In 2018, ESMO published the ESCAT (ESMO Scale of Clinical Actionability of molecular Targets) to harmonise and standardise the reporting of clinically relevant genomics data . The ESCAT is a unified framework that classifies targets for precision medicine based on clinical evidence of utility to aid clinicians in their prioritization of potential targets for clinical use based on results of sequencing panels.
The tiers of the ESCAT are shown in the following table . Of note, larotrectinib and entrectinib are ESCAT Tier 1C treatments (i.e. considered standard of care based on evidence from basket trials).
In conjunction with the ESMO guidelines published in 2019, a general algorithm for NTRK gene fusion testing to identify patients who would benefit from therapies targeting TRK fusion proteins is outlined in the following figure.
ESMO guidelines note:
- In the scenario where the presence of an NTRK gene fusion needs to be confirmed, which happens for patients affected by tumours in which NTRK gene fusion are known to be highly prevalent if not pathognomonic of the lesion, any technique could work in principle, however the best options as confirmatory techniques are FISH, RT-PCR or RNA-based targeted panels.
- In the scenario where the challenge is the identification of NTRK gene fusion in an unselected population, using a DNA- or RNA-based NGS targeted panel that reliably detects NTRK gene fusion would be ideal. In addition:
- Targeted RNA sequencing methods may represent the gold standard for screening, if the RNA quality is optimal
- If an NTRK gene fusion is identified, then the most exhaustive approach would be to include IHC to confirm protein expression of the detected NTRK fusions
- Alternatively, a “two-step approach” could be considered, which includes IHC first and confirmation of any positivity detected with IHC by NGS.
Algorithm for NTRK gene fusion testing [2, 3]a
aBased on ESMO 2019 guidelines for NTRK fusion detection and guidelines for TRK fusion cancer in children by Albert et al. 2019; bUsing specific probes for the rearrangement involving the known NTRK gene; cAlbert et al., note that RT-PCR is not routinely used in clinical practice and limited data are available using this technique for NTRK fusion detection; dESMO guidelines note that this population would be likely represented by “any malignancy at an advanced stage, in particular if it has been proven wild type for other known genetic alterations tested in routine practice, and especially if diagnosed in young patients”.
FISH, fluorescence in situ hybridization; IHC, immunohistochemistry; MPS, massively parallel sequencing; NGS, next-generation sequencing; RT-PCR, reverse transcriptase-polymerase chain reaction.
Alternative algorithms for NTRK gene fusions testing have also been proposed with the aim to incorporate the strengths and availability of different diagnostic techniques. Two of these are depicted in the figures below [4, 5]. Although not completely identical to each other, the key underlying feature of all algorithms is that they are based on the categorization of tumours into two groups according to the incidence of NTRK gene fusion (high or low/ yes or no).
NTRK gene fusion testing algorithm; as proposed by Penault-Llorca et al., 2019 (Adapted from )
NTRK gene fusion testing algorithm; as proposed by Garrido et al., 2021 (Adapted from )
- Mateo J, Chakravarty D, Dienstmann R et al. A framework to rank genomic alterations as targets for cancer precision medicine: the ESMO Scale for Clinical Actionability of molecular Targets (ESCAT). Ann Oncol 2018; 29: 1895-1902.
- Albert CM, Davis JL, Federman N et al. TRK Fusion Cancers in Children: A Clinical Review and Recommendations for Screening. J Clin Oncol 2019; 37: 513-524.
- Marchio C, Scaltriti M, Ladanyi M et al. ESMO recommendations on the standard methods to detect NTRK fusions in daily practice and clinical research. Ann Oncol. 2019; 30(9):1417-1427.
- Penault-Llorca F, Rudzinski ER, Sepulveda AR. Testing algorithm for identification of patients with TRK fusion cancer. J Clin Pathol. 2019;72(7):460-467. doi: 10.1136/jclinpath-2018-205679. Epub 2019 May 9. PMID: 31072837; PMCID: PMC6589488.
- Garrido P, Hladun R, de Álava E et al. Multidisciplinary consensus on optimising the detection of NTRK gene alterations in tumours. Clin Transl Oncol. 2021;23(8):1529-1541.