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  • 1
    In: Clinical Lymphoma Myeloma and Leukemia, Elsevier BV, Vol. 19, No. 10 ( 2019-10), p. e279-e280
    Type of Medium: Online Resource
    ISSN: 2152-2650
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2019
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  • 2
    In: Blood, American Society of Hematology, Vol. 122, No. 21 ( 2013-11-15), p. 482-482
    Abstract: Adult acute myeloid leukemia (AML) exemplifies the challenges of modern cancer drug discovery and development in that molecularly targeted therapies are yet to be translated into clinical use. No effective second-line therapy exists once standard chemotherapy fails. While many genetic events have been linked with the onset and progression of AML, the fundamental disease mechanisms remain poorly understood. There is significant genomic and molecular heterogeneity among patients. Several targeted therapies have been investigated for improved second-line AML therapy but none has been approved for clinical use to date. It would be critically important to identify patient subgroups that would benefit from such therapies and to identify combinations of drugs that are likely to be effective. Methods To identify and optimize novel therapies for AML, we studied 28 samples from 18 AML patients with an individualized systems medicine (ISM) approach. The ISM platform includes functional profiling of AML patient cells ex vivo with drug sensitivity and resistance testing (DSRT), comprehensive molecular profiling as well as clinical background information. Data integration was done to identify disease- and patient-specific molecular vulnerabilities for translation in the clinic. The DSRT platform comprises 306 anti-cancer agents, each tested in a dose response series. We calculated differential drug sensitivity scores by comparing AML responses to those of control cells in order to distinguish cancer-specific drug effects. Next generation RNA- and exome-sequencing was used to identify fusion transcripts and mutations that link to drug sensitivities. Results Individual AML patient samples had a distinct drug sensitivity pattern, but unsupervised hierarchical clustering of the drug sensitivity profiles of the 28 AML patient samples identified 5 functional AML drug response subtypes. Each subtype was characterized by distinct combinations of sensitivities: Bcl-2 inhibitors (e.g. navitoclax; Group 1), JAK inhibitors (e.g. ruxolitinib) (Group 2) and MEK inhibitors (e.g. trametinib) (Groups 2 and 4), PI3K/mTOR inhibitors (e.g. temsirolimus; Groups 4 and 5), broad spectrum receptor tyrosine kinase inhibitors (e.g. dasatinib) (Groups 3, 4 and 5) and FLT3 inhibitors (e.g. quizartinib, sunitinib) (Group 5). Correlation of overall drug responses with genomic profiles revealed that RAS and FLT3 mutations were significantly linked with the drug response subgroups 4 and 5, respectively. Activating FLT3 mutations contributed to sensitivity to FLT3 inhibitors, as expected, but also to tyrosine kinase inhibitors not targeting FLT3, such as dasatinib. Hence, these data point to the potential synergistic combinatorial effects of FLT3 inhibitors with dasatinib for improved therapy outcome (Figure). Early clinical translational results based on compassionate use support this hypothesis. Therefore, by combinations of drugs we expect to see synergistic drug responses that can be translated into efficacious and safe therapies for relapsed AML cases in the clinic. Clinical application of DSRT results in the treatment of eight recurrent chemorefractory patients led to objective responses in three cases according to ELN criteria, whereas four of the remaining five patients had meaningful responses not meeting ELN criteria. After disease progression, AML patient cells showed ex vivo resistance to the drugs administered to the patients, as well as significant changes in clonal architecture during treatment response. Furthermore, we saw genomic alterations potentially explaining drug resistance, such as appearance of novel fusion genes. Summary The ISM approach represents an opportunity for improving therapies for cancer patients, one patient at the time. We show that the platform can be used to identify functional groups of AML linking to vulnerabilities to single targeted drugs and, importantly, unexpected drug combinations. This information can in turn be used for personalized medicine strategies and for creating hypotheses to be explored in systematic clinical trials, both for approved and investigational drugs. Disclosures: Off Label Use: Many of the compounds included in our DSRT platform are not indicated for AML therapy. Mustjoki:BMS: Honoraria, Research Funding; Novartis: Honoraria. Porkka:Novartis: Honoraria, Research Funding; BMS: Honoraria, Research Funding. Kallioniemi:Medisapiens: Membership on an entity’s Board of Directors or advisory committees; Roche: Research Funding.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
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    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2013
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  • 3
    In: Blood, American Society of Hematology, Vol. 120, No. 21 ( 2012-11-16), p. 288-288
    Abstract: Abstract 288 Introduction: Recent genomic analyses of acute myeloid leukemia (AML) patients have provided new information on mutations contributing to the disease onset and progression. However, the genomic changes are often complex and highly diverse from one patient to another and often not actionable in clinical care. To rapidly identify novel patient-specific therapies, we developed a high-throughput drug sensitivity and resistance testing (DSRT) platform to experimentally validate therapeutic options for individual patients with relapsed AML. By integrating the results with exome and transcriptome sequencing plus proteomic analysis, we were able to define specific drug-sensitive subgroups of patients and explore predictive biomarkers. Methods: Ex vivo DSRT was implemented for 29 samples from 16 adult AML patients at the time of relapse and chemoresistance and from 5 healthy donors. Fresh mononuclear cells from bone marrow aspirates ( 〉 50% blast count) were screened against a comprehensive collection of cytotoxic chemotherapy agents (n=103) and targeted preclinical and clinical drugs (n=100, later 170). The drugs were tested over a 10,000-fold concentration range resulting in a dose-response curve for each compound and each leukemia sample. A leukemia-specific drug sensitivity score (sDSS) was derived from the area under each dose response curve in relation to the total area, and comparing leukemia samples with normal bone marrow results. The turnaround time for the DSRT assay was 4 days. All samples also underwent deep exome (40–100×) and transcriptome sequencing to identify somatic mutations and fusion transcripts, as well as phosphoproteomic array analysis to uncover active cell signaling pathways. Results: The drug sensitivity profiles of AML patient samples differed markedly from healthy bone marrow controls, with leukemia-specific responses mostly observed for molecularly targeted drugs. Individual AML patient samples clustered into distinct subgroups based on their chemoresponse profiles, thus suggesting that the subgroups were driven by distinct signaling pathways. Similarly, compounds clustered based on the response across the samples revealing functional groups of compounds of both expected and unexpected composition. Furthermore, subsets of patient samples stood out as highly sensitive to different compounds. Specifically, dasatinib, rapalogs, MEK inhibitors, ruxolitinib, sunitinib, sorafenib, ponatinib, foretinib and quizartinib were found to be selectively active in 5 (31%), 5 (31%), 4 (25%), 4 (25%), 3 (19%), 3 (19%), 2 (13%), 2 (13%), and 1 (6%) of the AML patients ex vivo, respectively. DSRT assays of serial samples from the same patient at different stages of leukemia progression revealed patterns of resistance to the clinically applied drugs, in conjunction with evidence of dynamic changes in the clonal genomic architecture. Emergence of vulnerabilities to novel pathway inhibitors was seen at the time of drug resistance, suggesting potential combinatorial or successive cycles of drugs to achieve remissions in an increasingly chemorefractory disease. Genomic and molecular profiling of the same patient samples not only highlighted potential biomarkers reflecting the ex vivo DSRT response patterns, but also made it possible to follow in parallel the drug sensitivities and the clonal progression of the disease in serial samples from the same patients. Summary: The comprehensive analysis of drug responses by DSRT in samples from human chemorefractory AML patients revealed a complex pattern of sensitivities to distinct inhibitors. Thus, these results suggest tremendous heterogeneity in drug response patterns and underline the relevance of individual ex vivo drug testing in selecting optimal therapies for patients (personalized medicine). Together with genomic and molecular profiling, the DSRT analysis resulted in a comprehensive view of the drug response landscape and the underlying molecular changes in relapsed AML. These data can readily be translated into the clinic via biomarker-driven stratified clinical trials. Disclosures: Mustjoki: Bristol-Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria. Kallioniemi:Roche: Research Funding; Medisapiens: Membership on an entity's Board of Directors or advisory committees. Porkka:Bristol-Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria, Research Funding.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
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    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2012
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  • 4
    In: New England Journal of Medicine, Massachusetts Medical Society, Vol. 366, No. 20 ( 2012-05-17), p. 1905-1913
    Type of Medium: Online Resource
    ISSN: 0028-4793 , 1533-4406
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    Language: English
    Publisher: Massachusetts Medical Society
    Publication Date: 2012
    detail.hit.zdb_id: 1468837-2
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  • 5
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 72, No. 8_Supplement ( 2012-04-15), p. 5067-5067
    Abstract: Despite significant advances in characterizing the molecular genetics of AML, the clonal evolution of leukemic cells and the dynamic impact of genomic changes on the development of the disease and progression to drug resistance are not well understood. Here, we applied next-generation sequencing to quantify aberrant tumor subclones carrying specific mutant alleles of key cancer genes and developed a method to extract quantitative high-resolution copy number changes across the genome using exome sequencing data from matching cancer and normal DNA. Serial bone marrow (BM) samples collected from diagnosis to relapse to post-treatment drug resistance in a patient-centric manner made it possible to trace the clonal evolution of AML and to identify variants potentially involved in drug resistance. Exome sequencing from AML blast cells and normal skin biopsies was performed as part of the Finnish Hematology Registry and Biobanking (FHRB) effort. Consecutive paired samples from different patients revealed unique genetic patterns of clonal evolution and cancer progression in each patient. In a pre-resistant sample of one AML M5 patient, we identified four closely spaced insertions in the Wilm's Tumor (WT1) suppressor gene, none of which appear on the same sequence reads. This suggests the presence of multiple distinct leukemic subclones even before treatment resistance and underscores the strong selective advantage conferred by WT1 mutations. After relapse, one of the subclones was lost, and another one significantly increased suggesting that the relapse arose from the expansion of a pre-existing resistant subclone. In this patient, recurrent clones otherwise featured similar copy number changes and the same fusion genes as the primary diagnostic sample. In another AML patient developing recurrence an opposite pattern was observed: The relapsed, drug-resistant cells displayed an enormous increase of small microdeletions compared to the diagnostic, pre-treatment sample, while almost all sequence-level alterations in potential cancer genes were the same between the two samples. This suggests that a distinct type of DNA repair deficiency may have contributed to the drug resistant clone in this patient. Conclusions: Exome sequencing from paired samples of AML cells before and after relapse makes it possible to trace the clonal evolution of the disease and study the impact of therapy both at the level of sequence alterations of key cancer genes and simultaneously at the level of copy number changes inferred from exome sequence data. This analysis has highlighted multiple genomic patterns by which resistance may evolve in vivo during cancer treatment. Refined bioinformatic analysis and interpretation of exome-seq data provides a rich resource to identify genetic biomarkers of drug response and minimal residual disease. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5067. doi:1538-7445.AM2012-5067
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2012
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  • 6
    In: Blood, American Society of Hematology, Vol. 128, No. 22 ( 2016-12-02), p. 4711-4711
    Abstract: Introduction The t(5;11)(q35;p15.4) translocation joining the nucleoporin-98 kD (NUP98) and nuclear receptor binding SET domain protein 1 (NSD1) genes is a recurrent chromosomal aberration in pediatric acute myeloid leukemia (AML). The NUP98-NSD1 frequently co-occurs with FLT3-ITD and with high-rates of induction failure. Analyzing primary samples and cell models by high-throughput drug testing, we aimed to identify alternative therapeutic approaches and better understand the impact of the NUP98-NSD1 and FLT3-ITDalterations on drug response. Methods Bone marrow mononuclear cells (BM MNCs) were prepared from 4 samples collected from two NUP98-NSD1, FLT3-ITD AML patients and 10 healthy donors. Experimental cell models included lineage-marker-depleted mouse bone marrow (BM) cells transduced with chimeric NUP98-NSD1 (NN) and FLT3-ITD (F) retroviruses alone and together (NNF),and Ba/F3 and 32D cells transduced with two NUP98-NSD1 transcripts, cloned from patient material. The cells were dispensed on plates containing up to 309 FDA/EMA-approved investigational small molecule inhibitors and chemotherapeutics in five concentrations over a 10,000-fold range. Cell viability was measured after 72h using the CellTiter-Glo® luminescence assay and drug sensitivity scores (DSS) calculated. In patient samples, a select drug sensitivity score (sDSS) was evaluated by subtracting the mean DSS of the healthy samples from the patient DSS, while in cell lines the DSS of mock-transduced parental cell line was substracted from the DSS of the respective experimental model. For evaluating selectively effective drugs, we considered compounds with DSS and sDSS values above 9 and 4, respectively. Results We identified 19 selectively effective drugs in the patient samples. The highest mean sDSS values were seen for pan-BCL-2 inhibitor navitoclax (11,8), the multikinase inhibitor dasatinib (11,0), and the HSP-90 inhibitor tanespimycin (10,3). Amongst the top selective compounds were also the multikinase inhibitor ponatinib and several HDAC-, MEK-, HSP-90, PI3K-, MTOR-inhibitors. Similar to patient samples, mouse BM cells expressing chimeric NUP98-NSD1, and mouse cells (Ba/F3, 32D) expressing a NUP98 exon-12/NSD1 exon-6 fusion had high mean sDSS to BCL-2 inhibitors: navitoclax (13,9), obatoclax (13,4), and venetoclax (9,6). Analyzing the BCL-2 inhibitor sensitive NUP98-NSD1cells, we identified 32 selectively effective drugs. The top 25 drugs included inhibitors of Aurora A, BRAF, VEGFR, MET, IGF1R, WEE-1, and PI3K. Contrary to the NUP98 exon-12/NSD1 exon-6 fusion, the cells (Ba/F3, 32D) expressing NUP98 exon-11/NSD1 exon-6 fusion were unresponsive to BCL-2 inhibitors. From these cells, we found 14 selectively effective drugs, including glucocorticoids, JAK-, PI3K-, MTOR-, and BET-inhibitors. As an indication of functional synergy, the mouse BM cells expressing both NUP98-NSD1 and FLT3-ITD had significantly increased selective sensitivity to non-specific and specific FLT3-inhibitors (N =11) compared to cells expressing FLT3-ITDalone (p = 0.001). The most selectively effective FLT3-inhibitor in the dual positive mouse BM cells was quizartinib (sDSS = 22,8). Based on the initial results, we designed drug combinations for the 72 most effective drugs. Synergy was observed between dasatinib and MEK1/2-, PI3K-, and several receptor tyrosine kinase inhibitors in the NNF expressing mouse BM cells. In vitally frozen primary cells, we observed synergy between dasatinib and BCL-2- (navitoclax), PI3K-, and MTOR-inhibitors (idelalisib, PF-04691502). Conclusions In summary, we identified potential candidate drugs and drug combinations for targeting NUP98-NSD1 and FLT3-ITD expressing cells. The sensitivity of NUP98-NSD1 cells to BCL-2 inhibitors suggests the fusion may induce BCL-2 mediated survival, while the addition of FLT3-ITD confers sensitivity to FLT3 inhibitors. The results also suggest that alternative NUP98-NSD1 transcripts may have different impacts on the drug responses. Finally, our data indicates that FLT3-inhibitors could offer therapeutic advantage to cells with dual NUP98-NSD1 and FLT3-ITD, and support clinical evaluation of FLT3-inhibitors in chemo-resistant t(5;11) positive AML. Disclosures Porkka: Bristol-Myers Squibb: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Heckman:Pfizer: Research Funding; Celgene: Research Funding.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
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    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2016
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  • 7
    In: Blood, American Society of Hematology, Vol. 118, No. 21 ( 2011-11-18), p. 2487-2487
    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.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
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    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2011
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  • 8
    In: Blood, American Society of Hematology, Vol. 126, No. 23 ( 2015-12-03), p. 2462-2462
    Abstract: BACKGROUND: BCL-2 family members play a critical role in the regulation of apoptosis. BCL-2 and BCL-XL promote cell survival by preventing mitochondrial apoptotic pore formation. BH3 mimetic drugs such as venetoclax (ABT-199) promote apoptosis by inhibiting BCL-2 while navitoclax (ABT-263) inhibits both BCL-2 and BCL-XL. In AML, the expression of anti-apoptotic proteins is highly variable. In a recent study venetoclax showed single-agent activity in 6/12 AML cell lines and 20/25 patient samples. (Pan et al. Cancer Disc 2014). The samples with complex cytogenetics were largely resistant. Sensitivity correlated with increased BCL-2 protein levels and negatively correlated with BCL-XL and MCL-1 protein levels. We aimed to expand these data to both newly diagnosed and relapsed AML patients and to isolate biomarkers for patient selection. METHODS: We assessed the ex vivo sensitivity of fresh leukemic cells from 16 diagnosed and 36relapsed/refractory AML patient samples to venetoclax (25 samples) and navitoclax (52 samples). Exome sequencing was performed on 32 samples and gene expression of BCL2 family members (BCL-2, BCL-XL, MCL-1, BIK, BAX, BAK1, BID, BCL2L12, BIM, BCL2A1, PUMA and BAD) was determined on 31 samples by qRT-PCR. Samples from primary cells of healthy individuals (n=10), and CMML (n=7) or CLL (n=2) patients were used as controls. Drug sensitivity was determined over a 10,000-fold concentration range (1-10 000 nM). A leukemia-specific drug sensitivity score (sDSS) derived from area under the dose response curve calculations was used as the efficacy variable by comparing leukemia results with those from normal bone marrow cells (Bhagwan et al., Sci Rep 2014, Pemovska et al., Cancer Disc 2014). RESULTS: Compared to healthy controls, CMML samples were largely non-sensitive, whereas CLL samples were highly sensitive to BCL-2 inhibitors ex vivo. The AML samples exhibited heterogeneous responses. 15/25 (60%) of AML samples were sensitive to venetoclax and 35/52 (67%) to navitoclax. Both diagnostic (12 of 16 samples, 75%) and relapsed/refractory samples (24 of 36 samples, 64%) were sensitive to navitoclax. Similarly, 6/7 (86%) of diagnostic samples and 9/18 (50%) of relapsed/refractory samples were sensitive to venetoclax. We observed responses to venetoclax and navitoclax in each patient to be similar, although navitoclax showed efficacy at lower concentrations: in 25 samples tested with both agents, mean sDSS values were lower in navitoclax-treated samples (paired t-test, p=0.02). All except one patient sample exhibited a difference in resistance between the two drugs showing sensitivity to navitoclax but not to venetoclax. We observed responses across all mutational profiles, including samples with mutations to FLT3 -ITD, NPM1, TP53, NRAS and IDH1 and IDH2, as well as in samples with complex karyotypes. Intriguingly, three of four samples with mutated TP53 exhibited sensitivity to BCL2 inhibition. No single mutation predicted sensitivity or resistance. At the RNA level, no statistical correlation between BCL2 or BCL-XL expression for BCL2 inhibitor response was observed. Instead we observed high levels of beta-2-microglobulin (B2M) mRNA expression in BCL2 inhibitor-resistant samples with a strong negative correlation to navitoclax sensitivity (r=-0.60, P=0.0008). DISCUSSION: We did not observe BCL2 and BCL-XL mRNA expression to be optimal predictors for BCL2 inhibitor response. On the other hand, we observed high expression of B2M mRNA expression in resistant samples suggesting that it could serve as a biomarker for sensitivity to BCL2 inhibitors. The high B2M expression has been previously linked to poor prognosis in solid tumors and in AML (Albitar et al., Leukemia 2007). In cell line models B2M leads to phosphorylation and inactivation of proapototic protein BAD (Nokura et al., J Urol 2007). This may affect the balance between pro- and antiapoptotic proteins and thus offer a escape route from BCL2 inhibition. To conclude, we observed BCL2 inhibition to be effective ex vivo in over half of all AML samples, tested both in primary and relapsed/refractory state as well as across different subgroups defined by AML driver mutations. Of the potential biomarkers that were assessed, B2M was the best mRNA-level indicator for anti-BCL-2 drug efficacy. Disclosures Off Label Use: BCL2 inhibitors are not approved for the treatment of AML. Heckman:Celgene: Honoraria, Research Funding; Pfizer: Research Funding. Porkka:Bristol-Myers Squibb: Honoraria; Celgene: Honoraria; Novartis: Honoraria; Pfizer: Honoraria.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
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    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2015
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  • 9
    In: Blood, American Society of Hematology, Vol. 126, No. 23 ( 2015-12-03), p. 867-867
    Abstract: BACKGROUND AND OBJECTIVES: The bone marrow (BM) microenvironment supports the survival of leukemic cells and influences their response to therapeutic agents by promoting drug tolerance and resistance. Novel therapeutic strategies are therefore needed that can override the BM mediated protection of AML cells in patients undergoing drug treatment. To address this we used a high-throughput drug screening method to identify novel drug combinations to reverse stromal-induced cytoprotection against the BCL2 antagonist venetoclax in primary AML samples. METHODS: Sensitivity of mononuclear cells collected from 18 AML BM aspirates or peripheral blood samples to a range of BCL2 inhibitors and tyrosine kinase inhibitors (TKIs) was assessed either in mononuclear cell medium (MCM, Promocell) or in a 25% HS-5 stromal cell-conditioned medium plus 75% RPMI medium mix (CM) to mimic cytoprotective bone marrow conditions. Cell viability was measured after 72 h and dose response curves generated for each tested drug. Drug sensitivity scores were calculated based on the area under the dose response curve. For the drug combination studies single agents (venetoclax, WEHI-539, ruxolitinib) were added simultaneously at fixed concentrations to AML cells and incubated for 72 h either in the MCM or CM medium. Cell viability was measured using the CellTiter-Glo assay. The expression of BCL2 genes was measured by qPCR after incubating the AML patient cells in either MCM or CM for 48 h. RESULTS: Incubation of primary AML cells in the CM culture condition led to reduced sensitivity to BCL2 family inhibitors, suggesting that stromal-derived factors in the CM promote cytoprotection. This effect was particularly pronounced for the selective BCL2 inhibitor venetoclax, where the CM-induced loss of sensitivity coincided with decreased BCL2 expression and increased BCL2L1 expression. In contrast, JAK inhibitors showed improved efficacy in CM compared to MCM culture conditions. To determine if the protective effects of CM stromal-like conditions against venetoclax could be diminished, the drug was tested in combination with the JAK1/2 inhibitor ruxolitinib using AML cells cultured in MCM or CM. When tested on AML cells from 4 patients with the FLT3-ITD alteration, we found that ruxolitinib rescued the sensitivity of venetoclax in leukemic cells in the presence of CM and the combination of two drugs exhibited synergistic effects in this setting. The combinatorial activity, however, was not recapitulated in the MCM condition. Since CM was found to induce BCL2L1 expression, venetoclax was also tested in combination with a BCLXLspecific inhibitor WEHI-539. Analogously to the ruxolitinib-venetoclax combination, synergistic activity between venetoclax and WEHI-539 was observed towards leukemic cells in the presence of CM. CONCLUSIONS: By applying a functional, drug-based approach to understand microenvironment-induced mechanisms of drug resistance in AML, we found that the activity of the selective BCL2 inhibitor venetoclax towards AML cells is adversely affected in stromal-based conditions, while JAK inhibitors, in contrast, exhibit increased efficacy in these conditions. Our results suggest stroma-derived cytokines induce JAK-STAT signaling in AML cells, which results in increased BCL2L1 expression and drives resistance to venetoclax. However, blocking JAK1/2 with ruxolitinib restores the sensitivity of AML cells to venetoclax. We found that JAK1/2 inhibitors such as ruxolitinib can act synergistically with BCL2/BCLXL inhibitors, suggesting clinically useful combination treatments. Disclosures Gjertsen: BerGenBio AS: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Boehringer Ingelheim: Membership on an entity's Board of Directors or advisory committees; Kinn Therapeutics AS: Equity Ownership. Porkka:Pfizer: Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Kallioniemi:Vysis-Abbot: Patents & Royalties; Medisapiens: Membership on an entity's Board of Directors or advisory committees; IMI-Project Predect: Research Funding; Roche: Research Funding; Pfizer: Research Funding. Wennerberg:Pfizer: Research Funding. Heckman:Celgene: Honoraria, Research Funding.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
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    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2015
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  • 10
    In: Cancer Discovery, American Association for Cancer Research (AACR), Vol. 3, No. 12 ( 2013-12-01), p. 1416-1429
    Abstract: We present an individualized systems medicine (ISM) approach to optimize cancer drug therapies one patient at a time. ISM is based on (i) molecular profiling and ex vivo drug sensitivity and resistance testing (DSRT) of patients' cancer cells to 187 oncology drugs, (ii) clinical implementation of therapies predicted to be effective, and (iii) studying consecutive samples from the treated patients to understand the basis of resistance. Here, application of ISM to 28 samples from patients with acute myeloid leukemia (AML) uncovered five major taxonomic drug-response subtypes based on DSRT profiles, some with distinct genomic features (e.g., MLL gene fusions in subgroup IV and FLT3-ITD mutations in subgroup V). Therapy based on DSRT resulted in several clinical responses. After progression under DSRT-guided therapies, AML cells displayed significant clonal evolution and novel genomic changes potentially explaining resistance, whereas ex vivo DSRT data showed resistance to the clinically applied drugs and new vulnerabilities to previously ineffective drugs. Significance: Here, we demonstrate an ISM strategy to optimize safe and effective personalized cancer therapies for individual patients as well as to understand and predict disease evolution and the next line of therapy. This approach could facilitate systematic drug repositioning of approved targeted drugs as well as help to prioritize and de-risk emerging drugs for clinical testing. Cancer Discov; 3(12); 1416–29. ©2013 AACR. See related commentary by Hourigan and Karp, p. 1336 This article is highlighted in the In This Issue feature, p. 1317
    Type of Medium: Online Resource
    ISSN: 2159-8274 , 2159-8290
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2013
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