Human cancer is caused by the accumulation of mutations in oncogenes and tumor suppressor genes. To catalog the genetic changes that occur during tumorigenesis, we isolated DNA from 11 breast and 11 colorectal tumors and determined the sequences of the genes in the Reference Sequence database in these samples. Based on analysis of exons representing 20,857 transcripts from 18,191 genes, we conclude that the genomic landscapes of breast and colorectal cancers are composed of a handful of commonly mutated gene "mountains" and a much larger number of gene "hills" that are mutated at low frequency. We describe statistical and bioinformatic tools that may help identify mutations with a role in tumorigenesis. These results have implications for understanding the nature and heterogeneity of human cancers and for using personal genomics for tumor diagnosis and therapy.

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

Medulloblastoma (MB) is the most common malignant brain tumor of children. To identify the genetic alterations in this tumor type, we searched for copy number alterations using high density microarrays and sequenced all known protein-coding genes and miRNA genes using Sanger sequencing in a set of 22 MBs. We found that, on average, each tumor had 11 gene alterations, 5 to 10-fold fewer than in the adult solid tumors that have been sequenced to date. In addition to alterations in the Hedgehog and Wnt pathways, our analysis led to the discovery of genes not previously known to be altered in MBs. Most notably, inactivating mutations of the histone-lysine N-methyltransferase genes MLL2 or MLL3 were identified in 16% of MB patients. These results demonstrate key differences between the genetic landscapes of adult and childhood cancers, highlight dysregulation of developmental pathways as an important mechanism underlying MBs, and identify a role for a specific type of histone methylation in human tumorigenesis.

Highlights d Comprehensive LUAD proteogenomics exposes multi-omic clusters and immune subtypes d Phosphoproteomics identifies candidate ALK-fusion diagnostic markers and targets d Candidate drug targets: PTPN11 (EGFR), SOS1 (KRAS), neutrophil degranulation (STK11) d Phospho and acetyl modifications denote tumor-specific markers and druggable proteins

Highlights d Integrated proteogenomic characterization in 103 ccRCC cases d Delineation of chromosomal translocation events leading to chromosome 3p loss d Tumor-specific proteomic/phosphoproteomic alterations unrevealed by mRNA analysis d Immune-based subtypes of ccRCC defined by mRNA, proteome, and phosphoproteome

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Highlights d Proteogenomic characterization reveals the functional impact of genomic alterations d Phosphoproteomics uncovers putative therapeutic targets downstream of KRAS d Multiomics links endothelial cell remodeling and glycolysis to immune exclusion d Proteomics and glycoproteomics reveal candidates for early detection or intervention