Prson Gautam

Long-Term ERK Inhibition in KRAS-Mutant Pancreatic Cancer Is Associated with MYC Degradation and Senescence-like Growth Suppression

Induction of compensatory mechanisms and ERK reactivation has limited the effectiveness of Raf and MEK inhibitors in RAS-mutant cancers. We determined that direct pharmacologic inhibition of ERK suppressed the growth of a subset of KRAS-mutant pancreatic cancer cell lines and that concurrent phosphatidylinositol 3-kinase (PI3K) inhibition caused synergistic cell death. Additional combinations that enhanced ERK inhibitor action were also identified. Unexpectedly, long-term treatment of sensitive cell lines caused senescence, mediated in part by MYC degradation and p16 reactivation. Enhanced basal PI3K-AKT-mTOR signaling was associated with de novo resistance to ERK inhibitor, as were other protein kinases identified by kinome-wide siRNA screening and a genetic gain-of-function screen. Our findings reveal distinct consequences of inhibiting this kinase cascade at the level of ERK.

Bioluminescent, Nonlytic, Real-Time Cell Viability Assay and Use in Inhibitor Screening

Abstract Real-time continuous monitoring of cellular processes offers distinct advantages over traditional endpoint assays. A comprehensive representation of the changes occurring in live cells over the entire length of an experiment provides information about the biological status of the cell and informs decisions about the timing of treatments or the use of other functional endpoint assays. We describe a homogeneous, nonlytic, bioluminescent assay that measures cell viability in real time. This time-dependent measurement allowed us to monitor cell health for 72 h from the same test samples, distinguish differential cell growth, and investigate drug mechanism of action by analyzing time- and dose-dependent drug effects. The real-time measurements also allowed us to detect cell death immediately (>75% signal decrease within 15 min of digitonin addition), analyze drug potency versus efficacy, and identify cytostatic versus toxic drug effects. We screened an oncology compound library (Z′ = 0.7) and identified compounds with varying activity at different time points (1.6% of the library showed activity within 3 h, whereas 35.4% showed a response by 47 h). The assay compared well with orthogonal endpoint cell viability assays and additionally provided data at multiple time points and the opportunity to multiplex assays on the same cells. To test the advantage of time-dependent measurements to direct optimal timing of downstream applications, we used the real-time cell viability assay to determine the ideal time to measure caspase activity by monitoring the onset of cell death and multiplexing a luminescent caspase activation assay on the same test samples.

Identification of selective cytotoxic and synthetic lethal drug responses in triple negative breast cancer cells

BackgroundTriple negative breast cancer (TNBC) is a highly heterogeneous and aggressive type of cancer that lacks effective targeted therapy. Despite detailed molecular profiling, no targeted therapy has been established. Hence, with the aim of gaining deeper understanding of the functional differences of TNBC subtypes and how that may relate to potential novel therapeutic strategies, we studied comprehensive anticancer-agent responses among a panel of TNBC cell lines.MethodThe responses of 301 approved and investigational oncology compounds were measured in 16 TNBC cell lines applying a functional profiling approach. To go beyond the standard drug viability effect profiling, which has been used in most chemosensitivity studies, we utilized a multiplexed readout for both cell viability and cytotoxicity, allowing us to differentiate between cytostatic and cytotoxic responses.ResultsOur approach revealed that most single-agent anti-cancer compounds that showed activity for the viability readout had no or little cytotoxic effects. Major compound classes that exhibited this type of response included anti-mitotics, mTOR, CDK, and metabolic inhibitors, as well as many agents selectively inhibiting oncogene-activated pathways. However, within the broad viability-acting classes of compounds, there were often subsets of cell lines that responded by cell death, suggesting that these cells are particularly vulnerable to the tested substance. In those cases we could identify differential levels of protein markers associated with cytotoxic responses. For example, PAI-1, MAPK phosphatase and Notch-3 levels associated with cytotoxic responses to mitotic and proteasome inhibitors, suggesting that these might serve as markers of response also in clinical settings. Furthermore, the cytotoxicity readout highlighted selective synergistic and synthetic lethal drug combinations that were missed by the cell viability readouts. For instance, the MEK inhibitor trametinib synergized with PARP inhibitors. Similarly, combination of two non-cytotoxic compounds, the rapamycin analog everolimus and an ATP-competitive mTOR inhibitor dactolisib, showed synthetic lethality in several mTOR-addicted cell lines.ConclusionsTaken together, by studying the combination of cytotoxic and cytostatic drug responses, we identified a deeper spectrum of cellular responses both to single agents and combinations that may be highly relevant for identifying precision medicine approaches in TNBC as well as in other types of cancers.

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

Genomic Perturbations Reveal Distinct Regulatory Networks in Intrahepatic Cholangiocarcinoma

Intrahepatic cholangiocarcinoma remains a highly heterogeneous malignancy that has eluded effective patient stratification to date. The extent to which such heterogeneity can be influenced by individual driver mutations remains to be evaluated. Here, we analyzed genomic (whole‐exome sequencing, targeted exome sequencing) and epigenomic data from 496 patients and used the three most recurrently mutated genes to stratify patients (IDH, KRAS, TP53, “undetermined”). Using this molecular dissection approach, each subgroup was determined to possess unique mutational signature preferences, comutation profiles, and enriched pathways. High‐throughput drug repositioning in seven patient‐matched cell lines, chosen to reflect the genetic alterations specific for each patient group, confirmed in silico predictions of subgroup‐specific vulnerabilities linked to enriched pathways. Intriguingly, patients lacking all three mutations (“undetermined”) harbored the most extensive structural alterations, while isocitrate dehydrogenase mutant tumors displayed the most extensive DNA methylome dysregulation, consistent with previous findings. Conclusion: Stratification of intrahepatic cholangiocarcinoma patients based on occurrence of mutations in three classifier genes (IDH, KRAS, TP53) revealed unique oncogenic programs (mutational, structural, epimutational) that influence pharmacologic response in drug repositioning protocols; this genome dissection approach highlights the potential of individual mutations to induce extensive molecular heterogeneity and could facilitate advancement of therapeutic response in this dismal disease. (Hepatology 2018).

Drug Target Commons: A Community Effort to Build a Consensus Knowledge Base for Drug-Target Interactions

Summary Knowledge of the full target space of bioactive substances, approved and investigational drugs as well as chemical probes, provides important insights into therapeutic potential and possible adverse effects. The existing compound-target bioactivity data resources are often incomparable due to non-standardized and heterogeneous assay types and variability in endpoint measurements. To extract higher value from the existing and future compound target-profiling data, we implemented an open-data web platform, named Drug Target Commons (DTC), which features tools for crowd-sourced compound-target bioactivity data annotation, standardization, curation, and intra-resource integration. We demonstrate the unique value of DTC with several examples related to both drug discovery and drug repurposing applications and invite researchers to join this community effort to increase the reuse and extension of compound bioactivity data.

A normalized drug response metric improves accuracy and consistency of drug sensitivity quantification in cell-based screening

Accurate quantification of drug effects is crucial for identifying pharmaceutically actionable cancer vulnerabilities. Current cell viability-based measurements often lead to biased response estimates due to varying growth rates and experimental artifacts that explain part of the inconsistency in high-throughput screening results. We developed an improved drug scoring model, normalized drug response (NDR), which makes use of both positive and negative control conditions to account for differences in cell growth rates and experimental noise to better characterize drug-induced effects. We demonstrate an improved consistency and accuracy of NDR compared to existing metrics in assessing drug responses of cancer cells in various culture models and experimental setups. Notably, NDR reliably captures both toxicity and viability responses, and differentiates a wider spectrum of drug behavior, including lethal, growth-inhibitory and growth-stimulatory modes, based on a single viability readout. The method will therefore substantially reduce the time and resources required in cell-based drug sensitivity screening.

Abstract A26: Prediction of synergistic anticancer drug combinations by integrating chemical and genetic screens

Identification of effective drug combinations inhibiting multiple target genes that are essential for cancer cell survival is seen as an effective strategy to overcome drug resistance. Cellular pathways are often redundant, and network rewiring in the face of an assault promotes the emergence of resistant cells that contribute to clinical relapse. However, the number of possible drug-target combinations to investigate by conducting experiments is exponentially large and impractical, therefore warranting the need for systematic approaches to prioritize the most effective drug combinations. Here, we aimed to integrate large-scale drug screening, drug-target affinity knowledge and public RNAi screening datasets on a huge compendium of cancer cell lines to predict synergistic drug combinations. In a case study in an individual breast cancer cell line, MDA-MB-231, we identified drugs selectively active in this cell line compared to the background distribution. We find that target genes of active drugs and other genes involved in related cellular processes were also differentially essential in RNAi screening data. Further, we observe that the drug combinations of these selectively active drugs were synergistic. We propose a systematic approach for integrating chemical screening, drug target and cellular processes knowledge, and functional genetic screens to optimize drug combination testing efforts. Citation Format: Alok Jaiswal, Prson Gautam, Jing Tang, Krister Wennerberg, Tero Aittokallio. Prediction of synergistic anticancer drug combinations by integrating chemical and genetic screens [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 A26.

Abstract B21: Enhanced understanding of drug responses and drug-drug interactions in triple negative breast cancer cells with a multiplexed cell viability and cell death readout

Triple negative breast cancers (TNBC) are a heterogeneous group of cancers that remain a considerable clinical challenge with no known effective targeted therapy, limiting the therapies to cytotoxic chemotherapy, radiation and surgery. Molecular sub-classifications of TNBC have been established but none of these define therapy. With an aim to establish potential novel stratified therapeutic strategies to target TNBC, we applied the Drug Sensitivity and Resistance Testing (DSRT) (Pemovska et al., Cancer Discov. 2013) chemo-sensitivity profiling approach in which the responses of cells to 304 approved and investigational oncology drugs were explored. We studied a diverse panel of 15 TNBC cell lines in an attempt to functionally classify the TNBCs. To go beyond standard growth inhibition drug sensitivity testing we utilized a multiplexed cell viability and cytotoxicity readout allowing us to differentiate between cytostatic and cytotoxic drug responses. Evaluating both types of drug responses, several discoveries were made: First, the multiplexed viability and cytotoxicity readout identified several drug classes that previously had been assumed to have cytotoxic effects based on their strong effects on cell viability but in fact only showed cytostatic effects in most cells. Drug classes exhibiting this type of response included anti-mitotics, mTOR, CDK, metabolic inhibitors, as well as many targeted agents selectively inhibiting oncogenically activated pathways in individual cell lines. Second, within the responses to these broad cytostatic-acting classes of drugs, there were subsets of the cell lines that responded by cell death, suggesting that these cell lines may represent the TNBC subtypes that might best respond to the drugs in the clinic. To explore this further, we tested whether the cytostatic vs. cytotoxic responses could be linked to expression of protein biomarkers using a published TNBC cell line reverse-phase protein array data set (Daemen et al, Genome Biol. 2013). This led to the identification of potential predictive biomarkers such as PAI-1, MAPK phosphatase and Notch-3 levels linking to toxic responses to mitotic and proteasome inhibitors, suggesting that these could be explored as predictors for clinical responses for the drugs. Third, drug response based clustering of cell lines was very distinctive to transcriptomics based TNBC subgroups and recurrent mutation patterns, highlighting the considerable challenges in converting genetic and transcriptomic data to therapeutic responses. Fourth, the cytotoxicity readout highlighted effective synergistic drug combinations that were not apparent using cell viability readouts. For instance, we identified selective synergistic combinations of the MEK inhibitor trametinib with PARP inhibitors or the tyrosine kinase inhibitor ponatinib in DU4475 cells; as well as of the rapamycin analog everolimus with the ATP-competitive mTOR inhibitor dactolisib in several mTOR addicted cell lines. In conclusion, we showed that tracking drug-induced toxic responses in drug sensitivity testing and drug combination evaluation provides novel critical information on drug vulnerabilities. This argues that high throughput chemo-sensitivity profiling of cancer need to go beyond the current standard cell viability testing. Citation Format: Prson Gautam, Leena Karhinen, Agnieszka Szwajda, Sawan Kumar Jha, Bhagwan Yadav, Tero Aittokallio, Krister Wennerberg. Enhanced understanding of drug responses and drug-drug interactions in triple negative breast cancer cells with a multiplexed cell viability and cell death readout. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr B21.

Abstract P6-02-01: Identification of subgroups of triple negative breast cancer cells with selective responses to mTOR, CDK, mitotic and proteasome inhibitors

Triple negative breast cancer (TNBC) is characterized by the lack of of estrogen, progesterone and HER2/ErbB2 receptors. It is a highly heterogeneous class of breast cancer and transcriptomics has recently been used to define 6 major subtypes of TNBC. We studied a panel of 15 TNBC cell lines using a chemical biology approach where we measure the responses to 306 approved and investigational oncology drugs. Clustering of cell lines based on their overall drug responses resulted in a strikingly different grouping compared to the gene expression derived one, highlighting that the current TNBC subtyping is not easily converted to differential sensitivities to drugs. To further evaluate the nature of the drug responses and to differentiate between their cell growth and cytotoxic effects, we multiplexed the standard cell viability readout in the cell line screening with detection of cytotoxicity. This simple multiplexed readout identified several drug classes that previously had been assumed to be cytotoxic based on strong effects on cell viability (cell numbers) while they in fact showed no or a very heterogeneous effect on cytotoxicity. Drug classes exhibiting this type of response included mTOR inhibitors, cyclin-dependent kinase inhibitors (eg. alvociclib), mitotic inhibitors (eg. paclitaxel) as well as proteasome inhibitors (eg. bortezomib) and RNA synthesis inhibitors (eg. dactinomycin). Further investigation of these drug classes showed that their static effects were reversible and in some cases the cells even overcame the inhibitory effect in the presence of the drug in a matter of a few days. Given the non-toxic responses to major classes of anticancer compounds such as mTOR inhibitors, we performed combination screens with these compounds to identify other drugs with which they may synergize to promote cancer cell specific killing. Surprisingly, we instead found that mTOR inhibitors had an antagonistic effect on the activity of many other cancer drugs such as different cytotoxic and antimitotic drugs, tyrosine kinase inhibitors, HDAC inhibitors and PARP inhibitors, suggesting that combining these classes of drugs may be counterproductive also in the clinic. We also found out that accessing a cytotoxic readout allowed us to identify effective synergistic drug combination concentrations that were not seen in cell viability readouts. For example, these synergistic toxic combination responses were seen in DU4475 cells when the MEK inhibitor trametinib was combined either with the PARP inhibitor iniparib or with the broad spectrum tyrosine kinase inhibitor ponatinib. In conclusion, multiplexed cell viability cell death readouts in drug sensitivity testing yields novel critical information on single drug and drug combination activities and liabilities. With this we were able to conclude that antimitotic, mTOR, CDK, proteasome and metabolic inhibitors have a heterogeneous cytotoxic effect across the panel of TNBC cell lines in contrast to their homogenous effect on metabolic inactivation. Citation Format: Prson Gautam, Leena Karhinen, Agnieszka Szwajda, Sawan Kumar Jha, Bhagwan Yadav, Tero Aittokallio, Krister Wennerberg. Identification of subgroups of triple negative breast cancer cells with selective responses to mTOR, CDK, mitotic and proteasome inhibitors [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P6-02-01.