Postdoctoral Fellow – Jones Lab

Background

The Personalized Approaches in the Treatment of Head and Neck Cancer (PATH) study aims to use longitudinal plasma cell‑free DNA sequencing to develop a minimally invasive method for detecting minimal residual disease (MRD) and recurrence in head and neck squamous cell carcinoma (HNSCC). Approximately 35 % of patients with HNSCC recur and die despite aggressive treatment. There are currently no validated biomarkers to monitor treatment response or detect residual disease. The proposed pilot will collect serial blood draws from 30–50 patients per year and perform whole‑genome sequencing (WGS) on Illumina and Oxford Nanopore Technology (ONT) platforms. It will integrate genomic, epigenomic and fragmentomic features (SNVs, copy‑number variants, methylation marks, fragment size, end‑motifs, nucleosomal positioning, viral integration) to sensitively detect low tumour fractions. The project’s third aim is to explore bioinformatics tools to develop a genome‑wide multimodal MRD analysis pipeline, analyze baseline plasma samples, and retrospectively analyze longitudinal samples to detect tumour burden. This position will be central to achieving those aims.

High‑level Summary of the Role

The post‑doctoral fellow will lead the development of an integrated analysis pipeline for detecting minimal residual disease in head and neck cancer patients by analyzing longitudinal plasma ctDNA. Using whole‑genome sequencing from both Illumina and Oxford Nanopore platforms, the fellow will combine genomic mutations, copy‑number alterations, methylation signatures, fragmentomics and viral integration data to sensitively track tumour burden over time. They will adapt and create bioinformatics methods, apply tumour‑informed analyses using matched tumour and normal WGS, and analyze baseline and follow‑up blood samples to identify early ctDNA signals before clinical standard of care recurrence. The fellow will collaborate within the GSC and PATH project to produce reports and manuscripts, and help scale the pipeline for future studies to impact cancer patient care.

Responsibilities

• Explore, design, and implement an analysis pipeline to sensitively detect low tumour fractions from plasma ctDNA using data from Illumina short‑read and ONT long‑read WGS. This includes integrating whole genomic alterations (SNVs, indels, copy‑number variants), epigenetic marks, fragmentomics (fragment length, end‑motifs, nucleosome positioning) and viral integration patterns.
• Adapt and evaluate existing tools and develop novel algorithms as required to improve MRD sensitivity and specificity.
• Perform longitudinal analyses of serial plasma samples from patients with and without recurrence, tracking ctDNA tumour fraction over time and correlating ctDNA trends with clinical events and imaging.
• Incorporate tumour‑informed analysis by leveraging matched tumour and normal WGS data from the PATH study; build HNSCC‑specific methylation classification models.
• Identify and characterize genomic features of HPV/EBV‑positive cancers, including viral integration and methylation patterns.
• Generate comprehensive reports, visualizations and manuscripts for internal review, conference presentations and peer‑reviewed publications.

Qualifications

• Ph.D. in Bioinformatics, Computational Biology, Genomics, Computer Science or a related discipline.
• Expertise in next‑generation sequencing data analysis; familiarity with both short‑read (Illumina) and long‑read (ONT) platforms; experience with variant calling, copy‑number analysis, methylation analysis, fragmentomics and viral integration analysis.
• Strong programming skills (Python, R, C/C++ or similar), proficiency with workflow management (Snakemake, Nextflow, CWL), version control (Git), and high‑performance computing.
• Knowledge of cancer genomics, liquid biopsy technologies, epigenetics and MRD; experience with tumour‑informed analysis or minimal residual disease detection is highly desirable.
• Experience developing or customizing bioinformatics tools and demonstrated ability to analyze complex genomic datasets.
• Excellent communication and interpersonal skills to collaborate with a multidisciplinary team.
• Proven publication record in cancer genomics or related fields and commitment to open‑science principles.