver mutations. Some non-coding regions exhibited high mutation frequencies, but most have distinctive structural features probably causing elevated mutation rates and do not contain driver mutations. Mutational signature analysis was extended to genome rearrangements and revealed twelve base substitution and six rearrangement signatures. Three rearrangement signatures, characterized by tandem duplications or deletions, appear associated with defective homologous-recombination-based DNA repair: one with deficient BRCA1 function, another with deficient BRCA1 or BRCA2 function, the cause of the third is unknown. This analysis of all classes of somatic mutation across exons, introns and intergenic regions highlights the repertoire of cancer genes and mutational processes operating, and progresses towards a comprehensive account of the somatic genetic basis of breast cancer.

Since first being described in 2000 (1), gene expression–based molecular subtypes have become an integral part of both basic and translational breast cancer research. Moreover, incremental research has found the subtypes to provide sufficient information on prognosis and systemic treatment selection to merit inclusion in international guidelines for breast cancer treatment (2). Although various subtype classifiers have been developed (3–6), the different classifiers generally agree on a taxonomy of breast cancer typically encompassing four subtypes (luminal A, luminal B, HER2-enriched, and basal-like). However, there has been considerable controversy regarding the ability of different molecular subtyping methods to robustly assign the same molecular subtype to a specific sample (7). First, it is not clear that molecular subtypes can be robustly defined in general (8), and the definition of the subtypes has changed over time from the initial classifier by Sorlie et al. (3) to the PAM50 classifier (5) often used today. Although each version could be considered an improvement (9), development is still ongoing, exemplified by the recent revision of the PAM50 classifier in the commercial ProSigna assay (NanoString Technologies). Second, current classifiers cannot robustly assign subtype to individual samples in a truly independent manner. Briefly, to put gene expression measurements of a sample on a scale that is required by the classifier methods, gene expression data for each individual test tumor have to be centered against a large and heterogeneous reference tumor set (10). This gene-centering step makes the assignment of subtype to a tumor highly dependent on the composition of other tumors in a reference set (11). The lack of robustness in assigning subtypes is not limited to gene expression assays like PAM50 (5) and SCMGENE (6), but is also observed in surrogate classifications based on clinico-pathological factors. For instance, there is considerable variation between laboratories in Ki-67 immunohistochemical measurement, which is used as a proliferation surrogate (2). Nevertheless, it has been argued, pending improved standardization, that local laboratory experience of Ki-67 levels could be used to stratify patients into Ki-67 “high” and “low” groups, classifications that may influence therapy decision (2). This argument parallels the de facto situation for gene expression–based molecular subtyping, where slightly different implementations are used in different studies. Although these differences may have limited impact on broad characterizations of the subtypes in terms of, for example, patient outcome, it is not satisfactory that classifications of individual patients are not robust. Thus, there is an imminent need for robust, standardized methods for the assignment of breast cancer molecular subtype to individual tumors independent of data from other tumors. The article published in this issue of the Journal by Paquet and Hallett describes an approach to making gene expression–based tumor subtyping of individual tumors robust and truly independent (12). At the heart of designing a true single sample predictor lies eliminating the gene-centering step. To this aim, the investigators developed a subtyping approach, AIMS, that relates raw expression measurements of subtype-specific genes to the levels of other genes within each tumor sample, instead of using a gene-centering step. To prove the concept, the investigators applied AIMS to mimic the PAM50 subtyping scheme and conducted a number of rigorous evaluations demonstrating that AIMS is a robust subtype classifier for new samples. AIMS is based on a collection of binary rules, for example, samples with higher raw expression levels for FOXC1 than ESR1 are more likely to be classified as basal-like. The stability of subtyping was evaluated on tumor sets with different proportions of specific subtypes. In these analyses, conventional subtyping tools were found to be highly unstable when single subtypes were excluded from the test set. For instance, 20% to 30% of samples changed subtype when PAM50 was applied to test sets with Luminal A tumors excluded, whereas AIMS was fully independent of the composition of the test set. The absolute nature of AIMS is of profound value when subtyping is applied to studies focusing on restricted cohorts of patients, for example, when characterizing responders to different regimes of therapy in a cohort of only ER-positive/HER2-negative cases or in neoadjuvant trials. In the first scenario, the investigators showed that conventional subtyping assigns the ER-positive/HER2-negative cases to all subtypes in proportions inherent to the method (for example, approximately 20% were incorrectly classified as basal-like by PAM50). In neoadjuvant trials, robust subtyping is even more critical, as such trials typically are small and often underpowered for clinically relevant endpoints, making them sensitive to inflated chance findings based on unstable conventional subtyping. The investigators’ analyses also demonstrate that AIMS is consistently more stable than PAM50 when subtyping is applied to samples with missing measurements for some predictor genes, or to samples with a large proportion of non-neoplastic cells. Subtyping approaches based on raw expression measurements also have limitations. One concern is that such methods may be highly sensitive to changes in the technology used to quantify expression levels. Since AIMS depends on the relative relations between expression levels of different genes in a sample, differences in measurement bias for a gene could affect its expression level relative to other genes in the sample, potentially causing instability in the subtyping. To explore this concern, the investigators analyzed samples profiled by both microarrays and RNA-sequencing and found that AIMS consistently assigns subtypes to samples across multiple platforms. Presumably, this stability is because of the construction of AIMS using relationships between genes having large differences in expression levels between subtypes, thereby making the relationships less sensitive to measurement bias. The development of methods that can robustly assign subtype to individual tumors, based on absolute measurements, opens the door to address the issues with different subtype classification schemes in breast cancer. It may serve as a first step to standardize methodologies. The separation of luminal tumors into A and B is based on expression of proliferation-related genes, but it is likely that proliferation in luminal tumors is a continuum (11). An absolute classifier is critical to optimize cutpoints in proliferation that reproducibly separates luminal tumors with respect to response to systemic treatments, and to standardize risk of recurrence scores that are based on subtype assignments. Eventually, such studies may return more stringent subtyping tools even better suited for incorporation into clinical trials and routine clinical practice.

Purpose: Lung cancer is the worldwide leading cause of death from cancer. DNA methylation in gene promoter regions is a major mechanism of gene expression regulation that may promote tumorigenesis. However, whether clinically relevant subgroups based on DNA methylation patterns exist in lung cancer remains unclear. Experimental Design: Whole-genome DNA methylation analysis using 450K Illumina BeadArrays was performed on 12 normal lung tissues and 124 tumors, including 83 adenocarcinomas, 23 squamous cell carcinomas (SqCC), 1 adenosquamous cancer, 5 large cell carcinomas, 9 large cell neuroendocrine carcinomas (LCNEC), and 3 small-cell carcinomas (SCLC). Unsupervised bootstrap clustering was performed to identify DNA methylation subgroups, which were validated in 695 adenocarcinomas and 122 SqCCs. Subgroups were characterized by clinicopathologic factors, whole-exome sequencing data, and gene expression profiles. Results: Unsupervised analysis identified five DNA methylation subgroups (epitypes). One epitype was distinctly associated with neuroendocrine tumors (LCNEC and SCLC). For adenocarcinoma, remaining four epitypes were associated with unsupervised and supervised gene expression phenotypes, and differences in molecular features, including global hypomethylation, promoter hypermethylation, genomic instability, expression of proliferation-associated genes, and mutations in KRAS, TP53, KEAP1, SMARCA4, and STK11. Furthermore, these epitypes were associated with clinicopathologic features such as smoking history and patient outcome. Conclusions: Our findings highlight one neuroendocrine and four adenocarcinoma epitypes associated with molecular and clinicopathologic characteristics, including patient outcome. This study demonstrates the possibility to further subgroup lung cancer, and more specifically adenocarcinomas, based on epigenetic/molecular classification that could lead to more accurate tumor classification, prognostication, and tailored patient therapy. Clin Cancer Res; 20(23); 6127–40. ©2014 AACR.

Purpose: Cigarette smoking is the major pathogenic factor for lung cancer. The precise mechanisms of tobacco-related carcinogenesis and its effect on the genomic and transcriptional landscape in lung cancer are not fully understood. Experimental Design: A total of 1,398 (277 never-smokers and 1,121 smokers) genomic and 1,449 (370 never-smokers and 1,079 smokers) transcriptional profiles were assembled from public lung adenocarcinoma cohorts, including matched next-generation DNA-sequencing data (n = 423). Unsupervised and supervised methods were used to identify smoking-related copy-number alterations (CNAs), predictors of smoking status, and molecular subgroups. Results: Genomic meta-analyses showed that never-smokers and smokers harbored a similar frequency of total CNAs, although specific regions (5q, 8q, 16p, 19p, and 22q) displayed a 20% to 30% frequency difference between the two groups. Importantly, supervised classification analyses based on CNAs or gene expression could not accurately predict smoking status (balanced accuracies ∼60% to 80%). However, unsupervised multicohort transcriptional profiling stratified adenocarcinomas into distinct molecular subgroups with specific patterns of CNAs, oncogenic mutations, and mutation transversion frequencies that were independent of the smoking status. One subgroup included approximately 55% to 90% of never-smokers and approximately 20% to 40% of smokers (both current and former) with molecular and clinical features of a less aggressive and smoking-unrelated disease. Given the considerable intragroup heterogeneity in smoking-defined subgroups, especially among former smokers, our results emphasize the clinical importance of accurate molecular characterization of lung adenocarcinoma. Conclusions: The landscape of smoking-related CNAs and transcriptional alterations in adenocarcinomas is complex, heterogeneous, and with moderate differences. Our results support a molecularly distinct less aggressive adenocarcinoma entity, arising in never-smokers and a subset of smokers. Clin Cancer Res; 20(18); 4912–24. ©2014 AACR.

Over the last decade, our knowledge in somatic genetic events related to breast cancer has increased -enormously. Through usage of various genome-wide molecular approaches, it has become increasingly clear that breast cancer is a vastly heterogeneous disease. Microarray-based gene expression profiling has divided breast cancer into five distinct intrinsic subtypes termed basal-like, HER2-enriched, normal-like, luminal A, and luminal B. Importantly, these subtypes are closely correlated to clinical variables as well as different outcomes, with luminal A tumors as the good prognostic group. Initial studies using genome-wide DNA copy number data broadly partitioned breast cancers into three types, complex, amplifier, and simple, and moreover associated distinct copy number changes with the intrinsic subtypes defined by gene expression profiles. More recently, this genomic classification was refined into six genomic subtypes demonstrating strong resemblance to the intrinsic gene expression classification. Additionally, inherited BRCA1- and BRCA2-mutated tumors were significantly correlated to specific subtypes. In this chapter, we will review the current status regarding genomic subtypes of nonfamilial breast cancer.

The rapidly growing collection of diverse genome-scale data from multiple tumor types sheds light on various aspects of the underlying tumor biology. With the objective to identify genes of importance for breast tumorigenesis in men and to enable comparisons with genes important for breast cancer development in women, we applied the computational framework COpy Number and EXpression In Cancer (CONEXIC) to detect candidate driver genes among all altered passenger genes. Unique to this approach is that each driver gene is associated with several gene modules that are believed to be altered by the driver. Thirty candidate drivers were found in the male breast cancers and 67 in the female breast cancers. We identified many known drivers of breast cancer and other types of cancer, in the female dataset (e.g. GATA3, CCNE1, GRB7, CDK4). In contrast, only three known cancer genes were found among male breast cancers; MAP2K4, LHP, and ZNF217. Many of the candidate drivers identified are known to be involved in processes associated with tumorigenesis, including proliferation, invasion and differentiation. One of the modules identified in male breast cancer was regulated by THY1, a gene involved in invasion and related to epithelial-mesenchymal transition. Furthermore, men with THY1 positive breast cancers had significantly inferior survival. THY1 may thus be a promising novel prognostic marker for male breast cancer. Another module identified among male breast cancers, regulated by SPAG5, was closely associated with proliferation. Our data indicate that male and female breast cancers display highly different landscapes of candidate driver genes, as only a few genes were found in common between the two. Consequently, the pathobiology of male breast cancer may differ from that of female breast cancer and can be associated with differences in prognosis; men diagnosed with breast cancer may consequently require different management and treatment strategies than women.

Purpose: For primary melanomas, tumor thickness, mitotic rate, and ulceration are well-laid cornerstones of prognostication. However, a molecular exposition of melanoma aggressiveness is critically missing. We recently uncovered a four-class structure in metastatic melanoma, which predicts outcome and informs biology. This raises the possibility that a molecular structure exists even in the early stages of melanoma and that molecular determinants could underlie histophenotype and eventual patient outcome. Experimental Design: We subjected 223 archival primary melanomas to a horizontally integrated analysis of RNA expression, oncogenic mutations at 238 lesions, histomorphometry, and survival data. Results: Our previously described four-class structure that was elucidated in metastatic lesions was evident within the expression space of primary melanomas. Because these subclasses converged into two larger prognostic and phenotypic groups, we used the metastatic lesions to develop a binary subtype-based signature capable of distinguishing between “high” and “low” grade forms of the disease. The two-grade signature was subsequently applied to the primary melanomas. Compared with low-grade tumors, high-grade primary melanomas were significantly associated with increased tumor thickness, mitotic rate, ulceration (all P < 0.01), and poorer relapse-free (HR = 4.94; 95% CI, 2.84–8.59), and overall (HR = 3.66; 95% CI, 2.40–5.58) survival. High-grade melanomas exhibited elevated levels of proliferation and BRCA1/DNA damage signaling genes, whereas low-grade lesions harbored higher expression of immune genes. Importantly, the molecular-grade signature was validated in two external gene expression data sets. Conclusions: We provide evidence for a molecular organization within melanomas, which is preserved across all stages of disease. Clin Cancer Res; 18(15); 4026–36. ©2012 AACR.

Introduction: Gene expression data derived from clinical cancer specimens provide an opportunity to characterize cancer-specific transcriptional programs. Here, we present an analysis delineating a correlation-based gene expression landscape of breast cancer that identifies modules with strong associations to breast cancer-specific and general tumor biology. Methods: Modules of highly connected genes were extracted from a gene co-expression network that was constructed based on Pearson correlation, and module activities were then calculated using a pathway activity score. Functional annotations of modules were experimentally validated with an siRNA cell spot microarray system using the KPL-4 breast cancer cell line, and by using gene expression data from functional studies. Modules were derived using gene expression data representing 1,608 breast cancer samples and validated in data sets representing 971 independent breast cancer samples as well as 1,231 samples from other cancer forms. Results: The initial co-expression network analysis resulted in the characterization of eight tightly regulated gene modules. Cell cycle genes were divided into two transcriptional programs, and experimental validation using an siRNA screen showed different functional roles for these programs during proliferation. The division of the two programs was found to act as a marker for tumor protein p53 (TP53) gene status in luminal breast cancer, with the two programs being separated only in luminal tumors with functional p53 (encoded by TP53). Moreover, a module containing fibroblast and stroma-related genes was highly expressed in fibroblasts, but was also up-regulated by overexpression of epithelial-mesenchymal transition factors such as transforming growth factor beta 1 (TGF-beta1) and Snail in immortalized human mammary epithelial cells. Strikingly, the stroma transcriptional program related to less malignant tumors for luminal disease and aggressive lymph node positive disease among basal-like tumors. Conclusions: We have derived a robust gene expression landscape of breast cancer that reflects known subtypes as well as heterogeneity within these subtypes. By applying the modules to TP53-mutated samples we shed light on the biological consequences of non-functional p53 in otherwise low-proliferating luminal breast cancer. Furthermore, as in the case of the stroma module, we show that the biological and clinical interpretation of a set of co-regulated genes is subtype-dependent.

Purpose: Even though urothelial cancer is the fourth most common tumor type among males, progress in treatment has been scarce. A problem in day-to-day clinical practice is that precise assessment of individual tumors is still fairly uncertain; consequently efforts have been undertaken to complement tumor evaluation with molecular biomarkers. An extension of this approach would be to base tumor classification primarily on molecular features. Here, we present a molecular taxonomy for urothelial carcinoma based on integrated genomics. Experimental Design: We use gene expression profiles from 308 tumor cases to define five major urothelial carcinoma subtypes: urobasal A, genomically unstable, urobasal B, squamous cell carcinoma like, and an infiltrated class of tumors. Tumor subtypes were validated in three independent publically available data sets. The expression of 11 key genes was validated at the protein level by immunohistochemistry. Results: The subtypes show distinct clinical outcomes and differ with respect to expression of cell-cycle genes, receptor tyrosine kinases particularly FGFR3, ERBB2, and EGFR, cytokeratins, and cell adhesion genes, as well as with respect to FGFR3, PIK3CA, and TP53 mutation frequency. The molecular subtypes cut across pathologic classification, and class-defining gene signatures show coordinated expression irrespective of pathologic stage and grade, suggesting the molecular phenotypes as intrinsic properties of the tumors. Available data indicate that susceptibility to specific drugs is more likely to be associated with the molecular stratification than with pathologic classification. Conclusions: We anticipate that the molecular taxonomy will be useful in future clinical investigations. Clin Cancer Res; 18(12); 3377–86. ©2012 AACR.

Introduction: Male breast cancer (MBC) is a rare and inadequately characterized disease. The aim of the present study was to characterize MBC tumors transcriptionally, to classify them into comprehensive subgroups, and to compare them with female breast cancer (FBC). less thanbrgreater than less thanbrgreater thanMethods: A total of 66 clinicopathologically well-annotated fresh frozen MBC tumors were analyzed using Illumina Human HT-12 bead arrays, and a tissue microarray with 220 MBC tumors was constructed for validation using immunohistochemistry. Two external gene expression datasets were used for comparison purposes: 37 MBCs and 359 FBCs. less thanbrgreater than less thanbrgreater thanResults: Using an unsupervised approach, we classified the MBC tumors into two subgroups, luminal M1 and luminal M2, respectively, with differences in tumor biological features and outcome, and which differed from the intrinsic subgroups described in FBC. The two subgroups were recapitulated in the external MBC dataset. Luminal M2 tumors were characterized by high expression of immune response genes and genes associated with estrogen receptor (ER) signaling. Luminal M1 tumors, on the other hand, despite being ER positive by immunohistochemistry showed a lower correlation to genes associated with ER signaling and displayed a more aggressive phenotype and worse prognosis. Validation of two of the most differentially expressed genes, class 1 human leukocyte antigen (HLA) and the metabolizing gene N-acetyltransferase-1 (NAT1), respectively, revealed significantly better survival associated with high expression of both markers (HLA, hazard ratio (HR) 3.6, P = 0.002; NAT1, HR 2.5, P = 0.033). Importantly, NAT1 remained significant in a multivariate analysis (HR 2.8, P = 0.040) and may thus be a novel prognostic marker in MBC. less thanbrgreater than less thanbrgreater thanConclusions: We have detected two unique and stable subgroups of MBC with differences in tumor biological features and outcome. They differ from the widely acknowledged intrinsic subgroups of FBC. As such, they may constitute two novel subgroups of breast cancer, occurring exclusively in men, and which may consequently require novel treatment approaches. Finally, we identified NAT1 as a possible prognostic biomarker for MBC, as suggested by NAT1 positivity corresponding to better outcome. Funding Agencies|Swedish Cancer Society||G Nilsson Cancer Foundation||Mrs. B Kamprad Foundation||Lund University Hospital Research Foundation||SCIBLU Genomics center||governmental funding of clinical research within the national health services (ALF)||Lund University

Epithelial to mesenchymal transition (EMT) is a critical event in cancer progression and is closely linked to the breast epithelial cancer stem cell phenotype. Given the close interaction between the vascular endothelium and cancer cells, especially at the invasive front, we asked whether endothelial cells might play a role in EMT. Using a 3D culture model we demonstrate that endothelial cells are potent inducers of EMT in D492 an immortalized breast epithelial cell line with stem cell properties. Endothelial induced mesenchymal-like cells (D492M) derived from D492, show reduced expression of keratins, a switch from E-Cadherin (E-Cad) to N-Cadherin (N-Cad) and enhanced migration. Acquisition of cancer stem cell associated characteristics like increased CD44(high)/CD24(low) ratio, resistance to apoptosis and anchorage independent growth was also seen in D492M cells. Endothelial induced EMT in D492 was partially blocked by inhibition of HGF signaling. Basal-like breast cancer, a vascular rich cancer with stem cell properties and adverse prognosis has been linked with EMT. We immunostained several basal-like breast cancer samples for endothelial and EMT markers. Cancer cells close to the vascular rich areas show no or decreased expression of E-Cad and increased N-Cad expression suggesting EMT. Collectively, we have shown in a 3D culture model that endothelial cells are potent inducers of EMT in breast epithelial cells with stem cell properties. Furthermore, we demonstrate that basal-like breast cancer contains cells with an EMT phenotype, most prominently close to vascular rich areas of these tumors. We conclude that endothelial cells are potent inducers of EMT and may play a role in progression of basal-like breast cancer.

Microarray-based gene expression analysis holds promise of improving prognostication and treatment decisions for breast cancer patients. However, the heterogeneity of breast cancer emphasizes the need for validation of prognostic gene signatures in larger sample sets stratified into relevant subgroups. Here, we describe a multifunctional user-friendly online tool, GOBO (http://co.bmc.lu.se/gobo), allowing a range of different analyses to be performed in an 1881-sample breast tumor data set, and a 51-sample breast cancer cell line set, both generated on Affymetrix U133A microarrays. GOBO supports a wide range of applications including: 1) rapid assessment of gene expression levels in subgroups of breast tumors and cell lines, 2) identification of co-expressed genes for creation of potential metagenes, 3) association with outcome for gene expression levels of single genes, sets of genes, or gene signatures in multiple subgroups of the 1881-sample breast cancer data set. The design and implementation of GOBO facilitate easy incorporation of additional query functions and applications, as well as additional data sets irrespective of tumor type and array platform.

Disseminated melanoma is an aggressive disease with fatal outcome. Better understanding of the underlying biology is needed to find effective treatment. We applied microarray-based comparative genomic hybridization, gene expression and CpG island methylation analysis of primary tumors and multiple metastases from five melanoma patients with the aim of analyzing the molecular patterns of melanoma progression. Epigenetic profiling showed that the multiple metastases after a single primary melanoma share similar methylation patterns for many genes, although differences in methylation between the lesions were evident for several genes, example, PTEN, TFAP2C, and RARB. In addition, DNA copy number and global gene expression profiles of tumors from individual patients were highly similar, confirming common origin of metastases. Some of the identified genomic aberrations, for example, gain of chromosome 6p and loss of chromosomes 6q and 10, persisted during progression, indicating early changes highly important for melanoma development. Homozygous deletions at 3p26.1 and 6q23.2-q23.3 appeared in two consecutive metastases originating from the same primary tumor, respectively, in a mutually exclusive manner that provides evidence for two genetically different subclones. However, in another case, the similarity of the copy number aberrations in subsequent metastatic lesions suggests sequential metastatic development through the clonal evolution. These data are further corroborated by a switch in CDH1 and CDH2 expression between metastases from the same patient. In conclusion, our results provide evidence for different models of metastatic progression in melanoma.

Introduction: HER2 gene amplification and protein overexpression (HER2+) define a clinically challenging subgroup of breast cancer with variable prognosis and response to therapy. Although gene expression profiling has identified an ERBB2 molecular subtype of breast cancer, it is clear that HER2+ tumors reside in all molecular subtypes and represent a genomically and biologically heterogeneous group, needed to be further characterized in large sample sets. Methods: Genome-wide DNA copy number profiling, using bacterial artificial chromosome (BAC) array comparative genomic hybridization (aCGH), and global gene expression profiling were performed on 200 and 87 HER2+ tumors, respectively. Genomic Identification of Significant Targets in Cancer (GISTIC) was used to identify significant copy number alterations (CNAs) in HER2+ tumors, which were related to a set of 554 non-HER2 amplified (HER2-) breast tumors. High-resolution oligonucleotide aCGH was used to delineate the 17q12-q21 region in high detail. Results: The HER2-amplicon was narrowed to an 85.92 kbp region including the TCAP, PNMT, PERLD1, HER2, C17orf37 and GRB7 genes, and higher HER2 copy numbers indicated worse prognosis. In 31% of HER2+ tumors the amplicon extended to TOP2A, defining a subgroup of HER2+ breast cancer associated with estrogen receptor-positive status and with a trend of better survival than HER2+ breast cancers with deleted (18%) or neutral TOP2A (51%). HER2+ tumors were clearly distinguished from HER2-tumors by the presence of recurrent high-level amplifications and firestorm patterns on chromosome 17q. While there was no significant difference between HER2+ and HER2-tumors regarding the incidence of other recurrent high-level amplifications, differences in the co-amplification pattern were observed, as shown by the almost mutually exclusive occurrence of 8p12, 11q13 and 20q13 amplification in HER2+ tumors. GISTIC analysis identified 117 significant CNAs across all autosomes. Supervised analyses revealed: (1) significant CNAs separating HER2+ tumors stratified by clinical variables, and (2) CNAs separating HER2+ from HER2-tumors. Conclusions: We have performed a comprehensive survey of CNAs in HER2+ breast tumors, pinpointing significant genomic alterations including both known and potentially novel therapeutic targets. Our analysis sheds further light on the genomically complex and heterogeneous nature of HER2+ tumors in relation to other subgroups of breast cancer.

Introduction: Five different molecular subtypes of breast cancer have been identified through gene expression profiling. Each subtype has a characteristic expression pattern suggested to partly depend on cellular origin. We aimed to investigate whether the molecular subtypes also display distinct methylation profiles. Methods: We analysed methylation status of 807 cancer-related genes in 189 fresh frozen primary breast tumours and four normal breast tissue samples using an array-based methylation assay. Results: Unsupervised analysis revealed three groups of breast cancer with characteristic methylation patterns. The three groups were associated with the luminal A, luminal B and basal-like molecular subtypes of breast cancer, respectively, whereas cancers of the HER2-enriched and normal-like subtypes were distributed among the three groups. The methylation frequencies were significantly different between subtypes, with luminal B and basal-like tumours being most and least frequently methylated, respectively. Moreover, targets of the polycomb repressor complex in breast cancer and embryonic stem cells were more methylated in luminal B tumours than in other tumours. BRCA2-mutated tumours had a particularly high degree of methylation. Finally, by utilizing gene expression data, we observed that a large fraction of genes reported as having subtype-specific expression patterns might be regulated through methylation. Conclusions: We have found that breast cancers of the basal-like, luminal A and luminal B molecular subtypes harbour specific methylation profiles. Our results suggest that methylation may play an important role in the development of breast cancers.

Introduction: Breast cancer is a profoundly heterogeneous disease with respect to biologic and clinical behavior. Gene-expression profiling has been used to dissect this complexity and to stratify tumors into intrinsic gene-expression subtypes, associated with distinct biology, patient outcome, and genomic alterations. Additionally, breast tumors occurring in individuals with germline BRCA1 or BRCA2 mutations typically fall into distinct subtypes. Methods: We applied global DNA copy number and gene-expression profiling in 359 breast tumors. All tumors were classified according to intrinsic gene-expression subtypes and included cases from genetically predisposed women. The Genomic Identification of Significant Targets in Cancer (GISTIC) algorithm was used to identify significant DNA copy-number aberrations and genomic subgroups of breast cancer. Results: We identified 31 genomic regions that were highly amplified in > 1% of the 359 breast tumors. Several amplicons were found to co-occur, the 8p12 and 11q13.3 regions being the most frequent combination besides amplicons on the same chromosomal arm. Unsupervised hierarchical clustering with 133 significant GISTIC regions revealed six genomic subtypes, termed 17q12, basal-complex, luminal-simple, luminal-complex, amplifier, and mixed subtypes. Four of them had striking similarity to intrinsic gene-expression subtypes and showed associations to conventional tumor biomarkers and clinical outcome. However, luminal A-classified tumors were distributed in two main genomic subtypes, luminal-simple and luminal-complex, the former group having a better prognosis, whereas the latter group included also luminal B and the majority of BRCA2-mutated tumors. The basal-complex subtype displayed extensive genomic homogeneity and harbored the majority of BRCA1-mutated tumors. The 17q12 subtype comprised mostly HER2-amplified and HER2-enriched subtype tumors and had the worst prognosis. The amplifier and mixed subtypes contained tumors from all gene-expression subtypes, the former being enriched for 8p12-amplified cases, whereas the mixed subtype included many tumors with predominantly DNA copy-number losses and poor prognosis. Conclusions: Global DNA copy-number analysis integrated with gene-expression data can be used to dissect the complexity of breast cancer. This revealed six genomic subtypes with different clinical behavior and a striking concordance to the intrinsic subtypes. These genomic subtypes may prove useful for understanding the mechanisms of tumor development and for prognostic and treatment prediction purposes.

A BAC-array platform for comparative genomic hybridization was constructed from a library of 32,433 clones providing complete genome coverage, and evaluated by screening for DNA copy number changes in 10 breast cancer cell lines (BT474, MCF7, HCC1937, SK-BR-3, L56Br-C1, ZR-75-1, JIMT1, MDA-MB-231, MDA-MB-361, and HCC2218) and one cell line derived from fibrocystic disease of the breast (MCF10A). These were also characterized by gene expression analysis and found to represent all five recently described breast cancer subtypes using the "intrinsic gene set" and centroid correlation. Three cell lines, HCC1937 and L56BrC1 derived from BRCA1 mutation carriers and MDA-MB-231, were of basal-like subtype and characterized by a high frequency of low-level gains and losses of typical pattern, including limited deletions on 5q. Four estrogen receptor positive cell lines were of luminal A subtype and characterized by a different pattern of aberrations and high-level amplifications, including ERBB2 and other 17q amplicons in BT474 and MDA-MB-361. SK-BR-3 cells, characterized by a complex genome including ERBB2 amplification, massive high-level amplifications on 8q and a homozygous deletion of CDH1 at 16q22, had an expression signature closest to luminal B subtype. The effects of gene amplifications were verified by gene expression analysis to distinguish targeted genes from silent amplicon passengers. JIMT1, derived from an ERBB2 amplified trastuzumab resistant tumor, was of the ERBB2 subtype. Homozygous deletions included other known targets such as PTEN (HCC1937) and CDKN2A (MDA-MB-231, MCF10A), but also new candidate suppressor genes such as FUSSEL18 (HCC1937) and WDR11 (L56Br-C1) as well as regions without known genes. The tiling BAC-arrays constitute a powerful tool for high-resolution genomic profiling suitable for cancer research and clinical diagnostics.

Multiple sclerosis (MS) and other T cell-mediated autoimmune diseases develop in individuals carrying a complex susceptibility trait, probably following exposure to various environmental triggers. Owing to the presumed weak influence of single genes on disease predisposition and the recognized genetic heterogeneity of autoimmune disorders in humans, candidate gene searches in MS have been difficult. In an attempt to identify molecular markers indicative of disease status rather than susceptibility genes for MS, we show that gene expression profiling of peripheral blood mononuclear cells by cDNA microarrays can distinguish MS patients from healthy controls. Our findings support the concept that the activation of autoreactive T cells is of primary importance for this complex organ-specific disorder and prompt further investigations on gene expression in peripheral blood cells aimed at characterizing disease phenotypes.

In the decade since their discovery, the two major breast cancer susceptibility genes BRCA1 and BRCA2 , have been shown conclusively to be involved in a significant fraction of families segregating breast and ovarian cancer. However, it has become equally clear that a large proportion of families segregating breast cancer alone are not caused by mutations in BRCA1 or BRCA2. Unfortunately, despite intensive effort, the identification of additional breast cancer predisposition genes has so far been unsuccessful, presumably because of genetic heterogeneity, low penetrance, or recessive/polygenic mechanisms. These non- BRCA1/2 breast cancer families (termed BRCAx families) comprise a histopathologically heterogeneous group, further supporting their origin from multiple genetic events. Accordingly, the identification of a method to successfully subdivide BRCAx families into recognizable groups could be of considerable value to further genetic analysis. We have previously shown that global gene expression analysis can identify unique and distinct expression profiles in breast tumors from BRCA1 and BRCA2 mutation carriers. Here we show that gene expression profiling can discover novel classes among BRCAx tumors, and differentiate them from BRCA1 and BRCA2 tumors. Moreover, microarray-based comparative genomic hybridization (CGH) to cDNA arrays revealed specific somatic genetic alterations within the BRCAx subgroups. These findings illustrate that, when gene expression-based classifications are used, BRCAx families can be grouped into homogeneous subsets, thereby potentially increasing the power of conventional genetic analysis.

Large proportions of hereditary breast cancers are due to mutations in the two breast cancer susceptibility genes BRCA1 and BRCA2. Considerable effort has gone into studying the function(s) of these tumor suppressor genes, both in attempts to better understand why individuals with these inherited mutations acquire breast (and ovarian) cancer and to potentially develop better treatment strategies. The advent of tools such as cDNA microarrays has enabled researchers to study global gene expression patterns in, for example, primary tumors, thus providing more comprehensive overviews of tumor development and progression. Our recent study (Hedenfalk et al., 2001) strongly supports the principle that genomic approaches to classification of hereditary breast cancers are possible, and that further studies will likely identify the most significant genes that discriminate between subgroups and may influence prognosis and treatment. A large number of hereditary breast cancer cases cannot be accounted for by mutations in these two genes and are believed to be due to as yet unidentified breast cancer predisposition genes (BRCAx). Subclassification of these non-BRCA1/2 breast cancers using cDNA microarray-based gene expression profiling, followed by linkage analysis and/or investigation of genomic alterations, may help in the recognition of novel breast cancer predisposition loci. To summarize, gene expression-based analysis of hereditary breast cancer can potentially be used for classification purposes, as well as to expand upon our knowledge of differences between different forms of hereditary breast cancer. Initial studies indicate that a patient's genotype does in fact leave an identifiable trace on her/his cancer's gene expression profile.

To investigate the phenotype associated with estrogen receptor alpha (ER) expression in breast carcinoma, gene expression profiles of 58 node-negative breast carcinomas discordant for ER status were determined using DNA microarray technology. Using artificial neural networks as well as standard hierarchical clustering techniques, the tumors could be classified according to ER status, and a list of genes which discriminate tumors according to ER status was generated. The artificial neural networks could accurately predict ER status even when excluding top discriminator genes, including ER itself. By reference to the serial analysis of gene expression database, we found that only a small proportion of the 100 most important ER discriminator genes were also regulated by estradiol in MCF-7 cells. The results provide evidence that ER+ and ER- tumors display remarkably different gene-expression phenotypes not solely explained by differences in estrogen responsiveness.

Background DNA methylation at CpG dinucleotides is modified in tumorigenesis with potential impact on transcriptional activity. Methods We used the Illumina 450 K platform to evaluate DNA methylation patterns of 50 metastatic melanoma tumors, with matched gene expression data. Results We identified three different methylation groups and validated the groups in independent data from The Cancer Genome Atlas. One group displayed hypermethylation of a developmental promoter set, genome-wide demethylation, increased proliferation and activity of the SWI/SNF complex. A second group had a methylation pattern resembling stromal and leukocyte cells, over-expressed an immune signature and had improved survival rates in metastatic tumors (p

Gastrointestinal stromal tumors (GISTs), the most common mesenchymal tumors of the digestive tract, are believed to arise from the interstitial cells of Cajal. GISTs are characterized by mutations in the proto-oncogene KIT that lead to constitutive activation of its tyrosine kinase activity. The tyrosine kinase inhibitor STI571, active against the BCR-ABL fusion protein in chronic myeloid leukemia, was recently shown to be highly effective in GISTs. We used 13,826-element cDNA microarrays to define the expression patterns of 13 KIT mutation-positive GISTs and compared them with the expression profiles of a group of spindle cell tumors from locations outside the gastrointestinal tract. Our results showed a remarkably distinct and uniform expression profile for all of the GISTs. In particular, hierarchical clustering of a subset of 113 cDNAs placed all of the GIST samples into one branch, with a Pearson correlation0.91. This homogeneity suggests that the molecular pathogenesis of a GIST results from expansion of a clone that has acquired an activating mutation in KIT without the extreme genetic instability found in the common epithelial cancers. The results provide insight into the histogenesis of GIST and the clinical behavior of this therapeutically responsive tumor.

Breast tumors from BRCA1 germ line mutation carriers typically exhibit features of the basal-like molecular subtype. However, the specific genes recurrently mutated as a consequence of BRCA1 dysfunction have not been fully elucidated. In this study, we used gene expression profiling to molecularly subtype 577 breast tumors, including 73 breast tumors from BRCA1/2 mutation carriers. Focusing on the RB1 locus, we analyzed 33 BRCA1-mutated, 36 BRCA2-mutated, and 48 non-BRCA1/2–mutated breast tumors using a custom-designed high-density oligomicroarray covering the RB1 gene. We found a strong association between the basal-like subtype and BRCA1-mutated breast tumors and the luminal B subtype and BRCA2-mutated breast tumors. RB1 was identified as a major target for genomic disruption in tumors arising in BRCA1 mutation carriers and in sporadic tumors with BRCA1 promoter methylation but rarely in other breast cancers. Homozygous deletions, intragenic breaks, or microdeletions were found in 33% of BRCA1-mutant tumors, 36% of BRCA1 promoter–methylated basal-like tumors, 13% of non-BRCA1–deficient basal-like tumors, and 3% of BRCA2-mutated tumors. In conclusion, RB1 was frequently inactivated by gross gene disruption in BRCA1 hereditary breast cancer and BRCA1-methylated sporadic basal-like breast cancer but rarely in BRCA2 hereditary breast cancer and non-BRCA1–deficient sporadic breast cancers. Together, our findings show the existence of genetic heterogeneity within the basal-like breast cancer subtype that is based upon BRCA1 status. Cancer Res; 72(16); 4028–36. ©2012 AACR.