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

Abstract

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.

A Comparative Analaysis of Inter-Site Gene Expression Heterogenicity of Normal Human Buccal Mucosa With Normal Gingival Mucosa

“…While studying differences between same type of cancer (for example oral squamous cell carcinoma) at different sites (of oral cavity), the inherent difference between the normal tissues would be highlighted more rather than differences between those that are independent of normal tissue physiology. [46] If not accounted, this phenomenon may lead to erroneous conclusions.…”

Tissue-adjusted pathway analysis of cancer (TPAC)

“…In this paper, we demonstrated through an analysis of solid tumor data from The Cancer Genome Atlas (TCGA) [20] and normal tissue data from the Human Protein Atla (HPA) [21] that the association between tissue-specific and cancer-specific expression values, i.e., the ratio of gene expression in a specific tissue or cancer relative to the mean across multiple tissues or cancers, can be used to improve survival analysis, the comparative analysis of distinct cancer types, and the analysis of cancer/normal tissue pairs. Specifically, we found that genes enriched in normal tissues are more likely to be down-regulated in the associated cancer with elevated expression associated with a favorable cancer prognosis (see the ρ cancer/norm and ρ surv columns in and 3 in Frost [19].…”

“…Cancer biology is highly tissue and cell type-specific [12][13][14][15][16] with most driving somatic alterations either occuring in only a small number of cancer types, e.g., KRAS mutations in pancreatic, lung and colorectal cancers [17], or having a functional impact that varies between impacted tissues, e.g., germline BRCA1/BRCA2 mutations that only drive cancer in estrogen-sensitive tissues [18]. As we explored in a recent paper [19], this tissue-specificity means that the pattern of gene activity (as quantified by mRNA expression) in normal tissues carries important information regarding the biology of the associated cancer types. In this paper, we demonstrated through an analysis of solid tumor data from The Cancer Genome Atlas (TCGA) [20] and normal tissue data from the Human Protein Atla (HPA) [21] that the association between tissue-specific and cancer-specific expression values, i.e., the ratio of gene expression in a specific tissue or cancer relative to the mean across multiple tissues or cancers, can be used to improve survival analysis, the comparative analysis of distinct cancer types, and the analysis of cancer/normal tissue pairs.…”

MLK4 as an immune marker and its correlation with immune infiltration in Cervical squamous cell carcinoma and endocervical adenocarcinoma(CESC)

“…To study immune microenvironmental effects in tumors, researchers developed a new algorithm that takes advantage of the unique properties of the transcription pro le of cancer samples to infer the contents of tumor cells and the different in ltrating normal cells, It is called Estimation of STromal and Immune cells in MAlignant tumor tissues using Expression data(ESTIMATE) [12,13] . ESTIMATE focuses on stromal and immune cells, which make up the majority of non-tumor components in tumor samples, and identi es speci c signals associated with stromal and immune cell in ltration in tumor tissue [13] .…”