Using singscore to predict mutation status in acute myeloid leukemia from transcriptomic signatures

Abstract

Advances in RNA sequencing (RNA-seq) technologies that measure the transcriptome of biological samples have revolutionised our ability to understand transcriptional regulatory programs that underpin diseases such as cancer. We recently published singscore - a single sample, rank-based gene set scoring method which quantifies how concordant the transcriptional profile of individual samples are relative to specific gene sets of interest. Here we demonstrate the application of singscore to investigate transcriptional profiles associated with specific mutations or genetic lesions in acute myeloid leukemia. Using matched genomic and transcriptomic data available through the TCGA we show that scoring of appropriate signatures can distinguish samples with corresponding mutations, reflecting the ability of these mutations to drive aberrant transcriptional programs involved in leukemogenesis. We believe the singscore method is particularly useful for studying heterogeneity within a specific subsets of cancers, and as demonstrated, we show the ability of singscore to identify where alternative mutations appear to drive similar transcriptional programs.

Partial epithelial-to-mesenchymal transition is prognostic and associates with Slug in head and neck cancer

“…primarily in oral cavity cancers. We have quantified p-EMT using a signature of n = 15 common p-EMT genes defined across patients with oral cavity cancers (Puram et al, 2017) by applying the recently published Single Sample Scoring of Molecular Phenotype (Bhuva et al, 2019; Foroutan et al, 2018), which represents a superior gene set enrichment analysis (GSEA) providing stable Singscores, within the TCGA HNSCC cohort (Cancer Genome Atlas, 2015). Even though not reliant on background samples, Singscores are a GSEA that depends on a sufficiently large number of total genes to quantify enrichment scores for a chosen gene set (n = 15 genes out of a pool of n = 10,000 in the present study).…”

“…In conclusion, reducing dimensional complexity of p-EMT by Singscoring allowed a distinctive stratification and prognosis of clinical outcome depending on disease sub-groups. Measuring the degree of p-EMT is feasible at the single patient level using Singscores in bulk sequencing information and provides a novel stratification parameter for HNSCC patients in the context of next generation sequencing-based precision medicine (Bhuva et al, 2019). A high p-EMT SING score in patients suffering from oral cavity cancers of the malignant-basal subtype might represent a supportive rationale for full treatment regimens, whereas p-EMT low-risk patients might profit from treatment de-escalation.…”

Stable gene expression for normalisation and single-sample scoring

“…Samples with the phrase ‘carcinoma’ in their annotation were considered carcinomas and were used to derive our stable gene list. TCGA breast cancer data were downloaded and processed using an alternate pipeline described in a R/Bioconductor-based workflow ( 26 ). This data was used to assess the impact of processing pipelines on putative stable genes.…”

Stable gene expression for normalisation and single-sample scoring

“…Samples with the phrase "carcinoma" in their annotation were considered carcinomas and were used to derive our stable gene list. TCGA breast cancer data were downloaded and processed using an alternate pipeline described in a R/Bioconductor-based workflow (25). This data was used to assess the impact of processing pipelines on putative stable genes.…”

“…We previously developed a method, singscore, to score individual samples against gene set signatures using transcriptomic data and showed that these scores can assist in assessing the molecular phenotype of tissues and cell lines (35). Though the method has been applied in diverse scenarios in an exploratory context (25,35,36,42,43), the potential for clinical translation is limited by a requirement for transcriptome-wide measurements.…”