Does the number of cycles of neoadjuvant therapy affect the efficacy of neoadjuvant chemoimmunotherapy for non-small cell lung cancer in locally advanced stage? Retrospective experience based on a single center.

BACKGROUND: The number of cycles of neoadjuvant therapy programmed cell death 1 (PD-1) inhibitor for locally advanced non-small cell lung cancer (NSCLC) remains controversial. METHODS: From October 2019 to March 2022, neoadjuvant chemoimmunotherapy followed by radical surgery for NSCLC patients with stage II-III were retrospectively reviewed in Shanghai Pulmonary Hospital. The radiologic response was assessed according to the Response Evaluation Criteria for Solid Tumors version 1.1. The major pathological response was defined as no more than 10% residual tumor. Student's t-test, chi-square test, and Mann-Whitney test were used for univariate analysis, logistic regression analysis was used for multivariate analysis. All statistical analyses were calculated by SPSS software (version 26). RESULTS: Among 108 patients, the number of patients who received 2-cycle (2-cycle group) and more than 2-cycle (>2-cycle group) neoadjuvant chemoimmunotherapy were 75 (69.4%) and 33 (30.6%), respectively. Compared with patients in the >2-cycle group, patients in the 2-cycle group had significantly smaller diagnostic radiological tumor size (37.0 mm vs. 49.6 mm, p = 0.022) and radiological tumor regression rate (36% vs. 49%, p = 0.007). However, no significant difference in pathological tumor regression rate was observed between patients in the 2-cycle group and >2-cycle group. Further logistic regression analysis demonstrated that the neoadjuvant chemoimmunotherapy cycle could independently affect the radiographic response (odds ratio [OR]: 0.173, 95% confidence interval [CI]: 0.051-0.584, p = 0.005) but not for pathological response (OR: 0.450, 95% CI: 0.161-1.257, p = 0.127). CONCLUSIONS: For patients diagnosed with stage II-III NSCLC, the number of neoadjuvant cycles administered can significantly influence the radiographic efficacy of chemoimmunotherapy.

Sulfasalazine inhibits esophageal cancer cell proliferation by mediating ferroptosis.

This study aimed to explore the potential mechanism by which sulfasalazine (SAS) inhibits esophageal cancer cell proliferation. A cell counting kit-8 (CCK-8) assay was used to detect the effect of SAS (0, 1, 2, and 4 mM) on the proliferation of TE-1 cells. Subsequently, TE-1 cells were divided into control group, SAS group, SAS + ferrostatin-1 (ferroptosis inhibitor) group, and SAS + Z-VAD (OH)-FMK (apoptosis inhibitor) group, and cell proliferation was measured using a CCK-8 assay. Real-time quantitative polymerase chain reaction and western blotting were used to determine the expression of solute carrier family member 7 11 (SLC7A11, also called xCT), glutathione peroxidase 4 (GPX4), and acyl-CoA synthase long-chain family member 4 (ACSL4) in TE-1 cells. Measurement of ferroptosis in TE-1 cells was achieved by flow cytometry. Compared with the control group (0 mM SAS), the proliferation of TE-1 cells was significantly inhibited by different concentrations of SAS for different time lengths, and 4 mM SAS treatment for 48 h could obtain the maximum inhibition rate (53.9%). In addition, SAS treatment caused a significant decrease in the mRNA and protein expression of xCT and GPX4, and a significant increase in ACSL4 expression in TE-1 cells treated with SAS. Flow cytometry results showed that the ferroptosis level was significantly increased after SAS treatment. However, the activation of ferroptosis by SAS was partially eliminated by treatment with ferrostatin-1 or Z-VAD (OH)-FMK. In conclusion, SAS inhibits the proliferation of esophageal carcinoma cells by activating the ferroptosis pathway.

Development and validation of nomogram prognostic model for early-stage T1-2N0M0 small cell lung cancer: A population-based analysis.

Background: Survival outcomes of early-stage T1-2N0M0 small cell lung cancer (SCLC) patients differ widely, and the existing Veterans Administration Lung Study Group (VALSG) or TNM staging system is inefficient at predicting individual prognoses. In our study, we developed and validated nomograms for individually predicting overall survival (OS) and lung cancer-specific survival (LCSS) in this special subset of patients. Methods: Data on patients diagnosed with T1-2N0M0 SCLC between 2000 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. All enrolled patients were split into a training cohort and a validation cohort according to the year of diagnosis. Using multivariable Cox regression, significant prognostic factors were identified and integrated to develop nomograms for 1-, 3-, and 5-year OS and LCSS prediction. The prognostic performance of our new model was measured by the concordance index (C-index) and calibration curve. We compared our latest model and the 8th AJCC staging system using decision curve analyses (DCA). Kaplan-Meier survival analyses were applied to test the application of the risk stratification system. Results: A total of 1,147 patients diagnosed from 2000 to 2011 were assigned to the training cohort, and 498 cases that were diagnosed from 2012 to 2015 comprised the validation cohort. Age, surgery, lymph node removal (LNR), and chemotherapy were independent predictors of LCSS. The variables of sex, age, surgery, LNR, and chemotherapy were identified as independent predictors of OS. The above-mentioned prognostic factors were entered into the nomogram construction of OS and LCSS. The C-index of this model in the training cohort was 0.663, 0.702, 0.733, and 0.658, 0.702, 0.733 for predicting 1-, 3-, and 5-year OS and LCSS, respectively. Additionally, in the validation cohort, there were 0.706, 0.707, 0.718 and 0.712, 0.691, 0.692. The calibration curve showed accepted prediction accuracy between nomogram-predicted survival and actual observed survival, regardless of OS or LCSS. In addition, there were significant distinctions in the survival curves of OS and LCSS between different risk groups stratified by prognostic scores. Compared with the 8th AJCC staging system, our new model also improved net benefits. Conclusions: We developed and validated novel nomograms for individual prediction of OS and LCSS, integrating the characteristics of patients and tumors. The model showed superior reliability and may help clinicians make treatment strategies and survival predictions for early-stage T1-2N0M0 SCLC patients.

Genome-wide screening for genomic aberrations in Kazakh patients with esophageal squamous cell cancer by comparative genomic hybridization.

Multiple chromosome aberrations are responsible for tumorigenesis of esophagus squamous cell carcinoma (ESCC). To characterize genetic alterations by comparative genomic hybridization (CGH) and their relation to ESCC, We enrolled 54 members with ESCC from Kazakh's patients. We found that the deletions of 3p (P = 0.032), 17p (P = 0.004), 22q (P = 0.000) and gains of 5p (P = 0.000), 11q (P = 0.000) were significantly correlated with the location of tumors. Losses of 1p (P = 0.005), 3p (P = 0.006), 22q (P = 0.024) and gains of 3q (P = 0.043), 8q (P = 0.038), 18q (P = 0.046) were also found more frequently in patients with larger diameter disease. The loss of 19q (P = 0.005) and gains of l3q (P = 0.045), 18p (P = 0.018) were significantly correlated with pathologic grade. The gain of 7p (P = 0.009) and deletion of 19q (P = 0.018) were seen more frequently in patients with Grade III-IV tumors. Chromosome amplifications in ESCC at 1q (P = 0.008), 7p (P = 0.008), 8q (P = 0.018) and deletions at 3p (P = 0.021), 11q (P = 0.002), 17p (P = 0.012) were related to lymph node metastasis; the gains of 1q (P = 0.026) and 6q (P = 0.017) and the loss of 11q (P = 0.001) were significant in different isoforms of HPV infection. We identified some chromosomes in which the genes were related to the tumorgenesis of ESCC, which may be a theme for future investigation.