Kinome-wide decoding of network-attacking mutations rewiring cancer signaling

Causal Reasoning on Boolean Control Networks Based on Abduction: Theory and Application to Cancer Drug Discovery

Complex diseases such as Cancer or Alzheimer's are caused by multiple molecular perturbations leading to pathological cellular behavior. However, the identification of disease-induced molecular perturbations and subsequent development of efficient therapies are challenged by the complexity of the genotype-phenotype relationship. Accordingly, a key issue is to develop frameworks relating molecular perturbations and drug effects to their consequences on cellular phenotypes. Such framework would aim at identifying the sets of causal molecular factors leading to phenotypic reprogramming. In this article, we propose a theoretical framework, called Boolean Control Networks, where disease-induced molecular perturbations and drug actions are seen as topological perturbations/actions on molecular networks leading to cell phenotype reprogramming. We present a new method using abductive reasoning principles inferring the minimal causal topological actions leading to an expected behavior at stable state. Then, we compare different implementations of the algorithm and finally, show a proof-of-concept of the approach on a model of network regulating the proliferation/apoptosis switch in breast cancer by automatically discovering driver genes and their synthetic lethal drug target partner.

Classifying tumors by supervised network propagation

Motivation

Network propagation has been widely used to aggregate and amplify the effects of tumor mutations using knowledge of molecular interaction networks. However, propagating mutations through interactions irrelevant to cancer leads to erosion of pathway signals and complicates the identification of cancer subtypes.

Results

To address this problem we introduce a propagation algorithm, Network-Based Supervised Stratification (NBS²), which learns the mutated subnetworks underlying tumor subtypes using a supervised approach. Given an annotated molecular network and reference tumor mutation profiles for which subtypes have been predefined, NBS² is trained by adjusting the weights on interaction features such that network propagation best recovers the provided subtypes. After training, weights are fixed such that mutation profiles of new tumors can be accurately classified. We evaluate NBS² on breast and glioblastoma tumors, demonstrating that it outperforms the best network-based approaches in classifying tumors to known subtypes for these diseases. By interpreting the interaction weights, we highlight characteristic molecular pathways driving selected subtypes.

Availability and implementation

The NBS² package is freely available at: https://github.com/wzhang1984/NBSS.

Supplementary information

Supplementary data are available at Bioinformatics online.

ActiveDriverDB: human disease mutations and genome variation in post-translational modification sites of proteins

Interpretation of genetic variation is needed for deciphering genotype-phenotype associations, mechanisms of inherited disease, and cancer driver mutations. Millions of single nucleotide variants (SNVs) in human genomes are known and thousands are associated with disease. An estimated 21% of disease-associated amino acid substitutions corresponding to missense SNVs are located in protein sites of post-translational modifications (PTMs), chemical modifications of amino acids that extend protein function. ActiveDriverDB is a comprehensive human proteo-genomics database that annotates disease mutations and population variants through the lens of PTMs. We integrated >385,000 published PTM sites with ∼3.6 million substitutions from The Cancer Genome Atlas (TCGA), the ClinVar database of disease genes, and human genome sequencing projects. The database includes site-specific interaction networks of proteins, upstream enzymes such as kinases, and drugs targeting these enzymes. We also predicted network-rewiring impact of mutations by analyzing gains and losses of kinase-bound sequence motifs. ActiveDriverDB provides detailed visualization, filtering, browsing and searching options for studying PTM-associated mutations. Users can upload mutation datasets interactively and use our application programming interface in pipelines. Integrative analysis of mutations and PTMs may help decipher molecular mechanisms of phenotypes and disease, as exemplified by case studies of TP53, BRCA2 and VHL. The open-source database is available at https://www.ActiveDriverDB.org.

Regulated Phosphosignaling Associated with Breast Cancer Subtypes and Druggability

Aberrant phospho-signaling is a hallmark of cancer. We investigated kinase-substrate regulation of 33,239 phosphorylation sites (phosphosites) in 77 breast tumors and 24 breast cancer xenografts. Our search discovered 2134 quantitatively correlated kinase-phosphosite pairs, enriching for and extending experimental or binding-motif predictions. Among the 91 kinases with auto-phosphorylation, elevated EGFR, ERBB2, PRKG1, and WNK1 phosphosignaling were enriched in basal, HER2-E, Luminal A, and Luminal B breast cancers, respectively, revealing subtype-specific regulation. CDKs, MAPKs, and ataxia-telangiectasia proteins were dominant, master regulators of substrate-phosphorylation, whose activities are not captured by genomic evidence. We unveiled phospho-signaling and druggable targets from 113 kinase-substrate pairs and cascades downstream of kinases, including AKT1, BRAF and EGFR. We further identified kinase-substrate-pairs associated with clinical or immune signatures and experimentally validated activated phosphosites of ERBB2, EIF4EBP1, and EGFR. Overall, kinase-substrate regulation revealed by the largest unbiased global phosphorylation data to date connects driver events to their signaling effects.

Systematic analysis of the intersection of disease mutations with protein modifications

BACKGROUND

Perturbed posttranslational modification (PTM) landscapes commonly cause pathological phenotypes. The Cancer Genome Atlas (TCGA) project profiles thousands of tumors allowing the identification of spontaneous cancer-driving mutations, while Uniprot and dbSNP manage genetic disease-associated variants in the human population. PhosphoSitePlus (PSP) is the most comprehensive resource for studying experimentally observed PTM sites and the only repository with daily updates on functional annotations for many of these sites. To elucidate altered PTM landscapes on a large scale, we integrated disease-associated mutations from TCGA, Uniprot, and dbSNP with PTM sites from PhosphoSitePlus. We characterized each dataset individually, compared somatic with germline mutations, and analyzed PTM sites intersecting directly with disease variants. To assess the impact of mutations in the flanking regions of phosphosites, we developed DeltaScansite, a pipeline that compares Scansite predictions on wild type versus mutated sequences. Disease mutations are also visualized in PhosphoSitePlus.

RESULTS

Characterization of somatic variants revealed oncoprotein-like mutation profiles of U2AF1, PGM5, and several other proteins, showing alteration patterns similar to germline mutations. The union of all datasets uncovered previously unknown losses and gains of PTM events in diseases unevenly distributed across different PTM types. Focusing on phosphorylation, our DeltaScansite workflow predicted perturbed signaling networks consistent with calculations by the machine learning method MIMP.

CONCLUSIONS

We discovered oncoprotein-like profiles in TCGA and mutations that presumably modify protein function by impacting PTM sites directly or by rewiring upstream regulation. The resulting datasets are enriched with functional annotations from PhosphoSitePlus and present a unique resource for potential biomarkers or disease drivers.

Analysis of the Human Kinome and Phosphatome by Mass Cytometry Reveals Overexpression-Induced Effects on Cancer-Related Signaling

Kinase and phosphatase overexpression drives tumorigenesis and drug resistance. We previously developed a mass-cytometry-based single-cell proteomics approach that enables quantitative assessment of overexpression effects on cell signaling. Here, we applied this approach in a human kinome- and phosphatome-wide study to assess how 649 individually overexpressed proteins modulated cancer-related signaling in HEK293T cells in an abundance-dependent manner. Based on these data, we expanded the functional classification of human kinases and phosphatases and showed that the overexpression effects include non-catalytic roles. We detected 208 previously unreported signaling relationships. The signaling dynamics analysis indicated that the overexpression of ERK-specific phosphatases sustains proliferative signaling. This suggests a phosphatase-driven mechanism of cancer progression. Moreover, our analysis revealed a drug-resistant mechanism through which overexpression of tyrosine kinases, including SRC, FES, YES1, and BLK, induced MEK-independent ERK activation in melanoma A375 cells. These proteins could predict drug sensitivity to BRAF-MEK concurrent inhibition in cells carrying BRAF mutations.

Associating expression and genomic data using co-occurrence measures

Abstract

Recent technological evolutions have led to an exponential increase in data in all the omics fields. It is expected that integration of these different data sources, will drastically enhance our knowledge of the biological mechanisms behind genomic diseases such as cancer. However, the integration of different omics data still remains a challenge. In this work we propose an intuitive workflow for the integrative analysis of expression, mutation and copy number data taken from the METABRIC study on breast cancer. First, we present evidence that the expression profile of many important breast cancer genes consists of two modes or ‘regimes’, which contain important clinical information. Then, we show how the co-occurrence of these expression regimes can be used as an association measure between genes and validate our findings on the TCGA-BRCA study. Finally, we demonstrate how these co-occurrence measures can also be applied to link expression regimes to genomic aberrations, providing a more complete, integrative view on breast cancer. As a case study, an integrative analysis of the identified MLPH-FOXA1 association is performed, illustrating that the obtained expression associations are intimately linked to the underlying genomic changes.

Reviewers

This article was reviewed by Dirk Walther, Francisco Garcia and Isabel Nepomuceno.

Electronic supplementary material

The online version of this article (10.1186/s13062-019-0240-2) contains supplementary material, which is available to authorized users.

Improved measures for evolutionary conservation that exploit taxonomy distances

Selective pressures on protein-coding regions that provide fitness advantages can lead to the regions' fixation and conservation in genome duplications and speciation events. Consequently, conservation analyses relying on sequence similarities are exploited by a myriad of applications across all biosciences to identify functionally important protein regions. While very potent, existing conservation measures based on multiple sequence alignments are so pervasive that improvements to solutions of many problems have become incremental. We introduce a new framework for evolutionary conservation with measures that exploit taxonomy distances across species. Results show that our taxonomy-based framework comfortably outperforms existing conservation measures in identifying deleterious variants observed in the human population, including variants located in non-abundant sequence domains such as intrinsically disordered regions. The predictive power of our approach emphasizes that the phenotypic effects of sequence variants can be taxonomy-level specific and thus, conservation needs to be interpreted accordingly.

Information on protein sequence variability and conservation can be leveraged to identify functionally important regions. Here, the authors develop new conservation measures that exploit taxonomy distances and LIST, a tool for predicting deleteriousness of human variants.

Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers

At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor’s genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses—all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.

Dynamic Targeting in Cancer Treatment

With the advent of personalized medicine, design and development of anti-cancer drugs that are specifically targeted to individual or sets of genes or proteins has been an active research area in both academia and industry. The underlying motivation for this approach is to interfere with several pathological crosstalk pathways in order to inhibit or at the very least control the proliferation of cancer cells. However, after initially conferring beneficial effects, if sub-lethal, these artificial perturbations in cell function pathways can inadvertently activate drug-induced up- and down-regulation of feedback loops, resulting in dynamic changes over time in the molecular network structure and potentially causing drug resistance as seen in clinics. Hence, the targets or their combined signatures should also change in accordance with the evolution of the network (reflected by changes to the structure and/or functional output of the network) over the course of treatment. This suggests the need for a "dynamic targeting" strategy aimed at optimizing tumor control by interfering with different molecular targets, at varying stages. Understanding the dynamic changes of this complex network under various perturbed conditions due to drug treatment is extremely challenging under experimental conditions let alone in clinical settings. However, mathematical modeling can facilitate studying these effects at the network level and beyond, and also accelerate comparison of the impact of different dosage regimens and therapeutic modalities prior to sizeable investment in risky and expensive clinical trials. A dynamic targeting strategy based on the use of mathematical modeling can be a new, exciting research avenue in the discovery and development of therapeutic drugs.

Kinome-wide identification of phosphorylation networks in eukaryotic proteomes

Motivation

Signaling and metabolic pathways are finely regulated by a network of protein phosphorylation events. Unraveling the nature of this intricate network, composed of kinases, target proteins and their interactions, is therefore of crucial importance. Although thousands of kinase-specific phosphorylations (KsP) have been annotated in model organisms their kinase-target network is far from being complete, with less studied organisms lagging behind.

Results

In this work, we achieved an automated and accurate identification of kinase domains, inferring the residues that most likely contribute to peptide specificity. We integrated this information with the target peptides of known human KsP to predict kinase-specific interactions in other eukaryotes through a deep neural network, outperforming similar methods. We analyzed the differential conservation of kinase specificity among eukaryotes revealing the high conservation of the specificity of tyrosine kinases. With this approach we discovered 1590 novel KsP of potential clinical relevance in the human proteome.

Availability and implementation

http://akid.bio.uniroma2.it.

Supplementary information

Supplementary data are available at Bioinformatics online.

Protein kinase C: perfectly balanced

Protein kinase C (PKC) isozymes belong to a family of Ser/Thr kinases whose activity is governed by reversible release of an autoinhibitory pseudosubstrate. For conventional and novel isozymes, this is effected by binding the lipid second messenger, diacylglycerol, but for atypical PKC isozymes, this is effected by binding protein scaffolds. PKC shot into the limelight following the discovery in the 1980s that the diacylglycerol-sensitive isozymes are "receptors" for the potent tumor-promoting phorbol esters. This set in place a concept that PKC isozymes are oncoproteins. Yet three decades of cancer clinical trials targeting PKC with inhibitors failed and, in some cases, worsened patient outcome. Emerging evidence from cancer-associated mutations and protein expression levels provide a reason: PKC isozymes generally function as tumor suppressors and their activity should be restored, not inhibited, in cancer therapies. And whereas not enough activity is associated with cancer, variants with enhanced activity are associated with degenerative diseases such as Alzheimer's disease. This review describes the tightly controlled mechanisms that ensure PKC activity is perfectly balanced and what happens when these controls are deregulated. PKC isozymes serve as a paradigm for the wisdom of Confucius: "to go beyond is as wrong as to fall short."

Personal Mutanomes Meet Modern Oncology Drug Discovery and Precision Health

Recent remarkable advances in genome sequencing have enabled detailed maps of identified and interpreted genomic variation, dubbed "mutanomes." The availability of thousands of exome/genome sequencing data has prompted the emergence of new challenges in the identification of novel druggable targets and therapeutic strategies. Typically, mutanomes are viewed as one- or two-dimensional. The three-dimensional protein structural view of personal mutanomes sheds light on the functional consequences of clinically actionable mutations revealed in tumor diagnosis and followed up in personalized treatments, in a mutanome-informed manner. In this review, we describe the protein structural landscape of personal mutanomes and provide expert opinions on rational strategies for more streamlined oncological drug discovery and molecularly targeted therapies for each individual and each tumor. We provide the structural mechanism of orthosteric versus allosteric drugs at the atom-level via targeting specific somatic alterations for combating drug resistance and the "undruggable" challenges in solid and hematologic neoplasias. We discuss computational biophysics strategies for innovative mutanome-informed cancer immunotherapies and combination immunotherapies. Finally, we highlight a personal mutanome infrastructure for the emerging development of personalized cancer medicine using a breast cancer case study.

Understanding molecular mechanisms in cell signaling through natural and artificial sequence variation

The functionally tolerated sequence space of proteins can now be explored in an unprecedented way, owing to the expansion of genomic databases and the development of high-throughput methods to interrogate protein function. For signaling proteins, several recent studies have shown how the analysis of sequence variation leverages the available protein-structure information to provide new insights into specificity and allosteric regulation. In this Review, we discuss recent work that illustrates how this emerging approach is providing a deeper understanding of signaling proteins.

Integrating molecular networks with genetic variant interpretation for precision medicine

More reliable and cheaper sequencing technologies have revealed the vast mutational landscapes characteristic of many phenotypes. The analysis of such genetic variants has led to successful identification of altered proteins underlying many Mendelian disorders. Nevertheless the simple one‐variant one‐phenotype model valid for many monogenic diseases does not capture the complexity of polygenic traits and disorders. Although experimental and computational approaches have improved detection of functionally deleterious variants and important interactions between gene products, the development of comprehensive models relating genotype and phenotypes remains a challenge in the field of genomic medicine. In this context, a new view of the pathologic state as significant perturbation of the network of interactions between biomolecules is crucial for the identification of biochemical pathways associated with complex phenotypes. Seminal studies in systems biology combined the analysis of genetic variation with protein–protein interaction networks to demonstrate that even as biological systems evolve to be robust to genetic variation, their topologies create disease vulnerabilities. More recent analyses model the impact of genetic variants as changes to the “wiring” of the interactome to better capture heterogeneity in genotype–phenotype relationships. These studies lay the foundation for using networks to predict variant effects at scale using machine‐learning or algorithmic approaches. A wealth of databases and resources for the annotation of genotype–phenotype relationships have been developed to support developments in this area. This overview describes how study of the molecular interactome has generated insights linking the organization of biological systems to disease mechanism, and how this information can enable precision medicine.

This article is categorized under:

Translational, Genomic, and Systems Medicine > Translational Medicine

Biological Mechanisms > Cell Signaling

Models of Systems Properties and Processes > Mechanistic Models

Analytical and Computational Methods > Computational Methods

Orange is the new black: Kinases are the new master regulators of tumor suppression

For many decades, kinases have predominantly been characterized as oncogenes and drivers of tumorigenesis, because activating mutations in kinases occur in cancer with high frequency. The oncogenic functions of kinases relate to their roles as growth factor receptors and as critical mediators of mitogen-activated pathways. Indeed, some of the most promising cancer therapeutic agents are kinase inhibitors. However, cancer genomics studies, especially screens that utilize high-throughput identification of loss-of-function somatic mutations, are beginning to shed light on a widespread role for kinases as tumor suppressors. The initial characterization of tumor-suppressing kinases- in particular members of the protein kinase C (PKC) family, MKK4 of the mitogen-activated protein kinase kinase family, and DAPK3 of the death-associated protein kinase family- laid the foundation for bioinformatic approaches that enable the identification of other tumor-suppressing kinases. In this review, we discuss the important role that kinases play as tumor suppressors, using several examples to illustrate the history of their discovery and highlight the modern approaches that presently aid in the identification of tumor-suppressing kinases. © 2018 IUBMB Life, 71(6):738-748, 2019.

A functional proteomics platform to reveal the sequence determinants of lysine methyltransferase substrate selectivity

Lysine methylation is a key regulator of histone protein function. Beyond histones, few connections have been made to the enzymes responsible for the deposition of these posttranslational modifications. Here, we debut a high-throughput functional proteomics platform that maps the sequence determinants of lysine methyltransferase (KMT) substrate selectivity without a priori knowledge of a substrate or target proteome. We demonstrate the predictive power of this approach for identifying KMT substrates, generating scaffolds for inhibitor design, and predicting the impact of missense mutations on lysine methylation signaling. By comparing KMT selectivity profiles to available lysine methylome datasets, we reveal a disconnect between preferred KMT substrates and the ability to detect these motifs using standard mass spectrometry pipelines. Collectively, our studies validate the use of this platform for guiding the study of lysine methylation signaling and suggest that substantial gaps exist in proteome-wide curation of lysine methylomes.

Typing tumors using pathways selected by somatic evolution

Many recent efforts to analyze cancer genomes involve aggregation of mutations within reference maps of molecular pathways and protein networks. Here, we find these pathway studies are impeded by molecular interactions that are functionally irrelevant to cancer or the patient’s tumor type, as these interactions diminish the contrast of driver pathways relative to individual frequently mutated genes. This problem can be addressed by creating stringent tumor-specific networks of biophysical protein interactions, identified by signatures of epistatic selection during tumor evolution. Using such an evolutionarily selected pathway (ESP) map, we analyze the major cancer genome atlases to derive a hierarchical classification of tumor subtypes linked to characteristic mutated pathways. These pathways are clinically prognostic and predictive, including the TP53-AXIN-ARHGEF17 combination in liver and CYLC2-STK11-STK11IP in lung cancer, which we validate in independent cohorts. This ESP framework substantially improves the definition of cancer pathways and subtypes from tumor genome data.

Informative pathways driving cancer pathogenesis and subtypes can be difficult to identify in the presence of many gene interactions irrelevant to cancer. Here, the authors describe an approach for cancer gene pathway analysis based on key molecular interactions that drive cancer in relevant tissue types, and they assemble a focused map of Evolutionarily Selected Pathways (ESP) with interactions supported by both protein–protein binding and genetic epistasis during somatic tumor evolution.

Hierarchical Organization Endows the Kinase Domain with Regulatory Plasticity

The functional diversity of kinases enables specificity in cellular signal transduction. Yet how more than 500 members of the human kinome specifically receive regulatory inputs and convey information to appropriate substrates-all while using the common signaling output of phosphorylation-remains enigmatic. Here, we perform statistical co-evolution analysis, mutational scanning, and quantitative live-cell assays to reveal a hierarchical organization of the kinase domain that facilitates the orthogonal evolution of regulatory inputs and substrate outputs while maintaining catalytic function. We find that three quasi-independent "sectors"-groups of evolutionarily coupled residues-represent functional units in the kinase domain that encode for catalytic activity, substrate specificity, and regulation. Sector positions impact both disease and pharmacology: the catalytic sector is significantly enriched for somatic cancer mutations, and residues in the regulatory sector interact with allosteric kinase inhibitors. We propose that this functional architecture endows the kinase domain with inherent regulatory plasticity.

The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine

Precision cancer medicine promises to tailor clinical decisions to patients using genomic information. Indeed, successes of drugs targeting genetic alterations in tumors, such as imatinib that targets BCR-ABL in chronic myelogenous leukemia, have demonstrated the power of this approach. However, biological systems are complex, and patients may differ not only by the specific genetic alterations in their tumor, but also by more subtle interactions among such alterations. Systems biology and more specifically, network analysis, provides a framework for advancing precision medicine beyond clinical actionability of individual mutations. Here we discuss applications of network analysis to study tumor biology, early methods for N-of-1 tumor genome analysis, and the path for such tools to the clinic.

A Methodological Assessment and Characterization of Genetically-Driven Variation in Three Human Phosphoproteomes

Phosphorylation of proteins on serine, threonine, and tyrosine residues is a ubiquitous post-translational modification that plays a key part of essentially every cell signaling process. It is reasonable to assume that inter-individual variation in protein phosphorylation may underlie phenotypic differences, as has been observed for practically any other molecular regulatory phenotype. However, we do not know much about the extent of inter-individual variation in phosphorylation because it is quite challenging to perform a quantitative high throughput study to assess inter-individual variation in any post-translational modification. To test our ability to address this challenge with SILAC-based mass spectrometry, we quantified phosphorylation levels for three genotyped human cell lines within a nested experimental framework, and found that genetic background is the primary determinant of phosphoproteome variation. We uncovered multiple functional, biophysical, and genetic associations with germline driven phosphopeptide variation. Variants affecting protein levels or structure were among these associations, with the latter presenting, on average, a stronger effect. Interestingly, we found evidence that is consistent with a phosphopeptide variability buffering effect endowed from properties enriched within longer proteins. Because the small sample size in this 'pilot' study may limit the applicability of our genetic observations, we also undertook a thorough technical assessment of our experimental workflow to aid further efforts. Taken together, these results provide the foundation for future work to characterize inter-individual variation in post-translational modification levels and reveal novel insights into the nature of inter-individual variation in phosphorylation.

Network Analysis as a Grand Unifier in Biomedical Data Science

Biomedical data scientists study many types of networks, ranging from those formed by neurons to those created by molecular interactions. People often criticize these networks as uninterpretable diagrams termed hairballs; however, here we show that molecular biological networks can be interpreted in several straightforward ways. First, we can break down a network into smaller components, focusing on individual pathways and modules. Second, we can compute global statistics describing the network as a whole. Third, we can compare networks. These comparisons can be within the same context (e.g., between two gene regulatory networks) or cross-disciplinary (e.g., between regulatory networks and governmental hierarchies). The latter comparisons can transfer a formalism, such as that for Markov chains, from one context to another or relate our intuitions in a familiar setting (e.g., social networks) to the relatively unfamiliar molecular context. Finally, key aspects of molecular networks are dynamics and evolution, i.e., how they evolve over time and how genetic variants affect them. By studying the relationships between variants in networks, we can begin to interpret many common diseases, such as cancer and heart disease.

Kinase Substrate Profiling Using a Proteome-wide Serine-Oriented Human Peptide Library

The human proteome encodes >500 protein kinases and hundreds of thousands of potential phosphorylation sites. However, the identification of kinase-substrate pairs remains an active area of research because the relationships between individual kinases and these phosphorylation sites remain largely unknown. Many techniques have been established to discover kinase substrates but are often technically challenging to perform. Moreover, these methods frequently rely on substrate reagent pools that do not reflect human protein sequences or are biased by human cell line protein expression profiles. Here, we describe a new approach called SERIOHL-KILR (serine-oriented human library-kinase library reactions) to profile kinase substrate specificity and to identify candidate substrates for serine kinases. Using a purified library of >100000 serine-oriented human peptides expressed heterologously in Escherichia coli, we perform in vitro kinase reactions to identify phosphorylated human peptide sequences by liquid chromatography and tandem mass spectrometry. We compare our results for protein kinase A to those of a well-established positional scanning peptide library method, certifying that SERIOHL-KILR can identify the same predominant motif elements as traditional techniques. We then interrogate a small panel of cancer-associated PKCβ mutants using our profiling protocol and observe a shift in substrate specificity likely attributable to the loss of key polar contacts between the kinase and its substrates. Overall, we demonstrate that SERIOHL-KILR can rapidly identify candidate kinase substrates that can be directly mapped to human sequences for pathway analysis. Because this technique can be adapted for various kinase studies, we believe that SERIOHL-KILR will have many new victims in the future.

A recurrent point mutation in PRKCA is a hallmark of chordoid gliomas

Chordoid glioma (ChG) is a characteristic, slow growing, and well-circumscribed diencephalic tumor, whose mutational landscape is unknown. Here we report the analysis of 16 ChG by whole-exome and RNA-sequencing. We found that 15 ChG harbor the same PRKCA^D463H mutation. PRKCA encodes the Protein kinase C (PKC) isozyme alpha (PKCα) and is mutated in a wide range of human cancers. However the hot spot PRKCA^D463H mutation was not described in other tumors. PRKCA^D463H is strongly associated with the activation of protein translation initiation (EIF2) pathway. PKCα^D463H mRNA levels are more abundant than wild-type PKCα transcripts, while PKCα^D463H is less stable than the PCKα^WT protein. Compared to PCKα^WT, the PKCα^D463H protein is depleted from the cell membrane. The PKCα^D463H mutant enhances proliferation of astrocytes and tanycytes, the cells of origin of ChG. In conclusion, our study identifies the hallmark mutation for chordoid gliomas and provides mechanistic insights on ChG oncogenesis.

Chordoid glioma is a slow growing diencephalic tumor whose mutational landscape is poorly characterized. Here, the authors perform whole-exome and RNA-sequencing and find that 15 of 16 chordoid glioma cases studied harbor the same PRKCA mutation which results in enhanced proliferation.

Proteomic analysis and prediction of amino acid variations that influence protein posttranslational modifications

Accumulative studies have indicated that amino acid variations through changing the type of residues of the target sites or key flanking residues could directly or indirectly influence protein posttranslational modifications (PTMs) and bring about a detrimental effect on protein function. Computational mutation analysis can greatly narrow down the efforts on experimental work. To increase the utilization of current computational resources, we first provide an overview of computational prediction of amino acid variations that influence protein PTMs and their functional analysis. We also discuss the challenges that are faced while developing novel in silico approaches in the future. The development of better methods for mutation analysis-related protein PTMs will help to facilitate the development of personalized precision medicine.

Truncation- and motif-based pan-cancer analysis reveals tumor-suppressing kinases

A major challenge in cancer genomics is identifying "driver" mutations from the many neutral "passenger" mutations within a given tumor. To identify driver mutations that would otherwise be lost within mutational noise, we filtered genomic data by motifs that are critical for kinase activity. In the first step of our screen, we used data from the Cancer Cell Line Encyclopedia and The Cancer Genome Atlas to identify kinases with truncation mutations occurring within or before the kinase domain. The top 30 tumor-suppressing kinases were aligned, and hotspots for loss-of-function (LOF) mutations were identified on the basis of amino acid conservation and mutational frequency. The functional consequences of new LOF mutations were biochemically validated, and the top 15 hotspot LOF residues were used in a pan-cancer analysis to define the tumor-suppressing kinome. A ranked list revealed MAP2K7, an essential mediator of the c-Jun N-terminal kinase (JNK) pathway, as a candidate tumor suppressor in gastric cancer, despite its mutational frequency falling within the mutational noise for this cancer type. The majority of mutations in MAP2K7 abolished its catalytic activity, and reactivation of the JNK pathway in gastric cancer cells harboring LOF mutations in MAP2K7 or the downstream kinase JNK suppressed clonogenicity and growth in soft agar, demonstrating the functional relevance of inactivating the JNK pathway in gastric cancer. Together, our data highlight a broadly applicable strategy to identify functional cancer driver mutations and define the JNK pathway as tumor-suppressive in gastric cancer.

Homing in: Mechanisms of Substrate Targeting by Protein Kinases

Protein phosphorylation is the most common reversible post-translational modification in eukaryotes. Humans have over 500 protein kinases, of which more than a dozen are established targets for anticancer drugs. All kinases share a structurally similar catalytic domain, yet each one is uniquely positioned within signaling networks controlling essentially all aspects of cell behavior. Kinases are distinguished from one another based on their modes of regulation and their substrate repertoires. Coupling specific inputs to the proper signaling outputs requires that kinases phosphorylate a limited number of sites to the exclusion of hundreds of thousands of off-target phosphorylation sites. Here, we review recent progress in understanding mechanisms of kinase substrate specificity and how they function to shape cellular signaling networks.

A fine balancing act: A delicate kinase-phosphatase equilibrium that protects against chromosomal instability and cancer

Cancer cells rewire signalling networks to acquire specific hallmarks needed for their proliferation, survival, and dissemination throughout the body. Although this is often associated with the constitutive activation or inactivation of protein phosphorylation networks, there are other contexts when the dysregulation must be much milder. For example, chromosomal instability is a widespread cancer hallmark that relies on subtle defects in chromosome replication and/or division, such that these processes remain functional, but nevertheless error-prone. In this article, we will discuss how perturbations to the delicate kinase-phosphatase balance could lie at the heart of this type of dysregulation. In particular, we will explain how the two principle mechanisms that safeguard the chromosome segregation process rely on an equilibrium between at least two kinases and two phosphatases to function correctly. This balance is set during mitosis by a central complex that has also been implicated in chromosomal instability - the BUB1/BUBR1/BUB3 complex - and we will put forward a hypothesis that could link these two findings. This could be relevant for cancer treatment because most tumours have evolved by pushing the boundaries of chromosomal instability to the limit. If this involves subtle changes to the kinase-phosphatase equilibrium, then it may be possible to exacerbate these defects and tip tumour cells over the edge, whilst still maintaining the viability of healthy cells.

Differential gene regulatory networks in development and disease

Gene regulatory networks, in which differential expression of regulator genes induce differential expression of their target genes, underlie diverse biological processes such as embryonic development, organ formation and disease pathogenesis. An archetypical systems biology approach to mapping these networks involves the combined application of (1) high-throughput sequencing-based transcriptome profiling (RNA-seq) of biopsies under diverse network perturbations and (2) network inference based on gene-gene expression correlation analysis. The comparative analysis of such correlation networks across cell types or states, differential correlation network analysis, can identify specific molecular signatures and functional modules that underlie the state transition or have context-specific function. Here, we review the basic concepts of network biology and correlation network inference, and the prevailing methods for differential analysis of correlation networks. We discuss applications of gene expression network analysis in the context of embryonic development, cancer, and congenital diseases.

Cancer genetics meets biomolecular mechanism-bridging an age-old gulf

Increasingly available genomic sequencing data are exploited to identify genes and variants contributing to diseases, particularly cancer. Traditionally, methods to find such variants have relied heavily on allele frequency and/or familial history, often neglecting to consider any mechanistic understanding of their functional consequences. Thus, while the set of known cancer-related genes has increased, for many, their mechanistic role in the disease is not completely understood. This issue highlights a wide gap between the disciplines of genetics, which largely aims to correlate genetic events with phenotype, and molecular biology, which ultimately aims at a mechanistic understanding of biological processes. Fortunately, new methods and several systematic studies have proved illuminating for many disease genes and variants by integrating sequencing with mechanistic data, including biomolecular structures and interactions. These have provided new interpretations for known mutations and suggested new disease-relevant variants and genes. Here, we review these approaches and discuss particular examples where these have had a profound impact on the understanding of human cancers.

Electrostatic Interactions in Protein Structure, Folding, Binding, and Condensation

Charged and polar groups, through forming ion pairs, hydrogen bonds, and other less specific electrostatic interactions, impart important properties to proteins. Modulation of the charges on the amino acids, e.g., by pH and by phosphorylation and dephosphorylation, have significant effects such as protein denaturation and switch-like response of signal transduction networks. This review aims to present a unifying theme among the various effects of protein charges and polar groups. Simple models will be used to illustrate basic ideas about electrostatic interactions in proteins, and these ideas in turn will be used to elucidate the roles of electrostatic interactions in protein structure, folding, binding, condensation, and related biological functions. In particular, we will examine how charged side chains are spatially distributed in various types of proteins and how electrostatic interactions affect thermodynamic and kinetic properties of proteins. Our hope is to capture both important historical developments and recent experimental and theoretical advances in quantifying electrostatic contributions of proteins.

Polypharmacology-based ceritinib repurposing using integrated functional proteomics

Targeted drugs are effective when they directly inhibit strong disease drivers, but only a small fraction of diseases feature defined actionable drivers. Alternatively, network-based approaches can uncover new therapeutic opportunities. Applying an integrated phenotypic screening, chemical and phosphoproteomics strategy, here we describe the anaplastic lymphoma kinase (ALK) inhibitor ceritinib as having activity across several ALK-negative lung cancer cell lines and identify new targets and network-wide signaling effects. Combining pharmacological inhibitors and RNA interference revealed a polypharmacology mechanism involving the noncanonical targets IGF1R, FAK1, RSK1 and RSK2. Mutating the downstream signaling hub YB1 protected cells from ceritinib. Consistent with YB1 signaling being known to cause taxol resistance, combination of ceritinib with paclitaxel displayed strong synergy, particularly in cells expressing high FAK autophosphorylation, which we show to be prevalent in lung cancer. Together, we present a systems chemical biology platform for elucidating multikinase inhibitor polypharmacology mechanisms, subsequent design of synergistic drug combinations, and identification of mechanistic biomarker candidates.

Phosphoproteomics-Based Profiling of Kinase Activities in Cancer Cells

Cellular signaling, predominantly mediated by phosphorylation through protein kinases, is found to be deregulated in most cancers. Accordingly, protein kinases have been subject to intense investigations in cancer research, to understand their role in oncogenesis and to discover new therapeutic targets. Despite great advances, an understanding of kinase dysfunction in cancer is far from complete.A powerful tool to investigate phosphorylation is mass-spectrometry (MS)-based phosphoproteomics, which enables the identification of thousands of phosphorylated peptides in a single experiment. Since every phosphorylation event results from the activity of a protein kinase, high-coverage phosphoproteomics data should indirectly contain comprehensive information about the activity of protein kinases.In this chapter, we discuss the use of computational methods to predict kinase activity scores from MS-based phosphoproteomics data. We start with a short explanation of the fundamental features of the phosphoproteomics data acquisition process from the perspective of the computational analysis. Next, we briefly review the existing databases with experimentally verified kinase-substrate relationships and present a set of bioinformatic tools to discover novel kinase targets. We then introduce different methods to infer kinase activities from phosphoproteomics data and these kinase-substrate relationships. We illustrate their application with a detailed protocol of one of the methods, KSEA (Kinase Substrate Enrichment Analysis). This method is implemented in Python within the framework of the open-source Kinase Activity Toolbox (kinact), which is freely available at http://github.com/saezlab/kinact/ .

PhosphoPredict: A bioinformatics tool for prediction of human kinase-specific phosphorylation substrates and sites by integrating heterogeneous feature selection

Protein phosphorylation is a major form of post-translational modification (PTM) that regulates diverse cellular processes. In silico methods for phosphorylation site prediction can provide a useful and complementary strategy for complete phosphoproteome annotation. Here, we present a novel bioinformatics tool, PhosphoPredict, that combines protein sequence and functional features to predict kinase-specific substrates and their associated phosphorylation sites for 12 human kinases and kinase families, including ATM, CDKs, GSK-3, MAPKs, PKA, PKB, PKC, and SRC. To elucidate critical determinants, we identified feature subsets that were most informative and relevant for predicting substrate specificity for each individual kinase family. Extensive benchmarking experiments based on both five-fold cross-validation and independent tests indicated that the performance of PhosphoPredict is competitive with that of several other popular prediction tools, including KinasePhos, PPSP, GPS, and Musite. We found that combining protein functional and sequence features significantly improves phosphorylation site prediction performance across all kinases. Application of PhosphoPredict to the entire human proteome identified 150 to 800 potential phosphorylation substrates for each of the 12 kinases or kinase families. PhosphoPredict significantly extends the bioinformatics portfolio for kinase function analysis and will facilitate high-throughput identification of kinase-specific phosphorylation sites, thereby contributing to both basic and translational research programs.

Whole-genome sequencing revealed novel prognostic biomarkers and promising targets for therapy of ovarian clear cell carcinoma

Background:

Ovarian clear cell carcinoma (OCCC) is mostly resistant to standard chemotherapy that results in poor patient survival. To understand the genetic background of these tumours, we performed whole-genome sequencing of OCCC tumours.

Methods:

Tumour tissue samples and matched blood samples were obtained from 55 Japanese women diagnosed with OCCC. Whole-genome sequencing was performed using the Illumina HiSeq platform according to standard protocols.

Results:

Alterations to the switch/sucrose non-fermentable (SWI/SNF) subunit, the phosphatidylinositol-3-kinase (PI3K)/Akt signalling pathway, and the receptor tyrosine kinase (RTK)/Ras signalling pathway were found in 51%, 42%, and 29% of OCCC tumours, respectively. The 3-year overall survival (OS) rate for patients with an activated PI3K/Akt signalling pathway was significantly higher than that for those with inactive pathway (91 vs 40%, hazard ratio 0.24 (95% confidence interval (CI) 0.10–0.56), P=0.0010). Similarly, the OS was significantly higher in patients with the activated RTK/Ras signalling pathway than in those with the inactive pathway (91 vs 53%, hazard ratio 0.35 (95% CI 0.13–0.94), P=0.0373). Multivariable analysis revealed that activation of the PI3K/Akt and RTK/Ras signalling pathways was an independent prognostic factor for patients with OCCC.

Conclusions:

The PI3K/Akt and RTK/Ras signalling pathways may be potential prognostic biomarkers for OCCC patients. Furthermore, our whole-genome sequencing data highlight important pathways for molecular and biological characterisations and potential therapeutic targeting in OCCC.

Comparison of algorithms for the detection of cancer drivers at subgene resolution

Understanding genetic events that lead to cancer initiation and progression remains one of the biggest challenges in cancer biology. Traditionally, most algorithms for cancer-driver identification look for genes that have more mutations than expected from the average background mutation rate. However, there is now a wide variety of methods that look for nonrandom distribution of mutations within proteins as a signal for the driving role of mutations in cancer. Here we classify and review such subgene-resolution algorithms, compare their findings on four distinct cancer data sets from The Cancer Genome Atlas and discuss how predictions from these algorithms can be interpreted in the emerging paradigms that challenge the simple dichotomy between driver and passenger genes.

Protein kinase C as a tumor suppressor

Protein kinase C (PKC) has historically been considered an oncoprotein. This stems in large part from the discovery in the early 1980s that PKC is directly activated by tumor-promoting phorbol esters. Yet three decades of clinical trials using PKC inhibitors in cancer therapies not only failed, but in some cases worsened patient outcome. Why has targeting PKC in cancer eluded successful therapies? Recent studies looking at the disease for insight provide an explanation: cancer-associated mutations in PKC are generally loss-of-function (LOF), supporting an unexpected function as tumor suppressors. And, contrasting with LOF mutations in cancer, germline mutations that enhance the activity of some PKC isozymes are associated with degenerative diseases such as Alzheimer's disease. This review provides a background on the diverse mechanisms that ensure PKC is only active when, where, and for the appropriate duration needed and summarizes recent findings converging on a paradigm reversal: PKC family members generally function by suppressing, rather than promoting, survival signaling.

Reversing the Paradigm: Protein Kinase C as a Tumor Suppressor

The discovery in the 1980s that protein kinase C (PKC) is a receptor for the tumor-promoting phorbol esters fueled the dogma that PKC is an oncoprotein. Yet 30+ years of clinical trials for cancer using PKC inhibitors not only failed, but in some instances worsened patient outcome. The recent analysis of cancer-associated mutations, from diverse cancers and throughout the PKC family, revealed that PKC isozymes are generally inactivated in cancer, supporting a tumor suppressive function. In keeping with a bona fide tumor suppressive role, germline causal loss-of-function (LOF) mutations in one isozyme have recently been identified in lymphoproliferative disorders. Thus, strategies in cancer treatment should focus on restoring rather than inhibiting PKC.

Methods, Tools and Current Perspectives in Proteogenomics

With combined technological advancements in high-throughput next-generation sequencing and deep mass spectrometry-based proteomics, proteogenomics, i.e. the integrative analysis of proteomic and genomic data, has emerged as a new research field. Early efforts in the field were focused on improving protein identification using sample-specific genomic and transcriptomic sequencing data. More recently, integrative analysis of quantitative measurements from genomic and proteomic studies have identified novel insights into gene expression regulation, cell signaling, and disease. Many methods and tools have been developed or adapted to enable an array of integrative proteogenomic approaches and in this article, we systematically classify published methods and tools into four major categories, (1) Sequence-centric proteogenomics; (2) Analysis of proteogenomic relationships; (3) Integrative modeling of proteogenomic data; and (4) Data sharing and visualization. We provide a comprehensive review of methods and available tools in each category and highlight their typical applications.

Emerging role of mutations in epigenetic regulators including MLL2 derived from The Cancer Genome Atlas for cervical cancer

BACKGROUND

Cervical cancer is the second most common cause of cancer deaths in women worldwide. The aim of this study is to exploit novel pathogenic genes in cervical carcinogenesis.

METHOD

The somatic mutations from 194 patients with cervical cancer were obtained from the Cancer Genome Atlas (TCGA) publically accessible exome-sequencing database. We investigated mutated gene enrichment in the 12 cancer core pathways and predicted possible post-translational modifications. Additionally, we predicted the impact of mutations by scores quantifying the deleterious effects of the mutations. We also examined the immunogenicity of the mutations based on the mutant peptides' strong binding with major histocompatibility complex class I molecules (MHC-I). The Kaplan-Meier method was used for the survival analysis.

RESULTS

We observed that the chromatin modification pathway was significantly mutated across all clinical stages. Among the mutated genes involved in this pathway, we observed that the histone modification regulators were primarily mutated. Interestingly, of the 197 mutations in the 26 epigenetic regulators in this pathway, 25 missense mutations in 13 genes were predicted in or around the phosphorylation sites. Only mutations in the histone methyltransferase MLL2 exhibited poor survival. Compared to other mutations in MLL2 mutant patients, we noticed that the mutational scores prioritized mutations in MLL2, which indicates that it is more likely to have deleterious effects to the human genome. Around half of all of the mutations were found to bind strongly to MHC-I, suggesting that patients are likely to benefit from immunotherapy.

CONCLUSIONS

Our results highlight the emerging role of mutations in epigenetic regulators, particularly MLL2, in cervical carcinogenesis, which suggests a potential disruption of histone modifications. These data have implications for further investigation of the mechanism of epigenetic dysregulation and for treatment of cervical cancer.

Tissue-Specific Signaling Networks Rewired by Major Somatic Mutations in Human Cancer Revealed by Proteome-Wide Discovery

Massive somatic mutations discovered by large cancer genome sequencing projects provide unprecedented opportunities in the development of precision oncology. However, deep understanding of functional consequences of somatic mutations and identifying actionable mutations and the related drug responses currently remain formidable challenges. Dysfunction of protein posttranslational modification plays critical roles in tumorigenesis and drug responses. In this study, we proposed a novel computational oncoproteomics approach, named kinome-wide network module for cancer pharmacogenomics (KNMPx), for identifying actionable mutations that rewired signaling networks and further characterized tumorigenesis and anticancer drug responses. Specifically, we integrated 746,631 missense mutations in 4,997 tumor samples across 16 major cancer types/subtypes from The Cancer Genome Atlas into over 170,000 carefully curated nonredundant phosphorylation sites covering 18,610 proteins. We found 47 mutated proteins (e.g., ERBB2, TP53, and CTNNB1) that had enriched missense mutations at their phosphorylation sites in pan-cancer analysis. In addition, tissue-specific kinase-substrate interaction modules altered by somatic mutations identified by KNMPx were significantly associated with patient survival. We further reported a kinome-wide landscape of pharmacogenomic interactions by incorporating somatic mutation-rewired signaling networks in 1,001 cancer cell lines via KNMPx. Interestingly, we found that cell lines could highly reproduce oncogenic phosphorylation site mutations identified in primary tumors, supporting the confidence in their associations with sensitivity/resistance of inhibitors targeting EGF, MAPK, PI3K, mTOR, and Wnt signaling pathways. In summary, our KNMPx approach is powerful for identifying oncogenic alterations via rewiring phosphorylation-related signaling networks and drug sensitivity/resistance in the era of precision oncology. Cancer Res; 77(11); 2810-21. ©2017 AACR.

Functional variomics and network perturbation: connecting genotype to phenotype in cancer

Proteins interact with other macromolecules in complex cellular networks for signal transduction and biological function. In cancer, genetic aberrations have been traditionally thought to disrupt the entire gene function. It has been increasingly appreciated that each mutation of a gene could have a subtle but unique effect on protein function or network rewiring, contributing to diverse phenotypic consequences across cancer patient populations. In this Review, we discuss the current understanding of cancer genetic variants, including the broad spectrum of mutation classes and the wide range of mechanistic effects on gene function in the context of signalling networks. We highlight recent advances in computational and experimental strategies to study the diverse functional and phenotypic consequences of mutations at the base-pair resolution. Such information is crucial to understanding the complex pleiotropic effect of cancer genes and provides a possible link between genotype and phenotype in cancer.

Cushing's syndrome mutant PKAL205R exhibits altered substrate specificity

The PKA^L205R hotspot mutation has been implicated in Cushing's syndrome through hyperactive gain-of-function PKA signaling; however, its influence on substrate specificity has not been investigated. Here, we employ the Proteomic Peptide Library (ProPeL) approach to create high-resolution models for PKA^WT and PKA^L205R substrate specificity. We reveal that the L205R mutation reduces canonical hydrophobic preference at the substrate P + 1 position, and increases acidic preference in downstream positions. Using these models, we designed peptide substrates that exhibit altered selectivity for specific PKA variants, and demonstrate the feasibility of selective PKA^L205R loss-of-function signaling. Through these results, we suggest that substrate rewiring may contribute to Cushing's syndrome disease etiology, and introduce a powerful new paradigm for investigating mutation-induced kinase substrate rewiring in human disease.

Active Interaction Mapping Reveals the Hierarchical Organization of Autophagy

We have developed a general progressive procedure, Active Interaction Mapping, to guide assembly of the hierarchy of functions encoding any biological system. Using this process, we assemble an ontology of functions comprising autophagy, a central recycling process implicated in numerous diseases. A first-generation model, built from existing gene networks in Saccharomyces, captures most known autophagy components in broad relation to vesicle transport, cell cycle, and stress response. Systematic analysis identifies synthetic-lethal interactions as most informative for further experiments; consequently, we saturate the model with 156,364 such measurements across autophagy-activating conditions. These targeted interactions provide more information about autophagy than all previous datasets, producing a second-generation ontology of 220 functions. Approximately half are previously unknown; we confirm roles for Gyp1 at the phagophore-assembly site, Atg24 in cargo engulfment, Atg26 in cytoplasm-to-vacuole targeting, and Ssd1, Did4, and others in selective and non-selective autophagy. The procedure and autophagy hierarchy are at http://atgo.ucsd.edu/.

Introduction: History of SH2 Domains and Their Applications

The Src Homology 2 (SH2) domain is the prototypical protein interaction module that lies at the heart of phosphotyrosine signaling. Since its serendipitous discovery, there has been a tremendous advancement in technologies and an array of techniques available for studying SH2 domains and phosphotyrosine signaling. In this chapter, we provide a glimpse of the history of SH2 domains and describe many of the tools and techniques that have been developed along the way and discuss future directions for SH2 domain studies. We highlight the gist of each chapter in this volume in the context of: the structural biology and phosphotyrosine binding; characterizing SH2 specificity and generating prediction models; systems biology and proteomics; SH2 domains in signal transduction; and SH2 domains in disease, diagnostics, and therapeutics. Many of the individual chapters provide an in-depth approach that will allow scientists to interrogate the function and role of SH2 domains.

Systems Approaches to Cancer Biology

Cancer systems biology aims to understand cancer as an integrated system of genes, proteins, networks, and interactions rather than an entity of isolated molecular and cellular components. The inaugural Systems Approaches to Cancer Biology Conference, cosponsored by the Association of Early Career Cancer Systems Biologists and the National Cancer Institute of the NIH, focused on the interdisciplinary field of cancer systems biology and the challenging cancer questions that are best addressed through the combination of experimental and computational analyses. Attendees found that elucidating the many molecular features of cancer inevitably reveals new forms of complexity and concluded that ensuring the reproducibility and impact of cancer systems biology studies will require widespread method and data sharing and, ultimately, the translation of important findings to the clinic. Cancer Res; 76(23); 6774-7. ©2016 AACR.