An artificial intelligence approach to variant calling of ALK resistance mutations.

Abstract

3079 Background: ALK tyrosine kinase inhibitors (TKIs) are effective in treating advanced anaplastic lymphoma kinase (ALK) fusion-positive non-small-cell lung cancers (NSCLC), and specific ALK variants are associated with the development of resistance to specific TKIs. Humans struggle to harness the full potential of the highly complex next-generation sequencing bioinformatics pipeline output. As a consequence, the decision to report a variant remains difficult, and we considered the discrete nature of the data and the binary decision (report vs. not-report) as an ideal setting to apply an artificial intelligence (AI) approach for variant reporting. Methods: We assessed diagnostic performance of an AI model in calling ALK-resistance mutations in n = 50 consecutive ALK fusion positive patients who relapsed on TKI-therapy and underwent repeat biopsy at MGH. The random forest model was derived from independent datasets (training and validation) capturing the reporting decision on > 36,000 variants with ~500 features per variant resulting in a matrix of > 18 million data points. The model output is a contiguous prediction score from 0 (not report) to 1 (report) and a visual drill-down functionality allows exploration of the underlying features that contributed to the decision. Results: Examination of n = 76 tests from n = 50 patients with a total of n = 130 reported variants (and = 115 not reported variants) included a total of n = 31 ALK point mutations: p.1156(n = 2), p.1171(n = 8), p.1174(n = 2), p.1180(n = 2), p.1196(n = 1), p.1198(n = 1), p.1202(n = 8), p.1203(n = 1), p.1204(n = 1), p.1206(n = 1), p.1269(n = 4). Setting a screening threshold of the model at > 10% for reporting showed only one false-negative (p.Ile1171Asn) variant and 96.7% sensitivity. The average model score for ALK variants was 0.664 (range: 0.08–0.98; median 0.8) and did not show significant differences from other reported variants (0.636; 0–1; 0.7; t-test 0.66). The model would have called n = 18 of the non-reported control variants (average 0.07; range < 0.001–0.64; P < 0.0001) and was 84% specific. Review of the drill-down function identified prior call frequency, allelic ratio, and predicted transcript consequences as common model features. Importantly, the model is currently agnostic to the medical literature and does not take clinical parameters (e.g. TKI type) into account, which may further improve performance. Conclusions: Applying artificial intelligence to large discrete datasets is one approach to help identify clinically relevant variants in the setting of ALK resistance in ALK-fusion positive NSCLC.