Multiscale Cancer Modeling and In Silico Oncology: Emerging Computational Frontiers in Basic and Translational Cancer Research.

Abstract

Cancer cells contain numerous mutations in the genome that are present in most or all malignant cells of a tumor. While not all mutations are significant for cancer progression, a subset of them, often termed driver mutations, have presumably been selected because they confer a distinctive fitness advantage for malignant cells in a heterogeneous tumor microenvironment [1,2]. Correlative studies on clinical samples profiling such mutations in various cancer types suggest that such drivers confer fitness advantage by providing a gain of function in several categories of cancer cell signaling including cell adhesion and motility, signaling, transcriptional regulation, cellular metabolism, and intracellular trafficking [3,4]. One of the grand challenges of the understanding of cancer progression is to find mechanistic links between such alterations and the hall marks of cancers such as increased proliferation and survival, aggressive invasion and metastasis, evasion of cell death, and increased metabolism [5,6]. This challenge is also of quintessential clinical importance because patient outcome to therapy (both in terms of initial response to therapy and subsequent development of resistance to therapy) is now shown to depend on the genetic alterations (primary or acquired) in the individual patients [7,8]. Traditional methods in cell biology and cancer biology such as phospho-proteomics, immuno-precipitation, polymerase chain reaction, in-situ hybridization and molecular imaging, and direct sequencing, along with network-based theories and bioinformatics are reasonably poised to probe some of these altered traits, such as those connected with signaling, transcriptional regulation, and