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Abstract Purpose To evaluate the efficacy and safety of induction chemotherapy combined with programmed death protein 1 (PD-1) inhibitor (sintilimab) followed by concurrent chemoradiotherapy (CCRT) plus sintilimab, and subsequent maintenance with sintilimab (IC-ICCRT-IO) for patients with unresectable locally advanced esophageal squamous cell carcinoma (ESCC) in a retrospective study. Methods Data from patients with histologically confirmed, locally advanced, inoperable ESCC who received IC-ICCRT-IO were retrospectively analyzed. Treatment effects were evaluated after 2 cycles of induction therapy and after CCRT by contrast-enhanced CT scans and esophagograms, followed by subsequent evaluations every 3 months post-treatment. The primary endpoints included progression-free survival (PFS) and PFS rates at 6, 12, and 18 months. Secondary endpoints involved overall response rate (ORR), disease control rate (DCR), overall survival (OS), and safety. The influence of the expression level of programmed death ligand-1 (PD-L1) as well as neutrophil-to-lymphocyte ratio (NLR) on efficacy of the IC-ICCRT-IO was analyzed. Results In total, 29 eligible patients were enrolled and analyzed. The median follow-up time was 20.5 months. The median PFS was not reached; the 6-, 12-, and 18-month PFS rates were 100.0%, 93.1%, and 82.8%, respectively. The median OS was not reached, and the 6-, 12-, and 18-month OS rates were all 100.0%. The ORR and DCR were 89.7% and 100.0%. Adverse events (AEs) were manageable, with grade 3 or higher AEs observed in 48.2% of patients, primarily nonimmune-related and clinically manageable. Hematologic toxicity was predominant. Two patients developed grade 3 immune-related rash, and two patients developed grade 3 radiation pneumonitis, all of whom were managed with appropriate symptomatic treatment. No significant differences in survival outcomes were observed with respect to PD-L1 and NLR. Conclusion Our results indicated that the IC-ICCRT-IO regimen for unresectable locally advanced ESCC provided a survival benefit with manageable safety profile. More prospective clinical studies should be warranted. Trial registration 2024-04-22, No. QYFY WZLL 28,684, retrospectively registered.

PurposeTo establish a model combining radiomic and clinicopathological factors based on magnetic resonance imaging to predict pathological complete response (pCR) after neoadjuvant chemotherapy in breast cancer patients.MethodMRI images and clinicopathologic data of 329 eligible breast cancer patients from the Affiliated Hospital of Qingdao University from August 2018 to August 2022 were included in this study. All patients received neoadjuvant chemotherapy (NAC), and imaging examinations were performed before and after NAC. A total of 329 patients were randomly allocated to a training set and a test set at a ratio of 7:3. We mainly studied the following three types of prediction models: radiomic models, clinical models, and clinical-radiomic models. All models were evaluated using subject operating characteristic curve analysis and area under the curve (AUC), decision curve analysis (DCA) and calibration curves.ResultsThe AUCs of the clinical prediction model, independent imaging model and clinical combined imaging model in the training set were 0.864 0.968 and 0.984, and those in the test set were 0.724, 0.754 and 0.877, respectively. According to DCA and calibration curves, the clinical-radiomic model showed good predictive performance in both the training set and the test set, and we found that we had developed a more concise clinical-radiomic nomogram.ConclusionWe have developed a clinical-radiomic model by integrating radiomic features and clinical factors to predict pCR after NAC in breast cancer patients, thereby contributing to the personalized treatment of patients.

BackgroundGastric cancer (GC) ranks as the fifth most prevalent malignancy and the second leading cause of oncologic mortality globally. Despite staging guidelines and standard treatment protocols, significant heterogeneity exists in patient survival and response to therapy for GC. Thus, an increasing number of research have examined prognostic models recently for screening high-risk GC patients.MethodsWe studied DEGs between GC tissues and adjacent non-tumor tissues in GEO and TCGA datasets. Then the candidate DEGs were further screened in TCGA cohort through univariate Cox regression analyses. Following this, LASSO regression was utilized to generate prognostic model of DEGs. We used the ROC curve, Kaplan-Meier curve, and risk score plot to evaluate the signature’s performance and prognostic power. ESTIMATE, xCell, and TIDE algorithm were used to explore the relationship between the risk score and immune landscape relationship. As a final step, nomogram was developed in this study, utilizing both clinical characteristics and a prognostic model.ResultsThere were 3211 DEGs in TCGA, 2371 DEGs in GSE54129, 627 DEGs in GSE66229, and 329 DEGs in GSE64951 selected as candidate genes and intersected with to obtain DEGs. In total, the 208 DEGs were further screened in TCGA cohort through univariate Cox regression analyses. Following this, LASSO regression was utilized to generate prognostic model of 6 DEGs. External validation showed favorable predictive efficacy. We studied interaction between risk models, immunoscores, and immune cell infiltrate based on six-gene signature. The high-risk group exhibited significantly elevated ESTIMATE score, immunescore, and stromal score relative to low-risk group. The proportions of CD4+ memory T cells, CD8+ naive T cells, common lymphoid progenitor, plasmacytoid dentritic cell, gamma delta T cell, and B cell plasma were significantly enriched in low-risk group. According to TIDE, the TIDE scores, exclusion scores and dysfunction scores for low-risk group were lower than those for high-risk group. As a final step, nomogram was developed in this study, utilizing both clinical characteristics and a prognostic model.ConclusionIn conclusion, we discovered a 6 gene signature to forecast GC patients’ OS. This risk signature proves to be a valuable clinical predictive tool for guiding clinical practice.

BackgroundLung squamous cell carcinoma (LUSC) represents 30% of all non-small cell lung carcinoma. Targeted therapy is not sufficient for LUSC patients because of the low frequency of targeted-effective mutation in LUSC whereas immunotherapy offers more options for patients with LUSC. We explored a ferroptosis-related prognostic signature that can potentially assess the prognosis and immunotherapy efficacy of LUSC patients.MethodsA total of 502 LUSC patients were downloaded from The Cancer Genome Atlas (TCGA). The external validation data were obtained from the Gene Expression Omnibus (GEO): GSE73403. Then, we identified the candidate genes and constructed the prognostic signature through the Cox survival regression analyses and least absolute shrinkage and selection operator (LASSO). Risk score plot, Kaplan–Meier curve, and ROC curve were used to assess the prognostic power and performance of the model. The CIBERSORT algorithm estimated the fraction of immune cell types. TIDE was utilized to predict the response to immunotherapy. IMvigor210 was used to explore the association between the risk scores and immunotherapy outcomes. A nomogram combined selected clinical characteristics, and the risk scores were constructed.ResultsWe screened 132 differentially expressed ferroptosis-related genes. According to KEGG and GO pathway analyses, these genes were mainly engaged in the positive regulation of cytokine production, cytokine metabolic process, and oxidoreductase activity. We then constructed a prognostic model via LASSO regression. The proportions of CD8+ T cells, CD4+ activated T cells, and follicular helper T cells were significantly different between low-risk and high-risk groups. TIDE algorithm indicated that low-risk LUSC patients might profit more from immune checkpoint inhibitors. The predictive value of the ferroptosis gene model in immunotherapy response was further confirmed in IMvigor210. Finally, we combined the clinical characteristics with a LASSO regression model to construct a nomogram that could be easily applied in clinical practice.ConclusionWe identified a prognostic model that provides an accurate and objective basis for guiding individualized treatment decisions for LUSC.

Abstract Background To investigate the impact of the elevation of tumor-infiltrating lymphocytes (TILs) in different molecular subtypes of primary breast cancer, i.e. each 10% increment of TILs and high-level TILs (TILs≥50%) in tumor, on overall survival (OS) and pathological complete response (pCR) and to compare the presentation of high-level TILs across these molecular subtypes. Methods Citation retrieval was performed in the PubMed, Cochrane Library, Embase and Web of Science databases. All statistical calculations were performed by the software of StataSE version 12.0. Results Twenty-two eligible clinical trials including 15,676 unique patients were included for meta-analysis. Each 10% increment of TILs significantly improved OS in human epidermal growth factor receptor 2 (HER2)-overexpression (pooled Hazard ratio (HR), 0.92; 95% CI, 0.89–0.95) and triple-negative (TN) (pooled HR, 0.90; 95% CI, 0.89–0.92) breast tumors but not in luminal tumor subtype (pooled HR, 1.06; 95% CI, 0.99–1.13). It was also associated with an increased pCR rate in breast cancers (pooled Odds ratio (OR), 1.27; 95% CI, 1.19–13.5). High-level TILs were significantly related with a higher pCR rate (pooled OR, 2.73; 95% CI, 2.40–3.01) than low-level TILs. The HER2-amplified (pooled OR, 3.14; 95% CI, 1.95–5.06) and TN (pooled OR, 4.09; 95% CI, 2.71–6.19) phenotypes of breast cancers expressed significantly more high-level TILs than the luminal tumor subtype, although the presentation of those between the former two subsets was not significantly different (pooled OR, 1.30; 95%CI, 0.83–2.04). Conclusions The elevation of TILs in breast tumors predicts favorable prognostic outcomes, particularly in the HER2-overexpression and TN subtypes.

Abstract Background To investigate the efficacy of neoadjuvant chemotherapy (NCT), neoadjuvant endocrine therapy (NET) and neoadjuvant chemoendocrine therapy (NCET) on the tumour response, including pathological complete response (pCR) rate and overall response rate (ORR), in postmenopausal women with hormone receptor (HR)-positive breast cancer. Methods Based on a PRISMA-IPD statement, the PubMed, Embase and Cochrane Library databases were used to identify eligible trials published from inception to 7 May 2019. Pooled odds ratio (OR) with 95% confidential interval (CI) was calculated to assess the pCR rate and ORR of tumours among those three treatments via fixed- or random-effect Mantel-Haenszel models in terms of a Heterogeneity Chi2 test with a significant level of p

Background/Aims: Several studies have shown secreted clusterin (sCLU) silencing directed against sCLU mRNA in sCLU-rich lung cancer cell lines sensitized cells to chemotherapy. However, the molecular mechanisms underlying the effect of sCLU silencing on lung cancer cell chemosensitivity is not known. In the present study, we aimed to determine that vector expressing short hairpin RNA against sCLU RNA (sCLU-shRNA) enhances the chemosensitivity in human small cell lung cancer A549 cells in vitro by inhibition of phosphorylated ERK1/2 (p-ERK1/2) and Akt (p-Akt). Methods: The pCDNA3.1-sCLU and control scrambled pCDNA3.1 plasmid was constructed. We investigated the effects of sCLU overexpression by pCDNA3.1-sCLU transfection on chemosensitivity to cisplatin (DDP) in A549 cells in vitro. We down-regulated sCLU expression by short hairpin RNA against sCLU RNA (sCLU-shRNA) and investigated the effects on chemosensitivity to DDP in A549 cells and A549DDPin vitro. In order to confirm the correlation between sCLU and AKT and ERK1/2 signals, cells were treated with wortmannin and U0126. Results: We found the chemotherapeutic agent DDP activated sCLU. Overexpression of sCLU increased cellular DDP chemoresistance in the A549DDP and pCDNA3. 1-sCLU transfected A549 cells via inhibition DDP-induced apoptosis. Whereas sCLU knockdown induced chemosensitization in the S549 and A549DDP cells via increase of DDP-induced apoptosis. sCLU overexpression activated pAKT Ser473 and pERK1/2Thr202/Tyr204, and vice versa. Inhibition of pAKT Ser473 and pERK1/2Thr202/Tyr204 was sufficient to induce significant recover y in chemosensitivity to DDP in A549DDP in the presence of sCLU overexpression. The DDP activated sCLU, which directly regulated pAKT and pERK1/2. Conclusions: This novel finding suggests that therapies directed against sCLU and its downstream signaling targets pAKT and pERK1/2 may have the potential to enhance the efficacy of DDP-based chemotherapy.

Agencies seeking to disseminate public use microdata, i.e., data on individual records, can replace confidential values with multiple draws from statistical models estimated with the collected data. We present a famework for evaluating disclosure risks inherent in releasing multiply-imputed, synthetic data. The basic idea is to mimic an intruder who computes posterior distributions of confidential values given the released synthetic data and prior knowledge. We illustrate the methodology with artificial fully synthetic data and with partial synthesis of the Survey of Youth in Custody.