An online clinical model and risk stratification system to predict progression-free survival for advanced non-small-cell lung cancer patients treated with PD-(L)1 inhibitor.

Background: Our study aimed to build a risk stratification system predicting the progression-free survival (PFS) to classify patients into diverse prognostic subgroups for advanced non-small-cell lung cancer patients treated with PD-(L)1 inhibitor. Methods: 404 patients from our center were enrolled in this study and 70% patients (n = 282) were randomly assigned into the training cohort and other 30% patients (n = 122) into the validation cohort. A testing cohort contained 81 patients from other centers were used to assess the generalizability of model. Cox regression analyses were used to identify the most significant clinical parameters. The model's performance was assessed by using concordance index (C-index), calibration curves, Decision Curve Analyses (DCAs), net reclassification improvement (NRI), integrated discrimination improvement (IDI) analyses, and survival curve. Results: Five clinical parameters were identified as the most significant predictors by using cox regression. We then integrated them into a Nomogram to Evaluate the relative PFS of ICIs Treatment (NEPIT). The C-index of NEPIT in the training cohort, the validation cohort and testing cohort was 0.789 (95%CI: 0.750-0.828), 0.745 (95%CI: 0.706-0.784), and 0.766 (95%CI: 0.744-0.788), respectively. The calibration curves presented a good congruence between the predictions and actual observations. The Decision Curve Analyses (DCAs) reflected positive net benefits can be obtained for NEPIT. The results from NRI and IDI analyses showed that the NEPIT could improve predictive power of TPS. In addition, the further constructed risk stratification system could effectively categorize patients into different risk subgroups. Conclusion: The tools developed in this study would have value in guiding the optimal patient selection for precision care.

Larysuicide: an online risk stratification system to identify patients at high risk of suicide after the laryngeal cancer diagnosis.

BACKGROUND: Patients with laryngeal cancer have more than five times the incidence of suicide compared with the general population. In this study, we aimed to develop an online risk stratification system, named Larysuicide, to identify patients at high risk of suicide after the laryngeal cancer diagnosis. METHODS: Forty-two thousand and sixty-six American patients from the SEER-18 database and 4207 Chinese patients from our center were included in this study. We randomly assigned American patients into the training set and validation set at a ratio of 7:3, and all Chinese patients remained as an independent external testing set. LASSO regression model was applied for data dimension reduction, feature selection, and Larysuicide building. The performance of model was evaluated and validated by C-index, AUC, calibration curves, decision curve analysis (DCA), and univariate regression analysis. RESULTS: The Larysuicide developed with seven selected features-age, race, cancer site, pathological subtype, grade, stage at presentation, and radiation. The model showed good discrimination, with a C-index of 0.745 (95% CI 0.723-0.767) in training set, 0.759 (95% CI 0.722-0.800) in validation set, and 0.749 (95% CI 0.730-0.769) in testing set. The AUC was 0.745 in training set, 0.759 in validation set, and 0.749 in testing set. The calibration curves showed good calibration. Decision curve analysis demonstrated that Larysuicide was clinically useful. The univariate regression analysis presented patients in the high-risk group identified by Larysuicide suffered a significantly higher risk of committing suicide after cancer diagnosis. CONCLUSION: We constructed an online risk stratification system which could help health-care professionals efficiently identify patients at high risk of suicide after the laryngeal cancer diagnosis. Larysuicide could be a useful tool for health-care professionals to implement an early and appropriate psychological intervention in context of precision medicine.

Real-world clinical analysis in 190 advanced NSCLC patients with uncommon EGFR mutations: A multi-center study.

Differently from epidermal growth factor receptor (EGFR) 19Del and L858R mutations, the panoramic description of uncommon EGFR mutations is far from mature. Our understanding of its population characteristics, treatment response, and drug resistance mechanisms needs urgent expansion and deepening. Our study enrolled 437 patients with non-small-cell lung cancer from four clinical centers and who had uncommon EGFR mutations. The clinical characteristics of all patients and the treatment outcomes of 190 advanced patients who received pharmacotherapy were analyzed. Moreover, the acquired resistance mechanisms were explored based on 53 tissue or liquid re-biopsy data in 45 patients. Patients with EGFR 20ins had a shorter survival time compared with patients with non-20ins mutations. In total, 149 cases had received EGFR-tyrosine kinase inhibitors (TKI); afatinib was significantly superior to other EGFR-TKIs both in ORR and mPFS in all uncommon mutations and especially in the L861Q group. The most common acquired drug resistance mechanism was MET amplification, followed by EGFR T790M, which was significantly different from common EGFR mutations.

A novel membrane-bound interleukin-2 promotes NK-92 cell persistence and anti-tumor activity.

A major challenge in natural killer (NK) cell immunotherapy is the limited persistence of NK cells in vivo. However, the proliferation of NK cells is dependent on cytokines such as interleukin-2 (IL-2). Although IL-2 is a critical cytokine for NK cell activation and survival, IL-2 administration in adoptive NK cell therapy can induce adverse toxicities. To improve the persistence of NK cells and attenuate the systemic toxicity of IL-2, we constructed a cell-restricted artificial IL-2, named membrane-bound IL-2 (mbIL-2), comprising human IL-2 and human IL-2Rα joined by a classic linker. We found that mbIL-2-activated NK-92 cells can survive and proliferate in vitro and in vivo, independent of exogenous IL-2, while mbIL-2-expressing NK-92 cells do not support bystander cell survival or proliferation. Additionally, mbIL-2 enhanced NK-92 cell-mediated antitumor activity by tuning the IL-2 receptor downstream signals and NK cell receptor repertoire expression. To conclude, our novel mbIL-2 improves NK-92 cell persistence and enhances NK-92 cell-mediated antitumor activity. NK-92 cells genetically modified to express the novel mbIL-2 with potential significance for clinical development.

Expression of Microtubule-Associated Proteins in Relation to Prognosis and Efficacy of Immunotherapy in Non-Small Cell Lung Cancer.

BACKGROUND: Microtubule-associated proteins (MAPs) have been considered to play significant roles in the tumor evolution of non-small cell lung cancer (NSCLC). Nevertheless, mRNA transcription levels and prognostic value of distinct MAPs in patients with NSCLC remain to be clarified. METHODS: In this study, the Oncomine database, Gene Expression Profiling Interactive Analysis (GEPIA) database, and Human Protein Atlas were utilized to analyze the relationship between mRNA/protein expression of different MAPs and clinical characteristics in NSCLC patients, including tumor type and pathological stage. The correlation between the transcription level of MAPs and overall survival (OS) of NSCLC patients was analyzed by Kaplan-Meier plotter. Besides, 50 frequently altered neighbor genes of the MAPs were screened out, and a network has been constructed via the cBioPortal and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) dataset. Meanwhile, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis on the expression data of MAPs and their 50 frequently altered neighbor genes in NSCLC tissues. Furthermore, The Cancer Immunome Atlas (TCIA) was utilized to analyze the relationship between MAP expression and the response to immunotherapy. Finally, we used reverse transcription-quantitative polymerase chain reaction (RT-qPCR) to verify the expression of MAPs in 20 patients with NSCLC. RESULTS: The present study discovered that the mRNA transcription levels of MAP7/7D2 were enriched in NSCLC tissues, while those of the MAP2/4/6/7D3 were lower in NSCLC specimens than those in control specimens. The mRNA transcription level of MAP6 was significantly associated with the advanced stage of NSCLC. Besides, survival analysis indicated that higher mRNA expressions of MAP2/4/6/7/7D3 were correlated considerably with favorable OS of NSCLC patients, whereas increased mRNA expression levels of MAP1A/1S were associated with poor OS. Moreover, the expression of MAP1A/1B/1S/4/6/7D1/7D3 was significantly correlated with immunophenoscore (IPS) in NSCLC patients. CONCLUSIONS: Our analysis indicated that MAP1A/1S could serve as potential personalized therapeutic targets for patients with NSCLC, and the enriched MAP2/4/6/7/7D3 expression could serve as a biomarker for favorable prognosis in NSCLC. Besides, the expression of MAP1A/1B/1S/4/6/7D1/7D3 was closely related to the response to immunotherapy. Taken together, MAP expression has potential application value in the clinical treatment and prognosis assessment of NSCLC patients, and further verifiable experiments can be conducted to verify our results.

Dexamethasone enhances the lung metastasis of breast cancer via a PI3K-SGK1-CTGF pathway.

Dexamethasone (Dex), as a pretreatment agent, is widely used to attenuate the side effects of chemotherapy in breast cancer treatment. However, whether and how Dex affects breast cancer metastasis remain to be furtherly understood. In this study, we established several mouse breast cancer metastatic models to study the effect of Dex in vitro and in vivo. Transwell, Western Blot and RNA interference were applied to study the molecular mechanism of Dex in promoting breast cancer cell migration. Meanwhile, the effect of Dex on lung metastasis of breast cancer in Dex combined with PTX chemotherapy was discussed. Our results confirmed that Dex could promote breast cancer cell metastasis both in vitro and in vivo. Mechanistic studies revealed that this pro-metastatic effect of Dex was mediated by the GR-PI3K-SGK1-CTGF pathway in tumor cells. Ligation of Dex and glucocorticoid receptor (GR) on tumor cells activated the PI3K signaling pathway and upregulated serum glucocorticoid-inducible kinase 1 (SGK1) expression, and then increased the expression of connective tissue growth factor (CTGF) through Nedd4l-Smad2. Moreover, Dex was the leading factor for lung metastasis in a standard regimen for breast cancer treatment with paclitaxel and Dex. Importantly, targeting SGK1 with the inhibitor GSK650394 remarkably reduced lung metastasis in this regimen. Our present data provide new insights into Dex-induced breast cancer metastasis and indicate that SGK1 could be a candidate target for the treatment of breast cancer metastasis.