Noah Berlow

Functionally defined therapeutic targets in diffuse intrinsic pontine glioma

Diffuse intrinsic pontine glioma (DIPG) is a fatal childhood cancer. We performed a chemical screen in patient-derived DIPG cultures along with RNA-seq analyses and integrated computational modeling to identify potentially effective therapeutic strategies. The multi–histone deacetylase inhibitor panobinostat demonstrated therapeutic efficacy both in vitro and in DIPG orthotopic xenograft models. Combination testing of panobinostat and the histone demethylase inhibitor GSK-J4 revealed that the two had synergistic effects. Together, these data suggest a promising therapeutic strategy for DIPG.

Erratum: Functionally defined therapeutic targets in diffuse intrinsic pontine glioma(Nature Medicine (2015) 21 (555-559) DOI: 10.1038/nm.3855)

Catherine S Grasso, Yujie Tang, Nathalene Truffaux, Noah E Berlow, Lining Liu, Marie-Anne Debily, Michael J Quist, Lara E Davis, Elaine C Huang, Pamelyn J Woo, Anitha Ponnuswami, Spenser Chen, Tessa B Johung, Wenchao Sun, Mari Kogiso, Yuchen Du, Lin Qi, Yulun Huang, Marianne Hütt-Cabezas, Katherine E Warren, Ludivine Le Dret, Paul S Meltzer, Hua Mao, Martha Quezado, Dannis G van Vuurden, Jinu Abraham, Maryam Fouladi, Matthew N Svalina, Nicholas Wang, Cynthia Hawkins, Javad Nazarian, Marta M Alonso, Eric H Raabe, Esther Hulleman, Paul T Spellman, Xiao-Nan Li, Charles Keller, Ranadip Pal, Jacques Grill & Michelle Monje Nat. Med. 21, 555–559 (2015); doi:10.1038/nm.3855; published online 4 May 2015; corrected after print 15 June 2015

A new approach for prediction of tumor sensitivity to targeted drugs based on functional data

BackgroundThe success of targeted anti-cancer drugs are frequently hindered by the lack of knowledge of the individual pathway of the patient and the extreme data requirements on the estimation of the personalized genetic network of the patient’s tumor. The prediction of tumor sensitivity to targeted drugs remains a major challenge in the design of optimal therapeutic strategies. The current sensitivity prediction approaches are primarily based on genetic characterizations of the tumor sample. We propose a novel sensitivity prediction approach based on functional perturbation data that incorporates the drug protein interaction information and sensitivities to a training set of drugs with known targets.ResultsWe illustrate the high prediction accuracy of our framework on synthetic data generated from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and an experimental dataset of four canine osteosarcoma tumor cultures following application of 60 targeted small-molecule drugs. We achieve a low leave one out cross validation error of <10% for the canine osteosarcoma tumor cultures using a drug screen consisting of 60 targeted drugs.ConclusionsThe proposed framework provides a unique input-output based methodology to model a cancer pathway and predict the effectiveness of targeted anti-cancer drugs. This framework can be developed as a viable approach for personalized cancer therapy.

A high-throughput in vitro drug screen in a genetically engineered mouse model of diffuse intrinsic pontine glioma identifies BMS-754807 as a promising therapeutic agent.

Diffuse intrinsic pontine gliomas (DIPGs) represent a particularly lethal type of pediatric brain cancer with no effective therapeutic options. Our laboratory has previously reported the development of genetically engineered DIPG mouse models using the RCAS/tv-a system, including a model driven by PDGF-B, H3.3K27M, and p53 loss. These models can serve as a platform in which to test novel therapeutics prior to the initiation of human clinical trials. In this study, an in vitro high-throughput drug screen as part of the DIPG preclinical consortium using cell-lines derived from our DIPG models identified BMS-754807 as a drug of interest in DIPG. BMS-754807 is a potent and reversible small molecule multi-kinase inhibitor with many targets including IGF-1R, IR, MET, TRKA, TRKB, AURKA, AURKB. In vitro evaluation showed significant cytotoxic effects with an IC50 of 0.13 μM, significant inhibition of proliferation at a concentration of 1.5 μM, as well as inhibition of AKT activation. Interestingly, IGF-1R signaling was absent in serum-free cultures from the PDGF-B; H3.3K27M; p53 deficient model suggesting that the antitumor activity of BMS-754807 in this model is independent of IGF-1R. In vivo, systemic administration of BMS-754807 to DIPG-bearing mice did not prolong survival. Pharmacokinetic analysis demonstrated that tumor tissue drug concentrations of BMS-754807 were well below the identified IC50, suggesting that inadequate drug delivery may limit in vivo efficacy. In summary, an unbiased in vitro drug screen identified BMS-754807 as a potential therapeutic agent in DIPG, but BMS-754807 treatment in vivo by systemic delivery did not significantly prolong survival of DIPG-bearing mice.

An integrated approach to anti-cancer drug sensitivity prediction

A framework for design of personalized cancer therapy requires the ability to predict the sensitivity of a tumor to anticancer drugs. The predictive modeling of tumor sensitivity to anti-cancer drugs has primarily focused on generating functions that map gene expressions and genetic mutation profiles to drug sensitivity. In this paper, we present a new approach for drug sensitivity prediction and combination therapy design based on integrated functional and genomic characterizations. The modeling approach when applied to data from the Cancer Cell Line Encyclopedia shows a significant gain in prediction accuracy as compared to elastic net and random forest techniques based on genomic characterizations. Utilizing a Mouse Embryonal Rhabdomyosarcoma cell culture and a drug screen of 60 targeted drugs, we show that predictive modeling based on functional data alone can also produce high accuracy predictions. The framework also allows us to generate personalized tumor proliferation circuits to gain further insights on the individualized biological pathway.

Inference of dynamic biological networks based on responses to drug perturbations

Drugs that target specific proteins are a major paradigm in cancer research. In this article, we extend a modeling framework for drug sensitivity prediction and combination therapy design based on drug perturbation experiments. The recently proposed target inhibition map approach can infer stationary pathway models from drug perturbation experiments, but the method is limited to a steady-state snapshot of the underlying dynamical model. We consider the inverse problem of possible dynamic models that can generate the static target inhibition map model. From a deterministic viewpoint, we analyze the inference of Boolean networks that can generate the observed binarized sensitivities under different target inhibition scenarios. From a stochastic perspective, we investigate the generation of Markov chain models that satisfy the observed target inhibition sensitivities.

Rb1 loss modifies but does not initiate alveolar rhabdomyosarcoma

BackgroundAlveolar rhabdomyosarcoma (aRMS) is a myogenic childhood sarcoma frequently associated with a translocation-mediated fusion gene, Pax3:Foxo1a.MethodsWe investigated the complementary role of Rb1 loss in aRMS tumor initiation and progression using conditional mouse models.ResultsRb1 loss was not a necessary and sufficient mutational event for rhabdomyosarcomagenesis, nor a strong cooperative initiating mutation. Instead, Rb1 loss was a modifier of progression and increased anaplasia and pleomorphism. Whereas Pax3:Foxo1a expression was unaltered, biomarkers of aRMS versus embryonal rhabdomyosarcoma were both increased, questioning whether these diagnostic markers are reliable in the context of Rb1 loss. Genome-wide gene expression in Pax3:Foxo1a,Rb1 tumors more closely approximated aRMS than embryonal rhabdomyosarcoma. Intrinsic loss of pRb function in aRMS was evidenced by insensitivity to a Cdk4/6 inhibitor regardless of whether Rb1 was intact or null. This loss of function could be attributed to low baseline Rb1, pRb and phospho-pRb expression in aRMS tumors for which the Rb1 locus was intact. Pax3:Foxo1a RNA interference did not increase pRb or improve Cdk inhibitor sensitivity. Human aRMS shared the feature of low and/or heterogeneous tumor cell pRb expression.ConclusionsRb1 loss from an already low pRb baseline is a significant disease modifier, raising the possibility that some cases of pleomorphic rhabdomyosarcoma may in fact be Pax3:Foxo1a-expressing aRMS with Rb1 or pRb loss of function.

Anticancer drug sensitivity analysis: An integrated approach applied to Erlotinib sensitivity prediction in the CCLE database

ABSTRACT The cancer cell line encyclopedia (CCLE), a joint academic and industry collaboration, provides a vast resource for analyzing the effectiveness of anti-cancer drugs across numerous cell lines. The predictive modeling of tumor sensitivity to targeted drugs has primarily focused on generating functions that map gene expressions and genetic mutation profiles to targeted drug sensitivity. The prediction accuracies of genomic signature based models are often limited as reported in initial analysis (Barretina et al.) of CCLE database. In this article, we illustrate that incorporating the target inhibition profile of the anticancer drugs and the functional behavior of related drugs will enable us to achieve much higher prediction accuracy.

Combination therapy design for targeted therapeutics from a drug-protein interaction perspective

ABSTRACT In the last decade, a number of drugs targeting specific proteins have been developed that are becoming common in cancer research as a basis for personalized therapy. How-ever, the numerous aberrations in molecular pathways that can produce cancer necessitate the use of drug combinations as compared to single drugs for treatment of individual cancers. In this article, we consider the design of combination therapy based on tumor sensitivity measurements over a panel of targeted drugs. We consider the following two optimization criteria (a) generating drug combinations with high sensitivity and minimal toxicity and (b) generating drug combinations targeting multiple parallel pathways for avoiding resistance. The optimization problem is solved using a set cover approach and a sequential search hill climbing technique. The effectiveness of our optimization procedure is illustrated on both synthetic and experimental models.

IGF1R as a Key Target in High Risk, Metastatic Medulloblastoma

Risk or presence of metastasis in medulloblastoma causes substantial treatment-related morbidity and overall mortality. Through the comparison of cytokines and growth factors in the cerebrospinal fluid (CSF) of metastatic medulloblastoma patients with factors also in conditioned media of metastatic MYC amplified medulloblastoma or leptomeningeal cells, we were led to explore the bioactivity of IGF1 in medulloblastoma by elevated CSF levels of IGF1, IGF-sequestering IGFBP3, IGFBP3-cleaving proteases (MMP and tPA), and protease modulators (TIMP1 and PAI-1). IGF1 led not only to receptor phosphorylation but also accelerated migration/adhesion in MYC amplified medulloblastoma cells in the context of appropriate matrix or meningothelial cells. Clinical correlation suggests a peri-/sub-meningothelial source of IGF-liberating proteases that could facilitate leptomeningeal metastasis. In parallel, studies of key factors responsible for cell autonomous growth in MYC amplified medulloblastoma prioritized IGF1R inhibitors. Together, our studies identify IGF1R as a high value target for clinical trials in high risk medulloblastoma.