Search

The FDA recently approved eight targeted therapies for acute myeloid leukemia (AML), including the BCL-2 inhibitor venetoclax. Maximizing efficacy of these treatments requires refining patient selection. To this end, we analyzed two recent AML studies profiling the gene expression and ex vivo drug response of primary patient samples. We find that ex vivo samples often exhibit a general sensitivity to (any) drug exposure, independent of drug target. We observe that this “general response across drugs” (GRD) is associated with FLT3-ITD mutations, clinical response to standard induction chemotherapy, and overall survival. Further, incorporating GRD into expression-based regression models trained on one of the studies improved their performance in predicting ex vivo response in the second study, thus signifying its relevance to precision oncology efforts. We find that venetoclax response is independent of GRD but instead show that it is linked to expression of monocyte-associated genes by developing and applying a multi-source Bayesian regression approach. The method shares information across studies to robustly identify biomarkers of drug response and is broadly applicable in integrative analyses.

We introduce the federated multi-view matrix factorization method that learns a multi-view model without transferring the user’s personal data to a central server. The method extends the federated learning framework to matrix factorization with multiple data sources. As far as we are aware, this is the first federated model to provide recommendations using multi-view matrix factorization. In addition, it is the first method to provide federated cold-start recommendations. The model is rigorously evaluated on three datasets on production settings. Empirical validation confirms that federated multi-view matrix factorization outperforms simpler methods that do not take into account the multi-view structure of the data. In addition, we also demonstrate the usefulness of the proposed method for the challenging prediction task of cold-start federated recommendations.

We introduce the payload optimization method for federated recommender systems (FRS). In federated learning (FL), the global model payload that is moved between the server and users depends on the number of items to recommend. The model payload grows when there is an increasing number of items. This becomes challenging for an FRS if it is running in production mode. To tackle the payload challenge, we formulated a multi-arm bandit solution that selected part of the global model and transmitted it to all users. The selection process was guided by a novel reward function suitable for FL systems. So far as we are aware, this is the first optimization method that seeks to address item dependent payloads. The method was evaluated using three benchmark recommendation datasets. The empirical validation confirmed that the proposed method outperforms the simpler methods that do not benefit from the bandits for the purpose of item selection. In addition, we have demonstrated the usefulness of our proposed method by rigorously evaluating the effects of a payload reduction on the recommendation performance degradation. Our method achieved up to a 90\% reduction in model payload, yielding only a $\sim$4\% - 8\% loss in the recommendation performance for highly sparse datasets, Comment: 15 pages, 3 figures, 4 tables

Introduction The availability of a variety of e-cigarettes flavors is one of the frequently cited reasons for their adoption. An active stream of discussion about flavoring can be observed online. Analyzing these real-time conversations offers nuanced insights into key factors related to the adoption of flavors, subsequently supporting public health interventions. Methods Google’s BERT, a state-of-the-art deep learning method was employed to model the first sentiment corpus on JUUL flavors. BERT, which is pre-trained with the complete English Wikipedia was fine-tuned by integrating a classification model, with human labeled Tweets, as training data. A collection of 30 075 Tweets about JUUL flavors was classified into positive and negative sentiments. Finally, using topic models, we identify and grouped thematic areas into positive and negative Tweets. Results With an average of 89% cross-validation precision for classifying Tweets, the fine-tuned BERT model classified 24 114 Tweets as positive and 5961 Tweets as negative. Through the topic modeling approach 10 thematic topics were identified from the predicted positive and negative sentiments expressed in the Tweets. Conclusions JUUL flavors, notably mango, mint, and cucumber, provoke overwhelmingly positive sentiments indicating a strong likeness due to favorable taste and odor. Negative discourse about JUUL flavors revolve around addictiveness, high nicotine content, and youth targeted marketing. Implications Limiting the content related to flavors and positive perceptions on social media is necessary to minimize exposure to youth. The novel methodology used in this study may be adopted to monitor e-cigarette discourse periodically, as well as other critical public health phenomena online.

We combined clinical, cytokine, genomic, methylation and dietary data from 43 young adult monozygotic twin pairs (aged 22–36 years, 53% female), where 25 of the twin pairs were substantially weight discordant (delta body mass index > 3 kg m −2 ). These measurements were originally taken as part of the TwinFat study, a substudy of The Finnish Twin Cohort study. These five large multivariate datasets (comprising 42, 71, 1587, 1605 and 63 variables, respectively) were jointly analysed using an integrative machine learning method called group factor analysis (GFA) to offer new hypotheses into the multi-molecular-level interactions associated with the development of obesity. New potential links between cytokines and weight gain are identified, as well as associations between dietary, inflammatory and epigenetic factors. This encouraging case study aims to enthuse the research community to boldly attempt new machine learning approaches which have the potential to yield novel and unintuitive hypotheses. The source code of the GFA method is publically available as the R package GFA.

Objective: The objective of this study was to sum up the medical experimentation, effects of estrogen on the appearance of indications of vasomotor and possessions of these similar amounts of estrogen on bothersome unpleasant proceedings. Background: In this study we again examine on the basis of indications beneficial and indications irritation that which amount of estrogen should be suggested. Doctors should know when less amount of estrogen be suggested to maximize the reception and persistence of patient. 65% reductions in vasomotor incidents are observed by giving reduced amount of estrogen. By giving the typical quantity of insulin we can reduce the effects up to 80%. Methods: The study was conducted in Services Hospital Lahore for one-year duration from May 2018 to April 2019. In spite of reprimands of less amount of estrogen, it has been observed in the latest survey of the country that most of the females are using the typical amount of estrogen. Result: Doctors should try to make the less amount of estrogen most satisfactory by patients. But this effort is not so impressive. An observation was made in 2004. In this observation, females undergoing HT were added. Only 28% females were suggested to use less amount of estrogen by doctors. It should be told that less amount of estrogen may take some time in lessening the indications of vasomotor but it is more effective treatment. Females using HT should use the less amount of estrogen for better results. Conclusion: Less amount of estrogen is the mostly favored alternative due to its higher acceptability and higher prospective for tolerability of sufferer. Doctors should try to make the less amount of estrogen most satisfactory by patients.. Females using HT should use the less amount of estrogen for better results. When most of the females using higher amount were shifted towards lower amount of estrogen, most of them succeeded. Keywords: Hormone therapy; Low dosage; Side effects; vasomotor symptoms

The diabetes type 2 is more frequently present in Hispanic habitants of US as compared to non-Hispanic. There are many reasons because of which diabetes type 2 is more prevalent. These include natural features, economic situations and educational characteristics. There is one essential cause of diabetes in Hispanic sufferers which is heredity. But these features related to heredity can never be improved. Other causes of its prevalence are social and economic aspects like less entrée to health clinics, fences of talking, artistic viewpoints and less capability related to health care giver etc. These factors should be made better to improve the treatment of diabetes type 2. At the clinical level, strategies should be made to lessen the differences among Hispanic and non-Hispanic sufferers. At physician and sufferers’ level, traditional viewpoints should be considered during the selection of suitable management strategy. By diagnosis of favored language and level of achievement for every sufferer overall organization of diabetes type 2 should be separated. These preferences need to develop the announcement by traditionally suitable learning resources and plans. Hurdles in the way of management of diabetes type 2 in Hispanic sufferers these plans may prove useful. Keywords: Cultural competence; Hispanic Latino; Treatment barrier; Type 2 diabetes mellitus

Motivation A prime challenge in precision cancer medicine is to identify genomic and molecular features that are predictive of drug treatment responses in cancer cells. Although there are several computational models for accurate drug response prediction, these often lack the ability to infer which feature combinations are the most predictive, particularly for high-dimensional molecular datasets. As increasing amounts of diverse genome-wide data sources are becoming available, there is a need to build new computational models that can effectively combine these data sources and identify maximally predictive feature combinations. Results We present a novel approach that leverages on systematic integration of data sources to identify response predictive features of multiple drugs. To solve the modeling task we implement a Bayesian linear regression method. To further improve the usefulness of the proposed model, we exploit the known human cancer kinome for identifying biologically relevant feature combinations. In case studies with a synthetic dataset and two publicly available cancer cell line datasets, we demonstrate the improved accuracy of our method compared to the widely used approaches in drug response analysis. As key examples, our model identifies meaningful combinations of features for the well known EGFR, ALK, PLK and PDGFR inhibitors. Availability and Implementation The source code of the method is available at https://github.com/suleimank/mvlr. Supplementary information Supplementary data are available at Bioinformatics online.

A key goal of computational personalized medicine is to systematically utilize genomic and other molecular features of samples to predict drug responses for a previously unseen sample. Such predictions are valuable for developing hypotheses for selecting therapies tailored for individual patients. This is especially valuable in oncology, where molecular and genetic heterogeneity of the cells has a major impact on the response. However, the prediction task is extremely challenging, raising the need for methods that can effectively model and predict drug responses. In this study, we propose a novel formulation of multi-task matrix factorization that allows selective data integration for predicting drug responses. To solve the modeling task, we extend the state-of-the-art kernelized Bayesian matrix factorization (KBMF) method with component-wise multiple kernel learning. In addition, our approach exploits the known pathway information in a novel and biologically meaningful fashion to learn the drug response associations. Our method quantitatively outperforms the state of the art on predicting drug responses in two publicly available cancer data sets as well as on a synthetic data set. In addition, we validated our model predictions with lab experiments using an in-house cancer cell line panel. We finally show the practical applicability of the proposed method by utilizing prior knowledge to infer pathway-drug response associations, opening up the opportunity for elucidating drug action mechanisms. We demonstrate that pathway-response associations can be learned by the proposed model for the well known EGFR and MEK inhibitors., Comment: Accepted in European Conference in Computational Biology, to be published in Bioinformatics 2016

With data from recent large-scale drug sensitivity measurement campaigns, it is now possible to build and test models predicting responses for more than one hundred anticancer drugs against several hundreds of human cancer cell lines. Traditional quantitative structure-activity relationship (QSAR) approaches focus on small molecules in searching for their structural properties predictive of the biological activity in a single cell line or a single tissue type. We extend this line of research in two directions: (1) an integrative QSAR approach predicting the responses to new drugs for a panel of multiple known cancer cell lines simultaneously and (2) a personalized QSAR approach predicting the responses to new drugs for new cancer cell lines. To solve the modeling task, we apply a novel kernelized Bayesian matrix factorization method. For maximum applicability and predictive performance, the method optionally utilizes genomic features of cell lines and target information on drugs in addition to chemical drug descriptors. In a case study with 116 anticancer drugs and 650 cell lines, we demonstrate the usefulness of the method in several relevant prediction scenarios, differing in the amount of available information, and analyze the importance of various types of drug features for the response prediction. Furthermore, after predicting the missing values of the data set, a complete global map of drug response is explored to assess treatment potential and treatment range of therapeutically interesting anticancer drugs.

With recent advancements in measurement technologies, many multi-way and tensor datasets have started to emerge. Exploiting the natural tensor structure in the data has been shown to be advantageous for both explorative and predictive studies in several application areas of bioinformatics and computational biology. Therefore, there has subsequently arisen a need for robust and flexible tools for effectively analyzing tensor data sets. We present the R package tensorBF, which is the first R package providing Bayesian factorization of a tensor. Our package implements a generative model that automatically identifies the number of factors needed to explain the tensor, overcoming a key limitation of traditional tensor factorizations. We also recommend best practices when using tensor factorizations for both, explorative and predictive analysis with an example application on drug response dataset. The package also implements tools related to the normalization of data, informative noise priors and visualization. Availability: The package is available at https://cran.r-project.org/package=tensorBF.

Providing accurate predictions is challenging for machine learning algorithms when the number of features is larger than the number of samples in the data. Prior knowledge can improve machine learning models by indicating relevant variables and parameter values. Yet, this prior knowledge is often tacit and only available from domain experts. We present a novel approach that uses interactive visualization to elicit the tacit prior knowledge and uses it to improve the accuracy of prediction models. The main component of our approach is a user model that models the domain expert's knowledge of the relevance of different features for a prediction task. In particular, based on the expert's earlier input, the user model guides the selection of the features on which to elicit user's knowledge next. The results of a controlled user study show that the user model significantly improves prior knowledge elicitation and prediction accuracy, when predicting the relative citation counts of scientific documents in a specific domain., in Proceedings of the 22nd International Conference on Intelligent User Interfaces (IUI 2017)

We consider regression under the "extremely small $n$ large $p$" condition, where the number of samples $n$ is so small compared to the dimensionality $p$ that predictors cannot be estimated without prior knowledge. This setup occurs in personalized medicine, for instance, when predicting treatment outcomes for an individual patient based on noisy high-dimensional genomics data. A remaining source of information is expert knowledge, which has received relatively little attention in recent years. We formulate the inference problem of asking expert feedback on features on a budget, propose an elicitation strategy for a simple "small $n$" setting, and derive conditions under which the elicitation strategy is optimal. Experiments on simulated experts, both on synthetic and genomics data, demonstrate that the proposed strategy can drastically improve prediction accuracy., Comment: In Proceedings of the 15th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA'16)

Background Following the positive outcome of the RATIFY phase 3 clinical trial, the multi-kinase inhibitor midostaurin was approved for the treatment of adult patients with newly diagnosed FLT3-mutated acute myeloid leukemia (AML). However, we and others have observed that single agent midostaurin yields responses also in a substantial portion of patients not carrying FLT3 mutations. The molecular basis and the kinase targets mediating these responses are poorly understood and no biomarkers predictive of response in FLT3 wildtype (wt) AML patients exist. To identify markers distinguishing the FLT3 wt responding subset of patients, we trained machine learning multi-marker models using AML patient baseline transcriptomic and mutational data to predict ex vivo responders vs. non-responders. Further, to better understand the molecular basis of midostaurin responses and to explore the unique signaling networks modulated by midostaurin, we profiled the sensitivities of AML patient samples to midostaurin in comparison to, and in combination with, several clinically relevant oncological targeted agents of diverse mechanistic classes. Results Midostaurin target space is unique and it retains anti-leukemic potency under cytoprotective conditions. We have previously established that single agent midostaurin is effective ex vivo in about 25% of FLT3 wt AML patient samples and retains potency in a cytoprotective medium that masks the effects of more selective FLT3 inhibitors such as quizartinib, crenolanib and sorafenib (Karjalainen et al, Blood 2017). To further investigate the unique pathways that midostaurin, but not other FLT3 inhibitors targets, we correlated the response patterns of 87 AML patient samples in cytoprotective medium to midostaurin and 261 other kinase inhibitors in our oncology compound collection. In unsupervised cluster analysis, midostaurin showed highly similar response patterns to AZD7762, OTS167, milciclib, pacritinib, ENMD-2076 and fostamatinib. Publicly available in vitro kinase profiling (Tang et al, Cell Chem. Biol. 2018) suggested that midostaurin does not inhibit most of the primary targets of these other inhibitors, with only aurora kinases, JAK kinases and SYK appearing to be shared potent targets. Midostaurin anti-leukemic potency is determined by the mutational background. Several multi-marker, supervised machine learning models were compared to extract biomarker signatures from either baseline transcriptomic or mutational data, in the task of predicting ex vivo midostaurin response in samples cultured in cytoprotective medium. In the full cohort (N=81), the presence of FLT3 mutations (both internal tandem repeat and tyrosine kinase domain mutations) was the strongest predictor of response. In the FLT3 wt cases (N=49), our results revealed that other select mutations correlated well with either response or non-response upon Bayesian Linear Regression analysis with cross-validation (Ammad-Ud-Din et al, Bioinformatics, 2017). Mutations in PTPN11, U2AF1, SRSF2, RUNX1, JAK2 and BCOR predicted midostaurin responders, while mutations in GATA2, WT1, NPM1 and IDH2 were enriched in non-responders (Figure 1). Baseline transcriptomic profiles, however, did not provide added value for the predictive power. Midostaurin efficacy can be enhanced by combination with other targeted agents. Combinatorial drug screening of midostaurin in cytoprotective medium revealed several synergizing drug classes, including BCL-2 and MDM2-p53 inhibitors. Further analysis of synergizing agents in broader AML patient sample cohorts is ongoing. Conclusions Our results show that midostaurin may reach its biological effects through inhibition of additional kinases than just FLT3. In both FLT3 mutant and wt cases, midostaurin responses are influenced by the overall mutational background. Furthermore, our data indicates that midostaurin efficacy can be enhanced through combination with other agents. Together, we have significantly expanded the understanding of molecular determinants of midostaurin response in primary AML cells, supporting predictive biomarker discovery efforts and development of synergistic drug combinations. The emerging hypotheses from this work will have to be tested in clinical studies. Disclosures Porkka: Novartis: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Marques Ramos:Novartis: Employment. Pallaud:Novartis: Employment. Aittokallio:Novartis: Research Funding. Wennerberg:Novartis: Research Funding.

Introduction: Therapeutic options for patients with AML were recently expanded with FDA approval of four drugs in 2017. As their efficacy is limited in some patient subpopulations and relapse ultimately ensues, there remains an urgent need for additional treatment options tailored to well-defined patient subpopulations to achieve durable responses. Two comprehensive profiling efforts were launched to address this need-the multi-center Beat AML initiative, led by the Oregon Health & Science University (OHSU) and the AML Individualized Systems Medicine program at the Institute for Molecular Medicine Finland (FIMM). Methods: We performed a comparative analysis of the two large-scale data sets in which patient samples were subjected to whole-exome sequencing, RNA-seq, and ex vivo functional drug sensitivity screens: OHSU (121 patients and 160 drugs) and FIMM (39 patients and 480 drugs). We predicted ex vivo drug response [quantified as area under the dose-response curve (AUC)] using gene expression signatures selected with standard regression and a novel Bayesian model designed to analyze multiple data sets simultaneously. We restricted analysis to the 95 drugs in common between the two data sets. Results: The ex vivo responses (AUCs) of most drugs were positively correlated (OHSU: median Pearson correlation r across all pairwise drug comparisons=0.27; FIMM: median r=0.33). Consistently, a samples's ex vivo response to an individual drug was often correlated with the patient's Average ex vivo Drug Sensitivity (ADS), i.e., the average response across the 95 drugs (OHSU: median r across 95 drugs=0.41; FIMM: median r=0.58). Patients with a complete response to standard induction therapy had a higher ADS than those that were refractory (p=0.01). Further, patients whose ADS was in the top quartile had improved overall survival relative to those having an ADS in the bottom quartile (p The BCL-2 inhibitor venetoclax was the only drug whose response was negatively correlated with ADS in both data sets. We hypothesized that, whereas the predictive performance of many other drugs was likely dependent on ADS, the predictive performance of venetoclax (LASSO: r=0.53, p=0.01; Ridge: r=0.63, p=1.3x10-3) reflected specific gene expression biomarkers. To identify biomarkers associated with venetoclax sensitivity, we developed an integrative Bayesian machine learning method that jointly modeled both data sets, revealing several candidate biomarkers positively (BCL2 and FLT3) or negatively (CD14, MAFB, and LRP1) correlated with venetoclax response. We assessed these biomarkers in an independent data set that profiled ex vivo response to the BCL-2/BCL-XL inhibitor navitoclax in 29 AML patients (Lee et al.). All five biomarkers were validated in the Lee data set (Fig 1). Conclusions: The two independent ex vivo functional screens were highly concordant, demonstrating the reproducibility of the assays and the opportunity for their use in the clinic. Joint analysis of the two data sets robustly identified biomarkers of drug response for BCL-2 inhibitors. Two of these biomarkers, BCL2 and the previously-reported CD14, serve as positive controls credentialing our approach. CD14, MAFB, and LRP1 are involved in monocyte differentiation. The inverse correlation of their expression with venetoclax and navitoclax response is consistent with prior reports showing that monocytic cells are resistant to BCL-2 inhibition (Kuusanmäki et al.). These biomarker panels may enable better selection of patient populations likely to respond to BCL-2 inhibition than would any one biomarker in isolation. References: Kuusanmäki et al. (2017) Single-Cell Drug Profiling Reveals Maturation Stage-Dependent Drug Responses in AML, Blood 130:3821 Lee et al. (2018) A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia, Nat Commun 9:42 Disclosures Druker: Cepheid: Consultancy, Membership on an entity's Board of Directors or advisory committees; ALLCRON: Consultancy, Membership on an entity's Board of Directors or advisory committees; Fred Hutchinson Cancer Research Center: Research Funding; Celgene: Consultancy; Vivid Biosciences: Membership on an entity's Board of Directors or advisory committees; Aileron Therapeutics: Consultancy; Third Coast Therapeutics: Membership on an entity's Board of Directors or advisory committees; Oregon Health & Science University: Patents & Royalties; Patient True Talk: Consultancy; Millipore: Patents & Royalties; Monojul: Consultancy; Gilead Sciences: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Leukemia & Lymphoma Society: Membership on an entity's Board of Directors or advisory committees, Research Funding; GRAIL: Consultancy, Membership on an entity's Board of Directors or advisory committees; Beta Cat: Membership on an entity's Board of Directors or advisory committees; MolecularMD: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Henry Stewart Talks: Patents & Royalties; Bristol-Meyers Squibb: Research Funding; Blueprint Medicines: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Aptose Therapeutics: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; McGraw Hill: Patents & Royalties; ARIAD: Research Funding; Novartis Pharmaceuticals: Research Funding. Heckman:Orion Pharma: Research Funding; Novartis: Research Funding; Celgene: Research Funding. Porkka:Novartis: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Tyner:AstraZeneca: Research Funding; Incyte: Research Funding; Janssen: Research Funding; Leap Oncology: Equity Ownership; Seattle Genetics: Research Funding; Syros: Research Funding; Takeda: Research Funding; Gilead: Research Funding; Genentech: Research Funding; Aptose: Research Funding; Agios: Research Funding. Aittokallio:Novartis: Research Funding. Wennerberg:Novartis: Research Funding.

Introduction: Ex vivo drug sensitivity studies of samples derived from acute myeloid leukemia (AML) patients have been shown to be predictive of in vivo response. These findings are based on a limited number of well-characterized agents for which in vivo patient response data and ex vivo drug sensitivity data—on that same patient—are available. To show the feasibility of scaling such ex vivo studies to large drug screens, we characterized the reproducibility of expression-based models of drug response across two independent data sets—one generated at the Oregon Health and Science University (OHSU) and the second at the Institute for Molecular Medicine Finland (FIMM). Methods: We harmonized two large-scale AML ex vivo studies screened for drug response and profiled transcriptomically—OHSU (303 AML patient samples and 160 drugs) and FIMM (48 AML samples and 480 drugs). The two panels have 94 drugs in common. Log-logistic curves were fit to the dose-response data and area under the dose-response curves (AUCs) were calculated. Predictive modeling using Ridge regression or an integrative Bayesian approach was performed for each drug AUC independently using 202 highly-variable and/or cancer-associated genes as features. Results: For each of the 94 drugs in common between the two data sets, we trained a Ridge regression model on the OHSU data set, used the model to predict response in the FIMM data set, and calculated the Pearson correlation between the predicted and observed FIMM responses. 41 of the 94 drug models had a positive and statistically significant correlation [false discovery rate (FDR) < 20%; mean ρ = 0.43; 95% CI = 0.29 – 0.77]. Drugs corresponding to the top decile of these significant models (mean ρ = 0.54; 95% CI = 0.48 – 0.77) clustered into four primary classes: MEK inhibitors (PD184352, Selumetinib, and Trametinib), EGFR/VEGFR inhibitors (Cabozantinib, Erlotinib, Foretinib, and Sorafenib), and singletons Venetoclax and Sirolimus. To confirm these results, we applied a second modeling approach—an integrative Bayesian machine learning method—that allows systematic combination of both data sets. Training and evaluation of this approach using 10-fold cross validation yielded 82 positive and statistically significant correlations (FDR < 20%; mean ρ = 0.35; 95% CI = 0.13 – 0.58). Five of nine drugs (Cabozantinib, Selumetinib, Sirolimus, Sorafenib, and Trametinib) corresponding to the top decile of these significant models (mean ρ = 0.54; 95% CI = 0.49 – 0.60) overlapped with drugs from the top decile of Ridge results (one-sided Fisher p = 2.5 x 10-4) Conclusions: Our results using independent data sets and two statistical approaches suggest that certain drugs (including MEK and EGFR/VEGFR inhibitors) are amenable to expression-based predictive modeling in AML. Future work will focus on inferring individual biomarkers of response. Citation Format: Brian S. White, Suleiman A. Khan, Muhammad Ammad-ud-din, Swapnil Potdar, Mike J. Mason, Cristina E. Tognon, Brian J. Druker, Caroline A. Heckman, Olli P. Kallioniemi, Stephen E. Kurtz, Kimmo Porkka, Jeffrey W. Tyner, Tero Aittokallio, Krister Wennerberg, Justin Guinney. Gene expression predicts ex vivo drug sensitivity in acute myeloid leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3883.