Cancer arises from complex interactions between genes and the biochemical pathways they regulate. The human genome contains approximately 4–5 million genetic variants that influence individual susceptibility to cancer. Mutations contribute to cancer risk by affecting uncontrolled cell proliferation, metastasis, and tumour aggressiveness, with effects varying across cancer types. While the genetic basis of sporadic and hereditary cancers is relatively well understood, knowledge of familial cancers remains limited. Most currently characterized variants are medium-to high-penetrance; however, low-penetrance variants may also disrupt cellular homeostasis and significantly influence cancer development and progression.

Current approaches to genetic variant analysis focus primarily on clinical interpretation, often overlooking structural and functional consequences at the protein level. This gap is driven by the lack of integrated datasets and accessible tools that combine three-dimensional protein visualization, detailed structural analysis, molecular dynamics, mechanistic interpretation, and thermodynamic stability assessment. Existing platforms such as PyMOL and AlphaFold provide valuable insights but require specialized expertise and multiple workflows. Consequently, comprehensive structural-functional analysis can take 7–24 hours per variant, delaying diagnostic reporting and genetic counselling. 

At the same time, artificial intelligence (AI) has transformed genomics by reducing the “diagnostic odyssey,” the prolonged period between symptom onset and accurate genetic diagnosis. AI-driven platforms such as Emedgene, AI-MARRVEL, DeepVariant, and AlphaMissense automate variant calling, prioritization, and interpretation, shortening diagnostic timelines from years to weeks. Nevertheless, 35–37% of variants identified in cancer and rare diseases remain classified as Variants of Uncertain Significance (VUS), limiting their clinical utility and hindering targeted interventions. 

We propose the development of an integrated, AI-powered, web-based, user-friendly application that combines clinical significance assessment with dynamic structural-functional analysis of genetic variants within a single platform. By enabling rapid, comprehensive reporting, this approach will improve variant classification, facilitate reclassification of VUS, particularly low-penetrance variants and enhance genetic counselling. Ultimately, the platform aims to advance proactive precision cancer medicine, improve patient care, and reduce avoidable healthcare costs

Shristi Biswas is a biotechnology researcher, educator, and cancer genomics specialist with over eight years of experience in research, higher education, and scientific mentorship. She earned her Ph.D. in Biotechnology from the Institute of Science, Nirma University, Ahmedabad, where she investigated constitutional genetic markers associated with familial cancers using next generation sequencing and bioinformatics approaches. Her research focuses on cancer genetics, germline variant analysis, precision oncology, bioinformatics, and the integration of artificial intelligence into genomic medicine. During her doctoral studies, she explored genetic predisposition in familial cancer cases, identifying novel germline variants and examining their structural and functional significance. She has expertise in genomic databases and bioinformatics tools, including ClinVar, Ensembl VEP, UCSC Genome Browser, UniProt, PDB, GATK, PyMOL, and DynaMut, for variant interpretation and protein structure analysis. Currently, she serves as an Assistant Professor at Silver Oak University, where she contributes to teaching, curriculum development, outcome-based education, and student research mentoring. She has successfully guided student projects recognized at the state and national levels. She has published several peer-reviewed research articles, presented her work at national and international conferences, and received the Government of Gujarat SHODH Scholarship for her doctoral research. Her contributions have enhanced the understanding of hereditary and familial cancers, particularly in the Indian population. She is dedicated to advancing precision medicine by integrating genomics, bioinformatics, and clinical applications to improve genetic diagnosis, counselling, and patient outcomes.