Matthew Montierth

PhD Student - Baylor College of Medicine
Wang Lab, UT MD Anderson Cancer Center

About Me

I am a sixth-year PhD student in the Quantitative and Computational Biosciences program at Baylor College of Medicine in Houston, Texas. My thesis work is conducted under the guidance of Dr. Wenyi Wang at The University of Texas MD Anderson Cancer Center. I received my B.S. in Genetics and Biotechnology from Brigham Young University in Utah, with a minor in statistics.

My research interests focus on developing and applying statistical methods and models to better understand the biology of human health and disease. My current research spans cancer genomics, computational deconvolution, miRNA regulation, and tumor evolution.

Outside the lab, I enjoy cycling, disc golf, reading, 3D printing, cooking, and gardening.

Ongoing Research Projects

Cancer Genomics

I have contributed to our lab's somatic mutation calling pipeline and software, MuSE. I've also collaborated with clinicians and specialists to define the mutational landscape of anaplastic thyroid cancer and esophageal cancer.

miRNAs Associated with TmS

miRNAs are key regulators of gene expression. Our lab has recently published TmS, a metric for quantifying the total mRNA expression of a tumor sample. Using data from TCGA, I am working to better understand TmS by identifying cancer-specific and pan-cancer associations with miRNAs.

Computational Deconvolution

Computational deconvolution is a powerful tool for identifying cell type- and tumor-specific patterns from bulk RNA-seq data. My main thesis project addresses the need for a tool specifically designed for the deconvolution of miRNA data.

Tumor Evolution & Subclonal Reconstruction

Cancers originate from a single cell that acquires oncogenic mutations. The descendants of that cell will share these clonal mutations, with subpopulations emerging that share subclonal mutations. Identifying these mutations is key to studying tumor evolution and heterogeneity and is critical for precision oncology. Our method, CliP, is an extremely fast and accurate tool enabling this study.

Publications

Estimation of Tumor Cell Total mRNA Expression in 15 Cancer Types Predicts Disease Progression

This study presents a method to quantify tumor-specific total mRNA expression (TmS) from bulk sequencing data, accounting for tumor transcript proportion, purity, and ploidy. High TmS is associated with increased risk of disease progression and death across cancers.

Impact of Somatic Mutations on Survival Outcomes in Patients with Anaplastic Thyroid Carcinoma

This research investigates the association between tumor mutations and survival outcomes in patients with anaplastic thyroid carcinoma, emphasizing the prognostic importance of mutations in BRAFV600E and RAS.

MuSE: A Novel Approach to Mutation Calling with Sample-Specific Error Modeling

MuSE is a novel approach for accurate detection of somatic mutations in genetically heterogeneous tumor cell populations. This publication describes the method and provides a tutorial on its installation and application.

CliP: Subclonal Architecture Reconstruction of Cancer Cells in DNA Sequencing Data Using a Penalized Likelihood Model

CliP is a penalized likelihood framework for subclonal reconstruction in cancer cells, offering high accuracy and speed compared to existing methods. This publication discusses its development and application.

A Pedigree-Based Prediction Model Identifies Carriers of Deleterious De Novo Mutations in Families with Li-Fraumeni Syndrome

This study introduces Famdenovo, a model for predicting deleterious de novo mutations in inherited cancer syndromes, with a focus on Li-Fraumeni syndrome. The model provides insights into the distinct molecular mechanisms of de novo mutations.