title: A novel model to combine clinical and pathway-based transcriptomic information for the prognosis prediction of breast cancer.
abstract: Breast cancer is the most common malignancy in women worldwide. With the increasing awareness of heterogeneity in breast cancers, better prediction of breast cancer prognosis is much needed for more personalized treatment and disease management. Towards this goal, we have developed a novel computational model for breast cancer prognosis by combining the Pathway Deregulation Score (PDS) based pathifier algorithm, Cox regression and L1-LASSO penalization method. We trained the model on a set of 236 patients with gene expression data and clinical information, and validated the performance on three diversified testing data sets of 606 patients. To evaluate the performance of the model, we conducted survival analysis of the dichotomized groups, and compared the areas under the curve based on the binary classification. The resulting prognosis genomic model is composed of fifteen pathways (e.g., P53 pathway) that had previously reported cancer relevance, and it successfully differentiated relapse in the training set (log rank p-value = 6.25e-12) and three testing data sets (log rank p-value < 0.0005). Moreover, the pathway-based genomic models consistently performed better than gene-based models on all four data sets. We also find strong evidence that combining genomic information with clinical information improved the p-values of prognosis prediction by at least three orders of magnitude in comparison to using either genomic or clinical information alone. In summary, we propose a novel prognosis model that harnesses the pathway-based dysregulation as well as valuable clinical information. The selected pathways in our prognosis model are promising targets for therapeutic intervention.
authors.affiliation: Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, Hawaii, United States of America; Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America.
authors.forename: Sijia
authors.initials: S
authors.lastname: Huang
authors.affiliation: Neurobiology Program of Biology Department, University of Washington, Seattle, Washington, United States of America.
authors.forename: Cameron
authors.initials: C
authors.lastname: Yee
authors.affiliation: Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, Hawaii, United States of America; Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America.
authors.forename: Travers
authors.initials: T
authors.lastname: Ching
authors.affiliation: Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America.
authors.forename: Herbert
authors.initials: H
authors.lastname: Yu
authors.affiliation: Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, Hawaii, United States of America; Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America.
authors.forename: Lana X
authors.initials: LX
authors.lastname: Garmire
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics* / Q000378:metabolism / Q000473:pathology*; D018450:Disease Progression; D005260:Female; D020869:Gene Expression Profiling; D023281:Genomics / Q000379:methods*; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D011379:Prognosis; D016016:Proportional Hazards Models; D012372:ROC Curve; D059467:Transcriptome