DeltaMSI: artificial intelligence-based modeling of microsatellite instability scoring on next-generation sequencing data

BackgroundDNA mismatch repair deficiency (dMMR) testing is crucial for detection of microsatellite unstable (MSI) tumors. MSI is detected by aberrant indel length distributions of microsatellite markers, either by visual inspection of PCR-fragment length profiles or by automated bioinformatic scoring on next-generation sequencing (NGS) data. The former is time-consuming and low-throughput while the latter typically relies on simplified binary scoring of a single parameter of the indel distribution. The purpose of this study was to use machine learning to process the full complexity of indel distributions and integrate it into a robust script for screening of dMMR on small gene panel-based NGS data of clinical tumor samples without paired normal tissue.MethodsScikit-learn was used to train 7 models on normalized read depth data of 36 microsatellite loci in a cohort of 133 MMR proficient (pMMR) and 46 dMMR tumor samples, taking loss of MLH1/MSH2/PMS2/MSH6 protein expression as reference method. After selection of the optimal model and microsatellite panel the two top-performing models per locus (logistic regression and support vector machine) were integrated into a novel script (DeltaMSI) for combined prediction of MSI status on 28 marker loci at sample level. Diagnostic performance of DeltaMSI was compared to that of mSINGS, a widely used script for MSI detection on unpaired tumor samples. The robustness of DeltaMSI was evaluated on 1072 unselected, consecutive solid tumor samples in a real-world setting sequenced using capture chemistry, and 116 solid tumor samples sequenced by amplicon chemistry. Likelihood ratios were used to select result intervals with clinical validity.ResultsDeltaMSI achieved higher robustness at equal diagnostic power (AUC = 0.950; 95% CI 0.910-0.975) as compared to mSINGS (AUC = 0.876; 95% CI 0.823-0.918). Its sensitivity of 90% at 100% specificity indicated its clinical potential for high-throughput MSI screening in all tumor types.Clinical Trial Number/IRB B1172020000040, Ethical Committee, AZ Delta General Hospital.

Automated summary

This study aimed to use machine learning to process the complexity of indel distributions and integrate it into a robust script for dMMR screening on small gene panel-based NGS data of clinical tumor samples. The results showed that the novel script (DeltaMSI) achieved higher robustness and diagnostic power compared to the widely used script mSINGS, indicating its clinical potential for high-throughput MSI screening in all tumor types.