Optimizing Drug Combinations for T-PLL: Restoring DNA Damage and P53-Mediated Apoptotic Responses.

T-prolymphocytic leukemia (T-PLL) is a mature T-cell neoplasm associated with marked chemotherapy resistance and continued poor clinical outcomes. Current treatments, i.e. the CD52-antibody alemtuzumab, offer transient responses, with relapses being almost inevitable without consolidating allogeneic transplantation. Recent more detailed concepts of T-PLL's pathobiology fostered the identification of actionable vulnerabilities: (i) altered epigenetics, (ii) defective DNA damage responses, (iii) aberrant cell-cycle regulation, and (iv) deregulated pro-survival pathways, including TCR and JAK/STAT signaling. To further develop related pre-clinical therapeutic concepts, we studied inhibitors of (H)DACs, BCL2, CDK, MDM2, and clas-sical cytostatics, utilizing (a) single-agent and combinatorial compound testing in 20 well-characterized and molecularly-profiled primary T-PLL (validated by additional 42 cases), and (b) 2 independent murine models (syngeneic transplants and patient-derived xenografts). Overall, the most efficient/selective single-agents and combinations (in vitro and in mice) in-cluded Cladribine, Romidepsin ((H)DAC), Venetoclax (BCL2), and/or Idasanutlin (MDM2). Cladribine sensitivity correlated with expression of its target RRM2. T-PLL cells revealed low overall apoptotic priming with heterogeneous dependencies on BCL2 proteins. In additional 38 T-cell leukemia/lymphoma lines, TP53 mutations were associated with resistance towards MDM2 inhibitors. P53 of T-PLL cells, predominantly in wild-type configuration, was amenable to MDM2 inhibition, which increased its MDM2-unbound fraction. This facilitated P53 activa-tion and down-stream signals (including enhanced accessibility of target-gene chromatin re-gions), in particular synergy with insults by Cladribine. Our data emphasize the therapeutic potential of pharmacologic strategies to reinstate P53-mediated apoptotic responses. The identified efficacies and their synergies provide an informative background on compound and patient selection for trial designs in T-PLL.

Epigenome-wide methylation study identified two novel CpGs associated with T2DM risk and a network of co-methylated CpGs capable of patient's classifications.

Prevention of Type 2 diabetes mellitus (T2DM) pandemic needs markers that can precisely predict the disease risk in an individual. Alterations in DNA methylations due to exposure towards environmental risk factors are widely sought markers for T2DM risk prediction. To identify such individual DNA methylation signatures and their effect on disease risk, we performed an epigenome-wide association study (EWAS) in 844 Indian individuals of Indo-European origin. We identified and validated methylation alterations at two novel CpG sites in MIR1287 (cg01178710) and EDN2-SCMH1 (cg04673737) genes associated with T2DM risk at the epigenome-wide-significance-level (P < 1.2 × 10-7). Further, we also replicated the association of two known CpG sites in TXNIP, and CPT1A in the Indian population. With 535 EWAS significant CpGs (P < 1.2 × 10-7) identified in the discovery phase samples, we created a co-methylation network using weighted correlation network analysis and identified four modules among the CpGs. We observed that methylation of one of the module associates with T2DM risk factors (e.g. BMI, insulin and C-peptide) and can be used as markers to segregate T2DM patients with good glycemic control (e.g. low HbA1c) and dyslipidemia (low HDL and high TG) from the other patients. Additionally, an intronic SNP (rs6503650) in the JUP gene, a member of the same module, associated with methylation at all the 14 hub CpG sites of that module as methQTL. Our network-assisted EWAS is the first to systematically explore DNA methylation variations conferring risks to T2DM in Indians and use the identified risk CpG sites for patient segregation with different clinical outcomes. These findings can be useful for better stratification of patients to improve the clinical management and treatment effects.

Genome-Wide Association Study Identifies 4 Novel Risk Loci for Small Intestinal Neuroendocrine Tumors Including a Missense Mutation in LGR5.

BACKGROUND & AIMS: Small intestinal neuroendocrine tumor (SI-NET) is a rare disease, but its incidence has increased over the past 4 decades. Understanding the genetic risk factors underlying SI-NETs can help in disease prevention and may provide clinically beneficial markers for diagnosis. Here the results of the largest genome-wide association study of SI-NETs performed to date with 405 cases and 614,666 controls are reported. METHODS: Samples from 307 patients with SI-NETs and 287,137 controls in the FinnGen study were used for the identification of SI-NET risk-associated genetic variants. The results were also meta-analyzed with summary statistics from the UK Biobank (n = 98 patients with SI-NET and n = 327,529 controls). RESULTS: We identified 6 genome-wide significant (P < 5 × 10-8) loci associated with SI-NET risk, of which 4 (near SEMA6A, LGR5, CDKAL1, and FERMT2) are novel and 2 (near LTA4H-ELK and in KIF16B) have been reported previously. Interestingly, the top hit (rs200138614; P = 1.80 × 10-19) was a missense variant (p.Cys712Phe) in the LGR5 gene, a bona-fide marker of adult intestinal stem cells and a potentiator of canonical WNT signaling. The association was validated in an independent Finnish collection of 70 patients with SI-NETs, as well as in the UK Biobank exome sequence data (n = 92 cases and n = 392,814 controls). Overexpression of LGR5 p.Cys712Phe in intestinal organoids abolished the ability of R-Spondin1 to support organoid growth, indicating that the mutation perturbed R-Spondin-LGR5 signaling. CONCLUSIONS: Our study is the largest genome-wide association study to date on SI-NETs and reported 4 new associated genome-wide association study loci, including a novel missense mutation (rs200138614, p.Cys712Phe) in LGR5, a canonical marker of adult intestinal stem cells.

SynergyFinder 3.0: an interactive analysis and consensus interpretation of multi-drug synergies across multiple samples.

SynergyFinder (https://synergyfinder.fimm.fi) is a free web-application for interactive analysis and visualization of multi-drug combination response data. Since its first release in 2017, SynergyFinder has become a popular tool for multi-dose combination data analytics, partly because the development of its functionality and graphical interface has been driven by a diverse user community, including both chemical biologists and computational scientists. Here, we describe the latest upgrade of this community-effort, SynergyFinder release 3.0, introducing a number of novel features that support interactive multi-sample analysis of combination synergy, a novel consensus synergy score that combines multiple synergy scoring models, and an improved outlier detection functionality that eliminates false positive results, along with many other post-analysis options such as weighting of synergy by drug concentrations and distinguishing between different modes of synergy (potency and efficacy). Based on user requests, several additional improvements were also implemented, including new data visualizations and export options for multi-drug combinations. With these improvements, SynergyFinder 3.0 supports robust identification of consistent combinatorial synergies for multi-drug combinatorial discovery and clinical translation.

SynergyFinder 2.0: visual analytics of multi-drug combination synergies.

SynergyFinder (https://synergyfinder.fimm.fi) is a stand-alone web-application for interactive analysis and visualization of drug combination screening data. Since its first release in 2017, SynergyFinder has become a widely used web-tool both for the discovery of novel synergistic drug combinations in pre-clinical model systems (e.g. cell lines or primary patient-derived cells), and for better understanding of mechanisms of combination treatment efficacy or resistance. Here, we describe the latest version of SynergyFinder (release 2.0), which has extensively been upgraded through the addition of novel features supporting especially higher-order combination data analytics and exploratory visualization of multi-drug synergy patterns, along with automated outlier detection procedure, extended curve-fitting functionality and statistical analysis of replicate measurements. A number of additional improvements were also implemented based on the user requests, including new visualization and export options, updated user interface, as well as enhanced stability and performance of the web-tool. With these improvements, SynergyFinder 2.0 is expected to greatly extend its potential applications in various areas of multi-drug combinatorial screening and precision medicine.

SynToxProfiler: An interactive analysis of drug combination synergy, toxicity and efficacy.

Drug combinations are becoming a standard treatment of many complex diseases due to their capability to overcome resistance to monotherapy. In the current preclinical drug combination screening, the top combinations for further study are often selected based on synergy alone, without considering the combination efficacy and toxicity effects, even though these are critical determinants for the clinical success of a therapy. To promote the prioritization of drug combinations based on integrated analysis of synergy, efficacy and toxicity profiles, we implemented a web-based open-source tool, SynToxProfiler (Synergy-Toxicity-Profiler). When applied to 20 anti-cancer drug combinations tested both in healthy control and T-cell prolymphocytic leukemia (T-PLL) patient cells, as well as to 77 anti-viral drug pairs tested in Huh7 liver cell line with and without Ebola virus infection, SynToxProfiler prioritized as top hits those synergistic drug pairs that showed higher selective efficacy (difference between efficacy and toxicity), which offers an improved likelihood for clinical success.

Directional dominance on stature and cognition in diverse human populations.

Homozygosity has long been associated with rare, often devastating, Mendelian disorders, and Darwin was one of the first to recognize that inbreeding reduces evolutionary fitness. However, the effect of the more distant parental relatedness that is common in modern human populations is less well understood. Genomic data now allow us to investigate the effects of homozygosity on traits of public health importance by observing contiguous homozygous segments (runs of homozygosity), which are inferred to be homozygous along their complete length. Given the low levels of genome-wide homozygosity prevalent in most human populations, information is required on very large numbers of people to provide sufficient power. Here we use runs of homozygosity to study 16 health-related quantitative traits in 354,224 individuals from 102 cohorts, and find statistically significant associations between summed runs of homozygosity and four complex traits: height, forced expiratory lung volume in one second, general cognitive ability and educational attainment (P < 1 × 10(-300), 2.1 × 10(-6), 2.5 × 10(-10) and 1.8 × 10(-10), respectively). In each case, increased homozygosity was associated with decreased trait value, equivalent to the offspring of first cousins being 1.2 cm shorter and having 10 months' less education. Similar effect sizes were found across four continental groups and populations with different degrees of genome-wide homozygosity, providing evidence that homozygosity, rather than confounding, directly contributes to phenotypic variance. Contrary to earlier reports in substantially smaller samples, no evidence was seen of an influence of genome-wide homozygosity on blood pressure and low density lipoprotein cholesterol, or ten other cardio-metabolic traits. Since directional dominance is predicted for traits under directional evolutionary selection, this study provides evidence that increased stature and cognitive function have been positively selected in human evolution, whereas many important risk factors for late-onset complex diseases may not have been.

Multifaceted genome-wide study identifies novel regulatory loci in SLC22A11 and ZNF45 for body mass index in Indians.

Obesity, a risk factor for multiple diseases (e.g. diabetes, hypertension, cancers) originates through complex interactions between genes and prevailing environment (food habit and lifestyle) that varies across populations. Indians exhibit a unique obesity phenotype with high abdominal adiposity for a given body weight compared to matched white populations suggesting presence of population-specific genetic and environmental factors influencing obesity. However, Indian population-specific genetic contributors for obesity have not been explored yet. Therefore, to identify potential genetic contributors, we performed a two-staged genome-wide association study (GWAS) for body mass index (BMI), a common measure to evaluate obesity in 5973 Indian adults and the lead findings were further replicated in 1286 Indian adolescents. Our study revealed novel association of variants-rs6913677 in BAI3 gene (p = 1.08 × 10-8) and rs2078267 in SLC22A11 gene (p = 4.62 × 10-8) at GWAS significance, and of rs8100011 in ZNF45 gene (p = 1.04 × 10-7) with near GWAS significance. As genetic loci may dictate the phenotype through modulation of epigenetic processes, we overlapped genetic data of identified signals with their DNA methylation patterns in 236 Indian individuals and performed methylation quantitative trait loci (meth-QTL) analysis. Further, functional roles of discovered variants and underlying genes were speculated using publicly available gene regulatory databases (ENCODE, JASPAR, GeneHancer, GTEx). The identified variants in BAI3 and SLC22A11 genes were found to dictate methylation patterns at unique CpGs harboring critical cis-regulatory elements. Further, BAI3, SLC22A11 and ZNF45 variants were located in repressive chromatin, active enhancer, and active chromatin regions, respectively, in human subcutaneous adipose tissue in ENCODE database. Additionally, these genomic regions represented potential binding sites for key transcription factors implicated in obesity and/or metabolic disorders. Interestingly, GTEx portal identify rs8100011 as a robust cis-expression quantitative trait locus (cis-eQTL) in subcutaneous adipose tissue (p = 1.6 × 10-7), and ZNF45 gene expression in skeletal muscle of Indian subjects showed an inverse correlation with BMI indicating its possible role in obesity. In conclusion, our study discovered 3 novel population-specific functional genetic variants (rs6913677, rs2078267, rs8100011) in 2 novel (SLC22A11 and ZNF45) and 1 earlier reported gene (BAI3) for BMI in Indians. Our study decodes key genomic loci underlying obesity phenotype in Indians that may serve as prospective drug targets in future.

Genome-wide association study of blood lipids in Indians confirms universality of established variants.

Lipids foster energy production and their altered levels have been coupled with metabolic ailments. Indians feature high prevalence of metabolic diseases, yet uncharacterized for genes regulating lipid homeostasis. We performed first GWAS for quantitative lipids (total cholesterol, LDL, HDL, and triglycerides) exclusively in 5271 Indians. Further to corroborate our genetic findings, we investigated DNA methylation marks in peripheral blood in Indians at the identified loci (N = 233) and retrieved gene regulatory features from public domains. Recurrent GWAS loci-CELSR2, CETP, LPL, ZNF259, and BUD13 cropped up as lead signals in Indians, reflecting their universal applicability. Besides established variants, we found certain unreported variants at sub-genome-wide level-QKI, REEP3, TMCC2, FAM129C, FAM241B, and LOC100506207. These variants though failed to attain GWAS significance in Indians, but largely turned out to be active CpG sites in human subcutaneous adipose tissue and showed robust association to two or more lipid traits. Of which, QKI variants showed significant association to all four lipid traits and their designated region was observed to be a key gene regulatory segment denoting active transcription particularly in human subcutaneous adipose tissue. Both established and novel loci were observed to be significantly associated with altered DNA methylation in Indians for specific CpGs that resided in key regulatory elements. Further, gene-based association analysis pinpointed novel GWAS loci-LINC01340 and IQCJ-SCHIP1 for TC; IFT27, IFT88, and LINC02141 for HDL; and TEX26 for TG. Present study ascertains universality of selected known genes and also identifies certain novel loci for lipids in Indians by integrating data from various levels of gene regulation.

DNA methylation profiling reveals the presence of population-specific signatures correlating with phenotypic characteristics.

Phenotypic characteristics are known to vary substantially among different ethnicities around the globe. These variations are mediated by number of stochastic events and cannot be attributed to genetic architecture alone. DNA methylation is a well-established mechanism that sculpts our epigenome influencing phenotypic variation including disease manifestation. Since DNA methylation is an important determinant for health issues of a population, it demands a thorough investigation of the natural differences in genome wide DNA methylation patterns across different ethnic groups. This study is based on comparative analyses of methylome from five different ethnicities with major focus on Indian subjects. The current study uses hierarchical clustering approaches, principal component analysis and locus specific differential methylation analysis on Illumina 450K methylation data to compare methylome of different ethnic subjects. Our data indicates that the variations in DNA methylation patterns of Indians are less among themselves compared to other global population. It empirically correlated with dietary, cultural and demographical divergences across different ethnic groups. Our work further suggests that Indians included in this study, despite their genetic similarity with the Caucasian population, are in close proximity with Japanese in terms of their methylation signatures.

Engineering CRISPR/Cas9 therapeutics for cancer precision medicine.

The discovery of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR-associated protein 9 (Cas9) technology has revolutionized field of cancer treatment. This review explores usage of CRISPR/Cas9 for editing and investigating genes involved in human carcinogenesis. It provides insights into the development of CRISPR as a genetic tool. Also, it explores recent developments and tools available in designing CRISPR/Cas9 systems for targeting oncogenic genes for cancer treatment. Further, we delve into an overview of cancer biology, highlighting key genetic alterations and signaling pathways whose deletion prevents malignancies. This fundamental knowledge enables a deeper understanding of how CRISPR/Cas9 can be tailored to address specific genetic aberrations and offer personalized therapeutic approaches. In this review, we showcase studies and preclinical trials that show the utility of CRISPR/Cas9 in disrupting oncogenic targets, modulating tumor microenvironment and increasing the efficiency of available anti treatments. It also provides insight into the use of CRISPR high throughput screens for cancer biomarker identifications and CRISPR based screening for drug discovery. In conclusion, this review offers an overview of exciting developments in engineering CRISPR/Cas9 therapeutics for cancer treatment and highlights the transformative potential of CRISPR for innovation and effective cancer treatments.

Genome-wide mRNA profiling in urinary extracellular vesicles reveals stress gene signature for diabetic kidney disease.

Urinary extracellular vesicles (uEV) are a largely unexplored source of kidney-derived mRNAs with potential to serve as a liquid kidney biopsy. We assessed ∼200 uEV mRNA samples from clinical studies by genome-wide sequencing to discover mechanisms and candidate biomarkers of diabetic kidney disease (DKD) in Type 1 diabetes (T1D) with replication in Type 1 and 2 diabetes. Sequencing reproducibly showed >10,000 mRNAs with similarity to kidney transcriptome. T1D DKD groups showed 13 upregulated genes prevalently expressed in proximal tubules, correlated with hyperglycemia and involved in cellular/oxidative stress homeostasis. We used six of them (GPX3, NOX4, MSRB, MSRA, HRSP12, and CRYAB) to construct a transcriptional "stress score" that reflected long-term decline of kidney function and could even identify normoalbuminuric individuals showing early decline. We thus provide workflow and web resource for studying uEV transcriptomes in clinical urine samples and stress-linked DKD markers as potential early non-invasive biomarkers or drug targets.

High-throughput screening for drug discovery targeting the cancer cell-microenvironment interactions in hematological cancers.

INTRODUCTION: The interactions between leukemic blasts and cells within the bone marrow environment affect oncogenesis, cancer stem cell survival, as well as drug resistance in hematological cancers. The importance of this interaction is increasingly being recognized as a potentially important target for future drug discoveries and developments. Recent innovations in the high throughput drug screening-related technologies, novel ex-vivo disease-models, and freely available machine-learning algorithms are advancing the drug discovery process by targeting earlier undruggable proteins, complex pathways, as well as physical interactions (e.g. leukemic cell-bone microenvironment interaction). AREA COVERED: In this review, the authors discuss the recent methodological advancements and existing challenges to target specialized hematopoietic niches within the bone marrow during leukemia and suggest how such methods can be used to identify drugs targeting leukemic cell-bone microenvironment interactions. EXPERT OPINION: The recent development in cell-cell communication scoring technology and culture conditions can speed up the drug discovery by targeting the cell-microenvironment interaction. However, to accelerate this process, collecting clinical-relevant patient tissues, developing culture model systems, and implementing computational algorithms, especially trained to predict drugs and their combination targeting the cancer cell-bone microenvironment interaction are needed.

Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data.

Identification of cell populations often relies on manual annotation of cell clusters using established marker genes. However, the selection of marker genes is a time-consuming process that may lead to sub-optimal annotations as the markers must be informative of both the individual cell clusters and various cell types present in the sample. Here, we developed a computational platform, ScType, which enables a fully-automated and ultra-fast cell-type identification based solely on a given scRNA-seq data, along with a comprehensive cell marker database as background information. Using six scRNA-seq datasets from various human and mouse tissues, we show how ScType provides unbiased and accurate cell type annotations by guaranteeing the specificity of positive and negative marker genes across cell clusters and cell types. We also demonstrate how ScType distinguishes between healthy and malignant cell populations, based on single-cell calling of single-nucleotide variants, making it a versatile tool for anticancer applications. The widely applicable method is deployed both as an interactive web-tool ( https://sctype.app ), and as an open-source R-package.

Analysis of the payments from the coronavirus food assistance program and the market facilitation program to minority producers.

This paper examines the payments made to minority producers, focused on African American producers, from the COVID-19 program, Coronavirus Food Assistance Program (CFAP), of the United States Department of Agriculture (USDA) and compares it with one of the other more recent ad hoc program payments, the Market Facilitation Program (MFP). There were two rounds of the CFAP, and combinedly (as of March 2022), the program made direct payments of $31.0 billion ($11.8 billion from CFAP 1 and $19.2 billion from CFAP 2) starting in 2020. The MFP made a total payment of $23.5 billion (in two rounds, MFP 2018 and MFP 2019) to producers affected by the retaliatory tariffs placed on US producers by trade partners across multiple years. CFAP made almost $600 million in direct payments to minority producers, including Black or African American producers. Black or African American only producers received more than $52 million in CFAP payments. CFAP payments were proportional to the value of agricultural commodity sold for most minority producers. The 2017 Census of Agriculture showed that the majority of minority producers, including African American producers but excluding Asian producers, raised livestock. CFAP made the highest payments to livestock minority producers. The CFAP payment distribution pattern shows that payments reached minority producers who often did not receive Government payments. CFAP made more payments and as a share of total program outlays to minority producers compared to MFP. However, for Black or African American only producers, even though the magnitude increased (because CFAP disbursed more funds compared to MFP), the share of payment received did not increase.

Patient-tailored design for selective co-inhibition of leukemic cell subpopulations.

The extensive drug resistance requires rational approaches to design personalized combinatorial treatments that exploit patient-specific therapeutic vulnerabilities to selectively target disease-driving cell subpopulations. To solve the combinatorial explosion challenge, we implemented an effective machine learning approach that prioritizes patient-customized drug combinations with a desired synergy-efficacy-toxicity balance by combining single-cell RNA sequencing with ex vivo single-agent testing in scarce patient-derived primary cells. When applied to two diagnostic and two refractory acute myeloid leukemia (AML) patient cases, each with a different genetic background, we accurately predicted patient-specific combinations that not only resulted in synergistic cancer cell co-inhibition but also were capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Our functional precision oncology approach provides an unbiased means for systematic identification of personalized combinatorial regimens that selectively co-inhibit leukemic cells while avoiding inhibition of nonmalignant cells, thereby increasing their likelihood for clinical translation.

Higher ETV5 Expression Associates With Poor 5-Florouracil-Based Adjuvant Therapy Response in Colon Cancer.

Discovery of markers predictive for 5-Fluorouracil (5-FU)-based adjuvant chemotherapy (adjCTX) response in patients with locally advanced stage II and III colon cancer (CC) is necessary for precise identification of potential therapy responders. PEA3 subfamily of ETS transcription factors (ETV1, ETV4, and ETV5) are upregulated in multiple cancers including colon cancers. However, the underlying epigenetic mechanism regulating their overexpression as well as their role in predicting therapy response in colon cancer are largely unexplored. In this study, using gene expression and methylation data from The Cancer Genome Atlas (TCGA) project, we showed that promoter DNA methylation negatively correlates with ETV4 expression (ρ = -0.17, p = 5.6 × 10-3) and positively correlates with ETV5 expression (ρ = 0.22, p = 1.43 × 10-4) in colon cancer tissue. Further, our analysis in 1,482 colon cancer patients from five different cohorts revealed that higher ETV5 expression associates with shorter relapse-free survival (RFS) of adjCTX treated colon cancer patients (Hazard ratio = 2.09-5.43, p = 0.004-0.01). The present study suggests ETV5 expression as a strong predictive biomarker for 5-FU-based adjCTX response in stage II/III CC patients.

Charting the challenges behind the testing of COVID-19 in developing countries: Nepal as a case study.

The infrastructure needed to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection (COVID-19) that complies completely with WHO guidelines is lacking across many parts of the globe, especially in developing countries, including Nepal. We outline the problems faced by such countries and suggest that the national and international community should collaborate in the development and adoption of novel protocols for the rapid detection of COVID-19 according to locally available infrastructure, in order to fight against the outbreak.

DNMT Inhibitors Increase Methylation in the Cancer Genome.

DNA methyltransferase inhibitors (DNMTi) decitabine and azacytidine are approved therapies for myelodysplastic syndrome and acute myeloid leukemia, and their combinations with other anticancer agents are being tested as therapeutic options for multiple solid cancers such as colon, ovarian, and lung cancer. However, the current therapeutic challenges of DNMTis include development of resistance, severe side effects and no or partial treatment responses, as observed in more than half of the patients. Therefore, there is a critical need to better understand the mechanisms of action of these drugs. In order to discover molecular targets of DNMTi therapy, we identified 638 novel CpGs with an increased methylation in response to decitabine treatment in HCT116 cell lines and validated the findings in multiple cancer types (e.g., bladder, ovarian, breast, and lymphoma) cell lines, bone marrow mononuclear cells from primary leukemia patients, as well as peripheral blood mononuclear cells and ascites from platinum resistance epithelial ovarian cancer patients. Azacytidine treatment also increased methylation of these CpGs in colon, ovarian, breast, and lymphoma cancer cell lines. Methylation at 166 identified CpGs strongly correlated (|r|≥ 0.80) with corresponding gene expression in HCT116 cell line. Differences in methylation at some of the identified CpGs and expression changes of the corresponding genes was observed in TCGA colon cancer tissue as compared to adjacent healthy tissue. Our analysis revealed that hypermethylated CpGs are involved in cancer cell proliferation and apoptosis by P53 and olfactory receptor pathways, hence influencing DNMTi responses. In conclusion, we showed hypermethylation of CpGs as a novel mechanism of action for DNMTi agents and identified 638 hypermethylated molecular targets (CpGs) common to decitabine and azacytidine therapy. These novel results suggest that hypermethylation of CpGs should be considered when predicting the DNMTi responses and side effects in cancer patients.