Drug Sensitivity And Resistance Testing Research Articles

Abstract 3100: Interactive analysis of drug testing data for functional precision oncology with Breeze 2.0

Abstract High throughput screening (HTS) is used to screen large numbers of small molecules in cell-based assays to predicting their potential therapeutic response and when applying approved drug libraries using HTS it becomes drug repurposing. The Drug Sensitivity and Resistance Testing (DSRT) platform established at FIMM has been extensively used in testing panels of approved and investigational drugs on cells, both conventional cell lines and primary cell models derived from cancer patient samples. The results help in deepen our understanding of drug efficacy in individual patients and across specific disease to guide future patient stratification. Breeze 2.0 is an open access, web-based platform for interactive analysis of drug screening data in real time. Breeze accepts raw data in a simple tabular format and generates detailed quality control metrics, plots, dose-response curve fits, drug response quantification in form of IC50/EC50/AUC and Drug Sensitivity Scores (DSS). Besides tabular results, Breeze generates interactive, informative, and intuitive visualizations, such as plate heatmaps, scatterplots, barplots, clustered heatmaps, circular plots, for further analysis. Based on manually-curated published datasets available through Breeze 2.0, users can instantly compare their drug response patterns with the existing DSRT profiles from patient samples, as well as cell lines. Importantly, Breeze 2.0 has integrated curated reference DSRT data tested on bone marrow samples from healthy control individuals, which plays a crucial role in understanding cancer-selective drug response by de-prioritizing generally toxic compounds. The end-users can select multiple healthy controls or relevant patient/cell line samples and choose the drugs of interest to visualize the overlaid dose responses, as well as response quantification metrics. Breeze 2.0 is built in R, PHP, MySQL and is freely accessible at https://breeze.fimm.fi/v2 Citation Format: Swapnil Potdar, Filipp Ianevski, Aleksandr Ianevski, Ziaurrehman Tanoli, Krister Wennerberg, Brinton Seashore-Ludlow, Olli Kallioniemi, Päivi Östling, Tero Aittokallio, Jani Saarela. Interactive analysis of drug testing data for functional precision oncology with Breeze 2.0 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3100.

WITHDRAWN: Towards robotic laboratory automation Plug & play: Survey and concept proposal on teaching-free robot integration with the LAPP digital twin

Establishment of drug testing of patient-derived cancer cells (PDCs) in physiologically relevant 3-dimensional (3D) culture is central for drug discovery and cancer research, as well as for functional precision medicine. Here, we describe the detailed protocol allowing the 3D drug testing of PDCs – or any type of cells of interest – in Matrigel in 384-well plate format using automation. We also provide an alternative protocol, which does not require supporting matrices. The cancer tissue is obtained directly from clinics (after surgery or biopsy) and processed into single cell suspension. Systematic drug sensitivity and resistance testing (DSRT) is carried out on the PDCs directly after cancer cell isolation from tissue or on cells expanded for a few passages. In the 3D-DSRT assay, the PDCs are plated in 384-well plates in Matrigel, grown as spheroids, and treated with compounds of interest for 72 h. The cell viability is directly measured using a luminescence-based assay. Alternatively, prior to the cell viability measurement, drug-treated cells can be directly subjected to automated high-content bright field imaging or stained for fluorescence (live) cell microscopy for further image analysis. This is followed by the quality control and data analysis. The 3D-DSRT can be performed within a 1–3-week timeframe of the clinical sampling of cancer tissue, depending on the amount of the obtained tissue, growth rate of cancer cells, and the number of drugs being tested. The 3D-DSRT method can be flexibly modified, e.g., to be carried out with or without supporting matrices with U-bottom 384-well plates when appropriate for the PDCs or other cell models used.

High-Throughput Screens Identify NEDD8 Inhibition As a Strategy to Augment Natural Killer Cell Cytotoxicity Against Blood Cancers

Natural killer (NK) cell-based therapies are amongst the emerging immunotherapeutic approaches that aim to target malignant cells refractory to standard therapies. Currently used strategies to augment the NK cell anti-cancer function of NK cells include cytokine treatments and the use of feeder expansion protocols for culturing NK cells. Recent studies have indicated that NK cell cytotoxicity and function may also be enhanced by certain anti-cancer drugs. However, the effects of the hundreds of available oncology drugs on NK cell function have not been systematically evaluated. Here, we perform large-scale high-throughput testing of NK cell-drug combinations using co-culture models with diverse blood cancer cells, identifying the NEDD8-activating enzyme inhibitor pevonedistat as a promising candidate for augmenting NK cell anti-cancer responses. To identify oncology drugs synergizing with NK cell-based immunotherapy, we established a high-throughput drug sensitivity and resistance testing (DSRT) platform to investigate effects of 527 approved drugs and investigational compounds on NK cell cytotoxicity against blood cancer cell lines. Overall, 10 cell lines, including acute myeloid leukemia (AML), chronic myeloid leukemia (CML), acute lymphoblastic leukemia (ALL), diffuse large B-cell lymphoma (DLBCL), and multiple myeloma (MM), were screened in combination with primary expanded NK cells. Target cells were treated for 24 hours with drugs at five different concentrations both with and without NK cells. In NK cell co-culture, an effector-target (ET) cell ratio at which half of the target cells were killed was selected for experiments. Most effective compounds were then selected for further testing using multiplexed single-cell RNA sequencing (scRNA-seq) and analysis of secreted cytokines (Figure 1). Overall, the results from our DSRT platform demonstrated varying effects of different drug classes on NK cell cytotoxicity against blood cancer cell lines. In general, 5-10% of drugs had NK cell cytotoxicity-enhancing effects, 30-40% inhibited NK function and 35-50% of drugs had no effect. Similarities in results between cell lines highlighted the dependence of NK cell-drug synergistic effects on disease pathobiology. Across multiple disease models, pevonedistat (MLN4924), a selective NEDD8 activating enzyme inhibitor that has been studied as treatment for relapsed/refractory AML, showed strong synergistic effects with NK cells. For example, in OCI-AML-3 (AML), pevonedistat alone showed no effect on target cell viability, but in combination with NK cells at a 1:4 ET ratio, it resulted in 98% target cell killing at 100 nM. Similar effects were also seen in MM1.S (MM), although a higher concentration (10000 nM) of pevonedistat was required (Figure 2). Results from scRNA-seq after combination treatment with NK cells and pevonedistat revealed distinct transcriptomic changes in both NK cells and target cells when compared to controls. In both NK cells and target cells, pevonedistat caused upregulation of TXN, TALDO1, TXNRD1, NQO1, SRXN1 and SLC7A11,collectively indicating effects on the NRF2 signaling pathway. The upregulation of these genes correlated with the tested concentration of pevonedistat, with notably higher expression when treated with 1000 nM pevonedistat compared to 100 nM. Changes were also observed in the secretion of cytokines detected from the cell culture media, where concentrations of IL-1β, IL-2, IL-6, IL-8, IL-10, IFN-γ and TNF-α all increased up to 300% compared to controls. In HEL (AML) and LAMA-84 (CML), where pevonedistat had only minor effects on NK cell cytotoxicity, the expression TNF and TNFRSF1B at baseline and after pevonedistat treatment were notably lower, when compared to cell lines where the combination therapy led to stronger responses. This suggests a possible mechanism for pevonedistat-NK cell synergy, given that previous reports have demonstrated a synergy between pevonedistat and TNF-α to activate apoptosis. In conclusion, we discovered a cell context-specific synergy between pevonedistat and NK cells, which increased NK cell cytotoxicity against malignant cells in vitro. In addition, our results demonstrate the potential of using a high-throughput platform for studying NK cell-drug interactions against malignant cells, while aiming to improve treatment for hematological malignancies through combination immunotherapies.

Comparison of two supporting matrices for patient-derived cancer cells in 3D drug sensitivity and resistance testing assay (3D-DSRT).

Central to the success of functional precision medicine of solid tumors is to perform drug testing of patient-derived cancer cells (PDCs) in tumor-mimicking ex vivo conditions. While high throughput (HT) drug screening methods have been well-established for cells cultured in two-dimensional (2D) format, this approach may have limited value in predicting clinical responses. Here, we describe the results of the optimization of drug sensitivity and resistance testing (DSRT) in three-dimensional (3D) growth supporting matrices in a HT mode (3D-DSRT) using the hepatocyte cell line (HepG2) as an example. Supporting matrices included widely used animal-derived Matrigel and cellulose-based hydrogel, GrowDex, which has earlier been shown to support 3D growth of cell lines and stem cells. Further, the sensitivity of ovarian cancer PDCs, from two patients included in the functional precision medicine study, was tested for 52 drugs in 5 different concentrations using 3D-DSRT. Shortly, in the optimized protocol, the PDCs are embedded with matrices and seeded to 384-well plates to allow the formation of the spheroids prior to the addition of drugs in nanoliter volumes with acoustic dispenser. The sensitivity of spheroids to drug treatments is measured with cell viability readout (here, 72 h after addition of drugs). The quality control and data analysis are performed with openly available Breeze software. We show the usability of both matrices in established 3D-DSRT, and report 2D vs 3D growth condition dependent differences in sensitivities of ovarian cancer PDCs to MEK-inhibitors and cytotoxic drugs. This study provides a proof-of-concept for robust and fast screening of drug sensitivities of PDCs in 3D-DSRT, which is important not only for drug discovery but also for personalized ex vivo drug testing in functional precision medicine studies. These findings suggest that comparing results of 2D- and 3D-DSRT is essential for understanding drug mechanisms and for selecting the most effective treatment for the patient.

Protocol for 3D drug sensitivity and resistance testing of patient-derived cancer cells in 384-well plates.

Establishment of drug testing of patient-derived cancer cells (PDCs) in physiologically relevant 3-dimensional (3D) culture is central for drug discovery and cancer research, as well as for functional precision medicine. Here, we describe the detailed protocol allowing the 3D drug testing of PDCs - or any type of cells of interest - in Matrigel in 384-well plate format using automation. We also provide an alternative protocol, which does not require supporting matrices. The cancer tissue is obtained directly from clinics (after surgery or biopsy) and processed into single cell suspension. Systematic drug sensitivity and resistance testing (DSRT) is carried out on the PDCs directly after cancer cell isolation from tissue or on cells expanded for a few passages. In the 3D-DSRT assay, the PDCs are plated in 384-well plates in Matrigel, grown as spheroids, and treated with compounds of interest for 72 h. The cell viability is directly measured using a luminescence-based assay. Alternatively, prior to the cell viability measurement, drug-treated cells can be directly subjected to automated high-content bright field imaging or stained for fluorescence (live) cell microscopy for further image analysis. This is followed by the quality control and data analysis. The 3D-DSRT can be performed within a 1-3-week timeframe of the clinical sampling of cancer tissue, depending on the amount of the obtained tissue, growth rate of cancer cells, and the number of drugs being tested. The 3D-DSRT method can be flexibly modified, e.g., to be carried out with or without supporting matrices with U-bottom 384-well plates when appropriate for the PDCs or other cell models used.

A Potential Role of NK Cells and ABCB9 in Modulating the Response to Melflufen in Multiple Myeloma

Introduction Melphalan flufenamide (melflufen), is a novel peptide-drug conjugate that delivers alkylating agents in tumours upon hydrolysis by aminopeptidases. Melflufen was recently approved by the EMA for the treatment of relapsed/refractory multiple myeloma (MM). Considering the challenges in treating this group of patients and their low overall survival, information that can support treatment selection and success is urgently needed. To discover possible biomarkers of melflufen sensitivity, we applied a multiparametric drug sensitivity assay to MM patient samples ex vivo and analysed the samples by single cell RNA sequencing (scRNAseq) and flow cytometry. Drug testing identified groups of MM samples with high and low sensitivity to melflufen, while differential gene expression and flow cytometry analysis revealed markers associated with response and cytotoxicity. Methods Bone marrow mononuclear cells from 12 newly diagnosed (ND) and 12 relapsed/refractory (RR) patients were used for multi-parametric flow cytometry-based drug sensitivity and resistance testing (DSRT) evaluation to melflufen and melphalan, and for scRNAseq after written informed consent following approved protocols in compliance with the Declaration of Helsinki. Based on the results from the DSRT assay and drug sensitivity scores (DSS), we divided the samples into three groups - high sensitivity (HS, DSS > 40 (melflufen) or DSS > 16 (melphalan)), intermediate sensitivity (IS, 31 ≤ DSS ≤ 40 (melflufen) or 10 ≤ DSS ≤ 16 (melphalan)), and low sensitivity (LS, DSS < 31 (melflufen) or DSS < 10 (melphalan)). To identify genes, responsible for the general sensitivity to we conducted differential gene expression (DGE) analyses between HS and LS samples for both drugs ("HS vs. LS melphalan” and "HS vs. LS for melflufen", respectively). In addition, we conducted flow cytometry analysis to evaluate the levels of HLA-B protein, associated with increased cytotoxicity of NK cells. Results We analyzed the DSRT and scRNAseq data for the MM samples of different melflufen sensitivity groups (HS, IS, LS). DGE analysis showed significantly lower levels of the ABCB9 gene in plasma cells populations of the HS compared to LS group (2.2-fold, HS vs. LS groups for melflufen, p < 0.01). ABCB9 belongs to a family of proteins known for drug efflux and multidrug resistance. A similar pattern was detected for the melphalan HS vs. LS (3.2-fold, p < 0.01) comparison suggesting that this alteration might be a common indicator of sensitivity to melflufen and melphalan. As a confirmation, we found that MM cell lines treated with melphalan (Fig. 1) also tend to express higher levels of ABCB9, as compared to control cells (1.2-fold, p<0.001), indicating its potential role in drug resistance. Based on scRNAseq and flow cytometry analysis, we also discovered that NK cells may be associated with melflufen cytotoxic effect. Our results demonstrated higher levels of NK and NKT subpopulations, as well as lower surface HLA-B protein level (p<0.01) and activation of NFKB signalling in the melflufen HS group in comparison to the LS group (Fig. 2). The latter two features are associated with increased NK cells cytotoxicity, thus, suggesting synergistic effect of innate immunity with melflufen. Conclusion In summary, we demonstrate the association of ABCB9 expression with resistance both to melphalan or melflufen. Furthermore, we observed that MM samples that were highly sensitive to melflufen exhibited markers associated with NK-mediated cytotoxicity, suggesting a potential role of NK cells for enhancing the efficacy of melflufen. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

The Novel Peptide Drug Conjugate OPDC3 Is Highly Active in Different Hematological Malignancies

Background: Peptide drug conjugates (PDCs) are an important emerging class of molecules that allow for targeted delivery of therapeutic agents. Melflufen, which was recently approved by the European Medicines Agency for relapsed refractory multiple myeloma patients, is one such PDC that allows for targeted delivery of the payload drug melphalan. Like melflufen, the novel PDC OPDC3 was designed to target cells with high peptidase activity to deliver toxic payload. In this study, we evaluated the activity of OPDC3 in models of acute myeloid leukemia (AML), myelodysplastic syndrome (MDS), and diffuse large B-cell lymphoma (DLBCL). Since the majority of AML patients eventually relapse, and only a limited set of therapies are available for MDS and DLBCL patients, new treatment options are urgently needed. Methods: The Finnish Hematology Research Biobank provided viably frozen bone marrow mononuclear cells from 29 AML and 6 MDS patients. All 29 AML samples and 1 MDS sample were from relapsed/refractory (R/R) patients, and 5 MDS samples were taken at diagnosis. Multi-parametric flow cytometry-based drug sensitivity and resistance testing (DSRT) was used to assess sample sensitivities to OPDC3, melflufen and venetoclax. Samples were incubated for 72h hours in seven different concentrations ranging from 0.01 nM to 10 000 nM of OPDC3, melflufen and venetoclax. Cells were stained using antibody panels that allowed detection of immature and mature blasts. Cell death and viability were assessed using Annexin V and DRAQ7. Dose response curves were generated for each detected cell subset. Drug sensitivity scores (DSS) and EC50 values were calculated. OPDC3 tumor growth inhibition efficacy was validated on xenografts in ovo initiated from SUDHL-4 cell line. OPDC3 and bendamustine effects as single agents were tested in three different concentrations (8.4; 33.5; 167.5 μM) on DLBCL cell line SUDHL-4. OPDC3 in combination with rituximab and bendamustine in combination with rituximab were also tested. The grafted tumors were quantitatively evaluated by tumor weight using the ChorioAllantoic Membrane (CAM) assay. Toxicity was followed daily by embryonic lethality and final quantitative evaluation was done on day18 after treatment per treatment group. Results: OPDC3 was effective in killing blast cells in samples from R/R AML patients. The most sensitive sample having the highest DSS 33 (IC50: 0.9 nM), least sensitive sample having the lowest DSS 15 (IC50: 19.5 nM), and median DSS being 24.1 (IC50: 8.5 nM) (Figure 1). For venetoclax the highest DSS was 27.9 (IC50: 2 nM), lowest DSS 0 (IC50: >10 000 nM), and median DSS 19.4 (Figure 1). The 3 least sensitive samples to OPDC3 were clearly sensitive to venetoclax (Figure 1). OPDC3 was also able to kill more mature malignant CD14+ cells (median DSS 21.5; median EC50 17.1 nM). OPDC3 was more effective in killing both blast and CD14+ cells than melflufen. Melflufen blast median DSS was 20.6 (EC50: 19.9 nM) and for CD14+ cells median DSS was 16.4 (EC50: 84.7 nM). OPDC3 was also effective in killing MDS blast cells in 4/6 of the patient samples. The highest OPDC3 DSS was 27.2 (IC50: 3.1 nM), lowest DSS 0 (IC50: >10 000), and median DSS 15.3. OPDC3 reduced the DLBCL cell line SUDHL-4 tumor growth at 8.4 μM when compared to the negative control (Figure 2). Bendamustine had little effect on tumor growth at 8.4 μM. At 33.5 μM bendamustine was unable to reduce tumor growth as much as 8.4 μM of OPDC3. OPDC3 also caused less toxicity at 33.5 and 167.5 μM compared to bendumustine at the same concentrations. OPDC3 in combination with rituximab was more effective in reducing tumor growth than both compounds alone, or bendamustine in combination with rituximab. OPDC3 in combination with rituximab caused also the least embryonic toxicity. Conclusions: OPDC3 can efficiently target patient derived AML and MDS blast cells at relatively low nM concentrations. It is also able to kill more differentiated malignant CD14+ AML cells. Especially in R/R AML samples in which the blast cells and CD14+ cells are resistant to venetoclax, OPDC3 was effective at targeting both cell populations. OPDC3 is more effective than bendamustine as a single drug in killing SUDHL-4 tumor cells, and when combined with rituximab compared to bendamustine in combination with rituximab. OPDC3 shows promise as a novel therapeutic for different hematological malignancies. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Effects of Wee1 inhibitor adavosertib on patient-derived high-grade serous ovarian cancer cells are multiple and independent of homologous recombination status.

ObjectiveA major challenge in the treatment of platinum-resistant high-grade serous ovarian cancer (HGSOC) is lack of effective therapies. Much of ongoing research on drug candidates relies on HGSOC cell lines that are poorly documented. The goal of this study was to screen for effective, state-of-the-art drug candidates using primary HGSOC cells. In addition, our aim was to dissect the inhibitory activities of Wee1 inhibitor adavosertib on primary and conventional HGSOC cell lines.MethodsA comprehensive drug sensitivity and resistance testing (DSRT) on 306 drug compounds was performed on three patient-derived genetically unique HGSOC cell lines and two commonly used ovarian cancer cell lines. The effect of adavosertib on the cell lines was tested in several assays, including cell-cycle analysis, apoptosis induction, proliferation, wound healing, DNA damage, and effect on nuclear integrity.ResultsSeveral compounds exerted cytotoxic activity toward all cell lines, when tested in both adherent and spheroid conditions. In further cytotoxicity tests, adavosertib exerted the most consistent cytotoxic activity. Adavosertib affected cell-cycle control in patient-derived and conventional HGSOC cells, inducing G2/M accumulation and reducing cyclin B1 levels. It induced apoptosis and inhibited proliferation and migration in all cell lines. Furthermore, the DNA damage marker γH2AX and the number of abnormal cell nuclei were clearly increased following adavosertib treatment. Based on the homologous recombination (HR) signature and functional HR assays of the cell lines, the effects of adavosertib were independent of the cells' HR status.ConclusionOur study indicates that Wee1 inhibitor adavosertib affects several critical functions related to proliferation, cell cycle and division, apoptosis, and invasion. Importantly, the effects are consistent in all tested cell lines, including primary HGSOC cells, and independent of the HR status of the cells. Wee1 inhibition may thus provide treatment opportunities especially for patients, whose cancer has acquired resistance to platinum-based chemotherapy or PARP inhibitors.

54P Targeted treatment options for childhood hepatoblastoma using high-throughput drug screening

Hepatoblastoma (HB) is a pediatric malignancy with prevalence of 1:1 000 000 in Europe. Median age at diagnosis is one year. Current treatment entails chemotherapy (platinum-based alone or combined with doxorubicin) followed by surgery (tumor resection or liver transplantation). Survival rate of HB patients has improved; however, high-risk HB tumors are still difficult to treat with 20-30% of HB patients responding poorly to current treatment modalities. Furthermore, while current treatment modalities improve the prognosis, side effects of the chemotherapeutics have a significant effect on the life quality of HB survivors. Therefore, safer, more effective, and targeted treatment options are needed for improved patient care. Drug sensitivity and resistance testing (DSRT) was completed using 3D spheroids of five histologically different high-risk hepatoblastoma PDX-derived models. Cells were cultured with 527 approved or emerging oncological compounds and 1 drug combination for 72 h in five concentrations. Viability was assessed with Cell-Titer Glo. Pediatric primary hepatocytes were used to evaluate the general toxicity of the compounds. Selective drug sensitivity score (sDSS, calculated DSS(PDX) - DSS(control)) was calculated for each drug. sDSS score was used to evaluate the efficacy and tumor-sensitivity of each compound. Between 14 and 71 compounds with sDSS > 10 were identified for each HB model. These compounds represent a wide range of mechanisms of action and include kinase inhibitors, HSP inhibitors, apoptotic modulators, as well as conventional chemotherapeutics. Of these, 8 compounds were found to be effective in all five HB models. These were onalespib (a heat shock protein inhibitor (HSPI)), fimepinostat (PI3K/HDAC inhibitor), idasanutlin (MDM2-antagonist), cabazitaxel (microtubule inhibitor), filanesib (kinesin inhibitor), BIIB021 (HSPI), eribulin (microtubule inhibitor), and luminespib (HSPI). Interestingly, standard treatments in HB (carboplatin and doxorubicin) only demonstrated a minor effect. The screen revealed many new promising compounds for HB treatment. The efficacy of these agents should be confirmed in in vivo models in the future.

Pancreatic Cancer Organoids in the Field of Precision Medicine: A Review of Literature and Experience on Drug Sensitivity Testing with Multiple Readouts and Synergy Scoring.

Simple SummaryNew treatments are urgently needed for pancreatic ductal adenocarcinoma because it is one of the most aggressive and lethal cancers, detected too late and resistant to conventional chemotherapy. Tumors in most patients feature a similar set of core mutations but so far it has not been possible to design a one-fits-all treatment strategy. Instead, efforts are underway to personalize the therapies. To find the treatments that might work the best for each patient, entirely new experimental platforms based on living miniature tumors, organoids, have been developed. We review here the latest international findings in designing personalized treatments pancreatic cancer patients using organoids as testing beds. Our own work adds important clues about how such testing could, and perhaps should, be conducted.Pancreatic ductal adenocarcinoma (PDAC) is a silent killer, often diagnosed late. However, it is also dishearteningly resistant to nearly all forms of treatment. New therapies are urgently needed, and with the advent of organoid culture for pancreatic cancer, an increasing number of innovative approaches are being tested. Organoids can be derived within a short enough time window to allow testing of several anticancer agents, which opens up the possibility for functional precision medicine for pancreatic cancer. At the same time, organoid model systems are being refined to better mimic the cancer, for example, by incorporation of components of the tumor microenvironment. We review some of the latest developments in pancreatic cancer organoid research and in novel treatment design. We also summarize our own current experiences with pancreatic cancer organoid drug sensitivity and resistance testing (DSRT) in 14 organoids from 11 PDAC patients. Our data show that it may be necessary to include a cell death read-out in ex vivo DSRT assays, as metabolic viability quantitation does not capture actual organoid killing. We also successfully adapted the organoid platform for drug combination synergy discovery. Lastly, live organoid culture 3D confocal microscopy can help identify individual surviving tumor cells escaping cell death even during harsh combination treatments. Taken together, the organoid technology allows the development of novel precision medicine approaches for PDAC, which paves the way for clinical trials and much needed new treatment options for pancreatic cancer patients.

Single Cell RNA Sequencing Identifies Potential Molecular Indicators of Response to Melflufen in Multiple Myeloma

IntroductionMelphalan flufenamide (melflufen), is a novel peptide-drug conjugate that targets aminopeptidases and selectively delivers alkylating agents in tumors. Melflufen was recently FDA approved for the treatment of relapsed/refractory multiple myeloma (MM) patients. Considering the challenges in treating this group of patients, and the availability of several new drugs for MM, information that can support treatment selection is urgently needed. To identify potential indicators of response and mechanism of resistance to melflufen, we applied a multiparametric drug sensitivity assay to MM patient samples ex vivo and analyzed the samples by single cell RNA sequencing (scRNAseq). Ex vivo drug testing identified MM samples that were distinctly sensitive or resistant to melflufen, while differential gene expression analysis revealed pathways associated with response.MethodsBone marrow (BM) aspirates from 24 MM patients were obtained after written informed consent following approved protocols in compliance with the Declaration of Helsinki. BM mononuclear cells from 12 newly diagnosed (ND) and 12 relapsed/refractory (RR) patients were used for multi-parametric flow cytometry-based drug sensitivity and resistance testing (DSRT) evaluation to melflufen and melphalan, and for scRNAseq. Based on the results from the DSRT tests and drug sensitivity scores (DSS), we divided the samples into three groups - high sensitivity (HS, DSS > 40 (melflufen) or DSS > 16 (melphalan)), intermediate sensitivity (IS, 31 ≤ DSS ≤ 40 (melflufen) or 10 ≤ DSS ≤ 16 (melphalan)), and low sensitivity (LS, DSS < 31 (melflufen) or DSS < 10 (melphalan)). To identify genes, responsible for the general sensitivity to melphalan-based drugs we conducted differential gene expression (DGE) analyses separately for melphalan and melflufen focusing on the plasma cell populations, comparing gene expression between HS and LS samples for both drugs (“HS vs. LS melphalan” and “HS vs. LS for melflufen”, respectively). In addition, to explain the increased sensitivity of RR samples, we conducted the DGE analysis for ND vs. RR samples and searched for similarities between these three datasets.ResultsDSRT data indicated that samples from RRMM patients were significantly more sensitive to melflufen compared to samples from NDMM (Fig. 1A). In addition, we observed that samples with a gain of 1q (+1q) were more sensitive to melflufen while those with deletion of 13q (del13q) appeared to be less sensitive, although these results lacked significance (Fig. 1A). After separating the samples into different drug sensitivity groups (HS, IS, LS), DGE analysis showed significant downregulation of the drug efflux and multidrug resistance protein family member ABCB9 in the melflufen HS group opposed to the LS group (2.2-fold, p < 0.001). A similar pattern was detected for the melphalan HS vs. LS comparison suggesting that this alteration might be a common indicator of sensitivity to melphalan-based drugs. Furthermore, in the melflufen HS group we observed downregulation of the matrix metallopeptidase inhibitors TIMP1 and TIMP2 (3-fold and 1.6-fold, p < 0.001, respectively), and cathepsin inhibitors CST3 and CSTB (3.2-fold and 1.3-fold, p < 0.001, respectively) (Fig. 1B). This effect was observed in both “ND vs. RR” and “HS vs. LS for melflufen” comparisons, but not for melphalan, suggesting that these changes are associated with disease progression and specific indicators of sensitivity to melflufen. Moreover, gene set enrichment analysis (GSEA) showed activation of pathways related to protein synthesis, as well as amino acid starvation for malignant and normal cell populations in the HS group.ConclusionIn summary, our results indicate that melflufen is more active in RRMM compared to NDMM. In addition, samples from MM patients with +1q, which is considered an indicator of high-risk disease, tended to be more sensitive to melflufen. Based on differential GSEA and pathway enrichment, several synergizing mechanisms could potentially explain the higher sensitivity to melflufen, such as decreased drug efflux and increased drug uptake. Although these results indicate potential indicators of response and mechanisms of drug efficacy, further validation of these findings is required using data from melflufen treated patients. [Display omitted] DisclosuresSlipicevic: Oncopeptides AB: Current Employment. Nupponen: Oncopeptides AB: Consultancy. Lehmann: Oncopeptides AB: Current Employment. Heckman: Orion Pharma: Research Funding; Oncopeptides: Consultancy, Research Funding; Novartis: Research Funding; Celgene/BMS: Research Funding; Kronos Bio, Inc.: Research Funding.

Phosphoproteomic Analysis of Primary Myeloma Patient Samples Identifies Distinct Phosphorylation Signatures Correlating with Chemo-Sensitivity Profiles in an Ex Vivo Drug Sensitivity Testing Platform

Introduction: Multiple myeloma (MM) is characterized by the clonal expansion of plasma cells in the bone marrow resulting in end-organ damage. Despite an extensive increase in the five-year survival rate in recent years, MM is still considered an incurable disease as patients will repeatedly relapse and develop resistance to current chemotherapies. A key focus for the personalization of myeloma therapy is understanding the biological mechanisms of drug resistance and identifying clinically useful biomarkers of therapeutic response. Highly efficient techniques for the enrichment of phosphorylated peptides followed by high resolution mass spectrometry facilitates the quantitation of thousands of site-specific phosphorylation events. Here, we have performed a phosphoproteomic analysis on MM cell lysates stratified based on their ex vivo drug response profiles to advance our understanding of drug resistance mechanisms.Materials and Methods: CD138 + plasma cells were isolated from 20 adult MM patient bone marrow aspirates at diagnosis (n=7) or relapse (n=13). Samples were grouped based on ex vivo drug sensitivity and resistance testing (DSRT) as follows: highly sensitive (Group I), sensitive (Group II), resistant (Group III), highly resistant (Group IV) [1]. For the phosphoproteomic analysis, peptides were generated and purified using the filter aided sample preparation (FASP) protocol. Peptide tandem mass tag (TMT) labelling, Fe 3+ immobilized metal ion affinity chromatography (IMAC), synchronous precursor selection (SPS), and triple stage tandem mass spectrometry (MS3) was performed. Nonenriched peptides were used for proteomic analysis. Resulting data was analysed using MaxQuant, followed by normalization of phosphosite intensities using the internal reference scale (IRS) method, and statistical analysis using Perseus. Functional enrichment and kinase enrichment analyses were performed on significant phosphoproteins using g:profiler and KEA2, respectively.Results: Our quantitative MS-based phosphoproteomic analysis identified 2,945 phosphorylation sites on 2,232 phosphopeptides from 690 phosphoproteins. Of these phosphorylation sites, 176 were significantly changed between all four DSRT groups and 267 were significantly changed between Group I and Group IV (False Discovery Rate (FDR) < 0.05). Hierarchical clustering was performed to highlight the distinct phosphoproteomic profiles associated with each DSRT group, of which the very sensitive (Group I) and very resistant (Group IV) subgroups demonstrated a well-defined separation (Fig. 1A, 1B). KEGG pathway analysis and gene ontology (GO) analysis of significantly increased phosphorylated proteins in Group IV compared to Group I MM patients demonstrated an increased phosphorylation of proteins associated with tight junctions, the Rap1 signalling pathway and the phosphatidylinositol signalling system; indicating an upregulation of cell adhesion associated processes in drug resistant MM. Phosphoproteins increased in abundance in Group I compared to Group IV MM patients revealed an increased phosphorylation of proteins involved in translation and RNA processing including the spliceosome, RNA transport and RNA binding pathways (Fig. 1C). We identified filamin A serine 2152, RAS guanyl-releasing protein 2 serine 576 and proto-oncogene tyrosine-protein kinase Src serine 17 as increased in Group IV MM, and nuclease-sensitive element-binding protein 1 (YBX1) serine 165, CD44 serine 697 and Bcl2-associated agonist of cell death (BAD) serine 99 as increased in Group I MM. KEA of the upregulated phosphoproteome in Group IV revealed an enrichment of cyclin dependent kinase 1 (CDK1) and ribosomal s6 kinases (RPS6K) whereas casein kinase 2 (CK2) and the glycolysis-associated kinases were enriched in Group I (Fig. 1D).Conclusion: Our study has generated a phosphoproteomic dataset demonstrating distinct phosphorylation signatures associated with drug sensitivity in clinical MM plasma cells. The identification of phosphorylation events associated with drug resistance provides a basis for further exploration of these events and associated signalling pathways to further understand drug resistance mechanisms in MM and identify potential biomarkers of therapeutic response and targets for drug re-sensitization in MM.

Gene-Drug Interactions in AML Patients Identified By RNA Sequencing and Comprehensive Ex Vivo Drug Sensitivity and Resistance Testing

IntroductionTranscriptome analysis of patient samples can provide crucial insight of disease biology and treatment response. In acute myeloid leukemia (AML) where treatment options are limited, combining functional genomics with transcriptomics can uncover novel therapeutically useful pharmacogenomic associations. We sought to determine associations between drug response and individual gene or gene expression modules to identify indicators of drug sensitivity and resistance. Here, we analyzed responses of primary cells from 123 diagnostic and relapsed/refractory AML patients to 527 different small molecule inhibitors along with gene expression profiles from the same samples, and applied different statistical and bioinformatic approaches to define high confident drug-gene interactions.MethodsRNA was prepared from bone marrow or blood mononuclear cells (MNCs) collected from diagnostic (n=61), relapsed (n=38) and refractory (n=24) AML patients after written informed consent. RNA-seq was performed and data analyzed as described previously1. Specifically, RSubread package was employed for read summarization from aligned bam files using (EnsEMBL v82). The R packages DESeq2 and edgeR were used for the count normalization and differential gene expression analysis. Ex vivo drug sensitivity and resistance testing (DSRT) was also performed on the MNCs with 527 approved and investigational oncology chemical compounds in a 10,000-fold concentration range as described earlier2. Weighted correlation network analysis (WGCNA) was performed using the WGCNA R package and the resulting eigengenes were correlated (Spearman) with drug response for each drug. In addition, linear regression analysis was performed for each drug with potential confounding factors including RNA-seq library preparation and media used for drug testing.ResultsTo identify expressed genes associated with drug sensitivity and resistance, we applied a weighted correlation network analysis method. After data pre-processing, 5000 most-highly variable genes were selected for co-expression gene module exploration. The gene co-expression modules and the corresponding hierarchical clustering of these genes resulted in 24 distinct gene modules (Fig. 1). These modules were then correlated with ex-vivo drug response profiles. The results indicated that BCL2 inhibitors navitoclax and venetoclax and MDM2 inhibitor idasanutlin strongly correlated (rho=0.69) with a module consisting of 142 genes. Gene set enrichment analysis suggested that genes within this module were enriched with gene involved in hematopoietic stem cells and nucleic acid binding/transcriptional regulation. Linear regression analysis for predictors of tyrosine kinase inhibitor responses (sorafeninb, sunitinib, tivozanib) revealed significant positive correlation with FLT3LG and KITLG and negative correlation with BEX2 and BEX5 genes. Similarly, the regression analysis for MEK inhibitors resulted in positive correlation with RRAS and JAK2 gene expression. Associations were considered significant with adjusted p-value <0.1.SummaryDetermining gene-drug interactions is a powerful strategy to identify clinically actionable pharmacogenomic associations. By applying different statistical and bioinformatic approaches to RNA-seq and ex vivo drug sensitivity data, we were able to identify potential drug-gene interactions, which could be used to monitor clinical drug response or identify responding vs. non-responding patients. Our results also indicated novel associations for drugs (e.g. TKIs and MEK inhibitors) that are being considered for AML therapy.Figure 1. Gene expression analysis of 123 AML patient samples resulted in 24 distinct gene modules.

Feasibility study of using high-throughput drug sensitivity testing to target recurrent glioblastoma stem cells for individualized treatment

BackgroundDespite the well described heterogeneity in glioblastoma (GBM), treatment is standardized, and clinical trials investigate treatment effects at population level. Genomics-driven oncology for stratified treatments allow clinical decision making in only a small minority of screened patients. Addressing tumor heterogeneity, we aimed to establish a clinical translational protocol in recurrent GBM (recGBM) utilizing autologous glioblastoma stem cell (GSC) cultures and automated high-throughput drug sensitivity and resistance testing (DSRT) for individualized treatment within the time available for clinical application.ResultsFrom ten patients undergoing surgery for recGBM, we established individual cell cultures and characterized the GSCs by functional assays. 7/10 GSC cultures could be serially expanded. The individual GSCs displayed intertumoral differences in their proliferative capacity, expression of stem cell markers and variation in their in vitro and in vivo morphology. We defined a time frame of 10 weeks from surgery to complete the entire pre-clinical work-up; establish individualized GSC cultures, evaluate drug sensitivity patterns of 525 anticancer drugs, and identify options for individualized treatment. Within the time frame for clinical translation 5/7 cultures reached sufficient cell yield for complete drug screening. The DSRT revealed significant intertumoral heterogeneity to anticancer drugs (p < 0.0001). Using curated reference databases of drug sensitivity in GBM and healthy bone marrow cells, we identified individualized treatment options in all patients. Individualized treatment options could be selected from FDA-approved drugs from a variety of different drug classes in all cases.ConclusionsIn recGBM, GSC cultures could successfully be established in the majority of patients. The individual cultures displayed intertumoral heterogeneity in their in vitro and in vivo behavior. Within a time frame for clinical application, we could perform DSRT in 50% of recGBM patients. The DSRT revealed a remarkable intertumoral heterogeneity in sensitivity to anticancer drugs in recGBM that could allow tailored therapeutic options for functional precision medicine.

Associations between Microrna Expression, Disease Progression and Ex Vivo Drug Response in Multiple Myeloma

Targeted drug treatment strategies have significantly prolonged the overall survival rate among multiple myeloma (MM) patients. However, high relapse rates and multiple drug resistance still pose major challenges. Although, the underlying molecular features of the disease have been explored both at the genomic and transcriptomic levels, the functional role of microRNAs (miRNA) in MM disease progression and prognosis is yet to be investigated at a personalized level. In earlier studies, microRNAs have been implicated to regulate gene expression and were determined to play crucial roles in the biology of MM by acting as oncogenes or tumor suppressors. Nevertheless, considering the heterogeneity of MM, little is known about the roles of miRNAs in controlling MM disease progression and drug response at an individualized systems level. We collected bone marrow aspirates from MM patients at diagnosis (n=20) and relapse (n=25) after informed consent and following approved protocols in accordance with the Declaration of Helsinki. CD138+ plasma cells were enriched from the bone marrow samples and used for miRNA-sequencing and drug sensitivity and resistance testing (DSRT). The miRNA was prepared from the CD138+ cells and subjected to sequencing using Illumina compatible technologies. DSRT was performed and responses to 83 clinically approved drugs and investigational compounds were measured as drug sensitivity scores (DSS) as described previously (Majumder et al., Oncotarget 2017). The pairwise comparative analysis of miRNA expression and drug responses was performed using Spearman's rank-order correlations, to elucidate significant associations of miRNA expression with drug sensitivity and resistance. Additionally, using DEseq2 the differential miRNA expression was determined for the newly diagnosed and relapse samples to deconvolute the role of miRNAs in MM disease progression. The comparative analysis of the miRNA expression and drug sensitivity scores revealed statistically significant associations between miRNA expression and drug sensitivity measures with the Spearman coefficient (r) ranging from -0.71 to 0.64 (adjusted p-value ≤ 0.05) (Figure 1A). Negative associations were more prevalent, with 40 miRNAs negatively associated with ≥1 drug response from the total of 30 predicted drugs. miR-486, which is known to be an effective biomarker in diagnosis and prognosis of multiple cancer types (Jiang et al., Oncotarget 2018), was found to have significant negative correlation (r= -0.71 to -0.52, p-value ≤ 0.01) with the responses of 14 drugs. Similarly, negative correlation was observed for miR-144 with 12 drugs and miR-584 with 9 drugs. We observed that PI3K/mTOR inhibitors and HDAC inhibitors were common amongst all the significant negative correlations predicted. Specifically, the PI3K/mTOR inhibitors apitosilib, omipalisib and buparlisib were found to be negatively associated with the expression of 18, 14 and 7 miRNAs respectively. These observations can lead to the understanding of miRNA mediated regulation of molecular pathways involved in drug resistance. Differential miRNA expression analysis between newly diagnosed and relapse MM samples revealed the involvement of miRNAs in disease progression. The analysis resulted in total of 31 significant differentially expressed miRNAs with fold change ≥2 and adjusted p-value ≤ 0.1 (Figure 1B). Several miRNAs known to play crucial roles in cancer diagnosis and prognosis were found to be significantly upregulated in the relapse samples. In particular, 25 miRNAs were upregulated, including following miR-17/92 cluster members: miR-18b, miR-20a, miR-92b and miR-106a, which are known to have an oncogenic role in various cancer types (Mogilyansky &amp; Rigoutsos, Cell Death and Differentiation 2013). Interestingly, 12/31 differentially regulated miRNAs were located on chromosome X. Although cytogenetic alteration data predicted that chromosome 1q gain is significantly prominent in the relapse samples (p-value = 0.009), only 3/31 differentially regulated miRNAs were located on chromosome 1. These results demonstrate the role of miRNAs in regulating drug response and disease progression in multiple myeloma. Monitoring miRNA expression profiles in MM patients can facilitate the assessment of treatment outcome and prognosis, and miRNAs could potentially be useful prognostic and treatment biomarkers for MM. Disclosures Silvennoinen: Amgen: Research Funding; Bristol-Myers Squibb (BMS): Research Funding; Takeda: Research Funding; Celgene: Research Funding. Heckman:Celgene: Research Funding; Novartis: Research Funding; Oncopeptides: Research Funding; Orion Pharma: Research Funding.

BCR-ABL1 p190 in CML: A Minor Breakpoint with a Major Impact

Introduction: The oncoprotein Bcr-Abl has two major isoforms, depending on the breakpoint in BCR gene, p190 and p210. While p210 is the hallmark of chronic myeloid leukemia (CML), p190 occurs in the majority of Philadelphia-positive acute lymphoblastic leukemia (Ph+ ALL) patients. p190 occurs as a sole transcript in 1-2% CML patients, associated with distinct features like monocytosis and frequent additional cytogenetic abnormalities (ACA) at diagnosis. It also confers a risk of treatment failure and progression in chronic phase (CP) CML patients. However, the underlying mechanisms are largely unknown. Here we explore the characteristics of p190 and p210 in CML and ALL patients using next generation sequencing, phospho-flowcytometry and high throughput drug testing. Patients and methods: Peripheral blood mononuclear cells (PMNC) were collected at diagnosis from four CP-CML patients harboring p190 isoform from Helsinki University Hospital. Genetic alterations were identified by whole exome sequencing. RNA sequencing was employed to analyze transcriptional profiles of p190 CML (n=3) in contrast to p210 CML patients (n=4). A thorough transcriptional, phosphorylation and drug sensitivity profiling were applied to five p190- and three p210-expressing Ph+ALL patients. Expression alterations were further characterized in two cell line models mimicking BCR-ABL positive leukemia (Ba/F3 and HPCLSK). Phosphorylation profiles were analyzed by flowcytometry and phospho-array (Tyrosine Phosphorylation ProArray, Full Moon Biosystems). For drug sensitivity and resistance testing (DSRT), a custom plate set comprising 75 approved and investigational oncology drugs was used for patient samples and more extensive 528-drugs plates were used for the cell lines. Results: CML patients with p190 had a median age of 72.5 years at the diagnosis (range: 50-80) and all received imatinib as a frontline treatment. Only one patient achieved a fluctuating major molecular response (MMR) by 12 months while the rest of the patients showed primary resistance to treatment and were shifted to a 2nd line TKI, nilotinib (n=2) or proceeded to HSCT (n=1). By exome sequencing we identified 26 variants in p190-CML samples (median per patient=7, range: 2-10), including variants in ASXL1, DNMT3A and KDM4D genes. RNA-sequencing analysis identified 19 and 97 dysregulated genes (Q &amp;lt;0.05) between p190- and p210 in CML and Ph+ ALL cells respectively. In CML, enrichment analysis revealed upregulation of TNF, interferon (IFN), IL1-R and Toll-like receptor (TLR) signaling, TP53-related, cell cycle and apoptosis pathways. Among Ph+ ALL samples, many CML-related genes were upregulated in samples encompassing p210 while IFN-, TP53- and cell cycle-related molecules were upregulated in p190 samples. p190 samples exhibited hyper-phosphorylation of Src kinase compared to p210 samples. DSRT results also revealed increased sensitivity of primary Ph+ ALL-p190 cells to Src-inhibitors (dasatinib and saracatinib), glucocorticoids and MDM2 inhibitors/TP53 activators (SAR405838 and idasanutlin). Regarding cell lines, Ba/F3-p190 showed the upregulation of interferon signaling pathways compared to p210. Src was also hyperphosphorylated in both Ba/F3 and HPCLSK p190 models. In addition to glucocorticoids and Src-inhibitors, compounds blocking the activity of the inhibitors of apoptosis protein (IAP) family were highly effective at reducing the viability of p190 compared to p210 cells in both cell lines. Conclusions: In CML, p190 isoform of BCR-ABL1 is associated with distinct features and should be considered as a high-risk group. Combining clinical, genomic, phosphorylation and drug sensitivity data, we demonstrated that p190 activates specific cancer pathways, notably Src signaling and interferon pathways. Data also suggests that CML patients with p190 could benefit from broad spectrum TKI with Src inhibiting activity or combination of TKI with MDM2- or IAP-inhibitors. Disclosures Heckman: Orion Pharma: Research Funding; Celgene: Research Funding; Novartis: Research Funding; Oncopeptides: Research Funding. Porkka:Celgene: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding. Mustjoki:Novartis: Research Funding; Pfizer: Research Funding; BMS: Honoraria, Research Funding.

Dasatinib Inhibits FLT3/ITD and PTPN11mutated Acute Myeloid Leukemia Cells Overexpressing SRC Tyrosine Kinases

Background: Recent advances in acute myeloid leukemia(AML) targeted therapy improve overall survival. While these targeted therapies can achieve prolonged remissions, most patients will eventually relapseunder therapy. Our recent studies suggest that relapse most often originates from several sub-clones of leukemic stem cells (LSCs), present before therapy initiation, and selected due to several resistance mechanisms. Eradication of these LSCs during treatment induction /remission could thus potentially prevent relapse. The overall goal of the current study was to identify drugs which can be safely administrated to patients at diagnosis and that will target LSCs. Since simultaneously testing multiple drugs in vivo is not feasible, we used an in vitrohigh throughput drug sensitivity assay to identify new targets in primary AML samples. Methods: Drug sensitivity and resistance testing (DSRT) was assessed in vitro (N=46 compounds) on primary AML samples from patients in complete remission (N=29). We performed whole exome sequencing and RNAseq on samples to identify correlations between molecular attributes and in vitro DSRT. Results:Unsupervised hierarchical clustering analysis of in vitro DSRT, measured by IC50, identified a subgroup of primary AML samples sensitive to various tyrosine kinase inhibitors (TKIs). In this subgroup, 52% (9/17) of AML samples displayed sensitivity to dasatinib (defined as a 10-fold decrease in IC50 compared to resistant samples). Dasatinib has broad TKI activity, and is safely administered in the treatment of leukemia. We therefore focused our analysis on predicting AML response to dasatinib, validating our results on the Beat AML cohort. Enrichment analysis of mutational variants in dasatinib-sensitive and resistant primary AML samples identified enrichment of FLT3/ITD (p=0.05) and PTPN11(p=0.05) mutations among dasatinib responders. Samples resistant to dasatinib were enriched with TP53 mutations (p=0.01). No global gene expression changes were observed between dasatinib-sensitive and resistant samples in our cohort, nor in the Beat AML cohort. Following this, we tested the differential expression of specific dasatinib-targeted genes between dasatinib-responding and resistant samples. No significant differences were identified. However, unsupervised hierarchical clustering of dasatinib targeted genes expression in our study and in the Beat AML cohort identified a subgroup of AML samples (enriched in dasatinib responders) that demonstrated overexpression of three SRC family tyrosine kinases:FGR, HCK and LYN as well as PTK6, CSK, GAK and EPHB2. Analysis of the PTPN11 mutant samples revealed that the IC50 for dasatinib in 23 carriers of the mutant PTPN11 was significantly lower compared to the IC50 of PTPN11 wild type samples (p=0.005). LYN was also upregulated (p&amp;lt;0.001) in the mutant samples. We therefore hypothesized that gene expression of dasatinib-targeted genes could be used as a predictive biomarker of dasatinib response among FLT3/ITD carriers. We found that among FLT3/ITD AML carriers in the Beat AML cohort LYN, HCK, CSK and EPHB2 were significantly over-expressed in the dasatinib responding samples (N=27) as compared to the dasatinib resistant samples (N=35). To predict response to dasatinib among FLT3/ITD carriers we used a decision tree classifier based on the expression levels of these four genes. Our prediction model yielded a sensitivity of 74% and specificity of 83% for differentiating dasatinib responders from non-responders with an AUC of 0.84. Based on our findings, we selected FLT3/ITD AML samples and injected them to NSG-SGM3 mice. We found that in a subset of these samples, dasatinib significantly inhibited LSCs engraftment. This subset of FLT3/ITD AML samples expressed higher levels of LYN, HCK,FGR and SRC as compared to the FLT3/ITD samples that were not sensitive to dasatinib therapy in vivo. In summary, we identified a subgroup of AML patients sensitive to dasatinib, based on mutational and expression profiles. Dasatinib has anti-leukemic effects on both blasts and LSCs. Further clinical studies are needed to demonstrate whether selection of tyrosine kinase inhibitors, based on specific biomarkers, could indeed prevent relapse. Disclosures Tavor: Novartis: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; Astellas: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; BMS companies: Membership on an entity's Board of Directors or advisory committees.

Abstract 2945: Clinical implementation of precision systems oncology in the treatment of ovarian cancer based on ex-vivo drug testing and molecular profiling

Abstract Ovarian cancer (OC) is difficult to treat and has a high mortality rate. There is a strong need for new therapies, but OC is often not the main indication for drug development activities. Hence, here we explored systematic drug repurposing based on clues from ex-vivo testing of human OC patient cells. We established patient-derived cancer cell (PDCs) cultures using ascites or tumor tissue from serous OC patients and applied them to ex vivo Drug Sensitivity and Resistance Testing (DSRT) with a panel of 528 approved and investigational oncology drugs. The representativity of the PDCs was confirmed by genomic and phenotypic profiling in comparison to the original tumor sample. Both 2D and 3D culture conditions were applied and compared for their drug responses. 9 PDCs and 11 high-grade serous OC cell lines were examined. High grade serous OC (HGSOC, 6 cases) with TP53 mutation and CCNE1, CCNE1/KRAS or MYC/KRAS amplifications showed highly resistant drug response profiles, which reflects the clinical challenges in treating this OC subtype. Some HGSOC PDCs showed vulnerability to SMAC mimetics, inhibitors of Apoptosis Protein Antagonists, (e.g. birinapant) and to prexasertib, a checkpoint kinase 1 and 2 inhibitor. In contrast, low-grade serous OC (LGSOC, 3 cases) showed subtype-specific sensitivity to several kinase inhibitors, including EGFR/Her2, MEK, mTOR and PI3K inhibitors. In a metastatic LGSOC patient, RNA-sequencing revealed a CLU-NRG1 fusion gene, which creates an autocrine activation loop of the ErbB family of receptor kinases. DSRT assay confirmed a strong sensitivity of the PDCs ex-vivo to dual-kinase inhibitors, including afatinib. Based on these data, the patient has received approved kinase inhibitors, including afatinib monotherapy followed by a combination of herceptin and pertuzumab. This strategy has managed to keep the disease in control for over 3 years as measured by clinical criteria, including imaging and serum CA125 antigen levels. In conclusion, systems precision cancer medicine with PDCs could provide a valuable approach to reveal patient-specific drug efficacies that can be translated to the clinic in real-time. Citation Format: Astrid Murumägi, Daniela Ungureanu, Suleiman Khan, Akira Hirasawa, Mariliina Arjama, Katja Välimäki, Piia Mikkonen, Wilhelmiina Niininen, Ashwini Kumar, Samuli Eldfors, Teijo Pellinen, Vilja Pietiäinen, Andrus Mägi, Riitta Koivisto-Korander, Johanna Tapper, Mikko Loukovaara, Tero Aittokallio, Ralf Bützow, Olli Kallioniemi. Clinical implementation of precision systems oncology in the treatment of ovarian cancer based on ex-vivo drug testing and molecular profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2945.

Abstract 2153: Ex vivo drug sensitivity testing of primary cells for precision cancer medicine

Abstract Introduction/Purpose: Cancer therapy is increasingly moving towards individualized care and therapy, but there are still gaps between what is known and described on the molecular level about cancers and what is applied in the clinic. In an attempt to bridge the knowledge gap, we at the Institute for Molecular Medicine Finland (FIMM) have set up an Individualized Systems Medicine program that integrates clinical information, molecular profiling and functional information about individual patients’ cancers (Pemovska et al, Cancer Discov, 2013). Central to this program is the Drug Sensitivity and Resistance Testing (DSRT) where we functionally profile the responses of primary cancer cells to a comprehensive clinical oncology and signal transduction inhibitor drug collection of 528 compounds. Methods: Acoustic dispensing platforms are integral to the success of this profiling activity. We have to date produced approximately 3000 drug sets as dose response assay ready plates. The acoustic dispensing allows for making pre-drugged single drug plate sets and/or drug combination plates within hours after sampling of the cells. The plates are also readily sent to researchers anywhere in the world for running comparable assays at other sites. The drugging reproducibility is excellent generating results with correlations of 0.98 or higher in replicate assays. We have developed in-house software solutions to aid these processes: a script for quick creation of transfer list for combination plates and automated analysis pipelines with web-based software interfaces to enable the screening biologists to analyze the screening results effectively. Results: The results of these assays are used to explore and understand cancer biology in terms of druggability, functional heterogeneity and mechanism of drug response and resistance. The profiling data can be used to stratify and position the relevance of specific drugs in different diseases and has been used to identify novel clinically relevant activities of existing and investigational drugs (see e.g. Pemovska et al, Nature, 2015). This information is further utilized to establish hypotheses on drug combinations selectively targeting individual cancers and their predictive biomarkers, which can be explored in the clinic by our clinical collaborators to guide the treatment of the individual patient. Conclusions: In summary, we describe our platform for a functional drug sensitivity testing within our individualized cancer systems medicine program, which generates consistent biological and clinically relevant data. Citation Format: Sergey G. Kuznetsov, Alexander Ianevski, Evgeny Kulessky, Karoliina Laamanen, Elina Lehtinen, Maria Nurmi, Swapnil Potdar, Jani Saarela, Katja Suomi, Laura Turunen, Krister Wennerberg, Päivi Tammela. Ex vivo drug sensitivity testing of primary cells for precision cancer medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2153.

RUNX1 Mutations Identify an Entity of Blast Phase Chronic Myeloid Leukemia (BP-CML) Patients with Distinct Phenotype, Transcriptional Profile and Drug Vulnerabilities

Introduction: Genetic alterations of RUNX1 gene (mutations and fusion genes) are common in hematological malignancies including AML, ALL, MDS, and MPN. Mutations of RUNX1 occur in 10% of newly diagnosed AML patients and associate with inferior prognosis. Similarly, point mutations in RUNX1 are frequent in BP-CML, but little is known about their role in BP patients.Methods: Bone marrow samples from four BP patients with RUNX1 mutations were collected from Helsinki University Hospital, and samples from four other BP patients without RUNX1 mutations were used as controls. Samples were analyzed by exome and RNA sequencing. For drug sensitivity and resistance testing (DSRT), a high-throughput platform comprising 195 approved and investigational oncology drugs was used. To further study the role of RUNX1 mutations in BP-CML, Baf3/BCR-ABL1+ RUNX1-/- knock-out and RUNX1-/mut heterozygous deletion mutant models were created using CRISPR-CAS9 technology. The effects of the mutations in the cell line models were characterized by flow cytometry, RNA sequencing, and DSRT analyses.Results: The median age of RUNX1-mutated BP patients was 47 years (range 36-56 years) and included 3 patients with myeloid-BP and 1 with lymphoid-BP phenotype. RUNX1-mutated patients tended to have higher mutational load and co-occurrence with mutations to the BCOR, EZH2 and PHF6 genes in agreement with the mutational profile of RUNX1-mutated AML. Signature analysis of RUNX1-mutated BP patients showed dominance of signature 1 (age-related), signature 6, and 15 (DNA mismatch repair) correlating with the control group signature profiles. In addition, the RUNX1 mutated group showed specific enrichment of signature 9, related to somatic hypermutation and AID/RAG axis activity, that was not observed in the control group. RNA sequencing revealed that 388 genes were significantly differentially expressed between RUNX1-mutated and control BP patients (139 upregulated, 249 downregulated). Interestingly, the antigen processing and presentation pathway was among the top upregulated pathways in RUNX1 mutated patients with overexpression of CIITA and HLA-DR genes. Stem cell differentiation, complement, and hemostasis pathways were enriched in the downregulated gene set. Lymphoid-specific transcription factors (DNTT, PAX5 and VPERB1) in addition to CD19 and CD25 were significantly upregulated in the RUNX1-mutated group. DSRT data showed greater sensitivity to glucocorticoids, MEK-, mTOR- and VEGFR-inhibitors in RUNX1-mutated patients compared to the control group. Data from RUNX1-/- and RUNX1-/mut cell line models confirmed the induced expression of CD19 surface marker with RUNX1 mutation. DSRT results showed increased activity of MEK inhibitor and mTOR inhibitors in RUNX1-/- knock-out and RUNX1-/mut cell lines.Conclusions:RUNX1 mutations in BP-CML patients associate with distinct phenotypic and transcriptional features. DSRT results together with transcriptional data support the potential benefits of glucocorticoids and MEK and mTOR inhibitors as well as immunotherapy targeting CD19 and CD25 in this group of BP-CML patients. DisclosuresKankainen:Medix Biochemica: Consultancy. Heckman:Celgene: Research Funding; Orion Pharma: Research Funding; Novartis: Research Funding. Porkka:Novartis: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Mustjoki:Bristol-Myers Squibb: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Ariad: Research Funding; Celgene: Honoraria.