  Extracting genetic information from a full range of sequencing data is
important for understanding diseases. We propose a novel method to effectively
explore the landscape of genetic mutations and aggregate them to predict cancer
type. We used multinomial logistic regression, nonsmooth non-negative matrix
factorization (nsNMF), and support vector machine (SVM) to utilize the full
range of sequencing data, aiming at better aggregating genetic mutations and
improving their power in predicting cancer types. Specifically, we introduced a
classifier to distinguish cancer types using somatic mutations obtained from
whole-exome sequencing data. Mutations were identified from multiple cancers
and scored using SIFT, PP2, and CADD, and grouped at the individual gene level.
The nsNMF was then applied to reduce dimensionality and to obtain coefficient
and basis matrices. A feature matrix was derived from the obtained matrices to
train a classifier for cancer type classification with the SVM model. We have
demonstrated that the classifier was able to distinguish the cancer types with
reasonable accuracy. In five-fold cross-validations using mutation counts as
features, the average prediction accuracy was 77.1% (SEM=0.1%), significantly
outperforming baselines and outperforming models using mutation scores as
features. Using the factor matrices derived from the nsNMF, we identified
multiple genes and pathways that are significantly associated with each cancer
type. This study presents a generic and complete pipeline to study the
associations between somatic mutations and cancers. The discovered genes and
pathways associated with each cancer type can lead to biological insights. The
proposed method can be adapted to other studies for disease classification and
pathway discovery.
