Discovery Of Therapeutic Biomarkers Research Articles

Quantitative proteomics for therapeutic biomarker discovery in pancreatic cancer

Quantitative proteomics for therapeutic biomarker discovery in pancreatic cancer

Quantitative proteomics for therapeutic biomarker discovery in pancreatic cancer

Quantitative proteomics for therapeutic biomarker discovery in pancreatic cancer

Multimodal spatiotemporal transcriptomic resolution of embryonic palate osteogenesis

The terminal differentiation of osteoblasts and subsequent formation of bone marks an important phase in palate development that leads to the separation of the oral and nasal cavities. While the morphogenetic events preceding palatal osteogenesis are well explored, major gaps remain in our understanding of the molecular mechanisms driving the formation of this bony union of the fusing palate. Through bulk, single-nucleus, and spatially resolved RNA-sequencing analyses of the developing secondary palate, we identify a shift in transcriptional programming between embryonic days 14.5 and 15.5 pinpointing the onset of osteogenesis. We define spatially restricted expression patterns of key osteogenic marker genes that are differentially expressed between these developmental timepoints. Finally, we identify genes in the palate highly expressed by palate nasal epithelial cells, also enriched within palatal osteogenic mesenchymal cells. This investigation provides a relevant framework to advance palate-specific diagnostic and therapeutic biomarker discovery.

A Precision Clinical-Omics Approach in Predicting Therapeutic Response to Pancreatic Cancer

Background: Pancreatic ductal adenocarcinoma is one of the most aggressive solid tumours. Despite numerous advances in surgical techniques, survival has remained largely unchanged over the last decade. Pancreatic cancer is thus considered a ‘systemic disease’ where chemotherapy plays a pivotal role in both the pre- and post-surgery setting. Tools that can predict the most effective chemotherapeutic regimens for patients are urgently needed to improve survival by optimising biological effects while minimising side effects, thereby not comprising their fitness for surgery. The aim of the study was to characterise the proteome of patient derived pancreatic cancer organoids to identify biomarkers predictive of chemotherapeutic sensitivity. Method: Using quantitative mass spectrometry we performed deep proteomic profiling of patient derived pancreatic cancer organoids with matched primary tumour tissue, and performed high-throughput combinatorial drug screening on organoid lines. Results: We demonstrated that common pancreatic cancer characteristics were conserved in organoids derived from different patients, and through comparative analysis between organoids revealed individual diversity that correlated with patient specific features. By correlating personalized organoid proteomes with their matched drug sensitivity profiles derived using high-throughput combinatorial drug screening, we identified protein biomarkers that correlated with therapeutic response to both standard of care and other novel therapeutics. Candidate biomarkers were verified by immunohistochemistry of organoids, with further validation to be performed in an interventional trial using organoids to predict second-line chemotherapy in patients with metastatic disease. Conclusions: We present the integration of deep proteomic profiling with a patient derived organoid platform for therapeutic biomarker discovery in pancreatic cancer.

Identifying Drug Sensitivity Subnetworks with NETPHIX

SummaryPhenotypic heterogeneity in cancer is often caused by different patterns of genetic alterations. Understanding such phenotype-genotype relationships is fundamental for the advance of personalized medicine. We develop a computational method, named NETPHIX (NETwork-to-PHenotype association with eXclusivity) to identify subnetworks of genes whose genetic alterations are associated with drug response or other continuous cancer phenotypes. Leveraging interaction information among genes and properties of cancer mutations such as mutual exclusivity, we formulate the problem as an integer linear program and solve it optimally to obtain a subnetwork of associated genes. Applied to a large-scale drug screening dataset, NETPHIX uncovered gene modules significantly associated with drug responses. Utilizing interaction information, NETPHIX modules are functionally coherent and can thus provide important insights into drug action. In addition, we show that modules identified by NETPHIX together with their association patterns can be leveraged to suggest drug combinations.

Identification of potential prognostic TF-associated lncRNAs for predicting survival in ovarian cancer.

The pathophysiology of ovarian cancer (OV) is complex and depends on multiple biological processes and pathways. Therefore, there is an urgent need to identify reliable prognostic biomarkers for predicting clinical outcomes and helping personalize treatment of OV. A long non‐coding RNA (lncRNA)‐based risk score model was constructed to infer the prognostic efficacy of transcription factors (TFs) based on the OV dataset from The Cancer Genome Atlas. The risk score model was further validated in other independent cohorts from Gene Expression Omnibus. Time‐dependent receiver operating characteristic curves were used to evaluate the survival prediction performance in comparison with other clinical and molecular variables. Our results revealed that the top‐ranked TF‐associating lncRNAs were significantly associated with overall survival, progression‐free survival and disease‐free survival. Stratification analysis according to clinical variables indicated the prognostic independence of POLR2A‐associating lncRNAs. In comparison, the signature of POLR2A‐associating lncRNAs was more sensitive and specific than existing clinical and molecular signatures. Functional and experimental analysis suggested that POLR2A‐associating lncRNAs may be involved in known biological processes and pathways of OV. Our findings revealed that the lncRNA‐based risk score model can provide helpful information on OV prognosis stratification and discovery of therapeutic biomarkers.

A Road Map for Precision Cancer Medicine Using Personalized Models.

<b/> A study by Pauli and colleagues in this issue of Cancer Discovery describes the creation of a precision cancer platform for patients with advanced disease, integrating DNA sequencing of patient tumors with the generation of patient-derived organoids and xenografts. They propose the use of this platform for drug testing to nominate therapeutic options for individual patients and for therapeutic biomarker discovery. Cancer Discov; 7(5); 456-8. ©2017 AACRSee related article by Pauli et al., p. 462.

Abstract CN08-02: Therapeutic biomarker discovery in cancer cells.

Abstract There is compelling evidence that alterations in cancer genomes strongly influence clinical responses to anticancer therapies. Indeed, there are now several examples where genomic changes are used as molecular biomarkers to stratify patients most likely to benefit from a treatment (e.g. BRAF in melanoma). Despite these successes, the majority of cancer drugs have not been linked to specific molecular features that could be used to direct their clinical use to maximize patient benefit. We are using pharmacogenomic profiling in a panel of &amp;gt;1,000 cancer cell lines as a biomarker discovery platform by systematically linking pharmacological data with extensive genomic information. I will present results from our most current large-scale analysis of drug screening results as well highlight some the new approaches we have developed to harness clinical sequencing data to guide our analyses. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):CN08-02. Citation Format: Mathew Garnett. Therapeutic biomarker discovery in cancer cells. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr CN08-02.

Abstract 2206: Genomics of Drug Sensitivity in Cancer (GDSC): A resource for therapeutic biomarker discovery in cancer cells.

Abstract The Genomic of Drug Sensitivity in Cancer (GDSC; www.cancerRxgene.org) resource facilitates development of targeted cancer therapies through pre-clinical identification of therapeutic biomarkers. GDSC is the largest public resource for information on drug sensitivity in cancer cells and links these data to extensive genomic information to identify molecular features that influence anticancer drug response. There is compelling evidence that alterations in cancer genomes strongly influence clinical responses to anticancer therapies. There are several examples where genomic changes are used as molecular biomarkers to stratify patients most likely to benefit from a treatment (e.g. BRAF in melanoma). Despite these successes, the majority of cancer drugs have not been linked to specific molecular features that could be used to direct their clinical use to maximize patient benefit. We are using pharmacogenomic profiling in cancer cell lines as a biomarker discovery platform by systematically linking pharmacological data with genomic information in cancer cells. The GDSC database contains drug sensitivity data generated from high-throughput screening performed by the Cancer Genome Project at the Wellcome Trust Sanger Institute and the Center for Molecular Therapeutics at Massachusetts General Hospital using a collection of &amp;gt;1,200 cancer cell lines. GDSC release v3 (November 2012) contains drug sensitivity data for almost 80,000 experiments, describing response to 142 anticancer drugs across over 700 cancer cell lines. To identify molecular markers of drug response, cell line drug sensitivity data are integrated with large genomic datasets obtained from COSMIC (Catalogue of Somatic Mutations in Cancer), including information on somatic mutations in cancer genes, gene amplification and deletion, tissue type and transcriptional data. Analysis of GDSC data is through a web portal based on queries of specific anticancer drugs or cancer genes. Interactive graphical representations of the data are used throughout with links to related resources, and all datasets are freely available and downloadable. The GDSC database will undergo significant expansion in coming years as drug sensitivity and genomic datasets increase in size and complexity. GDSC provides a unique public resource incorporating large drug sensitivity and genomic datasets to facilitate discovery of new therapeutic biomarkers for cancer therapies. Citation Format: Wanjuan Yang, Jorge Soares, Patricia Greninger, Elena Edelman, Howard Lightfoot, Simon Forbes, Ramaswamy Sridhar, P. Andrew Futreal, Daniel Haber, Michael Stratton, Cyril Benes, Ultan McDermott, Mathew Garnett. Genomics of Drug Sensitivity in Cancer (GDSC): A resource for therapeutic biomarker discovery in cancer cells. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2206. doi:10.1158/1538-7445.AM2013-2206

Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells

Alterations in cancer genomes strongly influence clinical responses to treatment and in many instances are potent biomarkers for response to drugs. The Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerRxgene.org) is the largest public resource for information on drug sensitivity in cancer cells and molecular markers of drug response. Data are freely available without restriction. GDSC currently contains drug sensitivity data for almost 75 000 experiments, describing response to 138 anticancer drugs across almost 700 cancer cell lines. To identify molecular markers of drug response, cell line drug sensitivity data are integrated with large genomic datasets obtained from the Catalogue of Somatic Mutations in Cancer database, including information on somatic mutations in cancer genes, gene amplification and deletion, tissue type and transcriptional data. Analysis of GDSC data is through a web portal focused on identifying molecular biomarkers of drug sensitivity based on queries of specific anticancer drugs or cancer genes. Graphical representations of the data are used throughout with links to related resources and all datasets are fully downloadable. GDSC provides a unique resource incorporating large drug sensitivity and genomic datasets to facilitate the discovery of new therapeutic biomarkers for cancer therapies.

Abstract PD04-10: Genome-Wide Copy Number Analysis of Circulating Tumor Cells (CTCs) from Metastatic Breast Cancer Patients

Abstract Background: Clinical studies have demonstrated the prognostic and predictive value of the number of CTCs in metastatic breast cancer. However, it is the molecular characterization of CTCs that offers insight into the biology of these tumor cells in the context of personalized treatment. Methods: Ten to twenty mLs of blood from 32 metastatic breast cancer patients with poor prognosis (≥5 CTCs in 7.5mLs blood) were subjected to immunomagnetic enrichment. Pools of ∼20 CTCs [EpCAM+, CD45-, nucleated] were isolated via fluorescence activated cell sorting, FACS. Genomic DNA was subjected to whole genome amplification followed by array comparative genomic hybridization (CGH) analysis. Serial genomic profiling was performed in three patients. Genomic profiles from archival primary tumor available from 4 patients were compared to matched CTCs. Copy number analysis was performed using the Nexus® Copy Number. Genomic alterations with p ≥0.05 were considered significant. Results: Genomic profiling of CTCs revealed significant recurrent (≥35%) genomic alterations including gains in 8q12.1, 8q24 and losses in 1p36, 4p16, 5q21, 10q22, 11q24-25, and 13q34. Good concordance of CGH profiles obtained from CTCs isolated from serial blood samples attested to the reproducibility of the assay. Furthermore, comparisons of CTCs with matched archival primary tumors confirmed shared lineage with some divergence. Conclusions: Copy number analysis revealed common genomic alterations in CTCs as well as shared aberrations with matched primary tumor. In addition, we demonstrated the feasibility of serial genomic profiling of CTCs. Future work will determine associations between genomic aberrations in CTCs and clinicopathological parameters. Uncovering genomic alterations in CTCs may lead to the discovery of therapeutic biomarkers to specifically target these cells in breast cancer patients who respond poorly to current aggressive therapies. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr PD04-10.

Clinical Proteomics and Breast Cancer: Strategies For Diagnostic and Therapeutic Biomarker Discovery

A major challenge of breast cancer research is the identification of accurate biomarkers that improve screening, early diagnosis, prediction of aggressiveness, and prediction of therapeutic response or toxicity, as well as the identification of new molecular therapeutic targets. The new proteomic techniques promise to be valuable for identifying such tissue and serum markers. The different techniques currently applied to clinical samples of breast cancer and the most important results obtained are summarized in this review.