Person:

Niemierko, Andrzej

Background: Data entry errors are common in clinical research databases. Omitted data are of particular concern because they are more common than erroneously inserted data and therefore could potentially affect research findings. However, few affordable strategies for their prevention are available. Methods: We have conducted a prospective observational study of the effect of a novel tool called "Summary Page" on the frequency of correction of omitted data errors in a radiation oncology research database between July 2008 and March 2009. "Summary Page" was implemented as an optionally accessed screen in the database that visually integrates key fields in the record. We assessed the frequency of omitted data on the example of the Date of Relapse field. We considered the data in this field to be omitted for all records that had empty Date of Relapse field and evidence of relapse elsewhere in the record. Results: A total of 1,156 records were updated and 200 new records were entered in the database over the study period. "Summary Page" was accessed for 44% of all updated records and for 69% of newly entered records. Frequency of correction of the omitted date of cancer relapse was six-fold higher in records for which "Summary Page" was accessed (p = 0.0003). Conclusions: "Summary Page" was strongly associated with an increased frequency of correction of omitted data errors. Further, controlled, studies are needed to confirm this finding and elucidate its mechanism of action.

Significant interest exists in establishing synergistic research in bioinformatics, systems biology and intelligent computing. Supported by the United States National Science Foundation (NSF), International Society of Intelligent Biological Medicine (http://www.ISIBM.org), International Journal of Computational Biology and Drug Design (IJCBDD) and International Journal of Functional Informatics and Personalized Medicine, the ISIBM International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing (ISIBM IJCBS 2009) attracted more than 300 papers and 400 researchers and medical doctors world-wide. It was the only inter/multidisciplinary conference aimed to promote synergistic research and education in bioinformatics, systems biology and intelligent computing. The conference committee was very grateful for the valuable advice and suggestions from honorary chairs, steering committee members and scientific leaders including Dr. Michael S. Waterman (USC, Member of United States National Academy of Sciences), Dr. Chih-Ming Ho (UCLA, Member of United States National Academy of Engineering and Academician of Academia Sinica), Dr. Wing H. Wong (Stanford, Member of United States National Academy of Sciences), Dr. Ruzena Bajcsy (UC Berkeley, Member of United States National Academy of Engineering and Member of United States Institute of Medicine of the National Academies), Dr. Mary Qu Yang (United States National Institutes of Health and Oak Ridge, DOE), Dr. Andrzej Niemierko (Harvard), Dr. A. Keith Dunker (Indiana), Dr. Brian D. Athey (Michigan), Dr. Weida Tong (FDA, United States Department of Health and Human Services), Dr. Cathy H. Wu (Georgetown), Dr. Dong Xu (Missouri), Drs. Arif Ghafoor and Okan K Ersoy (Purdue), Dr. Mark Borodovsky (Georgia Tech, President of ISIBM), Dr. Hamid R. Arabnia (UGA, Vice-President of ISIBM), and other scientific leaders. The committee presented the 2009 ISIBM Outstanding Achievement Awards to Dr. Joydeep Ghosh (UT Austin), Dr. Aidong Zhang (Buffalo) and Dr. Zhi-Hua Zhou (Nanjing) for their significant contributions to the field of intelligent biological medicine.

We present a report of the BIOCOMP'10 - The 2010 International Conference on Bioinformatics & Computational Biology and other related work in the area of systems biology.

Background: To evaluate feasibility and preliminary outcomes associated with sequential whole abdomen irradiation (WAI) as consolidative treatment following comprehensive surgery and systemic chemotherapy for advanced endometrial cancer. Methods: We conducted a retrospective analysis of patients treated at our institution from 2000 to 2011. Inclusion criteria were stage III-IV endometrial cancer patients with histological proof of one or more sites of extra-uterine abdomen-confined disease, treated with WAI as part of multimodal therapy. Endpoints were feasibility, acute toxicity, late effects, recurrence-free survival (RFS) and overall survival (OS). Twenty patients were identified. Chemotherapy consisted of 3 to 6 cycles of a platinum-paclitaxel regimen in 18 patients. WAI was delivered using conventional technique to a median total dose of 27.5 Gy. Results: No grade 4 toxicities occurred during chemotherapy or radiotherapy. No radiation dose reduction was necessary. Three patients developed small bowel obstruction, all in the context of recurrent intraperitoneal disease. Kaplan-Meier estimates and 95% confidence intervals for RFS and OS at one year were 63% (38–80%) and 83% (56-94%) and at 3 years 57% (33-76%) and 62% (34-81%), respectively. On univariate Cox analysis, stage IVB and serous papillary (SP) histology were found to be statistically significantly (at the p = 0.05 level) associated with worse RFS and OS. The peritoneal cavity was the most frequent site of initial failure. Conclusions: Consolidative WAI following chemotherapy is feasible and can be performed without interruption with manageable acute and late toxicity. Patients with endometrioid adenocarcinoma, especially stage FIGO III, had favorable outcomes possibly meriting prospective evaluation of the addition of WAI following chemotherapy in selected patients. Patients with SP do poorly and do not routinely benefit from this approach.

Purpose We investigated the metabolic response of lung cancer to radiotherapy or chemoradiotherapy by 18F-FDG PET and its utility in guiding timely supplementary therapy. Methods: Glucose metabolic rate (MRglc) was measured in primary lung cancers during the 3 weeks before, and 10–12 days (S2), 3 months (S3), 6 months (S4), and 12 months (S5) after radiotherapy or chemoradiotherapy. The association between the lowest residual MRglc representing the maximum metabolic response (MRglc-MMR) and tumor control probability (TCP) at 12 months was modeled using logistic regression. Results: We accrued 106 patients, of whom 61 completed the serial 18F-FDG PET scans. The median values of MRglc at S2, S3 and S4 determined using a simplified kinetic method (SKM) were, respectively, 0.05, 0.06 and 0.07 μmol/min/g for tumors with local control and 0.12, 0.16 and 0.19 μmol/min/g for tumors with local failure, and the maximum standard uptake values (SUVmax) were 1.16, 1.33 and 1.45 for tumors with local control and 2.74, 2.74 and 4.07 for tumors with local failure (p < 0.0001). MRglc-MMR was realized at S2 (MRglc-S2) and the values corresponding to TCP 95 %, 90 % and 50 % were 0.036, 0.050 and 0.134 μmol/min/g using the SKM and 0.70, 0.91 and 1.95 using SUVmax, respectively. Probability cut-off values were generated for a given level of MRglc-S2 based on its predicted TCP, sensitivity and specificity, and MRglc ≤0.071 μmol/min/g and SUVmax ≤1.45 were determined as the optimum cut-off values for predicted TCP 80 %, sensitivity 100 % and specificity 63 %. Conclusion: The cut-off values (MRglc ≤0.071 μmol/min/g using the SKM and SUVmax ≤1.45) need to be tested for their utility in identifying patients with a high risk of residual cancer after standard dose radiotherapy or chemoradiotherapy and in guiding a timely supplementary dose of radiation or other means of salvage therapy. Electronic supplementary material The online version of this article (doi:10.1007/s00259-013-2348-4) contains supplementary material, which is available to authorized users.

Purpose

To evaluate the feasibility of a respiratory-gated proton beam therapy for liver tumors.

Materials and Methods

Fifteen patients were enrolled on a prospective IRB-approved protocol. Eligibility criteria included Childs-Pugh A/B cirrhosis, unresectablebiopsy-proven hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), or metastatic disease (solid tumors only), 1-3 lesions, and tumor size of ≤6 cm. Patients received 15 fractions to a total dose of 45-75 GyE using respiratory-gated proton beam therapy. Gating was performed with an external respiratory position monitoring (RPM) based system.

Results

Of the15 patients enrolled on this clinical trial, 11 had HCC, 3 had ICC, and 1had metastasis from another primary. Ten patients had a single lesion, 3 patients had 2 lesions, and 2 patients 3 lesions. Toxicities were: Gr 3 bilirubinemia- 2, Gr 3 gastrointestinal bleed- 1, and Gr 5 stomach perforation-1. One patient had a marginal recurrence, 3 had hepatic recurrences elsewhere in the liver, and 2 had extrahepatic recurrence. With a median follow-up for survivors of 69 months, 1-yr, 2-yr, 3-yr OS is 53%, 40%, and 33% respectively. PFS is 40%,33% and 27% at 1, 2, and 3 years, respectively.

Conclusion

Respiratory-gated proton beam therapy for liver tumors is feasible. Phase II studies for primary liver tumors and metastatic tumorsare underway.

Synergistically integrating multi-layer genomic data at systems level not only can lead to deeper insights into the molecular mechanisms related to disease initiation and progression, but also can guide pathway-based biomarker and drug target identification. With the advent of high-throughput next-generation sequencing technologies, sequencing both DNA and RNA has generated multi-layer genomic data that can provide DNA polymorphism, non-coding RNA, messenger RNA, gene expression, isoform and alternative splicing information. Systems biology on the other hand studies complex biological systems, particularly systematic study of complex molecular interactions within specific cells or organisms. Genomics and molecular systems biology can be merged into the study of genomic profiles and implicated biological functions at cellular or organism level. The prospectively emerging field can be referred to as systems genomics or genomic systems biology. The Mid-South Bioinformatics Centre (MBC) and Joint Bioinformatics Ph.D. Program of University of Arkansas at Little Rock and University of Arkansas for Medical Sciences are particularly interested in promoting education and research advancement in this prospectively emerging field. Based on past investigations and research outcomes, MBC is further utilizing differential gene and isoform/exon expression from RNA-seq and co-regulation from the ChiP-seq specific for different phenotypes in combination with protein-protein interactions, and protein-DNA interactions to construct high-level gene networks for an integrative genome-phoneme investigation at systems biology level.

Advances of high-throughput technologies have rapidly produced more and more data from DNAs and RNAs to proteins, especially large volumes of genome-scale data. However, connection of the genomic information to cellular functions and biological behaviours relies on the development of effective approaches at higher systems level. In particular, advances in RNA-Seq technology has helped the studies of transcriptome, RNA expressed from the genome, while systems biology on the other hand provides more comprehensive pictures, from which genes and proteins actively interact to lead to cellular behaviours and physiological phenotypes. As biological interactions mediate many biological processes that are essential for cellular function or disease development, it is important to systematically identify genomic information including genetic mutations from GWAS (genome-wide association study), differentially expressed genes, bidirectional promoters, intrinsic disordered proteins (IDP) and protein interactions to gain deep insights into the underlying mechanisms of gene regulations and networks. Furthermore, bidirectional promoters can co-regulate many biological pathways, where the roles of bidirectional promoters can be studied systematically for identifying co-regulating genes at interactive network level. Combining information from different but related studies can ultimately help revealing the landscape of molecular mechanisms underlying complex diseases such as cancer.

Background: Kidney Renal Clear Cell Carcinoma (KIRC) is one of fatal genitourinary diseases and accounts for most malignant kidney tumours. KIRC has been shown resistance to radiotherapy and chemotherapy. Like many types of cancers, there is no curative treatment for metastatic KIRC. Using advanced sequencing technologies, The Cancer Genome Atlas (TCGA) project of NIH/NCI-NHGRI has produced large-scale sequencing data, which provide unprecedented opportunities to reveal new molecular mechanisms of cancer. We combined differentially expressed genes, pathways and network analyses to gain new insights into the underlying molecular mechanisms of the disease development. Results: Followed by the experimental design for obtaining significant genes and pathways, comprehensive analysis of 537 KIRC patients' sequencing data provided by TCGA was performed. Differentially expressed genes were obtained from the RNA-Seq data. Pathway and network analyses were performed. We identified 186 differentially expressed genes with significant p-value and large fold changes (P < 0.01, |log(FC)| > 5). The study not only confirmed a number of identified differentially expressed genes in literature reports, but also provided new findings. We performed hierarchical clustering analysis utilizing the whole genome-wide gene expressions and differentially expressed genes that were identified in this study. We revealed distinct groups of differentially expressed genes that can aid to the identification of subtypes of the cancer. The hierarchical clustering analysis based on gene expression profile and differentially expressed genes suggested four subtypes of the cancer. We found enriched distinct Gene Ontology (GO) terms associated with these groups of genes. Based on these findings, we built a support vector machine based supervised-learning classifier to predict unknown samples, and the classifier achieved high accuracy and robust classification results. In addition, we identified a number of pathways (P < 0.04) that were significantly influenced by the disease. We found that some of the identified pathways have been implicated in cancers from literatures, while others have not been reported in the cancer before. The network analysis leads to the identification of significantly disrupted pathways and associated genes involved in the disease development. Furthermore, this study can provide a viable alternative in identifying effective drug targets. Conclusions: Our study identified a set of differentially expressed genes and pathways in kidney renal clear cell carcinoma, and represents a comprehensive computational approach to analysis large-scale next-generation sequencing data. The pathway and network analyses suggested that information from distinctly expressed genes can be utilized in the identification of aberrant upstream regulators. Identification of distinctly expressed genes and altered pathways are important in effective biomarker identification for early cancer diagnosis and treatment planning. Combining differentially expressed genes with pathway and network analyses using intelligent computational approaches provide an unprecedented opportunity to identify upstream disease causal genes and effective drug targets.

Background: To study the association between functional single nucleotide polymorphisms (SNPs) in candidate genes from oxidative stress pathways, and risk of radiation pneumonitis (RP) in patients treated with thoracic radiation therapy (RT) for locally advanced lung cancer (LC).

Methods: We reviewed 136 patients treated with RT for LC between 2001 and 2007, and had prior genotyping of functional SNPs in oxidative stress genes including superoxide dismutase 2 (SOD2; rs4880) and methylenetetrahydrofolate reductase (MTHFR; rs1801131, rs1801133). RP events were retrospectively scored using the Common Terminology Criteria for Adverse Events, version 4.0. Cox proportional hazard regression was performed to identify clinical variables and genotypes associated with risk of grade ≥2 and grade ≥3 RP on univariate and multivariate analysis. P-values were corrected for multiple hypothesis testing.

Results: With a median follow-up of 21.4 months, the incidence of ≥grade 2 RP was 29% and ≥grade 3 RP was 14%. On multivariate analysis, after adjusting for clinical factors such as concurrent chemotherapy, and consolidation docetaxel, and lung dosimetric parameters such as V20 and mean lung dose, MTHFR genotype (rs1801131; AA versus AC/CC) was significantly associated with risk of ≥grade 2 RP (Hazard ratio [HR]: 0.37; 95% confidence interval [CI]: 0.18-0.76; p=0.006, corrected p=0.018) and ≥grade 3 RP (HR: 0.21; 95% CI: 0.06-0.70; p=0.01; corrected p=0.03). SOD2 genotype was not associated with RP.

Conclusions: Our study showed an association between MTHFR genotype and risk of clinically significant RP. Further study of MTHFR-related pathways may provide insight into the mechanisms behind RP.