Lasso-Cox Model Research Articles

A Novel Gene Signature Based on Immune Cell Infiltration Landscape Predicts Prognosis in Lung Adenocarcinoma Patients.

The tumor microenvironment (TME) is created by the tumor and dominated by tumor-induced interactions. Long-term survival of lung adenocarcinoma (LUAD) patients is strongly influenced by immune cell infiltration in TME. The current article intends to construct a gene signature from LUAD ICI for predicting patient outcomes. For the initial phase of the study, the TCGA-LUAD dataset was chosen as the training group for dataset selection. We found two datasets named GSE72094 and GSE68465 in the Gene Expression Omnibus (GEO) database for model validation. Unsupervised clustering was performed on the training cohort patients using the ICI profiles. We employed Kaplan-Meier estimators and univariate Cox proportional-hazard models to identify prognostic differentially expressed genes in immune cell infiltration (ICI) clusters. These prognostic genes are then used to develop a LASSO Cox model that generates a prognostic gene signature. Validation was performed using Kaplan-Meier estimation, Cox, and ROC analysis. Our signature and vital immune-relevant signatures were analyzed. Finally, we performed gene set enrichment analysis (GSEA) and immune infiltration analysis on our finding gene signature to further examine the functional mechanisms and immune cellular interactions. Our study found a sixteen-gene signature (EREG, HPGDS, TSPAN32, ACSM5, SFTPD, SCN7A, CCR2, S100P, KLK12, MS4A1, INHA, HOXB9, CYP4B1, SPOCK1, STAP1, and ACAP1) to be prognostic based on data from the training cohort. This prognostic signature was certified by Kaplan-Meier, Cox proportional-hazards, and ROC curves. 11/15 immune-relevant signatures were related to our signature. The GSEA results indicated our gene signature strongly correlates with immune-related pathways. Based on the immune infiltration analysis findings, it can be deduced that a significant portion of the prognostic significance of the signature can be attributed to resting mast cells. We used bioinformatics to determine a new, robust sixteen-gene signature. We also found that this signature's prognostic ability was closely related to the resting mast cell infiltration of LUAD patients.

A Hybrid CNN-Transformer Model for Predicting N Staging and Survival in Non-Small Cell Lung Cancer Patients Based on CT-Scan.

Accurate assessment of N staging in patients with non-small cell lung cancer (NSCLC) is critical for the development of effective treatment plans, the optimization of therapeutic strategies, and the enhancement of patient survival rates. This study proposes a hybrid model based on 3D convolutional neural networks (CNNs) and transformers for predicting the N-staging and survival rates of NSCLC patients within the NSCLC radiogenomics and Nsclc-radiomics datasets. The model achieved accuracies of 0.805, 0.828, and 0.819 for the training, validation, and testing sets, respectively. By leveraging the strengths of CNNs in local feature extraction and the superior performance of transformers in global information modeling, the model significantly enhances predictive accuracy and efficacy. A comparative analysis with traditional CNN and transformer architectures demonstrates that the CNN-transformer hybrid model outperforms N-staging predictions. Furthermore, this study extracts the one-year survival rate as a feature and employs the Lasso-Cox model for survival predictions at various time intervals (1, 3, 5, and 7 years), with all survival prediction p-values being less than 0.05, illustrating the time-dependent nature of survival analysis. The application of time-dependent ROC curves further validates the model's accuracy and reliability for survival predictions. Overall, this research provides innovative methodologies and new insights for the early diagnosis and prognostic evaluation of NSCLC.

Construction and evaluation of a risk model for adverse outcomes of necrotizing enterocolitis based on LASSO-Cox regression.

This study aimed to develop a nomogram to predict adverse outcomes in neonates with necrotizing enterocolitis (NEC). In this retrospective study on neonates with NEC, data on perinatal characteristics, clinical features, laboratory findings, and x-ray examinations were collected for the included patients. A risk model and its nomogram were developed using the least absolute shrinkage and selection operator (LASSO) Cox regression analyses. A total of 182 cases of NEC were included and divided into a training set (148 cases) and a temporal validation set (34 cases). Eight features, including weight [p = 0.471, HR = 0.99 (95% CI: 0.98-1.00)], history of congenital heart disease [p < 0.001, HR = 3.13 (95% CI:1.75-5.61)], blood transfusion before onset [p = 0.757, HR = 0.85 (95%CI:0.29-2.45)], antibiotic exposure before onset [p = 0.003, HR = 5.52 (95% CI:1.81-16.83)], C-reactive protein (CRP) at onset [p = 0.757, HR = 1.01 (95%CI:1.00-1.02)], plasma sodium at onset [p < 0.001, HR = 4.73 (95%CI:2.61-8.59)], dynamic abdominal x-ray score change [p = 0.001, HR = 4.90 (95%CI:2.69-8.93)], and antibiotic treatment regimen [p = 0.250, HR = 1.83 (0.65-5.15)], were ultimately selected for model building. The C-index for the predictive model was 0.850 (95% CI: 0.804-0.897) for the training set and 0.7880.788 (95% CI: 0.656-0.921) for the validation set. The area under the ROC curve (AUC) at 8-, 10-, and 12-days were 0.889 (95% CI: 0.822-0.956), 0.891 (95% CI: 0.829-0.953), and 0.893 (95% CI:0.832-0.954) in the training group, and 0.812 (95% CI: 0.633-0.991), 0.846 (95% CI: 0.695-0.998), and 0.798 (95%CI: 0.623-0.973) in the validation group, respectively. Calibration curves showed good concordance between the predicted and observed outcomes, and DCA demonstrated adequate clinical benefit. The LASSO-Cox model effectively identifies NEC neonates at high risk of adverse outcomes across all time points. Notably, at earlier time points (such as the 8-day mark), the model also demonstrates strong predictive performance, facilitating the early prediction of adverse outcomes in infants with NEC. This early prediction can contribute to timely clinical decision-making and ultimately improve patient prognosis.

Construction of a metabolism-malnutrition-inflammation prognostic risk score in patients with heart failure with preserved ejection fraction: a machine learning based Lasso-Cox model

BackgroundMetabolic disorder, malnutrition and inflammation are involved and interplayed in the mechanisms of heart failure with preserved ejection fraction (HFpEF). We aimed to construct a Metabolism-malnutrition-inflammation score (MIS) to predict the risk of death in patients with HFpEF.MethodsWe included patients diagnosed with HFpEF and without infective or systemic disease. 20 biomarkers were filtered by the Least absolute shrinkage and selection operator (Lasso)-Cox regression. 1000 times bootstrapping datasets were generated to select biomarkers that appeared above 95% frequency in repetitions to construct the MIS.ResultsAmong 1083 patients diagnosed with HFpEF, 342 patients (31.6%) died during a median follow-up period of 2.5 years. The MIS was finally constructed based on 6 biomarkers, they were albumin (ALB), red blood cell distribution width-standard deviation (RDW-SD), high-sensitivity C-reactive protein (hs-CRP), lymphocytes, triiodothyronine (T3) and uric acid (UA). Incorporating MIS into the basic predictive model significantly increased both discrimination (∆C-index = 0.034, 95% CI 0.013–0.050) and reclassification (IDI, 6.6%, 95% CI 4.0%-9.5%; NRI, 22.2% 95% CI 14.4%-30.2%) in predicting all-cause mortality. In the time-dependent receiver operating characteristic (ROC) analysis, the mean area under the curve (AUC) for the MIS was 0.778, 0.782 and 0.772 at 1, 3, and 5 years after discharge in the cross-validation sets. The MIS was independently associated with all-cause mortality (hazard ratio: 1.98, 95% CI [1.70–2.31], P < 0.001).ConclusionsA risk score derived from 6 commonly used inflammatory, nutritional, thyroid and uric acid metabolic biomarkers can effectively identify high-risk patients with HFpEF, providing potential individualized management strategies for patients with HFpEF.Graphical

Construction of an interpretable model for predicting survival outcomes in patients with middle to advanced hepatocellular carcinoma (≥5cm) using lasso-cox regression.

In this retrospective study, we aimed to identify key risk factors and establish an interpretable model for HCC with a diameter ≥ 5cm using Lasso regression for effective risk stratification and clinical decision-making. In this study, 843 patients with advanced hepatocellular carcinoma (HCC) and tumor diameter ≥ 5cm were included. Using Lasso regression to screen multiple characteristic variables, cox proportional hazard regression and random survival forest models (RSF) were established. By comparing the area under the curve (AUC), the optimal model was selected. The model was visualized, and the order of interpretable importance was determined. Finally, risk stratification was established to identify patients at high risk. Lasso regression identified 8 factors as characteristic risk factors. Subsequent analysis revealed that the lasso-cox model had AUC values of 0.773, 0.758, and 0.799, while the lasso-RSF model had AUC values of 0.734, 0.695, and 0.741, respectively. Based on these results, the lasso-cox model was chosen as the superior model. Interpretability assessments using SHAP values indicated that the most significant characteristic risk factors, in descending order of importance, were tumor number, BCLC stage, alkaline phosphatase (ALP), ascites, albumin (ALB), and aspartate aminotransferase (AST). Additionally, through risk score stratification and subgroup analysis, it was observed that the median OS of the low-risk group was significantly better than that of the middle- and high-risk groups. We have developed an interpretable predictive model for middle and late HCC with tumor diameter ≥ 5cm using lasso-cox regression analysis. This model demonstrates excellent prediction performance and can be utilized for risk stratification.

A LASSO Cox Regression Predictive Model for Patients Undergoing Surgery for Pancreatic Body and Tail Adenocarcinoma Patients: Comparative Long-Term Survival Analysis of Radical Antegrade Modular Pancreatosplenectomy (RAMPS) and Standard Retrograde Pancreatosplenectomy (SPRS).

This study was designed to assess the advantages of radical antegrade modular pancreatosplenectomy (RAMPS) over standard retrograde pancreatosplenectomy (SPRS) in terms of disease-free survival (DFS) by comparing clinical outcomes. Clinical data from 154 patients who underwent distal pancreatectomy at Tianjin Medical University Cancer Institute and Hospital between January 2015 and August 2018 were collected. We compared the preoperative conditions, postoperative complications, and survival outcomes of patients who underwent two different surgical procedures. By creating a LASSO-Cox model, we determined the parameters affecting DFS and the risk ratios of the two surgical procedures on DFS. The R0 resection rate (85.23% vs. 68.18%, P = 0.003), negative posterior margin rate (96.59% vs. 75.76%, P < 0.001), and tumor bed recurrence rate (15.29% vs. 40.00%, P = 0.001) significantly differed between the RAMPS and SPRS groups. The 1-, 3-, and 5-year survival and DFS rates of the RAMPS group were significantly better than those of the SPRS group (P < 0.05). Disease-free survival analysis based on Kaplan-Meier curves revealed that RAMPS was superior to SPRS (P < 0.001). We recommend RAMPS as the preferred procedure for treating ductal adenocarcinoma of the pancreatic body and tail due to its enhanced lymph node repair capacity and visualization of posterior pancreatic sections, which can increase DFS in patients.

Lasso-Cox interpretable model of AFP-negative hepatocellular carcinoma.

In AFP-negative hepatocellular carcinoma patients, markers for predicting tumor progression or prognosis are limited. Therefore, our objective is to establish an optimal predicet model for this subset of patients, utilizing interpretable methods to enhance the accuracy of HCC prognosis prediction. We recruited a total of 508 AFP-negative HCC patients in this study, modeling with randomly divided training set and validated with validation set. At the same time, 86 patients treated in different time periods were used as internal validation. After comparing the cox model with the random forest model based on Lasso regression, we have chosen the former to build our model. This model has been interpreted with SHAP values and validated using ROC, DCA. Additionally, we have reconfirmed the model's effectiveness by employing an internal validation set of independent periods. Subsequently, we have established a risk stratification system. The AUC values of the Lasso-Cox model at 1, 2, and 3years were 0.807, 0.846, and 0.803, and the AUC values of the Lasso-RSF model at 1, 2, and 3years were 0.783, 0.829, and 0.776. Lasso-Cox model was finally used to predict the prognosis of AFP-negative HCC patients in this study. And BCLC stage, gamma-glutamyl transferase (GGT), diameter of tumor, lung metastases (LM), albumin (ALB), alkaline phosphatase (ALP), and the number of tumors were included in the model. The validation set and the separate internal validation set both indicate that the model is stable and accurate. Using risk factors to establish risk stratification, we observed that the survival time of the low-risk group, the middle-risk group, and the high-risk group decreased gradually, with significant differences among the three groups. The Lasso-Cox model based on AFP-negative HCC showed good predictive performance for liver cancer. SHAP explained the model for further clinical application.

Pan-cancer analysis of T-cell proliferation regulatory genes as potential immunotherapeutic targets.

T cells are the key to killing tumor cells. However, the exact mechanism of their role in cancer is not fully understood. Therefore, a comprehensive understanding of the role of T-cell proliferation regulatory genes in tumors is needed. In our study, we investigated the expression levels of genes controlling T-cell proliferation, their impact on prognosis, and their genetic variations. Additionally, we explored their associations with TMB, MSI, ESTIMATEScore, ImmuneScore, StromalScore, and immune cell infiltration. We examined the role of these genes in cancer-related pathways using GSEA. Furthermore, we calculated their activity levels across various types of cancer. Drug analysis was also conducted targeting these genes. Single-cell analysis, LASSO Cox model construction, and prognosis analysis were performed. We observed distinct expression patterns of T-cell proliferation regulatory genes across different malignant tumors. Their abnormal expression may be caused by CNA and DNA methylation. In certain cancers, they also showed complex associations with TMB and MSI. Moreover, in many tumors, they exhibited significant positive correlations with ESTIMATEScores, ImmuneScore, and StromalScore. Additionally, in most tumors, their GSVA scores were significantly positively correlated with various T-cell subtypes. GSEA analysis revealed their involvement in multiple immune pathways. Furthermore, we found that model scores were associated with patient prognosis and related to tumor malignancy progression. T-cell proliferation regulatory genes are closely associated with the tumor immune microenvironment (TIM), especially T cells. Targeting them may be an essential approach for cancer immunotherapy.

Discovery and Validation of a 15-Gene Prognostic Signature for Clear Cell Renal Cell Carcinoma.

Develop and validate gene expression-based biomarker associated with recurrent disease to facilitate risk stratification of clear cell renal cell carcinoma (ccRCC). We retrospectively identified 110 patients who underwent radical nephrectomy for ccRCC (discovery cohort). Patients who recurred were matched on the basis of grade/stage to patients without recurrence. Capture whole-transcriptome sequencing was performed on RNA isolated from archival tissue using the Illumina platform. We developed a gene-expression signature to predict recurrence-free survival/disease-free survival (DFS) using a 15-fold lasso and elastic-net regularized linear Cox model. We derived the 31-gene cell cycle progression (mxCCP) score using RNA-seq data for each patient. Kaplan-Meier (KM) curves and multivariable Cox proportional hazard testing were used to validate the independent prognostic impact of the gene-expression signature on DFS, disease-specific survival (DSS), and overall survival (OS) in two validation data sets (combined n = 761). After quality control, the discovery cohort comprised 50 patients with recurrence and 41 patients without, with a median follow-up of 26 and 36 months, respectively. We developed a 15-gene (15G) signature, which was independently associated with worse DFS and DSS (DFS: hazard ratio [HR], 11.08 [95% CI, 4.9 to 25.1]; DSS: HR, 9.67 [95% CI, 3.4 to 27.7]) in a multivariable model adjusting for clinicopathologic parameters (including stage, size, grade, and necrosis [SSIGN] score and Memorial Sloan Kettering Cancer Center nomogram) and mxCCP score. The 15G signature was also independently associated with worse DFS and DSS in both validation data sets (Validation A [n = 382], DFS: HR, 2.6 [95% CI, 1.6 to 4.3]; DSS: HR, 3 [95% CI, 1.4 to 6.1] and Validation B (n = 379), DFS: HR, 2.1 [95% CI, 1.2 to 3.6]; OS: HR, 3 [95% CI, 1.6 to 5.7]) adjusting for clinicopathologic variables and mxCCP score. We developed and validated a novel 15G prognostic signature to improve risk stratification of patients with ccRCC. Pending further validation, this signature has the potential to facilitate optimal treatment allocation.

The molecular characteristics could supplement the staging system of pT2/T3N0M0 esophageal squamous cell carcinoma: a translational study based on a cohort with over 20 years of follow-up

ObjectiveThis study aimed to construct a model based on 23 enrolled molecules to evaluate prognoses of pT2/3N0M0 esophageal squamous cell carcinoma (ESCC) patients with up to 20 years of follow-up.MethodsThe lasso-Cox model was used to identify the candidate molecule. A nomogram was conducted to develop the survival model (molecular score, MS) based on the molecular features. Cox regression and Kaplan-Meier analysis were used in this study. The concordance index (C-index) was measured to compare the predicted ability between different models. The primary endpoint was overall survival (OS).ResultsA total of 226 patients and 23 proteins were enrolled in this study. Patients were classified into high-risk (MS-H) and low-risk (MS-L) groups based on the MS score of 227. The survival curves showed that the MS-L cohort had better 5-year and 10-year survival rates than the MS-H group (5-year OS: 51.0% vs. 8.0%; 10-year OS: 45.0% vs. 5.0%, all p < 0.001). Furthermore, multivariable analysis confirmed MS as an independent prognostic factor after eliminating the confounding factors (Hazard ratio 3.220, p < 0.001). The pT classification was confirmed to differentiate ESCC patients’ prognosis (Log-rank: p = 0.029). However, the combination of pT and MS could classify survival curves evidently (overall p < 0.001), which showed that the prognostic prediction efficiency was improved significantly by the combination of the pT and MS than by the classical pT classification (C-index: 0.656 vs. 0.539, p < 0.001).ConclusionsOur study suggested an MS for significant clinical stratification of T2/3N0M0 ESCC patients to screen out subgroups with poor prognoses. Besides, the combination of pT staging and MS could predict survival more accurately for this cohort than the pT staging system alone.

Abstract 7661: Integrating radiomic features from primary tumor and lymph node for survival prediction of HPV-associated oropharyngeal squamous cell carcinoma

Abstract Background: HPV-associated oropharyngeal squamous cell carcinoma (OPSCC) is a unique disease with improved prognosis which could benefit from treatment de-escalation. Current radiographic-based biomarkers mainly focus on primary tumor (PT) alone, neglecting the potential prognostic value from the lymph node (LN). In this work, we constructed a radiomic model integrating texture features from both PT and LN to predict overall survival (OS) in HPV-associated OPSCC patients. Methods: Pretreatment CT scans for 647 OPSCC patients treated with definitive radiation with or without chemotherapy were acquired from The Cancer Imaging Archive (TCIA) and the Cleveland Clinic (CCF), which were divided into training set D1 (n=294) and test set D2 (n=353). PT and LN contours were annotated by three. 2045 texture radiomic features were extracted from both PT and LN and were concatenated as input to a LASSO Cox model to predict OS. We used the harrell’s concordance index (C-index) and hazard ratio (HR) to evaluate the prognostic performance. We also performed the multivariable cox proportional hazard analysis adjusting for clinicopathological variables to validate the independent prognostic significance. Results: Among the 647 patients, median follow-up time was 70.23 months, with 155 patients (24%) experienced death. 22.6%, 38.8%, 23.2% and 15.4% had T stage I, II, III and IV tumors, respectively. 203 patients (31.4%) were treated with radiation alone. The model integrating PT and LN (M_PTLN) yielded higher C-index compared to PT alone (M_PT) on D1 (0.74 vs 0.66) and on D2 (0.64 vs 0.61). In univariate analysis on D2, M_PTLN is prognostic of OS (HR, 2.26; 95% CI, 1.32 - 3.87; log rank p = 0.003). In multivariable analysis, M_PTLN is still statistically significant (p = 0.04) accounting for clinicopathologic factors (age, gender, smoking pack-year [PY], T stage, N stage and treatment modality). Conclusions: We developed and validated a CT-based survival prediction model for HPV-associated OPSCC patients with significant prognostic values, accounting for both primary tumor and lymph node. If prospectively validated, the model has the potential to inform trial eligibility and treatment-related decision making. Citation Format: Bolin Song, Amaury Leroy, Kailin Yang, Qiyun Liu, Xiao Li, Jonathan Lee, Sarah Stock, Nabil F. Saba, James S. Lewis, Eric Deutsch, Mehmet Asim Bilen, Shlomo A. Koyfman, Anant Madabhushi. Integrating radiomic features from primary tumor and lymph node for survival prediction of HPV-associated oropharyngeal squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7661.

Association of serum concentrations of remnant cholesterol with incident cardiovascular disease in patients with rheumatoid arthritis: A real-world data from 2001 to 2022

BackgroundRemnant cholesterol (RC) promotes cardiovascular disease (CVD) in the general population, but its role among rheumatoid arthritis (RA) patients remains unknown. We aimed to investigate circulating RC levels associated with incident CVD among Chinese patients with RA. MethodsA total of 1018 RA patients free of baseline CVD were included and followed up in a prospective RA CVD cohort from 2001 to 2022. Fasting serum levels of triglycerides, total cholesterol (TC), low-density (LDL-C), and high-density lipoprotein cholesterol (HDL-C) were measured, while RC and Non-HDL-C levels were calculated. The primary exposure was RC levels. A LASSO Cox model was used to select covariates. The Fine-Gray competing risk model was used to estimate hazard ratios (HRs). ResultsRA patients had a mean age of 53.9 years, and 802 (78.8%) were females. After a median follow-up of 5.54 years, 131 patients developed CVD with an incidence rate of 21.6 per 1000 person-years. Continuous and quartile-categorized RC levels were associated with incident CVD before and after multivariate adjustment and Bonferroni correction (all P < 0.001). There were no robust associations of other lipids with incident CVD. The fully adjusted HRs for RC were 2.30 (95% CI 1.58–3.35) per 1 mmol/L increase, and 2.40 (1.36–4.25) and 2.81 (1.60–4.94) for patients in the 3rd and 4th versus the 1st quartile, respectively. ConclusionsCirculating RC levels are positively associated with incident CVD among Chinese RA patients independent of known risk factors, implying its clinically preferable use for improving the stratification of CVD risk in RA patients.

Comprehending the cuproptosis and cancer-immunity cycle network: delving into the immune landscape and its predictive role in breast cancer immunotherapy responses and clinical endpoints.

The role of cuproptosis, a phenomenon associated with tumor metabolism and immunological identification, remains underexplored, particularly in relation to the cancer-immunity cycle (CIC) network. This study aims to rigorously examine the impact of the cuproptosis-CIC nexus on immune reactions and prognostic outcomes in patients with breast cancer (BC), striving to establish a comprehensive prognostic model. In the study, we segregated data obtained from TCGA, GEO, and ICGC using CICs retrieved from the TIP database. We constructed a genetic prognostic framework using the LASSO-Cox model, followed by its validation through Cox proportional hazards regression. This framework's validity was further confirmed with data from ICGC and GEO. Explorations of the tumor microenvironment were carried out through the application of ESTIMATE and CIBERSORT algorithms, as well as machine learning techniques, to identify potential treatment strategies. Single-cell sequencing methods were utilized to delineate the spatial distribution of key genes within the various cell types in the tumor milieu. To explore the critical role of the identified CICs, experiments were conducted focusing on cell survival and migration abilities. In our research, we identified a set of 4 crucial cuproptosis-CICs that have a profound impact on patient longevity and their response to immunotherapy. By leveraging these identified CICs, we constructed a predictive model that efficiently estimates patient prognoses. Detailed analyses at the single-cell level showed that the significance of CICs. Experimental approaches, including CCK-8, Transwell, and wound healing assays, revealed that the protein HSPA9 restricts the growth and movement of breast cancer cells. Furthermore, our studies using immunofluorescence techniques demonstrated that suppressing HSPA9 leads to a notable increase in ceramide levels. This research outlines a network of cuproptosis-CICs and constructs a predictive nomogram. Our model holds great promise for healthcare professionals to personalize treatment approaches for individuals with breast cancer. The work provides insights into the complex relationship between the cuproptosis-CIC network and the cancer immune microenvironment, setting the stage for novel approaches to cancer immunotherapy. By focusing on the essential gene HSPA9 within the cancer-immunity cycle, this strategy has the potential to significantly improve the efficacy of treatments against breast cancer.

CT-based radiomics for predicting radio-chemotherapy response and overall survival in nonsurgical esophageal carcinoma.

To predict treatment response and 2 years overall survival (OS) of radio-chemotherapy in patients with esophageal cancer (EC) by radiomics based on the computed tomography (CT) images. This study retrospectively collected 171 nonsurgical EC patients treated with radio-chemotherapy from Jan 2010 to Jan 2019. 80 patients were randomly divided into training (n=64) and validation (n=16) cohorts to predict the radiochemotherapy response. The models predicting treatment response were established by Lasso and logistic regression. A total of 156 patients were allocated into the training cohort (n=110), validation cohort (n=23) and test set (n=23) to predict 2-year OS. The Lasso Cox model and Cox proportional hazards model established the models predicting 2-year OS. To predict the radiochemotherapy response, WFK as a radiomics feature, and clinical stages and clinical M stages (cM) as clinical features were selected to construct the clinical-radiomics model, achieving 0.78 and 0.75 AUC (area under the curve) in the training and validation sets, respectively. Furthermore, radiomics features called WFI and WGI combined with clinical features (smoking index, pathological types, cM) were the optimal predictors to predict 2-year OS. The AUC values of the clinical-radiomics model were 0.71 and 0.70 in the training set and validation set, respectively. This study demonstrated that planning CT-based radiomics showed the predictability of the radiochemotherapy response and 2-year OS in nonsurgical esophageal carcinoma. The predictive results prior to treatment have the potential to assist physicians in choosing the optimal therapeutic strategy to prolong overall survival.

Prognostic models for upper urinary tract urothelial carcinoma patients after radical nephroureterectomy based on a novel systemic immune-inflammation score with machine learning

PurposeThis study aimed to evaluate the clinical significance of a novel systemic immune-inflammation score (SIIS) to predict oncological outcomes in upper urinary tract urothelial carcinoma(UTUC) after radical nephroureterectomy(RNU).MethodThe clinical data of 483 patients with nonmetastatic UTUC underwent surgery in our center were analyzed. Five inflammation-related biomarkers were screened in the Lasso-Cox model and then aggregated to generate the SIIS based on the regression coefficients. Overall survival (OS) was assessed using Kaplan-Meier analyses. The Cox proportional hazards regression and random survival forest model were adopted to build the prognostic model. Then we established an effective nomogram for UTUC after RNU based on SIIS. The discrimination and calibration of the nomogram were evaluated using the concordance index (C-index), area under the time-dependent receiver operating characteristic curve (time-dependent AUC), and calibration curves. Decision curve analysis (DCA) was used to assess the net benefits of the nomogram at different threshold probabilities.ResultAccording to the median value SIIS computed by the lasso Cox model, the high-risk group had worse OS (p<0.0001) than low risk-group. Variables with a minimum depth greater than the depth threshold or negative variable importance were excluded, and the remaining six variables were included in the model. The area under the ROC curve (AUROC) of the Cox and random survival forest models were 0.801 and 0.872 for OS at five years, respectively. Multivariate Cox analysis showed that elevated SIIS was significantly associated with poorer OS (p<0.001). In terms of predicting overall survival, a nomogram that considered the SIIS and clinical prognostic factors performed better than the AJCC staging.ConclusionThe pretreatment levels of SIIS were an independent predictor of prognosis in upper urinary tract urothelial carcinoma after RNU. Therefore, incorporating SIIS into currently available clinical parameters helps predict the long-term survival of UTUC.

Blood immune inflammatory indicators predict prognosis in patients with coronary artery disease

AbstractBackgroundThe relationship between the combined hematological parameters and echocardiography and long‐term prognosis in patients with coronary artery disease (CAD) remains unclear.MethodsWe examined the ability of hematological parameters to predict all‐cause death and major adverse cardiovascular events (MACE) based on Lasso Cox regression analysis. The significant predictors of hematological parameters from the Lasso Cox model were analyzed via multivariate Cox regression analysis and by adjusting for echocardiographic data. We calculated the continuous net reclassification improvement (cNRI) and integrated discrimination improvement (IDI) of the hematological parameters to assess the improvement in prediction.ResultsA low hemoglobin and lymphocyte ratio and high hematocrit, red blood cell distribution width‐coefficient of variation, and monocyte ratio significantly increased the risk of MACE and death in CAD patients. Neutrophil‐to‐lymphocyte ratio was associated with MACE but not death in CAD patients. After adjustment for echocardiographic parameters, hemoglobin, hematocrit, and lymphocyte ratio remained independently related to death and MACE. The addition of hematological and echocardiographic parameters to the Framingham risk score model significantly improved the area under the curve of mortality (0.794 vs. 0.713, p = 0.0007) and reclassification with cNRI of 30.6% (p = 0.002) and IDI of 0.055 (p &lt; 0.001). Mendelian randomization analyses identified that fibrinogen and neutrophil‐to‐lymphocyte ratio were associated with increased brain natriuretic peptide and decreased left ventricular ejection fraction.ConclusionsThese findings suggest that the blood immune inflammatory indicators fibrinogen and neutrophil‐to‐lymphocyte ratio were causally associated with the risk of heart failure after CAD. The combination of hematological biomarkers and echocardiography parameters as predictor variables is a useful predictive tool for all‐cause mortality in patients with CAD.

A Lasso Cox score consisting of CTLA4+ regulatory T cells (Treg), monocytic myeloid derived suppressor cells (M-MDSC), and CXCR3+ CD8+ T cells: Association with adjuvant ipilimumab (ipi) survival outcomes in patients (pts) with high-risk melanoma.

e21557 Background: High-risk melanoma pts treated with ipi had a more favorable outcome than those treated with interferon-α2b (IFN) in phase III adjuvant trial E1609. We hypothesized that further profiling of circulating immune cells and cytokines would inform underlying mechanisms associated with immune response vs. resistance to CTLA4 blockade. Methods: Multiparameter flow cytometry was used to compare surface protein expression on PBMC collected at baseline, focusing on Treg, MDSC and effector T cells (N = 210 ipi, N = 119 IFN). Multiplex Luminex assays were used to test baseline serum for concentrations of candidate cytokines, chemokines and growth factors. A survival analysis tool, DRPPM-PATH-SURVEIOR, was then used to perform a univariate screening of these analytes using a Cox proportional hazards (PH) model. A median cut-point was derived for each marker, and pts were split into above or below median cohorts. The dichotomized scores were then processed through a Cox regression analysis. Features were ranked based on p-values calculated by a likelihood test for overall survival (OS) and recurrence-free survival (RFS). A Lasso Cox regression model was further developed using Glmnet by optimizing lambda at minimum mean cross-validated error. Pts were randomly and evenly split into training and testing datasets. Results: Initial univariate Cox PH regression model screening identified 6 cellular and 2 serum protein biomarkers associated with OS and RFS. Enriched populations of CTLA4+ Treg (CD3+/CD4+/CD25hi+/CD152+) and monocytic (M)-MDSC (Lin-CD33+/HLA-DRlo+/CD14+/CD15+) were identified as being associated with poor outcome, while enriched populations of CXCR3+/CD4+ T cells, CXCR3+/CD8+ T cells, CTLA4+/INFg+/CD8+ T cells and CD39+ Treg (CD3+/CD4+/CD25hi+/CD39+) were associated with better outcome. Higher levels of CCL3 and CXCL11 chemokines in serum were associated with improved OS and RFS. To develop a prognostic model predictive of ipi treatment benefits, we focused on top ranked biomarkers and generated and tested a Lasso Cox regression model based on CTLA4+ Treg, M-MDSC and CXCR3+/CD8+ T cell populations using a training subset (N = 105 ipi pts). This model was predictive of pt OS and RFS in the testing subset (N = 105) with C-index = 0.67. To examine broader applicability of our Lasso Cox model, we analyzed an additional 119 pts treated on the IFN control arm. By incorporating treatment arm as an interaction term with our 3 biomarker Lasso Cox score, our model was determined to be predictive of risk in pts treated with ipi but not IFN. Conclusions: Our analysis confirms the predictive value of a 3 biomarker Lasso Cox score predicated on CTLA4+ Treg, M-MDSC and CXCR3+/CD8+ T cell content in patient blood at baseline as it relates to adjuvant ipi treatment outcomes.

CT radiomic signature to predict overall survival and chemotherapy benefit in stage I and II HPV-associated oropharyngeal carcinoma.

6073 Background: Chemoradiation is the standard of care for human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC). However, not all patients would benefit from chemotherapy, especially patients with low-risk characteristics. We aim to develop and validate a prognostic and predictive radiomic image signature (pRiS) to inform survival and chemotherapy benefit in stage I and stage II HPV-associated OPSCC. Methods: Radiographic scans for 491 patients with stage I and stage II HPV-associated OPSCC were acquired from 4 independent sources and divided into three cohorts D1-D3. D1 comprised computed tomography (CT) scans from 60 radiotherapy treated patients and was used to identify prognostic features via a LASSO Cox model to predict overall and disease-free survival. The prognostic performance of pRiS was evaluated on two test sets (D2, n = 162; D3, n = 269) using concordance index (C-index). An integrated nomogram was developed to demonstrate the incremental value of the pRiS to the existing clinical factors for individualized survival estimation. Patients from D2 and D3 who received either radiotherapy alone or chemoradiation were used to validate pRiS as predictive of added benefit of chemotherapy. Results: Seven radiomic features were selected to construct the image biomarker pRiS, which was found to be prognostic of overall survival (OS) on univariate analysis in D2 (hazard ratio [HR] = 2.14, 95% confidence interval [CI], 1.1–4.16, p = 0.02) and D3 (HR = 2.74, 95% CI, 1.34–5.62, p = 0.006). Chemotherapy was associated with improved OS for high-pRiS patients in both D2 (radiation vs chemoradiation, HR = 4.47, 95% CI, 1.73–11.6, p = 0.002) and D3 (radiation vs chemoradiation, HR = 2.99, 95% CI, 1.04–8.63, p = 0.04). In contrast, chemotherapy did not improve OS for low-pRiS patients, which indicates these patients did not derive additional benefit from chemotherapy and could be considered for treatment de-escalation. Conclusions: The proposed radiomic signature was prognostic of patient survival and the binary pRiS group informed chemotherapy benefit for stage I and II HPV-associated OPSCC patients. [Table: see text]

An immune checkpoint-based signature predicts prognosis and chemotherapy response for patients with small cell lung cancer.

Therapeutic options for small cell lung cancer (SCLC), a particularly lethal malignancy, remain limited. Members of the B7-CD28 family are compelling targets for immune checkpoint blockade strategies, which involve activating, inhibiting, and fine-tuning the T cell immune response. However, their clinical features and significance have not been explored comprehensively. We enrolled 228 patients with an initial diagnosis of SCLC, including 77 cases from Cbioportal and a validation cohort of 151 cases with qPCR data. Kaplan-Meier analysis and LASSO Cox model were used to identify a signature based on the B7-CD28 family, which was applied for accurate prediction of chemotherapy benefit and prognosis for SCLC patients. In addition, we applied bioinformatics analysis to explore potential signature-related molecular mechanisms and the immune landscape. The mutation profiles of healthy tissues and SCLC tissues were distinct. A signature consisting of seven genes (CD86, ICOSLG, CD276, CD28, CTLA-4, PDCD1, and TMIGD2) was identified and applied to group patients based on risk level (high-risk and low-risk), producing two groups for which survival outcomes differed significantly (HR=3.81, 95% CI: 2.16-6.74, P<0.001). The immune checkpoint-based signature accurately predicted patient outcomes for the selected training and validation sets. Notably, low-risk patients were more likely to benefit from chemotherapy and showed greater immune activation. Additionally, time-dependent ROC curves and C-index analysis confirmed that the immune checkpoint-based signature has excellent predictive power for prognosis and chemotherapy benefit compared to clinically recognized parameters. Finally, multivariate analysis confirmed the identified signature as an independent risk factor for prognosis and chemotherapeutic response. We systematically obtained a comprehensive molecular profile for B7-CD28 family members in SCLC patients, from which we produced a reliable and robust prognostic immune checkpoint-based signature with the potential to improve prognostic stratification and therapy strategies for SCLC patients.

Molecular subtypes based on centrosome-related genes can predict prognosis and therapeutic responsiveness in patients with low-grade gliomas.

Abnormalities in centrosome regulatory genes can induce chromosome instability, cell differentiation errors, and tumorigenesis. However, a limited number of comprehensive analyses of centrosome-related genes have been performed in low-grade gliomas (LGG). LGG data were extracted from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. The ConsensusClusterPlus" R package was used for unsupervised clustering. We constructed a centrosome-related genes (CRGs) signature using a random forest model, lasso Cox model, and multivariate Cox model, and quantified the centrosome-related risk score (centS). The prognostic prediction efficacy of centS was evaluated using a Receiver Operating Characteristic (ROC) curve. Immune cell infiltration and genomic mutational landscapes were evaluated using the ESTIMATE algorithm, single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm, and "maftools" R package, respectively. Differences in clinical features, isocitrate dehydrogenase (IDH) mutation, 1p19q codeletion, O6-methylguanine-DNA methyltransferase promoter (MGMTp) methylation, and response to antitumor therapy between the high- and low-centS groups were explored. "pRRophetic" R packages were used for temozolomide (TMZ) sensitivity analysis. qRT-PCR verified the differential expression of the centrosomal gene team, the core of which is CEP135, between LGG cells and normal cells. Two distinct CRG-based clusters were identified using consensus unsupervised clustering analysis. The prognosis, biological characteristics, and immune cell infiltration of the two clusters differed significantly. A well-performing centS signature was developed to predict the prognosis of patients with LGG based on 12 potential CRGs. We found that patients in the high-centS group showed poorer prognosis and lower proportion of IDH mutation and 1p19q codeletion compared to those in the low-centS group. Furthermore, patients in the high-centS group showed higher sensitivity to TMZ, higher tumor mutation burden, and immune cell infiltration. Finally, we identified a centrosomal gene team whose core was CEP135, and verified their differential expression between LGG cells and normal glial cells. Our findings reveal a novel centrosome-related signature for predicting the prognosis and therapeutic responsiveness of patients with LGG. This may be helpful for the accurate clinical treatment of LGG.