Ancient hot and cold genes and chemotherapy resistance emergence.

We use a microfabricated ecology with a doxorubicin gradient and population fragmentation to produce a strong Darwinian selective pressure that drives forward the rapid emergence of doxorubicin resistance in multiple myeloma (MM) cancer cells. RNA sequencing of the resistant cells was used to examine (i) emergence of genes with high de novo substitution densities (i.e., hot genes) and (ii) genes never substituted (i.e., cold genes). The set of cold genes, which were 21% of the genes sequenced, were further winnowed down by examining excess expression levels. Both the most highly substituted genes and the most highly expressed never-substituted genes were biased in age toward the most ancient of genes. This would support the model that cancer represents a revision back to ancient forms of life adapted to high fitness under extreme stress, and suggests that these ancient genes may be targets for cancer therapy.

A preclinical assay for chemosensitivity in multiple myeloma.

Accurate preclinical predictions of the clinical efficacy of experimental cancer drugs are highly desired but often haphazard. Such predictions might be improved by incorporating elements of the tumor microenvironment in preclinical models by providing a more physiological setting. In generating improved xenograft models, it is generally accepted that the use of primary tumors from patients are preferable to clonal tumor cell lines. Here we describe an interdisciplinary platform to study drug response in multiple myeloma, an incurable cancer of the bone marrow. This platform uses microfluidic technology to minimize the number of cells per experiment, while incorporating three-dimensional extracellular matrix and mesenchymal cells derived from the tumor microenvironment. We used sequential imaging and a novel digital imaging analysis algorithm to quantify changes in cell viability. Computational models were used to convert experimental data into dose-exposure-response "surfaces," which offered predictive utility. Using this platform, we predicted chemosensitivity to bortezomib and melphalan, two clinical multiple myeloma treatments, in three multiple myeloma cell lines and seven patient-derived primary multiple myeloma cell populations. We also demonstrated how this system could be used to investigate environment-mediated drug resistance and drug combinations that target it. This interdisciplinary preclinical assay is capable of generating quantitative data that can be used in computational models of clinical response, demonstrating its utility as a tool to contribute to personalized oncology.

Evolutionary approaches to prolong progression-free survival in breast cancer.

Many cancers adapt to chemotherapeutic agents by upregulating membrane efflux pumps that export drugs from the cytoplasm, but this response comes at an energetic cost. In breast cancer patients, expression of these pumps is low in tumors before therapy but increases after treatment. While the evolution of therapeutic resistance is virtually inevitable, proliferation of resistant clones is not, suggesting strategies of adaptive therapy. Chemoresistant cells must consume excess resources to maintain resistance mechanisms, so adaptive therapy strategies explicitly aim to maintain a stable population of therapy-sensitive cells to suppress growth of resistant phenotypes through intratumoral competition. We used computational models parameterized by in vitro experiments to illustrate the efficacy of such approaches. Here, we show that low doses of verapamil and 2-deoxyglucose, to accentuate the cost of resistance and to decrease energy production, respectively, could suppress the proliferation of drug-resistant clones in vivo. Compared with standard high-dose-density treatment, the novel treatment we developed achieved a 2-fold to 10-fold increase in time to progression in tumor models. Our findings challenge the existing flawed paradigm of maximum dose treatment, a strategy that inevitably produces drug resistance that can be avoided by the adaptive therapy strategies we describe.