Artificial intelligence, molecular subtyping, biomarkers, and precision oncology

Abstract

A targeted cancer therapy is only useful if there is a way to accurately identify the tumors that are susceptible to that therapy. Thus rapid expansion in the number of available targeted cancer treatments has been accompanied by a robust effort to subdivide the traditional histological and anatomical tumor classifications into molecularly defined subtypes. This review highlights the history of the paired evolution of targeted therapies and biomarkers, reviews currently used methods for subtype identification, and discusses challenges to the implementation of precision oncology as well as possible solutions.

Transitioning to a Personalized Approach in Molecularly Subtyped Small-Cell Lung Cancer (SCLC)

“…AI can also prove invaluable in pathology, particularly in the analysis of small tissue samples that present significant diagnostic challenges. Shen (2021) argued that AI could be a valuable tool for introducing new molecular classification of cancers based on the molecular profile of tumors [48]. Moreover, AI can aid in histopathological diagnosis through the analysis of digital histopathologic slides and gene profiles, allowing for the differentiation of stromal cell types within the tumor microenvironment and facilitating pathological staging [49].…”

Adaptive Cancer Therapy in the Age of Generative Artificial Intelligence

“… 11 These treatment-related dimensions are in turn dependent on a myriad of specific tasks for which AI is increasingly explored as a feasible enabler, including cancer subtyping, patient stratification, prognosis prediction, treatment selection, and treatment response prediction. 12 - 17 In particular, the integration of clinical, radiomics, histopathology and molecular data has the potential to advance precision oncology, 12 and may be an essential ingredient towards the development of clinically effective AI-driven models of cancer subtyping and patient stratification. Catalyzing the convergence towards this objective is the increasing maturity of digital pathology 18 , 19 and radiomics 20 , 21 combined with advances in liquid biopsy 22 , 23 and next-generation sequencing (NGS).…”