PU.1 is a transcription factor absolutely required for normal hematopoiesis. Cumulating evidence indicates that precise levels of PU.1 expression are critical for differentiation to distinct blood ...lineages, and if perturbed, even modest decreases in PU.1 can lead to leukemogenesis. In contrast to extensive knowledge of regulation of PU.1 gene itself, the mechanism of how target genes senses different PU.1 levels remain largely unknown.
To address this, we used PU.1-/- mouse myeloid progenitors encoding inducible PU.1 transgene (PU.1ER, PUER, Walsh 2002) that allows tight control of PU.1 activity. Interestingly, intermediate PU.1 activity induced differentiation of PUER progenitors into granulocyte like cells, while high PU.1 produced macrophages, supporting the model that different PU.1 expression is not a consequence but a driver of cell fate choice. Global expression analysis using 4 different levels of PU.1 at 8 time points (2-96 hrs) revealed that granulocyte specific genes were activated exclusively by intermediate PU.1 levels in 3 distinct modes:
1. not expressed in progenitors while strongly induced at intermediate PU.1 (e.g. Gelatinase B (Mmp9) and Neutrophil collagenase (NC)
2. moderately expressed in progenitors while strongly activated at intermediate PU.1 and repressed at high PU.1 (e.g. Myeloperaxidase (Mpo)
3. highly expressed in unstimulated progenitors with expression maintained at intermediate PU.1 but strongly repressed at high PU.1 (e.g. Neutrophil elastase (NE), Proteinase 3 (primary granule proteins), Cebpe and Gfi1 (Growth factor independent1)
Majority of macrophage genes (incl. CD14, Csf1R, Egr2) were regulated as early PU.1 target genes; being gradually activated by high PU.1 activity within 8hrs. However, most granulocyte genes (NE, Mmp9, Mpo, NC but not Cebpe and GFI1) were late activated PU.1 targets (48 and 96hrs) indicating that these genes are coregulated by additional factor(s), likely an early PU.1 target.
Next we analyzed the regulatory sequences (+-50kb) of two genes activated exclusively by intermediate PU.1, Mpo and Mmp9, using own and public ChIP(seq) data of transcription factors (TFs) (PU.1, GFI.1), DNAseI hypersensitive sites, histone modifications (H3K4Me, H3K27Ac, H3K9Ac) and expression of enhancer specific bidirectional ncRNAs (eRNA) (CAGE).
14 Mpo and 16 Mmp9 putative enhancers, selected by above mentioned criteria, were cloned into luciferase vector containing their proximal promoter (PP) and were tested for functional activity in response to PU.1 levels. Interestingly, the PU.1 binding motifs within these regions have a low to intermediate affinity (log of score, Jaspar) and are often present in multiples and/or enriched for binding sites of other lineage determining transcription factors. Although PU.1 bound to all of these DNA regions resembling superenhancer, just a small fraction of PU.1 binding was functionally responsive. Specifically, we identified novel enhancer elements at -3.4 kb and -15kb of MPO which were activated by intermediate (but not high) PU.1 levels. Interestingly, activity of -3.4 kb enhancer required presence of PP, while the -15kb element required presence of both PP and the -3.4kb element. Similar phenomenon was observed at -5kb and +4.6kb (intronic) MMP9 enhancers. Collectively, these observations suggest that a cooperative assembly of several cell type-specific enhancers is required for optimal Mpo and Mmp9 activation.
This model is supported by our Chromosome conformation capture (3C) data identifying 3D interaction of these enhancer elements at intermediate PU.1 levels suggesting that PU.1 binding mediates DNA looping that allows enhancer cooperation. In addition, activity of these enhancers at intermediate PU.1 levels was associated with expression of bidirectional noncoding enhancer RNAs, confirming functionality of these elements.
In conclusion, our data support the model that PU.1 at intermediate concentration binds to low and intermediate affinity binding sites in several enhancers of granulocyte genes, causing their successive looping and interaction with proximal promoter that leads to transcription activation. The role of cooperating TFs, mechanisms of how granulocyte genes are switched off at high PU.1 concentration and deregulation of these mechanisms in AML are being further studied.
Grants 16-05649S P305/12/1033 16-31586A 16-27790A 16-31586A UNCE 204021 PRVOUK P24
No relevant conflicts of interest to declare.
CT perfusion imaging (CTP) plays an important role in decision making for the treatment of acute ischemic stroke with large vessel occlusion. Since the CT perfusion scan time is approximately one ...minute, the patient is exposed to a non-negligible dose of ionizing radiation. However, further dose reduction increases the level of noise in the data and the resulting perfusion maps. We present a method for reducing noise in perfusion data based on dimension reduction of time attenuation curves. For dimension reduction, we use either the fit of the first five terms of the trigonometric polynomial or the first five terms of the SVD decomposition of the time attenuation profiles. CTP data from four patients with large vessel occlusion and three control subjects were studied. To compare the noise level in the perfusion maps, we use the wavelet estimation of the noise standard deviation implemented in the scikit-image package. We show that both methods significantly reduce noise in the data while preserving important information about the perfusion deficits. These methods can be used to further reduce the dose in CT perfusion protocols or in perfusion studies using C-arm CT, which are burdened by high noise levels.
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.
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.
Introduction: Somatic mutation detection in myelodysplastic syndrome (MDS) is very important in deciphering clonal pathogenesis of every patient and if determined correctly will become useful tool in ...followup studies such as testing individual susceptibility to epigenetic therapy with azacitidine (AZA). While some patients respond to AZA by restoring hematologic parameters, others progress to AML. Recent identification of quite heterogeneous sets of mutated genes (Bejar R et al. 2013) suggested that: patients with specific mutation pattern/s may respond to epigenetic therapy differently.
Aim: We herein set to determine mutation profiles of MDS cohort indicated to and treated by AZA and utilized TrueSight DNA amplicon NGS sequencing approach containing 54 genes all previously associated with MDS or AML.
Patients: We analyzed immunomagnetically CD3-depleted bone marrows of two MDS patients - AZA responders. First patient (male, 68y), was diagnosed with RAEB2, IPSS int-2, transfusion dependent (4 TU/Mo), intermediate cytogenetics (tri21). Following 4 cycles of AZA (75 mg/m2 s.c., 5+2) the patient responded by partial remission, and AZA was discontinued after 17 cycles. Twelve months after discontinuation he progressed and AZA was readministered for additional 3 cycles and the patient achieved again partial remission. Analyzed are samples after 11 (P394) and 20 (P1380) cycles of Vidaza. Second patient (female, 64y), was diagnosed with RAEB2, IPSS high, transfusion dependent (2 TU/Mo), favorable cytogenetics (46XX). Following 4 cycles of AZA (75 mg/m2 s.c., 5+2) she responded by hematology improvement and later by partial remission. Analyzed is a sample after 4 (P1510) cycles of Vidaza. As negative controls we used two normal donor bone marrows from 41y male and 32y female. As a positive control we also used: 1 MDS/AML cell line MOLM-13 with previously identified mutations of CBL and FLT3 (DSMZ; ACC 554).
Methods and approach: Samples were sequenced on Illumina MiSeq sequencer. The mapping was performed using Burrows-Wheeler Aligner algorithm. Illumina Somatic variant caller was used to identify mutations. Then we applied following filters on the data: sequencing coverage should be higher than 1000 per mutation (~80% data left), mutation should be heterozygous (~95% data left), mutation frequency should be higher than 10% (~10% data left), Illumina Somatic variant caller should flag the mutation as "PASS" (~50% data left), mutation should not be synonymous (~75% data left) and mutation should be exonic (~40% data left). These filters were also applied to find mutations in the two control samples. Those mutations which were identified also in the control samples were removed from the analysis of patient samples (~50% data left).
Results: the MDS/AML cell line MOLM-13 contained mutations (SNVs or InDels) in ABL1 (SNV/frequency=46.5%), ASXL1 (SNV/49.8%), CEBPA (In/47.9%), HRAS (SNV/54.5%), TET2 (SNV/49.7%), and as expected also in the genes encoding CBL (delta-exon8/52.4%) and FLT3 (ITD/50.6%). Patient’ sample P1510 contained mutations in CBL (SNV/67.9%), CUX1 (SNV/51.8%), IKZF1 (2 different SNVs/41.9 and 50.7%), KDM6A (SNV/51.6%), SF3B1 (SNV/38.9%), and SMC3 (SNV/33.1%). Patient samples P394 and P1380 contained mutations in the ASXL1 (SNV/ 35.5% and 32.6% respectively), CUX1 (2x SNVs, first SNV/46.1->64.5%, second 48.4->57.9%), and IKZF1 (SNV, 50.4->44.5%) in similar frequencies in the sample before and after 2.5 years (including 9 cycles of AZA) suggesting limited genetic heterogeneity in this AZA-responding patient. Consequently, to gain more insight into how AZA modulates mutation pattern in MDS, we now analyze a set of fourty nine additional patients before and following at least 4 cycles on AZA treatment.
Conclusions: Our data support use of immunomagnetic CD3-depletion of bone marrow and addition of normal control samples in the sequencing of MDS patient samples and support this approach for testing genetic heterogeneity during MDS disease course upon AZA treatment.
Stopka:GAČR P305/12/1033 and UNCE 204021: Research Funding; Celgene: Research Funding; PersMed ltd.: Equity Ownership. Vargova:GAČR P305/12/1033 and P305/11/1745: Research Funding; UNCE 204021: Research Funding; PRVOUK P24/LF1/3: Research Funding. Kulvait:PersMed ltd.: Equity Ownership. Jonasova:PRVOUK P24/LF1/1: Research Funding; Celgene: Research Funding.
CT perfusion imaging (CTP) is used in the diagnostic workup of acute ischemic stroke (AIS). CTP may be performed within the angio suite using flat detector CT (FDCT) to help reduce patient management ...time. In order to significantly improve FDCT perfusion (FDCTP) imaging, data-processing algorithms need to be able to compensate for the higher levels of noise, slow rotation speed, and a lower frame rate of current FDCT devices. We performed a realistic simulation of FDCTP acquisition based on CTP data from seven subjects. We used the time separation technique (TST) as a model-based approach for FDCTP data processing. We propose a novel dimension reduction in which we approximate the time attenuation curves by a linear combination of trigonometric functions. Our goal was to show that the TST can be used even without prior assumptions on the shape of the attenuation profiles. We first demonstrated that a trigonometric basis is suitable for dimension reduction of perfusion data. Using simulated FDCTP data, we have shown that a trigonometric basis in the TST provided better results than the classical straightforward processing even with additional noise. Average correlation coefficients of perfusion maps were improved for cerebral blood flow (CBF), cerebral blood volume, mean transit time (MTT) maps. In a moderate noise scenario, the average Pearson's coefficient for the CBF map was improved using the TST from 0.76 to 0.81. For the MTT map, it was improved from 0.37 to 0.45. Furthermore, we achieved a total processing time from the reconstruction of FDCTP data to the generation of perfusion maps of under 5 min. In our study cohort, perfusion maps created from FDCTP data using the TST with a trigonometric basis showed equivalent perfusion deficits to classic CT perfusion maps. It follows, that this novel FDCTP technique has potential to provide fast and accurate FDCTP imaging for AIS patients.
Richter syndrome represents the transformation of the chronic lymphocytic leukemia (CLL) into an aggressive lymphoma, most frequently the diffuse large B‐cell lymphoma (DLBCL). In this report we ...describe a patient with CLL, who developed a clonally‐related pleomorphic highly‐aggressive mantle cell lymphoma (MCL) after five cycles of a fludarabine‐based second‐line therapy for the first relapse of CLL. Molecular cytogenetic methods together with whole‐exome sequencing revealed numerous gene alterations restricted to the MCL clone (apart from the canonical t(11;14)(q13;q32) translocation) including gain of one copy of ATM gene or emergence of TP53, CREBBP, NUP214, FUBP1 and SF3B1 gene mutations. Similarly, gene expression analysis revealed vast differences between the MCL and CLL transcriptome, including overexpression of cyclin D1, downregulation of cyclins D2 and D3, or downregulation of IL4R in the MCL clone. Backtracking analysis using quantitative PCR specifically detecting an MCL‐restricted focal deletion of TP53 revealed that the pre‐MCL clone appeared in the bone marrow and peripheral blood of the patient approximately 4 years before the clinical manifestation of MCL. Both molecular cytogenetic and sequencing data support the hypothesis of a slow development of the pre‐MCL clone in parallel to CLL over several years, and thereby exclude the possibility that the transformation event occurred at the stage of the CLL relapse clone by mere t(11;14)(q13;q32) acquisition.
What's new?
Richter syndrome represents the transformation of chronic lymphocytic leukemia (CLL) into an aggressive lymphoma. Here, the authors describe a CLL patient who developed a clonally‐related highly aggressive pleomorphic variant of mantle cell lymphoma (MCL) following repeated fludarabine‐based therapy for first relapse of CLL. Exome sequencing revealed MCL‐restricted mutations including TP53, CREBBP, NUP214, FUBP1 and SF3B1. Both sequencing and molecular cytogenetic data support the hypothesis of a slow development of the pre‐MCL clone in parallel to CLL over several years, thereby excluding the possibility that the transformation event occurred at the stage of the CLL relapse clone by mere t(11;14)(q13;q32) acquisition.
Perfusion imaging is an interesting new modality for evaluation and assessment of the liver cancer treatment. C-Arm CT provides a possibility to perform perfusion imaging scans intra-operatively for ...even faster evaluation. The slow speed of the C-Arm CT rotation and the presence of the noise, however, have an impact on the reconstruction and therefore model based approaches have to be applied. In this work we apply the Time separation technique (TST), to denoise data, speed up reconstruction and improve resulting perfusion images. We show on animal experiment data that Dynamic C-Arm CT Liver Perfusion Imaging together with the processing of the data based on the TST provides comparable results to standard CT liver perfusion imaging.
Mantle cell lymphoma (MCL) is an aggressive type of B-cell non-Hodgkin lymphoma (NHL) associated with poor prognosis. Animal models of MCL are scarce. We established and characterized various in vivo ...models of metastatic human MCL by tail vein injection of either primary cells isolated from patients with MCL or established MCL cell lines (Jeko-1, Mino, Rec-1, Hbl-2, and Granta-519) into immunodeficient NOD.Cg-Prkdc(scid) Il2rg(tm1Wjl)/SzJ mice. MCL infiltration was assessed with immunohistochemistry (tissues) and flow cytometry (peripheral blood). Engraftment of primary MCL cells was observed in 7 out of 12 patient samples. The pattern of engraftment of primary MCL cells varied from isolated involvement of the spleen to multiorgan infiltration. On the other hand, tumor engraftment was achieved in all five MCL cell lines used and lymphoma involvement of murine bone marrow, spleen, liver, and brain was observed. Overall survival of xenografted mice ranged from 22 ± 1 to 54 ± 3 days depending on the cell line used. Subsequently, we compared the gene expression profile (GEP) and phenotype of the engrafted MCL cells compared with the original in vitro growing cell lines (controls). We demonstrated that engrafted MCL cells displayed complex changes of GEP, protein expression, and sensitivity to cytotoxic agents when compared with controls. We further demonstrated that our MCL mouse models could be used to test the therapeutic activity of systemic chemotherapy, monoclonal antibodies, or angiogenesis inhibitors. The characterization of MCL murine models is likely to aid in improving our knowledge in the disease biology and to assist scientists in the preclinical and clinical development of novel agents in relapsed/refractory MCL patients.
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