Lynch syndrome is caused by germline mutations of DNA mismatch repair (MMR) genes. MMR deficiency has long been regarded as a secondary event in the pathogenesis of Lynch syndrome colorectal cancers. Recently, this concept has been challenged by the discovery of MMR-deficient crypt foci in the normal mucosa. We aimed to reconstruct colorectal carcinogenesis in Lynch syndrome by collecting molecular and histology evidence from Lynch syndrome adenomas and carcinomas. We determined the frequency of MMR deficiency in adenomas from Lynch syndrome mutation carriers by immunohistochemistry and by systematic literature analysis. To trace back the pathways of pathogenesis, histological growth patterns and mutational signatures were analyzed in Lynch syndrome colorectal cancers. Literature and immunohistochemistry analysis demonstrated MMR deficiency in 491 (76.7%) out of 640 adenomas (95% CI: 73.3% to 79.8%) from Lynch syndrome mutation carriers. Histologically normal MMR-deficient crypts were found directly adjacent to dysplastic adenoma tissue, proving their role as tumor precursors in Lynch syndrome. Accordingly, mutation signature analysis in Lynch colorectal cancers revealed that KRAS and APC mutations commonly occur after the onset of MMR deficiency. Tumors lacking evidence of polypous growth frequently presented with CTNNB1 and TP53 mutations. Our findings demonstrate that Lynch syndrome colorectal cancers can develop through three pathways, with MMR deficiency commonly representing an early and possibly initiating event. This underlines that targeting MMR-deficient cells by chemoprevention or vaccines against MMR deficiency-induced frameshift peptide neoantigens holds promise for tumor prevention in Lynch syndrome.

Tumor mutational burden (TMB) is a new biomarker for prediction of response to PD-(L)1 treatment. Comprehensive sequencing approaches (i.e., whole exome and whole genome sequencing) are ideally suited to measure TMB directly. However, as their applicability in routine diagnostics is currently limited by high costs, long turnaround times and poor availability of fresh tissue, targeted next-generation sequencing (NGS) of formalin-fixed and paraffin-embedded (FFPE) samples appears to be a more feasible and straightforward approach for TMB approximation, which can be seamlessly integrated in already existing diagnostic workflows and pipelines. In this work, we provide an overview of the clinical implications of TMB testing and highlight key parameters including pre-analysis, analysis and post-analytical steps that influence and shape TMB approximation by panel sequencing. Collectively, the data will not only serve as a field guide and state of the art knowledge source for molecular pathologists who consider implementation of TMB measurement in their lab, but also enable clinicians in understanding the specific parameters influencing TMB test results and reporting.

Tumor mutational burden (TMB) represents a new determinant of clinical benefit from immune checkpoint blockade that identifies responders independent of PD-L1 expression levels and is currently being explored in clinical trials. Although TMB can be measured directly by comprehensive genomic approaches such as whole-genome and exome sequencing, broad availability, short turnaround times, costs and amenability to formalin-fixed and paraffin-embedded tissue support the use of gene panel sequencing for approximating TMB in routine diagnostics. However, data on the parameters influencing panelbased TMB estimation are limited. Here, we report an extensive in silico analysis of the TCGA data set that simulates various panel sizes and compositions. We demonstrate that panel size is a critical parameter that influences confidence intervals (CIs) and cutoff values as well as important test parameters including sensitivity, specificity, and positive predictive value. Moreover, we evaluate the Illumina TSO500 panel, which will be made available for TMB estimation, and propose dynamic, entity-specific cutoff values based on current clinical trial data. Optimizing the cost-benefit ratio, our data suggest that panels between 1.5 and 3 Mbp are ideally suited to estimate TMB with small CIs, whereas smaller panels tend to deliver imprecise TMB estimates for low to moderate TMB (0-30 muts/Mbp), connected with insufficient separation of hypermutated tumors from non-hypermutated tumors.