Digital oncology, the dawn of a new era turning the clock back on tumor mass
Evolutionary path of cancer therapeutics which started around eighty-two years ago, following serendipitous discovery of nitrogen mustard [1] has been guided by an accelerated pace of new discoveries, an ever-increasing depth of understanding of intracellular biological processes [2] and application of advances made in other fields of science [3]. Success in any field, especially in cancer medicine, is based on embracing evolution in understanding other scientific fields [4] and its application to the forward move in cancer therapeutics. As such, we have gone through discovery of a diverse group of therapeutics, targeting different sub compartments of growth and proliferation pathways [5] and taking advantage of their synergistic efficacy, in different combinations [6]. Dissection of immune regulatory pathways [7] and their interaction with cancer cell has led to the generation of modern immune therapeutic agents. Single-cell sequencing technology [8] has paved the way for the development of spatial omics [9], which has opened the way on deeper understanding of tumor evolutionary path [10]. This would give us the opportunity to design a new generation of cancer therapeutics [11] intercepting with the forward evolutionary path of tumor mass. Along this path, we face insurmountable barriers in developing meaningful and game changing treatment strategies by directing single changes in a hyper complex biological system [12], manifested by complexities of genome [13], epigenome [14], microRNA network [15], gene regulatory mechanisms [16], and protein-protein interactions [17]. Consequently, we need to search for a solution that enables us to freeze or slow down the forward evolutionary path of tumor mass [18], by making a single change in a game changing variable of cancer cell.
Authors
- Kambiz Afrasiabi (ORCID: https://orcid.org/0000-0003-1294-2970)
Institutions
- University of California, Irvine (US)
Publication Details
- Journal
- Journal of Cancer Biology
- Published
- 2024-09-23
- DOI
- https://doi.org/10.46439/cancerbiology.5.065
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00