scholarly journals 5hmC Gene Signature Predicts the Treatment Response to Azacitidine with High-Dose Cytarabine and Mitoxantrone in AML

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 1305-1305
Author(s):  
Kirk Cahill ◽  
Linchen Wang ◽  
Guanghao Liang ◽  
Qiancheng You ◽  
Chuanyuan Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Treatment Response ◽  
Phase 1 ◽  
Treatment Resistance ◽  
Gene Signature ◽  
High Dose ◽  
Rna Seq ◽  
Epigenetic Effect ◽  
Machine Learning Model ◽  
Pre Treatment

Abstract Introduction Acute myeloid leukemia (AML) is an aggressive disease with genetic and phenotypic heterogeneity that results in a highly variable response to standard chemotherapy. Azacitidine (AZA) is a hypomethylating agent (HMA) and has been investigated in combination with intensive chemotherapy as an epigenetic primer to sensitize leukemic cells to treatment. In a phase 1 trial, this regimen was safe and well-tolerated with overall response rate (CR+CRi) of 61% and complete remission rate of 41% (Cahill et al, Blood Adv 2020). Predictive biomarkers for response to this treatment strategy have not yet been identified. Since 5-hydroxymethylcytosine (5hmC) is an epigenetic biomarker in cancer, we hypothesized that Nano-5hmC-Seal sequencing technology may serve as a novel approach to identifying 5hmC profiles predictive of treatment response to epigenetic priming. Methods We performed RNA-seq gene expression and Nano-5hmC-Seal DNA profiling from peripheral blood/bone marrow samples of patients with high-risk AML to identify potential 5hmC profile biomarkers and gene expression changes (Figure 1A). Patients (n=46) were treated in a 3+3 dose-escalation scheme of AZA (37.5 mg/m 2, 50 mg/m 2, or 75 mg/m 2) on days 1-5 followed by high-dose cytarabine (3000 mg/m 2) and mitoxantrone (30 mg/m 2) (AZA-HiDAC-Mito) on day 6 and day 10 in a phase 1 trial previously reported (Cahill et al, Blood Adv 2020). We compared pre-treatment RNA-seq gene expression and 5hmC DNA profiles between responders (CR+CRi) and non-responders, as well as between pre-treatment and after 5 days of AZA for individual patients. We used an XGBoost machine learning model in Python based on a training set of patients to develop a 5hmC gene signature to predict response to AZA-HiDAC-Mito in an independent test set of patients. We compared continuous variables with two-tailed Student's t-test and used the Kaplan-Meier method with log-rank test for survival analysis. Results Thirty-three patients (72%) had adequate RNA samples for RNA-seq gene expression analysis. Eighteen responded to treatment (CR +CRi) and were enriched with gene expression patterns involved in cell-cell interaction and activation of cell cycle, while non-responders (n=15) had a higher expression of leukemic stem cell (LSC) signatures. There was no difference in gene expression profile when comparing pre-treatment samples to day 5 samples after AZA exposure. From the 5hmC profiling [n=40 (87%) patients with adequate samples], increased 5hmC in LSC genes was associated with treatment resistance to AZA-HiDAC-Mito (p=0.044). The number of differentially hydroxy-methylated genes (DhMGs) increased with higher doses of AZA exposure suggesting a dose-dependent epigenetic effect from AZA. Patients with a greater number of DhMGs following 5 days of AZA treatment had improved survival (p=0.015) (Figure 1B). Using the 5hmC-based XGBoost machine learning model comparing 5hmC profiles between responders to non-responders from a training set of patients (n=22), we developed an 11-gene 5hmC pre-treatment signature (including SKP1, WNT8A, CYP2E1, and NBPF9) to predict treatment response. The model was highly effective in predicting response to therapy, with an area under the curve (AUC) of 0.86 in an independent test set of patients (n=18) treated with AZA-HiDAC-Mito (Figure 1C). Conclusion In patients with AML treated with AZA-HiDAC-Mito, a pre-treatment LSC gene expression signature enriched with 5hmC was associated with treatment resistance. More DhMGs at day 5 appear to be a dose-dependent epigenetic effect that is induced by AZA and is associated with longer survival despite the absence of an immediate change in gene expression levels. An 11-gene 5hmC pre-treatment signature may be a predictive biomarker for AZA-HiDAC-Mito therapy and other HMA-based approaches. These findings warrant validation in a larger prospective trial. Figure 1 Figure 1. Disclosures Zhang: Bristol-Myers Squibb: Current Employment. Stock: Pfizer: Consultancy, Honoraria, Research Funding; amgen: Honoraria; agios: Honoraria; jazz: Honoraria; kura: Honoraria; kite: Honoraria; morphosys: Honoraria; servier: Honoraria; syndax: Consultancy, Honoraria; Pluristeem: Consultancy, Honoraria. Odenike: Celgene, Incyte, AstraZeneca, Astex, NS Pharma, AbbVie, Gilead, Janssen, Oncotherapy, Agios, CTI/Baxalta, Aprea: Research Funding; AbbVie, Celgene, Impact Biomedicines, Novartis, Taiho Oncology, Takeda: Consultancy. He: Epican Genetech: Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company.

scholarly journals MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrea Delli Pizzi ◽  
Antonio Maria Chiarelli ◽  
Piero Chiacchiaretta ◽  
Martina d’Annibale ◽  
Pierpaolo Croce ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rectal Cancer ◽  
Treatment Response ◽  
Randomized Clinical Trials ◽  
Locally Advanced ◽  
Neoadjuvant Treatment ◽  
Learning Model ◽  
Partial Least Square ◽  
Machine Learning Model ◽  
Pre Treatment

AbstractNeoadjuvant chemo-radiotherapy (CRT) followed by total mesorectal excision (TME) represents the standard treatment for patients with locally advanced (≥ T3 or N+) rectal cancer (LARC). Approximately 15% of patients with LARC shows a complete response after CRT. The use of pre-treatment MRI as predictive biomarker could help to increase the chance of organ preservation by tailoring the neoadjuvant treatment. We present a novel machine learning model combining pre-treatment MRI-based clinical and radiomic features for the early prediction of treatment response in LARC patients. MRI scans (3.0 T, T2-weighted) of 72 patients with LARC were included. Two readers independently segmented each tumor. Radiomic features were extracted from both the “tumor core” (TC) and the “tumor border” (TB). Partial least square (PLS) regression was used as the multivariate, machine learning, algorithm of choice and leave-one-out nested cross-validation was used to optimize hyperparameters of the PLS. The MRI-Based “clinical-radiomic” machine learning model properly predicted the treatment response (AUC = 0.793, p = 5.6 × 10–5). Importantly, the prediction improved when combining MRI-based clinical features and radiomic features, the latter extracted from both TC and TB. Prospective validation studies in randomized clinical trials are warranted to better define the role of radiomics in the development of rectal cancer precision medicine.

scholarly journals Identifying inaccuracies in gene expression estimates from unstranded RNA-seq data

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Mikhail Pomaznoy ◽  
Ashu Sethi ◽  
Jason Greenbaum ◽  
Bjoern Peters
Keyword(s):  
Gene Expression ◽  
Differential Expression Analysis ◽  
Cell Types ◽  
Library Preparation ◽  
Rna Seq ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Machine Learning Model ◽  
Specific Manner ◽  
Library Preparation Protocol

Abstract RNA-seq methods are widely utilized for transcriptomic profiling of biological samples. However, there are known caveats of this technology which can skew the gene expression estimates. Specifically, if the library preparation protocol does not retain RNA strand information then some genes can be erroneously quantitated. Although strand-specific protocols have been established, a significant portion of RNA-seq data is generated in non-strand-specific manner. We used a comprehensive stranded RNA-seq dataset of 15 blood cell types to identify genes for which expression would be erroneously estimated if strand information was not available. We found that about 10% of all genes and 2.5% of protein coding genes have a two-fold or higher difference in estimated expression when strand information of the reads was ignored. We used parameters of read alignments of these genes to construct a machine learning model that can identify which genes in an unstranded dataset might have incorrect expression estimates and which ones do not. We also show that differential expression analysis of genes with biased expression estimates in unstranded read data can be recovered by limiting the reads considered to those which span exonic boundaries. The resulting approach is implemented as a package available at https://github.com/mikpom/uslcount.

Distinct gene expression profiles determine molecular treatment response in childhood acute lymphoblastic leukemia

Blood ◽  
2005 ◽  
Vol 105 (2) ◽  
pp. 821-826 ◽  
Author(s):  
Gunnar Cario ◽  
Martin Stanulla ◽  
Bernard M. Fine ◽  
Oliver Teuffel ◽  
Nils v. Neuhoff ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukemia ◽  
Treatment Response ◽  
Expression Profiles ◽  
Lymphoblastic Leukemia ◽  
Treatment Resistance ◽  
Childhood Acute Lymphoblastic Leukemia ◽  
Childhood All ◽  
Resistance To Chemotherapy ◽  
Intrinsic Feature

AbstractTreatment resistance, as indicated by the presence of high levels of minimal residual disease (MRD) after induction therapy and induction consolidation, is associated with a poor prognosis in childhood acute lymphoblastic leukemia (ALL). We hypothesized that treatment resistance is an intrinsic feature of ALL cells reflected in the gene expression pattern and that resistance to chemotherapy can be predicted before treatment. To test these hypotheses, gene expression signatures of ALL samples with high MRD load were compared with those of samples without measurable MRD during treatment. We identified 54 genes that clearly distinguished resistant from sensitive ALL samples. Genes with low expression in resistant samples were predominantly associated with cell-cycle progression and apoptosis, suggesting that impaired cell proliferation and apoptosis are involved in treatment resistance. Prediction analysis using randomly selected samples as a training set and the remaining samples as a test set revealed an accuracy of 84%. We conclude that resistance to chemotherapy seems at least in part to be an intrinsic feature of ALL cells. Because treatment response could be predicted with high accuracy, gene expression profiling could become a clinically relevant tool for treatment stratification in the early course of childhood ALL.

scholarly journals Transcriptome Analyses Revealed Adaptive-Responses in Livers of Mice Treated With Hua-Feng-Dan and Its “Guide Drug” Yaomu

2021 ◽  
Author(s):  
Jia-Jia Liu ◽  
Ya Zhang ◽  
Shang-Fu Xu ◽  
Feng Zhang ◽  
Jing-Shan Shi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Liver Toxicity ◽  
Cholesterol Metabolism ◽  
Enrichment Analysis ◽  
Stroke Recovery ◽  
Pathway Enrichment Analysis ◽  
High Dose ◽  
Hepatic Gene Expression ◽  
Rna Seq ◽  
Adaptive Responses

Abstract BackgroundHua-Feng-Dan is a patent Chinese medicine for stroke recovery and is effective against Parkinson’s disease models with modulatory effects on gut microbiota, but its effects on hepatic gene expression are unknown. This study used RNA-Seq to profile hepatic gene expression by Hua-Feng-Dan and its “Guide Drug” Yaomu.MethodsMice received orally Hua-Feng-Dan 1.2 g/kg, Yaomu 0.1-0.3 g/kg, or vehicle for 7 days. Liver pathology was examined, and total RNA was isolated for RNA-Seq. The bioinformatics, including GO and KEGG pathway enrichment analysis, two-dimensional clustering, Ingenuity Pathways Analysis (IPA), and Illumina BaseSpace Correlation Engine were used to analyze differentially expressed genes (DEGs). qPCR was performed to verify selected genes.ResultsHua-Feng-Dan and Yaomu did not produce liver toxicity as evidenced by histopathology and serum ALT and AST. GO Enrichment revealed Hua-Feng-Dan affected lipid homeostasis, protein folding and cell adhesion. KEGG showed activated cholesterol metabolism, bile secretion and PPAR signaling pathways. DEGs were identified by DESeq2 with p < 0.05 compared to controls. Hua-Feng-Dan produced 806 DEGs, Yaomu-0.1 had 235, and Yaomu-0.3 had 92 DEGs. qPCR on selected genes largely verified RNA-Seq results. IPA upstream regulator analysis revealed activation of MAPK and adaptive responses. Yaomu-0.1 had similar effects, but Yaomu-0.3 had little effects. Hua-Feng-Dan-induced DEGs were highly correlated with the GEO database of chemical-induced adaptive transcriptome changes in the liver. ConclusionHua-Feng-Dan at clinical dose did not produce liver pathological changes but induced metabolic and signaling pathway activations. Low dose of its Guide Drug Yaomu produced similar changes to a lesser extent, but high dose of Yaomu had little effects. The effects of Hua-Feng-Dan on liver transcriptome changes may produce adaptive responses to program the liver to produce beneficial or detrimental (over-dosed) pharmacological effects.

scholarly journals Impact of Variable RNA-Sequencing Depth on Gene Expression Signatures and Target Compound Robustness: Case Study Examining Brain Tumor (Glioma) Disease Progression

2018 ◽  
pp. 1-17 ◽  
Author(s):  
Alexey Stupnikov ◽  
Paul G. O’Reilly ◽  
Caitriona E. McInerney ◽  
Aideen C. Roddy ◽  
Philip D. Dunne ◽  
...  
Keyword(s):  
Gene Expression ◽  
Brain Tumor ◽  
Clinical Utility ◽  
Gene Signature ◽  
Sequencing Depth ◽  
Rna Seq ◽  
Data Loss ◽  
Target Compound ◽  
Gene Signatures ◽  
Connectivity Mapping

Purpose Gene expression profiling can uncover biologic mechanisms underlying disease and is important in drug development. RNA sequencing (RNA-seq) is routinely used to assess gene expression, but costs remain high. Sample multiplexing reduces RNA-seq costs; however, multiplexed samples have lower cDNA sequencing depth, which can hinder accurate differential gene expression detection. The impact of sequencing depth alteration on RNA-seq–based downstream analyses such as gene expression connectivity mapping is not known, where this method is used to identify potential therapeutic compounds for repurposing. Methods In this study, published RNA-seq profiles from patients with brain tumor (glioma) were assembled into two disease progression gene signature contrasts for astrocytoma. Available treatments for glioma have limited effectiveness, rendering this a disease of poor clinical outcome. Gene signatures were subsampled to simulate sequencing alterations and analyzed in connectivity mapping to investigate target compound robustness. Results Data loss to gene signatures led to the loss, gain, and consistent identification of significant connections. The most accurate gene signature contrast with consistent patient gene expression profiles was more resilient to data loss and identified robust target compounds. Target compounds lost included candidate compounds of potential clinical utility in glioma (eg, suramin, dasatinib). Lost connections may have been linked to low-abundance genes in the gene signature that closely characterized the disease phenotype. Consistently identified connections may have been related to highly expressed abundant genes that were ever-present in gene signatures, despite data reductions. Potential noise surrounding findings included false-positive connections that were gained as a result of gene signature modification with data loss. Conclusion Findings highlight the necessity for gene signature accuracy for connectivity mapping, which should improve the clinical utility of future target compound discoveries.

scholarly journals Mucosal Inflammatory and Wound Healing Gene Programmes Reveal Targets for Stricturing Behaviour in Paediatric Crohn’s Disease

2020 ◽  
Author(s):  
Yael Haberman ◽  
Phillip Minar ◽  
Rebekah Karns ◽  
Phillip J Dexheimer ◽  
Sudhir Ghandikota ◽  
...  
Keyword(s):  
Gene Expression ◽  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Small Molecules ◽  
Area Under The Curve ◽  
Gene Signature ◽  
Oncostatin M ◽  
Fibroblast Activation ◽  
Pre Treatment

Abstract Background and Aims Ileal strictures are the major indication for resective surgery in Crohn’s disease [CD]. We aimed to define ileal gene programmes present at diagnosis and linked with future stricturing behaviour during 5-year follow-up, and to identify potential small molecules to reverse these gene signatures. Methods Antimicrobial serologies and pre-treatment ileal gene expression were assessed in a representative subset of 249 CD patients within the RISK multicentre paediatric CD inception cohort study, including 113 that are unique to this report. These data were used to define genes associated with stricturing behaviour and for model testing to predict stricturing behaviour. A bioinformatics approach to define small molecules which may reverse the stricturing gene signature was applied. Results A total of 19 of the 249 patients developed isolated B2 stricturing behaviour during follow-up, while 218 remained B1 inflammatory. Using deeper RNA sequencing than in our previous report, we have now defined an inflammatory gene signature including an oncostatin M co-expression signature, tightly associated with extra-cellular matrix [ECM] gene expression, in those who developed stricturing complications. We further computationally prioritise small molecules targeting macrophage and fibroblast activation and angiogenesis which may reverse the stricturing gene signature. A model containing ASCA and CBir1 serologies and a refined eight ECM gene set was significantly associated with stricturing development by Year 5 after diagnosis {AUC (area under the curve) (95th CI [confidence interval]) = 0.82 [0.7–0.94)}. Conclusions An ileal gene programme for macrophage and fibroblast activation is linked to stricturing complications in treatment of naïve pediatric CD, and may inform novel small molecule therapeutic approaches.

scholarly journals A Stochastic Binary Model for Regulation of Gene Expression to Investigate Treatment Effects Targeting RKIP

2021 ◽  
Author(s):  
Guilherme Giovanini ◽  
Luciana Rodrigues Carvalho Barros ◽  
Leonardo dos Reis Gama ◽  
Tharcisio Citrangulo Tortelli Junior ◽  
Alexandre Ferreira Ramos
Keyword(s):  
Gene Expression ◽  
Treatment Response ◽  
Regulation Of Gene Expression ◽  
Drug Dosage ◽  
Synthesis Rate ◽  
State Duration ◽  
Binary Model ◽  
Kinetic Rates ◽  
Gene Expression Dynamics ◽  
Pre Treatment

In this manuscript we use an exactly solvable stochastic binary model for regulation of gene expression to analyse the dynamics of response to a treatment aiming to modulate the number of transcripts of RKIP gene. We demonstrate the usefulness of our method simulating three treatment scenarios aiming to reestablish RKIP gene expression dynamics towards pre-cancerous state: i. to increase the promoter&rsquo;s ON state duration; ii. to increase the mRNAs&rsquo; synthesis rate; iii. to increase both rates. We show that the pre-treatment kinetic rates of ON and OFF promoter switching speeds and mRNA synthesis and degradation will affect the heterogeneity and time for treatment response. Hence, we present a strategy for reducing drug dosage by simultaneously targeting multiple kinetic rates. That enables a reduction of treatment response time and heterogeneity which in principle diminishes the chances of emergence of resistance to treatment. This approach may be useful for inferring kinetic constants related to expression of antimetastatic genes or oncogenes and on the design of multi-drug therapeutic strategies targeting master regulatory genes.

scholarly journals Crosstalk between the IGF1 and CXCL12 Pathways Defines Objective Responses to the Farnesyl Transferase Inhibitor Tipifarnib in AML and PTCL Patients

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 5349-5349
Author(s):  
Antonio Gualberto ◽  
Catherine Scholz ◽  
Vishnu Mishra ◽  
Matthew R Janes ◽  
Linda Kessler
Keyword(s):  
Gene Expression ◽  
Mechanism Of Action ◽  
Cancer Genomics ◽  
Lymphoid Cells ◽  
Rna Seq ◽  
Employment Equity ◽  
Cxcl12 Expression ◽  
Best Response ◽  
Equity Ownership ◽  
Pre Treatment

Abstract Background CXCL12 is a chemokine that is essential for the maturation of myeloid and lymphoid cells. Tipifarnib is a potent and selective inhibitor of the enzyme farnesyltransferase (FT). Treatment with this agent may translate to durable responses in subsets of patients (pts) with acute myeloid leukemia (AML), chronic myelomonocytic leukemia (CMML) and peripheral T-cell lymphoma (PTCL) but its mechanism of action in these indications is poorly understood. We have previously reported that tipifarnib interferes with the CXCL12 pathway. Here we show that an interplay between the CXCL12 and IGF1 pathways may define those pts who may experience objective responses with tipifarnib monotherapy. Methods Gene expression profile (GEP) data generated using RNA-Seq and the Affymetrix U133A gene-chips of tumor samples from 129 pts enrolled into tipifarnib trials (CTEP-20, KO-TIP-002,KO-TIP-004, INT-17) were analyzed with respect to study outcomes and complemented with analyses of mRNA expression in data sets from the cBioportal for Cancer Genomics. Gene expression was further validated using RT-PCR assays. RNA-Seq and Whole Exome Sequencing were conducted using standard methodologies. Clinical trial information: NCT00027872, NCT02464228. NCT02807272, NCT00354146. Results In order to improve our understanding of the molecular pathology of tumor CXCL12 overexpression, we investigated GEPs from 8,401 cancer pts in 25 studies available at cBioportal (TCGA, Provisional). Notably, we found a highly significant correlation in the expression of the IGF1 and CXCL12 genes in 19 of those studies. Intriguingly, the highest IGF1/CXCL12 correlations were observed in indications, including AML (ρ=0.698, p<0.001), in which activity of tipifarnib as monotherapy has been previously reported (AML, breast and urothelial cancer). Based on these results, we investigated the effect of IGF1/CXCL12 co-expression on pt outcome in tipifarnib studies. In previously untreated AML, 3 subsets of pts were identified with respect to bone marrow (BM) IGF1/CXCL12 expression: (1) high IGF1, high CXCL12 with predominantly hematological improvement or stable disease (SD) as best response, (2) intermediate IGF1, low CXCL12, with predominantly disease progression (PD), and (3) low IGF1, variable CXCL12, with 6 complete responses in 15 pts that were associated with CXCL12 expression (p=0.013), supporting the notion that CXCL12 pathway activation determines objective responses with tipifarnib while IGF1 mediates drug resistance. Barely detectable levels of IGF1 (and IGF2) were observed in the BMs of CMML pts in whom only 1 best response of PD was reported. In contrast, elevated levels of both CXCL12 and IGF1 were observed in PTCL pts responding to tipifarnib. Further investigation revealed that tumors of PTCL pts experiencing a partial response (PR) with tipifarnib expressed high levels of IGFBP7 (p=0.03), a natural inhibitor of the IGF1 receptor. Sequencing of the CXCL12 and IGF1 genes in PTCL samples revealed the presence of polymorphisms in non-responding pts: 8 pts, 7 carrying CXCL12 rs2839695 and 1 with a novel 3UTR variant, experienced a best response of PD. No pts with a best response of PR or SD carried 3UTR variants in CXCL12 (0% vs 80%, p=0.007). No pt with a best response of PR, 1 of 4 pts with SD and 6 of 10 pts with PD carried the IGFBP7 variant L11F (rs11573021) (16% PR/SD vs 60% PD, p=0.15) Conclusions Pre-treatment tumor CXCL12, IGF1 and IGFBP7 expression may enable the identification of pts susceptible to experience objective responses with tipifarnib monotherapy. These data may contribute to the understanding of the mechanism of action of FT inhibitors. Disclosures Gualberto: Kura Oncology: Employment, Equity Ownership. Mishra:Kura Oncology: Employment, Equity Ownership. Janes:Wellspring Biosciences: Employment, Equity Ownership. Kessler:Kura Oncology: Employment, Equity Ownership.

scholarly journals Identification of a Prognostic Gene Expression Signature for AZA Response in MDS and CMML Patients

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 4601-4601
Author(s):  
Ashwin Unnikrishnan ◽  
Dominik Beck ◽  
Arjun Verma ◽  
Laura A Richards ◽  
Julie A I Thoms ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drug Treatment ◽  
Differential Expression ◽  
Cell Cycle Progression ◽  
Poor Response ◽  
Poor Responders ◽  
Rna Seq ◽  
Combination Drug ◽  
Pre Treatment ◽  
Combination Drug Treatment

Abstract Myelodysplastic syndrome (MDS) and chronic myelomonocytic leukaemia (CMML) are haematological disorders that develop in haematopoietic stem or progenitor cells (HSPCs) and are characterised by ineffective haematopoiesis. 5'-Azacitidine (AZA) is a DNA demethylating agent that is effective in treating MDS and CMML. However, response rates are less than 50% and the basis for poor response is currently unknown. A patient's potential to respond cannot be currently determined until after multiple cycles of AZA treatment and alternative treatment options for poor responders are limited. To address these fundamental questions, we enrolled patients on a compassionate access program prior to the listing of AZA on the pharmaceuticals benefit scheme in Australia. We have collected bone marrow from 18 patients (10 MDS, 8 CMML) at seven different stages of treatment, starting from before treatment until after six cycles of AZA treatment, and isolated high-purity CD34+ HSPCs at each stage. 10 of these patients (5 MDS and 5 CMML) responded completely to AZA while 8 did not achieve complete response. We performed next-generation sequencing (RNA-seq) of these HSPCs to identify the basis of poor response to AZA therapy. Analysis of the RNA-seq data from pre-treatment HSPCs has revealed a striking differential expression of 1148 genes between patients who were subsequently complete (CR) or non-complete responders (non-CR) to AZA therapy (Figure 1A). Using a Fluidigm nanofluidic system, we have validated the differential expression of a subset of these genes between CR and non-CR patients in two independent cohorts, totalling 67 patients, from the U.K. and Sweden. We have additionally confirmed that our gene signature does not simply segregate patients based on disease severity or poor overall survival, but rather uniquely prognosticates best AZA response. Pathway analyses of the differentially expressed genes indicates that the HSPCs of non-CR patients have decreased cell cycle progression and DNA damage pathways, while concomitantly possessing increased signalling through integrin and mTOR/AKT pathways. Using computational methods, we have determined that the expression of 15 genes (within the 1148 gene set) is sufficient to separate CRs from non-CRs across independent cohorts (Figure 1B). We have also developed a predictive AZA response algorithm that utilises the expression of these genes to identify potential complete and non-complete responders to AZA with high specificity and sensitivity (Figure 1C). Furthermore, we have identified statistically significant correlations between recurrent DNA mutations in MDS and our prognostic gene signature (SF3B1 & TET2 with CR, STAG2 and NUP98 with non-CR, p<0.05). We have used these findings to first, develop a clinically useful method to predict the likelihood of AZA response and second, use targeted therapies to promote AZA response in likely poor responders. To predict AZA response, we assess cell cycle progression of MDS/CMML CD34+ subsets by flow cytometry using unfractionated bone marrow aspirates. To improve drug response in predicted non-CR patients, we have performed combinatorial drug testing experiments with AZA using primary MDS/CMML CD34+ HSPCs, in a co-culture system using MS5 stromal cells, targeting up-regulated pathways identified from our RNA-seq data. (Figures 1D, 1E). Our findings have immediate clinical utility to both prospectively identify CR and non-CR patients prior to AZA therapy and to improve AZA response in the latter by using combination therapy targeting specific pathways. Fig 1. A.) Differential expression of 1148 genes in pre-treatment HSPCs of patients who were subsequently complete (CR) or non-complete responders (non-CR) to AZA. B.) The differential expression of a subset of 15 genes is sufficient to separate the two groups. C.) A predictive AZA response algorithm that utilises gene expression data to prospectively identify patients. D.) Representative images of CFU colonies illustrating improved colony formation following combination drug treatment. E.) Improved CFU colony counts following combination drug treatment. * p <0.05 Fig 1. A.) Differential expression of 1148 genes in pre-treatment HSPCs of patients who were subsequently complete (CR) or non-complete responders (non-CR) to AZA. B.) The differential expression of a subset of 15 genes is sufficient to separate the two groups. C.) A predictive AZA response algorithm that utilises gene expression data to prospectively identify patients. D.) Representative images of CFU colonies illustrating improved colony formation following combination drug treatment. E.) Improved CFU colony counts following combination drug treatment. * p <0.05 Disclosures Lynch: Celgene Pty Ltd: Employment, Equity Ownership. Mufti:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Pimanda:Celgene Pty Ltd: Research Funding.

Functional Plasticity in CD4+ CD25+ CD127- Treg Population in Primary Immune Thrombocytopenia

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 3470-3470 ◽  
Author(s):  
Boting Wu ◽  
Yanxia Zhan ◽  
Feng Li ◽  
Luya Cheng ◽  
Shanhua Zou ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Helper Cells ◽  
Foxp3 Expression ◽  
Post Treatment ◽  
High Dose ◽  
T Helper ◽  
Functional Plasticity ◽  
Helper Cells ◽  
High Dose Dexamethasone ◽  
Pre Treatment

Abstract Background As the most prevalent acquired bleeding disorder in adults, primary immune thrombocytopenia (ITP) and its underlying immune aberrations have been intensely investigated. Beyond the previously described Th1/Th2 imbalance, the role of Th17/Treg dysregulation has become the focus of attention. It was reported by our group that Tregs from untreated ITP patients demonstrated decreased IL-10 secretion and compromised control upon over-activated T effector cells (Li et al. 2015). The mechanism of Treg dysregulation under autoimmunity conditions is yet to be revealed. It has been recently argued by several research groups that the functional plasticity of Treg cell linage is dynamically regulated under inflammation via instability of Foxp3 expression, which means that Tregs can lose Foxp3 expression to gain effector T cell function in inflammatory milieu. The present study evaluated phenotypic features and gene expression traits of purified CD4+ T helper cells from ITP patients before and after glycocorticoids treatment, thus intending to investigate Treg functional plasticity among ITP patients. Methods CD4+ T helper cells were obtained via magnetic activated cell sorting from peripheral blood of 8 primary ITP patients before and after glycocorticoids treatment and 4 healthy volunteers. The phenotypic features were determined by FACS Canto II system with surface staining of CD25 and CD127, as well as cytoplasmic staining of IFN-γ and IL-17. Gene expression profiling was performed via QIAGEN Human T Helper Cell Differentiation PCR Array. Results The pre-treatment platelet count was (7±7)*109/L among 8 ITP patients (2 males and 6 females, median age 57.0 years), and the post-treatment platelet count was restored to (158±63)*109/L after high-dose dexamethasone regimen (40mg/d*4d). Gene expression profiling revealed that Foxp3 (-2.8 folds, p=0.001), TNF (-4.0 folds, p=0.003), and Stat1 (-2.3 folds, p=0.001) levels were significantly down-regulated among untreated ITP patients, while IL-17A (3.6 folds, p=0.05) was up-regulated with marginal statistical significance. The percentage of CD25+ CD127- population in CD4+ cells was similar among 3 groups (pre-treatment ITP: 3.1±0.6%, post-treatment ITP: 3.2±0.7%, health control: 3.4±0.8%). Among CD4+ CD25+ CD127- population, the percentage of IL-17+ cells was elevated in pre-treatment ITP patients (2.9±1.8% vs. 1.4±0.2%, p=0.17), and significantly decreased after high-dose dexamethasone regimen (1.5±1.1% vs. 2.9±1.8%, p=0.035), while the percentage of IFN-γ+ cells was similar among 3 groups (pre-treatment ITP: 18.5±12.6%, post-treatment ITP: 11.1±9.5%, health control: 18.5±3.8%). Conclusions Among primary ITP patients, Foxp3 expression was significantly decreased in their CD4+ T helper cells, which was inconsistent with the almost stable CD4+ CD25+ CD127- percentages determined by flow cytometry between ITP patients and healthy volunteers. Within the Treg population, we demonstrated elevated IL-17 expressing cells in pre-treatment ITP patients, which could be restored after high-dose dexamethasone regimen. These findings favored the argument of the functional plasticity of Treg cell linage during autoimmunity, and corresponded to Th17 dysregulation previously described in primary ITP. Disclosures No relevant conflicts of interest to declare.

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