An algorithm to discover gene signatures with predictive potential

Abstract

BackgroundThe advent of global gene expression profiling has generated unprecedented insight into our molecular understanding of cancer, including breast cancer. For example, human breast cancer patients display significant diversity in terms of their survival, recurrence, metastasis as well as response to treatment. These patient outcomes can be predicted by the transcriptional programs of their individual breast tumors. Predictive gene signatures allow us to correctly classify human breast tumors into various risk groups as well as to more accurately target therapy to ensure more durable cancer treatment.ResultsHere we present a novel algorithm to generate gene signatures with predictive potential. The method first classifies the expression intensity for each gene as determined by global gene expression profiling as low, average or high. The matrix containing the classified data for each gene is then used to score the expression of each gene based its individual ability to predict the patient characteristic of interest. Finally, all examined genes are ranked based on their predictive ability and the most highly ranked genes are included in the master gene signature, which is then ready for use as a predictor. This method was used to accurately predict the survival outcomes in a cohort of human breast cancer patients.ConclusionsWe confirmed the capacity of our algorithm to generate gene signatures with bona fide predictive ability. The simplicity of our algorithm will enable biological researchers to quickly generate valuable gene signatures without specialized software or extensive bioinformatics training.

E2F1 and KIAA0191 expression predicts breast cancer patient survival

“…To discover genes that might improve the capacity of E2F1 transcript levels to predict the prognosis of human breast cancer patients, we first ranked the level of gene expression for each gene in every patient's breast tumor as described previously[ 10 ]. We then adapted a similar approach to that we used previously, but instead of searching for genes whose expression was related to patient survival [ 10 ], we modified the algorithm to search for genes whose expression was predictive of patient survival in combination with that of E2F1. We then ranked all the genes present in the expression profiles using a scoring technique published previously [ 10 ].…”

“…We then adapted a similar approach to that we used previously, but instead of searching for genes whose expression was related to patient survival [ 10 ], we modified the algorithm to search for genes whose expression was predictive of patient survival in combination with that of E2F1. We then ranked all the genes present in the expression profiles using a scoring technique published previously [ 10 ].…”

“…Using an algorithm we published recently [ 10 ], we found that the expression of KIAA0191 transcripts can be used in conjunction with those of E2F1 to more accurately predict breast cancer patient survival than does E2F1 expression alone. KIAA0191 , commonly known as TUT4 or ZCCHC11 , encodes a canonical poly (A) polymerase, whose function involves the polyadenylation of pre-mRNA in the nucleus [ 11 ] .…”

The effects of lymph node status on predicting outcome in ER+ /HER2- tamoxifen treated breast cancer patients using gene signatures

“…Furthermore, in clinical practice patients with HER2+ ER+ tumours of 1 cm or more commonly receive adjuvant chemotherapy and Herceptin. A tumour was considered HER2 positive if either of the two HER2 probes on the Affymetrix chip were overexpressed as calculated using previously published methods [ 22 ].…”

Small Molecule Antagonists of the Wnt/Beta-Catenin Signaling Pathway Target Breast Tumor-Initiating Cells in a Her2/Neu Mouse Model of Breast Cancer

“…The expression of Wnt/β-catenin signaling pathway genes was used to divide patients into related and unrelated Wnt/β-catenin signature groups as previously described [36].…”