Chromosomal rearrangements involving the NTRK1, NTRK2, and NTRK3 genes (NTRK genes), which encode the high-affinity nerve growth factor receptor (TRKA), brain-derived neurotrophic factor/neurotrophin-3 (BDNF/NT-3) growth factor receptor (TRKB), and neurotrophin-3 (NT-3) growth factor receptor (TRKC) tyrosine kinases (TRK proteins), act as oncogenic drivers in a broad range of pediatric and adult tumor types. NTRK gene fusions have been shown to be actionable genomic events that are predictive of response to TRK kinase inhibitors, making their routine detection an evolving clinical priority. In certain exceedingly rare tumor types, NTRK gene fusions may be seen in the overwhelming majority of cases, whereas in a range of common cancers, reported incidences are in the range of 0.1% to 2%. Herein, we review the structure of the three NTRK genes and the nature and incidence of NTRK gene fusions in different solid tumor types, and we summarize the clinical data showing the importance of identifying tumors harboring such genomic events. We also outline the laboratory techniques that can be used to diagnose NTRK gene fusions in clinical samples. Finally, we propose a diagnostic algorithm for solid tumors to facilitate the identification of patients with TRK fusion cancer. This algorithm accounts for the widely varying frequencies by tumor histology and the underlying prevalence of TRK expression in the absence of NTRK gene fusions and is based on a combination of fluorescence in situ hybridization, next-generation sequencing, and immunohistochemistry assays.

The clinical management of pediatric liver tumors involves stratification into risk groups. One previously defined, high-risk group of hepatoblastomas is the small cell undifferentiated variant. In light of molecular studies showing SMARCB1 deletion in these tumors, it is now recognized that most small cell, undifferentiated liver tumors represent an aggressive unrelated tumor—the malignant rhabdoid tumor (MRT). SMARCB1 is a member of the chromatin remodeling SWI/SNF complex and encodes the INI1 protein. The histologic diagnosis of MRT is currently based on INI1 negative immunoreactivity and the presence of rhabdoid morphology. INI1-negative small cell liver tumors lacking classic rhabdoid morphology are often misclassified as small cell undifferentiated hepatoblastomas (SCUD-HB), according to the current classification. Pediatric liver tumors diagnosed between 2003–2017 as SCUD-HB (four cases) or MRT (two cases) were identified from the Columbia University Pathology Department Archives. All tumors were associated with normal or low serum alpha fetoprotein levels, and showed an absence of immunohistochemical staining of hepatocellular markers (Hep-par1, Arginase) and loss of INI1 staining. Two cases were initially diagnosed as MRT, one with prominent rhabdoid morphology, the other with predominant small cell morphology. The remaining four cases with small cell morphology were classified as SCUD-HB. Ancillary molecular studies confirmed the loss of SMARCB1, supporting the diagnosis of MRT in all cases, proving morphology an unreliable criterion. It is critical to eliminate the term INI1-negative hepatoblastoma from the current classification scheme, and classify INI1-negative tumors as MRT, particularly since high-risk HB-chemotherapy regimens are not effective for treating MRT.

Aim Microsatellite instability (MSI), a hallmark of DNA mismatch repair deficiency, is a key molecular biomarker with multiple clinical implications including the selection of patients for immunotherapy, identifying patients who may have Lynch syndrome and predicting prognosis in patients with colorectal tumours. Next-generation sequencing (NGS) provides the opportunity to interrogate large numbers of microsatellite loci concurrently with genomic variants. We sought to develop a method to detect MSI that would not require paired normal tissue and would leverage the sequence data obtained from a broad range of tumours tested using our 467-gene NGS Columbia Combined Cancer Panel (CCCP). Methods Altered mononucleotide and dinucleotide microsatellite loci across the CCCP region of interest were evaluated in clinical samples encompassing a diverse range of tumour types. The number of altered loci was used to develop a decision tree classifier model trained on the retrospectively collected cohort of 107 clinical cases sequenced by the CCCP assay. Results The classifier was able to correctly classify all cases and was then used to analyse a test set of clinical cases (n=112) and was able to correctly predict their MSI status with 100% sensitivity and specificity. Analysis of recurrently altered loci identified alterations in genes involved in DNA repair, signalling and transcriptional regulation pathways, many of which have been implicated in MSI tumours. Conclusion This study highlights the utility of this approach, which should be applicable to laboratories performing similar testing.