Assessment of Nine Driver Gene Mutations in Surgically Resected Samples from Patients with Non-Small-Cell Lung Cancer

Ability of 18F-FDG Positron Emission Tomography Radiomics and Machine Learning in Predicting KRAS Mutation Status in Therapy-Naive Lung Adenocarcinoma

OBJECTIVE

Considering the essential role of KRAS mutation in NSCLC and the limited experience of PET radiomic features in KRAS mutation, a prediction model was built in our current analysis. Our model aims to evaluate the status of KRAS mutants in lung adenocarcinoma by combining PET radiomics and machine learning.

METHOD

Patients were retrospectively selected from our database and screened from the NSCLC radiogenomic dataset from TCIA. The dataset was randomly divided into three subgroups. Two open-source software programs, 3D Slicer and Python, were used to segment lung tumours and extract radiomic features from ¹⁸F-FDG-PET images. Feature selection was performed by the Mann-Whitney U test, Spearman's rank correlation coefficient, and RFE. Logistic regression was used to build the prediction models. AUCs from ROCs were used to compare the predictive abilities of the models. Calibration plots were obtained to examine the agreements of observed and predictive values in the validation and testing groups. DCA curves were performed to check the clinical impact of the best model. Finally, a nomogram was obtained to present the selected model.

RESULTS

One hundred and nineteen patients with lung adenocarcinoma were included in our study. The whole group was divided into three datasets: a training set (n = 96), a validation set (n = 11), and a testing set (n = 12). In total, 1781 radiomic features were extracted from PET images. One hundred sixty-three predictive models were established according to each original feature group and their combinations. After model comparison and selection, one model, including wHLH_fo_IR, wHLH_glrlm_SRHGLE, wHLH_glszm_SAHGLE, and smoking habits, was validated with the highest predictive value. The model obtained AUCs of 0.731 (95% CI: 0.619~0.843), 0.750 (95% CI: 0.248~1.000), and 0.750 (95% CI: 0.448~1.000) in the training set, the validation set and the testing set, respectively. Results from calibration plots in validation and testing groups indicated that there was no departure between observed and predictive values in the two datasets (p = 0.377 and 0.861, respectively).

CONCLUSIONS

Our model combining ¹⁸F-FDG-PET radiomics and machine learning indicated a good predictive ability of KRAS status in lung adenocarcinoma. It may be a helpful non-invasive method to screen the KRAS mutation status of heterogenous lung adenocarcinoma before selected biopsy sampling.

Prediction of Resistance Mutations Against Upcoming Anaplastic Lymphoma Kinase Inhibitors

BACKGROUND

Chromosomal aberrations involving the anaplastic lymphoma kinase (ALK) gene have been observed in approximately 4% of patients with non-small cell lung cancer (NSCLC). Although these patients clinically benefit from treatment with various ALK tyrosine kinase inhibitors (ALK-TKIs), none of these can inhibit the development of resistance mutations. Considering inevitable drug resistance and the variety of available ALK-TKIs, it is necessary to predict the pattern of drug-resistance mutations to determine the optimal treatment strategy.

OBJECTIVE

We aimed to establish a polymerase chain reaction (PCR)-based system to predict the development of resistance mutations against ALK-TKIs and identify therapeutic strategies using the upcoming ALK-TKIs repotrectinib (TPX-0005) and ensartinib (X-396) following recurrence on first-line alectinib treatment for ALK-positive NSCLC.

METHODS

An error-prone PCR-based method for predicting drug resistance mutations was established and the half-maximal inhibitory concentration (IC₅₀) values of the predicted ALK mutations were evaluated in a Ba/F3 cell-based assay.

RESULTS

We predicted several resistance mutations against repotrectinib and ensartinib, and demonstrated that the next-generation ALK-TKI TPX-0131, was active against repotrectinib-resistant mutations and that the FLT3 inhibitor gilteritinib was active against ensartinib-resistant mutations.

CONCLUSIONS

We developed a PCR-based system for predicting drug resistance mutations. When this system was applied to repotrectinib and ensartinib, the results suggested that these drugs can be used for the second-line treatment of ALK-positive NSCLC. Predicting resistance mutations against TKIs will provide useful information to aid in the development of effective therapeutic strategies.

Vav1 accelerates Ras-driven lung cancer and modulates its tumor microenvironment

The potential impact of Vav1 on human cancer was only recently acknowledged, as it is detected as a mutant or an overexpressed gene in various cancers, including lung cancer. Vav1, which is normally and exclusively expressed in the hematopoietic system functions as a specific GDP/GTP nucleotide exchange factor (GEF), strictly regulated by tyrosine phosphorylation. To investigate whether Vav1 plays a causative or facilitating role in-vivo in lung cancer development and to examine whether it co-operates with other oncogenes, such as mutant K-Ras, we generated novel mouse strains that express: Vav1 or K-Ras^G12D in type II pneumocytes, as well as a transgenic mouse line that expresses both Vav1 and K-Ras^G12D in these cells. Coexpression of Vav1 and K-Ras^G12D in the lungs dramatically increased malignant lung cancer lesions, and did so significantly faster than K-Ras^G12D alone, strongly suggesting that these two oncogenes synergize to enhance lung tumor development. Vav1 expression alone had no apparent effects on lung tumorigenesis. The increase in lung cancer in K-Ras^G12D/Vav1 mice was accompanied by an increase in B-cell, T-cells, and monocyte infiltration in the tumor microenvironment. Concomitantly, ERK phosphorylation was highly elevated in the lungs of K-Ras^{G12 D}/Vav1 mice. Also, several cytokines such as IL-4 and IL-13 which play a significant role in the immune system, were elevated in lungs of Vav1 and K-Ras^{G12 D}/Vav1 mice. Our findings emphasize the contribution of Vav1 to lung tumor development through its signaling properties.

KRAS Mutations in Squamous Cell Carcinomas of the Lung

KRAS is one of the most commonly mutated oncogenes in cancer, enabling tumor proliferation and maintenance. After various approaches to target KRAS have failed over the past decades, the first specific inhibitor of the p.G12C mutation of KRAS was recently approved by the FDA after showing promising results in adenocarcinomas of the lung and other solid tumors. Lung cancer, the most common cancer worldwide, is a promising use case for these new therapies, as adenocarcinomas in particular frequently harbor KRAS mutations. However, in squamous cell carcinoma (SCC) of the lung, KRAS mutations are rare and their impact on clinical outcome is poorly understood. In this review, we discuss the current knowledge on the prevalence and prognostic and predictive significance of KRAS mutations in the context of SCC.

Multigene PCR using both cfDNA and cfRNA in the supernatant of pleural effusion achieves accurate and rapid detection of mutations and fusions of driver genes in patients with advanced NSCLC

Background

Pleural effusion from patients with advanced non‐small cell lung cancer (NSCLC) has been proved valuable for molecular analysis, especially when the tissue sample not available. However, simultaneous detection of multiple driver gene alterations especially the fusions is still challenging.

Methods

In this study, 77 patients with advanced NSCLC and pleural effusion were enrolled, 49 of whom had matched tumor tissues. Supernatants, cell sediments, and cell blocks were prepared from pleural effusion samples for detection of driver alterations by a PCR‐based 9‐gene mutation detection kit.

Results

Mutations in EGFR, KRAS, and HER2 were detected in DNA and cfDNA, fusions in ALK was detected in RNA and cfRNA. Compared with matched tumor tissue, the supernatant showed the highest overall sensitivity (81.3%), with 81.5% for SNV/Indels by cfDNA and 80% for fusions by cfRNA, followed by cell blocks (71.0%) and the cell sediments (66.7%). Within the group of treatment‐naïve patients or malignant cells observed in the cell sediments, supernatant showed higher overall sensitivity (89.5% and 92.3%) with both 100% for fusions.

Conclusions

CfDNA and cfRNA derived from pleural effusion supernatant have been successfully tested with a PCR‐based multigene detection kit. Pleural effusion supernatant seems a preferred material for detection of multigene alterations to guide treatment decision of advanced NSCLC.