Disha Malani

Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies

We developed a systematic algorithmic solution for quantitative drug sensitivity scoring (DSS), based on continuous modeling and integration of multiple dose-response relationships in high-throughput compound testing studies. Mathematical model estimation and continuous interpolation makes the scoring approach robust against sources of technical variability and widely applicable to various experimental settings, both in cancer cell line models and primary patient-derived cells. Here, we demonstrate its improved performance over other response parameters especially in a leukemia patient case study, where differential DSS between patient and control cells enabled identification of both cancer-selective drugs and drug-sensitive patient sub-groups, as well as dynamic monitoring of the response patterns and oncogenic driver signals during cancer progression and relapse in individual patient cells ex vivo. An open-source and easily extendable implementation of the DSS calculation is made freely available to support its tailored application to translating drug sensitivity testing results into clinically actionable treatment options.

HOX gene expression predicts response to BCL-2 inhibition in acute myeloid leukemia

Inhibitors of B-cell lymphoma-2 (BCL-2) such as venetoclax (ABT-199) and navitoclax (ABT-263) are clinically explored in several cancer types, including acute myeloid leukemia (AML), to selectively induce apoptosis in cancer cells. To identify robust biomarkers for BCL-2 inhibitor sensitivity, we evaluated the ex vivo sensitivity of fresh leukemic cells from 73 diagnosed and relapsed/refractory AML patients, and then comprehensively assessed whether the responses correlated to specific mutations or gene expression signatures. Compared with samples from healthy donor controls (nonsensitive) and chronic lymphocytic leukemia (CLL) patients (highly sensitive), AML samples exhibited variable responses to BCL-2 inhibition. Strongest CLL-like responses were observed in 15% of the AML patient samples, whereas 32% were resistant, and the remaining exhibited intermediate responses to venetoclax. BCL-2 inhibitor sensitivity was associated with genetic aberrations in chromatin modifiers, WT1 and IDH1/IDH2. A striking selective overexpression of specific HOXA and HOXB gene transcripts were detected in highly BCL-2 inhibitor sensitive samples. Ex vivo responses to venetoclax showed significant inverse correlation to β2-microglobulin expression and to a lesser degree to BCL-XL and BAX expression. As new therapy options for AML are urgently needed, the specific HOX gene expression pattern can potentially be used as a biomarker to identify venetoclax-sensitive AML patients for clinical trials.

Drug response prediction by inferring pathway-response associations with kernelized Bayesian matrix factorization

MOTIVATION 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. RESULTS 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 datasets as well as on a synthetic dataset. 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. AVAILABILITY AND IMPLEMENTATION The source code implementing the method is available at http://research.cs.aalto.fi/pml/software/cwkbmf/ CONTACTS muhammad.ammad-ud-din@aalto.fi or samuel.kaski@aalto.fi SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Consistency in drug response profiling

The comparative analysis by Haibe-Kains et al.1 concluded that data from two large-scale studies of cancer cell lines2,3 showed highly discordant results for drug sensitivity measurements, whereas gene expression data were reasonably concordant. Here, we crosscompared the two original datasets2,3 against our own data of drug response profiles in overlapping cancer cell line panels. Our results indicate that it is possible to achieve concordance between different laboratories for drug response measurements by paying attention to the harmonization of assays and experimental procedures. There is a Reply to this Comment by Safikhani, Z. et al. Nature 540, http://dx.doi.org/10.1038/ nature20172 (2016). Haibe-Kains et al.1 reported on a comparative evaluation of two drug sensitivity and molecular profiling datasets, one from the Cancer Genome Project (CGP)2 and the other from the Cancer Cell Line Encyclopedia (CCLE)3. In their analyses, gene expression profiles between hundreds of common cancer cell lines across all genes showed high consistency between the two studies (median rank correlation (MRC) = 0.85), whereas the drug response data for 15 common compounds were highly discordant (MRC = 0.28 for halfmaximum inhibitory concentration (IC50) values). This report1 and the accompanying commentary4 suggested that differences in laboratory protocols, compounds and their tested concentration ranges, and computational methods may account for the differences, but these reports did not elaborate which of these factors are important and whether they can be controlled for. Here, we reanalysed the dose–response data from both CGP and CCLE using a standardized area under the curve (AUC) response metric, which we call the drug sensitivity score (DSS)5. We then compared the CGP and CCLE data with a new dataset of drug responses profiled using the Institute for Molecular Medicine Finland (FIMM) compound testing assay6, covering 308 drugs across 106 cancer cell lines. The FIMM data included 45 compounds in common with CGP and 14 with the CCLE in 50 cell lines (Supplementary Data 1). In the AUC calculation, we unified the drug concentration ranges across the CGP, CCLE and FIMM assays. We observed a significantly higher level of consistency (P = 4.2 × 10−5), especially between the CCLE and FIMM drug response data (MRC = 0.74), as compared to the consistency between FIMM and CGP data (MRC = 0.54) (Fig. 1a). Similar experimental protocols were applied at FIMM and CCLE, including the same readout (CellTiter-Glo, Promega), similar controls (vehicle as negative control and positive controls of toxic compounds 100 μ M benzethonium chloride or 1 μ M MG132). However, there were also differences, such as the plate format used (1,536 versus 384 wells). Importantly, there was no effort made to standardize cell numbers used or any other parameters between the three laboratories, such as the source, passage number and media used for cells, nor the origin and handling of drugs. Therefore, this observed level of drug response agreement could be substantially improved by further standardization of the laboratory protocols. The CGP experimental protocol differed from the two others in terms of the readout (fluorescent nucleic acid stain Syto 60, Life Technologies), in the use of controls (drug-free cells as negative and no cells as positive controls), and the plate format used (96or 384-well plates). We compared the drug response profiles between the same cell lines from different laboratories, in line with the approach of Haibe-Kains et al.1, in which they showed consistency in gene expression profiles from CGP and CCLE (MRC = 0.85)1. The Haibe-Kains et al.1 approach, in which the correlation is calculated for each drug separately across the cell lines, showed more variability (Fig. 1b), owing to the fact that some drugs show minimal efficacy in all the tested cell lines. Analogously, gene expression correlations vary more widely when analysed at the level of genes across cell lines (MRC = 0.58 between CGP and CCLE), as certain genes are not expressed above technical noise. Although both ways to compare the data are relevant to the overall goal of personalized

Enhanced sensitivity to glucocorticoids in cytarabine-resistant AML.

We sought to identify drugs that could counteract cytarabine resistance in acute myeloid leukemia (AML) by generating eight resistant variants from MOLM-13 and SHI-1 AML cell lines by long-term drug treatment. These cells were compared with 66 ex vivo chemorefractory samples from cytarabine-treated AML patients. The models and patient cells were subjected to genomic and transcriptomic profiling and high-throughput testing with 250 emerging and clinical oncology compounds. Genomic profiling uncovered deletion of the deoxycytidine kinase (DCK) gene in both MOLM-13- and SHI-1-derived cytarabine-resistant variants and in an AML patient sample. Cytarabine-resistant SHI-1 variants and a subset of chemorefractory AML patient samples showed increased sensitivity to glucocorticoids that are often used in treatment of lymphoid leukemia but not AML. Paired samples taken from AML patients before treatment and at relapse also showed acquisition of glucocorticoid sensitivity. Enhanced glucocorticoid sensitivity was only seen in AML patient samples that were negative for the FLT3 mutation (P=0.0006). Our study shows that development of cytarabine resistance is associated with increased sensitivity to glucocorticoids in a subset of AML, suggesting a new therapeutic strategy that should be explored in a clinical trial of chemorefractory AML patients carrying wild-type FLT3.

Intrinsic resistance to PIM kinase inhibition in AML through p38α-mediated feedback activation of mTOR signaling

Although conventional therapies for acute myeloid leukemia (AML) and diffuse large B-cell lymphoma (DLBCL) are effective in inducing remission, many patients relapse upon treatment. Hence, there is an urgent need for novel therapies. PIM kinases are often overexpressed in AML and DLBCL and are therefore an attractive therapeutic target. However, in vitro experiments have demonstrated that intrinsic resistance to PIM inhibition is common. It is therefore likely that only a minority of patients will benefit from single agent PIM inhibitor treatment. In this study, we performed an shRNA-based genetic screen to identify kinases whose suppression is synergistic with PIM inhibition. Here, we report that suppression of p38α (MAPK14) is synthetic lethal with the PIM kinase inhibitor AZD1208. PIM inhibition elevates reactive oxygen species (ROS) levels, which subsequently activates p38α and downstream AKT/mTOR signaling. We found that p38α inhibitors sensitize hematological tumor cell lines to AZD1208 treatment in vitro and in vivo. These results were validated in ex vivo patient-derived AML cells. Our findings provide mechanistic and translational evidence supporting the rationale to test a combination of p38α and PIM inhibitors in clinical trials for AML and DLBCL.

Aggressive natural killer-cell leukemia mutational landscape and drug profiling highlight JAK-STAT signaling as therapeutic target

Aggressive natural killer-cell (NK-cell) leukemia (ANKL) is an extremely aggressive malignancy with dismal prognosis and lack of targeted therapies. Here, we elucidate the molecular pathogenesis of ANKL using a combination of genomic and drug sensitivity profiling. We study 14 ANKL patients using whole-exome sequencing (WES) and identify mutations in STAT3 (21%) and RAS-MAPK pathway genes (21%) as well as in DDX3X (29%) and epigenetic modifiers (50%). Additional alterations include JAK-STAT copy gains and tyrosine phosphatase mutations, which we show recurrent also in extranodal NK/T-cell lymphoma, nasal type (NKTCL) through integration of public genomic data. Drug sensitivity profiling further demonstrates the role of the JAK-STAT pathway in the pathogenesis of NK-cell malignancies, identifying NK cells to be highly sensitive to JAK and BCL2 inhibition compared to other hematopoietic cell lineages. Our results provide insight into ANKL genetics and a framework for application of targeted therapies in NK-cell malignancies.Aggressive natural killer-cell leukemia (ANKL) has few targeted therapies. Here ANKL patients are reported to harbor STAT3, RAS-MAPK pathway, DDX3X and epigenetic modifier mutations; and drug sensitivity profiling uncovers the importance of the JAK-STAT pathway, revealing potential ANKL therapeutic targets.

Abstract 4679: Acquisition of cytarabine resistance leads to increased glucocorticoid sensitivity in AML

Acquired resistance to standard chemotherapeutic agents, such as cytarabine, is a major challenge in the treatment of acute myeloid leukemia (AML). Here, we hypothesized that development of resistance to one chemotherapeutic agent may lead to increased sensitivity to other drugs. Hence, we sought to identify novel drug vulnerabilities that arise during the development of cytarabine resistance using both cytarabine resistant AML cell lines and samples from AML patients who had relapsed during cytarabine containing chemotherapy. We developed resistant variants of AML cell lines MOLM-13 and SHI-1 by long-term drug treatment with increasing doses of cytarabine. Profiling data from the in vitro generated cytarabine resistant cell line variants were systematically compared with corresponding data from 31 chemorefractory AML patient samples. All samples were subjected to genomic and transcriptomic profiling and high-throughput drug sensitivity and resistance testing with a panel of 250 chemical compounds (each in five doses). Cytarabine resistant AML cell line variants and patient samples showed co-resistance to other nucleoside analogues, such as cladribine, clofarabine and gemcitabine. Genomic profiling showed deletion of the deoxycytidine kinase gene DCK, a well-known genetic lesion related to cytarabine resistance, in both MOLM-13 and SHI-1 cytarabine resistant cell lines and in one chemorefractory AML patient. Importantly, comprehensive drug testing revealed that cytarabine resistant SHI-1 cell variants developed increased sensitivity to glucocorticoids, such as dexamethasone, methylprednisolone and prednisolone when compared to parental cells. This was accompanied by up-regulation of the glucocorticoid receptor NR3C1. We also observed acquisition of glucocorticoid sensitivity in paired samples from two AML patient cases who had relapsed after cytarabine containing chemotherapy. Systematic ex vivo drug testing of 31 relapsed and chemorefractory AML patient samples showed high sensitivity to dexamethasone in five (20%) and to prednisolone and methylprednisolone in four (13%) patient samples. In conclusion, our results from both cytarabine resistant AML cell lines and chemorefractory patient samples indicate that a subset of AML samples develop sensitivity to glucocorticoids. This novel finding indicates the need of detailed investigation of glucocorticoid efficacy in the clinic. Citation Format: Disha Malani, Astrid Murumagi, Bhagwan Yadav, Mika Kontro, Samuli Eldfors, Ashwini Kumar, Krister Wennerberg, Caroline Heckman, Kimmo Porkka, Maija Wolf, Tero Aittokallio, Olli Kallioniemi. Acquisition of cytarabine resistance leads to increased glucocorticoid sensitivity in AML. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4679.

Abstract 4184: Drug set enrichment analysis : A computational approach to identify functional drug sets

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA Most drug-testing approaches published so far focus on identifying a single drug that shows favorable response and is associated with a known cancer biomarker such as the drug Imatinib in BCR-ABL gene fusion positive cells. We developed and applied drug set enrichment analysis (DSEA) to find enriched patterns or statistically significant similarities (overlaps) between the drug responses of a test sample against a cohort of 182 previously screened cancer samples. The samples studied included established (ATCC) cancer cell lines, drug-resistant cancer cell models, ex-vivo patient cancer cells in primary cultures, including conditionally reprogrammed cancer cells from patients. DSEA is adopting Gene Set Enrichment Analysis statistics commonly used for gene expression analysis to high throughput drug testing data. Our drug screening (Pemovska et al., Cancer Discovery, 2013) was done with a panel of 306 established (FDA-approved) and emerging targeted cancer drugs such as tyrosine-kinase inhibitors (e.g. EGFR, PDGFR, BRAF, MET), S/T-type inhibitors, (e.g. MEK, Plk1, Akt, Aurora, Chk1), and inhibitors of other pathways (HDACs, Hh, BCL2, PI3K, PARP) and many others. The readout was based on viability of cells after a 72 hour culture. The DSEA approach is based on taking the top most sensitive drugs (above a defined sensitivity score cut-off) in an individual cancer sample and then identifying overlapping drug response profiles in previously screened reference samples. Our hypothesis is that the most sensitive drug sets in any given sample tend to show similar response profiles in a cohort of similar samples. We convey the correlations and drug set enrichment analysis results as dendrogram trees, plots and tables with enrichment and significance scores. Interestingly, our results show that clustering of drug sensitivity testing data does not place all cancer cell line samples within well-established subgroups based on biological features or histological origin. We find a similar tendency in ex vivo patient samples. Therefore, comprehensive drug response profiles seen may reveal novel biological data that reflect pharmacologically-relevant, phenotypic cancer cell states. DSEA could also provide novel means to subtype previously poorly characterized cancer samples based on their drug response profiles and thereby in the future facilitate the choice of therapies to patients whose cancers repond in an atypical way as compared to the expectations based on anatomical origin or genomic composition. Citation Format: John Patrick Mpindi, Dimitry Bychkov, Yadav Bhagwan, Disha Malani, Hirasawa Akira, Khalid Saeed, Susanne Hultsch, Sara Kangaspeska, Astrid Murumagi, Caroline A Heckman, Kimmo Porkka, Tero Aittokallio, Krister Wennerberg, Paivi Ostling, Olli Kallioniemi. Drug set enrichment analysis : A computational approach to identify functional drug sets. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4184. doi:10.1158/1538-7445.AM2014-4184

Abstract 5220: Identifying AML-specific key targeted drugs using high-throughput drug sensitivity and resistance testing profiles of AML cells.

Introduction: Conventional cytotoxic chemotherapy regimens for adult acute myeloid leukemia (AML) are effective in curing less than 50% of the patients, and there is a major need for targeted drugs with better anti-cancer selectivity. Here, our aim was to i) identify potential clinically used or emerging cancer drugs by quantitative drug sensitivity and resistance testing (DSRT) of 16 AML cell lines ii) compare the cell line data with results obtained from tested 24 ex vivo AML patient specimens iii) identify genomic correlations potentially explaining drug responsiveness. Methods: The cancer pharmacopeia-wide drug collection is composed of 119 FDA approved and 90 investigational chemical compounds including cytotoxic agents and cell signaling molecule inhibitors. Each drug was tested over a 10,000-fold concentration range and that has generated quantitative five point dose-response curves. AML cells were plated in 384 well plates (where the drugs were pre-printed using an acoustic nano-dispensing technology, Labcyte®) and incubated in standard cell culture conditions. Cell viability was measured by Cell Titer Glow® luminescence assays. Analysis of dose response curves using Dotmatics® software resulted in IC50 values. Moreover, the genomic profiles of the AML cell lines were determined by microarrays and/or next-gen sequencing data for further integration with drug responses. Results: Comprehensive data analysis of 16 AML cell lines indicated that specific targeted drugs were selectively killing AML cells. The data analysis revealed relatively strong responses for MEK inhibitors in most AML cell lines (e.g. refametinib 87%, trametinib 82%, selumetinib 75%) while 21% of ex vivo AML patient samples were sensitive to these MEK inhibitors. In case of rapalog sensitivity, 80% of AML cell lines (e.g. temsirolimus 82%, everolimus 71%, sirolimus 81%) and 25% of ex vivo AML patient cases were responsive to the mTOR inhibitors. The AML cell lines carrying FLT3-ITD mutations were extremely sensitive to FLT3 inhibitors (e.g. quizartinib, lestaurtinib, tandutinib, and sorafenib) but very few responses to FLT3 inhibitors were observed in AML patients carrying an ITD mutation in the FLT3 kinase. Summary: Systematic DSRT profiling of AML cell lines illustrates drug sensitivity patterns to classify the cell lines as sensitive or resistant to specific classes of drugs. mTOR and MEK inhibitors were among the most effective inhibitors for most cell lines and also in some ex vivo patient cases suggesting that these drugs may have potential as therapeutic agents in AML. Also, bioinformatics predictions can be used to identify key synergistic combinations of tested drugs for effective AML therapy. Further integration of molecular profiles and functional responses of AML cell lines will help provide better understanding of drug efficacy based on known genetic background of the disease. Citation Format: Disha Malani, Astrid Murumagi, Tea Pemovska, Bhagwan Yadav, Evgeny Kulesskiy, Jing Tang, John Patrick Mpindi, Maija Wolf, Riikka Karjalainen, Tero Aittokallio, Caroline Heckman, Kimmo Porkka, Krister Wennerberg, Olli Kallioniemi. Identifying AML-specific key targeted drugs using high-throughput drug sensitivity and resistance testing profiles of AML cells. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5220. doi:10.1158/1538-7445.AM2013-5220