Understanding resistance to combination chemotherapy

available in PMC 2014 April 04The current clinical application of combination chemotherapy is guided by a historically successful set of practices that were developed by basic and clinical researchers 50–60 years ago. Thus, in order to understand how emerging approaches to drug development might aid the creation of new therapeutic combinations, it is critical to understand the defining principles underlying classic combination therapy and the original experimental rationales behind them. One such principle is that the use of combination therapies with independent mechanisms of action can minimize the evolution of drug resistance. Another is that in order to kill sufficient cancer cells to cure a patient, multiple drugs must be delivered at their maximum tolerated dose – a condition that allows for enhanced cancer cell killing with manageable toxicity. In light of these models, we aim to explore recent genomic evidence underlying the mechanisms of resistance to the combination regimens constructed on these principles. Interestingly, we find that emerging genomic evidence contradicts some of the rationales of early practitioners in developing commonly used drug regimens. However, we also find that the addition of recent targeted therapies has yet to change the current principles underlying the construction of anti-cancer combinatorial regimens, nor have they made substantial inroads into the treatment of most cancers. We suggest that emerging systems/network biology approaches have an immense opportunity to impact the rational development of successful drug regimens. Specifically, by examining drug combinations in multivariate ways, next generation combination therapies can be constructed with a clear understanding of how mechanisms of resistance to multi-drug regimens differ from single agent resistance.Massachusetts Institute of Technology (Eisen and Chang Career Development Associate Professor of Biology)National Cancer Institute (U.S.) (NCI Integrative Cancer Biology Program (ICBP), #U54-CA112967-06)National Institutes of Health (U.S.) (NIH RO1-CA128803-04

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

Predicting cancer drug mechanisms of action using molecular network signatures

Molecular signatures are a powerful approach to characterize novel small molecules and derivatized small molecule libraries. While new experimental techniques are being developed in diverse model systems, informatics approaches lag behind these exciting advances. We propose an analysis pipeline for signature based drug annotation. We develop an integrated strategy, utilizing supervised and unsupervised learning methodologies that are bridged by network based statistics. Using this approach we can: 1, predict new examples of drug mechanisms that we trained our model upon; 2, identify “New” mechanisms of action that do not belong to drug categories that our model was trained upon; and 3, update our training sets with these “New” mechanisms and accurately predict entirely distinct examples from these new categories. Thus, not only does our strategy provide statistical generalization but it also offers biological generalization. Additionally, we show that our approach is applicable to diverse types of data, and that distinct biological mechanisms characterize its resolution of categories across different data types. As particular examples, we find that our predictive resolution of drug mechanisms from mRNA expression studies relies upon the analog measurement of a cell stress-related transcriptional rheostat along with a transcriptional representation of cell cycle state; whereas, in contrast, drug mechanism resolution from functional RNAi studies rely upon more dichotomous (e.g., either enhances or inhibits) association with cell death states. We believe that our approach can facilitate molecular signature-based drug mechanism understanding from different technology platforms and across diverse biological phenomena.National Cancer Institute (U.S.) (NCI Integrative Cancer Biology Program grant U54-CA112967

Disruption of Mycobacterium avium subsp. paratuberculosis-specific genes impairs in vivo fitness

Background: Mycobacterium avium subsp. paratuberculosis (MAP) is an obligate intracellular pathogen that infects many ruminant species. The acquisition of foreign genes via horizontal gene transfer has been postulated to contribute to its pathogenesis, as these genetic elements are absent from its putative ancestor, M. avium subsp. hominissuis (MAH), an environmental organism with lesser pathogenicity. In this study, high-throughput sequencing of MAP transposon libraries were analyzed to qualitatively and quantitatively determine the contribution of individual genes to bacterial survival during infection. Results: Out of 52384 TA dinucleotides present in the MAP K-10 genome, 12607 had a MycoMarT7 transposon in the input pool, interrupting 2443 of the 4350 genes in the MAP genome (56%). Of 96 genes situated in MAP-specific genomic islands, 82 were disrupted in the input pool, indicating that MAP-specific genomic regions are dispensable for in vitro growth (odds ratio = 0.21). Following 5 independent in vivo infections with this pool of mutants, the correlation between output pools was high for 4 of 5 (R = 0.49 to 0.61) enabling us to define genes whose disruption reproducibly reduced bacterial fitness in vivo. At three different thresholds for reduced fitness in vivo, MAP-specific genes were over-represented in the list of predicted essential genes. We also identified additional genes that were severely depleted after infection, and several of them have orthologues that are essential genes in M. tuberculosis. Conclusions: This work indicates that the genetic elements required for the in vivo survival of MAP represent a combination of conserved mycobacterial virulence genes and MAP-specific genes acquired via horizontal gene transfer. In addition, the in vitro and in vivo essential genes identified in this study may be further characterized to offer a better understanding of MAP pathogenesis, and potentially contribute to the discovery of novel therapeutic and vaccine targets. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-415) contains supplementary material, which is available to authorized users

Three-kinase inhibitor combination recreates multipathway effects of a geldanamycin analogue on hepatocellular carcinoma cell death

Multitarget compounds that act on a diverse set of regulatory pathways are emerging as a therapeutic approach for a variety of cancers. Toward a more specified use of this approach, we hypothesize that the desired efficacy can be recreated in terms of a particular combination of relatively more specific (i.e., ostensibly single target) compounds. We test this hypothesis for the geldanamycin analogue 17-Allylamino-17-demethoxygeldanamycin (17AAG) in hepatocellular carcinoma cells, measuring critical phosphorylation levels that indicate the kinase pathway effects correlating with apoptotic responsiveness of the Hep3B cell line in contrast to the apoptotic resistance of the Huh7 cell line. A principal components analysis (PCA) constructed from time course measurements of seven phosphoprotein signaling levels identified modulation of the AKT, IκB kinase, and signal transducer and activator of transcription 3 pathways by 17AAG treatment as most important for distinguishing these cell-specific death responses. The analysis correctly suggested from 17AAG-induced effects on these phosphoprotein levels that the FOCUS cell line would show apoptotic responsiveness similarly to Hep3B. The PCA also guided the inhibition of three critical pathways and rendered Huh7 cells responsive to 17AAG. Strikingly, in all three hepatocellular carcinoma lines, the three-inhibitor combination alone exhibited similar or greater efficacy to 17AAG. We conclude that (a) the PCA captures and clusters the multipathway phosphoprotein time courses with respect to their 17AAG-induced apoptotic responsiveness and (b) we can recreate, in a more specified manner, the cellular responses of a prospective multitarget cancer therapeutic.National Institute of General Medical Sciences (U.S.). Cell Decision Processes CenterNational Cancer Institute (U.S.). Integrative Cancer Biology ProgramMassachusetts Institute of Technology. Presidential FellowshipNational Institutes of Health (U.S.

BCL-2 family genetic profiling reveals microenvironment-specific determinants of chemotherapeutic response

The Bcl-2 family encompasses a diverse set of apoptotic regulators that are dynamically activated in response to various cell-intrinsic and -extrinsic stimuli. An extensive variety of cell culture experiments have identified effects of growth factors, cytokines, and drugs on Bcl-2 family functions, but in vivo studies have tended to focus on the role of one or two particular members in development and organ homeostasis. Thus, the ability of physiologically relevant contexts to modulate canonical dependencies that are likely to be more complex has yet to be investigated systematically. In this study, we report findings derived from a pool-based shRNA assay that systematically and comprehensively interrogated the functional dependence of leukemia and lymphoma cells upon various Bcl-2 family members across many diverse in vitro and in vivo settings. This approach permitted us to report the first in vivo loss of function screen for modifiers of the response to a front-line chemotherapeutic agent. Notably, our results reveal an unexpected role for the extrinsic death pathway as a tissue-specific modifier of therapeutic response. In particular, our findings show that particular tissue sites of tumor dissemination play critical roles in demarcating the nature and extent of cancer cell vulnerabilities and mechanisms of chemoresistance. Cancer Res; 71(17); 5850–8. ©2011 AACR.National Institutes of Health (U.S.) (NIH RO1 CA128803)National Cancer Institute (U.S.) (Integrated Cancer Biology Program grant NCI 1-U54-CA112967)David H. Koch Institute for Integrative Cancer Research at MIT (Ludwig Fellowship)Massachusetts Institute of Technology. Dept. of Biology (training grant

Tapering practices of New Zealand's elite raw powerlifters

Pritchard, HJ, Tod, DA, Barnes, MJ, Keogh, JW, and McGuigan, MR. Tapering practices of New Zealand's elite raw powerlifters. J Strength Cond Res 30(7): 1796-1804, 2016-The major aim of this study was to determine tapering strategies of elite powerlifters. Eleven New Zealand powerlifters (28.4 ± 7.0 years, best Wilks score of 431.9 ± 43.9 points) classified as elite were interviewed, using semistructured interviews, about their tapering strategies. Interviews were transcribed verbatim and content analyzed. Total training volume peaked 5.2 ± 1.7 weeks from competition while average training intensity (of 1 repetition maximum) peaked 1.9 ± 0.8 weeks from competition. During tapering, volume was reduced by 58.9 ± 8.4% while intensity was maintained (or slightly reduced) and the final weight training session was performed 3.7 ± 1.6 days out from competition. Participants generally stated that tapering was performed to achieve full recovery; that accessory work was removed around 2 weeks out from competition; and deadlifting takes longer to recover from than other lifts. Typically participants stated that trial and error, and changes based on "feel" were the sources of tapering strategies; equipment used and movements performed during tapering are the same as in competition; nutrition was manipulated during the taper (for weight cutting or performance aims); and poor tapering occurred when too long (1 week or more) was taken off training. These results suggest that athletes may benefit from continuing to strength train before important events with reduced volume and maintained intensity. Only exercises that directly assist sports performance should remain in the strength program during tapering, to assist with reductions in fatigue while maintaining/improving strength expression and performance

High-resolution definition of the Vibrio cholerae essential gene set with hidden Markov model–based analyses of transposon-insertion sequencing data

The coupling of high-density transposon mutagenesis to high-throughput DNA sequencing (transposon-insertion sequencing) enables simultaneous and genome-wide assessment of the contributions of individual loci to bacterial growth and survival. We have refined analysis of transposon-insertion sequencing data by normalizing for the effect of DNA replication on sequencing output and using a hidden Markov model (HMM)-based filter to exploit heretofore unappreciated information inherent in all transposon-insertion sequencing data sets. The HMM can smooth variations in read abundance and thereby reduce the effects of read noise, as well as permit fine scale mapping that is independent of genomic annotation and enable classification of loci into several functional categories (e.g. essential, domain essential or ‘sick’). We generated a high-resolution map of genomic loci (encompassing both intra- and intergenic sequences) that are required or beneficial for in vitro growth of the cholera pathogen, Vibrio cholerae. This work uncovered new metabolic and physiologic requirements for V. cholerae survival, and by combining transposon-insertion sequencing and transcriptomic data sets, we also identified several novel noncoding RNA species that contribute to V. cholerae growth. Our findings suggest that HMM-based approaches will enhance extraction of biological meaning from transposon-insertion sequencing genomic data

ARTIST: High-Resolution Genome-Wide Assessment of Fitness Using Transposon-Insertion Sequencing

Transposon-insertion sequencing (TIS) is a powerful approach for deciphering genetic requirements for bacterial growth in different conditions, as it enables simultaneous genome-wide analysis of the fitness of thousands of mutants. However, current methods for comparative analysis of TIS data do not adjust for stochastic experimental variation between datasets and are limited to interrogation of annotated genomic elements. Here, we present ARTIST, an accessible TIS analysis pipeline for identifying essential regions that are required for growth under optimal conditions as well as conditionally essential loci that participate in survival only under specific conditions. ARTIST uses simulation-based normalization to model and compensate for experimental noise, and thereby enhances the statistical power in conditional TIS analyses. ARTIST also employs a novel adaptation of the hidden Markov model to generate statistically robust, high-resolution, annotation-independent maps of fitness-linked loci across the entire genome. Using ARTIST, we sensitively and comprehensively define Mycobacterium tuberculosis and Vibrio cholerae loci required for host infection while limiting inclusion of false positive loci. ARTIST is applicable to a broad range of organisms and will facilitate TIS-based dissection of pathways required for microbial growth and survival under a multitude of conditions

A genome-scale in vivo loss-of-function screen identifies Phf6 as a lineage-specific regulator of leukemia cell growth

We performed a genome-scale shRNA screen for modulators of B-cell leukemia progression in vivo. Results from this work revealed dramatic distinctions between the relative effects of shRNAs on the growth of tumor cells in culture versus in their native microenvironment. Specifically, we identified many “context-specific” regulators of leukemia development. These included the gene encoding the zinc finger protein Phf6. While inactivating mutations in PHF6 are commonly observed in human myeloid and T-cell malignancies, we found that Phf6 suppression in B-cell malignancies impairs tumor progression. Thus, Phf6 is a “lineage-specific” cancer gene that plays opposing roles in developmentally distinct hematopoietic malignancies.Massachusetts Institute of Technology. Department of Biology (Training Grant)National Cancer Institute (U.S.). Integrative Cancer Biology Program (U54-CA112967-06)National Institutes of Health (U.S.) (RO1-CA128803-05