Development and validation of a machine learning model of radiation-induced hypothyroidism with clinical and dose-volume features.

BACKGROUND AND PURPOSE: Radiation-induced hypothyroidism (RIHT) is a common but underestimated late effect in head and neck cancers. However, no consensus exists regarding risk prediction or dose constraints in RIHT. We aimed to develop a machine learning model for the accurate risk prediction of RIHT based on clinical and dose-volume features and to evaluate its performance internally and externally. MATERIALS AND METHODS: We retrospectively searched two institutions for patients aged >20 years treated with definitive radiotherapy for nasopharyngeal or oropharyngeal cancer, and extracted their clinical information and dose-volume features. One was designated the developmental cohort, the other as the external validation cohort. We compared the performances of machine learning models with those of published normal tissue complication probability (NTCP) models. RESULTS: The developmental and external validation cohorts consisted of 378 and 49 patients, respectively. The estimated cumulative incidence rates of grade ≥1 hypothyroidism were 53.5% and 61.3% in the developmental and external validation cohorts, respectively. Machine learning models outperformed traditional NTCP models by having lower Brier scores at every time point and a lower integrated Brier score, while demonstrating a comparable calibration index and mean area under the curve. Even simplified machine learning models using only thyroid features performed better than did traditional NTCP algorithms. The machine learning models showed consistent performance between folds. The performance in a previously unseen external validation cohort was comparable to that of the cross-validation. CONCLUSIONS: Our model outperformed traditional NTCP models, with additional capabilities of predicting the RIHT risk at individual time points. A simplified model using only thyroid dose-volume features still outperforms traditional NTCP models and can be incorporated into future treatment planning systems for biological optimization.

Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients.

Histopathologic assessment is indispensable for diagnosing colorectal cancer (CRC). However, manual evaluation of the diseased tissues under the microscope cannot reliably inform patient prognosis or genomic variations crucial for treatment selections. To address these challenges, we develop the Multi-omics Multi-cohort Assessment (MOMA) platform, an explainable machine learning approach, to systematically identify and interpret the relationship between patients' histologic patterns, multi-omics, and clinical profiles in three large patient cohorts (n = 1888). MOMA successfully predicts the overall survival, disease-free survival (log-rank test P-value<0.05), and copy number alterations of CRC patients. In addition, our approaches identify interpretable pathology patterns predictive of gene expression profiles, microsatellite instability status, and clinically actionable genetic alterations. We show that MOMA models are generalizable to multiple patient populations with different demographic compositions and pathology images collected from distinctive digitization methods. Our machine learning approaches provide clinically actionable predictions that could inform treatments for colorectal cancer patients.

Literature-based translation from synthetic lethality screening into therapeutics targets: CD82 is a novel target for KRAS mutation in colon cancer.

Synthetic lethality (SL) is an emerging therapeutic paradigm in cancer. We introduced a different approach to prioritize SL gene pairs through literature mining and RAS-mutant high-throughput screening (HTS) data. We matched essential genes from text-mining and mutant genes from the COSMIC and CCLE HTS datasets to build a prediction model of SL gene pairs. CCLE gene expression data were used to enrich the essential-mutant SL gene pairs using Spearman's correlation coefficient and literature mining. In total, 223 essential trigger terms were extracted and ranked. The threshold of the essential gene score ( S g ) was set to 10. We identified 586 genes essential for the SL prediction model of colon cancer. Seven essential RAS-mutant SL gene pairs were identified in our model, including CD82-KRAS/NRAS, PEBP1-NRAS, MT-CO2-HRAS, IFI27-NRAS/KRAS, and SUMO1-HRAS gene pairs. Using RAS-mutant HTS data validation, we identified two potential SL gene pairs, including the CD82 (essential gene)-KRAS (mutant gene) pair and CD82-NRAS pair in the DLD-1 colon cancer cell line (Spearman's correlation p-values = 0.004786 and 0.00249, respectively). Based on further annotations by PubChem, we observed that digitonin targeted the complex comprising CD82, especially in KRAS-mutated HCT116 cancer cells. Moreover, we experimentally demonstrated that CD82 exhibited selective vulnerability in KRAS-mutant colorectal cancer. We used literature mining and HTS data to identify candidates for SL targets for RAS-mutant colon cancer.

DiaDeL: An Accurate Deep Learning-Based Model With Mutational Signatures for Predicting Metastasis Stage and Cancer Types.

Mutational signatures help identify cancer-associated genes that are being involved in tumorigenesis pathways. Hence, these pathways guide precision medicine approaches to find appropriate drugs and treatments. The pattern of mutations varies in different cancer types. Some mutations dysregulate protein function so that their accumulation is responsible for cancer development and might be associated with different cancer types. Therefore, mutations as a feature set can be used as an informative candidate to distinguish various cancer types. There are several options for demonstrating mutations. One might employ binary values to demonstrate mutation regions. Another potential method for extracting features is utilizing mutation interpreters. In this study, we investigate the trinucleotide mutational pattern of each cancer type. Moreover, we extract salient NMF-based mutational signatures across various cancer types. Then, we identify cancer-associated genes of a target cancer based on its salient signatures. We evaluate the cancer-associated genes using survival and gene expression analysis in different stages of cancer. Furthermore, we introduce DiaDeL, which is a deep learning-based binary classifier. The DiaDeL model uses mutational signatures as input features and distinct a cancer type from the others. Our proposed model outperforms six state-of-the-art methods with 0.824 and 0.88 for accuracy and AUC, respectively. The source code is available at https://github.com/sabdollahi/DiaDeL.

Contextualizing Genes by Using Text-Mined Co-Occurrence Features for Cancer Gene Panel Discovery.

Developing a biomedical-explainable and validatable text mining pipeline can help in cancer gene panel discovery. We create a pipeline that can contextualize genes by using text-mined co-occurrence features. We apply Biomedical Natural Language Processing (BioNLP) techniques for literature mining in the cancer gene panel. A literature-derived 4,679 × 4,630 gene term-feature matrix was built. The EGFR L858R and T790M, and BRAF V600E genetic variants are important mutation term features in text mining and are frequently mutated in cancer. We validate the cancer gene panel by the mutational landscape of different cancer types. The cosine similarity of gene frequency between text mining and a statistical result from clinical sequencing data is 80.8%. In different machine learning models, the best accuracy for the prediction of two different gene panels, including MSK-IMPACT (Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets), and Oncomine cancer gene panel, is 0.959, and 0.989, respectively. The receiver operating characteristic (ROC) curve analysis confirmed that the neural net model has a better prediction performance (Area under the ROC curve (AUC) = 0.992). The use of text-mined co-occurrence features can contextualize each gene. We believe the approach is to evaluate several existing gene panels, and show that we can use part of the gene panel set to predict the remaining genes for cancer discovery.

Deciphering genes associated with diffuse large B-cell lymphoma with lymphomatous effusions: A mutational accumulation scoring approach.

INTRODUCTION: Earlier studies have shown that lymphomatous effusions in patients with diffuse large B-cell lymphoma (DLBCL) are associated with a very poor prognosis, even worse than for non-effusion-associated patients with stage IV disease. We hypothesized that certain genetic abnormalities were associated with lymphomatous effusions, which would help to identify related pathways, oncogenic mechanisms, and therapeutic targets. METHODS: We compared whole-exome sequencing on DLBCL samples involving solid organs (n = 22) and involving effusions (n = 9). We designed a mutational accumulation-based approach to score each gene and used mutation interpreters to identify candidate pathogenic genes associated with lymphomatous effusions. Moreover, we performed gene-set enrichment analysis from a microarray comparison of effusion-associated versus non-effusion-associated DLBCL cases to extract the related pathways. RESULTS: We found that genes involved in identified pathways or with high accumulation scores in the effusion-based DLBCL cases were associated with migration/invasion. We validated expression of 8 selected genes in DLBCL cell lines and clinical samples: MUC4, SLC35G6, TP53BP2, ARAP3, IL13RA1, PDIA4, HDAC1 and MDM2, and validated expression of 3 proteins (MUC4, HDAC1 and MDM2) in an independent cohort of DLBCL cases with (n = 31) and without (n = 20) lymphomatous effusions. We found that overexpression of HDAC1 and MDM2 correlated with the presence of lymphomatous effusions, and HDAC1 overexpression was associated with the poorest prognosis.  CONCLUSION: Our findings suggest that DLBCL associated with lymphomatous effusions may be associated mechanistically with TP53-MDM2 pathway and HDAC-related chromatin remodeling mechanisms.

WinBinVec: Cancer-Associated Protein-Protein Interaction Extraction and Identification of 20 Various Cancer Types and Metastasis Using Different Deep Learning Models.

Biophysical protein-protein interactions perform dominant roles in the initiation and progression of many cancer-related pathways. A protein-protein interaction might play different roles in diverse cancer types. Hence, prioritizing the PPIs in each cancer type would help detect cancer-associated pathways, find a better understanding of cancer biology, and facilitate drug discovery. Several studies to date have proposed computational methods for extracting the PPI essentiality of different cancer types based on the PPI network. The main drawback of these studies is not using a rich source such as genomics variant data. An amino acid sequence encodes useful information about protein structure and behavior. We represent each amino acid sequence based on its variants/mutations in seven different ways: binary vectors, pathogenicity scores, binding affinity changes upon mutations, gene expression-based network of the interactions, biophysicochemical properties, g-gap dipeptide, and one-hot vectors. Based on these representations, we design and consider seven different deep learning models. Then, we compare the accuracy of these models in predicting 20 different cancer types from the TCGA cohort. WinBinVec is a window-based model that outperforms the other models. Moreover, WinBinVec contains a PPI essentiality module that helps extract the essentiality probability of each PPI for every cancer type. Source code and Data: https://github.com/sabdollahi/WinBinVec.

Comprehensive assessments of germline deletion structural variants reveal the association between prognostic MUC4 and CEP72 deletions and immune response gene expression in colorectal cancer patients.

BACKGROUND: Functional disruptions by large germline genomic structural variants in susceptible genes are known risks for cancer. We used deletion structural variants (DSVs) generated from germline whole-genome sequencing (WGS) and DSV immune-related association tumor microenvironment (TME) to predict cancer risk and prognosis. METHODS: We investigated the contribution of germline DSVs to cancer susceptibility and prognosis by silicon and causal inference models. DSVs in germline WGS data were generated from the blood samples of 192 cancer and 499 non-cancer subjects. Clinical information, including family cancer history (FCH), was obtained from the National Cheng Kung University Hospital and Taiwan Biobank. Ninety-nine colorectal cancer (CRC) patients had immune response gene expression data. We used joint calling tools and an attention-weighted model to build the cancer risk predictive model and identify DSVs in familial cancer. The survival support vector machine (survival-SVM) was used to select prognostic DSVs. RESULTS: We identified 671 DSVs that could predict cancer risk. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of the attention-weighted model was 0.71. The 3 most frequent DSV genes observed in cancer patients were identified as ADCY9, AURKAPS1, and RAB3GAP2 (p < 0.05). The DSVs in SGSM2 and LHFPL3 were relevant to colorectal cancer. We found a higher incidence of FCH in cancer patients than in non-cancer subjects (p < 0.05). SMYD3 and NKD2DSV genes were associated with cancer patients with FCH (p < 0.05). We identified 65 immune-associated DSV markers for assessing cancer prognosis (p < 0.05). The functional protein of MUC4 DSV gene interacted with MAGE1 expression, according to the STRING database. The causal inference model showed that deleting the CEP72 DSV gene affect the recurrence-free survival (RFS) of IFIT1 expression. CONCLUSIONS: We established an explainable attention-weighted model for cancer risk prediction and used the survival-SVM for prognostic stratification by using germline DSVs and immune gene expression datasets. Comprehensive assessments of germline DSVs can predict the cancer risk and clinical outcome of colon cancer patients.

Reproducible Machine Learning Methods for Lung Cancer Detection Using Computed Tomography Images: Algorithm Development and Validation.

BACKGROUND: Chest computed tomography (CT) is crucial for the detection of lung cancer, and many automated CT evaluation methods have been proposed. Due to the divergent software dependencies of the reported approaches, the developed methods are rarely compared or reproduced. OBJECTIVE: The goal of the research was to generate reproducible machine learning modules for lung cancer detection and compare the approaches and performances of the award-winning algorithms developed in the Kaggle Data Science Bowl. METHODS: We obtained the source codes of all award-winning solutions of the Kaggle Data Science Bowl Challenge, where participants developed automated CT evaluation methods to detect lung cancer (training set n=1397, public test set n=198, final test set n=506). The performance of the algorithms was evaluated by the log-loss function, and the Spearman correlation coefficient of the performance in the public and final test sets was computed. RESULTS: Most solutions implemented distinct image preprocessing, segmentation, and classification modules. Variants of U-Net, VGGNet, and residual net were commonly used in nodule segmentation, and transfer learning was used in most of the classification algorithms. Substantial performance variations in the public and final test sets were observed (Spearman correlation coefficient = .39 among the top 10 teams). To ensure the reproducibility of results, we generated a Docker container for each of the top solutions. CONCLUSIONS: We compared the award-winning algorithms for lung cancer detection and generated reproducible Docker images for the top solutions. Although convolutional neural networks achieved decent accuracy, there is plenty of room for improvement regarding model generalizability.

Literature-based discovery of new candidates for drug repurposing.

Drug development is an expensive and time-consuming process; these could be reduced if the existing resources could be used to identify candidates for drug repurposing. This study sought to do this by text mining a large-scale literature repository to curate repurposed drug lists for different cancers. We devised a pattern-based relationship extraction method to extract disease-gene and gene-drug direct relationships from the literature. These direct relationships are used to infer indirect relationships using the ABC model. A gene-shared ranking method based on drug target similarity was then proposed to prioritize the indirect relationships. Our method of assessing drug target similarity correlated to existing anatomical therapeutic chemical code-based methods with a Pearson correlation coefficient of 0.9311. The indirect relationships ranking method achieved a significant mean average precision score of top 100 most common diseases. We also confirmed the suitability of candidates identified for repurposing as anticancer drugs by conducting a manual review of the literature and the clinical trials. Eventually, for visualization and enrichment of huge amount of repurposed drug information, a chord diagram was demonstrated to rapidly identify two novel indications for further biological evaluations.

An epigenetic biomarker panel for glioblastoma multiforme personalized medicine through DNA methylation analysis of human embryonic stem cell-like signature.

Alterations of DNA methylation occur during the course of both stem cell development and tumorigenesis. We present a novel strategy that can be used to stratify glioblastoma multiforme (GBM) patients through the epigenetic states of genes associated with human embryonic stem cell (hESC) identity in order to 1) assess linkages between the methylation signatures of these stem cell genes and survival of GBM patients, and 2) delineate putative mechanisms leading to poor prognosis in some patient subgroups. A DNA methylation signature was established for stratifying GBM patients into several hESC methylator subgroups. The hESC methylator-negative phenotype has demonstrated poor survival and upregulation of glioma stem cell (GSC) markers, and is enriched in one of the previously defined transcriptomic phenotypes-the mesenchymal phenotype. We further identified a refined signature of 36 genes as the gene panel, including SOX2, POU3F2, FGFR2, GAP43, NTRK2, NTRK3, and NKX2-2, which are highly enriched in the nervous system. Both signatures outperformed the O6-methylguanine-DNA methyltransferase (MGMT) methylation test in predicting patient's outcome. These findings were also validated through an independent dataset of patients. Furthermore, through statistical analyses, both signatures were examined significantly. Hypomethylation of hESC-associated genes predicted poorer clinical outcome in GBM, supporting the idea that epigenetic activation of stem cell genes contributes to GBM aggression. The gene panel presented herein may be developed into clinical assays for patient stratification and future personalized medicine interventions.

CGPredictor: a systematic integrated analytic tool for mining and examining genome-scale cancer independent prognostic epigenetic marker panels.

BACKGROUND: Tumor biomarkers are potentially useful in several ways such as the identification of individuals at increased risk of developing cancer, in screening for early malignancies and in aiding cancer diagnoses; tumor biomarkers may also be used for determining prognosis, predicting therapeutic response, patient tracking following curative surgery for cancer and for monitoring therapy. Epigenetic alterations, especially aberrant DNA methylation, are recognized as common molecular alterations in a variety of tumors and also occur during the development of tumors. The Cancer Grade Predictor (CGPredictor) is an extendable package with functions designed to facilitate systematic integrated and rapid analysis of high-throughput methylation through the use of most self-similarity subgroups of patients supported by various validating examinations with regarded to survival outcome to obtain the identity of the target predictor. RESULTS: We used high-grade serous ovarian cancer (HGSOC) and invasive breast carcinoma (BRCA) to demonstrate the usefulness of the CGPredictor package. The clustering results and the identity predictors worked well and efficiently in producing significant results after various tests were used to validate the usefulness of CGPredictor package. Also, some of the markers for either the HGSOC or BRCA marker panel have been previously reported to reveal significant results. Even when performed using a different platform with an independent large population BRCA dataset for validation, the identity predictor provided an accurate assessment of patient conditions and produced significant results. CONCLUSIONS: CGPredictor package is not a customized analysis tool designed specifically for the identification of only one or a few specific types of cancer but can be applied more broadly; moreover, the results indicate that the extracted predictors may worthy of consideration for further clinical testing to identify their potential usefulness for clinical molecular diagnosis and targeted treatments of patients with HGSOC and BRCA. So, the use of CGPredictor is feasible for examining the statistical significance of specific markers of interest and shows great potential for use with other types of cancers for cancer biomarker mining.

Transcriptional activation of the Axl and PDGFR-α by c-Met through a ras- and Src-independent mechanism in human bladder cancer.

BACKGROUND: A cross-talk between different receptor tyrosine kinases (RTKs) plays an important role in the pathogenesis of human cancers. METHODS: Both NIH-Met5 and T24-Met3 cell lines harboring an inducible human c-Met gene were established. C-Met-related RTKs were screened by RTK microarray analysis. The cross-talk of RTKs was demonstrated by Western blotting and confirmed by small interfering RNA (siRNA) silencing, followed by elucidation of the underlying mechanism. The impact of this cross-talk on biological function was demonstrated by Trans-well migration assay. Finally, the potential clinical importance was examined in a cohort of 65 cases of locally advanced and metastatic bladder cancer patients. RESULTS: A positive association of Axl or platelet-derived growth factor receptor-alpha (PDGFR-α) with c-Met expression was demonstrated at translational level, and confirmed by specific siRNA knock-down. The transactivation of c-Met on Axl or PDGFR-α in vitro was through a ras- and Src-independent activation of mitogen-activated protein kinase/extracellular signal-regulated kinase (MEK/ERK) pathway. In human bladder cancer, co-expression of these RTKs was associated with poor patient survival (p < 0.05), and overexpression of c-Met/Axl/PDGFR-α or c-Met alone showed the most significant correlation with poor survival (p < 0.01). CONCLUSIONS: In addition to c-Met, the cross-talk with Axl and/or PDGFR-α also contributes to the progression of human bladder cancer. Evaluation of Axl and PDGFR-α expression status may identify a subset of c-Met-positive bladder cancer patients who may require co-targeting therapy.

An integrative approach to identifying cancer chemoresistance-associated pathways.

BACKGROUND: Resistance to chemotherapy severely limits the effectiveness of chemotherapy drugs in treating cancer. Still, the mechanisms and critical pathways that contribute to chemotherapy resistance are relatively unknown. This study elucidates the chemoresistance-associated pathways retrieved from the integrated biological interaction networks and identifies signature genes relevant for chemotherapy resistance. METHODS: An integrated network was constructed by collecting multiple metabolic interactions from public databases and the k-shortest path algorithm was implemented to identify chemoresistant related pathways. The identified pathways were then scored using differential expression values from microarray data in chemosensitive and chemoresistant ovarian and lung cancers. Finally, another pathway database, Reactome, was used to evaluate the significance of genes within each filtered pathway based on topological characteristics. RESULTS: By this method, we discovered pathways specific to chemoresistance. Many of these pathways were consistent with or supported by known involvement in chemotherapy. Experimental results also indicated that integration of pathway structure information with gene differential expression analysis can identify dissimilar modes of gene reactions between chemosensitivity and chemoresistance. Several identified pathways can increase the development of chemotherapeutic resistance and the predicted signature genes are involved in drug resistant during chemotherapy. In particular, we observed that some genes were key factors for joining two or more metabolic pathways and passing down signals, which may be potential key targets for treatment. CONCLUSIONS: This study is expected to identify targets for chemoresistant issues and highlights the interconnectivity of chemoresistant mechanisms. The experimental results not only offer insights into the mode of biological action of drug resistance but also provide information on potential key targets (new biological hypothesis) for further drug-development efforts.

Automated evaluation of electronic discharge notes to assess quality of care for cardiovascular diseases using Medical Language Extraction and Encoding System (MedLEE).

The objective of this study was to develop and validate an automated acquisition system to assess quality of care (QC) measures for cardiovascular diseases. This system combining searching and retrieval algorithms was designed to extract QC measures from electronic discharge notes and to estimate the attainment rates to the current standards of care. It was developed on the patients with ST-segment elevation myocardial infarction and tested on the patients with unstable angina/non-ST-segment elevation myocardial infarction, both diseases sharing almost the same QC measures. The system was able to reach a reasonable agreement (kappa value) with medical experts from 0.65 (early reperfusion rate) to 0.97 (beta-blockers and lipid-lowering agents before discharge) for different QC measures in the test set, and then applied to evaluate QC in the patients who underwent coronary artery bypass grafting surgery. The result has validated a new tool to reliably extract QC measures for cardiovascular diseases.

Analysis of chemotherapy response programs in ovarian cancers by the next-generation sequencing technologies.

OBJECTIVE: To understand the chemotherapy response program in ovarian cancer cells at deep transcript sequencing levels. METHODS: Two next-generation sequencing technologies--MPSS (massively parallel signature sequencing) and SBS (sequencing by synthesis)--were used to sequence the transcripts of IGROV1 and IGROV1-CP cells, and to sequence the transcripts of a highly chemotherapy responsive and a highly chemotherapy resistant ovarian cancer tissue. RESULTS: We identified 3422 signatures (2957 genes) that are significantly different between IGROV1 and IGROV1-CP cells (P<0.001). Gene Ontology (GO) term GO:0001837 (epithelial-to-mesenchymal transition) and GO:0034330 (cell junction assembly and maintenance) are enriched in genes that are over expressed in IGROV1-CP cells while apoptosis-related GO terms are enriched in genes over expressed in IGROV1 cells. We identified 1187 tags (corresponding to 1040 genes) that are differentially expressed between the chemotherapy responsive and the persistently chemotherapy resistant ovarian cancer tissues. GO term GO:0050673 (epithelial cell proliferation) and GO:0050678 (regulation of epithelial cell proliferation) are enriched in the genes over expressed in the chemotherapy resistant tissue while the GO:0007229 (integrin-mediated signaling pathway) is enriched in the genes over expressed in the chemotherapy sensitive tissue. An integrative analysis identified 111 common differentially expressed genes including two bone morphogenetic proteins (BMP4 and BMP7), six solute carrier proteins (SLC10A3, SLC16A3, SLC25A1, SLC35B3, SLC7A5 and SLC7A7), transcription factor POU5F1 (POU class 5 homeobox 1), and KLK10 (kallikrein-related peptidase 10). A network analysis revealed a subnetwork with three genes BMP7, NR2F2 and AP2B1 that were consistently over expressed in the chemoresistant tissue or cells compared to the chemosensitive tissue or cells. CONCLUSION: Our database offers the first comprehensive view of the digital transcriptomes of ovarian cancer cell lines and tissues with different chemotherapy response phenotypes.

Role of the histone variant H2A.Z/Htz1p in TBP recruitment, chromatin dynamics, and regulated expression of oleate-responsive genes.

The histone variant H2A.Z (Htz1p) has been implicated in transcriptional regulation in numerous organisms, including Saccharomyces cerevisiae. Genome-wide transcriptome profiling and chromatin immunoprecipitation studies identified a role for Htz1p in the rapid and robust activation of many oleate-responsive genes encoding peroxisomal proteins, in particular POT1, POX1, FOX2, and CTA1. The Swr1p-, Gcn5p-, and Chz1p-dependent association of Htz1p with these promoters in their repressed states appears to establish an epigenetic marker for the rapid and strong expression of these highly inducible promoters. Isw2p also plays a role in establishing the nucleosome state of these promoters and associates stably in the absence of Htz1p. An analysis of the nucleosome dynamics and Htz1p association with these promoters suggests a complex mechanism in which Htz1p-containing nucleosomes at fatty acid-responsive promoters are disassembled upon initial exposure to oleic acid leading to the loss of Htz1p from the promoter. These nucleosomes reassemble at later stages of gene expression. While these new nucleosomes do not incorporate Htz1p, the initial presence of Htz1p appears to mark the promoter for sustained gene expression and the recruitment of TATA-binding protein.

Dysregulated gene expression networks in human acute myelogenous leukemia stem cells.

We performed the first genome-wide expression analysis directly comparing the expression profile of highly enriched normal human hematopoietic stem cells (HSC) and leukemic stem cells (LSC) from patients with acute myeloid leukemia (AML). Comparing the expression signature of normal HSC to that of LSC, we identified 3,005 differentially expressed genes. Using 2 independent analyses, we identified multiple pathways that are aberrantly regulated in leukemic stem cells compared with normal HSC. Several pathways, including Wnt signaling, MAP Kinase signaling, and Adherens Junction, are well known for their role in cancer development and stem cell biology. Other pathways have not been previously implicated in the regulation of cancer stem cell functions, including Ribosome and T Cell Receptor Signaling pathway. This study demonstrates that combining global gene expression analysis with detailed annotated pathway resources applied to highly enriched normal and malignant stem cell populations, can yield an understanding of the critical pathways regulating cancer stem cells.

Modeling human cancer-related regulatory modules by GA-RNN hybrid algorithms.

BACKGROUND: Modeling cancer-related regulatory modules from gene expression profiling of cancer tissues is expected to contribute to our understanding of cancer biology as well as developments of new diagnose and therapies. Several mathematical models have been used to explore the phenomena of transcriptional regulatory mechanisms in Saccharomyces cerevisiae. However, the contemplating on controlling of feed-forward and feedback loops in transcriptional regulatory mechanisms is not resolved adequately in Saccharomyces cerevisiae, nor is in human cancer cells. RESULTS: In this study, we introduce a Genetic Algorithm-Recurrent Neural Network (GA-RNN) hybrid method for finding feed-forward regulated genes when given some transcription factors to construct cancer-related regulatory modules in human cancer microarray data. This hybrid approach focuses on the construction of various kinds of regulatory modules, that is, Recurrent Neural Network has the capability of controlling feed-forward and feedback loops in regulatory modules and Genetic Algorithms provide the ability of global searching of common regulated genes. This approach unravels new feed-forward connections in regulatory models by modified multi-layer RNN architectures. We also validate our approach by demonstrating that the connections in our cancer-related regulatory modules have been most identified and verified by previously-published biological documents. CONCLUSION: The major contribution provided by this approach is regarding the chain influences upon a set of genes sequentially. In addition, this inverse modeling correctly identifies known oncogenes and their interaction genes in a purely data-driven way.