Article Commentary: Bimodal Gene expression and Biomarker Discovery

Abstract

With insights gained through molecular profiling, cancer is recognized as a heterogeneous disease with distinct subtypes and outcomes that can be predicted by a limited number of biomarkers. Statistical methods such as supervised classification and machine learning identify distinguishing features associated with disease subtype but are not necessarily clear or interpretable on a biological level. Genes with bimodal transcript expression, however, may serve as excellent candidates for disease biomarkers with each mode of expression readily interpretable as a biological state. The recent article by Wang et al, entitled “The Bimodality Index: A Criterion for Discovering and Ranking Bimodal Signatures from Cancer Gene Expression Profiling Data,” provides a bimodality index for identifying and scoring transcript expression profiles as biomarker candidates with the benefit of having a direct relation to power and sample size. This represents an important step in candidate biomarker discovery that may help streamline the pipeline through validation and clinical application.

Molecular profiling of a lethal tumor microenvironment, as defined by stromal caveolin-1 status in breast cancers

“…17,18 Interestingly, Figure 11 shows that the Cav-1deficient stromal signature is clearly associated with increased this new paradigm for understanding tumor-stroma co-evolution as "The Autophagic Tumor Stroma Model of Cancer." 30 Many of the predictions of this new model were also validated using Cav-1 (-/-) null mice and stromal cells derived from these animals.…”

“…Table 1), with the transcriptional profiles of normal breast tissue and whole breast tumors. 17,18 Figure 10A and B shows that the Cav-1-deficient tumor stromal signature is upregulated in breast cancer(s), both ER(+) and ER(-) sub-types. However, a more significant association was observed with ER(-) breast cancers.…”

“…This was determined from the bimodal expression transcript profile of ESR1, previously described in Ertel et al 2010. 17,18 lists of the same size as the list under consideration to estimate the false discovery rate at a given p-value threshold. The final list of significant MSigDB gene sets was determined by setting a p-value threshold which ensures that the false discovery rate does not exceed 5%.…”

“…nlm.nih.gov/geo), 69 and ArrayExpress (www.ebi.ac.uk/arrayexpress), 70 was used to evaluate the stromal Cav-1 deficient gene signature in the context of clinical samples. 17,18 Samples were first analyzed in subsets based on their ER status. Additional subsets were defined by classifying samples among five canonical breast cancer subtypes, including luminal A, luminal B, normal-like, basal and Her-2-overexpressing disease.…”

Evidence for a stromal-epithelial “lactate shuttle” in human tumors

“…nlm.nih.gov/geo), 55 and ArrayExpress (www.ebi.ac.uk/arrayexpress), 56 was used to evaluate MCT4 expression in the context of clinical samples. 57,58 In the BoxPlots (See Fig. 8), only samples with ER IHC data were selected for analysis, including 959 ER-positive and 323 ER-negative, for a total of 1,282 samples; in addition, 102 normal healthy breast tissue controls were used ER-neg.)…”