Abstract 286: Transcriptomic features predicting drug sensitivity and resistance in acute myeloid leukemia

Author(s):  
Ashwini Kumar ◽  
Disha Malani ◽  
Bhagwan Yadav ◽  
Mika Kontro ◽  
Matti Kankainen ◽  
...  
2018 ◽  
Vol 36 (15_suppl) ◽  
pp. 7025-7025
Author(s):  
Mara Rosenberg ◽  
Guang Fan ◽  
Kevin Watanabe-Smith ◽  
Cristina Tognon ◽  
Brian J. Druker ◽  
...  

Oncotarget ◽  
2020 ◽  
Vol 11 (29) ◽  
pp. 2807-2818
Author(s):  
Mara W. Rosenberg ◽  
Kevin Watanabe-Smith ◽  
Jeffrey W. Tyner ◽  
Cristina E. Tognon ◽  
Brian J. Druker ◽  
...  

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1451-1451
Author(s):  
Sigal Tavor ◽  
Tali Shalit ◽  
Noa Chapal Ilani ◽  
Yoni Moskovitz ◽  
Nir Livnat ◽  
...  

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<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.


1993 ◽  
Vol 10 (1-2) ◽  
pp. 67-71 ◽  
Author(s):  
Alain Delmer ◽  
Jean-Pierre Marie ◽  
Danielle Thevenin ◽  
Anne-Marie Suberville ◽  
Robert Zittoun

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 3851-3851
Author(s):  
Pamela S. Becker ◽  
Michael W. Schmitt ◽  
Zhiyi Xie ◽  
Andrew R Carson ◽  
Bradley Patay ◽  
...  

Abstract Introduction: Whole genome sequencing has demonstrated tremendous heterogeneity in the mutations and chromosomal translocations associated with acute myeloid leukemia (AML), and there are several correlates with prognosis, yet we remain quite limited in our ability to predict specific chemotherapy drug sensitivity based on genomics with the exception of a few selected mutations or translocations, such as FLT3 -ITD or PML-RARA. One third of new diagnosis patients and over half of relapsed patients will not respond to initial chemotherapy regimens that incur appreciable toxicity and result in prolonged hospitalization. We therefore seek to define molecular information that might better predict response to conventional or novel therapies. Methods: MyAML™ uses next generation sequencing (NGS) to analyze the 3' and 5' UTR and exonic regions of 194 genes and potential genomic breakpoints within known somatic gene fusion breakpoints known to be associated with AML. Fragmented genomic DNA (~3.4Mb) is captured with a customized probe design, and sequenced with 300bp paired end reads on an Illumina MiSeq instrument to an average depth of coverage >1000x. Using a custom bioinformatics pipeline, MyInformatics™, single nucleotide variants (SNVs), insertion/deletions (indels), inversions and translocations are identified, annotated, characterized, and allelic frequencies calculated. Commonly associated variants in dbSNP and 1000 genomes may be eliminated, as well as variants with allele frequencies less than 5%. High throughput drug sensitivity testing was performed against a panel of 160 drugs, of which 56 are FDA approved and 104 are investigational. De-identified samples from 12 patients with de novo AML and 12 patients with relapsed AML were analyzed. For 2 patient samples, Duplex Sequencing was also performed to detect sub-clonal mutations below the detection limit of conventional NGS. Pearson and Spearman correlations were performed between all possible pairs of genes containing missense or indel mutations and the in vitro cytotoxicity response across the same set of 24 patients. Results: From the 24 patient samples analyzed to date, an average of 129 missense mutations were identified in each sample with an allelic frequency >5%. Of these, an average of over 21 missense variants were observed in COSMIC and less than 3 were novel (not in dbSNP). These samples also contained an average of over 12 coding indels (~5 frameshift and 7 inframe indels per sample). In addition, MyAML™ identified 3 samples with inv(16) and 6 samples with translocations, including the cryptic NUP98-NSD1 t(5;11) that was not detected by karyotyping. For 2 of the samples, Duplex Sequencing was performed at a depth of at least 6000X, and an accuracy of 10-7, and showed concordance of some of the mutations, with each method identifying additional mutations not observed by the other, an expected finding, as each method targeted distinct regions, and Duplex Sequencing had a greater depth of coverage. Fourteen genes were observed to exhibit at least one indel with a frameshift at frequency greater than 5% in more than one patient. In order to identify significantly associated drugs and genes containing indel mutations, we computed Pearson and Spearman correlations between drugs and these 14 genes across 24 patients. The correlation analyses revealed significant associations (p= 0.006 to 0.04) between indel mutations in three genes and chemosensitivity to drugs commonly used in AML such as cladribine, clofarabine, cytarabine, daunorubicin, etoposide, fludarabine and mitoxantrone. Similarly, significant associations (p<0.05) were identified between missense mutations in 5 genes and chemosensitivity to these drugs. Conclusion: Personalized data derived from a targeted genomic assay and in vitro chemotherapy sensitivity testing of individual patient AML samples will likely lead to innovation in treatment, identification of novel targeted agents, and improved outcomes in AML. Disclosures Xie: Invivoscribe: Employment. Carson:Genection: Employment. Patay:Genection: Employment.


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