Cancers are highly heterogeneous at molecular and phenotypic levels that it is essential to stratify these cancer patients for personalised cancer diagnosis and therapy.
To this end, my laboratory’s efforts build on our pioneering molecular stratification in different cancers including colorectal and pancreatic cancers. Nevertheless, we have specific projects in gastroesophageal, breast and pan-cancers (see high impact publications).
We systematically study tumour and immune/stromal heterogeneity by developing innovative artificial intelligence and machine-learning models to concurrently integrate multi-omics with phenome data. Multi-omics data include, but not limited to, image, transcriptome, genome and methylome. Phenome data include clinical outcomes and in vitro/in vivo data such as proliferation, migration, etc.
This careful, systematic approach of integration generates biomarkers and highly probable hypotheses for personalised cancer therapy.
Later, biomarkers are translated to potential molecular assays and tested in the clinic trial/study samples. Similarly, certain hypotheses are validated using mechanism-based pre-clinical cell line and mouse models and experiments.
This approach streamlines solutions to evolving areas in the field of multidisciplinary science including inter/intra-tumoural heterogeneity, companion diagnostic assay development, deconvolution statistical approaches, cell-of-origin/phenotypes-based evolution of tumour, and pre-clinical trials for modelling precision cancer therapy.
Translational cancer research and patient benefit
As a part of the ICR, my interdisciplinary (integrated experimental, computational and clinical biology) laboratory’s research focuses on translational cancer research and patient benefit and leverages national and international clinical trial and tissue resources. Our programme has three overlapping research themes:
1) defining clinically actionable inter/intra-tumoural heterogeneity by systematically integrating multi-omics profiles with phenome data;
2) developing prognostic and/or predictive biomarker-based companion diagnostic assays by dissecting tumour or drug-induced cancer heterogeneity; and
3) identifying and validating subtype-specific drug targets and therapies, specifically those involving immune/stroma pathways, for potential personalised/precision medicine.
Our research is deliberately interdisciplinary to maximise and expedite clinical translation and patient benefit.
Therefore, the existing team, along with clinical collaborators, has three key multidisciplinary components: basic/translational science (pre-clinical and mechanism-based experimental biology; and “Big” data generation); computational biology (development of artificial intelligence and machine learning tools and data analysis); and clinical science (companion diagnostics development; and collaboration-based clinical trial/study-relevant patient samples and data collection).
Our strategic national and international collaborations with industry, large consortia (such as the Colorectal Cancer Subtyping Consortium; CRCSC), leading clinicians across different continents and trial units, bioinformaticians, and biologists support and add value to my laboratory’s activities at the Institute of Cancer Research (ICR).
Furthermore, and focused on patient benefit, we have created an ICR-approved platform to make our companion diagnostic assays (patented already) available internationally for academic research purposes in collaboration.
Finally, we have developed novel bioinformatics and preclinical models, as resources, which are widely and internationally used. Moreover, our lab coordinates multiple cancer research projects related to Low and Middle Income Countries (LMIC) specifically related to India.
Our lab is exploring entrepreneurship through various resources for both Sadanandam and team members.
Overall, our team science-based research programme aligns well with the ICR/RMH Strategies, the UK’s and international key life sciences strategies, and developing a skilled workforce in interdisciplinary sciences including training clinicians/other disciplinarians in genomic pathology.