Biology subjects

Ideker, T.

Publications and source records attributed to Ideker, T..

3 recordsLinked to original sources

bioRxiv2018

Synthetic essentiality of metabolic regulator PDHK1 in PTEN-deficient cells and cancers

PTEN is a tumor suppressor that is often inactivated in cancer and possesses both lipid and protein phosphatase activities. We report the metabolic regulator PDHK1 (pyruvate dehydrogenase kinase1) is a synthetic-essential gene in PTEN-deficient cancer and normal cells. The predominant mechanism of PDHK1 regulation and dependency is the PTEN protein phosphatase dephosphorylates NF{kappa};B activating protein (NKAP) and limits NF{kappa}B activation to suppress expression of PDHK1, a NF{kappa}B target gene. Loss of the PTEN protein phosphatase upregulates PDHK1 to drive aerobic glycolysis and induce PDHK1 cellular dependence. PTEN-deficient human tumors harbor increased PDHK1, which is a biomarker of decreased patient survival, establishing clinical relevance. This study uncovers a PTEN-regulated signaling pathway and reveals PDHK1 as a potential target in PTEN-deficient cancers.\n\nSIGNIFICANCEThe tumor suppressor PTEN is widely inactivated in cancers and tumor syndromes. PTEN antagonizes PI3K/AKT signaling via its lipid phosphatase activity. The modest success of PI3K/AKT inhibition in PTEN-deficient cancer patients provides rationale for identifying other vulnerabilities in PTEN-deficient cancers to improve clinical outcomes. We show that PTEN-deficient cells are uniquely sensitive to PDHK1 inhibition. PTEN and PDHK1 co-suppression reduced colony formation and induced cell death in vitro and tumor regression in vivo. PDHK1 levels were high in PTEN-deficient patient tumors and associated with inferior patient survival, establishing clinical relevance. Our study identifies a PTEN-regulated signaling pathway linking the PTEN protein phosphatase to the metabolic regulator PDHK1 and provides a mechanistic basis for PDHK1 targeting in PTEN-deficient cancers.

cancer biology↗

bioRxiv2018

Deciphering signaling specificity with interpretable deep neural networks

Protein kinase phosphorylation is a prevalent post-translational modification (PTM) regulating protein function and transmitting signals throughout the cell. Defective signal transductions, which are associated with protein phosphorylation, have been revealed to link to many human diseases, such as cancer. Defining the organization of the phosphorylation-based signaling network and, in particular, identifying kinase-specific substrates can help reveal the molecular mechanism of the signaling network. Here, we present DeepSignal, a deep learning framework for predicting the substrate specificity for kinase/SH2 sequences with or without mutations. Empowered by the memory and selection mechanism of recurrent neural network, DeepSignal can identify important specificity-defining residues to predict kinase specificity and changes upon mutations. Evaluated on several public benchmark datasets, DeepSignal significantly outperforms current methods on predicting substrate specificity on both kinase and SH2 domains. Further analysis in The Cancer Genome Atlas (TCGA) demonstrated that DeepSignal is able to aggregate mutations on both kinase/SH2 domains and substrates to quantify binding specificity changes, predict cancer genes related to signaling transduction, and identify novel perturbed pathways.\n\nAvailabilityImplementation of DeepSignal is at https://github.com/luoyunan/DeepSignal

bioinformatics↗

bioRxiv2016

Pan-Cancer Analysis Reveals Technical Artifacts in The Cancer Genome Atlas (TCGA) Germline Variant Calls

The degree to which germline variation drives cancer development and shapes tumor phenotypes remains largely unexplored, possibly due to a lack of large scale publicly available germline data for a cancer cohort. Here we called germline variants on 9,618 cases from The Cancer Genome Atlas (TCGA) database representing 31 cancer types. We identified batch effects affecting loss of function (LOF) variant calls that can be traced back to differences in the way the sequence data were generated both within and across cancer types. Overall, LOF indel calls were more sensitive to technical artifacts than LOF Single Nucleotide Variant (SNV) calls. In particular, whole genome amplification of DNA prior to sequencing led to an artificially increased burden of LOF indel calls, which confounded association analyses relating germline variants to tumor type despite stringent indel filtering strategies. Due to the inherent noise we chose to remove all 614 amplified DNA samples, including all acute myeloid leukemia and virtually all ovarian cancer samples, from the final dataset. This study demonstrates how insufficient quality control can lead to false positive germlinetumor type associations and draws attention to the need to be sensitive to problems associated with a lack of uniformity in data generation in TCGA data.\n\nAuthor SummaryCancer research to date has largely focused on genetic aberrations specific to tumor tissue. In contrast, the degree to which germline, or inherited, variation contributes to tumorigenesis remains unclear, possibly due to a lack of accessible germline variant data. In this study we identify germline variants in 9,618 samples using raw germline exome data from The Cancer Genome Atlas (TCGA). There are substantial differences in the way exome sequence data was generated both across and within cancer types in TCGA. We observe that differences in sequence data generation introduced batch effects, or variation that is due to technical factors not true biological variation, in our variant data. Most notably, we observe that amplification of DNA prior to sequencing resulted in an excess of predicted damaging indel variants. We show how these batch effects can confound germline association analyses if not properly addressed. Our study highlights the difficulties of working with large public genomic datasets like TCGA where samples are collected over time and across data centers, and particularly cautions the use of amplified DNA samples for genetic association analyses.

genomics↗