Martin L. Miller

Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer

Immune checkpoint inhibitors, which unleash a patient’s own T cells to kill tumors, are revolutionizing cancer treatment. To unravel the genomic determinants of response to this therapy, we used whole-exome sequencing of non–small cell lung cancers treated with pembrolizumab, an antibody targeting programmed cell death-1 (PD-1). In two independent cohorts, higher nonsynonymous mutation burden in tumors was associated with improved objective response, durable clinical benefit, and progression-free survival. Efficacy also correlated with the molecular smoking signature, higher neoantigen burden, and DNA repair pathway mutations; each factor was also associated with mutation burden. In one responder, neoantigen-specific CD8+ T cell responses paralleled tumor regression, suggesting that anti–PD-1 therapy enhances neoantigen-specific T cell reactivity. Our results suggest that the genomic landscape of lung cancers shapes response to anti–PD-1 therapy. An anticancer drug is more effective against tumors that carry more mutations. More mutations predict better efficacy Despite the remarkable success of cancer immunotherapies, many patients do not respond to treatment. Rizvi et al. studied the tumors of patients with non–small-cell lung cancer undergoing immunotherapy. In two independent cohorts, treatment efficacy was associated with a higher number of mutations in the tumors. In one patient, a tumor-specific T cell response paralleled tumor regression. Science, this issue p. 124

Quantitative Phosphoproteomics Reveals Widespread Full Phosphorylation Site Occupancy During Mitosis

Protein phosphorylation during the cell cycle may be an all-or-none process in many instances. All-or-None Phosphorylation Phosphorylation is a key regulatory event that drives many cellular processes, including cell division. Olsen et al. undertook a phosphoproteomic analysis of HeLa cells at various stages in the cell cycle, which linked new phosphorylation sites and kinase substrates to specific stages. Furthermore, they established a method to calculate the fractional occupancy of particular phosphorylation sites (phosphorylation stoichiometry) on a global level and found that, contrary to expectations, many sites on functionally related proteins appeared to be nearly completely phosphorylated at particular stages of the cell cycle. They observed an inverse relationship in the phosphorylation occupancy of some sites in cells undergoing mitosis compared to those in S phase. The authors speculate that a high stoichiometry of phosphorylation may be necessary to inactivate an entire protein population to effectively block activity, whereas function may only require a low stoichiometry of phosphorylation, because only a small fraction of the protein population may be required for full activity. Eukaryotic cells replicate by a complex series of evolutionarily conserved events that are tightly regulated at defined stages of the cell division cycle. Progression through this cycle involves a large number of dedicated protein complexes and signaling pathways, and deregulation of this process is implicated in tumorigenesis. We applied high-resolution mass spectrometry–based proteomics to investigate the proteome and phosphoproteome of the human cell cycle on a global scale and quantified 6027 proteins and 20,443 unique phosphorylation sites and their dynamics. Co-regulated proteins and phosphorylation sites were grouped according to their cell cycle kinetics and compared to publicly available messenger RNA microarray data. Most detected phosphorylation sites and more than 20% of all quantified proteins showed substantial regulation, mainly in mitotic cells. Kinase-motif analysis revealed global activation during S phase of the DNA damage response network, which was mediated by phosphorylation by ATM or ATR or DNA-dependent protein kinases. We determined site-specific stoichiometry of more than 5000 sites and found that most of the up-regulated sites phosphorylated by cyclin-dependent kinase 1 (CDK1) or CDK2 were almost fully phosphorylated in mitotic cells. In particular, nuclear proteins and proteins involved in regulating metabolic processes have high phosphorylation site occupancy in mitosis. This suggests that these proteins may be inactivated by phosphorylation in mitotic cells.

Emerging landscape of oncogenic signatures across human cancers

Cancer therapy is challenged by the diversity of molecular implementations of oncogenic processes and by the resulting variation in therapeutic responses. Projects such as The Cancer Genome Atlas (TCGA) provide molecular tumor maps in unprecedented detail. The interpretation of these maps remains a major challenge. Here we distilled thousands of genetic and epigenetic features altered in cancers to ∼500 selected functional events (SFEs). Using this simplified description, we derived a hierarchical classification of 3,299 TCGA tumors from 12 cancer types. The top classes are dominated by either mutations (M class) or copy number changes (C class). This distinction is clearest at the extremes of genomic instability, indicating the presence of different oncogenic processes. The full hierarchy shows functional event patterns characteristic of multiple cross-tissue groups of tumors, termed oncogenic signature classes. Targetable functional events in a tumor class are suggestive of class-specific combination therapy. These results may assist in the definition of clinical trials to match actionable oncogenic signatures with personalized therapies.

Transfection of small RNAs globally perturbs gene regulation by endogenous microRNAs

Transfection of small RNAs (such as small interfering RNAs (siRNAs) and microRNAs (miRNAs)) into cells typically lowers expression of many genes. Unexpectedly, increased expression of genes also occurs. We investigated whether this upregulation results from a saturation effect—that is, competition among the transfected small RNAs and the endogenous pool of miRNAs for the intracellular machinery that processes small RNAs. To test this hypothesis, we analyzed genome-wide transcript responses from 151 published transfection experiments in seven different human cell types. We show that targets of endogenous miRNAs are expressed at significantly higher levels after transfection, consistent with impaired effectiveness of endogenous miRNA repression. This effect exhibited concentration and temporal dependence. Notably, the profile of endogenous miRNAs can be largely inferred by correlating miRNA sites with gene expression changes after transfections. The competition and saturation effects have practical implications for miRNA target prediction, the design of siRNA and short hairpin RNA (shRNA) genomic screens and siRNA therapeutics.

Linear Motif Atlas for Phosphorylation-Dependent Signaling

Created with both in vitro and in vivo data, NetPhorest is an atlas of consensus sequence motifs for 179 kinases and 104 phosphorylation-dependent binding domains and reveals new insight into phosphorylation-dependent signaling. An Atlas of Phosphorylation NetPhorest is a community resource that uses phylogenetic trees to organize data from both in vivo and in vitro experiments to derive sequence specificities for 179 kinases and 104 domains (SH2, PTB, BRCT, WW, and 14–3–3) that bind to phosphorylated sites. The resulting atlas of linear motifs revealed that oncogenic kinases tend to be less specific in the target sequences they phosphorylate than their non-oncogenic counterparts, that autophosphorylation sites tend to be more variable than other substrates of a given kinase, and that coupling interaction domains with kinase domains may allow phosphorylation site specificity to be low while still maintaining substrate specificity. Systematic and quantitative analysis of protein phosphorylation is revealing dynamic regulatory networks underlying cellular responses to environmental cues. However, matching these sites to the kinases that phosphorylate them and the phosphorylation-dependent binding domains that may subsequently bind to them remains a challenge. NetPhorest is an atlas of consensus sequence motifs that covers 179 kinases and 104 phosphorylation-dependent binding domains [Src homology 2 (SH2), phosphotyrosine binding (PTB), BRCA1 C-terminal (BRCT), WW, and 14–3–3]. The atlas reveals new aspects of signaling systems, including the observation that tyrosine kinases mutated in cancer have lower specificity than their non-oncogenic relatives. The resource is maintained by an automated pipeline, which uses phylogenetic trees to structure the currently available in vivo and in vitro data to derive probabilistic sequence models of linear motifs. The atlas is available as a community resource (http://netphorest.info).

KinomeXplorer: an integrated platform for kinome biology studies

or even impossible to be captured by cellular or in vivo experiments alone. Furthermore, it is difficult to design kinase perturbation experiments, because the kinome-wide selectivity and specificity of many kinase inhibitors is unknown3,4. As a result, knowledge is lacking on which of the ~540 human kinases phosphorylate a given site: of the 42,914 phosphorylation sites currently annotated in the Phospho.ELM database5, only ~20% have been linked to a kinase. Technological advances in mass spectrometry–based phosphoproteomics have accelerated the ability to identify phosphorylation sites but not to determine which kinases phosphorylate them. To systematically identify these dynamic interactions, computational methods to guide experiments must be deployed. We have shown that combining computational algorithms with quantitative mass spectrometry is a powerful approach to validate kinase-substrate relationships6. Notably, we have shown that kinase specificity can be described in terms of two main contributing elements: the recognition motif of the individual kinase (for example, X-S/T-Q-X for the ATM kinase) and proteins that can be functionally associated with it (i.e., not just proteins that directly interact with the kinase). The network context of kinases is crucial, as exemplified by the discovery that the phenotypic role of the JNK kinase depends entirely on the state of the cellular signaling networks before its activation7. In other words, it is crucial to assess the protein networks embedding kinases and how these are dynamically modulated (for example, through time or perturbations) to predict cell behavior8. KinomeXplorer (Fig. 1) provides workflows that enable researchers to efficiently analyze phosphorylationd e p e n d e nt i n t e r a c t i o n n e t w o r k s (Supplementary Fig. 1) and aids them in designing follow-up perturbation experiments. The platform includes improved versions of NetworKIN (an algorithm that integrates cellular context information and motif-based predictions)6 and NetPhorest (a phylogenetic tree–based algorithm to classify phosphorylation sites in terms of kinases and phosphobinding domains)9, conferring increased prediction accuracy through a novel Bayesian scoring scheme, broader kinome coverage, new phosphatome coverage and a redesigned unifying web interface. The framework also integrates the new KinomeSelector tool, which enables the user to select an optimal kinase panel to functionally perturb the predicted phosphorylation signaling networks. We re-engineered the NetworKIN algorithm to improve its performance and usability (Supplementary Note). To calculate the NetworKIN score, we combined the NetPhorest probability and the STRING-derived proximity score using KinomeXplorer: an integrated platform for kinome biology studies

NetPhosYeast: prediction of protein phosphorylation sites in yeast.

UNLABELLED We here present a neural network-based method for the prediction of protein phosphorylation sites in yeast--an important model organism for basic research. Existing protein phosphorylation site predictors are primarily based on mammalian data and show reduced sensitivity on yeast phosphorylation sites compared to those in humans, suggesting the need for an yeast-specific phosphorylation site predictor. NetPhosYeast achieves a correlation coefficient close to 0.75 with a sensitivity of 0.84 and specificity of 0.90 and outperforms existing predictors in the identification of phosphorylation sites in yeast. AVAILABILITY The NetPhosYeast prediction service is available as a public web server at http://www.cbs.dtu.dk/services/NetPhosYeast/.

Mitochondrial DNA copy number variation across human cancers.

Mutations, deletions, and changes in copy number of mitochondrial DNA (mtDNA), are observed throughout cancers. Here, we survey mtDNA copy number variation across 22 tumor types profiled by The Cancer Genome Atlas project. We observe a tendency for some cancers, especially of the bladder, breast, and kidney, to be depleted of mtDNA, relative to matched normal tissue. Analysis of genetic context reveals an association between incidence of several somatic alterations, including IDH1 mutations in gliomas, and mtDNA content. In some but not all cancer types, mtDNA content is correlated with the expression of respiratory genes, and anti-correlated to the expression of immune response and cell-cycle genes. In tandem with immunohistochemical evidence, we find that some tumors may compensate for mtDNA depletion to sustain levels of respiratory proteins. Our results highlight the extent of mtDNA copy number variation in tumors and point to related therapeutic opportunities. DOI: http://dx.doi.org/10.7554/eLife.10769.001

Perturbation biology: inferring signaling networks in cellular systems.

We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology.

The SH2 Domain Interaction Landscape

Members of the SH2 domain family modulate signal transduction by binding to short peptides containing phosphorylated tyrosines. Each domain displays a distinct preference for the sequence context of the phosphorylated residue. We have developed a high-density peptide chip technology that allows for probing of the affinity of most SH2 domains for a large fraction of the entire complement of tyrosine phosphopeptides in the human proteome. Using this technique, we have experimentally identified thousands of putative SH2-peptide interactions for more than 70 different SH2 domains. By integrating this rich data set with orthogonal context-specific information, we have assembled an SH2-mediated probabilistic interaction network, which we make available as a community resource in the PepspotDB database. A predicted dynamic interaction between the SH2 domains of the tyrosine phosphatase SHP2 and the phosphorylated tyrosine in the extracellular signal-regulated kinase activation loop was validated by experiments in living cells.