A multi-step classifier addressing cohort heterogeneity improves performance of prognostic biomarkers in three cancer types.
Cancer research continues to highlight the extensive genetic diversity that exists both between and within tumors. This intrinsic heterogeneity poses one of the central challenges to predicting patient clinical outcome and the personalization of treatments. Despite progress in some individual tumor types, it is not yet possible to prospectively, accurately classify patients by expected survival. One hypothesis proposed to explain this is that the prognostic classifiers developed to date are insufficiently sensitive and specific; however it is also possible that patients are not equally easy to classify by any given biomarker. We demonstrate in a cohort of 45 AJCC stage III melanoma patients that clinico-pathologic biomarkers can identify those patients that are most likely to be misclassified by a molecular biomarker. The process of modelling the classifiability of patients was then replicated in a cohort of 49 stage II breast cancer patients and 53 stage III colon cancer patients. A multi-step procedure incorporating this information not only improved classification accuracy but also indicated the specific clinical attributes that had made classification problematic in each cohort. These findings show that, even when cohorts are of moderate size, including features that explain the patient-specific performance of a prognostic biomarker in a classification framework can improve the modelling and estimation of survival.
School of Mathematics and Statistics, The University of Sydney, Sydney, Australia.|Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA.|Harvard Medical School, Boston, MA, USA.|Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA.|Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, USA.
Ellis
E
Patrick
The Westmead Millennium Institute for Medical Research, The University of Sydney, Sydney, Australia.|Melanoma Institute Australia, The University of Sydney, Sydney, Australia.
Sarah-Jane
SJ
Schramm
School of Mathematics and Statistics, The University of Sydney, Sydney, Australia.|ARC Centre of Excellence for Mathematical & Statistical Frontiers.
John T
JT
Ormerod
Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital, Sydney, Australia.|Discipline Pathology, Sydney Medical School, The University of Sydney, Sydney, Australia.
Richard A
RA
Scolyer
The Westmead Millennium Institute for Medical Research, The University of Sydney, Sydney, Australia.|Melanoma Institute Australia, The University of Sydney, Sydney, Australia.
Graham J
GJ
Mann
School of Mathematics and Statistics, The University of Sydney, Sydney, Australia.
Samuel
S
Mueller
School of Mathematics and Statistics, The University of Sydney, Sydney, Australia.|Melanoma Institute Australia, The University of Sydney, Sydney, Australia.
Jean Y H
JY
Yang
D014408:Biomarkers, Tumor; D019295:Computational Biology / Q000379:methods; D030561:Databases, Nucleic Acid; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D008545:Melanoma / Q000175:diagnosis / Q000235:genetics / Q000401:mortality; D009362:Neoplasm Metastasis; D009367:Neoplasm Staging / Q000379:methods; D009369:Neoplasms / Q000175:diagnosis* / Q000401:mortality*; D011379:Prognosis
D014408:Biomarkers, Tumor
biomarker; cancer; classification; pathology; prognosis