BackgroundHigh tumor mutational burden (TMB) is an emerging biomarker of sensitivity to immune checkpoint inhibitors and has been shown to be more significantly associated with response to PD-1 and PD-L1 blockade immunotherapy than PD-1 or PD-L1 expression, as measured by immunohistochemistry (IHC). The distribution of TMB and the subset of patients with high TMB has not been well characterized in the majority of cancer types.MethodsIn this study, we compare TMB measured by a targeted comprehensive genomic profiling (CGP) assay to TMB measured by exome sequencing and simulate the expected variance in TMB when sequencing less than the whole exome. We then describe the distribution of TMB across a diverse cohort of 100,000 cancer cases and test for association between somatic alterations and TMB in over 100 tumor types.ResultsWe demonstrate that measurements of TMB from comprehensive genomic profiling are strongly reflective of measurements from whole exome sequencing and model that below 0.5 Mb the variance in measurement increases significantly. We find that a subset of patients exhibits high TMB across almost all types of cancer, including many rare tumor types, and characterize the relationship between high TMB and microsatellite instability status. We find that TMB increases significantly with age, showing a 2.4-fold difference between age 10 and age 90 years. Finally, we investigate the molecular basis of TMB and identify genes and mutations associated with TMB level. We identify a cluster of somatic mutations in the promoter of the gene PMS2, which occur in 10% of skin cancers and are highly associated with increased TMB.ConclusionsThese results show that a CGP assay targeting ~1.1 Mb of coding genome can accurately assess TMB compared with sequencing the whole exome. Using this method, we find that many disease types have a substantial portion of patients with high TMB who might benefit from immunotherapy. Finally, we identify novel, recurrent promoter mutations in PMS2, which may be another example of regulatory mutations contributing to tumorigenesis.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-017-0424-2) contains supplementary material, which is available to authorized users.

Therapeutic antibodies blocking programmed death-1 and its ligand (PD-1/PD-L1) induce durable responses in a substantial fraction of melanoma patients. We sought to determine whether the number and/or type of mutations identified using a next generation sequencing (NGS) panel available in the clinic were correlated with response to anti–PD-1 in melanoma. Using archival melanoma samples from anti–PD-1/PD-L1-treated patients, we performed hybrid capture-based NGS on 236–315 genes and T-cell receptor (TCR) sequencing on initial and validation cohorts from two centers. Patients who responded to anti–PD-1/PD-L1 had higher mutational loads in an initial cohort (median 45.6 vs. 3.9 mutations/MB; P = 0.003), and a validation cohort (37.1 vs. 12.8 mutations/MB; P = 0.002) compared to nonresponders. Response rate, progression-free survival, and overall survival was superior in the high, compared to intermediate and low, mutation load groups. Melanomas with NF1 mutations harbored high mutational loads (median 62.7 mutations/MB) and high response rates (74%) whereas BRAF/NRAS/NF1 wild-type melanomas had a lower mutational load. In these archival samples, TCR clonality did not predict response. Mutation numbers in the 315 genes in the NGS platform strongly correlated with those detected by whole exome sequencing in The Cancer Genome Atlas samples, but was not associated with survival. In conclusion, mutational load, as determined by an NGS platform available in the clinic, effectively stratified patients by likelihood of response. This approach may provide a clinically feasible predictor of response to anti–PD-1/PD-L1.

Background Blood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer. Methods Whole-genome sequencing was performed on cfDNA extracted from plasma samples ( N = 546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validations to assess generalization performance. Results In a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91–0.93) with a mean sensitivity of 85% (95% CI 83–86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance. Conclusions A machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies. Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway. Electronic supplementary material The online version of this article (10.1186/s12885-019-6003-8) contains supplementary material, which is available to authorized users.

Background: Blood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumorderived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer.Methods: Whole-genome sequencing was performed on cfDNA extracted from plasma samples (N=546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validation to assess generalization performance. Results:In a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91-0.93) with a mean sensitivity of 85% (95% CI 83-86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance. Conclusions:A machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies.Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway.

Engineered bacterial cells that are designed to express therapeutic enzymes under the transcriptional control of remotely inducible promoters can mediate the de novo conversion of non-toxic prodrugs to their cytotoxic forms. In situ cellular expression of enzymes provides increased stability and control of enzyme activity as compared to isolated enzymes. We have engineered Escherichia coli (E. coli), designed to express cytosine deaminase at elevated temperatures, under the transcriptional control of thermo-regulatory λpL-cI857 promoter cassette which provides a thermal switch to trigger enzyme synthesis. Enhanced cytosine deaminase expression was observed in cultures incubated at 42 °C as compared to 30 °C, and enzyme expression was further substantiated by spectrophotometric assays indicating enhanced conversion of 5-fluorocytosine to 5-fluorouracil. The engineered cells were subsequently co-encapsulated with magnetic iron oxide nanoparticles in immunoprotective alginate microcapsules, and cytosine deaminase expression was triggered remotely by alternating magnetic field-induced hyperthermia. The combination of 5-fluorocytosine with AMF-activated microcapsules demonstrated tumor cell cytotoxicity comparable to direct treatment with 5-fluorouracil chemotherapy. Such enzyme-prodrug therapy, based on engineered and immunoisolated E. coli, may ultimately yield an improved therapeutic index relative to monotherapy, as AMF mediated hyperthermia might be expected to pre-sensitize tumors to chemotherapy under appropriate conditions.