Cancer classification and pathway discovery using non-negative matrix factorization

Abstract

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 crossvalidations 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.

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

“…107 Key to precision treatment has been the identification of the driver mutations specific to the cancer type. 108 Machine learning has been utilised for cancer classification 109,110 and discovery of relevant pathways. 109 Across the spectrum of autoimmune diseases, there has traditionally been a one-size-fits-all approach to patient therapeutics.…”

“…108 Machine learning has been utilised for cancer classification 109,110 and discovery of relevant pathways. 109 Across the spectrum of autoimmune diseases, there has traditionally been a one-size-fits-all approach to patient therapeutics. The expectation is that machine learning represents a necessary key tool that will use 'big' data to stratify patients and move towards personalised treatment approaches that have proven so effective in cancer.…”

Analysis of immune‑related signatures of colorectal cancer identifying two different immune phenotypes: Evidence for immune checkpoint inhibitor therapy

“…The NMF algorithm is a useful tool to perform clustering or subtyping, and extract genomic signatures; previous studies have shown its utility in distinct research directions, such as identification of image pattern, signal processing and text mining (36)(37)(38). The TIDE prediction score is a better predictor of ICI therapy compared with PD-L1 expression and TMB.…”

Tumor Immunity Microenvironment-based classifications of bladder cancer for enhancing cancer immunotherapy

“…Similarly to principal components analysis (PCA) or independent component analysis (ICA), the objective of NMF is to explain the observed data using a limited number of basic components, which could reflect the original data as accurately as possible 42 . NMF was applied to reveal the biomarkers, classify the tumor subtypes and predict the prognosis of tumors recent days 43-45 . As for the enrolled 4,003 bladder cancer patients.…”