Integration of quantitative methods and mathematical approaches for the modeling of cancer cell proliferation dynamics.

Physiological processes rely on the control of cell proliferation, and the dysregulation of these processes underlies various pathological conditions, including cancer. Mathematical modeling can provide new insights into the complex regulation of cell proliferation dynamics. In this review, we first examine quantitative experimental approaches for measuring cell proliferation dynamics in vitro and compare the various types of data that can be obtained in these settings. We then explore the toolbox of common mathematical modeling frameworks that can describe cell behavior, dynamics, and interactions of proliferation. We discuss how these wet-laboratory studies may be integrated with different mathematical modeling approaches to aid the interpretation of the results and to enable the prediction of cell behaviors, specifically in the context of cancer.

Applications of high-resolution clone tracking technologies in cancer.

Tumors are comprised of dynamic, heterogenous cell populations characterized by numerous genetic and non-genetic alterations that accumulate and change with disease progression and treatment. Retrospective analyses of tumor evolution have relied on the measurement of genetic markers (such as copy number variants) to infer clonal dynamics. However, these approaches neglect the critical contributions of non-genetic drivers of disease. Techniques that harness the power of prospective clone tracking via heritable barcode tags provide an alternative strategy. In this review, we discuss methods for high-resolution, quantitative clone tracking, including recent advancements to pair barcode-specific functionality with scRNA-seq, clonal cell isolation, and in situ hybridization and imaging. We discuss these approaches in the context of cancer cell heterogeneity and treatment resistance.

Paper-based detection of HIV-1 drug resistance using isothermal amplification and an oligonucleotide ligation assay.

Regular HIV-1 viral load monitoring is the standard of care to assess antiretroviral therapy effectiveness in resource-rich settings. Persistently elevated viral loads indicate virologic failure (VF), which warrants HIV drug resistance testing (HIVDRT) to allow individualized regimen switches. However, in settings lacking access to HIVDRT, clinical decisions are often made based on symptoms, leading to unnecessary therapy switches and increased costs of care. This work presents a proof-of-concept assay to detect M184V, the most common drug resistance mutation after first-line antiretroviral therapy failure, in a paper format. The first step isothermally amplifies a section of HIV-1 reverse transcriptase containing M184V using a recombinase polymerase amplification (RPA) assay. Then, an oligonucleotide ligation assay (OLA) is used to selectively label the mutant and wild type amplified sequences. Finally, a lateral flow enzyme-linked immunosorbent assay (ELISA) differentiates between OLA-labeled products with or without M184V. Our method shows 100% specificity and 100% sensitivity when tested with samples that contained 200 copies of mutant DNA and 800 copies of wild type DNA prior to amplification. When integrated with sample preparation, this method may detect HIV-1 drug resistance at a low cost and at a rural hospital laboratory.