scholarly journals Effects of TP53 mutational status on gene expression patterns across 10 human cancer types

2014 ◽  
Vol 232 (5) ◽  
pp. 522-533 ◽  
Author(s):  
Neha Parikh ◽  
Susan Hilsenbeck ◽  
Chad J Creighton ◽  
Tajhal Dayaram ◽  
Ryan Shuck ◽  
...  
Keyword(s):  
Gene Expression ◽  
Human Cancer ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Mutational Status ◽  
Cancer Types ◽  
Tp53 Mutational Status

scholarly journals GENT: Gene Expression Database of Normal and Tumor Tissues

2011 ◽  
Vol 10 ◽  
pp. CIN.S7226 ◽  
Author(s):  
Gwangsik Shin ◽  
Tae-Wook Kang ◽  
Sungjin Yang ◽  
Su-Jin Baek ◽  
Yong-Su Jeong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cancer Cell ◽  
Human Cancer ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Large Sample Size ◽  
Target Validation ◽  
Gene Expression Database ◽  
Tumor Tissues

Background Some oncogenes such as ERBB2 and EGFR are over-expressed in only a subset of patients. Cancer outlier profile analysis is one of computational approaches to identify outliers in gene expression data. A database with a large sample size would be a great advantage when searching for genes over-expressed in only a subset of patients. Description GENT (Gene Expression database of Normal and Tumor tissues) is a web-accessible database that provides gene expression patterns across diverse human cancer and normal tissues. More than 40000 samples, profiled by Affymetrix U133A or U133plus2 platforms in many different laboratories across the world, were collected from public resources and combined into two large data sets, helping the identification of cancer outliers that are over-expressed in only a subset of patients. Gene expression patterns in nearly 1000 human cancer cell lines are also provided. In each tissue, users can retrieve gene expression patterns classified by more detailed clinical information. Conclusions The large samples size (>24300 for U133plus2 and >16400 for U133A) of GENT provides an advantage in identifying cancer outliers. A cancer cell line gene expression database is useful for target validation by in vitro experiment. We hope GENT will be a useful resource for cancer researchers in many stages from target discovery to target validation. GENT is available at http://medicalgenome.kribb.re.kr/GENT/ or http://genome.kobic.re.kr/GENT/ .

scholarly journals Pan-cancer classification by regularized multi-task learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sk Md Mosaddek Hossain ◽  
Lutfunnesa Khatun ◽  
Sumanta Ray ◽  
Anirban Mukhopadhyay
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Cancer Classification ◽  
Functional Enrichment ◽  
Classification Model ◽  
Gene Expression Patterns ◽  
Task Learning ◽  
Cancer Types ◽  
Pan Cancer ◽  
Kernel Svm

AbstractClassifying pan-cancer samples using gene expression patterns is a crucial challenge for the accurate diagnosis and treatment of cancer patients. Machine learning algorithms have been considered proven tools to perform downstream analysis and capture the deviations in gene expression patterns across diversified diseases. In our present work, we have developed PC-RMTL, a pan-cancer classification model using regularized multi-task learning (RMTL) for classifying 21 cancer types and adjacent normal samples using RNASeq data obtained from TCGA. PC-RMTL is observed to outperform when compared with five state-of-the-art classification algorithms, viz. SVM with the linear kernel (SVM-Lin), SVM with radial basis function kernel (SVM-RBF), random forest (RF), k-nearest neighbours (kNN), and decision trees (DT). The PC-RMTL achieves 96.07% accuracy and 95.80% MCC score for a completely unknown independent test set. The only method that appears as the real competitor is SVM-Lin, which nearly equalizes the accuracy in prediction of PC-RMTL but only when complete feature sets are provided for training; otherwise, PC-RMTL outperformed all other classification models. To the best of our knowledge, this is a significant improvement over all the existing works in pan-cancer classification as they have failed to classify many cancer types from one another reliably. We have also compared gene expression patterns of the top discriminating genes across the cancers and performed their functional enrichment analysis that uncovers several interesting facts in distinguishing pan-cancer samples.

scholarly journals Molecular correlates and therapeutic targets in T cell-inflamed versus non-T cell-inflamed tumors across cancer types

2019 ◽  
Author(s):  
Riyue Bao ◽  
Jason J. Luke
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Hedgehog Signaling ◽  
Human Cancer ◽  
Expression Patterns ◽  
Gene Expression Signature ◽  
Gene Signature ◽  
Type I ◽  
Tumor Type ◽  
Cancer Types

AbstractThe T cell-inflamed tumor microenvironment, characterized by CD8 T cells and type I/II interferon transcripts, is an important cancer immunotherapy biomarker. Tumor mutational profile may also dictate response with some oncogenes (i.e. WNT/β-catenin) known to mediate immuno-suppression. Building on these observations we performed a multi-omic analysis of human cancer correlating the T cell-inflamed gene expression signature with the somatic mutanome and transcriptome for different immune phenotypes, by tumor type and across cancers. Strong correlations were noted between mutations in oncogenes and non-T cell-inflamed tumors with examples including IDH1 and GNAQ as well as less well-known genes including KDM6A, CD11c and genes with unknown functions. Conversely, we observe many genes associating with the T cell-inflamed phenotype including VHL and PBRM1, among others. Analyzing gene expression patterns, we identify oncogenic mediators of immune exclusion broadly active across cancer types including HIF1A and MYC. Novel examples from specific tumors include sonic hedgehog signaling in ovarian cancer or hormone signaling and novel transcription factors across multiple tumors. Using network analysis, somatic and transcriptomic events were integrated, demonstrating that most non-T cell-inflamed tumors are influenced by multiple pathways. Validating these analyses, we observe significant inverse relationships between protein levels and the T cell-inflamed gene signature with examples including NRF2 in lung, ERBB2 in urothelial and choriogonadotropin in cervical cancer. Finally, we integrate available databases for drugs that might overcome or augment the identified mechanisms. These results nominate molecular targets and drugs potentially available for immediate translation into clinical trials for patients with cancer.

scholarly journals Molecular correlates and therapeutic targets in T cell-inflamed versus non-T cell-inflamed tumors across cancer types

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Riyue Bao ◽  
Daniel Stapor ◽  
Jason J. Luke
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Hedgehog Signaling ◽  
Human Cancer ◽  
Expression Patterns ◽  
Gene Expression Signature ◽  
Type I ◽  
Tumor Type ◽  
Sonic Hedgehog Signaling ◽  
Cancer Types

Abstract Background The T cell-inflamed tumor microenvironment, characterized by CD8 T cells and type I/II interferon transcripts, is an important cancer immunotherapy biomarker. Tumor mutational burden (TMB) may also dictate response, and some oncogenes (i.e., WNT/β-catenin) are known to mediate immunosuppression. Methods We performed an integrated multi-omic analysis of human cancer including 11,607 tumors across multiple databases and patients treated with anti-PD1. After adjusting for TMB, we correlated the T cell-inflamed gene expression signature with somatic mutations, transcriptional programs, and relevant proteome for different immune phenotypes, by tumor type and across cancers. Results Strong correlations were noted between mutations in oncogenes and tumor suppressor genes and non-T cell-inflamed tumors with examples including IDH1 and GNAQ as well as less well-known genes including KDM6A, CD11c, and genes with unknown functions. Conversely, we observe genes associating with the T cell-inflamed phenotype including VHL and PBRM1. Analyzing gene expression patterns, we identify oncogenic mediators of immune exclusion across cancer types (HIF1A and MYC) as well as novel examples in specific tumors such as sonic hedgehog signaling, hormone signaling and transcription factors. Using network analysis, somatic and transcriptomic events were integrated. In contrast to previous reports of individual tumor types such as melanoma, integrative pan-cancer analysis demonstrates that most non-T cell-inflamed tumors are influenced by multiple signaling pathways and that increasing numbers of co-activated pathways leads to more highly non-T cell-inflamed tumors. Validating these analyses, we observe highly consistent inverse relationships between pathway protein levels and the T cell-inflamed gene expression across cancers. Finally, we integrate available databases for drugs that might overcome or augment the identified mechanisms. Conclusions These results nominate molecular targets and drugs potentially available for further study and potential immediate translation into clinical trials for patients with cancer.

scholarly journals Systematic variation in gene expression patterns in human cancer cell lines

10.1038/73432 ◽  
2000 ◽  
Vol 24 (3) ◽  
pp. 227-235 ◽  
Author(s):  
Douglas T. Ross ◽  
Uwe Scherf ◽  
Michael B. Eisen ◽  
Charles M. Perou ◽  
Christian Rees ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Human Cancer ◽  
Expression Patterns ◽  
Cancer Cell Lines ◽  
Systematic Variation ◽  
Gene Expression Patterns ◽  
Human Cancer Cell ◽  
Human Cancer Cell Lines

scholarly journals Application of Highly Immunocompromised Mice for Establishment of Patient-Derived Xenograft (PDX) Model

2019 ◽  
Author(s):  
Seiji Okada ◽  
Kulthida Vaeteewootthacharn ◽  
Ryusho Kariya
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Nk Cell ◽  
Human Cancer ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Patient Derived Xenograft ◽  
Pdx Model ◽  
Mutational Status ◽  
Immunocompromised Mice

Patient-derived xenograft (PDX) models are created by engraftment of patients’ tumor tissues into immunocompetent mice. Since PDX model keep the characteristics of primary patient’s tumor such as gene expression profiles and drug sensitivity, it now becomes most reliable in vivo human cancer model. The engraftment rate are increased with the introduction of NOD/Scid based immunocompromised mice, especially, NK cell defective NOD strains such as NOD/Scid/IL2Rγnu (NOG/ NSG) mice and NOD/Scid/Jak3null (NOJ) mice. Success ratio differs from the origin of tumor: Gastrointestinal tumors tend to higher success rate and breast cancer is lower. Subcutaneous transplantation is most popular method to establish PDX, but some tumor needs orthotropic or renal capsule transplantation, and human hormone treatment is needed to establish hormone dependent cancers such as prostate and breast cancer. PDX library with patient’s clinical data, gene-expression patterns, mutational status, drug responsiveness and tumor architecture will be the powerful tool for developing specific biomarker and novel individualized therapy and establishing precision cancer medicine.

Gene expression patterns in blood predict future severe endpoints in community acquired pneumonia

Pneumologie ◽  
2018 ◽  
Vol 72 (S 01) ◽  
pp. S8-S9
Author(s):  
M Bauer ◽  
H Kirsten ◽  
E Grunow ◽  
P Ahnert ◽  
M Kiehntopf ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Community Acquired Pneumonia ◽  
Gene Expression Patterns
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