Systematic identification of cancer driving signaling pathways based on mutual exclusivity of genomic alterations

Recent cancer genome studies have identified numerous genomic alterations in cancer genomes. It is hypothesized that only a fraction of these genomic alterations drive the progression of cancer -- often called driver mutations. Current sample sizes for cancer studies, often in the hundreds, are sufficient to detect pivotal drivers solely based on their high frequency of alterations. In cases where the alterations for a single function are distributed among multiple genes of a common pathway, however, single gene alteration frequencies might not be statistically significant. In such cases, we expect to observe that most samples are altered in only one of those alternative genes because additional alterations would not convey an additional selective advantage to the tumor. This leads to a mutual exclusion pattern of alterations, that can be exploited to identify these groups. We developed a novel method for the identification of sets of mutually exclusive gene alterations in a signaling network. We scan the groups of genes with a common downstream effect, using a mutual exclusivity criterion that makes sure that each gene in the group significantly contributes to the mutual exclusivity pattern. We have tested the method on all available TCGA cancer genomics datasets, and detected multiple previously unreported alterations that show significant mutual exclusivity and are likely to be driver events.

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Systematic identification of signaling pathways with potential to confer anticancer drug resistance

Science Signaling ◽

10.1126/scisignal.aaa1877 ◽

2014 ◽

Vol 7 (357) ◽

pp. ra121-ra121 ◽

Cited By ~ 95

Author(s):

C. A. Martz ◽

K. A. Ottina ◽

K. R. Singleton ◽

J. S. Jasper ◽

S. E. Wardell ◽

...

Keyword(s):

Drug Resistance ◽

Signaling Pathways ◽

Anticancer Drug ◽

Anticancer Drug Resistance ◽

Systematic Identification

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Subtractive Interaction Proteomics Reveal a Network of Signaling Pathways Activated By an Oncogenic Transcription Factor in High Risk AML

Blood ◽

10.1182/blood-2018-99-116631 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 3917-3917

Author(s):

Claudia Chiriches ◽

Nathalie Guillen ◽

Michal Rokicki ◽

Carol Guy ◽

Afsar Mian ◽

...

Keyword(s):

High Risk ◽

Signaling Pathways ◽

Research Funding ◽

Statistical Significance ◽

Driver Mutations ◽

Molecular Targeting ◽

Genomic Alterations ◽

Cancer Signaling ◽

Depth Analysis ◽

Interaction Proteomics

Abstract Acute myeloid leukemias (AML) are characterized by recurrent genomic alterations, often in transcriptional regulators, which form the basis on which current prognostication and therapeutic intervention is overlaid. Three subtypes of AML carrying specific translocations, namely t(15;17), t(11;17) and t(6;9), are notable for being associated with a smaller number of co-existing driver mutations than e.g. AML with normal karyotype. This strongly suggests that the function of their aberrant gene products, PML/RAR and DEK/CAN, respectively, may subsume the functions of other driver mutations. Thus we hypothesized that these functions, while as yet elusive, not necessarily require sequential acquisition of secondary genomic alterations. We elected to study AML with the t(6;9), defined as a distinct entity by the WHO classification, because of its particular biological and high risk clinical features and unmet clinical needs. Most t(6;9)-AML patients are young, with a median age of 23-40 years, complete remission rates do not exceed 50% and median survival after diagnosis is only about 1 year. We used a novel "subtractive interaction proteomics" (SIP) approach to understand the mechanisms by which the t(6;9)-DEK/CAN nuclear oncogene induces this highly resistant leukemic phenotype. Based on Tandem Affinity Precipitation (TAP) for the enrichment of proteins complexes associated with SILAC-technology followed by LC-MS/MS we developed SIP as a comparison between the interactome of an oncogene and those of its functionally inactive mutants in order to obtain eventually only relevant interaction partners (exclusive binders) in the same genetic background. This is achieved by the subtraction of binders that are common to four functionally inactive mutants classifying them as not relevant. Bioinformatic network analysis of the 9 exclusive binders of DEK/CAN revealed by SIP (RAB1A, RAB6A, S100A7, PCBD1, Clusterin, RPS14 and 19, IDH3A, SerpinB3) using BioGrid, IntAct and String together with Ingenuity© Pathway Analysis (IPA), indicated a functional relationship with ABL1-, AKT/mTOR-, MYC- and SRC family kinases-dependent signaling. Interestingly, we found all these signaling pathways strongly activated in an autonomous manner in four DEK/CAN-positive leukemia models, DEK/CAN expressing U937 cells, t(6;9)-positive FKH-1 cells, primary syngeneic murine DEK/CAN-driven leukemias, and t(6;9)-positive patient samples. Bioinformatic analysis of the phopshoproteomic profile of FKH1 cells upon molecular targeting of single pathways (imatinib for ABL1, PP2 for SFKs, dasatinib for ABL1/SFK and Torin1 or NVP-BEZ-235 for mTOR/AKT) revealed that these signaling pathways were organized in clusters creating a network with nodes that are credible candidates for combinatorial therapeutic interventions. On the other hand inhibition of individual outputs had the potential to activate interconnected pathways in a detrimental manner with consequential clinical impact e.g. the activation of STAT5 by the inhibition of mTOR/AKT in these cells. Treatment of mice injected with primary syngeneic DEK/CAN-induced leukemic cells with dasatinib (10mg/kg) and NVP-BEZ-235 (45mg/kg) alone and in combination for 14 days led to a strong reduction of leukemia burden in all cohorts (each cohort n=7). In fact, as compared to untreated controls (146.6 +/- 36mg), mice treated with NVP-BEZ 235 alone and in combination (61.7 +/-4.7mg and 65.3+/- 4.6mg, respectively) showed a statistically significant reduction of spleen size whereas those treated with dasatinib alone (77.5 8 +/- 5.4mg) did not reach statistical significance. Taken together the here presented results reveal specific interdependencies between a nuclear oncogene and kinase driven cancer signaling pathways providing a foundation for the design of therapeutic strategies to better address the complexity of cancer signaling. In addition, it provides evidence for the need of a more in depth analysis of indirect effects of molecular targeting strategies in a preclinical setting not only in AML but in all cancer types. Disclosures Ottmann: Novartis: Consultancy; Pfizer: Consultancy; Fusion Pharma: Consultancy, Research Funding; Amgen: Consultancy; Celgene: Consultancy, Research Funding; Takeda: Consultancy; Incyte: Consultancy, Research Funding.

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Mutually exclusive autism mutations point to the circadian clock and PI3K signaling pathways

10.1101/653527 ◽

2019 ◽

Cited By ~ 1

Author(s):

Hannah Manning ◽

Brian J. O’Roak ◽

Özgün Babur

Keyword(s):

Circadian Clock ◽

Signaling Pathways ◽

De Novo ◽

Autism Spectrum ◽

Akt Signaling ◽

Pi3k Signaling ◽

Whole Genome ◽

Mutual Exclusivity ◽

Sequencing Studies ◽

First Time

ABSTRACTMutual exclusivity analysis of genomic mutations has proven useful for detecting driver alterations in cancer patient cohorts. Here we demonstrate, for the first time, that this pattern is also present among de novo mutations in autism spectrum disorder. We analyzed three large whole genome sequencing studies and identified mutual exclusivity patterns within the most confident set of autism-related genes, as well as in the circadian clock and PI3K/AKT signaling pathways.

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Tumor Mutational Burden and Genomic Alterations in Chinese Small Cell Lung Cancer Measured by Whole-Exome Sequencing

BioMed Research International ◽

10.1155/2019/6096350 ◽

2019 ◽

Vol 2019 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Shan Su ◽

Jian-Jun Zou ◽

Yun-Yun Zeng ◽

Wen-Chang Cen ◽

Wei Zhou ◽

...

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Signaling Pathways ◽

Exome Sequencing ◽

Signaling Pathway ◽

Whole Exome Sequencing ◽

Small Cell ◽

Small Cell Lung ◽

Genomic Alterations ◽

Whole Exome

Purpose. Studies on genetic alterations of the heterogenous small cell lung cancer (SCLC) are rare. We carried out the present study to clarify the genomic alterations and TMB levels of Chinese SCLC patients by whole-exome sequencing. Materials and Methods. Whole-exome sequencing by next-generation sequencing technique was implemented on twenty SCLC samples. Significant somatic mutations and copy number variations were screened, followed by comparison with the data extracted from COSMIC. Besides, altered signaling pathways were examined in order to figure out actionable targets. Results. A total of 8,062 nonsynonymous mutations were defined. The number of mutations for each case ranged from 98 to 864. As for base substitutions, a total of 15,817 substitutions were detected with C > A conversion which was correlated to smoking occupying 25.57%. The TMB values ranged from 2.51/Mb to 22.1/Mb with a median value of 9.95/Mb. RB1 was the most frequently mutated gene altered in 18 (90%) cases, followed by TP53 altered in 17 (85%) cases. Other commonly changed genes were PTEN, and RBL1, with frequencies of 55% and 50%, respectively. SOX2 significantly amplified in 6 (30%) cases and MYCN amplified in 1 (5%) patient. Notch signaling pathway and PI3K/AKT/mTOR signaling pathway were universally and significantly changed. Major genomic alterations were in consistency with data from COSMIC, but frequencies of less common mutations were different. Conclusion. TP53 and RB1 inactivations were universally detected in SCLC. The Notch and PI3K/AKT/mTOR signaling pathways were both significantly altered, implying potential actionable targets.

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Abstract 21: Mutation profiling of patients with multiple primary cancers reveals some common genomic alterations in signaling pathways

10.1158/1557-3265.pmsclingen15-21 ◽

2016 ◽

Author(s):

Ruoyu Wang ◽

Dewei Zhao ◽

Zhiqiang Wang ◽

Xuesong Gu ◽

Shu Wen ◽

...

Keyword(s):

Signaling Pathways ◽

Multiple Primary Cancers ◽

Genomic Alterations ◽

Multiple Primary ◽

Mutation Profiling

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Comprehensive analysis of tumor NGS data to demonstrate pathways pointing to therapeutic targeting options.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.15_suppl.e23153 ◽

2017 ◽

Vol 35 (15_suppl) ◽

pp. e23153-e23153

Author(s):

Subha Krishnan ◽

Ally Perlina ◽

Alex Thomas ◽

Boyko Kakaradov ◽

Wayne Delport ◽

...

Keyword(s):

Cyclin D1 ◽

Signaling Pathways ◽

Tumor Biology ◽

Comprehensive Analysis ◽

Complex Nature ◽

Beta Catenin ◽

Genomic Alterations ◽

Genomic Variations ◽

Downstream Targets ◽

Therapeutic Decisions

e23153 Background: Recent advancements in NGS technology have enabled precision medicine based on tumor-specific genomic alterations. However, the complex nature of tumor biology demands an integrative analysis of genomic variations to identify converging downstream targets potentially regulated by multiple pathways. Methods: Genomic alterations from whole-genome sequencing results were interpreted in the context of pathways. Beyond listing the implicated known pathways based on genomic alterations, we explored protein interactions cross-linking the pathways to converging downstream targets. An in-house analytical pipeline was used to prioritize candidate pathways from MetaBase™, which contains manually curated pathways and interactions based on published experimental results. Custom pathway diagram was then designed manually to depict the most actionable pathways. The manual review consists of context-checking and addressing the concordant/discordant nature of all interactions. Factor such as mutational impact, directionality, mechanism of interaction and known targeted action are taken into account. Results: Comprehensive analysis of the genomic variations in three tumors types revealed activation of multiple oncogenic signaling pathways. Key events driving the tumor in a melanoma sample were NF1/RAS/MEK/ERK, WNT/beta-catenin pathway, and MITF signaling. Three interconnected signaling pathways, WNT, PI3K/AKT, and P53 were impacted by genomic alterations in a colorectal sample, which point to activation of converging downstream targets- CDK6, VEGFA, and COX-2. Integrated discovery of genomic alterations in an esophageal adenocarcinoma sample suggested potential activation of PI3K/AKT/MTOR and p16/Cyclin D1/CDK pathways. This could synergistically activate downstream converging targets- VEGFA, Cyclin D1, CDK6, CDK4, AURKA; some of which also showed relative RNA overexpression, supporting our pathway findings. Conclusions: Comprehensively analyzing the genomic alterations in context of cell signaling pathways provides us insights on how the pathways synergistically affect downstream targetable events, which in turn can impact therapeutic decisions.

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Revealing the impact of genomic alterations on cancer cell signaling with a partially transparent deep learning model

10.1101/2020.05.29.113605 ◽

2020 ◽

Author(s):

Jonathan D. Young ◽

Xinghua Lu

Keyword(s):

Deep Learning ◽

Signaling Pathways ◽

Latent Variables ◽

Learning Model ◽

Causal Relationships ◽

Genomic Alterations ◽

Cancer Signaling ◽

Causal Graphs ◽

Input Variables ◽

Deep Learning Model

AbstractCancer is a disease of aberrant cellular signaling and tumor-specific aberrations in signaling systems determine the aggressiveness of a cancer and response to therapy. Identifying such abnormal signaling pathways causing a patient’s cancer would enable more patient-specific and effective treatments. We interpret the cellular signaling system as a causal graphical model, where it is known that genomic alterations cause changes in the functions of signaling proteins, and the propagation of signals among proteins eventually leads to changed gene expression. To represent such a system, we developed a deep learning model, referred to as a redundant input neural network (RINN), with a redundant input architecture and an L1 regularized objective function to find causal relationships between input, latent, and output variables—when it is known a priori that input variables cause output variables. We hypothesize that training RINN on cancer omics data will enable us to map the functional impacts of genomic alterations to latent variables in a deep learning model, allowing us to discover the hierarchical causal relationships between variables perturbed by different genomic alterations. Importantly, the direct connections between all input and all latent variables in RINN make the latent variables partially interpretable, as they can be easily mapped to input space. We show that gene expression can be predicted from genomic alterations with reasonable accuracy when measured as the area under ROC curves (AUROCs). We also show that RINN is able to discover the shared functional impact of genomic alterations that perturb a common cancer signaling pathway, especially relationships in the PI3K, Nrf2, and TGFβ pathways, including some causal relationships. However, despite high regularization, the learned causal relationships were somewhat too dense to be easily and directly interpretable as causal graphs. We suggest promising future directions for RINN, including differential regularization, autoencoder pretrained representations, and constrained evolutionary strategies.Author summaryA modified deep learning model (RINN with L1 regularization) can be used to capture cancer signaling pathway relationships within its hidden variables and weights. We found that genomic alterations impacting the same known cancer pathway had interactions with a similar set of RINN latent variables. Having genomic alterations (input variables) directly connected to all latent variables in the RINN model allowed us to label the latent variables with a set of genomic alterations, making the latent variables partially interpretable. With this labeling, we were able to visualize RINNs as causal graphs and capture at least some of the causal relationships in known cancer signaling pathways. However, the graphs learned by RINN were somewhat too dense (despite large amounts of regularization) to compare directly to known cancer signaling pathways. We also found that differential expression can be predicted from genomic alterations by a RINN with reasonably high AUROCs, especially considering the very high dimensionality of the prediction task relative to the number of input variables and instances in the dataset. These are encouraging results for the future of deep learning models trained on cancer genomic data.

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A compendium of G-protein–coupled receptors and cyclic nucleotide regulation of adipose tissue metabolism and energy expenditure

Clinical Science ◽

10.1042/cs20190579 ◽

2020 ◽

Vol 134 (5) ◽

pp. 473-512 ◽

Cited By ~ 5

Author(s):

Ryan P. Ceddia ◽

Sheila Collins

Keyword(s):

Adipose Tissue ◽

Energy Expenditure ◽

Signaling Pathways ◽

G Protein ◽

Uncoupling Protein ◽

Beige Adipocytes ◽

Nucleotide Regulation ◽

Ucp1 Expression ◽

Thermogenic Adipocytes ◽

G Protein Coupled

Abstract With the ever-increasing burden of obesity and Type 2 diabetes, it is generally acknowledged that there remains a need for developing new therapeutics. One potential mechanism to combat obesity is to raise energy expenditure via increasing the amount of uncoupled respiration from the mitochondria-rich brown and beige adipocytes. With the recent appreciation of thermogenic adipocytes in humans, much effort is being made to elucidate the signaling pathways that regulate the browning of adipose tissue. In this review, we focus on the ligand–receptor signaling pathways that influence the cyclic nucleotides, cAMP and cGMP, in adipocytes. We chose to focus on G-protein–coupled receptor (GPCR), guanylyl cyclase and phosphodiesterase regulation of adipocytes because they are the targets of a large proportion of all currently available therapeutics. Furthermore, there is a large overlap in their signaling pathways, as signaling events that raise cAMP or cGMP generally increase adipocyte lipolysis and cause changes that are commonly referred to as browning: increasing mitochondrial biogenesis, uncoupling protein 1 (UCP1) expression and respiration.

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