Comparison of first-generation EGFR-TKIs (gefitinib, erlotinib, and icotinib) as adjuvant therapy in resected NSCLC patients with sensitive EGFR mutations

STAT3 and STAT6 polymorphisms predict the severity of adverse reactions in Chinese NSCLC patients receiving EGFR-TKIs therapy

A total of 162 non-small cell lung cancer (NSCLC) patients were divided into discovery (N = 68) and validation (N = 94) groups. Nine Janus kinase/Signal transducer and activator of transcription (JAK/STAT) pathway-related single nucleotide polymorphisms were selected to explore the potential associations between genetic polymorphisms and adverse drug reactions (ADRs). The TT genotype of STAT6 rs324011 was significantly associated with severe ADRs in the recessive genetic model (TT vs. CC + CT, OR = 13.5, 95% CI = 2.12-86.09, p = 0.006 in the discovery group; OR = 8.41, 95% CI = 1.95-36.19, p = 0.004 in the validation group). The T allele was associated with a higher incidence of severe ADRs than was the C allele of rs324011 (OR = 3.67, 95% CI = 1.46-9.19, p = 0.006 in the discovery group; OR = 3.17, 95% CI = 1.44-6.99, p = 0.004 in the validation group). Patients with the CC genotype in STAT3 rs1053023 (and rs1053005) or the TT genotype of STAT6 rs324011 were likely to experience severe epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) related ADRs.

Effectiveness comparison of third-generation EGFR-TKI as initial and sequential therapy in adjuvant treatment for EGFR mutation-sensitive stage IIIA non-small cell lung cancer after surgery

Introduction

Although third-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) Osimertinib has been approved as adjuvant therapy for resected stage IIIA non-small cell lung cancer (NSCLC) with EGFR-sensitive mutations, the optimal treatment sequencing of EGFR-TKIs, particularly whether Osimertinib should be the initial or sequential therapy following the first-generation EGFR-TKIs remains uncertain.

Methods

A retrospective analysis was conducted on a cohort of patients with EGFR-mutated stage IIIA NSCLC who received treatment with either first-generation EGFR-TKIs or Osimertinib (third-generation) alone, or in sequential combination, at a single institution. The data analysis involved using the Kaplan-Meier method, log-rank test, and Cox regression.

Results

Out of the total 148 patients with stage IIIA NSCLC included in the study, 76 individuals underwent treatment with either first-generation EGFR-TKIs (referred to as subgroup "1″) or exclusively Osimertinib (subgroup "0 + 3″), or a sequential combination of the two (subgroup "1 + 3″) following surgery. Both univariate and multivariate analyses demonstrated that there were no discernible disparities in terms of disease-free survival and overall survival between subgroup " 1″ and " 1 + 3," nor between subgroup " 0 + 3″ and "1 + 3".

Conclusion

The findings from this study indicate that the introduction of third-generation EGFR-TKI Osimertinib did not yield enhanced survival benefits when compared to the first-generation drug in patients with stage IIIA completely resected NSCLC who were administered EGFR-TKIs as part of their postoperative adjuvant treatment. Additionally, within the observed sample size of this cohort, the sequential use of Osimertinib alongside first-generation EGFR-TKI did not demonstrate superiority over using either the first-generation EGFR-TKI or Osimertinib alone in terms of postoperative survival.

Synergistic markers based on inter-covariate association estimate treatment option with lower propensity to covariates

Background

Propensity constitutes a common problem in identifying clinical outcome prediction model whose covariates include the treatment option, which is assumed to be randomly assigned but indeed dependent of other covariates in the training data. The genuine effect of treatment option cannot be elucidated under the influence of propensity. Existing approaches, such as matched-pairs study design, still cannot solve the problem for imbalanced or small datasets.

Methods

This work proposed an anti-propensity estimate of treatment option, which is generated by support vector classifier based on two synergistic markers that represent the lower and upper limits of inter-covariate association level. The algorithm for generating the synergistic markers was illustrated and the performance was evaluated on a public dataset of gene expression levels, which were obtained from surgically excised tumor samples in non-small cell lung cancer (NSCLC) patients where treatment option, i.e., adjuvant therapy or not, was known.

Results

Six covariates represented by the expression levels of ZNF217, ERCC3, PMS1, PIK3CB, BARD1, and MAPK1, were selected to generate two synergistic markers and classifier for estimating the adjuvant therapy option with substantially attenuated propensity. The estimation accuracy attained an area under the receiver-operating characteristics curve, 0.78, in the test set.

Conclusions

The proposed synergistic markers demonstrated a parsimonious and anti-propensity estimation of treatment option, which is ready for the further evaluation and application in the clinical outcome prediction model.

Insights into fourth generation selective inhibitors of (C797S) EGFR mutation combating non-small cell lung cancer resistance: a critical review

Lung cancer is the second most common cause of morbidity and mortality among cancer types worldwide, with non-small cell lung cancer (NSCLC) representing the majority of most cases. Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR TKIs) are among the most commonly used targeted therapy to treat NSCLC. Recent years have seen the evaluation of many synthetic EGFR TKIs, most of which showed therapeutic activity in pertinent models and were classified as first, second, and third-generation. The latest studies have concluded that their efficacy was also compromised by additional acquired mutations, including C797S. Because second- and third-generation EGFR TKIs are irreversible inhibitors, they are ineffective against C797S containing EGFR triple mutations (Del19/T790M/C797S and L858R/T790M/C797S). Therefore, there is an urgent unmet medical need to develop next-generation EGFR TKIs that selectively inhibit EGFR triple mutations via a non-irreversible mechanism. This review covers the fourth-generation EGFR-TKIs' most recent design with their essential binding interactions, the clinical difficulties, and the potential outcomes of treating patients with EGFR mutation C797S resistant to third-generation EGFR-TKIs was also discussed. Moreover, the utilization of various therapeutic strategies, including multi-targeting drugs and combination therapies, has also been reviewed.

Comprehensive analysis of histone methylation modification regulators for predicting prognosis and drug sensitivity in lung adenocarcinoma

Histone methylation is an epigenetic modification regulated by histone methyltransferases, histone demethylases, and histone methylation reader proteins that play important roles in the pathogenic mechanism of cancers. However, the prognostic value of histone methylation in lung adenocarcinoma (LUAD) remains unknown. Here, we found that LUAD cases could be divided into 2 subtypes by the 144 histone methylation modification regulators (HMMRs), with a significant difference in OS time. Ninety-five of the HMMRs were identified as differentially expressed genes (DEGs) between normal and tumor samples, and 13 of them were further discovered to be survival-related genes (SRGs). By applying the least absolute shrinkage and selector operator (LASSO) Cox regression, we constructed an 8-gene-based risk signature according to the TCGA (training) cohort, and the risk score calculated by the signature was proven to be an independent factor in both the training and validation cohorts. We then discovered that the immune functions were generally impaired in the high-risk groups defined by the HMMR signature (especially for the DCs and immune check-point pathway). Functional analyses showed that the DEGs between the low- and high-risk groups were related to the cell cycle. The drug sensitivity analysis indicated that our risk model could predict the sensitivity of commonly used drugs. Moreover, according to the DEGs between the low- and high-risk groups, we discovered several new compounds that showed potential therapeutic value for high-risk LUAD patients. In conclusion, our study demonstrated that HMMRs were promising predictors for the prognoses and drug therapeutic effects for LUAD patients.