SUMMARY The prevailing approach to addressing secondary drug resistance in cancer focuses on treating the resistance mechanisms at relapse. However, the dynamic nature of clonal evolution, along with potential fitness costs and cost compensations, may present exploitable vulnerabilities; a notion that we term ‘temporal collateral sensitivity’. Using a combined pharmacological screen and drug resistance selection approach in a murine model of Ph+ acute lymphoblastic leukemia, we indeed find that temporal and/or persistent collateral sensitivity to non-classical BCR-ABL1 drugs arises in emergent tumor subpopulations during the evolution of resistance toward initial treatment with BCR-ABL1 targeted inhibitors. We determined the sensitization mechanism via genotypic, phenotypic, signaling, and binding measurements in combination with computational models, and demonstrated significant overall survival extension in mice. Additional stochastic mathematical models and small molecule screens extended our insights, indicating the value of focusing on evolutionary trajectories and pharmacological profiles to identify new strategies to treat dynamic tumor vulnerabilities.

Transition metal perovskite chalcogenides, a class of materials with rich tunability in functionalities, are gaining increased attention as candidate materials for renewable energy applications. Perovskite oxides are considered excellent n-type thermoelectric materials.Compared to oxide counterparts, we expect the chalcogenides to possess more favorable thermoelectric properties such as lower lattice thermal conductivity and smaller band gap, making them promising material candidates for high temperature thermoelectrics. Thus, it is necessary to study the thermal properties of these materials in detail, especially thermal stability, to evaluate their potential. In this work, we report the synthesis and thermal stability study of five compounds, a-SrZrS 3 , b-SrZrS 3 , BaZrS 3 , Ba 2 ZrS 4 , and Ba 3 Zr 2 S 7 . These materials cover several structural types including distorted perovskite, needle-like, and Ruddlesden-Popper phases.Differential scanning calorimeter and thermo-gravimetric analysis measurements were performed up to 1200°C in air. Structural and chemical characterizations such as X-ray diffraction, Raman spectroscopy, and energy dispersive analytical X-ray spectroscopy were performed on all the samples before and after the heat treatment to understand the oxidation process. Our studies show that perovskite chalcogenides possess excellent thermal stability in air at least up to 600°C.

Recent tumor sequencing data suggests an urgent need to develop a methodology to directly address intra-tumor heterogeneity in the design of anti-cancer 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 pre-clinical lymphoma model. Altogether, our approach provides new insights concerning design principles for combination therapy in the context of intratumoral diversity, data that should inform the development of drug regimens superior for complex tumors.

The substantial spatial and temporal heterogeneity observed in patient tumors poses considerable challenges for the design of effective drug combinations with predictable outcomes. Currently, the implications of tissue heterogeneity and sampling bias during diagnosis are unclear for selection and subsequent performance of potential combination therapies. Here, we apply a multiobjective computational optimization approach integrated with empirical information on efficacy and toxicity for individual drugs with respect to a spectrum of genetic perturbations, enabling derivation of optimal drug combinations for heterogeneous tumors comprising distributions of subpopulations possessing these perturbations. Analysis across probabilistic samplings from the spectrum of various possible distributions reveals that the most beneficial (considering both efficacy and toxicity) set of drugs changes as the complexity of genetic heterogeneity increases. Importantly, a significant likelihood arises that a drug selected as the most beneficial single agent with respect to the predominant subpopulation in fact does not reside within the most broadly useful drug combinations for heterogeneous tumors. The underlying explanation appears to be that heterogeneity essentially homogenizes the benefit of drug combinations, reducing the special advantage of a particular drug on a specific subpopulation. Thus, this study underscores the importance of considering heterogeneity in choosing drug combinations and offers a principled approach toward designing the most likely beneficial set, even if the subpopulation distribution is not precisely known.systems biology | cancer | combination therapy G enetic intratumor heterogeneity has long been appreciated as present in cancer patients (1). Recent sequencing studies further revealed the extent of this tumor diversity, arising from highly complex clonal evolutionary processes (2). This phenomenon has been observed in many solid (3-5) and hematopoietic cancers (6-8). Moreover, treatments also may have dramatic effects on tumor composition-with a preexisting subclone at diagnosis often becoming dominant at relapse (9-12). To meet this challenge, rational drug combination treatments must be designed so as to account for intratumor heterogeneity to better predict reoccurrence.The history of theoretical studies aimed at drug optimization in cancer therapy is long. Some studies used differential equation models formulated as deterministic optimal control problems (13-16), whereas others used stochastic birth-death process models (17-21), to examine treatment regimens for tumors comprising a sensitive population along with a few resistant subpopulations. However, these studies, many of which dealt only with generic scenarios, focused primarily on drug scheduling, investigating the effects of frequency and dose intensity of drugs on resistance potential. Practical applications of such results have been limited, although some of these strategies recently were combined with experimental validation to examin...

Vaccines are among the most efficacious and cost-effective tools for reducing morbidity and mortality caused by infectious diseases. The vaccine investigation and online information network (VIOLIN) is a web-based central resource, allowing easy curation, comparison and analysis of vaccine-related research data across various human pathogens (e.g. Haemophilus influenzae, human immunodeficiency virus (HIV) and Plasmodium falciparum) of medical importance and across humans, other natural hosts and laboratory animals. Vaccine-related peer-reviewed literature data have been downloaded into the database from PubMed and are searchable through various literature search programs. Vaccine data are also annotated, edited and submitted to the database through a web-based interactive system that integrates efficient computational literature mining and accurate manual curation. Curated information includes general microbial pathogenesis and host protective immunity, vaccine preparation and characteristics, stimulated host responses after vaccination and protection efficacy after challenge. Vaccine-related pathogen and host genes are also annotated and available for searching through customized BLAST programs. All VIOLIN data are available for download in an eXtensible Markup Language (XML)-based data exchange format. VIOLIN is expected to become a centralized source of vaccine information and to provide investigators in basic and clinical sciences with curated data and bioinformatics tools for vaccine research and development. VIOLIN is publicly available at http://www.violinet.org