Dataset Information

Survival analysis of DNA mutation motifs with penalized proportional hazards.

ABSTRACT: Antibodies, an essential part of our immune system, develop through an intricate process to bind a wide array of pathogens. This process involves randomly mutating DNA sequences encoding these antibodies to find variants with improved binding, though mutations are not distributed uniformly across sequence sites. Immunologists observe this nonuniformity to be consistent with "mutation motifs", which are short DNA subsequences that affect how likely a given site is to experience a mutation. Quantifying the effect of motifs on mutation rates is challenging: a large number of possible motifs makes this statistical problem high dimensional, while the unobserved history of the mutation process leads to a nontrivial missing data problem. We introduce an ? 1-penalized proportional hazards model to infer mutation motifs and their effects. In order to estimate model parameters, our method uses a Monte Carlo EM algorithm to marginalize over the unknown ordering of mutations. We show that our method performs better on simulated data compared to current methods and leads to more parsimonious models. The application of proportional hazards to mutation processes is, to our knowledge, novel and formalizes the current methods in a statistical framework that can be easily extended to analyze the effect of other biological features on mutation rates.

SUBMITTER: Feng J

PROVIDER: S-EPMC7673484 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Survival analysis of DNA mutation motifs with penalized proportional hazards.

Feng Jean J Shaw David A DA Minin Vladimir N VN Simon Noah N Matsen Frederick A FA

The annals of applied statistics 20190617 2

Antibodies, an essential part of our immune system, develop through an intricate process to bind a wide array of pathogens. This process involves randomly mutating DNA sequences encoding these antibodies to find variants with improved binding, though mutations are not distributed uniformly across sequence sites. Immunologists observe this nonuniformity to be consistent with "mutation motifs", which are short DNA subsequences that affect how likely a given site is to experience a mutation. Quanti ...[more]

PMID: 33214798

Similar Datasets

Project description:An ever?increasing number of long noncoding (lnc)RNAs has been identified in breast cancer. The present study aimed to establish an lncRNA signature for predicting survival in breast cancer. RNA expression profiling was performed using microarray gene expression data from the National Center for Biotechnology Information Gene Expression Omnibus, followed by the identification of breast cancer?related preserved modules using weighted gene co?expression network (WGCNA) network analysis. From the lncRNAs identified in these preserved modules, prognostic lncRNAs were selected using univariate Cox regression analysis in combination with the L1?penalized (LASSO) Cox?proportional Hazards (Cox?PH) model. A risk score based on these prognostic lncRNAs was calculated and used for risk stratification. Differentially expressed RNAs (DERs) in breast cancer were identified using MetaDE. Gene Set Enrichment Analysis pathway enrichment analysis was conducted for these prognostic lncRNAs and the DERs related to the lncRNAs in the preserved modules. A total of five preserved modules comprising 73 lncRNAs were mined. An eight?lncRNA signature (IGHA1, IGHGP, IGKV2?28, IGLL3P, IGLV3?10, AZGP1P1, LINC00472 and SLC16A6P1) was identified using the LASSO Cox?PH model. Risk score based on these eight lncRNAs could classify breast cancer patients into two groups with significantly different survival times. The eight?lncRNA signature was validated using three independent cohorts. These prognostic lncRNAs were significantly associated with the cell adhesion molecules pathway, JAK?signal transducer and activator of transcription 5A pathway, and erbb pathway and are potentially involved in regulating angiotensin II receptor type 1, neuropeptide Y receptor Y1, KISS1 receptor, and C?C motif chemokine ligand 5. The developed eight?lncRNA signature may have clinical implications for predicting prognosis in breast cancer. Overall, this study provided possible molecular targets for the development of novel therapies against breast cancer.

Dataset Information

Survival analysis of DNA mutation motifs with penalized proportional hazards.

Publications

Survival analysis of DNA mutation motifs with penalized proportional hazards.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets