Biomarker and Target Discovery

The tumor microenvironment (TME) is a multi-faceted cellular environment that both constrains the evolving tumor and plays a pivotal role in tumor progression and therapeutic response. We are developing computational approaches to quantify and describe this environment. We are exploring fundamental propoerties of the TME, such as signaling between (Ghoshdastider et al.) and metabolism of (Rohatgi et al. 2020 ) cancer and stromal cells inside tumors. We are using similar approaches to study biomarkers of drug response and potential novel therapeutic targets.

Cancer Liquid Biopsies

In cancer patients, blood plasma can carry circulating tumor DNA (ctDNA) fragments originating from tumor cells, offering non-invasive access to somatic genetic alterations in tumors. The ctDNA profile of a cancer patient is clinically informative in at least two major ways. Firstly, the profile can provide information about specific actionable mutations that can guide therapy. Secondly, the profile can be used to infer tumor growth dynamics by estimating the amount of ctDNA in the blood. This latter information offers a promising non-invasive approach to track disease progression during clinical trials or therapy, offering a real-time tool to adjust therapy. In our lab, we are developing machine-learning based approaches to detect and quantify ctDNA in blood samples (Guo et al. 2019, Zhu et al. 2020). Secondly, we are using ctDNA profiles to study mechanisms of treatment resistance to chemotherapy and targeted therapy. We are also co-investigators in the new cancer liquid biopsy consortium in Singapore, CaLiBRe.

Integrative Cancer Genomics

We use computational approaches to study cancer biology and precision oncology. We have participated in several large scale tumor characterization projects in The Cancer Genome Atlas (TCGA) (E.g. TCGA ccRCC, Cerami et al. 2012, Gao et al. 2013). We have experience studying tumor whole genomes at scale (e.g. Weinhold, Jacobsen et al. 2014), non-coding mutations (Guo et al. 2018), and non-coding RNAs in cancer (e.g. Jacobsen et al. 2013). We collaborate with cancer research groups in Singapore and abroad (e.g. Hakimi et al. 2015, Chen et al. 2016, Montes et al. 2015, Hu et al. 2017, Montes et al. 2020). In Singapore, we are co-investigators in the national lung cancer (e.g. Tan et al. 2017, Chen et al. 2020) and gastrointestinal cancer (Ooi et al. 2020, Xing et al. 2020) consortiums.

cWords - motif discovery

cWords is an algorithm and software suite for analysis of regulatory sequence motifs in gene expression data. Originally developed for analysis of microRNA target sites in 3' UTRs (Jacobsen et al., Genome Research (2010)), but can easily be adapted to other settings. The newest version features an improved and much more efficient statistical approach (Rasmussen, Jacobsen & Krogh, Silence (2013)), and the software is now also available as a web service.

• URL: version 1.0 version 2.0 (web)
• Themes: Web-service, Method, miRNA, Non-coding

miRNA targets in cancer

CancerMiner is a comprehensive resource for discovery and prioritization of functional miRNA-mRNA target interactions in human cancer using molecular datasets from The Cancer Genome Atlas. Candidate interactions can be queried for specific miRNAs or mRNAs, in individual or across multiple cancer types. Our approach uses molecular profiles of >3,000 tumors from 11 human cancer types in The Cancer Genome Atlas. Using this data, we systematically analyzed expression of miRNAs and mRNAs across cancer types to infer recurrent cancer-associated miRNA-target relationships (Jacobsen et al., Nature Structural & Molecular Biology (2013)).

• URL: CancerMiner
• Themes: Web-service, Cancer genomics, miRNA, Non-coding

R + cBioPortal

CGDSR is an R-package to fetch and analyze data from The Cancer Genome Atlas and the cBioPortal for Cancer Genomics. These resources are further described here - Introduction to CGDSR, Cerami et al., Cancer Discovery (2012), Gao et al., Science Signaling (2013).

• URL: CGDSR
• Themes: Web-service, R, Cancer Genomics

Cancer Mutation Calling

Somatic Mutation calling method using a Random Forest (SMuRF) integrates predictions and auxiliary features from multiple somatic mutation callers using a supervised machine learning approach. SMuRF is trained on community-curated matched tumor and normal whole genome sequencing data. SMuRF predicts both SNVs and indels with high accuracy in genome or exome-level sequencing data. Furthermore, the method is robust across multiple tested cancer types and predicts low allele frequency variants with high accuracy. In contrast to existing ensemble-based somatic mutation calling approaches, SMuRF works out-of-the-box and is orders of magnitudes faster. See Huang et al. 2019.

• URL: SMuRF
• Themes: Cancer Genomics, Mutations

MutSpot

Recurrence and clustering of somatic mutations (hotspots) in cancer genomes may indicate positive selection and involvement in tumorigenesis. MutSpot performs genome-wide inference of mutation hotspots in non-coding and regulatory DNA of cancer genomes. MutSpot performs feature selection across hundreds of epigenetic and sequence features followed by estimation of position- and patient-specific background somatic mutation probabilities. MutSpot is user-friendly, works on a standard workstation, and scales to thousands of cancer genomes. See Guo et al. 2020.

• URL: MutSpot
• Themes: Cancer Genomics, Mutations

TME Crosstalk

CITE (Crosstalk Interactions within Tumor microEnvironment) provides a database and dynamic visualization of ligand-receptor (LR) signaling interactions within the tumor microenvironment (TME). Crosstalk between ligands and receptors on cancer and stromal cells were estimated in the TME of 20 solid tumor types, based on cancer and stroma specific expression estimated using a tumor transcriptome deconvolution approach. See Ghoshdastider et al. 2020.

• URL: CITE
• Themes: Cancer Genomics, Tumor Microenvironment

TME Metabolic Pathways

The Cancer Metabolic Pathway viewer (CaMP) contains metabolic activity profiles of cancer cells in vivo, non-malignant (stroma) cells within tumors, normal tissues and cancer cells in vitro. Metabolic profiles of cancer cells and stromal cells for 20 solid tumor types is estimated using a data deconvolution approach (Ghoshdastider, 2019) based on transcriptomes of 7865 tumors obtained from the cancer genome atlas (TCGA). Metabolic profiles of normal tissues is estimated using transcriptomes of 815 normal tissue samples from TCGA and genotype-tissue expression (GTEX). 620 cancer cell line from the cancer cell line encyclopedia (CCLE) transcriptomes are used to estimate the metabolic activity of cancer cells in vitro. The database contains KEGG maps of 55 metabolic pathways based on 1845 metabolic genes. See Rohatgi et al. 2020

• URL: CaMP
• Themes: Cancer Genomics, Metabolomics, Tumor Microenvironment

Cancer Genomics Datahub

The field of cancer research is advancing at a rapid pace due to the declining cost of next-generation DNA sequencing. Massive DNA sequence data, complex molecular profiles, and rich clinical data are being generated from thousands of cancer patients worldwide. Several such projects are also underway in Singapore. However, considerable computational expertise and resources are required to integrate and mine these datasets. This presents a significant barrier for clinicians and use of this data to study cancer precision medicine. To meet this demand, we are developing the Singapore Oncology Data Portal (OncoSG). OncoSG will enable integration, visualization, analysis, and sharing of cancer genomics datasets in Singapore. This will speed-up translation of cancer genomics data into new biological insights and clinical applications. OncoSG currently stores data for more than 50,000 patient tumor samples, including more than 2,000 samples from Singapore.

• URL: OncoSG
• Themes: Cancer Genomics, Database