Purpose Microsatellite instability (MSI) is a pattern of hypermutation that occurs at genomic microsatellites and is caused by defects in the mismatch repair system. Mismatch repair deficiency that leads to MSI has been well described in several types of human cancer, most frequently in colorectal, endometrial, and gastric adenocarcinomas. MSI is known to be both predictive and prognostic, especially in colorectal cancer; however, current clinical guidelines only recommend MSI testing for colorectal and endometrial cancers. Therefore, less is known about the prevalence and extent of MSI among other types of cancer. Methods Using our recently published MSI-calling software, MANTIS, we analyzed whole-exome data from 11,139 tumor-normal pairs from The Cancer Genome Atlas and Therapeutically Applicable Research to Generate Effective Treatments projects and external data sources across 39 cancer types. Within a subset of these cancer types, we assessed mutation burden, mutational signatures, and somatic variants associated with MSI. Results We identified MSI in 3.8% of all cancers assessed—present in 27 of tumor types—most notably adrenocortical carcinoma (ACC), cervical cancer (CESC), and mesothelioma, in which MSI has not yet been well described. In addition, MSI-high ACC and CESC tumors were observed to have a higher average mutational burden than microsatellite-stable ACC and CESC tumors. Conclusion We provide evidence of as-yet-unappreciated MSI in several types of cancer. These findings support an expanded role for clinical MSI testing across multiple cancer types as patients with MSI-positive tumors are predicted to benefit from novel immunotherapies in clinical trials.

In current clinical practice, microsatellite instability (MSI) and mismatch repair deficiency detection is performed with MSI-PCR and immunohistochemistry. Recent research has produced several computational tools for MSI detection with next-generation sequencing (NGS) data; however a comprehensive analysis of computational methods has not yet been performed. In this study, we introduce a new MSI detection tool, MANTIS, and demonstrate its favorable performance compared to the previously published tools mSINGS and MSISensor. We evaluated 458 normal-tumor sample pairs across six cancer subtypes, testing classification performance on variable numbers of target loci ranging from 10 to 2539. All three computational methods were found to be accurate, with MANTIS exhibiting the highest accuracy with 98.91% of samples from all six diseases classified correctly. MANTIS displayed superior performance among the three tools, having the highest overall sensitivity (MANTIS 97.18%, MSISensor 96.48%, mSINGS 76.06%) and specificity (MANTIS 99.68%, mSINGS 99.68%, MSISensor 98.73%) across six cancer types, even with loci panels of varying size. Additionally, MANTIS also had the lowest resource consumption (<1% of the space and <7% of the memory required by mSINGS) and fastest running times (49.6% and 8.7% of the running times of MSISensor and mSINGS, respectively). This study highlights the potential utility of MANTIS in classifying samples by MSI-status, allowing its incorporation into existing NGS pipelines.

Next‐generation sequencing has aided characterization of genomic variation. While whole‐genome sequencing may capture all possible mutations, whole‐exome sequencing remains cost‐effective and captures most phenotype‐altering mutations. Initial strategies for exome enrichment utilized a hybridization‐based capture approach. Recently, amplicon‐based methods were designed to simplify preparation and utilize smaller DNA inputs. We evaluated two hybridization capture‐based and two amplicon‐based whole‐exome sequencing approaches, utilizing both Illumina and Ion Torrent sequencers, comparing on‐target alignment, uniformity, and variant calling. While the amplicon methods had higher on‐target rates, the hybridization capture‐based approaches demonstrated better uniformity. All methods identified many of the same single‐nucleotide variants, but each amplicon‐based method missed variants detected by the other three methods and reported additional variants discordant with all three other technologies. Many of these potential false positives or negatives appear to result from limited coverage, low variant frequency, vicinity to read starts/ends, or the need for platform‐specific variant calling algorithms. All methods demonstrated effective copy‐number variant calling when evaluated against a single‐nucleotide polymorphism array. This study illustrates some differences between whole‐exome sequencing approaches, highlights the need for selecting appropriate variant calling based on capture method, and will aid laboratories in selecting their preferred approach.

The fibroblast growth factor receptor (FGFR) signaling pathway is aberrantly activated in approximately 15% to 20% of patients with intrahepatic cholangiocarcinoma. Currently, several FGFR kinase inhibitors are being assessed in clinical trials for patients with FGFR-altered cholangiocarcinoma. Despite evidence of initial responses and disease control, virtually all patients eventually develop acquired resistance. Thus, there is a critical need for the development of innovative therapeutic strategies to overcome acquired drug resistance. Here, we present findings from a patient with FGFR2-altered metastatic cholangiocarcinoma who enrolled in a phase II clinical trial of the FGFR inhibitor, infigratinib (BGJ398). Treatment was initially effective as demonstrated by imaging and tumor marker response; however, after 8 months on trial, the patient exhibited tumor regrowth and disease progression. Targeted sequencing of tumor DNA after disease progression revealed the FGFR2 kinase domain p.E565A and p.L617M single-nucleotide variants (SNV) hypothesized to drive acquired resistance to infigratinib. The sensitivities of these FGFR2 SNVs, which were detected post-infigratinib therapy, were extended to include clinically relevant FGFR inhibitors, including AZD4547, erdafitinib (JNJ-42756493), dovitinib, ponatinib, and TAS120, and were evaluated in vitro. Through a proteomics approach, we identified upregulation of the PI3K/AKT/mTOR signaling pathway in cells harboring the FGFR2 p.E565A mutation and demonstrated that combination therapy strategies with FGFR and mTOR inhibitors may be used to overcome resistance to FGFR inhibition, specific to infigratinib. Collectively, these studies support the development of novel combination therapeutic strategies in addition to the next generation of FGFR inhibitors to overcome acquired resistance in patients.

Activation of fibroblast growth factor receptor (FGFR) signaling through mutations, amplifications, or fusions involving FGFR1, 2, 3, or 4 are seen in multiple tumors including lung, bladder, and cholangiocarcinoma. Currently, several clinical trials are evaluating the role of novel FGFR inhibitors in solid tumors. As we move forward with FGFR inhibitors clinically, we anticipate emergence of resistance with treatment. Consequently, we sought to study the mechanism(s) of acquired resistance to FGFR inhibitors using annotated cancer cell lines. We identified cancer cell lines that have activating mutations in FGFR1, 2, or 3, and treated them chronically with the selective FGFR inhibitor, BGJ398. We observed resistance to chronic BGJ398 exposure in DMS114 (small cell lung cancer, FGFR1 amplification), and RT112 (urothelial carcinoma, FGFR3 fusion/amplification) cell lines based on viability assays. Reverse phase protein array (RPPA) analysis showed increased phosphorylation of Akt (T308 and S473) and its downstream target GSK3 (S9 and S21) in both the resistant cell lines when compared to matching controls. Results of RPPA were confirmed using immunoblots. Consequently, the addition of an Akt inhibitor (GSK2141795) or siRNA was able to restore sensitivity to BGJ398 in resistant cell lines. These data suggest a role for Akt pathway in mediating acquired resistance to FGFR inhibition.