East Asian patients who received immunotherapy-based therapy associated with improved survival benefit in advanced non-small cell lung cancer: An updated meta-analysis.

BACKGROUND: Immune checkpoint inhibitors (ICIs) combined with chemotherapy have been recommended as the standard treatment for advanced NSCLC patients without driver-gene mutations. However, there are different genetic characteristics and biological traits of tumors between non-East Asian (nEA) and East Asian (EA) patients with NSCLC, which may contribute to differences in the efficacy of ICIs in different ethnic populations. Previous findings regarding differences in the efficacy of ICIs among ethnic groups have been inconsistent. Therefore, we performed a meta-analysis by collecting published data to investigate the clinical outcomes of ICIs for EA NSCLC patients compared to nEA patients. METHODS: Overall survival (OS) and progression-free survival (PFS) were used to access the difference in survival outcomes between the two populations. Subgroup analyses were performed based on the line of ICIs, the use of ICIs alone or in combination, and the type of ICIs. RESULTS: A total of 9826 NSCLC patients from 21 randomized controlled trials (RCTs) with 4064 EAs were included, which involved PD-1, PD-L1, and CTLA-4 inhibitors. EA NSCLC patients who received ICIs-based therapy were associated with significantly improved survival benefits in OS (p = 0.02) compared with nEA patients. Subgroup analysis indicated that EA patients receiving first-line ICIs showed significantly superior OS compared with nEA patients (p = 0.007). Chemo-ICIs treatment showed significant advantages in terms of OS (p = 0.002) and PFS (p = 0.02) among EA patients compared to nEA patients. In addition, PD-1 inhibitors were associated with improved OS among both EA patients and nEA patients compared with PD-L1 inhibitors. CONCLUSION: EA NSCLC patients who received ICIs-based therapy were associated with significantly improved survival benefits compared with nEA NSCLC patients. Earlier intervention with ICIs and combination treatment was more recommended for EA NSCLC patients. Moreover, PD-1 inhibitors are associated with prolonged survival among both EA and nEA patients.

Efficacy and safety of thoracic radiotherapy in extensive-stage small-cell lung cancer patients receiving first-line immunotherapy plus chemotherapy: a propensity score matched multicentre retrospective analysis.

BACKGROUND: Platinum-etoposide chemotherapy combined with immune checkpoint inhibitors (ICIs) has been recommended as the first-line standard treatment for extensive-stage small-cell lung cancer (ES-SCLC). However, the effect of thoracic radiotherapy (TRT) on these patients is still unknown. This study aimed to evaluate the efficacy and safety of TRT for ES-SCLC patients who responded to first-line ICIs and chemotherapy (CHT). METHODS: Patients who received 4 to 6 cycles of ICIs and CHT as first-line therapy at three hospitals between 2018 and 2022 were included in the analysis. All patients were divided into two groups based on whether they received TRT as first-line treatment, and propensity score matching (PSM) was performed to ensure that the characteristics of two groups were well-balanced. The primary endpoints were overall survival (OS) and progression-free survival (PFS), and the secondary endpoint was toxic effects. RESULTS: A total of 276 patients were included, and the median follow-up time was 22.3 (range, 4.0-53.73) months. After PSM, 197 patients were further analysed, and 99 of whom received TRT. The baseline characteristics were well-balanced between patients in the TRT and non-TRT groups. There were significant differences in PFS between the TRT and non-TRT groups, with the median PFS of 10.76 and 7.63 months, respectively (P = 0.014). Significantly improved OS was observed in the TRT group (21.67 vs. 16.6 months, P = 0.009). In addition, the use of TRT was an independent prognostic factor for PFS and OS of ES-SCLC patients receiving ICIs plus CHT. In terms of safety, no significant increase of any grades adverse event (AE) (P = 0.874) and G3-4 AE (P = 0.909) was observed for patients receiving TRT. Radiation esophagitis, gastrointestinal and hematologic toxicities were the most common AEs in TRT group, which were tolerable. And high-dose radiotherapy was associated with higher incidence of pneumonitis. CONCLUSION: Addition of TRT showed significant survival benefits and well tolerability in ES-SCLC patients receiving platinum-etoposide CHT and ICIs, which could be a feasible first-line treatment strategy for ES-SCLC patients.

From pixels to patient care: deep learning-enabled pathomics signature offers precise outcome predictions for immunotherapy in esophageal squamous cell cancer.

BACKGROUND: Immunotherapy has significantly improved survival of esophageal squamous cell cancer (ESCC) patients, however the clinical benefit was limited to only a small portion of patients. This study aimed to perform a deep learning signature based on H&E-stained pathological specimens to accurately predict the clinical benefit of PD-1 inhibitors in ESCC patients. METHODS: ESCC patients receiving PD-1 inhibitors from Shandong Cancer Hospital were included. WSI images of H&E-stained histological specimens of included patients were collected, and randomly divided into training (70%) and validation (30%) sets. The labels of images were defined by the progression-free survival (PFS) with the interval of 4 months. The pretrained ViT model was used for patch-level model training, and all patches were projected into probabilities after linear classifier. Then the most predictive patches were passed to RNN for final patient-level prediction to construct ESCC-pathomics signature (ESCC-PS). Accuracy rate and survival analysis were performed to evaluate the performance of ViT-RNN survival model in validation cohort. RESULTS: 163 ESCC patients receiving PD-1 inhibitors were included for model training. There were 486,188 patches of 1024*1024 pixels from 324 WSI images of H&E-stained histological specimens after image pre-processing. There were 120 patients with 227 images in training cohort and 43 patients with 97 images in validation cohort, with balanced baseline characteristics between two groups. The ESCC-PS achieved an accuracy of 84.5% in the validation cohort, and could distinguish patients into three risk groups with the median PFS of 2.6, 4.5 and 12.9 months (P < 0.001). The multivariate cox analysis revealed ESCC-PS could act as an independent predictor of survival from PD-1 inhibitors (P < 0.001). A combined signature incorporating ESCC-PS and expression of PD-L1 shows significantly improved accuracy in outcome prediction of PD-1 inhibitors compared to ESCC-PS and PD-L1 anlone, with the area under curve value of 0.904, 0.924, 0.610 for 6-month PFS and C-index of 0.814, 0.806, 0.601, respectively. CONCLUSIONS: The outcome supervised pathomics signature based on deep learning has the potential to enable superior prognostic stratification of ESCC patients receiving PD-1 inhibitors, which convert the images pixels to an effective and labour-saving tool to optimize clinical management of ESCC patients.

Update of latest data for combined therapy for esophageal cancer using radiotherapy and immunotherapy: A focus on efficacy, safety, and biomarkers.

Esophageal cancer usually has a poor prognosis. Given the significant breakthrough with tumor immunotherapy, an increasing number of clinical studies have demonstrated that the combination of radiotherapy and immune checkpoint inhibitors (ICIs) may have a synergistic effect and good outcome in esophageal cancer. Clinical studies of immunoradiotherapy (iRT) for esophageal cancer have proliferated enormously from 2021 to the present. However, a summary of the efficacy and toxicity of combined therapy to guide esophageal cancer treatment in clinical practice is lacking. For this review, we integrate the latest data to analyze and assess the efficacy and safety of iRT for esophageal cancer. In addition, we discuss better predictive biomarkers, therapeutic options for specific populations, and other challenges to identify directions for future research design.

Pneumonitis with combined immune checkpoint inhibitors and chemoradiotherapy in locally advanced non-small-cell lung cancer: a systematic review and meta-analysis.

Aims: This study systematically evaluated cases of pneumonitis following combined immune checkpoint inhibitors (ICI) and chemoradiotherapy (CRT) for locally advanced non-small-cell lung cancer (LA-NSCLC). Methods: Studies from Embase, PubMed and the Cochrane Library on patients with LA-NSCLC who received CRT and ICIs were reviewed. The primary outcomes were rates of all-grade, grade 3-5 and grade 5 pneumonitis. Results: Overall, 35 studies involving 5000 patients were enrolled. The pooled rates of all-grade, grade 3-5 and grade 5 pneumonitis were 33.0% (95% CI: 23.5-42.6), 6.1% (95% CI: 4.7-7.4) and 0.8% (95% CI: 0.3-1.2), respectively, with 7.6% of patients discontinuing ICIs because of pneumonitis. Conclusion: The incidence rates of pneumonitis following combined CRT and ICIs for LA-NSCLC were acceptable. However, the pulmonary toxicity of concurrent CRT and nivolumab plus ipilimumab should be noted.

Combined immune checkpoint inhibitors (ICI) and chemoradiotherapy (CRT) may cause severe pneumonitis due to overlapped pulmonary toxicity. However, the safety data on pneumonitis are limited to a small number of prospective clinical trials and retrospective studies with limited evidence. Thus we conducted a systematic review of pneumonitis in relation to the combination treatment. A total of 35 studies, involving 5000 patients, were included for the final analysis. The pooled rates of all-grade, grade 3­5 and grade 5 pneumonitis were 33.0, 6.1 and 0.8%, respectively, and 7.6% of patients stopped taking ICIs because of pneumonitis. The pneumonitis rates following combined CRT and ICIs for LA-NSCLC were acceptable, but the pulmonary toxicity of concurrent CRT and nivolumab plus ipilimumab should be noted.

Outcome-Supervised Deep Learning on Pathologic Whole Slide Images for Survival Prediction of Immunotherapy in Patients with Non-Small Cell Lung Cancer.

Although programmed death-(ligand) 1 (PD-(L)1) inhibitors are marked by durable efficacy in patients with non-small cell lung cancer (NSCLC), approximately 60% of the patients still suffer from recurrence and metastasis after PD-(L)1 inhibitor treatment. To accurately predict the response to PD-(L)1 inhibitors, we presented a deep learning model using a Vision Transformer (ViT) network based on hematoxylin and eosin (H&E)-stained specimens of patients with NSCLC. Two independent cohorts of patients with NSCLC receiving PD-(L)1 inhibitors from Shandong Cancer Hospital and Institute and Shandong Provincial Hospital were enrolled for model training and external validation, respectively. Whole slide images (WSIs) of H&E-stained histologic specimens were obtained from these patients and patched into 1024 × 1024 pixels. The patch-level model was trained based on ViT to identify the predictive patches, and patch-level probability distribution was performed. Then, we trained a patient-level survival model based on the ViT-Recursive Neural Network framework and externally validated it in the Shandong Provincial Hospital cohort. A total of 291 WSIs of H&E-stained histologic specimens from 198 patients with NSCLC in Shandong Cancer Hospital and 62 WSIs from 30 patients with NSCLC in Shandong Provincial Hospital were included in the model training and validation. The model achieved an accuracy of 88.6% in the internal validation cohort and 81% in the external validation cohort. The survival model also remained a statistically independent predictor of survival from PD-(L)1 inhibitors. In conclusion, the outcome-supervised ViT-Recursive Neural Network survival model based on pathologic WSIs could be used to predict immunotherapy efficacy in patients with NSCLC.

Comparison of the Incidence Rate of Radiation Pneumonitis Observed in Patients with Advanced Lung Adenocarcinoma Treated with Simultaneous Thoracic Radiotherapy and 1G/2G/3G EGFR-TKIs.

Purpose: The present study aimed to evaluate the incidence rate of radiation pneumonitis (RP) in patients with advanced lung adenocarcinoma treated with first-generation (1G), second-generation (2G), or third-generation (3G) epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) combined with thoracic radiotherapy (TRT). Patients and Methods: Patients with advanced lung adenocarcinoma simultaneously treated with 1G/2G/3G EGFR-TKIs and TRT between 2015-2021 at Shandong Cancer Hospital and Institute were screened. The incidence rate of clinical and imaging RP was compared between the three groups. Results: A total of 200 patients treated with EGFR-TKIs were enrolled in this study, including 100 patients who were treated with 1G EGFR-TKIs, 50 patients who were treated with 2G EGFR-TKIs, and 50 patients who were treated with 3G EGFR-TKIs (patients matched in a 2:1:1 ratio for tumor characteristics). The overall incidence of clinical RP in the 1G, 2G, and 3G EGFR-TKI groups were 29%, 48%, and 28% (p=0.043), respectively, and that of imaging RP were 33%, 58%, and 36% (p=0.010), respectively. The incidence of RP with a clinical grade ≥3 in the three groups were 14%, 28%, and 12% (p=0.055), respectively, and that with an imaging grade ≥3 in the three groups were 11%, 32%, and 10% (p=0.002), respectively. The incidence of clinical RP was higher in the CFRT group than in the SBRT group, with an overall clinical grade of 38% vs 10% (p<0.001) and imaging grade of 46% vs 10% (p<0.001), respectively. In the multivariate analysis, only GTV volume was an independent predictive factor for all risks of clinical and imaging RP. V20 and grouping of 1G/2G/3G EGFR-TKIs were other independent predictive factors for the risk factors of RP for imaging grades. Conclusion: Compared with 2G EGFR-TKIs combined with TRT, 1G or 3G EGFR-TKIs combined with TRT achieved a lower incidence of RP.

Deep Learning With an Attention Mechanism for Differentiating the Origin of Brain Metastasis Using MR images.

BACKGROUND: Brain metastasis (BM) is a serious neurological complication of cancer of different origins. The value of deep learning (DL) to identify multiple types of primary origins remains unclear. PURPOSE: To distinguish primary site of BM and identify the best DL models. STUDY TYPE: Retrospective. POPULATION: A total of 449 BM derived from 214 patients (49.5% for female, mean age 58 years) (100 from small cell lung cancer [SCLC], 125 from non-small cell lung cancer [NSCLC], 116 from breast cancer [BC], and 108 from gastrointestinal cancer [GIC]) were included. FIELD STRENGTH/SEQUENCE: A 3-T, T1 turbo spin echo (T1-TSE), T2-TSE, T2FLAIR-TSE, DWI echo-planar imaging (DWI-EPI) and contrast-enhanced T1-TSE (CE T1-TSE). ASSESSMENT: Lesions were divided into training (n = 285, 153 patients), testing (n = 122, 93 patients), and independent testing cohorts (n = 42, 34 patients). Three-dimensional residual network (3D-ResNet), named 3D ResNet6 and 3D ResNet 18, was proposed for identifying the four origins based on single MRI and combined MRI (T1WI + T2-FLAIR + DWI, CE-T1WI + DWI, CE-T1WI + T2WI + DWI). DL model was used to distinguish lung cancer from non-lung cancer; then SCLC vs. NSCLC for lung cancer classification and BC vs. GIC for non-lung cancer classification was performed. A subjective visual analysis was implemented and compared with DL models. Gradient-weighted class activation mapping (Grad-CAM) was used to visualize the model by heatmaps. STATISTICAL TESTS: The area under the receiver operating characteristics curve (AUC) assess each classification performance. RESULTS: 3D ResNet18 with Grad-CAM and AIC showed better performance than 3DResNet6, 3DResNet18 and the radiologist for distinguishing lung cancer from non-lung cancer, SCLC from NSCLC, and BC from GIC. For single MRI sequence, T1WI, DWI, and CE-T1WI performed best for lung cancer vs. non-lung cancer, SCLC vs. NSCLC, and BC vs. GIC classifications. The AUC ranged from 0.675 to 0.876 and from 0.684 to 0.800 regarding the testing and independent testing datasets, respectively. For combined MRI sequences, the combination of CE-T1WI + T2WI + DWI performed better for BC vs. GIC (AUCs of 0.788 and 0.848 on testing and independent testing datasets, respectively), while the combined MRI approach (T1WI + T2-FLAIR + DWI, CE-T1WI + DWI) could not achieve higher AUCs for lung cancer vs. non-lung cancer, SCLC vs. NSCLC. Grad-CAM helped for model visualization by heatmaps that focused on tumor regions. DATA CONCLUSION: DL models may help to distinguish the origins of BM based on MRI data. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Integrated multi-dimensional deep neural network model improves prognosis prediction of advanced NSCLC patients receiving bevacizumab.

Background: The addition of bevacizumab was found to be associated with prolonged survival whether in combination with chemotherapy, tyrosine kinase inhibitors or immune checkpoint inhibitors in the treatment landscape of advanced non-small cell lung cancer (NSCLC) patients. However, the biomarkers for efficacy of bevacizumab were still largely unknown. This study aimed to develop a deep learning model to provide individual assessment of survival in advanced NSCLC patients receiving bevacizumab. Methods: All data were retrospectively collected from a cohort of 272 radiological and pathological proven advanced non-squamous NSCLC patients. A novel multi-dimensional deep neural network (DNN) models were trained based on clinicopathological, inflammatory and radiomics features using DeepSurv and N-MTLR algorithm. And concordance index (C-index) and bier score was used to demonstrate the discriminatory and predictive capacity of the model. Results: The integration of clinicopathologic, inflammatory and radiomics features representation was performed using DeepSurv and N-MTLR with the C-index of 0.712 and 0.701 in testing cohort. And Cox proportional hazard (CPH) and random survival forest (RSF) models were also developed after data pre-processing and feature selection with the C-index of 0.665 and 0.679 respectively. DeepSurv prognostic model, indicated with best performance, was used for individual prognosis prediction. And patients divided in high-risk group were significantly associated with inferior PFS (median PFS: 5.4 vs 13.1 months, P<0.0001) and OS (median OS: 16.4 vs 21.3 months, P<0.0001). Conclusions: The integration of clinicopathologic, inflammatory and radiomics features representation based on DeepSurv model exhibited superior predictive accuracy as non-invasive method to assist in patients counseling and guidance of optimal treatment strategies.

Integrating Preoperative Computed Tomography and Clinical Factors for Lymph Node Metastasis Prediction in Esophageal Squamous Cell Carcinoma by Feature-Wise Attentional Graph Neural Network.

PURPOSE: This study aimed to propose a regional lymph node (LN) metastasis prediction model for patients with esophageal squamous cell carcinoma (ESCC) that can learn and adaptively integrate preoperative computed tomography (CT) image features and nonimaging clinical parameters. METHODS AND MATERIALS: Contrast-enhanced CT scans taken 2 weeks before surgery and 20 clinical factors, including general, pathologic, hematological, and diagnostic information, were collected from 357 patients with ESCC between October 2013 and November 2018. There were 999 regional LNs (857 negative, 142 positive) with pathologically confirmed status after surgery. All LNs were randomly divided into a training set (n = 738) and a validation set (n = 261) for testing. The feature-wise attentional graph neural network (FAGNN) was composed of (1) deep image feature extraction by the encoder of 3-dimensional UNet and high-level nonimaging factor representation by the clinical parameter encoder; (2) a feature-wise attention module for feature embedding with learnable adaptive weights; and (3) a graph attention layer to integrate the embedded features for final LN level metastasis prediction. RESULTS: Among the 4 models we constructed, FAGNN using both CT and clinical parameters as input is the model with the best performance, and the area under the curve (AUC) reaches 0.872, which is better than manual CT diagnosis method, multivariable model using CT only (AUC = 0.797), multivariable model with combined CT and clinical parameters (AUC = 0.846), and our FAGNN using CT only (AUC = 0.853). CONCLUSIONS: Our adaptive integration model improved the metastatic LN prediction performance based on CT and clinical parameters. Our model has the potential to foster effective fusion of multisourced parameters and to support early prognosis and personalized surgery or radiation therapy planning in patients with ESCC.

Association between immune-related adverse events and the efficacy of PD-1 inhibitors in advanced esophageal cancer.

Introduction: Recent developments in immune checkpoint inhibitors (ICIs) have improved the treatment outcomes of esophageal cancer (EC); however, it may initiate immune-related adverse events (irAEs) in some patients. The ICIs' therapeutic efficacy is associated with irAEs in patients with non-small cell lung cancer or renal cell carcinoma, although this association is unknown in EC. The purpose of this study was to explore the association between irAEs and the efficacy of programmed death 1 (PD-1) inhibitors in EC patients. Patients and methods: This study included patients with advanced EC treated with PD-1 inhibitors. The patients were divided into two groups according to the occurrence of irAEs. Afterward, the efficacy was compared between the irAE-negative and irAE-positive groups, and we analyzed the predictive factors of irAEs and survival. Results: Overall, 295 patients were included in this study. Baseline characteristics were balanced in the irAE-negative and irAE-positive groups. In total, 143 (48.47%) patients experienced irAEs. The most frequent irAEs were anemia (49, 16.61%), hyperthyroidism (45, 15.25%), and pneumonitis (44, 14.92%). In total, 33 (11.19%) patients had grade ≥ 3 irAEs and pneumonitis have 15 (5.08%). No grade 5 adverse events were observed. A total of 52 (17.63%) and 91 (30.85%) patients had single and multiple irAEs, respectively. Compared with patients without irAEs, those with irAEs had significantly higher objective response rate (ORR) (37.76% vs. 25.00%, p = 0.018) and disease control rate (DCR) (92.31% vs. 83.55%, p = 0.022). Univariate Cox analyses indicated the significant association between irAEs and improved median progression-free survival (PFS) (10.27 vs. 6.2 months, p < 0.001) and overall survival (OS) (15.4 vs. 9.2 months, p < 0.001). In multivariate analyses, irAEs were independently associated with longer PFS (p = 0.011) and OS (p = 0.002). Moreover, multivariate analysis revealed that cycles > 8, radiation, as well as antiangiogenic therapy were strongly associated with irAEs development (p < 0.001, p = 0.002, and p = 0.025, respectively). Conclusion: In advanced EC, patients with irAEs showed markedly better efficacy in ORR, DCR, PFS, and OS compared with patients without irAEs.

Genome-wide DNA methylation signature predict clinical benefit of bevacizumab in non-small cell lung cancer.

BACKGROUND: The efficacy of bevacizumab in non-small cell lung cancer (NSCLC) patients is unsatisfactory, and the selection of suitable patients is still challenging. Given the epigenetic modifications can contribute to an aberrant regulation of angiogenesis and microenvironment, we investigated DNA methylation profiles to determine clinical benefit of bevacizumab in NSCLC patients. METHODS: Genome-wide DNA methylation profiling was performed in NSCLC patients treated with chemotherapy in combination with bevacizumab. Patients were divided into better prognosis group (A group) and inferior prognosis group (B group) based on their survival. The difference of methylation patterns and respective functional enrichment analysis were performed between two groups. Prognostic DNA methylation signature for bevacizumab was established with the least absolute shrinkage and selection operator regression analyses. TISIDB database was further used to infer immunological relationship for prognostic related DNA methylation. RESULTS: Twenty patients were included in this study, and significantly distinct methylation patterns were observed between patients with different prognosis. Related genes of different methylation regions were significantly enriched in the biological process of cell projection assembly, neutrophil mediated immunity, and pathway of VEGFA-VEGFR2 signaling pathway, neutrophil degranulation. A 10-gene DNA methylation signature for prognosis prediction was established with the C-index of 0.76. And host genes of signature were found to be related to the abundance of ActCD4, Th1, ActCD8, NKT and neutrophil cells. CONCLUSION: The 10-gene DNA methylation signature could serve as a novel biomarker to predict the clinical benefit of bevacizumab therapy and improve this anti-tumor approach for NSCLC patients.

NcRNAs-mediated P2RX1 expression correlates with clinical outcomes and immune infiltration in patients with breast invasive carcinoma.

The development of novel treatments for breast invasive carcinoma (BC) has been stagnant. P2RX1, a member of the purinergic receptor family, has been found to have a prognostic impact in several tumors. Therefore, we analyzed the expression pattern of P2RX1 in pan-cancers including BC and its impact on survival and found that the expression level of P2RX1 was lower in BC compared with para-cancerous tissues, and higher P2RX1 expression indicated better prognoses. But real-time quantitative reverse transcription PCR (RT-qPCR) and Western blot detected that the P2RX1 expression in normal mammary epithelial cells was lower than that in tumor cells. Then we comprehensively analyzed the regulatory mechanism and protein-protein interaction network, and found that P2RX1 was significantly positively linked with immune cell infiltration and immune checkpoints.

Three models that predict the efficacy of immunotherapy in Chinese patients with advanced non-small cell lung cancer.

BACKGROUND: Many tools have been developed to predict the efficacy of immunotherapy, such as lung immune prognostic index (LIPI), EPSILoN [Eastern Cooperative Oncology Group performance status (ECOG PS), smoking, liver metastases, lactate dehydrogenase (LDH), neutrophil-to-lymphocyte ratio (NLR)], and modified lung immune predictive index (mLIPI) scores. The aim of this study was to determine the ability of three predictive scores to predict the outcomes in Chinese advanced non-small cell lung cancer (aNSCLC) patients treated with immune checkpoint inhibitors (ICIs). METHODS: We retrospectively analyzed 429 patients with aNSCLC treated with ICIs at our institution. The predictive ability of these models was evaluated using area under the curve (AUC) in receiver operating characteristic curve (ROC) analysis. Calibration was assessed using the Hosmer-Lemeshow test (H-L test) and Spearman's correlation coefficient. Progression-free survival (PFS) and overall survival (OS) curves were generated using the Kaplan-Meier method. RESULTS: The AUC values of LIPI, mLIPI, and EPSILoN scores predicting PFS at 6 months were 0.642 [95% confidence interval (CI):0.590-0.694], 0.720 (95% CI: 0.675-0.762), and 0.633 (95% CI: 0.585-0.679), respectively (p < 0.001 for all models). The AUC values of LIPI, mLIPI, and EPSILON scores predicting objective response rate (ORR) were 0.606 (95% CI: 0.546-0.665), 0.683 (95% CI: 0.637-0.727), and 0.666 (95% CI: 0.620-0.711), respectively (p < 0.001 for all models). The C-indexes of LIPI, mLIPI, and EPSILoN scores for PFS were 0.627 (95% CI 0.611-6.643), 0.677 (95% CI 0.652-0.682), and 0.631 (95% CI 0.617-0.645), respectively. CONCLUSIONS: As mLIPI scores had the highest accuracy when used to predict the outcomes in Chinese aNSCLC patients, this tool could be used to guide clinical immunotherapy decision-making.

Toxicity Profile of Combining PD-1/PD-L1 Inhibitors and Thoracic Radiotherapy in Non-Small Cell Lung Cancer: A Systematic Review.

Background: The combination of immune checkpoint inhibitors (ICIs) and thoracic radiotherapy (TRT) has shown significant clinical activity in patients with non-small cell lung cancer (NSCLC). However, the currently available data on adverse events (AEs) were derived from a small subset of patients included in prospective clinical trials or retrospective studies. Thus, we conducted this systematic review to determine the AEs associated with this combination treatment. Methods: An electronic literature search was performed in databases and conference proceedings of prospective clinical trials assessing the combination of ICIs and TRT for patients with NSCLC. The systematic analysis was conducted to determine the profile and incidence of AEs of combination treatment. We further performed the comparison of AEs between programmed cell death 1 (PD-1) and programmed cell death ligand 1 (PD-L1) inhibitors, and sequential and concurrent administration of ICIs and TRT to help identify high risk patients. The systematic analyses were conducted with the Review Manager (version 5.3; The Cochrane Collaboration, Oxford, United Kingdom) and Stata version 12.0 (StataCorp, College Station, TX, USA) software. Results: Eleven clinical trials involving 1,113 patients with NSCLC were eligible for analysis. The incidence of all-grade AEs was 95.5%; that of high-grade AEs (grade ≥3) was 30.2%. The most frequent all-grade AE was fatigue (49.7%), while pneumonitis was the most common high-grade AE (3.8%) and grade 5 AE (0.6%). Notably, the toxicity profiles of PD-1 and PD-L1 inhibitors were similar. Concurrent treatment was associated with a higher incidence of higher-grade AEs (41.6% vs 24.8%, P=0.17) and pneumonitis (7.1% vs 3.9%, P=0.14) compared to sequential treatment, but no significant difference was observed. Conclusion: Most AEs of this combination treatment are tolerable; as the most common high-grade AE, pneumonitis deserves the utmost attention of physicians. The toxicity profiles of patients receiving PD-1 or PD-L1 were similar, and no significant difference was observed between concurrent and sequential treatment.

Clinical implications of germline BCL2L11 deletion polymorphism in pretreated advanced NSCLC patients with osimertinib therapy.

INTRODUCTION: B-cell lymphoma 2-like 11 (BCL-2-like 11, BCL2L11, also known as BIM) deletion polymorphism (BIM-del) has been associated with resistance to first-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs), and is a poor prognostic factor for EGFR-mutant non-small-cell lung cancer (NSCLC) patients. Nevertheless, the impact of BIM-del in advanced NSCLC patients treated with the third-generation EGFR-TKI osimertinib remains undetermined. This study aims to evaluate the relationship between BIM-del and therapeutic efficacy of osimertinib in pretreated NSCLC patients. METHODS: Patients subjected to EGFR T790 M detection and prior osimertinib treatment between December 2015 and December 2019 in our hospital were enrolled in this study. Peripheral blood samples from these patients were collected to detect BIM-del by polymerase chain reaction. Cox proportional hazards models were used to analyze the clinical outcomes of patients with and without BIM-del. RESULTS: In total, 152 Chinese Han NSCLC patients-including 143 T790M-positive and nine T790M-negative patients-were enrolled. BIM-del was detected in only 17.5 % of T790M-positive patients (25/143). The majority of patients were aged <65 years (81.8 %, 117/143), were female (58.7 %, 84/143), were non-smokers (82.5 %, 118/143), had Eastern Cooperative Oncology Group (ECOG) performance status (PS) 0-1 (88.8 %, 129/143), and exhibited metastases in the central nervous system (CNS) (54.5 %, 78/143). There were no associations between the BIM-del and clinical characteristics (including age, sex, histology, smoking status, stage, ECOG PS score, and CNS metastases). Patients with BIM-del had a poorer objective response rate than those without (28.0 % versus 52.5 %, p = 0.026). Besides, BIM-del was associated with a significantly shorter progression-free survival (PFS) and a moderately shorter overall survival (OS) (8.3 versus 10.5 months, p = 0.031 and 15.9 versus 25.2 months, p = 0.1, respectively). Multivariate analysis indicated that BIM-del was an independent prognostic factor for PFS but not for OS in EGFR T790 M NSCLC patients. CONCLUSIONS: BIM-del is associated with poor clinical responses and outcomes, and might be a negative predictive and prognostic biomarker in EGFR T790 M NSCLC patients with osimertinib treatment.

Multiple Immune Features-Based Signature for Predicting Recurrence and Survival of Inoperable LA-NSCLC Patients.

INTRODUCTION: The immune status of the tumor microenvironment is extremely complex. One single immune feature cannot reflect the integral immune status, and its prognostic value was limited. We postulated that the immune signature based on multiple immuno-features could markedly improve the prediction of post-chemoradiotherapeutic survival in inoperable locally advanced non-small-cell lung cancer (LA-NSCLC) patients. METHODS: In this study, 100 patients who were diagnosed as having inoperable LA-NSCLC between January 2005 and January 2016 were analyzed. A five immune features-based signature was then constructed using the nested repeat 10-fold cross validation with least absolute shrinkage and selection operator (LASSO) Cox regression model. Nomograms were then established for predicting prognosis. RESULTS: The immune signature combining five immuno-features was significantly associated with overall survival (OS) and progression-free survival (PFS) (P = 0.002 and P = 0.014, respectively) in patients with inoperable LA-NSCLC, and at a cutoff of -0.05 stratified patients into two groups with 5-year OS rates of 39.8 and 8.8%, and 2-year PFS rates of 22.2 and 5.5% for the high- and low-immune signature groups, respectively. Integrating immune signature, we proposed predictive nomograms that were better than the traditional TNM staging system in terms of discriminating ability (OS: 0.692 vs. 0.588; PFS: 0.672 vs. 0.586, respectively) or net weight classification (OS: 32.96%; PFS: 9.22%), suggesting that the immune signature plays a significant role in improving the prognostic value. CONCLUSION: Multiple immune features-based immune signature could effectively predict recurrence and survival of inoperable LA-NSCLC patients and complemented the prognostic value of the TNM staging system.

Prognostic Value of a Nomogram Based on the Dynamic Albumin-to-Alkaline Phosphatase Ratio for Patients with Extensive-Stage Small-Cell Lung Cancer.

PURPOSE: Small-cell lung cancer (SCLC) is known as the characteristics of high invasion, rapid progression, and poor prognosis. Therefore, identification of patients with high risk of progression and death is critical to improve the survival of patients with extensive-stage SCLC (ES-SCLC). This study was designed to determine the prognostic importance of the albumin-to-alkaline phosphatase ratio (AAPR) in the survival of patients with ES-SCLC and to develop a nomogram based on AAPR dynamics for ES-SCLC prognosis. PATIENTS AND METHODS: Characteristics were reviewed from 300 patients with ES-SCLC. Training and validation cohorts included 200 and 100 patients, respectively. We applied univariate and multivariate Cox models to assess the prognostic value of AAPR for ES-SCLC. The nomogram for progression-free survival (PFS) and overall survival (OS) of ES-SCLC patients was developed based on the multivariate survival analysis of the training cohort. External validation of the established nomogram was performed using the validation cohort. RESULTS: N3 stage, thoracic radiotherapy, and post-AAPR were the independent factors identified for PFS. T stage, thoracic radiotherapy, and high post-AAPR were the independent risk factors identified for death. The prognostic nomogram was established by integrating the independent significant factors for PFS and OS in the training cohort with the c-indices of 0.675 and 0.662, respectively, and validated in the validation cohort. The nomogram had superior prognosis prediction ability than did TNM stage. Decision curve analysis (DCA) also indicated clinical net benefits from the nomogram. CONCLUSION: AAPR was valuable for prognosis prediction in patients with ES-SCLC and was recommended to be dynamically evaluated to guide patient treatment. Additionally, the nomogram covering post-AAPR accurately predicted individual survival probability.

Development and validation of a nomogram prognostic model for esophageal cancer patients with oligometastases.

Platinum-based chemotherapy is recommended as the standard treatment for metastatic esophageal cancer (EC) patients; however, the outcome is poor. Oligometastasis is less aggressive and has limited growth potential. However, the prognostic factors for EC patients with oligometastases was largely unknown. Thus, we intend to determine the prognostic factors, and develop and validate nomograms for prediction of survival for EC patients with oligometastases. In this study, characteristics of 273 oligometastatic EC patients were analyzed using univariate and multivariate Cox models to determine the independent prognostic factors for progression-free survival (PFS) and overall survival (OS). The result showed that history of alcohol consumption, longer tumor, no local radiotherapy for EC, and no local treatment for metastases were independent factors for PFS. Sex, esophageal fistula, number of metastatic organs, and local radiotherapy for EC were independent prognostic factors for OS. On the basis of Cox models, the respective nomogram for prediction of PFS and OS was established with the corrected concordance index of 0.739 and 0.696 after internal cross-validation. In conclusion, local treatment for metastases and local radiotherapy for EC were demonstrated to be beneficial for oligometastatic EC patients, and the validated nomograms are valuable in prognosis prediction and could guide individualized management for these patients.