Spiro, O. | bioRxiv Science

bioRxiv2024

Comprehensive Longitudinal ctDNA Monitoring in Metastatic Cancer Patients Treated with an Individualized Neoantigen-directed Vaccine

PurposeCirculating-tumor DNA (ctDNA) is an emerging, minimally invasive diagnostic and prognostic biomarker for patients receiving a variety of cancer therapies. Comprehensive and robust longitudinal monitoring of ctDNA can provide an understanding of tumor burden, heterogeneity, and response or resistance to treatment. Experimental DesignctDNA of 28 metastatic cancer patients receiving an individualized neoantigen-directed immunotherapy was monitored longitudinally, up to two years, using a unique hybrid next generation sequencing assay targeting tumor-informed and tumor-naive variants. Patient-specific panels were designed targeting an average of 144 variants per patient. A tumor-naive universal panel was also designed for inclusion with patient-specific panels to monitor recurrently mutated tumor hotspots (e.g., KRAS and TP53) and genes implicated in immunotherapy resistance (B2M, TAP1/2). ResultsAnalytical characterization of the assay established linearity with a mean variant allele frequency (VAF) [≥]0.049%, and a variant-level limit of detection (LOD95) of 0.12%. Tumor-informed variants were detected in 26/28 patients, and de novo variants were observed in 25/28 patients. HLA LOH was also observed. Longitudinal ctDNA data provided key insights into patients responses to vaccine treatment. ConclusionsThe hybrid design of the ctDNA monitoring assay provides the sensitivity and specificity required for evaluating patient samples undergoing individualized therapy. It provides an improved capability to understand patient response to experimental therapies and further supports the utility of ctDNA as a cancer biomarker.

genomics↗

bioRxiv2018

Comprehensive analysis of tumour initiation, spatial and temporal progression under multiple lines of treatment

Driver mutations alter cells from normal to cancer through several evolutionary epochs: premalignancy, early malignancy, subclonal diversification, metastasis and resistance to therapy. Later stages of disease can be explored through analyzing multiple samples collected longitudinally, on or between successive treatments, and finally at time of autopsy. It is also possible to study earlier stages of cancer development through probabilistic reconstruction of developmental trajectories based on mutational information preserved in the genome. Here we present a suite of tools, called Phylogic N-Dimensional with Timing (PhylogicNDT), that statistically model phylogenetic and evolutionary trajectories based on mutation and copy-number data representing samples taken at single or multiple time points. PhylogicNDT can be used to infer: (i) the order of clonal driver events (including in pre-cancerous stages); (ii) subclonal populations of cells and their phylogenetic relationships; and (iii) cell population dynamics. We demonstrate the use of PhylogicNDT by applying it to whole-exome and whole-genome data of 498 lung adenocarcinoma samples (434 previously available and 64 of newly generated data). We identify significantly different progression trajectories across subtypes of lung adenocarcinoma (EGFR mutant, KRAS mutant, fusion-driven and EGFR/KRAS wild type cancers). In addition, we study the progression of fusion-driven lung cancer in 21 patients by analyzing samples from multiple timepoints during treatment with 1st and next generation tyrosine kinase inhibitors. We characterize their subclonal diversification, dynamics, selection, and changes in mutational signatures and neoantigen load. This methodology will enable a systematic study of tumour initiation, progression and resistance across cancer types and therapies.

bioinformatics↗