Dual suppression of the cyclin-dependent kinase inhibitors CDKN2C and CDKN1A in human melanoma.

Resistance to BRAF(V600E) inhibitors is associated with reactivation of mitogen-activated protein kinase (MAPK) signaling at different levels in melanoma. To identify downstream effectors of MAPK signaling that could be used as potential additional therapeutic targets for BRAF(V600E) inhibitors, we used hTERT/CDK4R24C/p53DD-immortalized primary human melanocytes genetically modified to ectopically express BRAF ( V600E ) or NRAS ( G12D ) and observed induction of the AP-1 transcription factor family member c-Jun. Using a dominant negative approach, in vitro cell proliferation assays, western blots, and flow cytometry showed that MAPK signaling via BRAF(V600E) promotes melanoma cell proliferation at G1 through AP-1-mediated negative regulation of the INK4 family member, cyclin-dependent kinase inhibitor 2C (CDKN2C), and the CIP/KIP family member, cyclin-dependent kinase inhibitor 1A (CDKN1A). These effects were antagonized by pharmacological inhibition of CDKN2C and CDKN1A targets CDK2 and CDK4 in vitro. In contrast to BRAF ( V600E ) or NRAS ( G12D )-expressing melanocytes, melanoma cells have an inherent resistance to suppression of AP-1 activity by BRAF(V600E)- or MEK-inhibitors. Here, CDK2/4 inhibition statistically significantly augmented the effects of BRAF(V600E)- or MEK-inhibitors on melanoma cell viability in vitro and growth in athymic nude Foxn1 ( nu ) mice (P = .03 when mean tumor volume at day 13 was compared for BRAF(V600E) inhibitor vs BRAF(V600E) inhibitor plus CDK2/4 inhibition; P = .02 when mean tumor volume was compared for MEK inhibitor vs MEK inhibitor plus CDK2/4 inhibition; P values were calculated by a two-sided Welch t test; n = 4-8 mice per group).

Initial genome sequencing and analysis of multiple myeloma.

Multiple myeloma is an incurable malignancy of plasma cells, and its pathogenesis is poorly understood. Here we report the massively parallel sequencing of 38 tumour genomes and their comparison to matched normal DNAs. Several new and unexpected oncogenic mechanisms were suggested by the pattern of somatic mutation across the data set. These include the mutation of genes involved in protein translation (seen in nearly half of the patients), genes involved in histone methylation, and genes involved in blood coagulation. In addition, a broader than anticipated role of NF-κB signalling was indicated by mutations in 11 members of the NF-κB pathway. Of potential immediate clinical relevance, activating mutations of the kinase BRAF were observed in 4% of patients, suggesting the evaluation of BRAF inhibitors in multiple myeloma clinical trials. These results indicate that cancer genome sequencing of large collections of samples will yield new insights into cancer not anticipated by existing knowledge.

Integrative transcriptome analysis reveals common molecular subclasses of human hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a highly heterogeneous disease, and prior attempts to develop genomic-based classification for HCC have yielded highly divergent results, indicating difficulty in identifying unified molecular anatomy. We performed a meta-analysis of gene expression profiles in data sets from eight independent patient cohorts across the world. In addition, aiming to establish the real world applicability of a classification system, we profiled 118 formalin-fixed, paraffin-embedded tissues from an additional patient cohort. A total of 603 patients were analyzed, representing the major etiologies of HCC (hepatitis B and C) collected from Western and Eastern countries. We observed three robust HCC subclasses (termed S1, S2, and S3), each correlated with clinical parameters such as tumor size, extent of cellular differentiation, and serum alpha-fetoprotein levels. An analysis of the components of the signatures indicated that S1 reflected aberrant activation of the WNT signaling pathway, S2 was characterized by proliferation as well as MYC and AKT activation, and S3 was associated with hepatocyte differentiation. Functional studies indicated that the WNT pathway activation signature characteristic of S1 tumors was not simply the result of beta-catenin mutation but rather was the result of transforming growth factor-beta activation, thus representing a new mechanism of WNT pathway activation in HCC. These experiments establish the first consensus classification framework for HCC based on gene expression profiles and highlight the power of integrating multiple data sets to define a robust molecular taxonomy of the disease.

Patterns of gene expression and copy-number alterations in von-hippel lindau disease-associated and sporadic clear cell carcinoma of the kidney.

Recent insights into the role of the von-Hippel Lindau (VHL) tumor suppressor gene in hereditary and sporadic clear-cell renal cell carcinoma (ccRCC) have led to new treatments for patients with metastatic ccRCC, although virtually all patients eventually succumb to the disease. We performed an integrated, genome-wide analysis of copy-number changes and gene expression profiles in 90 tumors, including both sporadic and VHL disease-associated tumors, in hopes of identifying new therapeutic targets in ccRCC. We identified 14 regions of nonrandom copy-number change, including 7 regions of amplification (1q, 2q, 5q, 7q, 8q, 12p, and 20q) and 7 regions of deletion (1p, 3p, 4q, 6q, 8p, 9p, and 14q). An analysis aimed at identifying the relevant genes revealed VHL as one of three genes in the 3p deletion peak, CDKN2A and CDKN2B as the only genes in the 9p deletion peak, and MYC as the only gene in the 8q amplification peak. An integrated analysis to identify genes in amplification peaks that are consistently overexpressed among amplified samples confirmed MYC as a potential target of 8q amplification and identified candidate oncogenes in the other regions. A comparison of genomic profiles revealed that VHL disease-associated tumors are similar to a subgroup of sporadic tumors and thus more homogeneous overall. Sporadic tumors without evidence of biallelic VHL inactivation fell into two groups: one group with genomic profiles highly dissimilar to the majority of ccRCC and a second group with genomic profiles that are much more similar to tumors with biallelic inactivation of VHL.

Gene expression changes in an animal melanoma model correlate with aggressiveness of human melanoma metastases.

Metastasis is the deadliest phase of cancer progression. Experimental models using immunodeficient mice have been used to gain insights into the mechanisms of metastasis. We report here the identification of a "metastasis aggressiveness gene expression signature" derived using human melanoma cells selected based on their metastatic potentials in a xenotransplant metastasis model. Comparison with expression data from human melanoma patients shows that this metastasis gene signature correlates with the aggressiveness of melanoma metastases in human patients. Many genes encoding secreted and membrane proteins are included in the signature, suggesting the importance of tumor-microenvironment interactions during metastasis.

Subclass mapping: identifying common subtypes in independent disease data sets.

Whole genome expression profiles are widely used to discover molecular subtypes of diseases. A remaining challenge is to identify the correspondence or commonality of subtypes found in multiple, independent data sets generated on various platforms. While model-based supervised learning is often used to make these connections, the models can be biased to the training data set and thus miss inherent, relevant substructure in the test data. Here we describe an unsupervised subclass mapping method (SubMap), which reveals common subtypes between independent data sets. The subtypes within a data set can be determined by unsupervised clustering or given by predetermined phenotypes before applying SubMap. We define a measure of correspondence for subtypes and evaluate its significance building on our previous work on gene set enrichment analysis. The strength of the SubMap method is that it does not impose the structure of one data set upon another, but rather uses a bi-directional approach to highlight the common substructures in both. We show how this method can reveal the correspondence between several cancer-related data sets. Notably, it identifies common subtypes of breast cancer associated with estrogen receptor status, and a subgroup of lymphoma patients who share similar survival patterns, thus improving the accuracy of a clinical outcome predictor.

The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease.

To pursue a systematic approach to the discovery of functional connections among diseases, genetic perturbation, and drug action, we have created the first installment of a reference collection of gene-expression profiles from cultured human cells treated with bioactive small molecules, together with pattern-matching software to mine these data. We demonstrate that this "Connectivity Map" resource can be used to find connections among small molecules sharing a mechanism of action, chemicals and physiological processes, and diseases and drugs. These results indicate the feasibility of the approach and suggest the value of a large-scale community Connectivity Map project.

The melanocyte differentiation program predisposes to metastasis after neoplastic transformation.

The aggressive clinical behavior of melanoma suggests that the developmental origins of melanocytes in the neural crest might be relevant to their metastatic propensity. Here we show that primary human melanocytes, transformed using a specific set of introduced genes, form melanomas that frequently metastasize to multiple secondary sites, whereas human fibroblasts and epithelial cells transformed using an identical set of genes generate primary tumors that rarely do so. Notably, these melanomas have a metastasis spectrum similar to that observed in humans with melanoma. These observations indicate that part of the metastatic proclivity of melanoma is attributable to lineage-specific factors expressed in melanocytes and not in other cell types analyzed. Analysis of microarray data from human nevi shows that the expression pattern of Slug, a master regulator of neural crest cell specification and migration, correlates with those of other genes that are important for neural crest cell migrations during development. Moreover, Slug is required for the metastasis of the transformed melanoma cells. These findings indicate that melanocyte-specific factors present before neoplastic transformation can have a pivotal role in governing melanoma progression.

Metagenes and molecular pattern discovery using matrix factorization.

We describe here the use of nonnegative matrix factorization (NMF), an algorithm based on decomposition by parts that can reduce the dimension of expression data from thousands of genes to a handful of metagenes. Coupled with a model selection mechanism, adapted to work for any stochastic clustering algorithm, NMF is an efficient method for identification of distinct molecular patterns and provides a powerful method for class discovery. We demonstrate the ability of NMF to recover meaningful biological information from cancer-related microarray data. NMF appears to have advantages over other methods such as hierarchical clustering or self-organizing maps. We found it less sensitive to a priori selection of genes or initial conditions and able to detect alternative or context-dependent patterns of gene expression in complex biological systems. This ability, similar to semantic polysemy in text, provides a general method for robust molecular pattern discovery.