Banding Together: A Systematic Comparison of The Cancer Genome Atlas and the Mitelman Databases.

Cytogenetic aberrations at the single-cell level represent an important characteristic of cancer cells relevant to tumor evolution and prognosis. However, with the advent of The Cancer Genome Atlas (TCGA), there has been a major shift in cancer research to the use of data from aggregate cell populations. Given that tumor cells harbor hundreds to thousands of biologically relevant genetic alterations that manifest as intratumor heterogeneity, these aggregate analyses may miss alterations readily observable at single-cell resolution. Using the Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer, we developed an algorithm to parse International System for Cytogenetic Nomenclature notation for quantitative abnormalities. Comparison of the Mitelman database and TCGA demonstrated that the Mitelman database is a powerful resource, and that cytogenetic aberrations captured by traditional approaches used in Mitelman database are on par with population-based genomic analyses used in TCGA. This algorithm will help nonspecialists to overcome the challenges associated with the format and syntax of the Mitelman database. SIGNIFICANCE: A novel in silico approach compares cytogenetic data between the Mitelman database and TCGA, highlighting the advantages and limitations of both datasets.

Humanized yeast genetic interaction mapping predicts synthetic lethal interactions of FBXW7 in breast cancer.

BACKGROUND: Synthetic lethal interactions (SLIs) that occur between gene pairs are exploited for cancer therapeutics. Studies in the model eukaryote yeast have identified ~ 550,000 negative genetic interactions that have been extensively studied, leading to characterization of novel pathways and gene functions. This resource can be used to predict SLIs that can be relevant to cancer therapeutics. METHODS: We used patient data to identify genes that are down-regulated in breast cancer. InParanoid orthology mapping was performed to identify yeast orthologs of the down-regulated genes and predict their corresponding SLIs in humans. The predicted network graphs were drawn with Cytoscape. CancerRXgene database was used to predict drug response. RESULTS: Harnessing the vast available knowledge of yeast genetics, we generated a Humanized Yeast Genetic Interaction Network (HYGIN) for 1009 human genes with 10,419 interactions. Through the addition of patient-data from The Cancer Genome Atlas (TCGA), we generated a breast cancer specific subnetwork. Specifically, by comparing 1009 genes in HYGIN to genes that were down-regulated in breast cancer, we identified 15 breast cancer genes with 130 potential SLIs. Interestingly, 32 of the 130 predicted SLIs occurred with FBXW7, a well-known tumor suppressor that functions as a substrate-recognition protein within a SKP/CUL1/F-Box ubiquitin ligase complex for proteasome degradation. Efforts to validate these SLIs using chemical genetic data predicted that patients with loss of FBXW7 may respond to treatment with drugs like Selumitinib or Cabozantinib. CONCLUSIONS: This study provides a patient-data driven interpretation of yeast SLI data. HYGIN represents a novel strategy to uncover therapeutically relevant cancer drug targets and the yeast SLI data offers a major opportunity to mine these interactions.

Expression-based analyses indicate a central role for hypoxia in driving tumor plasticity through microenvironment remodeling and chromosomal instability.

Can transcriptomic alterations drive the evolution of tumors? We asked if changes in gene expression found in all patients arise earlier in tumor development and can be relevant to tumor progression. Our analyses of non-mutated genes from the non-amplified regions of the genome of 158 triple-negative breast cancer (TNBC) cases identified 219 exclusively expression-altered (EEA) genes that may play important role in TNBC. Phylogenetic analyses of these genes predict a "punctuated burst" of multiple gene upregulation events occurring at early stages of tumor development, followed by minimal subsequent changes later in tumor progression. Remarkably, this punctuated burst of expressional changes is instigated by hypoxia-related molecular events, predominantly in two groups of genes that control chromosomal instability (CIN) and those that remodel tumor microenvironment (TME). We conclude that alterations in the transcriptome are not stochastic and that early-stage hypoxia induces CIN and TME remodeling to permit further tumor evolution.