This is an internal event mainly to share the research work at CSI, NUS, Singapore and IBSE, IITM in the field of cancer biology. The main purpose of the meeting is to identify and explore areas for potential collaborations between t he two institutes. The symposium will feature talks by faculty in IBSE and CSI about their ongoing research projects. IBSE is proud to organize this jointly with CSI.
| Time | Speaker | Talk title | ||
|---|---|---|---|---|
| 10:00-10:30 | Prof. Mahesh (IITM) | Inauguration | ||
| 10:30-11:00 | Coffee Break | |||
| 11:00-11:30 | Dr. Jason Pitt (CSI) | Identification and characterization of copy number signatures in breast cancer | ||
| 12:00-12:30 | Dr. Yang Zhang (CSI) | Progress and challenges in AI-based protein structure prediction | ||
| 11:30-12:00 | Dr. Nirav Bhatt (IITM) | Understanding Diseases using Integrated Biological Models from Data | ||
| 12:30-14:00 | Lunch | |||
| 14:00-14:30 | Dr. Yvonne Tay (CSI) | An isoform-resolution transcriptomic atlas of colorectal cancer from single-cell long-read sequencing | ||
| 14:30-15:00 | Dr. Swaminathan Rajaraman (WIA) | Cancer surveillance through registries in Tamil Nadu | ||
| 15:00-15:30 | Dr. Venkatraman Radhakrishnan (WIA) | Clinical trials: Cancer Institute (WIA) experience | 15:30-16:00 | Coffee Break |
| 16:00-16:30 | Dr. Maziya Ibrahim (IITM) | Investigating Cancer Metabolism With Genome-Scale Metabolic Models | ||
| 16:30-18:00 | Research Park visit | |||
Dr. Jason Pitt received his Ph.D. in Genetics, Genomics, and Systems Biology from the University of Chicago where he used data-intensive computing to explore germline and somatic variation in cancer. He is currently a Special Fellow and Head of the Genomics and Data Analytics Core at the Cancer Science Institute of Singapore. There his laboratory uses a combination of computation, software engineering, and biological knowledge to analyze and democratize large-scale cancer genomics data. Scientifically, they leverage this data to identify features and correlates of genome instability – a cancer hallmark with therapeutic implications. Their efforts promote precision oncology through both novel biomarker discovery and innovative digital health solutions.
Dr Yang Zhang is a Professor and Senior Principal Investigator in the Cancer Science Institute of Singapore. He also serves as a Professor in the Department of Computer Science at the School of Computing, and the Department of Biochemistry at the Yong Loo Lin School of Medicine, National University of Singapore (NUS). Prior to joining NUS, Dr Yang Zhang worked as a Professor in the Department of Computational Medicine & Bioinformatics, the Department of Biological Chemistry, and the Department of Macromolecular Science & Engineering, University of Michigan. The research interests of the Zhang Lab are in artificial intelligence and deep neural network learning, protein folding and structure prediction, and protein design and engineering. The I-TASSER algorithm (https://zhanggroup.org/I-TASSER/) developed in his laboratory was ranked as the No 1 most accurate method for automated protein structure prediction in the community-wide CASP experiments nine times in a row since 2006. Among the recognitions that Dr Zhang received includes the Alfred P Sloan Award, the US National Science Foundation Career Award, ASBMB Delano Award and the University of Michigan Basic Science Research Award. He was selected as the Thomson Reuters/Clarivate Analytics Highly Cited Researcher for six times since 2015.
Yvonne began her research career in Bing Lim’s lab at the Genome Institute of Singapore, where she studied miRNA function and mechanisms of action (Tay et al, Nature 2008). She then pursued her postdoctoral training in the Pandolfi lab at Harvard Medical School, where she investigated how transcripts can co-regulate each other by competing for shared miRNAs (Tay et al, Cell 2011). Now based at the Cancer Science Institute of Singapore and National University of Singapore, Yvonne’s research group studies non-coding RNAs as well as the non-coding untranslated regions (UTRs) of protein-coding mRNAs (Kwok et al, Cancer Res 2021; Chan et al, Nature Cell Biology 2022). As many mRNA populations comprise transcripts with different UTRs, and these UTRs control key processes such as stability, localization and transport, a better understanding of their function may lead to insights into the regulation of key cancer genes.
Sriram Sridharan obtained his Masters and PhD degree both in Electrical Engineering from Indian Institute of Technology, Roorkee and Texas A&M University, College Station respectively. For his postdoctoral training, he joined the lab of Dr. Andre Nussenzweig (Laboratory of Genome Integrity) at National Institutes of Health, Bethesda, USA. During his time at NIH, he worked on understanding mechanisms of DNA replication stress and fragility during DNA replication. To this end, he was instrumental in developing algorithms to analyze short read Illumina sequencing data for a newly developed in-house assay termed ENDseq, to map sites of DNA double strand breaks (DSBs) genome-wide at nucleotide resolution. Subsequently, using ENDseq as a tool he studied mechanisms of DNA fragility under the context of physiologically relevant replication stress and DNA damage conditions. His postdoctoral work showed that simple tandem repeat sequences (STRs) are a major source of replication stress causing replication fork stalling, DSBs and induces genome instability. Since May 2022 he is a Special Fellow at Cancer Science Institute of Singapore where his lab is focused on understanding the role of SWI/SNF chromatin remodeler complex in mediating response to replication stress and effectuate faithful DNA replication.
Karthik Raman is a Professor at the Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras. Karthik’s research group works on the development of algorithms and computational tools to understand, predict and manipulate complex biological networks. Broadly spanning computational aspects of synthetic and systems biology, key areas of research in his group encompass microbiome analysis, in silico metabolic engineering, biological network design and biological data analysis. Karthik also co-ordinates the Centre for Integrative Biology and Systems mEdicine (IBSE) at IIT Madras and is a core member of the Robert Bosch Centre for Data Science and Artificial Intelligence (RBC-DSAI). Karthik teaches courses on computational biology and systems biology at IIT Madras, and has also authored a textbook on Computational Systems Biology.
Manikandan Narayanan enjoys research at the interface between computer science and biology -- he is fascinated with using the power of computation to tease apart the molecular interactions underlying life, especially multicellular life, from large-scale genomic data. He obtained his Ph.D. from the University of California at Berkeley and held senior research scientist positions at Merck Research Labs and National Institutes of Health (NIH), before taking up his current position as Associate Professor at IIT Madras. He has obtained the Siebel Scholar, and the WellcomeTrust/DBT India Alliance Intermediate Fellow awards.
Veerendra Gadekar holds a Ph.D. in Life and Biomolecular Sciences from the Open University of the UK. He pursued postdoctoral studies at the University of Vienna and the University of Copenhagen. His primary research has centered on the evolution, structure, and function of RNA molecules, particularly non-coding RNAs. He has extensive experience in working with transcriptomics data related to neurodegenerative diseases. With a keen interest in AI/ML applications in the life sciences, he joined the Centre for Integrative Biology and Systems Medicine (IBSE) last year as a research scientist. His current research focuses on maternal and child health, involving clinical data analysis. Additionally, he is engaged in projects related to transcriptome analysis of the functional domains within the IRF family of transcription factors and studying the microbiome profile of Chennai City to understand its functionality and impact on health.
Nathiya obtained her Master’s degree in Biotechnology from The University of Madras. She commenced her PhD under the guidance of Prof. Daniel Murphy at the University Wurzburg, Germany before transferring to University of Glasgow-UK to complete her studies in 2015. Later, she worked at the Beatson Institute for cancer research as a Postdoctoral Fellow until Feb 2021, before joining IIT Madras as a faculty. The overarching goal of Dr. Nathiya’s research is to understand, at the molecular and cellular level how oncogenes control neuroendocrine plasticity, how this contributes to metastasis and how we can target this to improve the clinical outcome of patients
Dr. Kulandai Arockia Rajesh Packiam joined the Centre for Integrative Biology and Systems medicinE (IBSE), IIT Madras, as a Senior Project Scientist very recently. Kulandai works on a project with the Multi-Omics for Mothers and Infants (MOMI) Consortium, where he applies machine-learning-based approaches towards multi-omics data analysis to predict preterm birth. He graduated with a Ph.D. in Chemical Engineering from Monash University Malaysia. During his doctoral thesis, Kulandai focused on developing a machine learning-based tool, PERISCOPE-Opt, to predict the periplasmic protein yields and corresponding fermentation conditions in E. coli. Prior to joining IBSE, Kulandai was a post-doctoral Research Associate at Computer Science & Engineering Discipline, IIT Gandhinagar, where he was involved in identifying biomarkers for oral cancer using multi-omics data analysis. He is interested in the application of bioinformatics, machine learning, and systems biology in healthcare.
Professor Balaraman Ravindran is the head of the Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI) and the Centre for Responsible AI (CeRAI) at IIT-Madras, a core member at IBSE as well as being a Mindtree Faculty Fellow and Professor in the Department of Computer Science and Engineering. Professor Ravindran has previously been a Visiting Researcher at Google, Karnataka and a visiting Faculty at the Indian Institute of Science. Having received his Master’s degree from the Indian Institute of Science, Bangalore and his PhD in Computer Science from the University of Massachusetts, Professor Ravindran’s research interests span from Spatio-temporal Abstractions in Reinforcement Learning to social network analysis and Data Mining.
Dr. Himanshu Sinha leads the Systems Genetics Laboratory and Initiative for Biological Systems Engineering (IBSE), is a core member of RBCDSAI and an Associate Professor in the Department of Biotechnology, Bhupat and Jyothi Mehta School of Biosciences. Previously Dr. Sinha has worked at the Tata Institute of Fundamental Research, European Molecular Biology Laboratories and Duke University Medical Centre as a Post-Doctoral Fellow. He received his Master’s Degree in Plant Breeding at the Punjab Agricultural University, Ludhiana and his PhD in Plant Sciences at the University f Cambridge. The current areas of focus of Dr. Sinha’s lab include Population and quantitative genetics, systems and computational biology of yeast, Clinical data analytics and modelling for disease risk, incidence and progression and Building models for gestational age determination and preterm risk categorisation of Indian women using GARBH-Ini project in collaboration with THSTI.
Professor Ashok Venkitaraman is the Director of the Cancer Science Institute of Singapore. Professor of Medicine at the National University of Singapore and Program Director at A*STAR, Singapore. He was the inaugural holder of the Ursula Zoellner Professorship of Cancer Research at the University of Cambridge, a Professorial Fellow at Pembroke College, Cambridge, the Director of their Medical Research Council Cancer Unit. Having studied Medicine at Christian Medical College, Vellore, India, Professor Venkitaraman, earned his PhD at University College London. He has been elected a Fellow of the Academy of Medical Sciences and a Member of the European Molecular Biology Organization (EMBO). Moreover, he has been awarded the Basser Global Prize in recognition of his discoveries concerning the breast cancer gene BRCA2. Being among the first to discover the suppressive activities of BRCA2, Professor Venkitaraman’s lab seeks to explore the mechanisms behind cancer susceptibility. They are currently developing innovative approaches to identify and validate therapeutic targets in complex pathways, regulate enzyme activity and interrogate cellular signalling pathways using new tools in light microscope. Professor Venkitaraman’s multi-disciplinary research team seeks to bridge the gap from bench to bedside.
The talk would highlight the clinical trials conducted at Cancer Institute. The challenges of doing clinical trials will be discussed.
Copy number alterations (CNAs), or gains and losses of DNA segments, are common somatic changes implicated in oncogenesis and genomic instability (GI) in cancer. Akin to single base substitutions, the accumulation of CNAs within cancer genomes is often non-random and can be attributed to aberrant activity of specific DNA damage and repair processes. Studying the higher-order patterns of CNAs – especially in heterogenous, CNA-prevalent diseases such as breast cancer – can illuminate the types and determinants of GI as well as their clinical implications. These patterns can be characterized using pre-defined scores or, more recently, through de novo CNA signature extraction via matrix factorization. Applying these approaches to 2,646 breast cancer patients from The Cancer Genome Atlas (TCGA) and METABRIC, we found CNA scores and signatures associated with genomic architecture, driver genes, clinical features, and patient outcomes. Critical oncogenes, such as ERBB2, FGF19, and MYC, are preferentially amplified through tandem duplication-like processes in a subtype-specific manner. We also identified CNA signatures that capture diploid and tetraploid samples with relatively stable genomes – the latter of which represents a unique subset of whole genome doubled tumors. Moreover, multiple signatures co-occurred with the loss of BRCA1/2 – genes critical for proficient homologous recombination (HR). These BRCA1/2-associated signatures captured multiple components of the standard HR deficiency (HRD) score as well as tandem duplications. Lastly, we identified a signature (CN-BRCA-B) that strongly associates with chromothripsis-like patterns and the presence of extrachromosomal DNA (ecDNA) – particularly in HER2+ cases. Copy number segments assigned to this signature are often attributed to ecDNA – suggesting that CN-BRCA-B activity may have a direct role in ecDNA generation. Our findings demonstrate how deep interrogation of scores and signatures can help characterize the various processes underlying CNA-based GI in breast cancer.
In era of Big data in Biology, it is important to focus on the first-principles models that integrates different Big data to mimic the biological process of interests, particularly, disease conditions. This talk will discuss modelling approaches that allows us to integration of different types of biological information to understand biological processes. First, we will discuss integration metabolic and signaling networks and their interactions, and demonstrate its ability to capture biological processes. In the second example, we will discuss the integration of reactive species module to understand cancer metabolism, particularly, ferroptosis. The talk will conclude with some future directions for importance of integrating different data sets to understand diseases, particularly, cancer.
The past decade has witnessed significant progress in protein structure prediction, which is mainly driven by the advancement of artificial intelligence (AI) and deep learning techniques. In this talk, I will first review the new progress and challenge of the field based on blind test results of the most recent community wide CASP experiments. Next, I will give my personal prospect of future developments on the AI-based structure modeling of non-coding RNAs and protein-protein/RNA interactions, as well as the important roles played by metagenome sequencing in AI-driven structural biology.
Single-cell RNA sequencing (scRNA-seq) has been instrumental in deciphering tumor cell heterogeneities. Current scRNA-seq studies mainly quantify gene expression levels but neglect widespread dysregulated transcript structures (DTS) arising from differences in the 5’ and 3’ ends, and alternative splicing (AS) in cancer. Here, we provide an isoform-resolution transcriptomic atlas of colorectal cancer (CRC) using matched short- and long-read scRNA-seq profiles of primary samples from 12 CRC patients. Over 270 DTS events, many caused by the coupling of multiple AS events, and isoform-specific RNA-editing changes were identified in the tumor epithelial cells. Additionally, we characterized genes and isoforms associated with different epithelial differentiation lineages and tumor cell subpopulations exhibiting distinct prognostic implications. Finally, by integrating mass spectrometry data with the predicted proteome based on the atlas, we built an algorithm to curate a panel of recurrent neoepitopes that can potentially aid the development of universal neoantigen-based cancer vaccines.
The talk would highlight the clinical trials conducted at Cancer Institute. The challenges of doing clinical trials will be discussed.
The mammalian genome is highly organized at different scales starting from nucleosomes to topologically associated domains (TADs) which occupy discrete territories in a non-random fashion within the nucleus. The 3D genome organization has been studied using a variety of chromatin conformation capture assays ranging from 3C to HiC as well as immunoprecipitation (IP)-based assays such as ChIA-PET. HiC is the standard assay employed to probe genome-wide chromatin organization in an unbiased manner. It has been extensively applied to study regulation of gene expression and more recently to understand chromatin reorganization during double strand break (DSB) induction and repair. However, little work has been done on studying chromatin reorganization under condition of replication stress which results in stalled replication forks. Here I present a modified HiC protocol termed “Rep-HiC” to map chromatin organization in replicating cells at high resolution. Our method improves up on the standard in-situ HiC by first enriching for actively replicating DNA. This enrichment step is followed by the standard in-situ HiC protocol to capture genomic contacts that exist at actively replicating DNA at high resolution. Further, we extend the same assay to understand how chromatin organization changes upon induction of DNA replication stress.
Microbes are ubiquitous and occur in complex microbial communities, or microbiomes. Understanding how microbes interact metabolically, is essential for understanding how microbes thrive together in communities. It is of great interest to study the possible interactions between microbes in communities, identify the keystone species in these microbiomes, and how they influence one another and ultimately shape the structure of the microbiome. In this talk, I will focus on computational approaches we have developed, to understand the organisation of a variety of microbiomes. We use complementary approaches to systematically study microbiomes ranging from those in the human gut and the eye, to extreme environments such as those aboard the International Space Station and deep-sea hydrothermal vents. In each of these environments, we identify unique interaction patterns and possible metabolic exchanges and dependencies amongst the organisms. Our results point toward key dependencies of microorganisms in diverse environments. Our approach also underscores the importance of complementary modelling approaches in dissecting complex microbiomes and understanding various possible interactions. Our methodology is fairly generic and can be readily extended to predict microbial interactions in other interesting environments, and generate testable hypotheses for wet lab experiments.
Testing if two correlated variables are causally related is a fundamental problem in many sciences, including biological science. Addressing this problem requires separating causality from confounding using data from interventions (e.g., randomized controlled trials), or applying mediation tests on data observed in the absence of interventions. Statistical tests of mediation or conditional independence within the established framework of Mendelian Randomization (MR) allows us to infer causal relations between two variables that are each associated with a third instrument variable (e.g., two gene expression or clinical traits A, B associated with a genetic variant L, with all variables observed in the same population). Most existing MR methods determine the causal direction (A->B vs. B->A) and effect assuming a linear relationship between the traits and assuming perfect error-free measurements. Both these assumptions are routinely violated in real-world genomic datasets to varying extents. In this talk, I will present two methods that we've developed for error-aware and non-linear causal discovery between two variables. We've specifically extended a baseline linear causal discovery method (CIT for Causal Inference Test) to develop (i) a robust method that estimates and corrects for measurement errors when performing multiple statistical tests of causality, and (ii) another method that estimates conditional feature importance scores in non-linear regression models to learn a non-linear causal relationship. In comparison to the baseline method, our methods perform significantly better in various simulation scenarios, and also yield meaningful causal gene networks on real-world yeast or human genomics datasets. I will conclude the talk by briefly outlining how such causal gene networks could be used to inform other downstream genomic analyses.
Next, we focus on the critical concern of preterm birth (PTB) in India, which significantly contributes to global PTB-related mortality. We utilise a comprehensive dataset from over 8,000 pregnant participants, employing clustering and enrichment analyses to uncover specific PTB risk factors. Our analysis identifies distinct patterns and subphenotypes, emphasising the intricate, multifactorial nature of PTB risk in India. Given India's high PTB-related mortality, we stress the urgency of customised preventive strategies and the importance of monitoring specific subphenotypes and related factors to inform public health efforts and enhance maternal and neonatal well-being.
Pancreatic cancer (PC) is a deadly disease accounting for 4.5% of all global cancer related deaths. Despite the advancement in cancer detection and treatment strategies, the 5-year survival rate of pancreatic cancer patients still stands at dismal 9%. Pancreatic ductal adenocarcinoma (PDAC) and Pancreatic neuroendocrine tumours (PNET) are 2 distinct histological subtypes with different molecular and clinical features. Our recent work using genetically engineered mouse models (GEMM) of pancreatic cancer revealed that oncogene MYC can drive PNET tumours, whereas it drives PDAC when combined with mutant KRAS. These GEMM models with distinct tumours give the opportunity to unravel molecular signatures of PNET tumours, with the aim of identifying novel therapeutic targets for the same.
In this presentation, we delve into two critical aspects of maternal and neonatal health in lower and middle-income countries (LMICs). Firstly, we address the challenge of late antenatal care and the inaccuracies associated with the most widely accepted formula for estimating gestational age (GA). We introduce a population-specific GA model developed from data in North India's GARBH-Ini pregnancy cohort to tackle this issue. This model is validated in a South Indian cohort, highlighting its superiority over the widely-used Hadlock formula. This underscores the importance of tailored GA formulas to enhance antenatal care for diverse Indian populations.
The Centre for Integrative Biology and Systems medicinE (IBSE), a CoE, at IIT Madras is an interdisciplinary centre dedicated to pioneering innovative approaches and algorithms that integrate multi-dimensional data across scales, to understand, predict and manipulate complex biological systems. The centre closely collaborates with Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), one of the premier data science and AI centres in India.
The main sponsor, Indian Institute of Technology Madras, Chennai, is the top-ranked engineering institute in the country; and encourages interdisciplinary research at the interface of engineering, including computer science and biomedicine. While being situated in the heart of a busy metropolis, Chennai, the campus boasts hundreds of acres of forests, which are also home to various birds and animals, including the endangered blackbuck.
Register - This event is open for all IITM faculty, students and researchers. Please check this website regularly for updates.
Address
Centre for Integrative Biology and Systems Medicine (IBSE)
Indian Institute of Technology Madras
Chennai - 600036
INDIA
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