Identification of Somatic Mutational Patterns with Biological and Clinical Significance in Solid and Hematological Malignancies
Creators: name: McNeil, Reid Evan
Publisher: name: Graduate Studies
Year: 2024
Subjects: subject: Cancer Science
subject: Bioinformatics
subject: FOS: Computer and information sciences
subject: Squamous Cell Carcinoma
subject: Myeloid Malignancy
subject: Next-Generation Sequencing
subject: Biology--Cell
subject: Biostatistics
subject: Bioinformatics
subject: Biology--Molecular
subject: Oncology
subject: Genetics
subject: FOS: Biological sciences

description: Cancers use Darwinian evolution to naturally select for advantageous DNA alterations that allow these malignancies to escape normal biological control mechanisms. Advantageous alterations are known as driver mutations that modify cellular phenotype causing cancer cells to proliferate, invade adjacent tissues, and metastasize to other organs. Even across very different types of cancers, DNA alteration events are an underlying commonality. Using DNA alterations as a lens to analyze cancers can potentially identify cross cancer biomarkers implicated in disease development, progression, prognostication, and therapeutic drug targeting and response. In this MSc thesis, we hypothesis that in squamous cell carcinomas a subtype of alteration called DNA amplification events can modulate tumor biology through gene expression, and that point mutations in myeloid malignancies can predict clinical outcomes like disease relapse after bone marrow transplant. Aim one of my thesis investigated DNA amplifications to identify how they modulate mRNA and protein expression in head and neck and lung SCCs. Using genomic, transcriptomic, and proteomics data we identify amplification driven expression programs with biological and clinical significance in SCCs. Aim two of my thesis investigated point mutation profiles of myeloid malignancies using the Illumina TruSight Tumor (TST) Myeloid sequencing panel and machine learning analysis. Mutational landscape analysis of all myeloid samples annotated for DNA mutations of clinical relevance. Machine learning analysis was used point mutation profiles to predict relapse status of patients after transplant. The main research findings indicated that in SCCs, the 3q22-29 and 11q13 DNA amplifications were the top events in HNSCs and LUSCs. Several genes from 3q22-29 (ABCC5, ALG3, FXR1, TFRC, and RFC4) and 11q13 amplified regions (CTTN, FADD, and PPFIA1) were overexpressed on the mRNA and protein level. These genes were majority expressed in tumoral tissue and cancerous cells, and when overexpressed led to worse patient survival. The 3q22-29 amplified samples were negatively correlated with immune related pathways in the tumor microenvironment. Specifically, TRAIL and IFNG signaling levels were lower in 3q22-29 amplified samples, along with lower levels of immune cell infiltration of natural killer and cytotoxic cells. In myeloid malignancies, point mutations of clinical significance were identified, the most common mutations across 545 AML samples were TET2, ASXL1, and DNMT3A. The TET2 gene modulates DNA methylation levels and mutations to this gene trigger malignant transformation of myeloid progenitor cells and development of cancer. Patients with TET2 mutations can be treated with azacitidine in combination with chemotherapies to increase overall patient survival. The machine learning analysis of mutation profiles of myeloid patients who went through a bone marrow transplant were able to predict relapse status of patients from mutations profiles generated at disease diagnosis. The random forest model was the best performing model (AUC = 0.845) to predict relapse status after transplantation. Altogether, this analysis demonstrated the ability and importance DNA alterations have in diseases like cancer.