Introduction: Myelodysplastic syndrome (MDS) is characterized by differentiation blockade, cytopenias with commontransfusion dependency and immune defects. Upon progression the myeloblasts accumulate ...and the patients become vulnerable to severe infection complications. Based on the Prague Charles University General Hospital registry (N=164, median age 73), the AZA therapy in higher-risk MDS patients results in median OS 13.8 Mo with ORR 48.5%. We also noted from our retrospective data that AZA-treated patients with higher G-CSF consumption had significantly reduced occurrence of Grade 4 neutropenias and longer OS (19 vs 16 Mo, p value 0.039).
Rationale: To improve poor clinical outcomes we initiated a randomized open labeled academic trial that compares standard AZA therapy (A) with novel AZA-based therapy combination involving use of G-CSF added prior AZA (GA). Both AZA and also decitabine were preclinically shown to induce myeloid differentiation upon G-CSF preincubation. G-CSF binds its receptor in granulocytic precursors and neutrophils to stimulate their survival, proliferation, and differentiation via myeloid master regulator transcription factor and leukemia-suppressor PU.1. We also have previously shown that AZA increases PU.1. expression.
Study design & Methods: GA/MDS-2013 (EudraCT No 2013-001639-38). Expected for enrollment are 134 patients, currently enrolled 53 patients (GA arm N=29, A arm N=24) with median age 74 years, M:F ratio 32:21 (GA 16:16, A 13:8),median IPSS-R 6, median follow up 11.2 Mo, median cycles of therapy 6. Diagnosis included:MDS (EB1, EB 2) with IPSS int-2/high (75%), MDS/AML<30% MB (22.5%), and CMML II (2.5%). Transplant candidates were excluded. Randomization is 2:1 for GA vs A arm. Primary endpoints: OS, PFS, time to AML transformation, ORR, infections & QoL. Secondary endpoints: biomarkers. Therapy schedule: 75mg/m2 of AZA 5-2-2, in GA: G-CSF s.c. injected 48 hrs before dose 1 and dose 6. G-CSF is measured in patient sera (prior therapy), myeloid surface markers are determined by flow cytometry (day -2, day 1, and day 9 of cycle 1). Genomic libraries from whole bone marrow are prepared by NEBNext Direct Kit involving 33 gene panel, sequencing runs are performed on Illumina platform. Statistics involved longitudinal multivariate data analysis including the joint models for the OS and response.
Results: The presented data include 2.5 years since the beginning of the trial. Median survival for GA arm was 11 vs 6 Mo in the A arm. ORR (CR, CRm, PR, HI) was 56% in GA arm vs 33% in the A arm. Transformation to AML for both arms was comparable. The stratified longitudinal Cox proportional hazards model containing time-varying covariates together with the ordinal multilevel logistic mixed model were utilized. From this joint fitted model, a negative coefficient for the G-AZA treatment (significant p-value 0.0442) can be noticed in the case of the Cox Proportional Hazard part of the model. This means that G-AZA treatment improves patient survival. The estimated odds for the GA arm that responded to the therapy with remission rather than progression is 12.4x higher than for the A arm, controlling for the remaining patients' characteristics (p-value 0.0016).Both the GA and A arms are comparably tolerated. Data on serious infections and neutropenia gr4 were not yet available. The levels of G-CSF in sera prior the study in both arms (GA vs A) were comparable. Flow cytometry revealed G-CSF mediated upregulation of FCgRI (CD64) in the GA but not in the A arm. Multivariate analysis indicates the following: mutated genes: DNMT3A (p-value 0.0157), EZH2 (p-value 0.0091), TP53 (borderline p-value 0.0510), & CSF3R (p-value 0.0057) shorten the overall survival. The significant negative effects on response was noted for mutated EZH2 (p-value 0.0208) and CSF3R (p-value 0.0424) genes.
Conclusions: The current results supported by different methods and statistics indicates a beneficial effect of G-CSF pre-treatment to standard AZA therapy in higher risk MDS patients. G-CSF pre-treatment to AZA increases OS and ORR. In addition, we identified biomarkers that are negatively associated with patient survival and response including EZH2, DNMT3A, TP53, & CSF3R.
Grant Support: Ministry of Health, #16-27790A. Institutional resources: Progres Q49 & Q26, UNCE/MED/016, LQ1604, SVV 260374/2017, RVO-64165.
No relevant conflicts of interest to declare.
Introduction: Myelodysplastic syndromes (MDS) are clonal disorders of myeloid hematopoietic stem cells. Recent studies has shown that nearly 90% of patients with MDS carry somatic mutations in bone ...marrow (BM). These findings triggered a number of studies to identify potential uses of these mutations for diagnostics and prognostics purposes. We focused on a group of 38 patients with advanced stages of the disease that were selected for Azacitidine (AZA) therapy. We then utilized a set of 98 BM samples from the patient cohort that were collected in different stages before, during, and after the period of 4-12 cycles of the therapy. Each patient provided 3 samples on average. This study excludes patients that died early on AZA. Median OS on AZA therapy was 31 Mo. Most prevalent MDS subtypes were RAEB-2 (55%), RAEB-1 (24%), and MDS/AML (13%). 20% of patients had complex karyotype or poor cytogenetics (MedOS=22Mo) and the rest had mostly normal karyotype or intermediate cytogenetics (MedOS=40Mo) prior to AZA. Progression to AML was observed in 55% of patients (PFS= 16 Mo). After 4 cycles, PR was achieved in 59% of patients, CR in 12%, while SD was maintained in 21%, and 9% of patients progressed (PD) to AML.
Methods: We detected relevant mutations in MDS samples using the following approach. We collected genomic DNA from separated BM samples: either a CD3-negative population containing the myeloid compartment, or CD3-positive T cells representing an internal control. We prepared amplicon libraries from these samples using the Illumina TruSight Myeloid Panel that covers 54 key genes involved in myeloid malignancies (notably MDS and AML). We sequenced these libraries using the Illumina NGS platform. To achieve greater sensitivity in detecting SNVs and InDels we utilized two different variant calling pipelines (using samtools mpileup or FreeBayes). Since the (PCR) validation efficacy of each mutation from the single NGS run was below 60%, we improved specificity by using two independently prepared sequencing libraries. The intersection of the variant detections from both libraries was considered accurate and only these data were reported as variants.
Results: When we excluded all germinal variants, 43 somatic variants in ~18 genes were identified per patient on average. The majority (31/43) of these variants had an intermediate impact (on amino acid sequence), while 12/43 had high impact on the protein structure. Importantly, the majority of them had ~1% VAF (variant allele frequency) representing putative clones with low proliferative potential. In contrast, only 8 genes (~14 variants) were mutated with VAF>2%. The following genes were mutated most frequently: TET2, STAG2, ASXL1 in approximately 60-80% of patients. Data from repeatedly analyzed patient samples on AZA therapy led to an unexpected observation that the variants with WAF>2% often exhibit dynamically changing mutation pattern while the variants of non-proliferating clones (with VAF ~1%) remain very stable. We observed prominent development of some variants (ASXL1, STAG2, CUX1, BCOR) as well as an increase in VAF of others (TP53, RUNX1, CUX1) on AZA therapy. Most of these genes when mutated were reported previously as altering prognosis of MDS (Bejar R et al, 2014). Surprisingly, in some samples we found a mutation in the RUNX1 gene before AZA therapy that was not present after the treatment however, after the treatment another not previously observed mutation of RUNX1 emerged. Furthermore, the presence of any of the mutations before AZA including SF3B1 or TP53 did not have any prognostically significant association with OS or PFS. This contention is supported by the fact that many mutations actually disappeared on AZA.
Conclusions: Using an internal sample control combined with a duplicate NGS library preparation we achieved a very high accuracy of detecting somatic variants in MDS-BM sub-separated samples. We observed that variants above 2% VAF change dynamically over the course of AZA therapy while the variants with ~1% VAF remain stable. Our data suggest that development of somatic mutations in AZA-treated MDS patients is a dynamic process, which involves previously identified high risk genes including TP53, RUNX1, CUX1, ASXL1 and BCOR.
Grant support: GAČR 16-05649S & P305/12/1033, AZV: 16-27790A, CZ.1.05/1.1.00/02.0109, UNCE 204021, LH15170, PRVOUK P24, LQ1604 and RVO-VFN64165.
No relevant conflicts of interest to declare.
Azacitidine (AZA) for higher risk MDS patients is a standard therapy with limited durability. To monitor mutation dynamics during AZA therapy we utilized massive parallel sequencing of 54 genes ...previously associated with MDS/AML pathogenesis. Serial sampling before and during AZA therapy of 38 patients (reaching median overall survival 24 months (Mo) with 60% clinical responses) identified 116 somatic pathogenic variants with allele frequency (VAF) exceeding 5%. High accuracy of data was achieved via duplicate libraries from myeloid cells and T-cell controls. We observed that nearly half of the variants were stable while other variants were highly dynamic. Patients with marked decrease of allelic burden upon AZA therapy achieved clinical responses. In contrast, early-progressing patients on AZA displayed minimal changes of the mutation pattern. We modeled the VAF dynamics on AZA and utilized a joint model for the overall survival and response duration. While the presence of certain variants associated with clinical outcomes, such as the mutations of
were adverse predictors while
mutations yield lower risk of dying, the data also indicate that allelic burden volatility represents additional important prognostic variable. In addition, preceding 5q- syndrome represents strong positive predictor of longer overall survival and response duration in high risk MDS patients treated with AZA. In conclusion, variants dynamics detected via serial sampling represents another parameter to consider when evaluating AZA efficacy and predicting outcome.
The transcription factor PU.1 (Purine-rich DNA binding, SPI1) is a key regulator of hematopoiesis, whose level is influenced by transcription through its enhancers and its post-transcriptional ...degradation via microRNA-155 (miR-155). The degree of transcriptional regulation of the PU.1 gene is influenced by repression via DNA methylation, as well as other epigenetic factors, such as those related to progenitor maturation status, which is modulated by the transcription factor Myeloblastosis oncogene (MYB). In this work, we show that combinatorial treatment of acute myeloid leukemia (AML) cells with DNA methylation inhibitors (5-Azacytidine), MYB inhibitors (Celastrol), and anti-miR-155 (AM155) ideally leads to overproduction of PU.1. We also show that PU.1 reactivation can be compensated by miR-155 and that only a combined approach leads to sustained PU.1 derepression, even at the protein level. The triple effect on increasing PU.1 levels in myeloblasts stimulates the myeloid transcriptional program while inhibiting cell survival and proliferation, leading to partial leukemic differentiation.
BACKGROUND:TET2 (Ten-eleven-translocation-2) mutations found in 10-30% of MDS and AML patients are actionable for Azacitidine (AZA) treatment (PMID: 21828143). Although response rate of AZA in older ...AML patients is ~47%, rate of relapse is high due to evolution of disease clones and development of new mutations during treatment cycle. Currently, there is no standard of care for AZA-resistant MDS and AML, and treating these individuals is challenging. Thus, there is an urgent need to identify mechanisms for resistance to AZA for TET2-mutant cancers.
AIM: The objective of the study was to predict response to AZA in OCI-M2 disease model and its AZA-resistant (AZA-R) sub-clones using computational biology modeling (CBM) to determine mechanisms of resistance to AZA and identify new therapy options for AZA chemoresistance.
METHODS: Parental OCI-M2 cells, representing an AML transformed from MDS, were treated with 8 mM AZA added to media every 2 days (IC50=2µM) for a period of 6 weeks to generate OCI-M2 AZA resistant subclones. AZA-R subclones together with parental AZA-sensitive cells were profiled using cytogenetics and NGS (TruSight Myeloid Sequencing, Illumina). Genomic data were analyzed via CBM system (Cellworks Group), which generates disease-specific protein network maps and enables digital drug simulations. Impact of digital drug treatments were quantified by measuring predicted effects on AML cell growth score, which is a composite of cell proliferation, viability and apoptosis.
RESULTS: CBM correctly predicted the response of parental OCI-M2 AML cells to be sensitive to AZA and all its AZA-resistant subclones to be resistant to AZA treatment. Genomic analysis using CBM showed that OCI-M2 AML model harboured TET2mutation (Q1084P), KAT6A amplification and DNMT3B amplification, which are putative determinants of AZA sensitivity as the AZA-resistant subclones lacked cells harbouring these mutations (TET2, KAT6Aamp and DNMT3Bamp). Instead, AZA treatment selected clones with frameshift or nonsense mutations in the following genes: ASXL1, EZH2, CUX1, ATRX, BCOR, PTPN11, FLT3 along with preserved TET2 in all the clones. Mutations in ASXL1 and EZH2 were previously shown to reduce the importance of methylation in driving disease, thus potentially leading to subclone proliferation regardless of AZA. Also 4 of 6 AZA-resistant subclones had DNMT3A frameshift mutations which may enhance AZA resistance.
CBM identified new drug combinations based on overlapping as well as unique mutations in AZA-resistant subclones. These combinations include Ruxolitinib + Venetoclax, Nelfinavir + Ruxolitinib or Venetoclax, Sorafenib or Dasatinib + Venetoclax. Some of these drugs were shown to effectively substitute AZA on the parental cell lines (Venetoclax IC=0.9µM, Ruxolitinib IC50=1.7µM, Sorafenib IC50=5.3µM). Whether AZA enhanced sensitivity to these drugs in AZA-R clones is currently under investigation.
Conclusions: CBM-based genomic analysis identified secondary mutations in ASXL1, EZH2, CUX1, ATRX, BCOR, PTPN11 and FLT3 that associated with AZA chemoresistance. Digital drug screening identified new drug combinations including Venetoclax for validation testing in AZA-resistant MDS and AML.
Abbasi:Cell Works Group Inc.: Employment. Singh:Cellworks Research India Private Limited: Employment. Kumar:Cellworks Research India Private Limited: Employment. Suseela:Cellworks Research India Private Limited: Employment. Patil:Cellworks Research India Private Limited: Employment. Dattatraya:Cellworks Research India Private Limited: Employment. Tiwari:Cellworks Research India Private Limited: Employment. Vali:Cell Works Group Inc.: Employment. Cogle:Celgene: Other: Steering Committee Member of Connect MDS/AML Registry.
BACKGROUND: Azacitidine (AZA) is currently a drug of choice for most of high-risk MDS patients. However, only 40-50% of MDS patients achieve clinical improvement with AZA. There is a need for a ...predictive clinical decision support tool that can identify MDS patients with higher or lower likelihood of AZA response. Ideally, patients with no chance of response would be spared of life-threatening toxicities and expense; while patients with high chance for response would receive maximized treatment.
AIM: The objective of this study was to predict response to AZA in a cohort of intermediate- and high-risk MDS patients using CBM approach in retrospective blinded manner.
METHODS: We analyzed the clinical and genomic (NGS, cytogenetics and FISH) data for a cohort of 48 Int-2 and high risk MDS patients treated with AZA for median of 12 (4-34) cycles. Median age was 70.4 years, M:F ratio 1:1. MDS subtypes: EB-2 (35.4%, N=17), EB-1 (39.6%, N=19), MDS/AML (18.6%, N=9), CMML1/2 (4.2%, N=2), and RARS (2.1%, N=1). Median IPSS-R was 5 (3-10), cytogenetic score was 1. Median BM blasts were 10% (0.88-43.12), Hemoglobin 91g/L (62-145), ANC 1.24x109/mL (0.09-11.64), Platelets 84x109/mL (2-576). Patients were treated by AZA until progression to AML. Median AZA cycles was 14. Median overall survival (OS) on therapy was 24.2 months (4.4-61.6). Median progression free survival (PFS) was 15.9 months (4.0-61.6). Clinical responders were defined by IWG2006 criteria. Overall response rate (ORR) was 60.4% (CR/PR: 18 of 48 patients, stable disease with hematology improvement (SD-HI): 11/48). While 29 of 48 (60.4%) patients progressed to AML following the AZA therapy; 5 patients (10.4%) were primarily AZA-resistant.
CBM for each MDS patient was created utilizing genomic data to create a predictive workflow (Cellworks Group) complemented with digital mechanistic model of AZA and other FDA-approved drugs. Drugs were modeled by programming their mechanism of action on pathways and simulated individually and in combination. A disease inhibition score (DIS) characterized the drug impact to which malignant phenotypes was inhibited. For AZA non-responder profiles, unique combinations were selected that reduced DIS.
RESULTS: CBM cohort analysis performed on 37 out of 48 patients predicted 20 clinical responders (54.05%) and 17 clinical non-responders (45.95%). CBM accurately predicted the clinical outcomes of 14 out of 20 responders and 17 out of 17 non-responders with overall accuracy 83.78%. Sensitivity of identifying a responder is 70% while non-responders are called with 100% specificity. The CBM identified AZA based combination in 17 patients who did not respond to AZA monotherapy: AZA+Lenalidomide (N=6), AZA+Dasatinib (1), AZA+Ruxolitinib (2), AZA+Sorafenib (1), and AZA+Venetoclax (7). In the patient AZA014 (EB1 with isolated 5q-) who underwent AZA for 17 cycles and responded with mCR (MLD) without HI, the CBM predicted Lenalidomide sensitivity. Following the treatment with Lenalidomide the patient entered a long lasting CR (> 3 years). Digital droplet ddPCR technology during patient follow-up confirmed loss of the clone characterized by TP53(p.Thre377Pro) mutation upon Lenalidomide therapy. In contrast, ddPCR of AZA048 utilized SF3B1 (p.Lys700Glu) mutation tracking during patient follow-up that predicted the relapse by 8 months.
CONCLUSION: The CBM prediction of AZA clinical response in newly diagnosed, higher risk MDS patients showed high predictive accuracy of AZA resistant patients. The study validates the approach to a priori predict response and identify the right therapy option for the patient and could be used to establish criteria for precision enrollment in drug development trials. In addition, analysis uncovered possible mechanisms for AZA resistance that could be targeted to induce response. Clonal architecture is trackable using ddPCR technology providing time for additional NGS analysis to target the progressing clone. Ultimately, improving patient selection and mutation tracking may avoid unnecessary chemotherapy toxicity and reduce health care expenses.
Abbasi:Cell Works Group Inc.: Employment. Singh:Cellworks Research India Private Limited: Employment. Ullal:Cellworks Research India Private Limited: Employment. Narayanabhatia:Cellworks Research India Private Limited: Employment. Alam:Cellworks Research India Private Limited: Employment. Roy:Cellworks Research India Private Limited: Employment. Choudhary:Cellworks Research India Private Limited: Employment. Vali:Cell Works Group Inc.: Employment. Cogle:Celgene: Other: Steering Committee Member of Connect MDS/AML Registry.
•Lenalidomide is highly effective in low risk myelodysplastic syndrome with del(5q).•Borderline bone marrow blasts and TP53 mutation signal a risk of progression.•Addition of erythropoietin can ...trigger response in refractory or relapsed patients.•Further improvement can be obtained with the addition of prednisone.
Lenalidomide therapy represents meaningful progress in the treatment of anemic patients with myelodysplastic syndromes with del(5q). We present our initial lenalidomide experience and the positive effect of combining erythropoietin and steroids with lenalidomide in refractory and relapsed patients. We treated by lenalidomide 55 (42 female; 13 male; median age 69) chronically transfused lower risk MDS patients with del(5q) (45) and non-del(5q) (10). Response, meaning transfusion independence (TI) lasting ≥ eight weeks, was achieved in 38 (90%) of analyzed patients with del(5q), of whom three achieved TI only by adding erythropoietin ± prednisone. Another five patients responded well to this combination when their anemia relapsed later during the treatment. In the non-del(5q) group only one patient with RARS-T reached TI. Cytogenetic response was reached in 64% (32% complete, 32% partial response). The TP53 mutation was detected in 7 (18%) patients; four patients progressed to higher grade MDS or acute myeloid leukemia (AML). All seven RAEB-1 patients cleared bone marrow blasts during lenalidomide treatment and reached complete remission (CR); however, three later progressed to higher grade MDS or AML. Lenalidomide represents effective treatment for del(5q) group and combination with prednisone and erythropoietin may be used for non-responders or therapy failures.
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