Prognosis and oncogenomic profiling of patients with tropomyosin receptor kinase fusion cancer in the 100,000 genomes project

INTRODUCTION

Neurotrophic tyrosine receptor kinase (NTRK) gene fusions are oncogenic drivers in various tumor types. Limited data exist on the overall survival (OS) of patients with tumors with NTRK gene fusions and on the co-occurrence of NTRK fusions with other oncogenic drivers.

MATERIALS AND METHODS

This retrospective study included patients enrolled in the Genomics England 100,000 Genomes Project who had linked clinical data from UK databases. Patients who had undergone tumor whole genome sequencing between March 2016 and July 2019 were included. Patients with and without NTRK fusions were matched. OS was analyzed along with oncogenic alterations in ALK, BRAF, EGFR, ERBB2, KRAS, and ROS1, and tumor mutation burden (TMB) and microsatellite instability (MSI).

RESULTS

Of 15,223 patients analyzed, 38 (0.25%) had NTRK gene fusions in 11 tumor types, the most common were breast cancer, colorectal cancer (CRC), and sarcoma. Median OS was not reached in both the NTRK gene fusion-positive and -negative groups (hazard ratio 1.47, 95% CI 0.39-5.57, P = 0.572). A KRAS mutation was identified in two (5%) patients with NTRK gene fusions, and both had hepatobiliary cancer. High TMB and MSI were both more common in patients with NTRK gene fusions, due to the CRC subset. While there was a higher risk of death in patients with NTRK gene fusions compared to those without, the difference was not statistically significant.

CONCLUSION

This study supports the hypothesis that NTRK gene fusions are primary oncogenic drivers and the co-occurrence of NTRK gene fusions with other oncogenic alterations is rare.

Abstract 2682: Small molecule targeting the lipoic acid post-translational modification impacts proliferation of colorectal and PIK3CA-mutant cell lines

To identify novel therapeutic targets, we utilize the PRISM platform, a multiplexed cell line viability technology of 500 solid tumor cell lines and correlate responses to functional genomic and baseline genetic data. We describe ESD0140656, a small molecule with selective anti-proliferative effect on colorectal and PIK3CA-mutant cell lines. Response to ESD0140656 is correlated to sensitivity to CRISPR/Cas9 KO of components of the protein lipoylation pathway and OGDH complex members, which catalyze a step of the TCA cycle. Lipoylation is a rare post-translational modification attached to just four enzymes in humans, including the OGDH complex. Knockout of the protein that transfers lipoic acid to these four enzymes (LIPT1) sensitizes cells to ESD0140656, and ESD0140656 treatment leads to reduction of lipoic acid in cells. These results suggest ESD0140656 targets the lipoylation pathway and may represent a novel therapeutic angle for colorectal and PIK3CA-mutant tumors.

Citation Format: Laura Doherty, Tenzin Sangpo, Peter Tsvetkov, John Davis, Navid Dianati, Wolfgang Schwede, Katja Zimmermann, Laura Evans, Aldo Amatucci, Henrik Seidel, Atanas Kamburov, Gizem Akcay, Todd Golub, Ashley Eheim, Nils Burkhardt, Knut Eis, Sven Christian, Matt Rees, Jennifer Roth. Small molecule targeting the lipoic acid post-translational modification impacts proliferation of colorectal and PIK3CA-mutant cell lines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2682.

ConsensusPathDB 2022: molecular interactions update as a resource for network biology

Molecular interactions are key drivers of biological function. Providing interaction resources to the research community is important since they allow functional interpretation and network-based analysis of molecular data. ConsensusPathDB (http://consensuspathdb.org) is a meta-database combining interactions of diverse types from 31 public resources for humans, 16 for mice and 14 for yeasts. Using ConsensusPathDB, researchers commonly evaluate lists of genes, proteins and metabolites against sets of molecular interactions defined by pathways, Gene Ontology and network neighborhoods and retrieve complex molecular neighborhoods formed by heterogeneous interaction types. Furthermore, the integrated protein–protein interaction network is used as a basis for propagation methods. Here, we present the 2022 update of ConsensusPathDB, highlighting content growth, additional functionality and improved database stability. For example, the number of human molecular interactions increased to 859 848 connecting 200 499 unique physical entities such as genes/proteins, metabolites and drugs. Furthermore, we integrated regulatory datasets in the form of transcription factor–, microRNA– and enhancer–gene target interactions, thus providing novel functionality in the context of overrepresentation and enrichment analyses. We specifically emphasize the use of the integrated protein–protein interaction network as a scaffold for network inferences, present topological characteristics of the network and discuss strengths and shortcomings of such approaches.

Abstract 394: Prognosis and molecular characteristics of patients with TRK fusion cancer in the 100,000 Genomes Project

Background: NTRK gene fusions are oncogenic drivers in various tumor types. Overall survival (OS) of patients (pts) with tumors harboring NTRK fusions compared to pts without is unknown, and data on co-occurrence of NTRK fusions with other oncogenic drivers are limited.1

Methods: This retrospective study used genomic data generated by the 100,000 Genomes Project with linked clinical data from UK cancer databases. Pts with cancer who had undergone tumor whole genome sequencing between Mar 2016 and Jul 2019 were included. OS of pts with and without NTRK fusions, matched by exact and distance matching with a 1:4 NTRK+:NTRK− ratio, was analyzed by Kaplan-Meier method and Cox regression. Key alterations in ALK, BRAF, EGFR, HER2, KRAS and ROS1, and tumor mutation burden (TMB) and microsatellite instability (MSI), were evaluated along with NTRK gene fusions.

Results: Of 15,223 pts analyzed, 38 (0.2%) had NTRK fusions, identified in 11 cancers (Genomics England classification), the most common being breast cancer (n=9), colorectal cancer (CRC; n=9), and sarcoma (n=7). While there was no significant OS difference in pts with and without NTRK fusions, the HR was 1.47 (95% CI 0.39-5.57; Table). Of the tested oncogenic drivers, only KRAS mutation was identified in 2 (5%) pts with an NTRK fusion (both hepatobiliary cancer), while oncogenic driver frequency in pts without NTRK fusions ranged from 0.1-11.6%. High TMB was more common in pts with NTRK fusions than in those without (21% vs 6%), as was high MSI (18% vs 6%); all pts with NTRK fusions and high TMB and/or MSI had CRC.

Conclusions: While no statistical difference in OS was observed, there was a trend to higher risk of death in pts with NTRK fusions compared to those without, consistent with a recent US study.1 Co-occurrence of NTRK fusions and other biomarkers was rare, except for high TMB and high MSI in CRC. These results highlight NTRK fusions as actionable biomarkers and emphasize the need for NTRK gene fusion testing.

NTRK− (n=72†)NTRK+ (n=18†)Median follow-up (IQR), years2.28 (1.57-2.98)2.01 (1.40-2.97)Median OS (IQR), yearsNE (NE-NE)NE (NE-NE)Landmark OS, % (95% CI)1 year96 (91-100)94 (84-100)2 years94 (89-100)87 (71-100)3 years88 (78-99)87 (71-100)HR (95% CI)1.47 (0.39-5.57)†Only patients with linked clinical data and who were matched were included in the OS analysis.1CI, confidence interval; HR, hazard ratio; IQR, interquartile range; NE, not estimable; NTRK; neurotrophic tyrosine receptor kinase; OS, overall survival.1. Bazhenova L, et al. Clin Cancer Res. 2020;26(12 Suppl 1):09-09.

Citation Format: John Bridgewater, Xiaolong Jiao, Mounika Parimi, Clare Flach, Jeran Stratford, Atanas Kamburov, Arndt Schmitz, Jihong Zong, John A. Reeves, Karen Keating, Amanda Bruno, Marc Fellous, Lyudmila Bazhenova. Prognosis and molecular characteristics of patients with TRK fusion cancer in the 100,000 Genomes Project [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 394.

Identification of Small Molecules that Modulate Mutant p53 Condensation

Structural mutants of p53 induce global p53 protein destabilization and misfolding, followed by p53 protein aggregation. First evidence indicates that p53 can be part of protein condensates and that p53 aggregation potentially transitions through a condensate-like state. We show condensate-like states of fluorescently labeled structural mutant p53 in the nucleus of living cancer cells. We furthermore identified small molecule compounds that interact with the p53 protein and lead to dissolution of p53 structural mutant condensates. The same compounds lead to condensation of a fluorescently tagged p53 DNA-binding mutant, indicating that the identified compounds differentially alter p53 condensation behavior depending on the type of p53 mutation. In contrast to p53 aggregation inhibitors, these compounds are active on p53 condensates and do not lead to mutant p53 reactivation. Taken together our study provides evidence for structural mutant p53 condensation in living cells and tools to modulate this process.

Abstract A29: Genome-wide CRISPR/Cas9 screens for the identification of novel YAP1/TAZ modulators

Aberrant activation of the Hippo pathway effectors YAP1/TAZ promotes cell proliferation and tumorigenesis. To identify novel regulators of YAP1/TAZ in cancer, we established a FACS-based screening system monitoring YAP1/TAZ activity in MDA-MB-231 breast cancer cells. Using these cells, we performed pooled genome-wide CRISPR/Cas9 knockout and CRISPR activation/interference (a/i) screens. The list of hits included previously known YAP1/TAZ modulators such as LATS2, AJUBA, and TAZ itself, demonstrating the robustness of the screen. Moreover, we identified about 30 novel candidate genes with potential inhibitory activity on YAP1/TAZ and about 50 candidate genes that may play a role in YAP1/TAZ activation. These genes represent diverse cellular functions such as regulation of actin cytoskeleton, integrin signaling, and ER protein processing, among others. Modulation of endogenous YAP1/TAZ target genes was assessed by individual gene knockout using crRNAs. Functional characterization of the novel potential YAP1/TAZ modulators will aid to the further understanding of YAP1/TAZ biology in health and disease.

Citation Format: Jan Naujoks, Lisette Potze, Julia Kuehnlenz, Atanas Kamburov, Ekaterina Nevedomskaya, Andreas Steffen, Claudia Luther, Anna Anurin, Anne Buttgereit, Stefan Prechtl, Benjamin Bader, Ralf Lesche, Peter Staller, Martin Lange, Barbara Nicke. Genome-wide CRISPR/Cas9 screens for the identification of novel YAP1/TAZ modulators [abstract]. In: Proceedings of the AACR Special Conference on the Hippo Pathway: Signaling, Cancer, and Beyond; 2019 May 8-11; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2020;18(8_Suppl):Abstract nr A29.

Abstract A115: Mechanisms of resistance toward the FGFR inhibitor rogaratinib in preclinical urothelial bladder cancer models

Introduction: Approximately one in three cases of urothelial bladder carcinoma (UBC) shows high expression of fibroblast growth factor receptor (FGFR) genes, predominantly of FGFR3 and FGFR1. The antitumor activity of the small molecule pan-FGFR-inhibitor rogaratinib (BAY1163877) has been demonstrated in preclinical models1, and the drug is currently being investigated in phase II and III clinical trials. However, clinical experience suggests the emergence of resistance with single-agent therapy. Here, we investigated the changes that occur with long-term FGFR inhibition and the development of resistance in preclinical UBC models, in order to identify possible combination strategies. Methods: We generated 13 resistant cell lines from the rogaratinib-sensitive cell lines JMSU1, RT112, RT4, and SW780, through continuous culture with various, increasing concentrations of rogaratinib. Transcriptomic and proteomic characterization was performed to identify potential therapeutic targets, of which a subset was evaluated using in vitro drug testing. We furthermore generated an in vivo resistant model by repeatedly transplanting JMSU1-derived xenografts. Results: Rogaratinib resistance developed reproducibly in vitro within several months and up to one year. We confirmed the lack of rogaratinib response in proliferation and viability assays. Transcriptomic analysis of resistant cell lines revealed changes in gene expression, with 300 to 2000 differentially expressed genes per sub-line (2-fold, p-adj. = 0.01). Hallmark gene sets such as MYC targets, epithelial-mesenchymal transition, or KRAS signaling were either negatively or positively correlated with resistance, but there was no single pathway change common to all cell lines. Genomic analysis found no mutations of the original driver FGFR genes and only few mutations in other receptor tyrosine kinase (RTK), MAPK, or PI3K signaling pathways. Proteomic analysis revealed activation of other RTKs in the rogaratinib-resistant models compared with the parental, rogaratinib-sensitive cells. These included EGFR, ErbB3, and MET, for which clinically approved inhibitors are available. We tested these as single-agents and in combination with rogaratinib in resistant cell lines to delineate potentially synergistic combinations. The JMSU1 in vivo resistance model confirmed the in vitro observations, where MET was upregulated upon resistance. The best anti-tumor efficacy was achieved with combined FGFR- and MET-inhibition, showing that FGFR signaling can remain a relevant oncogenic driver in addition to other RTKs activated as an escape mechanism. Conclusion: Continuous in vitro culture of cell lines with rogaratinib over several months led to resistance in all four evaluated models. Gene expression was considerably changed in these resistant cell lines, and proteomic analysis revealed activation of several RTKs. While the significance of many gene expression changes still remains to be determined, potent therapeutic targets were identified in several resistant cell lines, which point out opportunities for combination therapy. 1. Grunewald S, Politz O, Bender S, et al. Rogaratinib: A potent and selective pan-FGFR inhibitor with broad antitumor activity in FGFR-overexpressing preclinical cancer models. Int J Cancer. 2019.

Citation Format: Isabel S Jerchel, Atanas Kamburov, Sabine Zitzmann-Kolbe, Ralf Lesche, Alexander Walter, Dominik Mumberg, Oliver Politz, Sylvia Gruenewald. Mechanisms of resistance toward the FGFR inhibitor rogaratinib in preclinical urothelial bladder cancer models [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr A115. doi:10.1158/1535-7163.TARG-19-A115

Abstract 3055: Genome-wide CRISPR/Cas9 screen for the identification of novel YAP1/TAZ modulators

Aberrant activation of the Hippo pathway effectors YAP1/TAZ promotes cell proliferation and tumorigenesis. To identify novel regulators of YAP1/TAZ as a possible means to treat cancer, we established a novel, FACS-based screening system monitoring YAP1/TAZ activity in MDA-MB-231 breast cancer cells. Using these cells, we performed a pooled genome-wide CRISPR/Cas9 knockout screen. We identified approximately 50 genes potentially activating YAP1/TAZ with functions in the Actin Cytoskeleton signaling, p53 signaling, cell polarity or ER stress, amongst others. Moreover, we identified about 30 potential targets which when knocked out induce activity of YAP1/TAZ. The list of hits included genes known to affect the YAP1/TAZ activity such as AJUBA, LATS2 and TEAD, demonstrating the validity of the screen. Functional validation of the novel potential YAP1/TAZ modulators will aid to the further understanding of YAP1/TAZ biology and may open the door to new therapeutic avenues for targeting YAP1/TAZ in cancer.

Citation Format: Jan Naujoks, Lisette Potze, Anna Anurin, Julia Kuehnlenz, Ralf Lesche, Atanas Kamburov, Ekaterina Nevedomskaya, Andreas Steffen, Martin Lange, Barbara Nicke. Genome-wide CRISPR/Cas9 screen for the identification of novel YAP1/TAZ modulators [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3055.

Abstract 3080: Activity of pan-FGFR inhibitor rogaratinib and PI3K inhibitor copanlisib in preclinical urothelial bladder cancer models

Rogaratinib is a potent small molecule pan-FGFR inhibitor that leads to downregulation of MAPK and PI3K signaling (1). In a recent Phase I study rogaratinib demonstrated higher ORR in locally advanced or metastatic urothelial bladder cancer (UBC) with FGFR mRNA overexpression in patients with PIK3CA and RASwildtype than in those harboring PIK3CA or RAS mutations (2). In UBC, about 25% of tumors contain mutated PIK3CA of which 75% represent hot spot mutations E545K or E542K (3). Copanlisib is a potent pan class I PI3K inhibitor with predominant activity against the α and δ isoforms being approved for treatment of relapsed follicular lymphoma (4). We evaluated the activity of rogaratinib in cellular and in vivo efficacy studies in bladder cancer models in monotherapy and in combination with copanlisib. Cell proliferation and viability in response to rogaratinib, copanlisib or the combination of both was studied in rogaratinib-sensitive UBC cell lines like RT112 as well as in a recombinant RT112-PIK3CAE545K cell line. Rogaratinib potently inhibited cell proliferation with IC50 values in the double nM range. Copanlisib’s potency varied between 20 and 900 nM. In combination, rogaratinib and copanlisib increased cell death and were synergistic as shown by their proliferation combination indices for the absolute IC50 and IC80. Expression of the PIK3CAE545K hot spot mutant in RT112 cells decreased the anti-proliferative efficacy of rogaratinib but did not change the sensitivity to copanlisib. In vivo, rogaratinib led to tumor regression in the FGFR1-expressing JMSU1 xenograft and strongly inhibited tumor growth in the FGFR3-driven RT112 model. Copanlisib did not show significant inhibition of tumor growth in monotherapy in either model. In the JMSU1 model the combination was not superior to rogaratinib alone at maximal tolerated doses. In the RT112 as well as in the RT112-PIK3CAE545K model, the combination significantly improved anti-tumor activity. In a PDX model with FGFR3 overexpression and a PIK3CAH1047R hot spot mutation rogaratinib did not inhibit tumor growth significantly while copanlisib displayed significant anti-tumor effects. The combination of both drugs reduced tumor growth compared to either monotherapy group. In conclusion, in UBC tumor models overexpressing FGFR with differing sensitivities to rogaratinib, its anti-tumor activity could be further enhanced when combined with copanlisib and suggests that PIK3CA mutations may play a role in reduced sensitivity of UBC to FGFR inhibition. These promising results warrant further development of rogaratinib in monotherapy and in combination. Clinical trials with rogaratinib are currently recruiting (NCT03517956, NCT03410693, NCT03473756).

References:

Jerchel et al. Cancer Res. 2018; 78: Abs. 4781.

Joerger et al. JCO 2018; 36: Abs. 4513.

Platt et al. Clin. Cancer Res. 2009; 15:6008.

Markham, A. Drugs. 2017; 77:2057.

Citation Format: Isabel S. Jerchel, Pascale Lejeune, Rita Lampignano, Alexander Walter, Ralf Lesche, Atanas Kamburov, Dominik Mumberg, Peter Ellinghaus, Oliver Politz, Sylvia Gruenewald. Activity of pan-FGFR inhibitor rogaratinib and PI3K inhibitor copanlisib in preclinical urothelial bladder cancer models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3080.

Preclinical Efficacy of the Novel Monocarboxylate Transporter 1 Inhibitor BAY-8002 and Associated Markers of Resistance

The lactate transporter SLC16A1/monocarboxylate transporter 1 (MCT1) plays a central role in tumor cell energy homeostasis. In a cell-based screen, we identified a novel class of MCT1 inhibitors, including BAY-8002, which potently suppress bidirectional lactate transport. We investigated the antiproliferative activity of BAY-8002 in a panel of 246 cancer cell lines and show that hematopoietic tumor cells, in particular diffuse large B-cell lymphoma cell lines, and subsets of solid tumor models are particularly sensitive to MCT1 inhibition. Associated markers of sensitivity were, among others, lack of MCT4 expression, low pleckstrin homology like domain family A member 2, and high pellino E3 ubiquitin protein ligase 1 expression. The antitumor effect of MCT1 inhibition was less pronounced on tumor xenografts, with tumor stasis being the maximal response. BAY-8002 significantly increased intratumor lactate levels and transiently modulated pyruvate levels. In order to address potential acquired resistance mechanisms to MCT1 inhibition, we generated MCT1 inhibitor-resistant cell lines and show that resistance can occur by upregulation of MCT4 even in the presence of sufficient oxygen, as well as by shifting energy generation toward oxidative phosphorylation. These findings provide insight into novel aspects of tumor response to MCT1 modulation and offer further rationale for patient selection in the clinical development of MCT1 inhibitors. Mol Cancer Ther; 17(11); 2285-96. ©2018 AACR.

ERCC2 Helicase Domain Mutations Confer Nucleotide Excision Repair Deficiency and Drive Cisplatin Sensitivity in Muscle-Invasive Bladder Cancer

PURPOSE

DNA-damaging agents comprise the backbone of systemic treatment for many tumor types; however, few reliable predictive biomarkers are available to guide use of these agents. In muscle-invasive bladder cancer (MIBC), cisplatin-based chemotherapy improves survival, yet response varies widely among patients. Here, we sought to define the role of the nucleotide excision repair (NER) gene ERCC2 as a biomarker predictive of response to cisplatin in MIBC.

EXPERIMENTAL DESIGN

Somatic missense mutations in ERCC2 are associated with improved response to cisplatin-based chemotherapy; however, clinically identified ERCC2 mutations are distributed throughout the gene, and the impact of individual ERCC2 variants on NER capacity and cisplatin sensitivity is unknown. We developed a microscopy-based NER assay to profile ERCC2 mutations observed retrospectively in prior studies and prospectively within the context of an institution-wide tumor profiling initiative. In addition, we created the first ERCC2-deficient bladder cancer preclinical model for studying the impact of ERCC2 loss of function.

RESULTS

We used our functional assay to test the NER capacity of clinically observed ERCC2 mutations and found that most ERCC2 helicase domain mutations cannot support NER. Furthermore, we show that introducing an ERCC2 mutation into a bladder cancer cell line abrogates NER activity and is sufficient to drive cisplatin sensitivity in an orthotopic xenograft model.

CONCLUSIONS

Our data support a direct role for ERCC2 mutations in driving cisplatin response, define the functional landscape of ERCC2 mutations in bladder cancer, and provide an opportunity to apply combined genomic and functional approaches to prospectively guide therapy decisions in bladder cancer.See related commentary by Grivas, p. 907.

Abstract 4781: Changes in intracellular signaling following chronic FGFR inhibition in urothelial bladder cancer models

Preclinical and clinical studies have validated the therapeutic potential of FGFR inhibition in urothelial bladder cancer (UBC) patients with FGFR genetic aberrations. Rogaratinib is a potent small-molecule pan-FGFR inhibitor being studied in phase I trials in UBC. Here, we studied the effects of chronic exposure of bladder cancer cells in vitro to FGFR inhibition by rogaratinib to identify changes in signaling and gene expression patterns that may identify possible drug combinations that may enhance efficacy of rogaratinib and help overcome inherent and/or acquired treatment resistance. Cell proliferation in response to rogaratinib was evaluated using crystal violet staining in a panel of 13 UBC cell lines. Continuous culture of two cell lines–JMSU1 and RT112–with either constant or increasing concentrations of rogaratinib in several independent approaches generated the resistant sublines, JMSU1-R1 to -R4 and RT112-R1 to -R4. In both cellular models rogaratinib resistance (defined as >30-fold [JMSU1] or >100-fold [RT112] difference in absolute IC50 of sublines vs. parental lines) arose reproducibly and with various treatment schedules. Morphologic changes were also observed. Transcriptomic (RNAseq) and proteomic (R&D Systems proteome profiler arrays) analyses of these cell lines or rogaratinib-treated versus untreated parental cells, respectively, revealed changes that co-occur with the development of resistance. Analysis of the phospho-proteome showed increased phosphorylation of several receptor tyrosine kinases compared to the parental cell lines. Phosphorylation levels varied among the 4 resistant sublines that were derived from the same parental line. This resulted in alteration of downstream signaling pathways in rogaratinib-treated sublines compared to parental cell lines. In conclusion, exposure of selected bladder cancer cell lines to rogaratinib resulted in development of resistance and changes in FGFR signaling and gene expression pathways that may identify strategies to optimize treatment with FGFR inhibitors.

Citation Format: Isabel S. Jerchel, Atanas Kamburov, Ralf Lesche, Sabine Zitzmann-Kolbe, Alexander Walter, Peter Ellinghaus, Dominik Mumberg, Oliver Politz, Sylvia Gruenewald. Changes in intracellular signaling following chronic FGFR inhibition in urothelial bladder cancer models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4781.

Distinct mutational signatures characterize concurrent loss of polymerase proofreading and mismatch repair

Fidelity of DNA replication is maintained using polymerase proofreading and the mismatch repair pathway. Tumors with loss of function of either mechanism have elevated mutation rates with characteristic mutational signatures. Here we report that tumors with concurrent loss of both polymerase proofreading and mismatch repair function have mutational patterns that are not a simple sum of the signatures of the individual alterations, but correspond to distinct, previously unexplained signatures: COSMIC database signatures 14 and 20. We then demonstrate that in all five cases in which the chronological order of events could be determined, polymerase epsilon proofreading alterations precede the defect in mismatch repair. Overall, we illustrate that multiple distinct mutational signatures can result from different combinations of a smaller number of mutational processes (of either damage or repair), which can influence the interpretation and discovery of mutational signatures.

Polymerase proofreading and the mismatch repair pathway maintain the fidelity of DNA replication. Here the authors show that tumors with concurrent loss of function of both pathways lead to mutation signatures that are not simply a sum of the signatures found in tumors involving single alteration.

NetSig: network-based discovery from cancer genomes

Methods that integrate molecular network information and tumor genome data could complement gene-based statistical tests to identify likely new cancer genes; but such approaches are challenging to validate at scale, and their predictive value remains unclear. We developed a robust statistic (NetSig) that integrates protein interaction networks with data from 4,742 tumor exomes. NetSig can accurately classify known driver genes in 60% of tested tumor types and predicts 62 new driver candidates. Using a quantitative experimental framework to determine in vivo tumorigenic potential in mice, we found that NetSig candidates induce tumors at rates that are comparable to those of known oncogenes and are ten-fold higher than those of random genes. By reanalyzing nine tumor-inducing NetSig candidates in 242 patients with oncogene-negative lung adenocarcinomas, we find that two (AKT2 and TFDP2) are significantly amplified. Our study presents a scalable integrated computational and experimental workflow to expand discovery from cancer genomes.

Phenotypic Characterization of a Comprehensive Set of MAPK1/ERK2 Missense Mutants

Tumor-specific genomic information has the potential to guide therapeutic strategies and revolutionize patient treatment. Currently, this approach is limited by an abundance of disease-associated mutants whose biological functions and impacts on therapeutic response are uncharacterized. To begin to address this limitation, we functionally characterized nearly all (99.84%) missense mutants of MAPK1/ERK2, an essential effector of oncogenic RAS and RAF. Using this approach, we discovered rare gain- and loss-of-function ERK2 mutants found in human tumors, revealing that, in the context of this assay, mutational frequency alone cannot identify all functionally impactful mutants. Gain-of-function ERK2 mutants induced variable responses to RAF-, MEK-, and ERK-directed therapies, providing a reference for future treatment decisions. Tumor-associated mutations spatially clustered in two ERK2 effector-recruitment domains yet produced mutants with opposite phenotypes. This approach articulates an allele-characterization framework that can be scaled to meet the goals of genome-guided oncology.

A mutational signature reveals alterations underlying deficient homologous recombination repair in breast cancer

Biallelic inactivation of BRCA1 or BRCA2 is associated with a pattern of genome-wide mutations known as signature 3. By analyzing ∼1,000 breast cancer samples, we confirmed this association and established that germline nonsense and frameshift variants in PALB2, but not in ATM or CHEK2, can also give rise to the same signature. We were able to accurately classify missense BRCA1 or BRCA2 variants known to impair homologous recombination (HR) on the basis of this signature. Finally, we show that epigenetic silencing of RAD51C and BRCA1 by promoter methylation is strongly associated with signature 3 and, in our data set, was highly enriched in basal-like breast cancers in young individuals of African descent.

Abstract 5567: A scalable and integrated computational and experimental workflow to identify new driver genes in cancer genome data

High throughput sequencing has revolutionized the study of the cancer genome, enabling numerous discoveries in basic and clinical research. However, considerable sample sizes are required to find cancer driver genes with intermediate and low mutation frequencies, and for a large proportion of patients the molecular cause (e.g. driver gene(s)) of disease is unknown. Here, we describe an integrated computational and experimental workflow that combines cancer genome data, molecular network information, multiplexed in vivo tumorigenesis assays, and reanalysis of driver-gene-negative cancer patients to predict and validate new driver genes. We develop a statistic, network mutation burden, that combines molecular network information with data from 4,742 cancer genomes to accurately classify known driver genes across 21 tumor types and predict 62 driver gene candidates.Of these, 35 gene candidates were tested in multiplexed in vivo tumorigenesis cell assays using sensitized immortalized human embryonic kidney (HA1E-M) and immortalized human lung epithelial (SALE-Y) cell lines.Tumor formation in vivo was observed for 11 genes (2 in HA1E-M, 3 in SALE-Y, 6 in both). By reanalyzing 242 lung adenocarcinoma patients with an unknown molecular cause of disease we show that two of these candidates, TFDP2 and AKT2, are significantly amplified in multiple samples.Overall, we describe a scalable combined computational and experimental framework to predict and validate driver genes across many tumor types. Our proof-of-concept approach should become increasingly useful as the number of cancer genomes continues to grow.

Citation Format: Heiko Horn, Michael S. Lawrence, Candace R. Chouinard, Yashaswi Shrestha, Jessica Xin Hu, Elizabeth Worstell, Emily Shea, Nina Ilic, Eejung Kim, Atanas Kamburov, Alireza Kashani, William C. Hahn, Joshua D. Campbell, Jesse S. Boehm, Gad Getz, Kasper Lage. A scalable and integrated computational and experimental workflow to identify new driver genes in cancer genome data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5567. doi:10.1158/1538-7445.AM2017-5567

Abstract LB-280: The landscape of somatic microsatellite indels across cancer: detection and identification of driver events

Microsatellites (MSs) are tracts of variable-length repeats of short DNA motifs that are abundant in the human genome and exhibit high rates of mutations in the form of insertions or deletions of the repeated motif (MS indels). Despite their prevalence, the contribution of somatic MS indels to cancer is largely unexplored due to difficulties in detecting them and assessing their significance. Here, we present a comprehensive analysis of MS indels across 20 tumor types. We characterize the overall MS indel landscape and detect genes with candidate driver MS indel events. We present two novel tools: MSMuTect for accurate detection of somatic MS indels and MSMutSig for identifying candidate cancer genes containing events at higher frequency than expected by chance. We observe high variability of the frequency of MS indels across tumors and demonstrate that the number and pattern of MS indels can accurately distinguish microsatellite stable (MSS) tumors from tumors with microsatellite instability (MSI). Applying MSMutSig across 6,788 tumors from 20 different tumor types identified 7 genes with significant MS indel hotspots: ACVR2A, RNF43, DOCK3, MSH3, ESRP1, PRDM2 and JAK1. In the four genes that have been previously implicated in cancer (ACVR2A, RNF43, JAK1 and MSH3), we identified previously unreported MS indels events. Three of the genes with significant loci - DOCK3, PRDM2 and ESRP1- had not been previously listed as cancer genes. MS indels in DOCK3, a negative regulator of the WNT pathway, were mutually exclusive with mutations in CTNNB1. MS indels in ESRP1, an RNA processing gene, correlated with alternative splicing of FGFR2, an event associated with the epithelial-to-mesenchymal transition. Overall, our comprehensive analysis of somatic MS indels across cancer highlights their importance, particularly in

MSI tumors, significantly contributes to the ongoing global efforts to detect cancer genes, and may improve classification of patients into clinically-relevant subgroups.

Citation Format: Yosef E. Maruvka, Kent W. Mouw, Rosa Karlic, Rasanna Parasuraman, Atanas Kamburov, Paz Polak, Nicholas J. Haradhvala, Julian M. Hess, Esther Rheinbay, Yehuda Brody, Lior Z. Braunstein, Alan D’Andrea, Michael S. Lawrence, Adam Bass, Andre Bernards, Franziska Michor, Gad Getz. The landscape of somatic microsatellite indels across cancer: detection and identification of driver events [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-280. doi:10.1158/1538-7445.AM2017-LB-280

High-throughput Phenotyping of Lung Cancer Somatic Mutations

Recent genome sequencing efforts have identified millions of somatic mutations in cancer. However, the functional impact of most variants is poorly understood. Here we characterize 194 somatic mutations identified in primary lung adenocarcinomas. We present an expression-based variant-impact phenotyping (eVIP) method that uses gene expression changes to distinguish impactful from neutral somatic mutations. eVIP identified 69% of mutations analyzed as impactful and 31% as functionally neutral. A subset of the impactful mutations induces xenograft tumor formation in mice and/or confers resistance to cellular EGFR inhibition. Among these impactful variants are rare somatic, clinically actionable variants including EGFR S645C, ARAF S214C and S214F, ERBB2 S418T, and multiple BRAF variants, demonstrating that rare mutations can be functionally important in cancer.

Abstract 4368: High-throughput phenotyping of lung cancer somatic mutations

Recent cancer genome sequencing and analysis has identified millions of somatic mutations in cancer. However, the functional impact of most variants is poorly understood, limiting the use of this genetic knowledge for clinical decision-making. Here we describe a new high-throughput approach, expression-based variant impact phenotyping (eVIP), which uses gene expression changes to infer somatic mutation impact. We generated a lentiviral expression library representing 53 genes and 194 somatic mutations identified in primary lung adenocarcinomas. Next, we introduced this library into A549 lung adenocarcinoma cells and 96 hours later performed gene expression profiling using Luminex-based L1000 profiling. We built a computational pipeline, eVIP, to compare mutant and wild-type expression signatures to infer whether variants were gain-of-function, change-of-function, loss-of-function, or neutral. Overall, eVIP identified 69% of mutations as impactful whereas 31% appeared functionally neutral. A very high rate, 92%, of missense mutations in the KEAP1 and STK11 tumor suppressor genes were found to inactivate or diminish protein function. As a complementary approach, we assessed which mutations are epistatic to EGFR or capable of initiating xenograft tumor formation in vivo. A subset of the impactful mutations identified by eVIP could induce xenograft tumor formation in mice and/or confer resistance to cellular EGFR inhibition. Among these mutations were 20 rare or non-canonical somatic variants in clinically-actionable or -relevant oncogenes including EGFR S645C, ARAF S214C and S214F, ERBB2 S418T, and PIK3CA E600K. eVIP can, in principle, characterize any genetic variant, independent of prior knowledge of gene function. Further application of eVIP should significantly advance the pace of functional characterization of mutations identified from genome sequencing.

Citation Format: Alice H. Berger, Angela N. Brooks, Xiaoyun Wu, Yashaswi Shrestha, Candace Chouinard, Federica Piccioni, Mukta Bagul, Atanas Kamburov, Marcin Imielinski, Larson Hogstrom, Cong Zhu, Xiaoping Yang, Sasha Pantel, Ryo Sakai, Nathan Kaplan, David Root, Rajiv Narayan, Ted Natoli, David Lahr, Itay Tirosh, Pablo Tamayo, Gad Getz, Bang Wong, John Doench, Aravind Subramanian, Todd R. Golub, Matthew Meyerson, Jesse S. Boehm. High-throughput phenotyping of lung cancer somatic mutations. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4368.

Somatic ERCC2 mutations are associated with a distinct genomic signature in urothelial tumors

Alterations in DNA repair pathways are common in tumors and can result in characteristic mutational signatures; however, a specific mutational signature associated with somatic alterations in the nucleotide- excision repair (NER) pathway has not yet been identified. Here we examine the mutational processes operating in urothelial cancer, a tumor type in which the core NER gene ERCC2 is significantly mutated. Analysis of three independent urothelial tumor cohorts demonstrates a strong association between somatic ERCC2 mutations and the activity of a mutational signature characterized by a broad spectrum of base changes. In addition, we note an association between the activity of this signature and smoking that is independent of ERCC2 mutation status, providing genomic evidence of tobacco-related mutagenesis in urothelial cancer. Together, these analyses identify an NER-related mutational signature and highlight the related roles of DNA damage and subsequent DNA repair in shaping tumor mutational landscape.

Systematic Functional Interrogation of Rare Cancer Variants Identifies Oncogenic Alleles

Cancer genome characterization efforts now provide an initial view of the somatic alterations in primary tumors. However, most point mutations occur at low frequency, and the function of these alleles remains undefined. We have developed a scalable systematic approach to interrogate the function of cancer-associated gene variants. We subjected 474 mutant alleles curated from 5,338 tumors to pooled in vivo tumor formation assays and gene expression profiling. We identified 12 transforming alleles, including two in genes (PIK3CB, POT1) that have not been shown to be tumorigenic. One rare KRAS allele, D33E, displayed tumorigenicity and constitutive activation of known RAS effector pathways. By comparing gene expression changes induced upon expression of wild-type and mutant alleles, we inferred the activity of specific alleles. Because alleles found to be mutated only once in 5,338 tumors rendered cells tumorigenic, these observations underscore the value of integrating genomic information with functional studies.

SIGNIFICANCE

Experimentally inferring the functional status of cancer-associated mutations facilitates the interpretation of genomic information in cancer. Pooled in vivo screen and gene expression profiling identified functional variants and demonstrated that expression of rare variants induced tumorigenesis. Variant phenotyping through functional studies will facilitate defining key somatic events in cancer. Cancer Discov; 6(7); 714-26. ©2016 AACR.See related commentary by Cho and Collisson, p. 694This article is highlighted in the In This Issue feature, p. 681.

ToxDB: pathway-level interpretation of drug-treatment data

Motivation: Extensive drug treatment gene expression data have been generated in order to identify biomarkers that are predictive for toxicity or to classify compounds. However, such patterns are often highly variable across compounds and lack robustness. We and others have previously shown that supervised expression patterns based on pathway concepts rather than unsupervised patterns are more robust and can be used to assess toxicity for entire classes of drugs more reliably.

Results: We have developed a database, ToxDB, for the analysis of the functional consequences of drug treatment at the pathway level. We have collected 2694 pathway concepts and computed numerical response scores of these pathways for 437 drugs and chemicals and 7464 different experimental conditions. ToxDB provides functionalities for exploring these pathway responses by offering tools for visualization and differential analysis allowing for comparisons of different treatment parameters and for linking this data with toxicity annotation and chemical information.

Database URL:http://toxdb.molgen.mpg.de

Mutational Strand Asymmetries in Cancer Genomes Reveal Mechanisms of DNA Damage and Repair

Mutational processes constantly shape the somatic genome, leading to immunity, aging, cancer, and other diseases. When cancer is the outcome, we are afforded a glimpse into these processes by the clonal expansion of the malignant cell. Here, we characterize a less explored layer of the mutational landscape of cancer: mutational asymmetries between the two DNA strands. Analyzing whole-genome sequences of 590 tumors from 14 different cancer types, we reveal widespread asymmetries across mutagenic processes, with transcriptional ("T-class") asymmetry dominating UV-, smoking-, and liver-cancer-associated mutations and replicative ("R-class") asymmetry dominating POLE-, APOBEC-, and MSI-associated mutations. We report a striking phenomenon of transcription-coupled damage (TCD) on the non-transcribed DNA strand and provide evidence that APOBEC mutagenesis occurs on the lagging-strand template during DNA replication. As more genomes are sequenced, studying and classifying their asymmetries will illuminate the underlying biological mechanisms of DNA damage and repair.

Abstract PR07: Towards precision functional genomics via next-generation functional mapping of cancer variants

With the comprehensive analysis of cancer genomes approaching completion, the research community stands poised to rapidly advance genome-guided therapeutic hypotheses into clinical settings. However, for the vast majority of cancer patients, existing knowledge of the function(s) of the newly discovered mutant genes harbored by their tumor is incomplete or non-existent since most cancer mutations are exceedingly rare. As a result, we now have long lists of candidate alleles but a paucity of targets whose biology is sufficiently well understood to guide therapeutics.

Here we present an interim progress report on a pilot effort aiming to create a generalizable framework to systematically map the molecular consequences of cancer variants at scale (Target Accelerator). First, we created an efficient pipeline to generate cancer variants and generated an initial library of 1300 mutant cDNA clones corresponding to variants in lung cancer and diffuse large B-cell lymphoma as well as those nominated by “pan-cancer” computational analyses.

Second, we established an industry-scale, next-generation pipeline to generate new cancer models (Cell Line Factory) directly from patient samples. We have leveraged this pipeline to process over 330 samples from 208 patients across 16 cancer types, with over 60% growing through at least 5 population doublings. We show that tumor genomics can be retained in such patient-derived models and that drug testing to discover clinically validated dependencies within 3 months is feasible. In addition, we use combinatorial molecular barcoding to rapidly generate a panel of pathway-primed human tumorigenesis models that are suitable for massively parallel multiplexed tumorigenesis assays in vivo (TumorPlex).

We hypothesized that this integrated framework could be utilized to generate meaningful functional hypotheses from cancer variants of unknown significance in a high-throughput manner. To test this hypothesis, we introduced over 1000 cancer mutations into cell models and created gene expression signatures together with phenotypic data. In lung cancer, we show that the mutational impact of mutant alleles with known and unknown functions can be rapidly assessed by comparing signatures of wild-type and mutant alleles. We show that this generalizable approach, which does not require prior knowledge, can place variants of unknown significance into dominant gain-of-function and loss-of-function categories. As a complementary approach, we have used TumorPlex assays to test the tumorigenic potential of 550 mutant alleles nominated by Pan-Cancer computational analyses and discovered unexpected variants in the KRAS, AKT1, MAP2K1, ERBB2, PIK3CB, NFE2L2, FAM200A and POT1 genes as being potently tumorigenic.

These proof-of-concept studies demonstrate initial feasibility of mapping cancer variant function at scale. Importantly, they demarcate a path by which mapping variant function and predicting vulnerabilities might soon be possible on a patient-by-patient basis, achieving the promise of precision functional genomics.

Citation Format: Alice H. Berger, Eejung Kim, Angela Brooks, Nina Ilic, Yashaswi Shrestha, Yuen-Yi Tseng, Xiaoyun Wu, Lihua Zou, Atanas Kamburov, Xiaoping Yang, Cong Zhu, Paula Keskula, Sara Seepo, Andrew Hong, Philip Kantoff, Keith L. Ligon, Levi A. Garraway, John G. Doench, David E. Root, Matthew Meyerson, William C. Hahn, Gad Getz, Todd R. Golub, Jesse S. Boehm. Towards precision functional genomics via next-generation functional mapping of cancer variants. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr PR07.

Abstract 957: Towards precision functional genomics via next-generation functional mapping of cancer variants

For the vast majority of cancer patients, existing knowledge of the function(s) of the mutant genes harbored by their tumor and the dependencies they induce is incomplete or non-existent since most cancer mutations are exceedingly rare. As a result, we now have long lists of candidate alleles but a paucity of targets whose biology is sufficiently well understood to guide therapeutics. Here we present an interim progress report on a pilot effort aiming to create a generalizable framework to systematically map the molecular consequences of cancer variants at scale (Target Accelerator). First, we created an efficient pipeline to generate cancer variants and generated an initial library of 1300 mutant cDNA clones corresponding to variants in lung cancer and diffuse large B-cell lymphoma as well as those nominated by “pan-cancer” computational analyses. Second, we established an industry-scale, next-generation pipeline to generate new cancer models (Cell Line Factory) directly from patient samples. We have leveraged this pipeline to process over 330 samples from 208 patients across 16 cancer types, with over 60% growing through at least 5 population doublings. We show that tumor genomics can be retained in such patient-derived models and that drug testing within 3 months is feasible. In addition, we use combinatorial molecular barcoding to rapidly generate a panel of pathway-primed human tumorigenesis models that are suitable for massively parallel multiplexed tumorigenesis assays in vivo (TumorPlex). We hypothesized that this integrated framework could be utilized to generate meaningful functional hypotheses in a high-throughput manner. To test this hypothesis, we introduced over 1000 cancer mutations into cell models and created gene expression signatures together with phenotypic data. In lung cancer, we show that the mutational impact of mutant alleles with known and unknown functions can be rapidly assessed by comparing signatures of wild-type and mutant alleles. We show that this generalizable approach, which does not require prior knowledge, can place variants of unknown significance into dominant gain-of-function and loss-of-function categories. As a complementary approach, we have used TumorPlex assays to test the tumorigenic potential of 550 mutant alleles nominated by Pan-Cancer computational analyses and discovered unexpected variants in the KRAS, AKT1, MAP2K1, ERBB2, PIK3CB, NFE2L2, FAM200A and POT1 genes as being potently tumorigenic. These proof-of-concept studies demonstrate initial feasibility of mapping cancer variant function at scale. Importantly, they demarcate a path by which mapping variant function and predicting vulnerabilities might soon be possible on a patient-by-patient basis, achieving the promise of precision functional genomics.

Citation Format: Alice Berger, Eejung Kim, Angela Brooks, Yashaswi Shrestha, Yuen-Yi Tseng, Xiaoyun Wu, Nina Ilic, Lihua Zou, Atanas Kamburov, Xiaoping Yang, Cong Zhu, Paula Keskula, Sara Seepo, Andrew Hong, John Doench, Aravind Subramanian, Keith Ligon, Philip Kantoff, Katherine Janeway, Levi Garraway, David Root, Todd Golub, Matthew Meyerson, William Hahn, Gad Getz, Jesse Boehm. Towards precision functional genomics via next-generation functional mapping of cancer variants. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 957. doi:10.1158/1538-7445.AM2015-957

Widespread macromolecular interaction perturbations in human genetic disorders

How disease-associated mutations impair protein activities in the context of biological networks remains mostly undetermined. Although a few renowned alleles are well characterized, functional information is missing for over 100,000 disease-associated variants. Here we functionally profile several thousand missense mutations across a spectrum of Mendelian disorders using various interaction assays. The majority of disease-associated alleles exhibit wild-type chaperone binding profiles, suggesting they preserve protein folding or stability. While common variants from healthy individuals rarely affect interactions, two-thirds of disease-associated alleles perturb protein-protein interactions, with half corresponding to "edgetic" alleles affecting only a subset of interactions while leaving most other interactions unperturbed. With transcription factors, many alleles that leave protein-protein interactions intact affect DNA binding. Different mutations in the same gene leading to different interaction profiles often result in distinct disease phenotypes. Thus disease-associated alleles that perturb distinct protein activities rather than grossly affecting folding and stability are relatively widespread.

A proteome-scale map of the human interactome network

Just as reference genome sequences revolutionized human genetics, reference maps of interactome networks will be critical to fully understand genotype-phenotype relationships. Here, we describe a systematic map of ?14,000 high-quality human binary protein-protein interactions. At equal quality, this map is ?30% larger than what is available from small-scale studies published in the literature in the last few decades. While currently available information is highly biased and only covers a relatively small portion of the proteome, our systematic map appears strikingly more homogeneous, revealing a "broader" human interactome network than currently appreciated. The map also uncovers significant interconnectivity between known and candidate cancer gene products, providing unbiased evidence for an expanded functional cancer landscape, while demonstrating how high-quality interactome models will help "connect the dots" of the genomic revolution.

Dual coordination of post translational modifications in human protein networks

Post-translational modifications (PTMs) regulate protein activity, stability and interaction profiles and are critical for cellular functioning. Further regulation is gained through PTM interplay whereby modifications modulate the occurrence of other PTMs or act in combination. Integration of global acetylation, ubiquitination and tyrosine or serine/threonine phosphorylation datasets with protein interaction data identified hundreds of protein complexes that selectively accumulate each PTM, indicating coordinated targeting of specific molecular functions. A second layer of PTM coordination exists in these complexes, mediated by PTM integration (PTMi) spots. PTMi spots represent very dense modification patterns in disordered protein regions and showed an equally high mutation rate as functional protein domains in cancer, inferring equivocal importance for cellular functioning. Systematic PTMi spot identification highlighted more than 300 candidate proteins for combinatorial PTM regulation. This study reveals two global PTM coordination mechanisms and emphasizes dataset integration as requisite in proteomic PTM studies to better predict modification impact on cellular signaling.

Normal cellular functioning is maintained by a vast array of macro-molecular machines that control both core and specialised molecular tasks. These machines are in large part multi-subunit protein complexes that undergo regulation at multiple levels, from expression of requisite components to a vast array of post translational modifications (PTMs). PTMs such as phosphorylation, ubiquitination and acetylation currently number up to more than 100,000 in the human proteome yet how, or if, they coordinate remains poorly understood. Here we show two mechanisms of systematic modification coordination that likely combine to provide finer control of protein complex function. Firstly, individual modifications selectively target protein complexes to execute specific molecular functions. Secondly, highly modified subunits of these complexes further accumulate multiple distinct modifications and contain regions of dense modification patterns, termed PTM integration (PTMi) spots. Through multiple PTM inputs, PTMi spots represent key regions for integrating multiple signals within these complexes, allowing finer regulation of protein function. Here we highlight the large extent of coordinated PTM regulation of protein complexes, and hence cellular function. Systematic dataset integration revealed biological insight into PTM mediated cellular regulatory mechanisms and further provides a resource for future hypothesis-driven studies.

The ConsensusPathDB interaction database: 2013 update

Knowledge of the various interactions between molecules in the cell is crucial for understanding cellular processes in health and disease. Currently available interaction databases, being largely complementary to each other, must be integrated to obtain a comprehensive global map of the different types of interactions. We have previously reported the development of an integrative interaction database called ConsensusPathDB (http://ConsensusPathDB.org) that aims to fulfill this task. In this update article, we report its significant progress in terms of interaction content and web interface tools. ConsensusPathDB has grown mainly due to the integration of 12 further databases; it now contains 215 541 unique interactions and 4601 pathways from overall 30 databases. Binary protein interactions are scored with our confidence assessment tool, IntScore. The ConsensusPathDB web interface allows users to take advantage of these integrated interaction and pathway data in different contexts. Recent developments include pathway analysis of metabolite lists, visualization of functional gene/metabolite sets as overlap graphs, gene set analysis based on protein complexes and induced network modules analysis that connects a list of genes through various interaction types. To facilitate the interactive, visual interpretation of interaction and pathway data, we have re-implemented the graph visualization feature of ConsensusPathDB using the Cytoscape.js library.

Cluster-based assessment of protein-protein interaction confidence

Background

Protein-protein interaction networks are key to a systems-level understanding of cellular biology. However, interaction data can contain a considerable fraction of false positives. Several methods have been proposed to assess the confidence of individual interactions. Most of them require the integration of additional data like protein expression and interaction homology information. While being certainly useful, such additional data are not always available and may introduce additional bias and ambiguity.

Results

We propose a novel, network topology based interaction confidence assessment method called CAPPIC (cluster-based assessment of protein-protein interaction confidence). It exploits the network’s inherent modular architecture for assessing the confidence of individual interactions. Our method determines algorithmic parameters intrinsically and does not require any parameter input or reference sets for confidence scoring.

Conclusions

On the basis of five yeast and two human physical interactome maps inferred using different techniques, we show that CAPPIC reliably assesses interaction confidence and its performance compares well to other approaches that are also based on network topology. The confidence score correlates with the agreement in localization and biological process annotations of interacting proteins. Moreover, it corroborates experimental evidence of physical interactions. Our method is not limited to physical interactome maps as we exemplify with a large yeast genetic interaction network. An implementation of CAPPIC is available at http://intscore.molgen.mpg.de.

IntScore: a web tool for confidence scoring of biological interactions

Knowledge of all molecular interactions that potentially take place in the cell is a key for a detailed understanding of cellular processes. Currently available interaction data, such as protein–protein interaction maps, are known to contain false positives that inevitably diminish the accuracy of network-based inferences. Interaction confidence scoring is thus a crucial intermediate step after obtaining interaction data and before using it in an interaction network-based inference approach. It enables to weight individual interactions according to the likelihood that they actually take place in the cell, and can be used to filter out false positives. We describe a web tool called IntScore which calculates confidence scores for user-specified sets of interactions. IntScore provides six network topology- and annotation-based confidence scoring methods. It also enables the integration of scores calculated by the different methods into an aggregate score using machine learning approaches. IntScore is user-friendly and extensively documented. It is freely available at http://intscore.molgen.mpg.de.

DIPSBC--data integration platform for systems biology collaborations

Background

Modern biomedical research is often organized in collaborations involving labs worldwide. In particular in systems biology, complex molecular systems are analyzed that require the generation and interpretation of heterogeneous data for their explanation, for example ranging from gene expression studies and mass spectrometry measurements to experimental techniques for detecting molecular interactions and functional assays. XML has become the most prominent format for representing and exchanging these data. However, besides the development of standards there is still a fundamental lack of data integration systems that are able to utilize these exchange formats, organize the data in an integrative way and link it with applications for data interpretation and analysis.

Results

We have developed DIPSBC, an interactive data integration platform supporting collaborative research projects, based on Foswiki, Solr/Lucene, and specific helper applications. We describe the main features of the implementation and highlight the performance of the system with several use cases. All components of the system are platform independent and open-source developments and thus can be easily adopted by researchers. An exemplary installation of the platform which also provides several helper applications and detailed instructions for system usage and setup is available at http://dipsbc.molgen.mpg.de.

Conclusions

DIPSBC is a data integration platform for medium-scale collaboration projects that has been tested already within several research collaborations. Because of its modular design and the incorporation of XML data formats it is highly flexible and easy to use.

Dynamic protein-protein interaction wiring of the human spliceosome

More than 200 proteins copurify with spliceosomes, the compositionally dynamic RNPs catalyzing pre-mRNA splicing. To better understand protein - protein interactions governing splicing, we systematically investigated interactions between human spliceosomal proteins. A comprehensive Y2H interaction matrix screen generated a protein interaction map comprising 632 interactions between 196 proteins. Among these, 242 interactions were found between spliceosomal core proteins and largely validated by coimmunoprecipitation. To reveal dynamic changes in protein interactions, we integrated spliceosomal complex purification information with our interaction data and performed link clustering. These data, together with interaction competition experiments, suggest that during step 1 of splicing, hPRP8 interactions with SF3b proteins are replaced by hSLU7, positioning this second step factor close to the active site, and that the DEAH-box helicases hPRP2 and hPRP16 cooperate through ordered interactions with GPKOW. Our data provide extensive information about the spliceosomal protein interaction network and its dynamics.

Construction of a pig physical interactome using sequence homology and a comprehensive reference human interactome

The analysis of interaction networks is crucial for understanding molecular function and has an essential impact for genomewide studies. However, the interactomes of most species are largely incomplete and computational strategies that take into account sequence homology can help compensating for this lack of information using cross-species analysis. In this work we report the construction of a porcine interactome resource. We applied sequence homology matching and carried out bi-directional BLASTp searches for the currently available protein sequence collections of human and pig. Using this homology we were able to recover, on average, 71% of the proteins annotated for human pathways for the pig. Porcine protein-protein interactions were deduced from homologous proteins with known interactions in human. The result of this work is a resource comprising 204,699 predicted porcine interactions that can be used in genome analyses in order to enhance functional interpretation of data. The data can be visualized and downloaded from http://cpdb.molgen.mpg.de/pig.

Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA

SUMMARY

Pathway-level analysis is a powerful approach enabling interpretation of post-genomic data at a higher level than that of individual biomolecules. Yet, it is currently hard to integrate more than one type of omics data in such an approach. Here, we present a web tool 'IMPaLA' for the joint pathway analysis of transcriptomics or proteomics and metabolomics data. It performs over-representation or enrichment analysis with user-specified lists of metabolites and genes using over 3000 pre-annotated pathways from 11 databases. As a result, pathways can be identified that may be disregulated on the transcriptional level, the metabolic level or both. Evidence of pathway disregulation is combined, allowing for the identification of additional pathways with changed activity that would not be highlighted when analysis is applied to any of the functional levels alone. The tool has been implemented both as an interactive website and as a web service to allow a programming interface.

AVAILABILITY

The web interface of IMPaLA is available at http://impala.molgen.mpg.de. A web services programming interface is provided at http://impala.molgen.mpg.de/wsdoc.

CONTACT

kamburov@molgen.mpg.de; r.cavill@imperial.ac.uk; h.keun@imperial.ac.uk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Human embryonic stem cell derived hepatocyte-like cells as a tool for in vitro hazard assessment of chemical carcinogenicity

Hepatocyte-like cells derived from the differentiation of human embryonic stem cells (hES-Hep) have potential to provide a human relevant in vitro test system in which to evaluate the carcinogenic hazard of chemicals. In this study, we have investigated this potential using a panel of 15 chemicals classified as noncarcinogens, genotoxic carcinogens, and nongenotoxic carcinogens and measured whole-genome transcriptome responses with gene expression microarrays. We applied an ANOVA model that identified 592 genes highly discriminative for the panel of chemicals. Supervised classification with these genes achieved a cross-validation accuracy of > 95%. Moreover, the expression of the response genes in hES-Hep was strongly correlated with that in human primary hepatocytes cultured in vitro. In order to infer mechanistic information on the consequences of chemical exposure in hES-Hep, we developed a computational method that measures the responses of biochemical pathways to the panel of treatments and showed that these responses were discriminative for the three toxicity classes and linked to carcinogenesis through p53, mitogen-activated protein kinases, and apoptosis pathway modules. It could further be shown that the discrimination of toxicity classes was improved when analyzing the microarray data at the pathway level. In summary, our results demonstrate, for the first time, the potential of human embryonic stem cell--derived hepatic cells as an in vitro model for hazard assessment of chemical carcinogenesis, although it should be noted that more compounds are needed to test the robustness of the assay.

Consensus-phenotype integration of transcriptomic and metabolomic data implies a role for metabolism in the chemosensitivity of tumour cells

Using transcriptomic and metabolomic measurements from the NCI60 cell line panel, together with a novel approach to integration of molecular profile data, we show that the biochemical pathways associated with tumour cell chemosensitivity to platinum-based drugs are highly coincident, i.e. they describe a consensus phenotype. Direct integration of metabolome and transcriptome data at the point of pathway analysis improved the detection of consensus pathways by 76%, and revealed associations between platinum sensitivity and several metabolic pathways that were not visible from transcriptome analysis alone. These pathways included the TCA cycle and pyruvate metabolism, lipoprotein uptake and nucleotide synthesis by both salvage and de novo pathways. Extending the approach across a wide panel of chemotherapeutics, we confirmed the specificity of the metabolic pathway associations to platinum sensitivity. We conclude that metabolic phenotyping could play a role in predicting response to platinum chemotherapy and that consensus-phenotype integration of molecular profiling data is a powerful and versatile tool for both biomarker discovery and for exploring the complex relationships between biological pathways and drug response.

Resistance to chemotherapy drugs in cancer sufferers is very common. Using a panel of 59 cell lines obtained from different types of cancer we study the links between the genes and metabolites measured in these cells and the resistance the cells show to common cancer drugs containing platinum. In order to combine the information given by the genes and metabolites we introduce a new pathway-based approach, which allows us to explore synergy between the different types of data. We then extend the procedure to look at a wider panel of drugs and show that the pathways we found were associated with platinum are not just the pathways which are frequently selected for a large number of drugs. Given the increasing use of multiple sets of measurements (genes, metabolites, proteins etc.) in biological studies, we demonstrate a powerful, yet straightforward method for dealing with the resulting large datasets and integrating their knowledge. We believe that this work could contribute to developing a personalised medicine approach to treating tumours, where the genetic and metabolic changes in the tumour are measured and then used for prediction of the optimal treatment regime.

ConsensusPathDB: toward a more complete picture of cell biology

ConsensusPathDB is a meta-database that integrates different types of functional interactions from heterogeneous interaction data resources. Physical protein interactions, metabolic and signaling reactions and gene regulatory interactions are integrated in a seamless functional association network that simultaneously describes multiple functional aspects of genes, proteins, complexes, metabolites, etc. With 155 432 human, 194 480 yeast and 13 648 mouse complex functional interactions (originating from 18 databases on human and eight databases on yeast and mouse interactions each), ConsensusPathDB currently constitutes the most comprehensive publicly available interaction repository for these species. The Web interface at http://cpdb.molgen.mpg.de offers different ways of utilizing these integrated interaction data, in particular with tools for visualization, analysis and interpretation of high-throughput expression data in the light of functional interactions and biological pathways.

Evidence mining and novelty assessment of protein-protein interactions with the ConsensusPathDB plugin for Cytoscape

Summary: Protein–protein interaction detection methods are applied on a daily basis by molecular biologists worldwide. After generating a set of potential interactions, biologists face the problem of highlighting the ones that are novel and collecting evidence with respect to literature and annotation. This task can be as tedious as searching for every predicted interaction in several interaction data repositories, or manually screening the scientific literature. To facilitate the task of evidence mining and novelty assessment of protein–protein interactions, we have developed a Cytoscape plugin that automatically mines publication references, database references, interaction detection method descriptions and pathway annotation for a user-supplied network of interactions. The basis for the annotation is ConsensusPathDB—a meta-database that integrates numerous protein–protein, signaling, metabolic and gene regulatory interaction repositories for currently three species: Homo sapiens, Saccharomyces cerevisiae and Mus musculus.

Availability: The ConsensusPathDB plugin for Cytoscape (version 2.7.0 or later) can be installed within Cytoscape on a major operating system (Windows, Mac OS, Unix/Linux) with Sun Java 1.5 or later installed through Cytoscape's Plugin manager (category ‘Network and Attribute I/O’). The plugin is freely available for download on the ConsensusPathDB web site (http://cpdb.molgen.mpg.de).

Supplementary information:Supplementary data are available at Bioinformatics online.

Contact:kamburov@molgen.mpg.de

An atlas of combinatorial transcriptional regulation in mouse and man

Combinatorial interactions among transcription factors are critical to directing tissue-specific gene expression. To build a global atlas of these combinations, we have screened for physical interactions among the majority of human and mouse DNA-binding transcription factors (TFs). The complete networks contain 762 human and 877 mouse interactions. Analysis of the networks reveals that highly connected TFs are broadly expressed across tissues, and that roughly half of the measured interactions are conserved between mouse and human. The data highlight the importance of TF combinations for determining cell fate, and they lead to the identification of a SMAD3/FLI1 complex expressed during development of immunity. The availability of large TF combinatorial networks in both human and mouse will provide many opportunities to study gene regulation, tissue differentiation, and mammalian evolution.

ConsensusPathDB--a database for integrating human functional interaction networks

ConsensusPathDB is a database system for the integration of human functional interactions. Current knowledge of these interactions is dispersed in more than 200 databases, each having a specific focus and data format. ConsensusPathDB currently integrates the content of 12 different interaction databases with heterogeneous foci comprising a total of 26 133 distinct physical entities and 74 289 distinct functional interactions (protein–protein interactions, biochemical reactions, gene regulatory interactions), and covering 1738 pathways. We describe the database schema and the methods used for data integration. Furthermore, we describe the functionality of the ConsensusPathDB web interface, where users can search and visualize interaction networks, upload, modify and expand networks in BioPAX, SBML or PSI-MI format, or carry out over-representation analysis with uploaded identifier lists with respect to substructures derived from the integrated interaction network. The ConsensusPathDB database is available at: http://cpdb.molgen.mpg.de

Denoising inferred functional association networks obtained by gene fusion analysis

Background

Gene fusion detection – also known as the 'Rosetta Stone' method – involves the identification of fused composite genes in a set of reference genomes, which indicates potential interactions between its un-fused counterpart genes in query genomes. The precision of this method typically improves with an ever-increasing number of reference genomes.

Results

In order to explore the usefulness and scope of this approach for protein interaction prediction and generate a high-quality, non-redundant set of interacting pairs of proteins across a wide taxonomic range, we have exhaustively performed gene fusion analysis for 184 genomes using an efficient variant of a previously developed protocol. By analyzing interaction graphs and applying a threshold that limits the maximum number of possible interactions within the largest graph components, we show that we can reduce the number of implausible interactions due to the detection of promiscuous domains. With this generally applicable approach, we generate a robust set of over 2 million distinct and testable interactions encompassing 696,894 proteins in 184 species or strains, most of which have never been the subject of high-throughput experimental proteomics. We investigate the cumulative effect of increasing numbers of genomes on the fidelity and quantity of predictions, and show that, for large numbers of genomes, predictions do not become saturated but continue to grow linearly, for the majority of the species. We also examine the percentage of component (and composite) proteins with relation to the number of genes and further validate the functional categories that are highly represented in this robust set of detected genome-wide interactions.

Conclusion

We illustrate the phylogenetic and functional diversity of gene fusion events across genomes, and their usefulness for accurate prediction of protein interaction and function.