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The information on candidate cancer driver alterations available from public databases is often descriptive and of limited mechanistic insight, which poses difficulties for reliable distinction between true driver and passenger events. To address this challenge, we performed in-depth analysis of whole-exome sequencing data from cell lines generated by a barrier bypass-clonal expansion (BBCE) protocol. The employed strategy is based on carcinogen-driven immortalization of primary mouse embryonic fibroblasts and recapitulates early steps of cell transformation. Among the mutated genes were almost 200 COSMIC Cancer Gene Census genes, many of which were recurrently affected in the set of 25 immortalized cell lines. The alterations affected pathways regulating DNA damage response and repair, transcription and chromatin structure, cell cycle and cell death, as well as developmental pathways. The functional impact of the mutations was strongly supported by the manifestation of several known cancer hotspot mutations among the identified alterations. We identified a new set of genes encoding subunits of the BAF chromatin remodeling complex that exhibited Ras-mediated dependence on PRC2 histone methyltransferase activity, a finding that is similar to what has been observed for other BAF subunits in cancer cells. Among the affected BAF complex subunits, we determined Smarcd2 and Smarcc1 as putative driver candidates not yet fully identified by large-scale cancer genome sequencing projects. In addition, Ep400 displayed characteristics of a driver gene in that it showed a mutually exclusive mutation pattern when compared with mutations in the Trrap subunit of the TIP60 complex, both in the cell line panel and in a human tumor data set. We propose that the information generated by deep sequencing of the BBCE cell lines coupled with phenotypic analysis of the mutant cells can yield mechanistic insights into driver events relevant to human cancer development.

Epigenetic 5-azacitidine (AZA) therapy of high-risk myelodysplastic syndromes (MDS) and acute myelogenous leukemia (AML) represents a promising, albeit not fully understood, approach. Hematopoietic transcription factor PU.1 is dynamically regulated by upstream regulatory element (URE), whose deletion causes downregulation of PU.1 leading to AML in mouse. In this study a significant group of the high-risk MDS patients, as well as MDS cell lines, displayed downregulation of PU.1 expression within CD34+ cells, which was associated with DNA methylation of the URE. AZA treatment in vitro significantly demethylated URE, leading to upregulation of PU.1 followed by derepression of its transcriptional targets and onset of myeloid differentiation. Addition of colony-stimulating factors (CSFs; granulocyte-CSF, granulocyte-macrophage-CSF and macrophage-CSF) modulated AZA-mediated effects on reprogramming of histone modifications at the URE and cell differentiation outcome. Our data collectively support the importance of modifying the URE chromatin structure as a regulatory mechanism of AZA-mediated activation of PU.1 and induction of the myeloid program in MDS.

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. Disclosures 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. Disclosures 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.

The transcription factor PU.1 and its inhibitory microRNA-155 (miR-155) are important regulators of B-cell differentiation. PU.1 downregulation coupled with oncogenic miR-155 upregulation has been reported in lymphoid malignancies; however, these data have not been studied across different subtypes in relation to clinical outcomes. We studied expression of miR-155 and PU.1 in the six most prevalent human B-cell lymphomas (n = 131) including aggressive (DLBCL, HL, MCL) and indolent (B-CLL/SLL, MZL, FL) types. Levels of miR-155 and PU.1 inversely correlated in DLBCL, B-CLL/SLL, and FL tumor tissues. In HL tissues, an exceptionally high level of miR-155 was found in patients with unfavorable responses to first-line therapy and those who had shorter survival times. PU.1 downregulation was noted in B-CLL/SLL samples positive for the adverse prognostic markers CD38 and ZAP-70. Upregulation of miR-155 and downregulation of PU.1 expression are integral aspects of lymphoma biology that could mark aggressive behavior of some, but not all, lymphoma types.

PU.1 downregulation within hematopoietic stem and progenitor cells (HSPCs) is the primary mechanism for the development of acute myeloid leukemia (AML) in mice with homozygous deletion of the upstream regulatory element (URE) of PU.1 gene. p53 is a well-known tumor suppressor that is often mutated in human hematologic malignancies including AML and adds to their aggressiveness; however, its genetic deletion does not cause AML in mouse. Deletion of p53 in the PU.1(ure/ure) mice (PU.1(ure/ure)p53(-/-)) results in more aggressive AML with shortened overall survival. PU.1(ure/ure)p53(-/-) progenitors express significantly lower PU.1 levels. In addition to URE deletion we searched for other mechanisms that in the absence of p53 contribute to decreased PU.1 levels in PU.1(ure/ure)p53(-/-) mice. We found involvement of Myb and miR-155 in downregulation of PU.1 in aggressive murine AML. Upon inhibition of either Myb or miR-155 in vitro the AML progenitors restore PU.1 levels and lose leukemic cell growth similarly to PU.1 rescue. The MYB/miR-155/PU.1 axis is a target of p53 and is activated early after p53 loss as indicated by transient p53 knockdown. Furthermore, deregulation of both MYB and miR-155 coupled with PU.1 downregulation was observed in human AML, suggesting that MYB/miR-155/PU.1 mechanism may be involved in the pathogenesis of AML and its aggressiveness characterized by p53 mutation.

Nucleosome movement is, at least in part, facilitated by ISWI ATPase Smarca5 (Snf2h). Smarca5 gene inactivation in mouse demonstrated its requirement at blastocyst stage; however its role at later stages is not completely understood. We herein determined nuclear distribution of Smarca5 and histone marks associated with actively transcribed and repressed chromatin structure in embryonic and adult murine tissues and in tumor cells. Confocal microscopy images demonstrate that Smarca5 is localized mainly in euchromatin and to lesser extent also in heterochromatin and nucleoli. Smarca5 heterozygous mice for a null allele display decreased levels of histone H3 modifications and defects in heterochromatin foci supporting role of Smarca5 as a key regulator of global chromatin structure.

Introduction and hypothesis: Somatic gene mutations develop in ~78% Myelodysplastic syndrome (MDS) patients. MDS progresses into an unstable phase characterized by an accumulation of myeloblasts that is indicated for the DNA demethylation therapy with Azacitidine (AZA). To understand whether AZA is capable to eliminate tumor cells and whether a mutation pattern responds to AZA we herein tracked mutations in the bone marrow (BM) during AZA treatment. Two scenarios were postulated: 1) AZA treatment will/ will not eliminate clones characterized by specific mutations and this will relate to the clinical outcome, 2) during clinical progression on the AZA treatment either the new mutations will develop or the original mutations will be detected. Patients: 40 int-2/high-risk MDS patients (176 samples, median age 70, 22F/18M) indicated for AZA (75mg/m2, 5+2+2) were sequenced. The MDS subtypes included RAEB1, RAEB2, and MDS/AML. Half of the patients progressed from the 5q-syndrome. Libraries were prepared using TruSight DNA amplicon kit. Set of 54 myeloid genes (associated with MDS or AML) were sequenced by Illumina platform HiSeq 2500 with depth >100 per mutation, mutation should be heterozygous, non-synonymous, exonic with frequency >10%. As controls: 4 normal BM samples and 2 cord bloods (also applied for data filtering) and MOLM-13 cell line carrying FLT3-ITD mutation were used. Additional controls included CD3+ T cells isolated from the patient BM samples. Results: 70% of patients were informative for at least one somatic mutation with high impact on the amino acid sequence. Mutations in TET2 (in 13%) were overall the most frequent before AZA was started (followed by mutations in BCOR, RUNX1, STAG2, and NOTCH genes). In 36% of informative patients, the mutation pattern developed on AZA. Interestingly, the most frequent mutations after AZA were in ASXL1 in 10% (followed by mutations in TET2, BCOR, and CUX1 genes). While mutations in ASXL1 and NOTCH genes developped only in the non-5q derived patients, the mutations in RUNX1 developped only in the 5q-derived higher-risk MDS patients. Average number of mutations before AZA and on AZA was 2 per patient. Complete elimination of the mutation pattern was noted in 57% of informative patients during the first 8 months of AZA treatment and this was associated with the therapy responsiveness (PR or CR). In contrast in 7% of informative patients the mutation pattern remained the same and this was associated with the stable disease (leading to progression). Upon progression on the AZA treatment we have observed appearance of the new mutations in 73% of informative patients. 20% of patients progressing after 14 cycles of AZA were non-informative which suggests that these patients may carry mutations in genes not included within the tested set. We have assessed overall survival (OS) according to the mutation status in the following subgroups: non-informative patients, informative patients with mutations that were eliminated by AZA, and those retaining or gaining the mutations upon AZA. The group that developed or retained mutations during first 4-8 cycles of AZA displayed significantly lower OS (p=0.004, median OS:11 months) compared to either non-informative patients (26 months) or those where AZA completely eliminated the mutation pattern (28 months). Conclusions: Tracking the mutations in MDS patients during AZA therapy provides opportunity to detect clonal development in 2/3 of the MDS patients and relate these data to the clinical outcome. Moreover, progression on AZA therapy is usually associated with the development of a new mutation pattern and this coincides with the significantly lower OS. Grants: GACR P305/12/1033, UNCE 204021, PRVOUK-P24/LF1/1. Disclosures Stopka: Celgene: Research Funding.

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. Disclosures No relevant conflicts of interest to declare.

Mantle cell lymphoma (MCL) is a relatively distinct B-cell non-Hodgkin lymphoma subtype with aggressive and often recurrent clinical course. At diagnosis, MCL often manifests with leukemization, a feature more common to chronic lymphocytic leukemia (CLL). Common features and differences between MCL and CLL were not yet explored by comprehensive global approaches, despite such understanding potentially being very neat for deciphering pathogenesis and tailoring therapies of these clinically distinct diseases. In our study, we have compared MCL(n=10), CLL(n=10) and normal control(n=8) B-cell samples using the Affymetrix Human Genome HG-U133 Plus 2.0 Array. We studied different mRNA levels of ~47.000 transcripts represented on the array. The comparative analyses identified a set of 892 differentially expressed genes between MCL and NBC; and 774 differentially expressed genes between CLL and NBC. In order to find MCL/CLL-specific biomarkers we focused on the intersection of differently expressed genes in both groups (CLL vs NBC and MCL vs NBC). There were 222 mRNAs in the intersection, 216 of them were deregulated in the same direction in both groups while 6 mRNAs were deregulated in the opposite direction. This set of 6 disease-specific mRNAs contained previously reported biomarkers (CD200, LEF1), and also the Myristoylated alanine-rich C-kinase substrate (MARCKS) that has not yet been studied in MCL. Thus we utilized the validation patient groups (NMCL=6, NCLL=8) and confirmed differential expression of MARCKS on protein levels by flow cytometry and immunofluorescence. As MARCKS was previously shown to either bound to the cell membrane, to reside in the cytosol, or alternatively become transmitted to nuclei, we investigated subcellular localization of MARCKS using immunofluorescence (IF). The cytoplasmic MARCKS signal in MCL was significantly higher than in CLL while the opposite was observed for the nuclear IF signal. The ratio between cytoplasmic and nuclear signal was 2.5 for MCL and 0.8 for CLL (p < 0,0001). The active forms of MARCKS were shown to become phosphorylated on serineresidues and this prompted us to study the phosphorylation forms of MARCKS in MCL. Indeed, one of the residues, Ser159/163, was hyperphosphorylated in the MCL cytoplasm and its level and distribution markedly differed from CLL or NBC. We next searched for regulatory mechanisms upstream of the MARCKS expression in MCL vs CLL. MARCKS is a predicted target of several microRNAs (according to DIANA-TarBase v7.0), among them also of miR-155 (that is differentially expressed between MCL and CLL). To further investigate the regulatory relationship between mir-155 and MARCKS we utilized a CLL cell line MEC-1 and using the CRISPR/Cas9 technology we prepared individual cell clones that were mutated within the mature miR-155 sequence that recognizes MARCKS mRNA. As expected, the miR-155-MEC-1 mutants expressed markedly higher level of MARCKS compared to the control MEC-1 cells. In conclusion, our work identified a set of six differentially expressed mRNAs when comparing MCL and CLL, among them, MARCKS. We further showed that MARCKS is differentially expressed, localized, and phosphorylated between MCL and CLL, and that MARCKS is partly controlled by oncogenic microRNA miR-155. MARCKS may play an important role in MCL pathogenesis and can serve as useful MCL biomarker. Grant support: GAČR 16-05649S & P305/12/1033, AZV: 16-27790A and 16-31586A. Institutional support: CZ.1.05/1.1.00/02.0109, UNCE 204021, LH15170, PRVOUK P24, LQ1604. Disclosures No relevant conflicts of interest to declare.