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PURPOSE Given regulatory approval of immune checkpoint inhibitors in patients with mismatch repair–deficient (MMR-D) cancers agnostic to tumor type, it has become important to characterize occurrence of MMR-D and develop cost-effective screening approaches. Using a next-generation sequencing (NGS) panel (OncoPanel), we developed an algorithm to identify MMR-D frequency in tumor samples and applied it in a clinical setting with pathologist review. METHODS To predict MMR-D, we adapted methods described previously for use in NGS panels, which assess patterns of single base-pair insertion or deletion events occurring in homopolymer regions. Tumors assayed with OncoPanel between July 2013 and July 2018 were included. For tumors tested after June 2017, sequencing results were presented to pathologists in real time for clinical MMR determination, in the context of tumor mutation burden, other mutational signatures, and clinical data. RESULTS Of 20,301 tumors sequenced, 2.7% (553) were retrospectively classified as MMR-D by the algorithm. Of 4,404 samples with pathologist sign-out of MMR status, the algorithm classified 147 (3.3%) as MMR-D: in 116 cases, MMR-D was confirmed by a pathologist, five cases were overruled by the pathologist, and 26 were assessed as indeterminate. Overall, the highest frequencies of OncoPanel-inferred MMR-D were in endometrial (21%; 152/723), colorectal (9.7%; 169/1,744), and small bowel (9.3%; 9/97) cancers. When algorithm predictions were compared with historical MMR immunohistochemistry or polymerase chain reaction results in a set of 325 tumors sequenced before initiation of pathologist assessment, the overall sensitivity and specificity of the algorithm were 91.1% and 98.2%, respectively. CONCLUSION We show that targeted, tumor-only NGS can be leveraged to determine MMR signatures across tumor types, suggesting that broader biomarker screening approaches may have clinical value.

Meningiomas are the most common primary tumor of the central nervous system, with ~28,000 new diagnoses annually in the United States1. Currently, there are no approved systemic therapies for meningiomas that recur following local treatment: chemotherapy and hormonal agents have demonstrated minimal benefit in numerous clinical trials2–4. Meningioma comprises a heterogeneous group of neoplasms driven by mutations in a wide array of tumor suppressor genes and oncogenes5–17. Characterization of these mutations has revealed opportunities for rational therapy18–20. For example, a durable therapeutic response has been reported for a metastatic AKT1(E17K)-mutant meningioma treated with a pan-AKT inhibitor.21 Studies also suggest the potential for treating meningioma with immune checkpoint modulators22–24: programmed death receptor 1 ligand (PD-L1) is expressed in a subset of meningiomas and the tumor microenvironment is immunosuppressive22–28. Higher-grade meningiomas also harbor mutations predicted to generate neoantigens, which may foster susceptibility to immunotherapies29. Based on these data, we initiated a phase II study of nivolumab, a humanized IgG4 PD-1 blocking monoclonal antibody, in patients with higher-grade meningiomas that recurred following surgery and radiotherapy ({"type":"clinical-trial","attrs":{"text":"NCT02648997","term_id":"NCT02648997"}}NCT02648997). We report here a patient with an atypical meningioma that was not controlled by repeated surgery and radiation but which was highly response to nivolumab.

PURPOSE Heterogeneity in tumor mutational burden (TMB) quantification across sequencing platforms limits the application and further study of this potential biomarker of response to immune checkpoint inhibitors (ICIs). We hypothesized that harmonization of TMB across platforms would enable integration of distinct clinical data sets to better characterize the association between TMB and ICI response. METHODS Cohorts of patients with non–small-cell lung cancer sequenced by 1 of 3 targeted panels or by whole-exome sequencing (WES) were compared (N = 7,297). TMB was calculated uniformly and compared across cohorts. TMB distributions were harmonized by applying a normal transformation followed by standardization to z scores. In subcohorts of patients treated with ICIs (Dana-Farber Cancer Institute n = 272; Memorial Sloan Kettering Cancer Center n = 227), the association between TMB and outcome was assessed. Durable clinical benefit (DCB) was defined as responsive/stable disease lasting ≥ 6 months. RESULTS TMB values were higher in the panel cohorts than in the WES cohort. Average mutation rates per gene were highly concordant across cohorts (Pearson’s correlation coefficients, 0.842-0.866). Subsetting the WES cohort by gene panels only partially reproduced the observed differences in TMB. Standardization of TMB into z scores harmonized TMB distributions and enabled integration of the ICI-treated subcohorts. Simulations indicated that cohorts > 900 are necessary for this approach. TMB did not associate with response in never-smokers or patients who harbor targetable driver alterations, although these analyses were underpowered. An increase of TMB thresholds increased DCB rate, but DCB rates within deciles varied. Receiver operating characteristic curves yielded an area under the curve of 0.614, with no natural inflection point. CONCLUSION The z score conversion harmonizes TMB values and enables integration of data sets derived from different sequencing panels. Clinical and biologic features may provide context to the clinical application of TMB and warrant additional study.