A journey from omics to clinicomics in solid cancers: Success stories and challenges

• Published in: Advances in protein chemistry and structural biology Vol. 139; pp. 89 - 139
• Main Authors: Mehrotra, Sanjana, Sharma, Sankalp, Pandey, Rajeev Kumar
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 2024
• Subjects: Algorithms; Artificial Intelligence; Breast cancer; Breast Neoplasms; Clinicomics; Computational Biology; Female; Humans; Machine Learning; Multi-omics; Solid cancers; Breast cancer; Clinicomics; Artificial Intelligence; Multi-omics; Solid cancers
• ISSN: 1876-1623; 1876-1631; 1876-1631
• DOI: 10.1016/bs.apcsb.2023.11.008

The word ‘cancer’ encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor’s shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient’s molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.