Boyang Zhao

Intratumor heterogeneity alters most effective drugs in designed combinations

Significance Tumors within each cancer patient have been found to be extensively heterogeneous both spatially across distinct regions and temporally in response to treatment. This poses challenges for prognostic/diagnostic biomarker identification and rational design of optimal drug combinations to minimize reoccurrence. Here we present a computational approach incorporating drug efficacy and drug side effects to derive effective drug combinations and study how tumor heterogeneity affects drug selection. We find that considering subpopulations beyond just the predominant subpopulation in a heterogeneous tumor may result in nonintuitive drug combinations. Additional analyses reveal general properties of effective drugs. This study highlights the importance of optimizing drug combinations in the context of intratumor heterogeneity and offers a principled approach toward their rational design. 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.

Acquisition of a single EZH2 D1 domain mutation confers acquired resistance to EZH2-targeted inhibitors

Although targeted therapies have revolutionized cancer treatment, overcoming acquired resistance remains a major clinical challenge. EZH2 inhibitors (EZH2i), EPZ-6438 and GSK126, are currently in the early stages of clinical evaluation and the first encouraging signs of efficacy have recently emerged in the clinic. To anticipate mechanisms of resistance to EZH2i, we used a forward genetic platform combining a mutagenesis screen with next generation sequencing technology and identified a hotspot of secondary mutations in the EZH2 D1 domain (Y111 and I109). Y111D mutation within the WT or A677G EZH2 allele conferred robust resistance to both EPZ-6438 and GSK126, but it only drove a partial resistance within the Y641F allele. EZH2 mutants required histone methyltransferase (HMT) catalytic activity and the polycomb repressive complex 2 (PRC2) components, SUZ12 and EED, to drive drug resistance. Furthermore, D1 domain mutations not only blocked the ability of EZH2i to bind to WT and A677G mutant, but also abrogated drug binding to the Y641F mutant. These data provide the first cellular validation of the mechanistic model underpinning the oncogenic function of WT and mutant EZH2. Importantly, our findings suggest that acquired-resistance to EZH2i may arise in WT and mutant EZH2 patients through a single mutation that remains targetable by second generation EZH2i.

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 towards 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 promising opportunities these inter-disciplinary 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.

Inherited Disease Genetics Improves the Identification of Cancer-Associated Genes.

The identification of biologically significant variants in cancer genomes is critical to therapeutic discovery, but it is limited by the statistical power needed to discern driver from passenger. Independent biological data can be used to filter cancer exomes and increase statistical power. Large genetic databases for inherited diseases are uniquely suited to this task because they contain specific amino acid alterations with known pathogenicity and molecular mechanisms. However, no rigorous method to overlay this information onto the cancer exome exists. Here, we present a computational methodology that overlays any variant database onto the somatic mutations in all cancer exomes. We validate the computation experimentally and identify novel associations in a re-analysis of 7362 cancer exomes. This analysis identified activating SOS1 mutations associated with Noonan syndrome as significantly altered in melanoma and the first kinase-activating mutations in ACVR1 associated with adult tumors. Beyond a filter, significant variants found in both rare cancers and rare inherited diseases increase the unmet medical need for therapeutics that target these variants and may bootstrap drug discovery efforts in orphan indications.

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.

Abstract PR07: Collateral sensitivity in chemotherapy resistance

The current 5-year survival rate across all cancers is only 68%. The ability of tumors to develop resistance to chemotherapy is a major factor keeping survival rates down. Understanding the evolutionary paths tumors take towards resistance will aid in creating drug regimens designed to avoid resistant tumor states. Research in bacteria suggests that a specific type of drug pair, where resistance to each drug confers sensitivity to the other drug, can slow evolution toward drug resistance. This drug pair relationship, termed collateral sensitivity, has not been systematically investigated in cancer. Here, using a preclinical Eμ-myc; Arf-/- murine model of Burkitt lymphoma we investigate collateral sensitivities in this malignancy. We derived resistant cell lines in vitro by treating the parental cell line with dose escalating concentrations of doxorubicin or cisplatin. We screened a subset of these resistant cell lines against a panel of drugs and found several collateral sensitivities conferred by doxorubicin or cisplatin resistance. We are currently investigating the mechanism of one of these collateral sensitivities. We plan to derive cell lines resistant to a variety of other chemotherapies to learn about collateral sensitivities across many chemotherapeutics. Collateral sensitivity is a promising avenue towards rationally designing combination therapies that reduce the emergence of chemotherapy resistant disease. Citation Format: Simona Dalin, Boyang Zhao, Michael T. Hemann. Collateral sensitivity in chemotherapy resistance [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr PR07.

Abstract 35: Evolution of resistance to glucocorticoid receptor agonists

Chemotherapy resistance is a major obstacle to curing cancer patients. Over 90% of metastatic tumors are resistant to chemotherapy, leading to treatment failure. Although glucocorticoid receptor agonists (GRAs) are a very commonly used class of component drugs in front-line combination chemotherapies for hematopoietic cancer, our understanding of its mechanism(s) of action and resistance are very limited. Here using a preclinical murine model of Burkitt9s lymphoma, we investigated the mechanism of resistance to the glucocorticoid receptor agonist dexamethasone. Dose responses of dexamethasone on the parental E μ -myc; Arf-null cell line revealed a consistently small population of persistent survivors, suggesting a pre-existing subpopulation with innate dexamethasone resistance. To further address the evolution and selection of these survivors in the presence of GRAs, we derived resistant cell lines in vitro by treating the parental cell line with dose escalating concentrations of dexamethasone. These resistant cell lines were expectedly cross-resistant to other GRAs. However, loss of expression of the glucocorticoid receptor (GR) was not observed – ruling out one possible resistance mechanism. To explore alternative or downstream pathways that may be implicated, we subsequently acquired dose responses against a broad set of targeted kinase inhibitors, and observed modest sensitivity to the HSP90 inhibitor 17-AAG. This suggests that the resistance mechanism could be related to HSP909s role of holding ligand-free glucocorticoid receptor (GR) in the cytoplasm. We are now re-evolving the parental populations, and deriving clonal populations to confirm this phenotype. A better understanding of how hematopoietic cancer cells evolve resistance to GCAs will lead to improved design of drug combinations for prolonged survival. Citation Format: Simona Dalin, Boyang Zhao, Michael T. Hemann. Evolution of resistance to glucocorticoid receptor agonists. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Integrating Clinical Genomics and Cancer Therapy; Jun 13-16, 2015; Salt Lake City, UT. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(1_Suppl):Abstract nr 35.

Abstract A31: Combination therapies guided by evolution of tumor heterogeneity.

Successful treatment of hematologic malignancies is challenged by genetic heterogeneity of tumors and the dynamic evolution of the heterogeneity in response to targeted drugs as well as chemotherapeutics. We are applying an integrated computational/experimental approach to elucidating principles which can guide selection of effective combination therapies by mathematical analysis of how tumors evolve with respect to drug sensitivity and resistance during treatment. In an initial pharmacological screen of targeted drugs and chemotherapeutics on murine Bcr-Abl; p19Arf-/- ALL cells, we found that crizotinib was particularly effective (IC50 = ~500 nM) with comparable efficacy to that for imatinib (IC50 = ~200 nM). Moreover, in a derivative cell line bearing spontaneous Bcr-AblT315I mutation, crizotinib exhibited efficacy comparable to that of the Bcr-AblWT parental cell line. This same Bcr-AblT315I cell line showed strong resistance to Abl inhibitors imatinib, dasatinib, nilotinib, and bosutinib. Imatinib and crizotinib also exhibited comparable in vivo efficacy, based on overall survival of syngeneic immunocompetent recipient mice transplanted with Bcr-Abl; p19Arf-/- ALL cells. Cell cycle profiles and signaling analyses suggest that crizotinib induces G2/M arrest and subsequently bim-dependent caspase-mediated apoptosis. This phenotype has been subsequently validated in the human K562 Ph+ CML and murine Baf3 Bcr-AblWT and Bcr-AblT315I cell lines. Resistant populations were also derived through dose escalation treatments with imatinib, dasatinib, nilotinib, foretinib, and crizotinib. Bcr-Abl; p19Arf-/- ALL cells were treated at IC90, and recovered populations were subsequently split into: [a] no-drug media, with sensitivity to different sets of drugs assessed at specific time points post-recovery; and [b] new media with drug concentration at 2x the previous dose (e.g., IC90 2x). Cell populations that were resistant to dasatinib at 1x and 2x IC90 became even more sensitive to crizotinib and foretinib. The drug sensitivity does not appear to be a transient behavior as a result of intracellular rewiring, but rather a change in the tumor heterogeneity via selection of particular subpopulations, as evidenced by sustained sensitivity when these populations were analyzed 24h, 48h, 72h, 5 days, and 9 days post drug selection in no-drug media. The sensitivity was abrogated at IC90 4x and above. Cell populations resistant at these higher doses of dasatinib also became cross-resistant to imatinib and foretinib. This potentially suggests particular stages in the tumor evolution under continued drug selection for which they are extremely sensitive to other pharmacological agents. These findings suggest a novel combination treatment strategy taking into account the evolutionary trajectories of tumor heterogeneity, with drugs dosed sequentially in a multi-course regimen. The sequential treatment is not to exploit vulnerabilities of intracellular rewiring induced by the first drug, but rather vulnerabilities of resulting expanded subpopulations upon selection by the first drug. Citation Format: Boyang Zhao, Michael T. Hemann, Douglas A. Lauffenburger. Combination therapies guided by evolution of tumor heterogeneity. [abstract]. In: Proceedings of the AACR Special Conference on Hematologic Malignancies: Translating Discoveries to Novel Therapies; Sep 20-23, 2014; Philadelphia, PA. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(17 Suppl):Abstract nr A31.

Reply to Azuaje: Predicting effective combined therapies for heterogeneous tumors.

We thank Francisco J. Azuaje (1) for his comments regarding our article (2). His letter offers an opportunity for further discussion on the clinical relevance of heterogeneity drawn from our study using a computational optimization approach based on random sampling of RNAi-based perturbations. Our objective was to learn how to improve the design of drug combinations for diverse tumor subpopulations. We experimentally modeled heterogeneity using RNAi, which is a versatile and robust tool, to generate potentially phenotypically distinct subpopulations. We emphasize, however, that our computational analysis and consequent conclusions are not restricted to only loss-of-function perturbations, as long as there are functional phenotypic distinctions (of relevance here is therapeutic response) among the various subpopulations (this can be at the genomic, transcriptional, or signaling level). Indeed, one can produce similar datasets by using perturbations other than RNAi (e.g., ORF cDNAs) to generate subpopulations and subject to a broad panel of targeted and chemotherapeutic treatments; the associated optimization models and analyses would be similar. The subpopulations are thus generalizable across diverse sources of heterogeneity. In addition, as first iteration of this work, we sampled subpopulation frequencies from a uniform distribution, but this can be extended to examine—for example, in a more clinically relevant setting—sampling based on a prior distribution of genetic alterations for a specific cancer type. Although these distributions may be extracted from public datasets (e.g., The Cancer Genome Atlas), information regarding therapeutic effects on subpopulations is less comprehensive for us to extend this in our present work. Nevertheless, we share interest in moving even closer toward such clinical relevancy with regards to sampling distributions, as we endeavored to describe in the Discussion section of our article (2).

Abstract IA10: Targeting intratumoral genetic heterogeneity through rationally designed combination therapy

Deep sequencing of distinct tumor biopsies from a single cancer has revealed dramatic genetic intratumoral heterogeneity in patients. Additionally, sequencing of tumors at multiple time points has led to the characterization of minor tumor subclones prior to treatment that expand to become the predominate tumor subpopulation at relapse. These data suggest an urgent need to directly address the issue of intratumoral heterogeneity in the design of chemotherapeutic regimens. Here we model tumor heterogeneity using RNA interference (RNAi), and show, both in vitro and in vivo, how optimized drug combinations can yield predictable effects on the composition of heterogeneous tumors. The ability to predict population trajectories has allowed us to mathematically optimize combination therapies to minimize the effect of heterogeneity in three-component heterogeneous tumors. We discover that for certain ensembles of tumor cells, knowledge of the predominant subpopulation is insufficient in deriving an optimal drug combination for the heterogeneous tumor. With optimized drug combinations, our in vitro and in vivo validations confirmed that we can control the predicted trajectories of a heterogeneous tumor, with improved survival in mice. Such approaches will enable us to gain a better understanding of the underlying design principles for combination therapy in the context of intratumoral diversity and construct drug regimens that are optimized for more complex tumors. Citation Format: Douglas A. Lauffenburger, Boyang Zhao, Justin R. Pritchard, Michael T. Hemann. Targeting intratumoral genetic heterogeneity through rationally designed combination therapy. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities; May 17-20, 2013; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(5 Suppl):Abstract nr IA10.