Nivolumab and Ipilimumab Combination Immunotherapy for Patients with Metastatic Collecting Duct Carcinoma

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.

Complete response to combination therapy using nivolumab and ipilimumab for metastatic, sarcomatoid collecting duct carcinoma presenting with high expression of programmed death-ligand 1: a case report

BACKGROUND

Collecting duct carcinoma and sarcomatoid renal cell carcinoma are tumors with poor prognosis. Immune checkpoint inhibitors have been established as the standard treatment for advanced renal cell carcinoma. Some cases of remission of collecting duct carcinoma and sarcomatoid renal cell carcinoma have been reported using immune checkpoint inhibitor interventions. Specifically, sarcomatoid renal cell carcinoma expresses high levels of programmed death-ligand 1, an immune checkpoint protein, and immune checkpoint inhibitors have been reported to be highly effective for treating sarcomatoid renal cell carcinoma.

CASE PRESENTATION

We describe the case of a 70-year-old Japanese male who underwent radical right nephrectomy for a right renal mass identified on computed tomography. The pathological examination demonstrated that the renal mass was urothelial carcinoma and collecting duct carcinoma with sarcomatoid changes, and programmed death-ligand 1 was highly expressed with a tumor proportion score of more than 10%. There was no evident submucosal connective tissue invasion in the urothelial carcinoma component, and collecting duct carcinoma was diagnosed as primary cancer. The tumor-node-metastasis classification was pT3aN0, venous invasion 1, lymphovascular invasion 0, and Fuhrman nuclear grade 4. Two months after the nephrectomy, multiple metastases were observed in both lungs, the right hilar lymph node, and the S6 segment of the right liver lobe. We initiated first-line combination therapy with nivolumab (240 mg, fixed dose) and ipilimumab (1 mg/kg). One day after administration, the patient developed drug-induced interstitial pneumonia, thus we applied steroid injections. After one administration of immunotherapy, the metastatic lesion showed complete response within 6 months, which was maintained after 3 years.

CONCLUSION

We report the first case of complete response to a single dose of combination therapy with nivolumab and ipilimumab for metastatic collecting duct carcinoma with sarcomatoid changes and high expression of programmed death-ligand 1. This case suggests high expectations for immune checkpoint inhibitors as treatment for sarcomatoid-transformed renal carcinoma tumors that express high levels of programmed death-ligand 1.

A Novel Machine Learning Algorithm Combined With Multivariate Analysis for the Prognosis of Renal Collecting Duct Carcinoma

Objectives

To investigate the clinical and non-clinical characteristics that may affect the prognosis of patients with renal collecting duct carcinoma (CDC) and to develop an accurate prognostic model for this disease.

Methods

The characteristics of 215 CDC patients were obtained from the U.S. National Cancer Institute’s surveillance, epidemiology and end results database from 2004 to 2016. Univariate Cox proportional hazard model and Kaplan-Meier analysis were used to compare the impact of different factors on overall survival (OS). 10 variables were included to establish a machine learning (ML) model. Model performance was evaluated by the receiver operating characteristic curves (ROC) and calibration plots for predictive accuracy and decision curve analysis (DCA) were obtained to estimate its clinical benefits.

Results

The median follow-up and survival time was 16 months during which 164 (76.3%) patients died. 4.2, 32.1, 50.7 and 13.0% of patients were histological grade I, II, III, and IV, respectively. At diagnosis up to 61.9% of patients presented with a pT3 stage or higher tumor, and 36.7% of CDC patients had metastatic disease. 10 most clinical and non-clinical factors including M stage, tumor size, T stage, histological grade, N stage, radiotherapy, chemotherapy, age at diagnosis, surgery and the geographical region where the care delivered was either purchased or referred and these were allocated 95, 82, 78, 72, 49, 38, 36, 35, 28 and 21 points, respectively. The points were calculated by the XGBoost according to their importance. The XGBoost models showed the best predictive performance compared with other algorithms. DCA showed our models could be used to support clinical decisions in 1-3-year OS models.

Conclusions

Our ML models had the highest predictive accuracy and net benefits, which may potentially help clinicians to make clinical decisions and follow-up strategies for patients with CDC. Larger studies are needed to better understand this aggressive tumor.

Persistent Response to a Combination Treatment Featuring a Targeted Agent and an Immune Checkpoint Inhibitor in a Patient With Collecting Duct Renal Carcinoma: A Case Report and Literature Review

Collecting duct carcinoma (CDC) is a rare and highly aggressive subtype of kidney cancer that is associated with a poor prognosis. At present, there is no effective treatment for CDC. Herein, we report a case of metastatic CDC treated with a combination of a tyrosine kinase inhibitor and an immune checkpoint inhibitor. A 67-year-old male was diagnosed with CDC with lung and bone metastasis. Pazopanib and camrelizumab were administered after cytoreductive nephrectomy. The patient achieved a partial response after one cycle of treatment; however, he then experienced serious drug-induced hepatic injury. Therefore, we discontinued camrelizumab and administered monotherapy with pazopanib. Three months later, the cancer had progressed and axitinib and sintilimab were administered. The patient achieved a partial response, accompanied by the complete disappearance of the metastatic lesion in the lung. The patient had an excellent physical status after 11 months. This is the first reported case of metastatic CDC successfully treated with a combination of a tyrosine kinase inhibitor and an immune checkpoint inhibitor. This form of combination treatment may be an effective option for treating metastatic CDC.