Semi-supervised recursively partitioned mixture models for identifying cancer subtypes.
BACKGROUND
Patients with identical cancer diagnoses often progress differently. The disparity we see in disease progression and treatment response can be attributed to the idea that two histologically similar cancers may be completely different diseases on the molecular level. Methods for identifying cancer subtypes associated with patient survival have the capacity to be powerful instruments for understanding the biochemical processes that underlie disease progression as well as providing an initial step toward more personalized therapy for cancer patients. We propose a method called semi-supervised recursively partitioned mixture models (SS-RPMM) that utilizes array-based genetic and patient-level clinical data for finding cancer subtypes that are associated with patient survival.


RESULTS
In the proposed SS-RPMM, cancer subtypes are identified using a selected subset of genes that are associated with survival time. Since survival information is used in the gene selection step, this method is semi-supervised. Unlike other semi-supervised clustering classification methods, SS-RPMM does not require specification of the number of cancer subtypes, which is often unknown. In a simulation study, our proposed method compared favorably with other competing semi-supervised methods, including: semi-supervised clustering and supervised principal components analysis. Furthermore, an analysis of mesothelioma cancer data using SS-RPMM, revealed at least two distinct methylation profiles that are informative for survival.


BACKGROUND
The analyses implemented in this article were carried out using R (http://www.r.project.org/).


BACKGROUND
devin_koestler@brown.edu; e_andres_houseman@brown.edu


BACKGROUND
Supplementary data are available at Bioinformatics online.
Koestler
Devin C
DC
Department of Community Health, Section for Biostatistics, Center for Environmental Health and Technology, Brown University, Providence, RI 02912, USA. devin_koestler@brown.edu
Marsit
Carmen J
CJ
Christensen
Brock C
BC
Karagas
Margaret R
MR
Bueno
Raphael
R
Sugarbaker
David J
DJ
Kelsey
Karl T
KT
Houseman
E Andres
EA
D016000:Cluster Analysis; D016208:Databases, Factual; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000145:classification* / Q000235:genetics / Q000401:mortality; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods