Modeling Tumor Clonal Evolution for Drug Combinations Design

Cancer is a clonal evolutionary process. This presents challenges for effective therapeutic intervention, given the constant selective pressure toward drug resistance. Mathematical modeling from population genetics, evolutionary dynamics, and engineering perspectives are being increasingly employed to study tumor progression, intratumoral heterogeneity, drug resistance, and rational drug scheduling and combinations design. In this review we discuss the promising opportunities that these interdisciplinary approaches hold for advances in cancer biology and treatment. We propose that quantitative modeling perspectives can complement emerging experimental technologies to facilitate enhanced understanding of disease progression and improved capabilities for therapeutic drug regimen designs.David H. Koch Cancer Research Fund (Grant P30-CA14051)National Cancer Institute (U.S.). Integrative Cancer Biology Program (Grant U54-CA112967)National Institute of General Medical Sciences (U.S.). Interdepartmental Biotechnology Training Program (5T32GM008334

Addressing Genetic Tumor Heterogeneity through Computationally Predictive Combination Therapy

Recent tumor sequencing data suggest an urgent need to develop a methodology to directly address intratumoral heterogeneity in the design of anticancer treatment regimens. We use RNA interference to model heterogeneous tumors, and demonstrate successful validation of computational predictions for how optimized drug combinations can yield superior effects on these tumors both in vitro and in vivo. Importantly, we discover here that for many such tumors knowledge of the predominant subpopulation is insufficient for determining the best drug combination. Surprisingly, in some cases, the optimal drug combination does not include drugs that would treat any particular subpopulation most effectively, challenging straightforward intuition. We confirm examples of such a case with survival studies in a murine preclinical lymphoma model. Altogether, our approach provides new insights about design principles for combination therapy in the context of intratumoral diversity, data that should inform the development of drug regimens superior for complex tumors.National Cancer Institute (U.S.) (NCI Integrative Cancer Biology Program (ICBP), Grant U54-CA112967-06)National Institutes of Health (U.S.) (NIH/National Institute of General Medical Sciences (NIGMS) Interdepartmental Biotechnology Training Program, 5T32GM008334)National Cancer Institute (U.S.) (Koch Institute Support (core) Grant P30-CA14051

Differential selective pressure alters rate of drug resistance acquisition in heterogeneous tumor populations

Recent drug discovery and development efforts have created a large arsenal of targeted and chemotherapeutic drugs for precision medicine. However, drug resistance remains a major challenge as minor pre-existing resistant subpopulations are often found to be enriched at relapse. Current drug design has been heavily focused on initial efficacy, and we do not fully understand the effects of drug selective pressure on long-term drug resistance potential. Using a minimal two-population model, taking into account subpopulation proportions and growth/kill rates, we modeled long-term drug treatment and performed parameter sweeps to analyze the effects of each parameter on therapeutic efficacy. We found that drugs with the same overall initial kill may exert differential selective pressures, affecting long-term therapeutic outcome. We validated our conclusions experimentally using a preclinical model of Burkitt’s lymphoma. Furthermore, we highlighted an intrinsic tradeoff between drug-imposed overall selective pressure and rate of adaptation. A principled approach in understanding the effects of distinct drug selective pressures on short-term and long-term tumor response enables better design of therapeutics that ultimately minimize relapse.Koch Institute for Integrative Cancer Research (Support (core) Grant P30-CA14051)National Cancer Institute (U.S.). Integrative Cancer Biology Program (Grant U54-CA112967)National Institute of General Medical Sciences (U.S.) (Interdepartmental Biotechnology Training Program 5T32GM008334

Crystal growth and structural analysis of perovskite chalcogenide BaZrS3_3 and Ruddlesden-Popper phase Ba3_3Zr2_2S7_7

Perovskite chalcogenides are gaining substantial interest as an emerging class of semiconductors for optoelectronic applications. High quality samples are of vital importance to examine their inherent physical properties. We report the successful crystal growth of the model system, BaZrS$_3$ and its Ruddlesden-Popper phase Ba$_3$Zr$_2$S$_7$ by flux method. X-ray diffraction analyses showed space group of $Pnma$ with lattice constants of $a$ = 7.056(3) \AA\/, $b$ = 9.962(4) \AA\/, $c$ = 6.996(3) \AA\/ for BaZrS$_3$ and $P4_2/mnm$ with $a$ = 7.071(2) \AA\/, $b$ = 7.071(2) \AA\/, $c$ = 25.418(5) \AA\/ for Ba$_3$Zr$_2$S$_7$. Rocking curves with full-width-at-half-maximum of 0.011$^\circ$ for BaZrS$_3$ and 0.027$^\circ$ for Ba$_3$Zr$_2$S$_7$ were observed. Pole figure analysis, scanning transmission electron microscopy images and electron diffraction patterns also establish high quality of grown crystals. The octahedra tilting in the corner-sharing octahedra network are analyzed by extracting the torsion angles.Comment: 4 Figures, 2 Table

Whole-genome sequencing reveals high-risk clones of Pseudomonas aeruginosa in Guangdong, China

The ever-increasing prevalence of infections produced by multidrug-resistant or extensively drug-resistant Pseudomonas aeruginosa is commonly linked to a limited number of aptly-named epidemical \u27high-risk clones\u27 that are widespread among and within hospitals worldwide. The emergence of new potential high-risk clone strains in hospitals highlights the need to better and further understand the underlying genetic mechanisms for their emergence and success. P. aeruginosa related high-risk clones have been sporadically found in China, their genome sequences have rarely been described. Therefore, the large-scale sequencing of multidrug-resistance high-risk clone strains will help us to understand the emergence and transmission of antibiotic resistances in P. aeruginosa high-risk clones. In this study, 212 P. aeruginosa strains were isolated from 2 tertiary hospitals within 3 years (2018-2020) in Guangdong Province, China. Whole-genome sequencing, multi-locus sequence typing (MLST) and antimicrobial susceptibility testing were applied to analyze the genomic epidemiology of P. aeruginosa in this region. We found that up to 130 (61.32%) of the isolates were shown to be multidrug resistant, and 196 (92.45%) isolates were Carbapenem-Resistant Pseudomonas aeruginosa. MLST analysis demonstrated high diversity of sequence types, and 18 reported international high-risk clones were identified. Furthermore, we discovered the co-presence of exoU and exoS genes in 5 collected strains. This study enhances insight into the regional research of molecular epidemiology and antimicrobial resistance of P. aeruginosa in China. The high diversity of clone types and regional genome characteristics can serve as a theoretical reference for public health policies and help guide measures for the prevention and control of P. aeruginosa resistance