scholarly journals Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Arika Fukushima ◽  
Masahiro Sugimoto ◽  
Satoru Hiwa ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response To Therapy ◽  
Hcv Infection ◽  
Entire Treatment Period ◽  
Time Points ◽  
Time Course Data ◽  
Conventional Methods

Abstract Background Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. Results We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. Conclusions The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases.

scholarly journals Effectiveness of Gene Expression Profiling for Response Prediction of Rectal Adenocarcinomas to Preoperative Chemoradiotherapy

2005 ◽  
Vol 23 (9) ◽  
pp. 1826-1838 ◽  
Author(s):  
B. Michael Ghadimi ◽  
Marian Grade ◽  
Michael J. Difilippantonio ◽  
Sudhir Varma ◽  
Richard Simon ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response Prediction ◽  
Preoperative Chemoradiotherapy ◽  
Response To Therapy ◽  
Phase Iii ◽  
Correct Prediction

Purpose There is a wide spectrum of tumor responsiveness of rectal adenocarcinomas to preoperative chemoradiotherapy ranging from complete response to complete resistance. This study aimed to investigate whether parallel gene expression profiling of the primary tumor can contribute to stratification of patients into groups of responders or nonresponders. Patients and Methods Pretherapeutic biopsies from 30 locally advanced rectal carcinomas were analyzed for gene expression signatures using microarrays. All patients were participants of a phase III clinical trial (CAO/ARO/AIO-94, German Rectal Cancer Trial) and were randomized to receive a preoperative combined-modality therapy including fluorouracil and radiation. Class comparison was used to identify a set of genes that were differentially expressed between responders and nonresponders as measured by T level downsizing and histopathologic tumor regression grading. Results In an initial set of 23 patients, responders and nonresponders showed significantly different expression levels for 54 genes (P < .001). The ability to predict response to therapy using gene expression profiles was rigorously evaluated using leave-one-out cross-validation. Tumor behavior was correctly predicted in 83% of patients (P = .02). Sensitivity (correct prediction of response) was 78%, and specificity (correct prediction of nonresponse) was 86%, with a positive and negative predictive value of 78% and 86%, respectively. Conclusion Our results suggest that pretherapeutic gene expression profiling may assist in response prediction of rectal adenocarcinomas to preoperative chemoradiotherapy. The implementation of gene expression profiles for treatment stratification and clinical management of cancer patients requires validation in large, independent studies, which are now warranted.

scholarly journals Prediction of Pharmacological and Xenobiotic Responses to Drugs Based on Time Course Gene Expression Profiles

PLoS ONE ◽  
2009 ◽  
Vol 4 (12) ◽  
pp. e8126 ◽  
Author(s):  
Tao Huang ◽  
WeiRen Cui ◽  
LeLe Hu ◽  
KaiYan Feng ◽  
Yi-Xue Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles

Sub-Classification of Hyperdiploid Myeloma Using Global Gene Expression Profiling and SNP-Based Mapping Arrays.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 3390-3390
Author(s):  
Brian A. Walker ◽  
Paola E. Leone ◽  
Matthew W. Jenner ◽  
David C. Johnson ◽  
David Gonzalez ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hierarchical Clustering ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Poor Response ◽  
Response To Therapy ◽  
Genomic Change ◽  
Group 5 ◽  
Mapping Arrays ◽  
Group 1

Abstract The translocation/cyclin classification system in myeloma does not neatly define subgroups of hyperdiploidy (HRD) and we sought a more definitive sub-classification. Using 131 pre-treatment samples (49 HRD with no split IgH locus by FISH) we defined subgroups using both supervised and unsupervised hierarchical clustering of gene expression profiles. RNA was purified from CD138+ cells, amplified using a 2-cycle IVT and hybridised onto U133 Plus 2 GeneChips. On 30 of the 49 HRD samples we also performed 500K SNP mapping arrays to define the true extent of the genomic change in HRD. The most common trisomic chromosomes were 15 (97%), 9 (86%), 19 (80%), 5 (77%), 11 (74%), 3 (64%), 21 (54%) and 7 (54%). There was no association between HRD and any of the major genetic abnormalities (1p, 1q, 6q, 8p, 13, 16q and 17p) compared to the non-HRD (NHRD) group. Many interstitial deletions were seen in all HRD samples, on both odd and even numbered chromosomes. However, using gene mapping alone it was not possible to globally sub-classify HRD myeloma. We compared NHRD and HRD sample gene expression profiles, removing differences between t(4;14) and t(11;14) cases in the NHRD group. This analysis showed that HRD samples segregate into 2 groups; one with a pattern distinct to NHRD samples and another containing genes that are up-regulated in both HRD and NHRD samples. In this analysis 176 genes were up-regulated in the HRD samples and were predominantly located on the trisomic chromosomes, especially 19, 11, 9 and 5. These genes showed a predominant upregulation of HGF and TRAIL, and down-regulation of TRAIL-R2 compared to NHRD samples. Unsupervised hierarchical clustering split the HRD samples into 5 distinct groups suggesting that there are distinct pathological entities. Group 1 overexpressed 90 genes including BCL2, CCNL1 (cyclin L1) and CDK6, consistent with a proliferation signature. Group 2 overexpressed interferon inducible genes including IFI6, IFI27, IFIT1 as well as TRAIL. Group 3 upregulated genes included IL8, MMP9 and TIMP2. Group 4 upregulated transcripts include neurexophilin 3. Group 5 was less well defined but contained transcripts for CCND2, WNT5A and CXCR4. To define clinically relevant subgroups the HRD samples were clustered comparing response or no response to induction chemotherapy. Analysis showed that Group 1 cases cluster together and were either non or minimal responders. This is consistent with the Group 1 cases over-expressing cell-cycle and proliferation related genes. Group 5 clustered together and were either complete or partial responders, and had a low expression of the genes over expressed by Group 1. The non-responder group overexpressed 58 genes and include MMSET-like 1 (in a region on 8p paralogous to 4p containing FGFR1), DVL3 (dishevelled homolog 3) and CCNL1. 23 genes were over expressed in the complete response group including caspase 1 and manic fringe homolog. The unsupervised HRD cluster and the supervised response cluster shared 10 genes, including CCNL1 and ASS. We have used both genetic and expression data to further define the HRD sub-group in terms of gene expression signatures and response to therapy and have identified 5 groups, of which Group 1 has a proliferation signature and poor response to induction therapy.

Gene Expression Profiling of Murine HOXB4-Transduced HSCs Shows Multiple Counter-Regulatory Signals That May Control HSC Pool Size

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 2361-2361
Author(s):  
Hui Yu ◽  
Sheng Zhou ◽  
Geoffrey A. Neale ◽  
Brian P. Sorrentino
Keyword(s):  
Gene Expression ◽  
Bone Marrow ◽  
Bone Marrow Cells ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Expression Array ◽  
Self Renewal ◽  
Time Points ◽  
Time Point

Abstract Abstract 2361 HOXB4 is a homeobox transcription factor that can induce hematopoietic stem cell (HSC) expansion both in vivo and in vitro. An interesting feature of HOXB4-induced HSC expansion is that HSC numbers do not exceed normal levels in vivo due to an unexplained physiological capping mechanism. To gain further insight into HOXB4 regulatory signals, we transplanted mice with bone marrow cells that had been transduced with a MSCV-HOXB4-ires-YFP vector and analyzed gene expression profiles in HSC-enriched populations 20 weeks after transplant, a time point at which HSC numbers have expanded to normal levels but no longer increasing beyond physiologic levels. We used Affymetrix arrays to analyze gene expression profiles in bone marrow cells sorted for a Lin−Sca-1+c-Kit+ (LSK), YFP+ phenotype. Using ANOVA, we identified1985 probe sets with >2 fold difference in expression (FDR<, 0.1) relative to a control vector-transduced LSK cells. A cohort of genes was identified that were known positive regulators of HSC self-renewal and proliferation. Hemgn, which we identified in a previous screen as a positive regulator of expansion and a direct transcriptional target of HOXB4, was 3.5 fold up-regulated in HOXB4 transduced LSKs. Other genes known to be important for HSCs survival, self-renewal and differentiation were upregulated to significant levels including N-myc, Meis1, Hoxa9, Hoxa10 and GATA2. Microarray data for selected genes was validated by quantitative real-time PCR on HOXB4 transduced CD34low LSK cells, a highly purified HSC population, obtained from another set of transplanted mice at the 20 week time point. In contrast, other gene expression changes were noted that would potentially limit or decrease stem cell numbers. PRDM16, a set domain transcription factor critical for HSC maintenance and associated with clonal hematopoietic expansions when inadvertently activated as a result of retroviral insertion, was dramatically down-regulated on the expression array and 7.6 fold decreased in the real time PCR assay of CD34low LSK cells. TFG-beta signaling is a well defined inhibitor HSC proliferation and utilize Smad proteins as downstream effectors. Expression of Smad1 and Smad7 were significantly upregulated on the LSK expression array and 8.1 and 3.5 fold up-regulated by qPCR in CD34low LSK cells. Another potential counter-regulatory signal was down regulation of Bcl3 mRNA, a potential anti-apoptotic effector in HSCs. We hypothesize that the HOXB4 expansion program involves activation of genes that lead to increased HSC numbers with later activation of counter-regulatory signals that limit expansion to physiologic numbers of HSCs in vivo. We are now examining how this program changes at various time points after transplantation and hypothesize the capping limits are set at relatively later time points during reconstitution. We also are studying the functional effects of these gene expression changes, and in particular, whether enforced expression of HOXB4 and PRMD16 will result in uncontrolled HSC proliferation and/or leukemia. Disclosures: No relevant conflicts of interest to declare.

Gene expression profiling of normal and malignant CD34-derived megakaryocytic cells

Blood ◽  
2004 ◽  
Vol 104 (10) ◽  
pp. 3126-3135 ◽  
Author(s):  
Elena Tenedini ◽  
Maria Elena Fagioli ◽  
Nicola Vianelli ◽  
Pier Luigi Tazzari ◽  
Francesca Ricci ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Mitochondrial Permeability Transition ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Annexin V ◽  
Permeability Transition Pore ◽  
Permeability Transition ◽  
Course Evaluation ◽  
Oligonucleotide Microarray Analysis

Abstract Gene expression profiles of bone marrow (BM) CD34-derived megakaryocytic cells (MKs) were compared in patients with essential thrombocythemia (ET) and healthy subjects using oligonucleotide microarray analysis to identify differentially expressed genes and disease-specific transcripts. We found that proapoptotic genes such as BAX, BNIP3, and BNIP3L were down-regulated in ET MKs together with genes that are components of the mitochondrial permeability transition pore complex, a system with a pivotal role in apoptosis. Conversely, antiapoptotic genes such as IGF1-R and CFLAR were up-regulated in the malignant cells, as was the SDF1 gene, which favors cell survival. On the basis of the array results, we characterized apoptosis of normal and ET MKs by time-course evaluation of annexin-V and sub-G1 peak DNA stainings of immature and mature MKs after culture in serum-free medium with an optimal thrombopoietin concentration, and annexin-V–positive MKs only, with decreasing thrombopoietin concentrations. ET MKs were more resistant to apoptosis than their normal counterparts. We conclude that imbalance between proliferation and apoptosis seems to be an important step in malignant ET megakaryocytopoiesis.

scholarly journals Comparative analyses of time-course gene expression profiles of the long-lived sch9Δ mutant

2009 ◽  
Vol 38 (1) ◽  
pp. 143-158 ◽  
Author(s):  
Huanying Ge ◽  
Min Wei ◽  
Paola Fabrizio ◽  
Jia Hu ◽  
Chao Cheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Comparative Analyses
Sign in / Sign up

Export Citation Format

Share Document