receptLoss

receptLoss identifies nuclear hormone receptor (NHR) expression loss in cancer by detecting gene expression loss in tumor subsets relative to adjacent normal tissue to reveal biomarkers and molecular subgroups associated with clinical outcomes.

Key Features:

• Expression loss detection: receptLoss detects loss of gene expression in subsets of tumors relative to adjacent normal tissue and is designed to operate when the number of normal samples is limited.
• Gaussian modeling: receptLoss approximates the distribution of normal tissue gene expression using a Gaussian model to support detection with few normal samples.
• Transcriptome-wide analysis: Initially developed for NHR expression loss, receptLoss can be applied transcriptome-wide to identify expression loss across genes.
• TCGA-based discovery: Analysis of gene expression data from The Cancer Genome Atlas (TCGA) identified six NHRs that are typically expressed in normal tissue but reduced in subsets of endometrial tumors, including estrogen, progesterone, and androgen receptors.
• THRB association: receptLoss identified thyroid hormone receptor beta (THRB) expression loss as a biomarker associated with improved survival in endometrial cancer independent of estrogen and progesterone receptor status.

Scientific Applications:

• Patient stratification: Classifying patients into subgroups based on NHR expression profiles to inform prognosis and potential treatment strategies in endometrial cancer.
• Molecular subtype discovery: Uncovering previously unrecognized molecular subgroups defined by NHR expression loss.
• Cross-cancer biomarker discovery: Extending transcriptome-wide application to other cancer types to identify biomarkers associated with disease progression and survival and to guide targeted therapy research.

Methodology:

receptLoss approximates normal tissue gene expression distributions with a Gaussian model and detects tumor-specific expression loss by comparing tumor subsets to adjacent normal samples using gene expression data from The Cancer Genome Atlas (TCGA).