scholarly journals Gene Expression Signatures Based on Variability can Robustly Predict Tumor Progression and Prognosis

2015 ◽  
Vol 14 ◽  
pp. CIN.S23862 ◽  
Author(s):  
Wikum Dinalankara ◽  
Héctor Corrada Bravo
Keyword(s):  
Gene Expression ◽  
Normal Tissue ◽  
Tissue Expression ◽  
Cancer Prognosis ◽  
Clinical Settings ◽  
Cancer Type ◽  
Genomic Signatures ◽  
Normal Expression ◽  
Gene Expression Signatures ◽  
Cancer Types

Gene expression signatures are commonly used to create cancer prognosis and diagnosis methods, yet only a small number of them are successfully deployed in the clinic since many fail to replicate performance on subsequent validation. A primary reason for this lack of reproducibility is the fact that these signatures attempt to model the highly variable and unstable genomic behavior of cancer. Our group recently introduced gene expression anti-profiles as a robust methodology to derive gene expression signatures based on the observation that while gene expression measurements are highly heterogeneous across tumors of a specific cancer type relative to the normal tissue, their degree of deviation from normal tissue expression in specific genes involved in tissue differentiation is a stable tumor mark that is reproducible across experiments and cancer types. Here we show that constructing gene expression signatures based on variability and the anti-profile approach yields classifiers capable of successfully distinguishing benign growths from cancerous growths based on deviation from normal expression. We then show that this same approach generates stable and reproducible signatures that predict probability of relapse and survival based on tumor gene expression. These results suggest that using the anti-profile framework for the discovery of genomic signatures is an avenue leading to the development of reproducible signatures suitable for adoption in clinical settings.

scholarly journals Analyzing cancer gene expression data through the lens of normal tissue-specificity

2021 ◽  
Author(s):  
H. Robert Frost
Keyword(s):  
Gene Expression ◽  
Normal Tissue ◽  
Tissue Specificity ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Genetic Alterations ◽  
Cancer Type ◽  
Tissue Specific ◽  
Normal Tissues ◽  
Cancer Types

AbstractThe genetic alterations that underlie cancer development are highly tissue-specific with the majority of driving alterations occurring in only a few cancer types and with alterations common to multiple cancer types often showing a tissue-specific functional impact. This tissue-specificity means that the biology of normal tissues carries important information regarding the pathophysiology of the associated cancers, information that can be leveraged to improve the power and accuracy of cancer genomic analyses. Research exploring the use of normal tissue data for the analysis of cancer genomics has primarily focused on the paired analysis of tumor and adjacent normal samples. Efforts to leverage the general characteristics of normal tissue for cancer analysis has received less attention with most investigations focusing on understanding the tissue-specific factors that lead to individual genomic alterations or dysregulated pathways within a single cancer type. To address this gap and support scenarios where adjacent normal tissue samples are not available, we explored the genome-wide association between the transcriptomes of 21 solid human cancers and their associated normal tissues as profiled in healthy individuals. While the average gene expression profiles of normal and cancerous tissue may appear distinct, with normal tissues more similar to other normal tissues than to the associated cancer types, when transformed into relative expression values, i.e., the ratio of expression in one tissue or cancer relative to the mean in other tissues or cancers, the close association between gene activity in normal tissues and related cancers is revealed. As we demonstrate through an analysis of tumor data from The Cancer Genome Atlas and normal tissue data from the Human Protein Atlas, this association between tissue-specific and cancer-specific expression values can be leveraged to improve the prognostic modeling of cancer, the comparative analysis of different cancer types, and the analysis of cancer and normal tissue pairs.

Mutation burden as a biomarker of response to immune checkpoint therapy in nine solid cancers.

2017 ◽  
Vol 35 (7_suppl) ◽  
pp. 35-35 ◽  
Author(s):  
Shridar Ganesan ◽  
Gyan Bhanot ◽  
Janice M. Mehnert ◽  
Ann W. Silk ◽  
Jeffrey S. Ross ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Response ◽  
T Cells ◽  
Immune Checkpoint ◽  
Solid Cancer ◽  
Cancer Type ◽  
Her2 Breast Cancer ◽  
Mutation Burden ◽  
Gene Expression Signatures ◽  
Cancer Types

35 Background: A high mutation burden a biomarker of response to immune checkpoint therapy in melanoma, non-small cell lung cancer, and colorectal cancer. It is unknown whether mutation burden is predictive of response in other cancer types, and whether it is identifiable using available clinical assays. Methods: Using data for 10,745 tumors from 33 solid cancer types in TCGA, we looked for a mutation burden threshold (iCAM) in each cancer type associated with gene expression signatures of a blocked immune response of robust CD8+T cell response and up-regulation of immune checkpoint genes. Results: A unique iCAM threshold was identified in nine cancers: melanoma and lung, colon, endometrial, and gastric adenocarcinoma; serous ovarian, bladder urothelial, cervical, and ER+ HER2− breast cancer. iCAM thresholds applied to published clinical data for patients treated with immune checkpoint therapy showed that iCAM+ patients had significantly better response. iCAM+ tumors can be identified from clinical-grade NGS assays with high accuracy . In a prospective melanoma cohort of patients treated with anti- PD-1 patients with iCAM+ tumors had significantly better objective response rates, progression free survival, and overall survival compared patients to iCAM− tumors. Pattern of somatic mutations were different in iCAM+ and iCAM− tumors, suggesting different mechanism of carcinogenesis in iCAM+ versus iCAM− tumors. Higher fractions of leukocytes were NK (Natural Killer) cells and macrophages were M1 polarized, and a lower fraction of T cells were regulatory T cells in iCAM+ tumors compared to iCAM− tumors. Over 75% of the genes significantly up-regulated in iCAM+ tumors in every cancer types were in the immune response pathway, confirming immune response to be the main differentiator in iCAM+ veruss iCAM− tumors. Conclusions: In nine cancer types, a clinically useful mutation burden threshold (iCAM) associated with gene expression signatures of blocked immune response can identify likely responders to immune checkpoint therapy, and iCAM status is identifiable using currently available clinical NGS assays.

scholarly journals Analyzing cancer gene expression data through the lens of normal tissue-specificity

2021 ◽  
Vol 17 (6) ◽  
pp. e1009085
Author(s):  
H. Robert Frost
Keyword(s):  
Gene Expression ◽  
Normal Tissue ◽  
Tissue Specificity ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Genetic Alterations ◽  
Cancer Type ◽  
Tissue Specific ◽  
Normal Tissues ◽  
Cancer Types

The genetic alterations that underlie cancer development are highly tissue-specific with the majority of driving alterations occurring in only a few cancer types and with alterations common to multiple cancer types often showing a tissue-specific functional impact. This tissue-specificity means that the biology of normal tissues carries important information regarding the pathophysiology of the associated cancers, information that can be leveraged to improve the power and accuracy of cancer genomic analyses. Research exploring the use of normal tissue data for the analysis of cancer genomics has primarily focused on the paired analysis of tumor and adjacent normal samples. Efforts to leverage the general characteristics of normal tissue for cancer analysis has received less attention with most investigations focusing on understanding the tissue-specific factors that lead to individual genomic alterations or dysregulated pathways within a single cancer type. To address this gap and support scenarios where adjacent normal tissue samples are not available, we explored the genome-wide association between the transcriptomes of 21 solid human cancers and their associated normal tissues as profiled in healthy individuals. While the average gene expression profiles of normal and cancerous tissue may appear distinct, with normal tissues more similar to other normal tissues than to the associated cancer types, when transformed into relative expression values, i.e., the ratio of expression in one tissue or cancer relative to the mean in other tissues or cancers, the close association between gene activity in normal tissues and related cancers is revealed. As we demonstrate through an analysis of tumor data from The Cancer Genome Atlas and normal tissue data from the Human Protein Atlas, this association between tissue-specific and cancer-specific expression values can be leveraged to improve the prognostic modeling of cancer, the comparative analysis of different cancer types, and the analysis of cancer and normal tissue pairs.

scholarly journals Estimating the predictive power of silent mutations on cancer classification and prognosis

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Tal Gutman ◽  
Guy Goren ◽  
Omri Efroni ◽  
Tamir Tuller
Keyword(s):  
Gene Expression ◽  
Predictive Power ◽  
Predictive Ability ◽  
Cancer Classification ◽  
Silent Mutation ◽  
Cancer Type ◽  
Coding Region ◽  
Probability Of Survival ◽  
Diagnosis And Prognosis ◽  
Cancer Types

AbstractIn recent years it has been shown that silent mutations, in and out of the coding region, can affect gene expression and may be related to tumorigenesis and cancer cell fitness. However, the predictive ability of these mutations for cancer type diagnosis and prognosis has not been evaluated yet. In the current study, based on the analysis of 9,915 cancer genomes and approximately three million mutations, we provide a comprehensive quantitative evaluation of the predictive power of various types of silent and non-silent mutations over cancer classification and prognosis. The results indicate that silent-mutation models outperform the equivalent null models in classifying all examined cancer types and in estimating the probability of survival 10 years after the initial diagnosis. Additionally, combining both non-silent and silent mutations achieved the best classification results for 68% of the cancer types and the best survival estimation results for up to nine years after the diagnosis. Thus, silent mutations hold considerable predictive power over both cancer classification and prognosis, most likely due to their effect on gene expression. It is highly advised that silent mutations are integrated in cancer research in order to unravel the full genomic landscape of cancer and its ramifications on cancer fitness.

scholarly journals Hard wiring of normal tissue-specific chromosome-wide gene expression levels is an additional factor driving cancer type-specific aneuploidies

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sushant Patkar ◽  
Kerstin Heselmeyer-Haddad ◽  
Noam Auslander ◽  
Daniela Hirsch ◽  
Jordi Camps ◽  
...  
Keyword(s):  
Gene Expression ◽  
Normal Tissue ◽  
Cancer Type ◽  
Tissue Specific ◽  
Expression Levels ◽  
Specific Distribution ◽  
Tumor Origin ◽  
Chromosome Arm ◽  
Gains And Losses ◽  
Gene Expression Levels

Abstract Background Many carcinomas have recurrent chromosomal aneuploidies specific to the tissue of tumor origin. The reason for this specificity is not completely understood. Methods In this study, we looked at the frequency of chromosomal arm gains and losses in different cancer types from the The Cancer Genome Atlas (TCGA) and compared them to the mean gene expression of each chromosome arm in corresponding normal tissues of origin from the Genotype-Tissue Expression (GTEx) database, in addition to the distribution of tissue-specific oncogenes and tumor suppressors on different chromosome arms. Results This analysis revealed a complex picture of factors driving tumor karyotype evolution in which some recurrent chromosomal copy number reflect the chromosome arm-wide gene expression levels of the their normal tissue of tumor origin. Conclusions We conclude that the cancer type-specific distribution of chromosomal arm gains and losses is potentially “hardwiring” gene expression levels characteristic of the normal tissue of tumor origin, in addition to broadly modulating the expression of tissue-specific tumor driver genes.

scholarly journals Protein structure-based gene expression signatures

2020 ◽  
Author(s):  
R. Rahman ◽  
Y. Xiong ◽  
J. G. C. van Hasselt ◽  
J. Hansen ◽  
E. A. Sobie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Structure ◽  
Mrna Expression ◽  
Expression Patterns ◽  
Drug Effects ◽  
Tissue Expression ◽  
Structural Features ◽  
Biological Phenomena ◽  
Gene Expression Signatures ◽  
Cellular Phenotypes

AbstractGene expression signatures (GES) connect phenotypes to mRNA expression patterns, providing a powerful approach to define cellular identity, function, and the effects of perturbations. However, the use of GES has suffered from vague assessment criteria and limited reproducibility. The structure of proteins defines the functional capability of genes, and hence, we hypothesized that enrichment of structural features could be a generalizable representation of gene sets. We derive structural gene expression signatures (sGES) using features from various levels of protein structure (e.g. domain, fold) encoded by the transcribed genes in GES, to describe cellular phenotypes. Comprehensive analyses of data from the Genotype-Tissue Expression Project (GTEx), ARCHS4, and mRNA expression of drug effects on cardiomyocytes show that structural GES (sGES) are useful for identifying robust signatures of biological phenomena. sGES also enables the characterization of signatures across experimental platforms, facilitates the interoperability of expression datasets, and can describe drug action on cells.

scholarly journals Putatively cancer-specific exon–exon junctions are shared across patients and present in developmental and other non-cancer cells

NAR Cancer ◽  
2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Julianne K David ◽  
Sean K Maden ◽  
Benjamin R Weeder ◽  
Reid F Thompson ◽  
Abhinav Nellore
Keyword(s):  
Rna Splicing ◽  
Tissue Expression ◽  
The Body ◽  
The Cancer Genome Atlas ◽  
Cancer Type ◽  
Rna Seq ◽  
Normal Tissues ◽  
Cancer Types ◽  
Exon Junctions ◽  
Cancer Tissues

Abstract This study probes the distribution of putatively cancer-specific junctions across a broad set of publicly available non-cancer human RNA sequencing (RNA-seq) datasets. We compared cancer and non-cancer RNA-seq data from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx) Project and the Sequence Read Archive. We found that (i) averaging across cancer types, 80.6% of exon–exon junctions thought to be cancer-specific based on comparison with tissue-matched samples (σ = 13.0%) are in fact present in other adult non-cancer tissues throughout the body; (ii) 30.8% of junctions not present in any GTEx or TCGA normal tissues are shared by multiple samples within at least one cancer type cohort, and 87.4% of these distinguish between different cancer types; and (iii) many of these junctions not found in GTEx or TCGA normal tissues (15.4% on average, σ = 2.4%) are also found in embryological and other developmentally associated cells. These findings refine the meaning of RNA splicing event novelty, particularly with respect to the human neoepitope repertoire. Ultimately, cancer-specific exon–exon junctions may have a substantial causal relationship with the biology of disease.

Identification of novel melanoma gene expression signatures using a Bayesian approach to filter out gene expression noise from normal tissues.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e19053-e19053
Author(s):  
David A. Liebner ◽  
Jeffrey Parvin ◽  
Kun Huang
Keyword(s):  
Gene Expression ◽  
Pathway Analysis ◽  
Normal Tissue ◽  
Latent Factors ◽  
Latent Factor ◽  
Gene Expression Noise ◽  
Normal Tissues ◽  
Expression Noise ◽  
Gene Expression Signatures ◽  
Melanoma Subtypes

e19053 Background: Identification of pertinent gene expression signatures may be confounded by failure to account for admixture of tumor specimens with normal tissue. We propose a novel method for filtering out gene expression “noise” from normal tissues in a set of melanoma samples derived from the NCBI Gene Expression Omnibus (GEO) public database. Methods: Using customized software implemented in MATLAB, we performed a preliminary search of GEO using the query term “melanoma”. We manually identified 695 clinical samples within 19 different gene expression studies (median 43 samples per series). Gene expression results were merged across different platforms using rank normalization for the target gene of the respective probes. Standardized gene expression signatures for 36 normal tissues were identified in GEO (GSE2361). We performed a modified sparse Bayesian factor regression analysis that incorporated signatures of normal tissues. Additional regression allowed for discovery of latent factors characteristic of melanoma subtypes. Pathway analysis was performed using Ingenuity Pathway Analysis (IPA) on characteristic genes for inferred melanoma-specific latent factors. Results: Normal tissue contributed significantly to the reported gene expression profiles, explaining a median of 11.5% of specimen variability (range 1.3-55.0%). In a subset analysis of 40 primary or in situ melanoma lesions versus 16 metastatic lesions (GSE7553), primary lesions were more likely contaminated with genes characteristic of normal skin (p<0.001). The top 5 latent factors explained a median of 17.0% of residual sample variance (range 0.6-64%). On IPA analysis, latent factor 1 was strongly associated with dermatologic disease (p = 7.46 e-36), latent factor 2 was associated with inflammatory response (p = 9.71e-25), and latent factor 3 was associated with upregulation of the MITF transcription factor regulation (p = 4.16e-19). Conclusions: Appropriate methods that filter out normal tissue contamination of melanoma specimens can improve our understanding of gene expression patterns in melanoma subtypes, and potentially improve future translational studies.

scholarly journals Integrative Analysis of Cancer Omics Data for Prognosis Modeling

Genes ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 604 ◽  
Author(s):  
Wang ◽  
Wu ◽  
Ma
Keyword(s):  
Integrative Analysis ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Sufficient Information ◽  
Cancer Type ◽  
Multiple Cancer ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Information Borrowing ◽  
Penalization Technique

Prognosis modeling plays an important role in cancer studies. With the development of omics profiling, extensive research has been conducted to search for prognostic markers for various cancer types. However, many of the existing studies share a common limitation by only focusing on a single cancer type and suffering from a lack of sufficient information. With potential molecular similarity across cancer types, one cancer type may contain information useful for the analysis of other types. The integration of multiple cancer types may facilitate information borrowing so as to more comprehensively and more accurately describe prognosis. In this study, we conduct marginal and joint integrative analysis of multiple cancer types, effectively introducing integration in the discovery process. For accommodating high dimensionality and identifying relevant markers, we adopt the advanced penalization technique which has a solid statistical ground. Gene expression data on nine cancer types from The Cancer Genome Atlas (TCGA) are analyzed, leading to biologically sensible findings that are different from the alternatives. Overall, this study provides a novel venue for cancer prognosis modeling by integrating multiple cancer types.

scholarly journals A novel gene selection method for gene expression data for the task of cancer type classification

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
N. Özlem ÖZCAN ŞİMŞEK ◽  
Arzucan ÖZGÜR ◽  
Fikret GÜRGEN
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Selection Method ◽  
Specific Method ◽  
Cancer Type ◽  
Expression Data ◽  
Gene Selection Method ◽  
Novel Gene ◽  
Cancer Types

AbstractCancer is a poligenetic disease with each cancer type having a different mutation profile. Genomic data can be utilized to detect these profiles and to diagnose and differentiate cancer types. Variant calling provide mutation information. Gene expression data reveal the altered cell behaviour. The combination of the mutation and expression information can lead to accurate discrimination of different cancer types. In this study, we utilized and transferred the information of existing mutations for a novel gene selection method for gene expression data. We tested the proposed method in order to diagnose and differentiate cancer types. It is a disease specific method as both the mutations and expressions are filtered according to the selected cancer types. Our experiment results show that the proposed gene selection method leads to similar or improved performance metrics compared to classical feature selection methods and curated gene sets.

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