Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer

Reclassified the phenotypes of cancer types and construct a nomogram for predicting bone metastasis risk: A pan-cancer analysis

BACKGROUND

Numerous of models have been developed to predict the bone metastasis (BM) risk; however, due to the variety of cancer types, it is difficult for clinicians to use these models efficiently. We aimed to perform the pan-cancer analysis to create the cancer classification system for BM, and construct the nomogram for predicting the BM risk.

METHODS

Cancer patients diagnosed between 2010 and 2018 in the Surveillance, Epidemiology, and End Results (SEER) database were included. Unsupervised hierarchical clustering analysis was performed to create the BM prevalence-based cancer classification system (BM-CCS). Multivariable logistic regression was applied to investigate the possible associated factors for BM and construct a nomogram for BM risk prediction. The patients diagnosed between 2017 and 2018 were selected for validating the performance of the BM-CCS and the nomogram, respectively.

RESULTS

A total of 50 cancer types with 2,438,680 patients were included in the construction model. Unsupervised hierarchical clustering analysis classified the 50 cancer types into three main phenotypes, namely, categories A, B, and C. The pooled BM prevalence in category A (17.7%; 95% CI: 17.5%-17.8%) was significantly higher than that in category B (5.0%; 95% CI: 4.5%-5.6%), and category C (1.2%; 95% CI: 1.1%-1.4%) (p < 0.001). Advanced age, male gender, race, poorly differentiated grade, higher T, N stage, and brain, lung, liver metastasis were significantly associated with BM risk, but the results were not consistent across all cancers. Based on these factors and BM-CCS, we constructed a nomogram for predicting the BM risk. The nomogram showed good calibration and discrimination ability (AUC in validation cohort = 88%,95% CI: 87.4%-88.5%; AUC in construction cohort = 86.9%,95% CI: 86.8%-87.1%). The decision curve analysis also demonstrated the clinical usefulness.

CONCLUSION

The classification system and prediction nomogram may guide the cancer management and individualized BM screening, thus allocating the medical resources to cancer patients. Moreover, it may also have important implications for studying the etiology of BM.

Novel model for cancer-specific survival of patients with pulmonary sarcomatoid carcinoma: A population-based analysis and external validation

BACKGROUND/OBJECTIVE

We aimed to develop a comprehensive and effective nomogram for predicting cancer-specific survival (CSS) in patients with pulmonary sarcomatoid carcinoma (PSC).

METHODS

Data for patients diagnosed with PSC between 2004 and 2018 from the Surveillance, Epidemiology, and End Results database were retrospectively collected and randomly divided into training and internal validation sets. We then retrospectively recruited patients diagnosed with PSC to construct an external validation cohort from the Southwest Hospital. A prognostic nomogram for CSS was established using independent prognostic factors that were screened from the multivariate Cox regression analysis. The performance of the nomogram was evaluated using area under the receiver operating characteristic (ROC) curves, Harrell's concordance index (C-index), calibration diagrams, and decision curve analysis (DCA). The clinical value of the nomogram and tumor, nodes, and metastases (TNM) staging system was compared using the C-index and net reclassification index (NRI).

RESULTS

Overall, 1356 patients with PSC were enrolled, including 876, 377, and 103 in the training, internal validation, and external validation sets, respectively. The C-index and ROC curves, calibration, and DCA demonstrated satisfactory nomogram performance for CSS in patients with PSC. In addition, the C-index and NRI of the nomogram suggested a significantly higher nomogram value than that of the TNM staging system. Subsequently, a web-based predictor was developed to help clinicians obtain this model easily.

CONCLUSIONS

The prognostic nomogram developed in this study can conveniently and precisely estimate the prognosis of patients with PSC and individualize treatment, thereby assisting clinicians in their shared decision-making with patients.

Predicting survival of patients with bone metastasis of unknown origin

Purpose

Bone metastasis of unknown origin is a rare and challenging situation, which is infrequently reported. Therefore, the current study was performed to analyze the clinicopathologic features and risk factors of survival among patients with bone metastasis of unknown origin.

Patients and methods

We retrospectively analyzed the clinical data for patients with bone metastasis of unknown origin between 2010 and 2016 based on the Surveillance, Epidemiology, and End Results (SEER) database. Overall survival (OS) and cancer-specific survival (CSS) were first analyzed by applying univariable Cox regression analysis. Then, we performed multivariable analysis to confirm independent survival predictors.

Results

In total, we identified 1224 patients with bone metastasis of unknown origin for survival analysis, of which 704 males (57.5%) and 520 females (42.5%). Patients with bone metastasis of unknown origin had a 1-year OS rate of 14.50% and CSS rate of 15.90%, respectively. Race, brain metastasis, liver metastasis, radiotherapy, and chemotherapy were significant risk factors of OS on both univariable and multivariable analyses (p <0.05). As for CSS, both univariable and multivariable analyses revealed that no brain metastasis, no liver metastasis, radiotherapy, and chemotherapy were associated with increased survival (p <0.05).

Conclusion

Patients with bone metastasis of unknown origin experienced an extremely poor prognosis. Radiotherapy and chemotherapy were beneficial for prolonging the survival of those patients.

Development and validation of prognostic nomogram for elderly patients with clear cell renal cell carcinoma based on the SEER database

This study sought to establish nomogram models of overall survival (OS) in patients with elderly clear cell renal cell carcinoma (ECCRCC). The Surveillance, Epidemiology, and End Results database provided data of the ECCRCC-afflicted patients diagnosed during the period from 2010 to 2015. This data was subsequently segregated into the training and validation sets randomly in a 7:3 ratio. The calibration curves, the receiver operating characteristic curves, the decision curve analysis and the Concordance index (C-index) were applied for the model evaluation. 9201 eligible cases from 2010 to 2015 were extracted; 6441 were included in the training cohort and 2760 in the validation cohort. The C-index for the training and validation sets were 0.710 and 0.709, respectively. The receiver operating characteristic and decision curve analysis curves demonstrated that nomograms outperformed the AJCC stage in predictive performance. Moreover, the nomogram was found to match closely with the actual observation, as indicated by the calibration plots. To make predictions with regard to the survival of the ECCRCC-afflicted individuals, and as a guide for treatment, the new nomogram could be used.

A decision tree model to predict liver cirrhosis in hepatocellular carcinoma patients: a retrospective study

Background

The severity of liver cirrhosis in hepatocellular carcinoma (HCC) patients is essential for determining the scope of surgical resection. It also affects the long-term efficacy of systemic anti-tumor therapy and transcatheter arterial chemoembolization (TACE). Non-invasive tools, including aspartate aminotransferase to platelet ratio index (APRI), fibrosis-4 (FIB-4), and γ-glutamyl transferase to platelet ratio (GPR), are less accurate in predicting cirrhosis in HCC patients. We aimed to build a novel decision tree model to improve diagnostic accuracy of liver cirrhosis.

Patients and Methods

The Mann-Whitney U test, χ2 test, and multivariate logistic regression analysis were used to identify independent cirrhosis predictors. A decision tree model was developed using machine learning algorithms in a training cohort of 141 HCC patients. Internal validation was conducted in 99 HCC patients. The diagnostic accuracy and calibration of the established model were evaluated using receiver operating characteristic (ROC) and calibration curves, respectively.

Results

Sex and platelet count were identified as independent cirrhosis predictors. A decision tree model integrating imaging-reported cirrhosis, APRI, FIB-4, and GPR was established. The novel model had an excellent diagnostic performance in the training and validation cohorts, with area under the curve (AUC) values of 0.853 and 0.817, respectively. Calibration curves and the Hosmer-Lemeshow test showed good calibration of the novel model. The decision curve analysis (DCA) indicated that the decision tree model could provide a larger net benefit to predict liver cirrhosis.

Conclusion

Our developed decision tree model could successfully predict liver cirrhosis in HCC patients, which may be helpful in clinical decision-making.

Individualized assessment predictive models for risk and overall survival in elderly patients of primary kidney cancer with bone metastases: A large population-based study

Background

Elderly people are at high risk of metastatic kidney cancer (KC), and, the bone is one of the most common metastatic sites for metastatic KC. However, studies on diagnostic and prognostic prediction models for bone metastases (BM) in elderly KC patients are still vacant. Therefore, it is necessary to establish new diagnostic and prognostic nomograms.

Methods

We downloaded the data of all KC patients aged more than 65 years during 2010-2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses were used to study independent risk factors of BM in elderly KC patients. Univariate and multivariate Cox regression analysis for the study of independent prognostic factors in elderly KCBM patients. Survival differences were studied using Kaplan-Meier (K-M) survival analysis. The predictive efficacy and clinical utility of nomograms were assessed by receiver operating characteristic (ROC) curve, the area under curve (AUC), calibration curve, and decision curve analysis (DCA).

Results

A final total of 17,404 elderly KC patients (training set: n = 12,184, validation set: n = 5,220) were included to study the risk of BM. 394 elderly KCBM patients (training set: n = 278, validation set: n = 116) were included to study the overall survival (OS). Age, histological type, tumor size, grade, T/N stage and brain/liver/lung metastasis were identified as independent risk factors for developing BM in elderly KC patients. Surgery, lung/liver metastasis and T stage were identified as independent prognostic factors in elderly KCBM patients. The diagnostic nomogram had AUCs of 0.859 and 0.850 in the training and validation sets, respectively. The AUCs of the prognostic nomogram in predicting OS at 12, 24 and 36 months were: training set (0.742, 0.775, 0.787), and validation set (0.721, 0.827, 0.799), respectively. The calibration curve and DCA also showed excellent clinical utility of the two nomograms.

Conclusion

Two new nomograms were constructed and validated to predict the risk of developing BM in elderly KC patients and 12-, 24-, and 36-months OS in elderly KCBM patients. These models can help surgeons provide more comprehensive and personalized clinical management programs for this population.

A degradome-based prognostic signature that correlates with immune infiltration and tumor mutation burden in breast cancer

Introduction

Female breast cancer is the most common malignancy worldwide, with a high disease burden. The degradome is the most abundant class of cellular enzymes that play an essential role in regulating cellular activity. Dysregulation of the degradome may disrupt cellular homeostasis and trigger carcinogenesis. Thus we attempted to understand the prognostic role of degradome in breast cancer by means of establishing a prognostic signature based on degradome-related genes (DRGs) and assessed its clinical utility in multiple dimensions.

Methods

A total of 625 DRGs were obtained for analysis. Transcriptome data and clinical information of patients with breast cancer from TCGA-BRCA, METABRIC and GSE96058 were collected. NetworkAnalyst and cBioPortal were also utilized for analysis. LASSO regression analysis was employed to construct the degradome signature. Investigations of the degradome signature concerning clinical association, functional characterization, mutation landscape, immune infiltration, immune checkpoint expression and drug priority were orchestrated. Cell phenotype assays including colony formation, CCK8, transwell and wound healing were conducted in MCF-7 and MDA-MB-435S breast cancer cell lines, respectively.

Results

A 10-gene signature was developed and verified as an independent prognostic predictor combined with other clinicopathological parameters in breast cancer. The prognostic nomogram based on risk score (calculated based on the degradome signature) showed favourable capability in survival prediction and advantage in clinical benefit. High risk scores were associated with a higher degree of clinicopathological events (T4 stage and HER2-positive) and mutation frequency. Regulation of toll-like receptors and several cell cycle promoting activities were upregulated in the high-risk group. PIK3CA and TP53 mutations were dominant in the low- and high-risk groups, respectively. A significantly positive correlation was observed between the risk score and tumor mutation burden. The infiltration levels of immune cells and the expressions of immune checkpoints were significantly influenced by the risk score. Additionally, the degradome signature adequately predicted the survival of patients undergoing endocrinotherapy or radiotherapy. Patients in the low-risk group may achieve complete response after the first round of chemotherapy with cyclophosphamide and docetaxel, whereas patients in the high-risk group may benefit from 5-flfluorouracil. Several regulators of the PI3K/AKT/mTOR signaling pathway and the CDK family/PARP family were identified as potential molecular targets in the low- and high-risk groups, respectively. In vitro experiments further revealed that the knockdown of ABHD12 and USP41 significantly inhibit the proliferation, invasion and migration of breast cancer cells.

Conclusion

Multidimensional evaluation verified the clinical utility of the degradome signature in predicting prognosis, risk stratification and guiding treatment for patients with breast cancer.

Bone metastasis risk and prognosis assessment models for kidney cancer based on machine learning

Background

Bone metastasis is a common adverse event in kidney cancer, often resulting in poor survival. However, tools for predicting KCBM and assessing survival after KCBM have not performed well.

Methods

The study uses machine learning to build models for assessing kidney cancer bone metastasis risk, prognosis, and performance evaluation. We selected 71,414 kidney cancer patients from SEER database between 2010 and 2016. Additionally, 963 patients with kidney cancer from an independent medical center were chosen to validate the performance. In the next step, eight different machine learning methods were applied to develop KCBM diagnosis and prognosis models while the risk factors were identified from univariate and multivariate logistic regression and the prognosis factors were analyzed through Kaplan-Meier survival curve and Cox proportional hazards regression. The performance of the models was compared with current models, including the logistic regression model and the AJCC TNM staging model, applying receiver operating characteristics, decision curve analysis, and the calculation of accuracy and sensitivity in both internal and independent external cohorts.

Results

Our prognosis model achieved an AUC of 0.8269 (95%CI: 0.8083-0.8425) in the internal validation cohort and 0.9123 (95%CI: 0.8979-0.9261) in the external validation cohort. In addition, we tested the performance of the extreme gradient boosting model through decision curve analysis curve, Precision-Recall curve, and Brier score and two models exhibited excellent performance.

Conclusion

Our developed models can accurately predict the risk and prognosis of KCBM and contribute to helping improve decision-making.

Recent Trends in Synchronous Brain Metastasis Incidence and Mortality in the United States: Ten-Year Multicenter Experience

BACKGROUND

Large epidemiological studies describing the trends in incidence rates and mortality of synchronous brain metastases (SBMs) are lacking. The study aimed to provide a comprehensive understanding of the changes in the incidence and mortality of SBMs over the previous ten years.

METHODS

Trends in the incidence of solid malignancies outside of the CNS in patients with SBMs and incidence-based mortality rates were assessed using data from the Surveillance, Epidemiology, and End Results database. Joinpoint analyses were used to calculate annual percent changes (APCs) and 95% CIs.

RESULTS

Between 2010 and 2019, 66,655 patients, including 34,821 (52.24%) men and 31,834 (47.76%) women, were found to have SBMs, and 57,692 deaths occurred over this period. Lung cancer SBMs, melanoma SBMs, and breast cancer SBMs were ranked in the top three, having the highest age-standardized incidence rates. The incidence of SBMs decreased significantly with an APC of -0.6% from 2010 to 2019, while the APC was 1.2% for lung cancer SBMs, 2.5% for melanoma SBMs, and 0.6% for breast cancer SBMs. The SBM mortality first experienced a rapid increase (APC = 28.6%) from 2010 to 2012 and then showed a significant decline at an APC of -1.8% from 2012 to 2019. Lung cancer SBMs showed similar trends, while melanoma SBM and breast cancer SBM mortality increased continuously.

CONCLUSIONS

SBMs incidence (2010-2019) and incidence-based mortality (2012-2019) declined significantly. These findings can advance our understanding of the prevalence of SBMs.

Dynamic Predictive Models with Visualized Machine Learning for Assessing the Risk of Lung Metastasis in Kidney Cancer Patients

OBJECTIVE

To establish and verify the clinical prediction model of lung metastasis in renal cancer patients.

METHOD

Kidney cancer patients from January 1, 2010, to December 31, 2017, in the SEER database were enrolled in this study. In the first section, LASSO method was adopted to select variables. Independent influencing factors were identified after multivariate logistic regression analysis. In the second section, machine learning (ML) algorithms were implemented to establish models and 10-foldcross-validation was used to train the models. Finally, receiver operating characteristic curves, probability density functions, and clinical utility curve were applied to estimate model's performance. The final model was shown by a website calculator.

RESULT

Lung metastasis was confirmed in 7.43% (3171 out of 42650) of study population. In multivariate logistic regression, bone metastasis, brain metastasis, grade, liver metastasis, N stage, T stage, and tumor size were independent risk factors of lung metastasis in renal cancer patients. Primary site and sequence number were independent protection factors of LM in renal cancer patients. The above 9 impact factors were used to develop the prediction models, which included random forest (RF), naive Bayes classifier (NBC), decision tree (DT), xgboost (XGB), gradient boosting machine (GBM), and logistic regression (LR). In 10-foldcross-validation, the average area under curve (AUC) ranked from 0.907 to 0.934. In ROC curve analysis, AUC ranged from 0.879-0.922. We found that the XGB model performed best, and a Web-based calculator was done according to XGB model.

CONCLUSION

This study provided preliminary evidence that the ML algorithm can be used to predict lung metastases in patients with kidney cancer. This low cost, noninvasive and easy to implement diagnostic method is useful for clinical work. Of course this model still needs to undergo more real-world validation.

Effectiveness and Safety of Molecular-Targeted Therapy after Nivolumab Plus Ipilimumab for Advanced or Metastatic Renal Cell Carcinoma: A Multicenter, Retrospective Cohort Study

Simple Summary

We evaluated the efficacy and safety of molecular-targeted therapies (MTTs) in 29 patients who discontinued the combination therapy of nivolumab plus ipilimumab (NIVO+IPI) for advanced or metastatic renal cell carcinoma as real-world outcomes. Patients receiving MTTs had a median follow-up of 8 months. The objective response rate was 44.8%, and the disease control rate was 72.4%. After NIVO+IPI, the median overall survival was 18 months, and progression-free survival (PFS) was 8 months. Patients with bone metastases had a significantly shorter median PFS when treated with MTTs after NIVO+IPI than those without bone metastases (4 vs. 12 months, p = 0.012). MTTs may be a useful secondary treatment option after the discontinuation of NIVO+IPI.

Abstract

This study aimed to evaluate the effectiveness and safety of molecular-targeted therapies (MTTs) after the discontinuation of nivolumab and ipilimumab (NIVO+IPI) combination therapy in patients who had been diagnosed with advanced/metastatic renal cell carcinoma as real-world outcomes. We enrolled patients treated with MTTs following initial therapy with NIVO+IPI at nine institutions in Japan. We evaluated the objective response rate (ORR) as the primary endpoint and disease control rate (DCR), best overall response, and oncological outcomes (overall survival (OS) and progression-free survival (PFS)) as the secondary endpoints. We also evaluated factors predictive of disease progression after the administration of MTTs. Patients were followed up for a median of 8 months. The ORR was 44.8%, and the DCR was 72.4%. The median OS and PFS of MTTs after NIVO+IPI were 18 months and 8 months, respectively. A total of 31% of patients experienced grade 3/4 MTT-related adverse events. The median PFS in patients with bone metastases was significantly shorter than that in those without bone metastases (4 vs. 12 months, p = 0.012). MTTs may be a useful secondary treatment option after the discontinuation of NIVO+IPI.

Risk Factors, Prognostic Factors, and Nomograms for Bone Metastasis in Patients with Newly Diagnosed Clear Cell Renal Cell Carcinoma: A Large Population-Based Study

Background

This study aimed to investigate risk factors and prognostic factors in patients with clear cell renal cell carcinoma (ccRCC) with bone metastasis (BM) and establish nomograms to provide a quantitative prediction of the risk of BM and survival probability.

Methods

The clinicopathological characteristics of patients with ccRCC between January 2010 and December 2015 were obtained from the Surveillance, Epidemiology and End Results (SEER) database. Independent factors for BM in ccRCC patients were identified using univariate and multivariate logistic regression analyses. Prognostic factors for predicting cancer-specific death were evaluated using univariate and multivariate analyses based on a competing risk regression model. We then constructed a diagnostic nomogram and a prognostic nomogram. The two nomograms were evaluated using calibration curves, receiver operating characteristic curves, and decision curve analysis.

Results

Our study included 34,659 patients diagnosed with ccRCC in the SEER database, with 1,415 patients who presented with bone metastasis. Risk factors for BM in patients with ccRCC included age, stage T, stage N, brain metastasis, liver metastasis, lung metastasis, tumor size, and laterality. Independent prognostic factors for patients with ccRCC patients with BM were Fuhrman grade, tumor size, T stage, N stage, brain metastases, lung metastasis, and surgery. For the diagnostic nomogram, the area under the curve values in the training and testing cohorts were 0.863 (95% CI, 0.851–0.875) and 0.859 (95% CI, 0.839–0.878), respectively. In the prognostic cohort, the area under the curve values for 1-, 2-, and 3-year cancer-specific survival rates in the training cohort were 0.747, 0.774, and 0.780, respectively, and 0.671, 0.706, and 0.696, respectively, in the testing cohort. Through calibration curves and decision curve analyses, the nomograms displayed excellent performance.

Conclusions

Several factors related to the development and prognosis of BM in patients with ccRCC were identified. The nomograms constructed in this study are expected to become effective and precise tools for clinicians to improve cancer management.