Therapeutic exploration of uncommon EGFR exon 20 insertion mutations in advanced non-small cell lung cancer: breaking through brambles and thorns

The novel EGFR mutations (p.E746_S752delinsI, p.T751_I759delinsG, p.L747_S752delinsAA) in patients with non-small cell lung cancer and the clinical treatment strategy: three case reports

Epidermal growth factor receptor (EGFR) is an established driver gene in non-small cell lung cancer (NSCLC) and the common Exon 19 del mutation (p.E746_A750 del) has exhibited remarkable responses for EGFR tyrosine kinase inhibitors (TKIs). However, there is even less comprehension of the treatment strategy in NSCLC patients harboring uncommon Exon 19 delins mutation. Here, we identified three novel EGFR Exon 19 mutations (p.E746_S752delinsI, p.T751_I759delinsG, p.L747_S752delinsAA), and described the clinical treatment process. To our knowledge, the EGFR p.E746_S752delinsI mutation of the patient with advanced NSCLC could benefit from the treatment with Icotinib. Otherwise, for the NSCLC patients with early-stage, one harboring p.T751_I759delinsG mutation had an excellent recovery and the other harboring p.L747_S752delinsAA experienced a relapse after receiving horacoscopic radical resection, which means the patients with different Exon 19 delins mutation might have different prognosis. Our study also demonstrated that next-generation sequencing (NGS) is a crucial tool in guiding clinical treatment decisions in NSCLC. Furthermore, the real incidence of these mutation is not known, the routinely use of NGS surely will increase the detection of EGFR del-ins respect to the old tools used to screen for EGFR mutations.

Real-World Clinical Outcomes for Patients with EGFR and HER2 Exon 20 Insertion-Mutated Non-Small-Cell Lung Cancer

(1) Background: Exon 20 insertion mutations (ex20ins) in EGFR and HER2 are uncommon driver mutations in non-small-cell lung cancer (NSCLC), with a poor prognosis and few targeted therapy options, and there are limited real-world data. Here, we report the clinicopathologic features and outcomes for patients with ex20ins NSCLC across British Columbia, Canada. (2) Methods: NSCLC patients with ex20ins in EGFR or HER2 were identified via tumour testing between 1 January 2016 and 31 December 2021 (n = 7233). Data were collected by chart review. Survival analyses were performed using the Kaplan-Meier method using the log-rank test. (3) Results: A total of 131 patients were identified. The median age was 66. Thirty-three percent of patients had brain metastases. For the EGFR cohort, the median OS was 18.6 months for patients who received any systemic therapy (ST) vs. 2.6 months for patients who did not (p < 0.001). Median OS was similar for patients treated with ex20ins-specific tyrosine kinase inhibitors (TKIs) vs. other STs (18.6 vs. 15.9 months; p = 0.463). The median first-line PFS was 4.1 vs. 7.4 months for patients treated with a TKI vs. other ST (p = 0.744). For the HER2 cohort, the median OS was 9.0 months for patients who received any ST vs. 4.9 months for patients who did not (p = 0.015). The median OS was 23.0 months for patients treated with an ex20ins TKI vs. 5.6 months for patients who were not (p = 0.019). The median first-line PFS was 5.4 vs. 2.1 months for patients treated with a TKI vs. other ST (p = 0.343). (4) Conclusions: Overall survival was significantly longer among ex20ins patients who received any systemic therapy vs. those who did not. Overall survival was significantly better among HER2 ex20ins patients who received ex20ins-specific TKIs.

A predictive model integrating deep and radiomics features based on gadobenate dimeglumine-enhanced MRI for postoperative early recurrence of hepatocellular carcinoma

PURPOSE

Hepatocellular carcinoma (HCC) is the most common liver cancer worldwide, and early recurrence of HCC after curative hepatic resection is indicative of poor prognoses. We aim to develop a predictive model for postoperative early recurrence of HCC based on deep and radiomics features from multi-phasic magnetic resonance imaging (MRI).

MATERIALS AND METHODS

A total of 472 HCC patients were included and divided into the training (n = 378) and validation (n = 94) cohorts in the retrospective study. We separately extracted radiomics features and deep features from eight phases of gadoxetic acid-enhanced MRI and utilized the least absolute shrinkage and selection operator logistic regression algorithm for feature selection and model construction. We integrated the selected two types of features into a combined model and established a radiomics model as well as a deep learning (DL) model for comparison.

RESULTS

In the training and validation cohorts, the combined model demonstrated better performance for stratifying patients at high risk of early recurrence (AUC of 0.911 and 0.840, accuracy of 0.779 and 0.777, sensitivity of 0.927 and 0.769, specificity 0.720 and 0.779) than the radiomics model (AUC of 0.740 and 0.780) and the DL model (AUC of 0.887 and 0.813).

CONCLUSION

The combined model integrating deep and radiomics features from multi-phasic MRI is efficient for noninvasively stratifying patients at high risk of early HCC recurrence after resection.