Comparative analysis of molecular signatures reveals a hybrid approach in breast cancer: Combining the Nottingham Prognostic Index with gene expressions into a hybrid signature

Abstract

The diagnosis of breast cancer—including determination of prognosis and prediction—has been traditionally based on clinical and pathological characteristics such as tumor size, nodal status, and tumor grade. The decision-making process has been expanded by the recent introduction of molecular signatures. These signatures, however, have not reached the highest levels of evidence thus far. Yet they have been brought to clinical practice based on statistical significance in prospective as well as retrospective studies. Intriguingly, it has also been reported that most random sets of genes are significantly associated with disease outcome. These facts raise two highly relevant questions: What information gain do these signatures procure? How can one find a signature that is substantially better than a random set of genes? Our study addresses these questions. To address the latter question, we present a hybrid signature that joins the traditional approach with the molecular one by combining the Nottingham Prognostic Index with gene expressions in a data-driven fashion. To address the issue of information gain, we perform careful statistical analysis and comparison of the hybrid signature, gene expression lists of two commercially available tests as well as signatures selected at random, and introduce the Signature Skill Score—a simple measure to assess improvement on random signatures. Despite being based on in silico data, our research is designed to be useful for the decision-making process of oncologists and strongly supports association of random signatures with outcome. Although our study shows that none of these signatures can be considered as the main candidate for providing prognostic information, it also demonstrates that both the hybrid signature and the gene expression list of the OncotypeDx signature identify patients who may not require adjuvant chemotherapy. More importantly, we show that combining signatures substantially improves the identification of patients who do not need adjuvant chemotherapy.

Re-evaluation of publicly available gene-expression databases using machine-learning yields a maximum prognostic power in breast cancer

“…To achieve more precise predictions, it may be necessary to refine prognostic models by incorporating multiple complementary factors. Our recent research has demonstrated that a hybrid model that integrates gene expression and clinical information can significantly improve prognostic power 20 . This approach has also been highlighted in various studies on cancer prognosis [37][38][39][40] .…”

Molecular phenotyping of malignant canine mammary tumours: Detection of high‐risk group and its relationship with clinicomolecular characteristics

“…If we have more samples, it is possible to confirm this hypothesis. Furthermore, in many cases, molecular classifications have been confirmed to be more robust in predicting survival rates than conventional histopathology methods 24–28 . With the development of more advanced molecular methods, it is possible to develop clinically relevant and practical molecular signatures 29,30 .…”

“…Furthermore, in many cases, molecular classifications have been confirmed to be more robust in predicting survival rates than conventional histopathology methods. [24][25][26][27][28] With the development of more advanced molecular methods, it is possible to develop clinically relevant and practical molecular signatures. 29,30 Proliferation and cell cycle-based gene signatures have been vastly assessed to determine molecular subtypes and to predict the clinical outcome in different human cancers.…”

Identification of lipid droplet-associated genes in breast cancer patients

“…These changes would not have been expected from the gene expression data because the effects regarding elongases (that leads to increased chain length) are weak. This fact emphasizes that the exclusive consideration of changes in the genome is a not very reliable approach, but experimental compositional data are also required (a similar conclusion that gene expression data is not su cient as a single signature was also made by [32]). Additionally, even if changes in the protein levels often accompany changes in the genes, the concentrations alone are not indicative.…”