Combined Targeting of BET Family Proteins and BCL2 Is Synergistic in Acute Myeloid Leukemia Cells Overexpressing S100A8 and S100A9

Abstract Background The 5-year survival rate for acute myeloid leukemia (AML) remains poor with most patients succumbing to relapse or refractory disease. Recently, the BCL2 specific inhibitor venetoclax has shown promising anti-leukemia activity in high-risk AML patients. Most patients, however, ultimately develop resistance to monotherapy and novel combination treatments with venetoclax are needed for patients with no other therapy options available. In this study we identified high expression of calcium binding protein S100A8/S100A9 to be associated with venetoclax resistance and looked for drug combinations to overcome the resistance in AML patient samples overexpressing S100A8 and S100A9. Methods Gene expression was assessed by RNA sequencing of AML patient samples and validated by qPCR. Gene enrichment analysis was performed on differentially expressed genes between venetoclax highly sensitive (n=3) and resistant (n=4) samples. Sensitivity of AML patient derived mononuclear cells was assessed to 304 different small molecule inhibitors by measuring cell viability after 72 h incubation with 5 different concentrations (1-10,000 nM) using the CellTiter-Glo (CTG) assay. A drug sensitivity score (DSS) was calculated based on a modified area under the dose response curve calculation. Drug combination studies were performed using AML cell lines resistant to venetoclax and confirmed with primary patient cells (n=15). Data of the drug combination studies were analyzed with the Zero Interaction Potency (ZIP) model by considering a dose-response matrix where two drugs are tested at 8 concentrations in a serially diluted manner. Statistical dependence between gene expression and drug sensitivity data was assessed by Pearson's correlation coefficient modelling. Results Venetoclax resistant AML patient samples were found to overexpress genes related to immune responses including inflammatory related proteins S100A8 and S100A9. The expression of S100A8 and S100A9 was upregulated in a sub-group of AML patients with somatic mutations in DNA methylation genes IDH2 and TET2 and chromatin modifier ASXL1. Functional studies with AML cell lines validated high expression of the S100 proteins in cells insensitive to venetoclax (NOMO-1, SKM-1 and SHI-1) whereas sensitive cell lines (MOLM-13, Kasumi-1 and ML-2) did not express the proteins. Integrated analysis of S100A8 and S100A9 expression and ex vivo drug sensitivity data indicated positive correlation of S100 expression with sensitivity to BET inhibitor (birabresib), PI3K inhibitor (TG100-115) and MEK1/2 inhibitor (AZD8330). In contrast, sensitivity to venetoclax and the FLT3 inhibitor quizartinib negatively correlated with S100 gene expression. Subsequently, we combined positively correlating drugs with venetoclax and tested the efficacy of these combinations in AML cell lines and patient samples. From the drug combination studies we found that BET inhibitor birabresib was able to overcome resistance to venetoclax treatment. The BCL2/BET inhibitor combination was synergistic in venetoclax resistant cell lines NOMO-1 and SKM-1, which express high-levels of S100A8 and S100A9 (Figure 1A). Efficacy of the combination on primary AML patient samples correlated with the expression level of the S100 genes. Nine of 11 high expression samples were sensitive to the venetoclax/birabresib combination (Figure 1B-C), whereas no synergy was observed in 3 of 4 samples with a low level of S100 expression. Conclusions The calcium binding proteins S100A8 and S100A9 are abundant in myeloid cells and are associated with poor prognosis in AML (Edgeworth et al, J Biol Chem. 1991; Nicolas et al, Leukemia 2011). From ex vivo and in vitro analyses of AML, we found that high expression of S100A8 and S100A9 is highly correlative with resistance to the BCL2 inhibitor venetoclax. In contrast, high S100A8 and S100A9 expression correlates with sensitivity to BET inhibitor birabresib. Interestingly, our studies showed that these two drugs act synergistically in venetoclax resistant AML cell lines and AML patient samples and may be a beneficial novel combination that should be further confirmed in preclinical and clinical investigations. Disclosures Porkka: Celgene: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Heckman:Orion Pharma: Research Funding; Novartis: Research Funding; Celgene: Research Funding.

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Scattome: A Single-Cell Analysis of Targeted Transcriptome Program to Predict Drug Sensitivity of Single Cells within Human Myeloma Tumors

Blood ◽

10.1182/blood.v126.23.4249.4249 ◽

2015 ◽

Vol 126 (23) ◽

pp. 4249-4249

Author(s):

Amit Kumar Mitra ◽

Ujjal Mukherjee ◽

Taylor Harding ◽

Holly Stessman ◽

Ying Li ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cell Lines ◽

Research Funding ◽

Drug Response ◽

Drug Sensitivity ◽

Single Cell Analysis ◽

Single Cells ◽

Pcr Analysis ◽

Cell Analysis

Abstract Multiple myeloma (MM) is characterized by significant genetic diversity at subclonal levels that likely plays a defining role in the heterogeneity of tumor progression, clinical aggressiveness and drug sensitivity. Such heterogeneity is a driving factor in the evolution of MM, from founder clones through outgrowth of subclonal fractions. DNA Sequencing studies on MM samples have indeed demonstrated such heterogeneity in subclonal architecture at diagnosis based on recurrent mutations in pathologically relevant genes that may ultimately to lead to relapse. However, no study so far has reported a predictive gene expression signature that can identify, distinguish and quantify drug sensitive and drug-resistant subpopulations within a bulk population of myeloma cells. In recent years, our laboratory has successfully developed a gene expression profile (GEP)-based signature that could not only distinguish drug response of MM cell lines, but also was effective in stratifying patient outcomes when applied to GEP profiles from MM clinical trials using proteasome inhibitors (PI) as chemotherapeutic agents. Further, we noted myeloma cell lines that responded to the drug often contained residual sub-population of cells that did not respond, and likely were selectively propagated during drug treatment in vitro, and in patients. In this study, we performed targeted qRT-PCR analysis of single cells using a gene panel that included PI sensitivity genes and gene signatures that could discriminate between low and high-risk myeloma followed by intensive bioinformatics and statistical analysis for the classification and prediction of PI response in individual cells within bulk multiple myeloma tumors. Fluidigm's C1 Single-Cell Auto Prep System was used to perform automated single-cell capture, processing and cDNA synthesis on 576 pre-treatment cells from 12 cell lines representing a wide range of PI-sensitivity and 370 cells from 7 patient samples undergoing PI treatment followed by targeted gene expression profiling of single cells using automated, high-throughput on-chip qRT-PCR analysis using 96.96 Dynamic Array IFCs on the BioMark HD System. Probability of resistance for each individual cell was predicted using a pipeline that employed the machine learning methods Random Forest, Support Vector Machine (radial and sigmoidal), LASSO and kNN (k Nearest Neighbor) for making single-cell GEP data-driven predictions/ decisions. The weighted probabilities from each of the algorithms were used to quantify resistance of each individual cell and plotted using Ensemble forecasting algorithm. Using our drug response GEP signature at the single cell level, we could successfully identify distinct subpopulations of tumor cells that were predicted to be sensitive or resistant to PIs. Subsequently, we developed a R Statistical analysis package (http://cran.r-project.org), SCATTome (Single Cell Analysis of Targeted Transcriptome), that can restructure data obtained from Fluidigm qPCR analysis run, filter missing data, perform scaling of filtered data, build classification models and successfully predict drug response of individual cells and classify each cell's probability of response based on the targeted transcriptome. We will present the program output as graphical displays of single cell response probabilities. This package provides a novel classification method that has the potential to predict subclonal response to a variety of therapeutic agents. Disclosures Kumar: Skyline: Consultancy, Honoraria; BMS: Consultancy; Onyx: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Novartis: Research Funding; Takeda: Consultancy, Research Funding; Celgene: Consultancy, Research Funding.

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

Blood ◽

10.1182/blood-2021-147191 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 1194-1194

Author(s):

Philipp Sergeev ◽

Sadiksha Adhikari ◽

Juho J. Miettinen ◽

Maiju-Emilia Huppunen ◽

Minna Suvela ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Single Cell ◽

Research Funding ◽

Drug Sensitivity ◽

Ex Vivo ◽

Cell Populations ◽

Drug Efflux ◽

Single Cell Rna Sequencing ◽

Differential Gene

Abstract Introduction Melphalan 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. Methods Bone 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. Results DSRT 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. Conclusion In 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. Figure 1 Figure 1. Disclosures Slipicevic: 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.

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AML Specific Targeted Drugs Identified By Drug Sensitivity and Resistance Testing: Comparison of Ex Vivo Patient Cells with in Vitro Cell Lines

Blood ◽

10.1182/blood.v124.21.2163.2163 ◽

2014 ◽

Vol 124 (21) ◽

pp. 2163-2163

Author(s):

Disha Malani ◽

Astrid Murumägi ◽

Bhagwan Yadav ◽

Tea Pemovska ◽

John Patrick Mpindi ◽

...

Keyword(s):

Cell Lines ◽

Cancer Cell ◽

Myeloid Leukemia ◽

Research Funding ◽

Drug Sensitivity ◽

Ex Vivo ◽

Cancer Cell Lines ◽

Resistance Testing ◽

Targeted Drugs ◽

Drug Responses

Abstract Introduction Many drug discovery efforts and pharmacogenomic studies are based on testing established cancer cell lines for their sensitivity to a given drug or a panel of drugs. This approach has been criticized due to high selectivity and fast proliferation rate of cancer cell lines. To explore new therapeutic avenues for acute myeloid leukemia (AML) and to compare experimental model systems, we applied high-throughput Drug Sensitivity and Resistance Testing (DSRT) platform with 305 approved and investigational drugs for 28 established AML cell lines and compared their drug responses with our earlier study of 28 ex vivo AML patient samples (Pemovska et al., 2013). We then correlated drug sensitivities with genomic and molecular profiles of the samples. Methods DSRT was carried out with 305 clinical, emerging and experimental drugs and small molecule chemical inhibitors. The drugs were tested at five different concentrations over a 10,000-fold concentration range. Cell viability was measured after 72 hours using Cell Titre Glow assay. IC50 values were calculated with Dotmatics software and drug sensitivity scores (DSS, a modified area under the curve metric) were derived for each drug (Yadav et al., 2014). Nimblegen's SeqCap EZ Designs Comprehensive Cancer Design kit was used to identify mutations from 578 oncogenes in cell lines. Results The 28 established AML cell lines were in general more sensitive to the drugs as compared to the 28 ex vivo patient samples, with some important exceptions. Sensitivity towards many targeted drugs was observed in both AML cell lines and in patient samples. These included inhibitors of MEK (e.g. trametinib in 56% of cell lines and 36% of ex vivo samples), mTOR (e.g. temsirolimus in 42% and 32%) and FLT3 (quizartinib in 28% and 18%). Overall, drug responses between cell lines and ex vivo patient cells in AML showed an overall correlation coefficient of r=0.81. BCL2 inhibitors (venetoclax and navitoclax) showed more sensitivity in ex vivo patient cells than in AML cancer cell lines, whereas responses to anti-mitotic agents (docetaxel, camptothecin, vincristine) showed stronger responses in cell lines (Figure). Only 7% of AML cell lines exhibited responses to a broad-spectrum tyrosine kinase inhibitor dasatinib, in contrast to 36% patient samples. AML cell lines that carried FLT3 mutations showed high sensitivity to FLT3 inhibitors. Similarly, cell lines harbouring mutations in RAS or RAF were strongly sensitive to MEK inhibitors. MEK and FLT3 inhibitor responses were mutually exclusive, indicating alternative pathway dependencies in cell lines. However, these pharmacogenomics correlations were not as clearly seen in the clinical samples. Summary These data revealed a few important differences as well as many similarities between established AML cell lines and primary AML patient samples in terms of their response to a panel of cancer drugs. The hope is that patient-derived primary cells in ex vivo testing predict clinical response better as compared to the established cancer cell lines, which indeed seem to overestimate the likelihood of responses to many drugs. On the other hand, cancer cell line studies may also underestimate the potential of dasatinib and BCL2 inhibitors as emerging AML therapeutics. References 1. Pemovska T, Kontro M, Yadav B, Edgren H, Eldfors S, Szwajda A, et al. Individualized systems medicine strategy to tailor treatments for patients with chemorefractory acute myeloid leukemia. Cancer Discovery. 2013 Dec;3(12):1416-29 2. Yadav B, Pemovska T, Szwajda A, Kulesskiy E, Kontro M, Karjalainen R, et al. Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Scientific reports. 2014;4:5193. Figure: Correlation of average drug responses (n=305) between 28 AML cell lines and 28 AML ex vivo patient samples Figure:. Correlation of average drug responses (n=305) between 28 AML cell lines and 28 AML ex vivo patient samples Disclosures Heckman: Celgene: Research Funding. Porkka:BMS: Honoraria; BMS: Research Funding; Novartis: Honoraria; Novartis: Research Funding; Pfizer: Research Funding. Kallioniemi:Medisapiens: Consultancy, Membership on an entity's Board of Directors or advisory committees.

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A Profound Biological Difference of Chronic and Blast Phase Chronic Myeloid Leukemia in Ex Vivo Drug Responses

Blood ◽

10.1182/blood.v124.21.3139.3139 ◽

2014 ◽

Vol 124 (21) ◽

pp. 3139-3139

Author(s):

Paavo Pietarinen ◽

Tea Pemovska ◽

Emma I Andersson ◽

Perttu Koskenvesa ◽

Mika Kontro ◽

...

Keyword(s):

Cell Line ◽

Cell Lines ◽

Myeloid Leukemia ◽

Research Funding ◽

Drug Sensitivity ◽

Ex Vivo ◽

Primary Cells ◽

Newly Diagnosed ◽

Biological Difference ◽

Resistant Patient

Abstract BACKGROUND Most patients with chronic phase (CP) chronic myeloid leukemia (CML) are successfully treated with tyrosine kinase inhibitors (TKIs) targeting ABL1. Despite the good results, TKI treatment rarely results in cure, and some patients relapse and progress to advanced phases of CML. Accelerated phase and blast crisis (BC) have remained a therapy challenge. We set out to identify novel candidate drugs for chronic and advanced phase CML by using an unbiased high-throughput drug testing platform and utilizing both primary patient cells (CP and BC) and cell lines. METHODS CML BC cell lines used: K562 (erythroleukemic), MOLM-1 (megakaryocytic) and EM-2 (myeloid). Primary bone marrow (BM) and peripheral blood (PB) samples were derived from 3 CML patients with BC, two of which were TKI-resistant. Patient 1 had developed resistance to imatinib and nilotinib due to an E274K mutation in ABL1 kinase domain, whereas patient 2 was resistant to imatinib, nilotinib, and dasatinib due to a T315I mutation. In addition to BC patients, samples from 23 newly diagnosed CML CP patients were screened. BM cells from 4 healthy individuals were used as controls. Functional profiling of drug responses was performed with a high-throughput drug sensitivity and resistance testing (DSRT) platform comprising 306 anti-cancer agents. Cells were dispensed to pre-drugged 384-well plates and incubated for 72 h. Cell viability was measured using a luminescence cell viability assay (CellTiter-Glo, Promega). A Drug Sensitivity Score (DSS) was calculated for each drug using normalized dose response curve values. The drug sensitivities of the primary cells were further normalized against the median values from healthy controls, resulting in leukemia-specific sensitivity scores (sDSS). RESULTS Drug sensitivities of CML cell lines correlated closely (EM-2 vs. K-562, rS=0.89; EM-2 vs. MOLM-1, rS=0.82; K-562 vs. MOLM-1, rS=0.78; p<0.0001 for all correlations). Similarly, patient samples had good correlation with cell line samples (rS=0.82 based on median values; p<0.0001). The cell lines were highly sensitive to ABL1-targeted TKIs, with the exception of the MOLM-1, which showed only modest sensitivity (Figure). The clinically TKI-resistant patient samples were also resistant to BCR-ABL1 inhibitors ex vivo (e.g. T315I sensitive only to ponatinib), further validating the DSRT assay data. Other drugs that exhibited high DSS in the CML cell lines and high sDSS in the BC patient samples included mTORC1/2 inhibitors (e.g. AZD8055, AZD2014, INK128), HSP90 inhibitors (e.g. NVP-AUY922, BIIB021) and a NAMPT inhibitor daporinad. Remarkably, the DSRT results from newly diagnosed CML CP differed clearly from those derived from the cell line and CML BC samples. In the clustering analysis, CML BC and cell line samples clustered together, whereas CML CP samples formed a separate group (Figure). The leukemia-specific scores were generally much lower in CML CP samples, which made identifying novel candidate compounds challenging. Most surprisingly the responses to TKIs were practically nonexistent in CML CP samples. CP TKI insensitivity was further assessed with primary cells sorted in CD34pos and CD34neg fractions. Preliminary results from two patients suggested that CD34pos cells were more sensitive to TKIs when compared to CD34neg or whole mononuclear fraction. CONCLUSIONS DSRT is a powerful platform for identifying novel candidate molecules for CML BC patients. Our results indicate that mTORC1/2 inhibitors (such as AZD8055, or AZD2014), HSP90 (such as NVP-AUY922/luminespib) and NAMPT inhibitors in particular warrant further clinical evaluation. TKI-insensitivity of CML CP samples suggests that the survival of mature myeloid cells in vitro is not BCR-ABL1 dependent and reflects a clear biological difference between CP and BC patient cells. Figure 1 Figure 1. Disclosures Kallioniemi: Medisapiens: Consultancy, Membership on an entity's Board of Directors or advisory committees. Mustjoki:Bristol-Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Porkka:Bristol-Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria, Research Funding.

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Targeting BCL-2, BCL-XL, BCL-W and MDM2 in B-Cell Acute Lymphoblastic Leukemia Is Highly Effective Ex Vivo

Blood ◽

10.1182/blood-2018-99-110610 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 3975-3975

Author(s):

Helena Hohtari ◽

Shady Awad ◽

Olli Dufva ◽

Swapnil Potdar ◽

Caroline A Heckman ◽

...

Keyword(s):

Gene Expression ◽

Acute Lymphoblastic Leukemia ◽

Tyrosine Kinase ◽

B Cell ◽

Research Funding ◽

Drug Sensitivity ◽

Kinase Inhibitors ◽

Ex Vivo ◽

Lymphoblastic Leukemia ◽

Highly Sensitive

Abstract Despite the advances in the treatment of acute lymphoblastic leukemia (ALL), a major fraction of adult patients still succumb to leukemia- or treatment-related events. In particular, the outcome of elderly ALL patients remains dismal. Our aim was to discover new or repurposed drugs for B-cell ALL in a clinically relevant ex vivo drug sensitivity testing platform. We analyzed 19 primary B-ALL samples using a well-established drug sensitivity and resistance testing platform and a drug panel including 65 drugs in five different concentrations. The main drug classes were glucocorticoids, MDM2 antagonists, and inhibitors of BCR-ABL1, VEGFR, BCL-2, BCL-XL, BET, MEK, JAK, Aurora kinase, PI3K, MTOR, IGF1R, ERK, STAT3, STAT5, HSP90 and NAMPT proteins. The samples were viably frozen bone marrow (BM) mononuclear cells collected at diagnosis. The cohort included both Philadelphia-positive (Ph+) (n=10) and Ph-negative (Ph-) (n=9) patients with a median age of 43 years (range 22-68). Cell viability (CellTiter-Glo) was measured after plating and after a three-day incubation with the drugs. A drug sensitivity score (DSS) was calculated from the viability readouts, which takes into account the area under the dose response curve, measuring both drug efficacy and potency. DSS values >10 are considered effective and >20 highly effective. As an overall view of drug sensitivity, a heatmap and dendrograms from DSS values are shown in Figure 1A. As expected, most patients were sensitive to glucocorticoids and tyrosine kinase inhibitors (TKIs) showed efficacy in Ph+ ALL. In addition, two Ph-negative patients were sensitive to TKIs, suggesting a Philadelphia-like disease. Drugs that showed pan-ALL efficacy included BCL-2 family inhibitors, idasanutlin (MDM2 inhibitor), luminespib (HSP90 inhibitor), daporinad (NMPRT inhibitor) and plicamycin (antineoplastic antibiotic). For the other drugs, only individual patients showed sensitivity, in line with the diverse molecular background of ALL. Strikingly, 17/19 (89%) of patients in our cohort were highly sensitive (DSS>20) to navitoclax (a BCL-2, BCL-XL and BCL-W inhibitor), whereas the BCL-2-specific inhibitor venetoclax was effective only in a distinct subset of patients (Figure 1B). 6/19 (32%) of patients were highly sensitive (DSS>20) to venetoclax and represented all risk classes based on age, white blood cell counts and karyotype, but interestingly, all were Ph-negative. Overall, response to venetoclax correlated with response to navitoclax (Spearman, r=0.85; P<0.0001). To examine differential gene expression of anti-apoptotic proteins between Ph+ and Ph- patients, we analyzed microarray gene expression data from ArrayExpress public database (www.ebi.ac.uk/arrayexpress, E-MTAB-5035). The analyzed cohort included 96 Ph- and 41 Ph+ adult B-ALL patients. Ph-negative samples were characterized with higher BCL-2 expression, whereas Ph-positive samples showed higher BCL-W expression and a trend to higher BCL-XL expression (Figure 1C). Thus, lack of venetoclax efficacy ex vivo in Ph-positive ALL indicated dependence on BCL-W and BCL-XL, as also reflected in the gene expression analyses. Inhibitors of BCL-2, such as navitoclax and venetoclax, potently induce apoptosis in a variety of cancer cells. Both inhibitors showed promising efficacy in our B-ALL samples. Dose-limiting thrombocytopenia has limited the use of navitoclax in solid tumors. However, in our assay navitoclax showed more uniform potency, particularly in Ph+ samples suggesting a rational combination with tyrosine kinase inhibitors. Similar to conventional cytotoxic agents used in ALL, a therapeutic window may exist for safe use of navitoclax in acute leukemia. In conclusion, targeting the multidomain anti-apoptotic proteins (BCL-2, BCL-XL, BCL-W, MCL-1) and TP53 with MDM2, possibly in combination, is a promising strategy for improving outcome of adult B-ALL. Figure 1. Figure 1. Disclosures Hohtari: Incyte: Research Funding. Heckman:Novartis: Research Funding; Celgene: Research Funding; Orion Pharma: Research Funding. Wennerberg:Novartis: Research Funding. Mustjoki:Ariad: Research Funding; Pfizer: Honoraria, Research Funding; Celgene: Honoraria; Novartis: Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria, Research Funding. Porkka:Celgene: Honoraria, Research Funding; Novartis: Honoraria, Research Funding.

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Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines

BMC Genomics ◽

10.1186/s12864-021-07581-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Yuanyuan Li ◽

David M. Umbach ◽

Juno M. Krahn ◽

Igor Shats ◽

Xiaoling Li ◽

...

Keyword(s):

Gene Expression ◽

Cell Lines ◽

Cancer Cell ◽

Predictive Models ◽

Drug Sensitivity ◽

Human Cancer ◽

Cancer Cell Lines ◽

The Cancer Genome Atlas ◽

Differential Sensitivity ◽

Tumor Type

Abstract Background Human cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients’ care. Tremendous progress has been made. Results In this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC50) from cancer cell lines. We identified many known drug-gene interactions and uncovered several potentially novel drug-gene associations. Importantly, we further applied these predictive models to ~ 17,000 bulk RNA-seq samples from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to predict drug sensitivity for both normal and tumor tissues. We created a web site for users to visualize and download our predicted data (https://manticore.niehs.nih.gov/cancerRxTissue). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug. Conclusions We demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our prediction could have relevance for preclinical drug testing and in phase I clinical design.

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Myeloma Drug Sensitivity Testing to Optimize Retreatment with Anti-CD38 Monoclonal Antibodies in Daratumumab-Refractory Patients

Blood ◽

10.1182/blood-2020-137819 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 37-38

Author(s):

Olivia Perez De Acha ◽

Beau M Idler ◽

Zachary J Walker ◽

Peter A Forsberg ◽

Tomer M Mark ◽

...

Keyword(s):

Research Funding ◽

Drug Sensitivity ◽

Drug Class ◽

Ex Vivo ◽

Partner Choice ◽

Sensitivity Testing ◽

Drug Sensitivity Testing ◽

Cd38 Expression ◽

Cell Subpopulations ◽

20 Nm

Background: Daratumumab-refractory multiple myeloma (MM) patients have limited treatment options and a dire prognosis. Daratumumab (Dara) targets the overexpressed myeloma antigen CD38, and its mechanism of resistance has been partially associated with downregulation of CD38 expression (Nijhof et al. Blood, 2016). Dara is a key agent in the relapsed setting and is being integrated into upfront treatment. In addition, isatuximab (Isa) has become the second FDA-approved anti-CD38 monoclonal antibody (mAb). Thus, when and how patients can be retreated with this important drug-class has become a critical question. We developed a platform for measuring drug efficacy ex vivo, including mAbs, termed Myeloma Drug Sensitivity Testing (My-DST) (Walker et al. Blood Advances, 2020). Here, we addressed whether My-DST could determine if retreatment with the anti-CD38 mAbs Dara and Isa can be effective in patients that had previously developed Dara-resistance. Methods: Bone marrow aspirates were obtained from the hematologic tissue bank (HTB) from patients at the University of Colorado Blood Cancer Program after informed consent and IRB approval. Mononuclear cells (MNCs) were isolated by Ficoll density gradient centrifugation, cells incubated for 48 h with 20 nM Dara, Isa and untreated controls in triplicate wells. Flow cytometry was performed on a BD FACSCelesta. To identify the viable MM population, samples were stained with fluorophore-conjugated mAbs to CD38, CD138, CD45, CD19, CD56 and CD46. Stained samples were washed and resuspended in Live/Dead dye Near-IR. Results were analyzed through FlowJo and Graphpad Prism software. Results: My-DST for Dara (20 nM) cytotoxicity has been performed in 67 patients, 52 of which were Dara-naïve and 15 of which were clinically Dara-refractory. Of Dara-naïve patients, 38/52 (73%) showed >20% reduction in viable MM cells whereas only 3/15 (20%) of the Dara-refractory patients showed >20% decrease (Fig 1A). Provocatively, the patients that showed potential "re-sensitization" had been off Dara for >12 months, and there was a significant correlation between response to Dara and months off treatment (r= -0.5096, p= 0.0457, Fig 1B). Furthermore, we evaluated two timepoints in one of the responders (HTB1749), and the later sample (HTB1749.3) showed a deeper response, further supporting the correlation. Surprisingly, in Dara-refractory patients the median CD38 expression was not significantly different between the three ex vivo responders and those who did not respond (p = 0.439, Fig 1C). Isa was tested in seven of the Dara-refractory patient samples, showing >20% decrease in viable MM cells in five, of which three did not respond ex vivo to Dara (Fig 1D). Interestingly, the Isa responders were only off Dara treatment for an average of 7.5 months. We further investigated HTB1059.3 and HTB1749.2, which responded to Dara, and found that these patients had multiple distinct MM cell subpopulations with different levels of CD38 expression, with the subpopulation with higher CD38 MFI accounting for the decreased viability (Fig 1E-F). When we compared HTB1059.3 with a prior sample available, these differential populations were not present before Dara treatment (data not shown), indicating these subpopulations evolved after treatment. Likewise, the same phenomenon of subpopulations with differential CD38 expression was observed in the Dara-refractory patients who responded to Isa (Fig 1G-H). Conclusion: These data support the possibility of retreatment with anti-CD38 mAbs in patients who once became refractory to Dara. Although our findings need to be confirmed with additional samples, they suggest that Isa may have efficacy earlier in this setting, supporting an approach to switch agents when retreating with this drug class. Anti-CD38 mAb sensitivity in the Dara-refractory population appears to be heavily influenced by the different CD38 expression levels on the heterogeneous MM cell subpopulations that emerge when a patient is off Dara for a period of time. Furthermore, My-DST with anti-CD38 mAbs may be applied to help guide the treatment approach in this population. Still, the presence of CD38-low subpopulations in these patients may represent resistant cells that shorten the remission times on retreatment. Thus, the combination drug partner choice will likely be critical to successfully optimizing depth and duration of response in Dara-refractory patients. Figure 1 Disclosures Forsberg: Genentech, Inc., Sanofi, Karyopharm, Abbvie: Research Funding; Celgene: Speakers Bureau. Mark:Bristol-Myers Squibb: Research Funding; Janssen: Research Funding; Celgene: Consultancy; Amgen: Consultancy; Kayopharm: Consultancy; Janssen: Consultancy; Sanofi: Consultancy; Takeda: Consultancy. Sherbenou:Oncopeptides Inc.: Research Funding.

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Abstract 5422: Drug combination studies in a panel of 30 cell lines established from patient-derived tumor xenograft

10.1158/1538-7445.am10-5422 ◽

2010 ◽

Author(s):

Gerhard Kelter ◽

Victoria Smith ◽

Thomas Metz ◽

Heinz-Herbert Fiebig ◽

Thomas Beckers

Keyword(s):

Cell Lines ◽

Drug Combination ◽

Tumor Xenograft ◽

Combination Studies

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Development of a Cancer Pharmacopeia-Wide Ex-Vivo Drug Sensitivity and Resistance Testing (DSRT) Platform: Identification of MEK and mTOR As Patient-Specific Molecular Drivers of Adult AML and Potent Therapeutic Combinations with Dasatinib

Blood ◽

10.1182/blood.v118.21.2487.2487 ◽

2011 ◽

Vol 118 (21) ◽

pp. 2487-2487

Author(s):

Mika Kontro ◽

Caroline Heckman ◽

Evgeny Kulesskiy ◽

Tea Pemovska ◽

Maxim Bespalov ◽

...

Keyword(s):

Bone Marrow ◽

Research Funding ◽

Drug Response ◽

Drug Sensitivity ◽

Ex Vivo ◽

Molecular Profiling ◽

Resistance Testing ◽

Patient Specific ◽

Targeted Drugs ◽

Response Profiles

Abstract Abstract 2487 Introduction: The molecular drivers of adult AML as well as the determinants of drug response are poorly understood. While AML genomes have recently been sequenced, many cases do not harbor druggable mutations. Treatment options are particularly limited for relapsed and refractory AML. Due to the molecular heterogeneity of the disease, optimal therapy would likely consist of individualized combinations of targeted and non-targeted drugs, which poses significant challenges for the conventional paradigm of clinical drug testing. In order to better understand the molecular driver signals, identify individual variability of drug response, and to discover clinically actionable therapeutic combinations and future opportunities with emerging drugs, we established a diagnostic ex-vivo drug sensitivity and resistance testing (DSRT) platform for adult AML covering the entire cancer pharmacopeia as well as many emerging anti-cancer compounds. Methods: DSRT was implemented for primary cells from adult AML patients, focusing on relapsed and refractory cases. Fresh mononuclear cells from bone marrow aspirates (>50% blast count) were screened in a robotic high-throughput screening system using 384-well plates. The primary screening panel consisted of a comprehensive collection of FDA/EMA-approved small molecule and conventional cytotoxic drugs (n=120), as well as emerging, investigational and pre-clinical oncology compounds (currently n=90), such as major kinase (e.g. RTKs, checkpoint and mitotic kinases, Raf, MEK, JAKs, mTOR, PI3K), and non-kinase inhibitors (e.g. HSP, Bcl, activin, HDAC, PARP, Hh). The drugs are tested over a 10,000-fold concentration range resulting in a dose-response curve for each compound and with combinations of effective drugs explored in follow-up screens. The same samples also undergo deep molecular profiling including exome- and transcriptome sequencing, as well as phosphoproteomic analysis. Results: DSRT data from 11 clinical AML samples and 2 normal bone marrow controls were bioinformatically processed and resulted in several exciting observations. First, overall drug response profiles of the AML samples and the controls were distinctly different suggesting multiple leukemia-selective inhibitory effects. Second, the MEK and mTOR signaling pathways emerged as potential key molecular drivers of AML cells when analyzing targets of leukemia-specific active drugs. Third, potent new ex-vivo combinations of approved targeted drugs were uncovered, such as mTOR pathway inhibitors with dasatinib. Fourth, data from ex-vivo DSRT profiles showed excellent agreement with clinical response when serial samples were analyzed from leukemia patients developing clinical resistance to targeted agents. Summary: The rapid and comprehensive DSRT platform covering the entire cancer pharmacopeia and many emerging agents has already generated powerful insights into the molecular events underlying adult AML, with significant potential to facilitate individually optimized combinatorial therapies, particularly for recurrent leukemias. DSRT will also serve as a powerful hypothesis-generator for clinical trials, particularly for emerging drugs and drug combinations. The ability to correlate response profiles of hundreds of drugs in clinical ex vivo samples with deep molecular profiling data will yield exciting new translational and pharmacogenomic opportunities for clinical hematology. Disclosures: Mustjoki: Novartis: Honoraria; Bristol-Myers Squibb: Honoraria. Porkka:Novartis: Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria, Research Funding. Kallioniemi:Abbot/Vysis: Patents & Royalties; Medisapiens: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Bayer Schering Pharma: Research Funding; Roche: Research Funding.

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In Silico Prediction of Novel Drug Combinations to Combat Bortezomib-Resistant Multiple Myeloma

Blood ◽

10.1182/blood.v120.21.1344.1344 ◽

2012 ◽

Vol 120 (21) ◽

pp. 1344-1344

Author(s):

Holly A. F. Stessman ◽

Tian Xia ◽

Aatif Mansoor ◽

Raamesh Deshpande ◽

Linda B. Baughn ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Cell Lines ◽

In Silico ◽

Research Funding ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Mouse Cell ◽

Resistant Mouse ◽

Novel Therapeutic

Abstract Abstract 1344 Bortezomib/VELCADE® (Bz) is a proteasome inhibitor that has been used successfully in the treatment of multiple myeloma (MM) patients. However, acquired resistance to Bz is an emerging problem. Thus, there is a need for novel therapeutic combinations that enhance Bz sensitivity or re-sensitize Bz resistant MM cells to Bz. The Connectivity Map (CMAP; Broad Institute) database contains treatment-induced transcriptional signatures from 1,309 bioactive compounds in 4 human cancer cell lines. An input signature can be used to query the database for correlated drug signatures, a technique that has been used previously to identify drugs that combat chemoresistance in cancer (Wei, et al. Cancer Cell (2006) 10:331). In this study we used in silico bioinformatic screening of gene expression profiles from isogenic pairs of Bz sensitive and resistant mouse cell lines derived from the iMycCα/Bcl-xL mouse model of plasma cell malignancy to identify compounds that combat Bz resistance. We established Bz-induced kinetic gene expression profiles (GEPs) in 3 pairs of Bz sensitive and resistant mouse cell lines over the course of 24 hours. GEPs were collected in the absence of large-scale cell death. The 16 and 24 hour time points were averaged and compared between each Bz sensitive and resistant pair. Genes in the sensitive cell line with a fold change greater than 2, relative to the resistant line, were given the binary distinction of “up” or “down” depending on the direction of change. Genes that met these criteria were assembled into signatures, and then used as inputs for CMAP queries to identify compounds that induce similar transcriptional responses. In all pairs, treatment of the Bz sensitive line correlated with GEPs of drugs that target the proteasome, NF-κB, HSP90 and microtubules, as indicated by positive connectivity scores. However eight compounds, all classified as Topoisomerase (Topo) I and/or II inhibitors, were negatively correlated to our input signature. A negative connectivity score could have two interpretations: (1) this could indicate simply that Topos are upregulated by Bz treatment in Bz sensitive lines, which has been previously reported (Congdan, et al. Biochem. Pharmacol. (2008) 74: 883); or (2) this score could be interpreted as Topos are inhibited in Bz resistant cells upon Bz treatment. This led us to ask whether Topo inhibitors could target Bz resistant MM cells and re-sensitize them to Bz. Indeed, we found that multiple Topo inhibitors were significantly more active against Bz resistant cells as single agents and restored sensitivity to Bz when combined with Bz as a cocktail regimen. This work demonstrates the potential of this in silico bioinformatic approach for identifying novel therapeutic combinations that overcome Bz resistance in MM. Furthermore, it identifies Topo inhibitors – drugs that are already approved for clinical use – as agents that may have utility in combating Bz resistance in refractory MM patients. Disclosures: Stessman: Millennium: The Takeda Oncology Company: Research Funding. Van Ness:Millennium: The Takeda Oncology Company: Research Funding.

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