A war on many fronts: cross disciplinary approaches for novel cancer treatment strategies

Abstract

Cancer is a disease characterized by uncontrolled cellular growth where cancer cells take advantage of surrounding cellular populations to obtain resources and promote invasion. Carcinomas are the most common type of cancer accounting for almost 90% of cancer cases. One of the major subtypes of carcinomas are adenocarcinomas, which originate from glandular cells that line certain internal organs. Cancers such as breast, prostate, lung, pancreas, colon, esophageal, kidney are often adenocarcinomas. Current treatment strategies include surgery, chemotherapy, radiation, targeted therapy, and more recently immunotherapy. However, patients with adenocarcinomas often develop resistance or recur after the first line of treatment. Understanding how networks of tumor cells interact with each other and the tumor microenvironment is crucial to avoid recurrence, resistance, and high-dose therapy toxicities. In this review, we explore how mathematical modeling tools from different disciplines can aid in the development of effective and personalized cancer treatment strategies. Here, we describe how concepts from the disciplines of ecology and evolution, economics, and control engineering have been applied to mathematically model cancer dynamics and enhance treatment strategies.

Keywords: cancer; control theory; ecology; economics; engineering; evolution; mathematical modeling.

FIGURE 1

Mathematical modeling of cancer research must bridge the multi-scale nature of the different types of data that can be collected from a patient’s tumor. Input data types ranging from the tumor level to the cellular level. Tumor biopsies can provide information about tumor composition, histology, stage, grade, and sequencing can be performed on these samples to get at mutation and clonal information. Primary tumor cells can also be isolated and cultured in vitro to obtain cell counts. Patient lab values and imaging can collect information about tumor volume, cell counts and biomarker levels. At the cellular level, different aspects of cellular (dys)function can be assayed dependent on the type of data collected (e.g., DNA, RNA, protein, epigenetic).

FIGURE 2

Pushing beyond computational systems biology, mathematical modeling frameworks from disciplines such as ecology and evolution, economics, and control engineering can be leveraged to better understand tumor progression and treatment response.

FIGURE 3

Representation of most commonly applied mathematical frameworks leveraged to answer open questions in cancer research using insights from ecology and evolution, economics and control engineering.