TMBur: a distributable tumor mutation burden approach for whole genome sequencing

Abstract

Background Tumor mutation burden (TMB) is a key characteristic used in a tumor-type agnostic context to inform the use of immune checkpoint inhibitors (ICI). Accurate and consistent measurement of TMB is crucial as it can significantly impact patient selection for therapy and clinical trials, with a threshold of 10 mutations/Mb commonly used as an inclusion criterion. Studies have shown that the most significant contributor to variability in mutation counts in whole genome sequence (WGS) data is differences in analysis methods, even more than differences in extraction or library construction methods. Therefore, tools for improving consistency in whole genome TMB estimation are of clinical importance. Methods We developed a distributable TMB analysis suite, TMBur, to address the need for genomic TMB estimate consistency in projects that span jurisdictions. TMBur is implemented in Nextflow and performs all analysis steps to generate TMB estimates directly from fastq files, incorporating somatic variant calling with Manta, Strelka2, and Mutect2, and microsatellite instability profiling with MSISensor. These tools are provided in a Singularity container downloaded by the workflow at runtime, allowing the entire workflow to be run identically on most computing platforms. To test the reproducibility of TMBur TMB estimates, we performed replicate runs on WGS data derived from the COLO829 and COLO829BL cell lines at multiple research centres. The clinical value of derived TMB estimates was then evaluated using a cohort of 90 patients with advanced, metastatic cancer that received ICIs following WGS analysis. Patients were split into groups based on a threshold of 10/Mb, and time to progression from initiation of ICIs was examined using Kaplan–Meier and cox-proportional hazards analyses. Results TMBur produced identical TMB estimates across replicates and at multiple analysis centres. The clinical utility of TMBur-derived TMB estimates were validated, with a genomic TMB ≥ 10/Mb demonstrating improved time to progression, even after correcting for differences in tumor type (HR = 0.39, p = 0.012). Conclusions TMBur, a shareable workflow, generates consistent whole genome derived TMB estimates predictive of response to ICIs across multiple analysis centres. Reproducible TMB estimates from this approach can improve collaboration and ensure equitable treatment and clinical trial access spanning jurisdictions.

Immune Activation Following Irbesartan Treatment in a Colorectal Cancer Patient: A Case Study

“…Mutations were correlated against a catalogue of known mutation signatures (COSMIC [ 34 ]) using an NNLS approach. TMB was calculated using TMBur [ 35 ].…”

Mappingin silicogenetic networks of theKMT2Dtumour suppressor gene to uncover novel functional associations and cancer cell vulnerabilities

“…Tumour mutation burden (TMB, mutations per megabase) is calculated by adding small mutations and small indels within the tumour sample generated in the above 160 and dividing by the effective genome size (2934.876451 Mb). MSI were detected using genome data according to Pleasance et al 156 and HRDetect scores were calculated using a logistic regression model 161 .…”

“…Enzymatic Variant calling and KMT2D mutation selection Somatic point mutations and small insertion and deletions are identified using Strelka 2.6.2 157 and Mutect2 from GATK 4.0.10.0 158 . Variants from these tools are intersected using RTGTools 159 to generate the calls as previously described 160 . We defined POG cases with KMT2D LOF alterations as those having somatic nonsense mutations, frameshift insertions, frameshift deletions, or deep copy number loss indicating likely homozygous deletions.…”

Detecting Somatic Mutations Without Matched Normal Samples Using Long Reads

“…In all experiments we mixed reads from the tumour and normal together, without knowing the origin of each read, to simulate tumour-only sequencing with a controlled purity and sequencing depth. COLO829 has a high mutation rate of ≈14 mutations per megabase (Titmuss et al 2022) due to ultraviolet light damage (Pleasance et al 2010), and is frequently used to benchmark the performance of sequencing technologies and analysis methods (Arora et al 2019;Espejo Valle-Inclan et al 2022).…”