The Addition of Peripheral Blood Inflammatory Indexes to Nomogram Improves the Predictive Accuracy of Survival in Limited-Stage Small Cell Lung Cancer Patients

Long-term, 13-year survival after immune cell therapy combined with chemotherapy for extensive-stage small-cell lung cancer: a case report

While the incidence of small-cell lung cancer is low, it has a poor prognosis. Patients with extensive small-cell lung cancer account for about 70% of all cases of small-cell lung cancer, with a median overall survival duration of 8-13 months and a 5-year overall survival rate of only 1%-5%. Herein, we report small-cell lung cancer diagnosed by bronchoscopic biopsy in an adult male patient in 2011. The patient had a clinical stage of cT2N2M1 and stage IV disease (i.e., extensive small-cell lung cancer). Still, he survived for 13 years through a combination of chemotherapy, radiotherapy, and cytokine-induced killer (CIK) immunocell thera. Comprehensive tumor markers, lymphocyte subsets, and lung CT images were obtained through long-term follow-up. After 12 cycles of chemotherapy (CE/IP regimen) and 5940cgy/33f radiotherapy, we found that the patient was in an immunosuppressive state, so the patient was given CIK cell therapy combined with chemotherapy. After 2 years of immunocell-combined chemotherapy, there were no significant changes in the primary lesion or other adverse events. In the 13 years since the patient's initial diagnosis, we monitored the changes in the patient's indicators such as CEA, NSE, CD4/CD8 ratio, and CD3+CD4+ lymphocytes, suggesting that these may be the factors worth evaluating regarding the patient's immune status and the effectiveness of combination therapy. In this case, CIK cell immunotherapy combined with chemotherapy was applied to control tumor progression. With a good prognosis, we concluded that CIK cell immunotherapy combined with chemotherapy can prolong patient survival in cases of extensive small-cell lung cancer, and the advantages of combined therapy are reflected in improving the body's immune capacity and enhancing the killing effect of immune cells.

Establishment of a prognostic nomogram for elderly patients with limited-stage small cell lung cancer receiving radiotherapy

The present study explored the risk factors associated with radiotherapy in seniors diagnosed with limited-stage small cell lung cancer (LS-SCLC) to construct and validate a prognostic nomogram. The study retrospectively included 137 elderly patients with LS-SCLC who previously received radiotherapy. Univariate and multivariate COX analyses were conducted to identify independent risk factors and determine optimal cut-off values. Kaplan-Meier survival curves and nomograms were constructed to predict survival. Calibration and receiver operating characteristic (ROC) curves were used to evaluate the accuracy and consistency of the nomogram. Illness rating scale-geriatric (CIRS-G) score, treatment strategy, lymphocyte-to-monocyte ratio (LMR), white blood cell-to-monocyte ratio (WMR), and prognostic nutritional index (PNI) were discovered to be independent prognostic factors. Based on the findings of our multivariate analysis, a risk nomogram was developed to assess patient prognosis. Internal bootstrap resampling was utilized to validate the model, and while the accuracy of the AUC curve at 1 year was modest at 0.657 (95% CI 0.458-0.856), good results were achieved in predicting 3- and 5 year survival with AUCs of 0.757 (95% CI 0.670-0.843) and 0.768 (95% CI 0.643-0.893), respectively. Calibration curves for 1-, 3-, and 5 year overall survival probabilities demonstrated good cocsistency between expected and actual outcomes. Patients with concurrent chemoradiotherapy, CIRS-G score > 5 points and low PNI, WMR and LMR correlated with poor prognosis. The nomogram model developed based on these factors demonstrated good predictive performance and provides a simple, accessible, and practical tool for clinicians to guide clinical decision-making and study design.

Association of immune cells and the risk of esophageal cancer: A Mendelian randomization study in a East Asian population

Immunotherapy has been used in esophageal cancer (EC), but the causal relationship between EC and immune cells is not clear. Although the cellular phenotype has been reported as a biomarker for immunotherapy, the biomarker studies for immunotherapy in EC still face great challenges. Comprehensive 2-sample Mendelian randomization (MR) analysis was performed to determine the causal association between immune cell signatures and EC in this study. Based on publicly available genetic data, we explored causal associations between 731 immune cell signatures and EC risk. EC had no statistically significant effect on immunophenotypes. Nine immunophenotype types were positively associated with the risk of EC: CD20-%B cell, CD20% lymphocytes, CD25 on IgD- CD27-, CD25 on IgD+ CD24+, CD27 on IgD+ CD24+, CD28+ CD45RA- CD8br AC, CD3 on TD CD8br, IgD-CD38dim%B cells, and Mo MDSC AC. In addition, a total of 15 immunophenotypes were identified as causally associated with EC. IgD+ CD38- %B cell, IgD- CD24- %lymphocyte, CD19 on IgD- CD38dim, CD20 on IgD+ CD24+, CD62L-myeloid DC AC, CD4+ AC, Lymphocyte %leukocyte, CD3 on HLA-DR+ T cell, CD3 on CD45RA- CD4+, HVEM on naive CD4+ AC, HVEM on CD45RA- CD4+, CD4 on TD CD4+, CD4 on CD4 Treg, and CD4 on CD39+ resting Treg, and CD4 on activated & secreting Treg. Our study has demonstrated the close connection between immune cells and EC by genetic means, thus providing guidance for future clinical research.

Effectiveness of CT radiomic features combined with clinical factors in predicting prognosis in patients with limited-stage small cell lung cancer

BACKGROUND

The prognosis of SCLC is poor and difficult to predict. The aim of this study was to explore whether a model based on radiomics and clinical features could predict the prognosis of patients with limited-stage small cell lung cancer (LS-SCLC).

METHODS

Simulated positioning CT images and clinical features were retrospectively collected from 200 patients with histological diagnosis of LS-SCLC admitted between 2013 and 2021, which were randomly divided into the training (n = 140) and testing (n = 60) groups. Radiomics features were extracted from simulated positioning CT images, and the t-test and the least absolute shrinkage and selection operator (LASSO) were used to screen radiomics features. We then constructed radiomic score (RadScore) based on the filtered radiomics features. Clinical factors were analyzed using the Kaplan-Meier method. The Cox proportional hazards model was used for further analyses of possible prognostic features and clinical factors to build three models including a radiomic model, a clinical model, and a combined model including clinical factors and RadScore. When a model has prognostic predictive value (AUC > 0.7) in both train and test groups, a nomogram will be created. The performance of three models was evaluated using area under the receiver operating characteristic curve (AUC) and Kaplan-Meier analysis.

RESULTS

A total of 1037 features were extracted from simulated positioning CT images which were contrast enhanced CT of the chest. The combined model showed the best prediction, with very poor AUC for the radiomic model and the clinical model. The combined model of OS included 4 clinical features and RadScore, with AUCs of 0.71 and 0.70 in the training and test groups. The combined model of PFS included 4 clinical features and RadScore, with AUCs of 0.72 and 0.71 in the training and test groups. T stages, ProGRP and smoke status were the independent variables for OS in the combined model, whereas T stages, ProGRP and prophylactic cranial irradiation (PCI) were the independent factors for PFS. There was a statistically significant difference between the low- and high-risk groups in the combined model of OS (training group, p < 0.0001; testing group, p = 0.0269) and PFS (training group, p < 0.0001; testing group, p < 0.0001).

CONCLUSION

Combined models involved RadScore and clinical factors can predict prognosis in LS-SCLC and show better performance than individual radiomics and clinical models.

Construction of the prognostic model for small-cell lung cancer based on inflammatory markers: A real-world study of 612 cases with eastern cooperative oncology group performance score 0-1

OBJECTIVES

This research aimed to explore the relationship between pre-treatment inflammatory markers and other clinical characteristics and the survival of small-cell lung cancer (SCLC) patients who received first-line platinum-based treatment and to construct nomograms for predicting overall survival (OS) and progression-free survival (PFS).

METHODS

A total of 612 patients diagnosed with SCLC between March 2008 and August 2021 were randomly divided into two cohorts: a training cohort (n = 459) and a validation cohort (n = 153). Inflammatory markers, clinicopathological factors, and follow-up information of patients were collected for each case. Cox regression was used to conduct univariate and multivariate analyses and the independent prognostic factors were adopted to develop the nomograms. Harrell's concordance index (C-index) and time-dependent receiver operating characteristic curve were used to verify model differentiation, calibration curve was used to verify consistency, and decision curve analysis was used to verify the clinical application value.

RESULTS

Our results showed that baseline C-reactive protein/albumin ratio, neutrophil/lymphocyte ratio, NSE level, hyponatremia, the efficacy of first-line chemotherapy, and stage were independent prognostic factors for both OS and PFS in SCLC. In the training cohort, the C-index of PFS and OS was 0.698 and 0.666, respectively. In the validation cohort, the C-index of PFS and OS was 0.727 and 0.747, respectively. The nomograms showed good predictability and high clinical value. Also, our new clinical models were superior to the US Veterans Administration Lung Study Group (VALG) staging for predicting the prognosis of SCLC.

CONCLUSIONS

The two prognostic nomograms of SCLC including inflammatory markers, VALG stage, and other clinicopathological factors had good predictive value and could individually assess the survival of patients.

The CCL27-CCR10 axis contributes to promoting proliferation, migration, and invasion of lung squamous cell carcinoma

Lung cancer is characterized by its high mortality and morbidity. A deep understanding of the molecular mechanisms of lung cancer tumorigenesis helps to develop novel lung cancer diagnostic and therapeutic strategies. However, the picture of the associated molecular landscape is not yet complete. As understood, chemokine-receptor interactions contribute much to lung cancer tumorigenesis, in which CCR10 also plays an important role. This study aimed to expand the knowledge of CCR10 in lung squamous cell carcinoma (LUSC) in the manner of molecular mechanism and biological functions. Using GEPIA database, the survival analysis between LUSC patients with high and low CCR10 expressions was performed, showing that CCR10 could be regarded as a risk factor for LUSC patients. Subsequently, CCR10 protein and mRNA expressions in LUSC were examined by qRT-PCR and western blot respectively. The results indicated that CCR10 was highly expressed in LUSC cells. The results of CCK-8, colony formation, and Transwell assays presented that CCL27, the ligand of CCR10, promoted proliferative, migratory, and invasive abilities of LUSC cells by activating CCR10. Also, the PI3K/AKT signaling pathway was verified as the involved pathway by western blot. Overall, it could be concluded that the CCL27-CCR10 regulatory axis can activate the PI3K/AKT pathway fostering the malignant features of LUSC cells.

Inflammation-based prognostic scoring system for predicting the prognosis of advanced small cell lung cancer patients receiving anlotinib monotherapy

BACKGROUND

According to the randomized multicenter phase II trial (ALTER1202), anlotinib has been approved as a third-line therapy for advanced small-cell lung cancer (SCLC). Some studies showed the predictive function of inflammatory markers, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) in the different cancers treated with anti-vascular targeting drugs. However, none of the studies showed the roles of NLR, PLR, and LMR in SCLC patients receiving anlotinib. Thus, our objective was to establish a scoring system based on inflammation to individuate patient stratification and selection based on NLR, PLR, and LMR.

METHODS

NLR, PLR, and LMR and their variations were calculated in 53 advanced SCLC patients receiving anlotinib as a third- or further-line treatment at Ningbo Medical Center Lihuili Hospital between January 2019 and December 2021. Kaplan-Meier curves were plotted. Both univariate and multivariate Cox regressions were used to identify predictors of survival.

RESULTS

Disease control rate was related to pre-NLR, pre-PLR, pre-LMR, post-NLR elevation, post-PLR elevation, and post-LMR elevation. The multivariate analysis determined post-NLR elevation, pre-PLR > 240.56, and pre-LMR ≤1.61 to be independently associated with progression-free survival, not overall survival. The inflammation-based prognostic scoring system demonstrated favorable predictive ability from the receiver operating characteristic curve (AUC: 0.791, 95% CI: 0.645-0.938).

CONCLUSIONS

Post-NLR variation, pre-PLR, and pre-LMR were independent prognostic factors for PFS in advanced SCLC receiving anlotinib monotherapy. The inflammation-based prognostic scoring system can accurately predict effectiveness and survival.

Genetic variation within the pri-let-7f-2 in the X chromosome predicting stroke risk in a Chinese Han population from Liaoning, China: From a case-control study to a new predictive nomogram

BACKGROUND AND OBJECTIVES

Stroke is the most common cause of disability and the second cause of death worldwide. Therefore, there is a need to identify patients at risk of developing stroke. This case-control study aimed to create and verify a gender-specific genetic signature-based nomogram to facilitate the prediction of ischemic stroke (IS) risk using only easily available clinical variables.

MATERIALS AND METHODS

A total of 1,803 IS patients and 1,456 healthy controls from the Liaoning province in China (Han population) were included which randomly divided into training cohort (70%) and validation cohort (30%) using the sample function in R software. The distribution of the pri-let-7f-2 rs17276588 variant genotype was analyzed. Following genotyping analysis, statistical analysis was used to identify relevant features. The features identified from the multivariate logistic regression, the least absolute shrinkage and selection operator (LASSO) regression, and univariate regression were used to create a multivariate prediction nomogram model. A calibration curve was used to determine the discrimination accuracy of the model in the training and validation cohorts. External validity was also performed.

RESULTS

The genotyping analysis identified the A allele as a potential risk factor for IS in both men and women. The nomogram identified the rs17276588 variant genotype and several clinical parameters, including age, diabetes mellitus, body mass index (BMI), hypertension, history of alcohol use, history of smoking, and hyperlipidemia as risk factors for developing IS. The calibration curves for the male and female models showed good consistency and applicability.

CONCLUSION

The pri-let-7f-2 rs17276588 variant genotype is highly linked to the incidence of IS in the northern Chinese Han population. The nomogram we devised, which combines genetic fingerprints and clinical data, has a lot of promise for predicting the risk of IS within the Chinese Han population.

Prognostic prediction by a novel integrative inflammatory and nutritional score based on least absolute shrinkage and selection operator in esophageal squamous cell carcinoma

BACKGROUND

This study aimed to establish and validate a novel predictive model named integrative inflammatory and nutritional score (IINS) for prognostic prediction in esophageal squamous cell carcinoma (ESCC).

MATERIALS AND METHODS

We retrospectively recruited 494 pathologically confirmed ESCC patients with surgery and randomized them into training (n = 346) or validation group (n = 148). The least absolute shrinkage and selection operator (LASSO) Cox proportional hazards (PH) regression analysis was initially used to construct a novel predictive model of IINS. The clinical features and prognostic factors with hazard ratio (HRs) and 95% confidence intervals (CIs) grouped by IINS were analyzed. Nomogram was also established to verify the prognostic value of IINS.

RESULTS

According to the LASSO Cox PH regression analysis, a novel score of IINS was initially constructed based on 10 inflammatory and nutritional indicators with the optimal cut-off level of 2.35. The areas under the curve (AUCs) of IINS regarding prognostic ability in 1-year, 3-years, and 5-years prediction were 0.814 (95% CI: 0.769-0.854), 0.748 (95% CI: 0.698-0.793), and 0.792 (95% CI: 0.745-0.833) in the training cohort and 0.802 (95% CI: 0.733-0.866), 0.702 (95% CI: 0.621-0.774), and 0.748 (95% CI: 0.670-0.816) in the validation cohort, respectively. IINS had the largest AUCs in the two cohorts compared with other prognostic indicators, indicating a higher predictive ability. A better 5-years cancer-specific survival (CSS) was found in patients with IINS ≤ 2.35 compared with those with IINS > 2.35 in both training cohort (54.3% vs. 11.1%, P < 0.001) and validation cohort (53.7% vs. 18.2%, P < 0.001). The IINS was then confirmed as a useful independent factor (training cohort: HR: 3.000, 95% CI: 2.254-3.992, P < 0.001; validation cohort: HR: 2.609, 95% CI: 1.693-4.020, P < 0.001). Finally, an IINS-based predictive nomogram model was established and validated the CSS prediction (training set: C-index = 0.71 and validation set: C-index = 0.69, respectively).

CONCLUSION

Preoperative IINS is an independent predictor of CSS in ESCC. The nomogram based on IINS may be used as a potential risk stratification to predict individual CSS and guide treatment in ESCC with radical resection.

Construction of Brain Metastasis Prediction Model and Optimization of Prophylactic Cranial Irradiation Selection for Limited-Stage Small-Cell Lung Cancer

Prophylactic cranial irradiation (PCI), as an essential part of the treatment of limited-stage small-cell lung cancer (LS-SCLC), inevitably leads to neurotoxicity. This study aimed to construct a brain metastasis prediction model and identify low-risk patients to avoid PCI; 236 patients with LS-SCLC were retrospectively analyzed and divided into PCI (63 cases) and non-PCI groups (173 cases). The nomogram was developed based on variables determined by univariate and multivariate analyses in the non-PCI group. According to the cutoff nomogram score, all patients were divided into high- and low-risk cohorts. A log-rank test was used to compare the incidence of brain metastasis between patients with and without PCI in the low-risk and high-risk groups, respectively. The nomogram included five variables: chemotherapy cycles (ChT cycles), time to radiotherapy (RT), lactate dehydrogenase (LDH), pro-gastrin-releasing peptide precursor (ProGRP), and lymphocytes−monocytes ratio (LMR). The area under the receiver operating characteristics (AUC) of the nomogram was 0.763 and 0.782 at 1 year, and 0.759 and 0.732 at 2 years in the training and validation cohorts, respectively. Based on the nomogram, patients were divided into high- and low-risk groups with a cutoff value of 165. In the high-risk cohort, the incidence of brain metastasis in the non-PCI group was significantly higher than in the PCI group (p < 0.001), but there was no difference in the low-risk cohort (p = 0.160). Propensity score-matching (PSM) analysis showed similar results; the proposed nomogram showed reliable performance in assessing the individualized brain metastasis risk and has the potential to become a clinical tool to individualize PCI treatment for LS-SCLC.

Risk Prediction Model for Synchronous Oligometastatic Non-Small Cell Lung Cancer: Thoracic Radiotherapy May Not Prolong Survival in High-Risk patients

Background and Purpose

On the basis of the promising clinical study results, thoracic radiotherapy (TRT)¹ has become an integral part of treatment of synchronous oligometastatic non–small cell lung cancer (SOM-NSCLC). However, some of them experienced rapid disease progression after TRT and showed no significant survival benefit. How to screen out such patients is a more concerned problem at present. In this study, we developed a risk-prediction model by screening hematological and clinical data of patients with SOM-NSCLC and identified patients who would not benefit from TRT.

Materials and Methods

We investigated patients with SOM-NSCLC between 2011 and 2019. A formula named Risk-Total was constructed using factors screened by LASSO-Cox regression analysis. Stabilized inverse probability treatment weight analysis was used to match the clinical characteristics between TRT and non-TRT groups. The primary endpoint was overall survival (OS).

Results

We finally included 283 patients divided into two groups: 188 cases for the training cohort and 95 for the validation cohort. Ten prognostic factors included in the Risk-Total formula were age, N stage, T stage, adrenal metastasis, liver metastasis, sensitive mutation status, local treatment status to metastatic sites, systemic inflammatory index, CEA, and Cyfra211. Patients were divided into low- and high-risk groups based on risk scores, and TRT was found to have improved the OS of low-risk patients (46.4 vs. 31.7 months, P = 0.083; 34.1 vs. 25.9 months, P = 0.078) but not that of high-risk patients (14.9 vs. 11.7 months, P = 0.663; 19.4 vs. 18.6 months, P = 0.811) in the training and validation sets, respectively.

Conclusion

We developed a prediction model to help identify patients with SOM-NSCLC who would not benefit from TRT, and TRT could not improve the survival of high-risk patients.

External Application of a Nomogram to Predict Survival and Benefit of Peripheral Blood Inflammatory Indexes in Limited-Stage Small Cell Lung Cancer

Background

Qi et al. recently proposed a nomogram to reveal the prognostic value of peripheral blood inflammatory indexes (named Risk) and predict overall survival (OS) in limited-stage small cell lung cancer (LS-SCLC). However, it hasn’t undergone external application so far. This study aimed to verify the role of Risk as a prognostic variable of OS and apply the nomogram externally.

Methods

We used a retrospective analysis of clinical data of 254 patients diagnosed as LS-SCLC in Shanxi Cancer Hospital from January 2015 to December 2018 to apply Qi’s nomogram externally. We also performed subgroup analysis to explore the predictive value of Risk. The model was evaluated in terms of discrimination (the area under the ROC curve (AUC ROC) and calibration (calibration plots).

Results

The prognosis of patients with low-Risk was significantly better than those with high-Risk in our cohort (p<0.01). The AUC of 1-, 2-, and 3-year OS was 0.644, 0.666, and 0.635, respectively. The calibration curve showed a nearly ideal calibration-slope of 1-, 2-, and 3-year OS (1.00 (0.41-1.59), 1.00 (0.54-1.46) and 1.00 (0.43-1.57), respectively).

Conclusion

The external application of nomogram added Risk for predicting OS in LS-SCLC patients showed a moderate-to-good performance using a cohort with different case-mix characteristics. The external application confirmed the predictive value of Risk and the usefulness of the nomogram for the prediction of OS.

Blood immune indexes can predict lateral lymph node metastasis of thyroid papillary carcinoma

OBJECTIVE

To explore the clinical significance of blood immune indexes in predicting lateral lymph node metastasis (LLNM) of thyroid papillary carcinoma (PTC).

METHODS

The pathological data and preoperative blood samples of 713 patients that underwent thyroid surgery at affiliated Hangzhou First People's Hospital Zhejiang University School of Medicine from January 2013 to June 2021 were collected as the model group. The pathological data and preoperative blood samples of 177 patients that underwent thyroid surgery in the same hospital from July 2021 to October 2021 were collected as the external validation group. Univariate and multivariate logistic regression analyses were used to determine the independent risk factors of LLNM in PTC patients. A predictive model for assessing LLNM in PTC patients was established and externally validated using the external data.

RESULTS

According to univariate and multivariate logistic regression analyses, tumor diameter (P < 0.001, odds ratios (OR): 1.205, 95% confidence interval (CI): 1.162-1.249) and the preoperative systemic immune-inflammation index (SII) (P = 0.032, OR: 1.001, 95% CI: 1.000-1.002) were independent risk factors for distinguishing LLNM in PTC patients. When the Youden index was the highest, the area under the curve (AUC) was 0.860 (P < 0.001, 95% CI: 0.821-0.898). The externally validated AUC was 0.827 (P < 0.001, 95% CI: 0.724-0.929), the specificity was 86.4%, and the sensitivity was 69.6%. The calibration curve and the decision curve indicated that the model had good diagnostic value.

CONCLUSION

Blood immune indexes can reflect the occurrence of LLNM and the biological behavior of PTC. The predictive model established in combination with SII and tumor diameter can effectively predict the occurrence of LLNM in PTC patients.