Progress and potential of immune checkpoint blockade for treating advanced renal cell carcinoma

Integration of Tumor Microenvironment in Patient-Derived Organoid Models Help Define Precision Medicine of Renal Cell Carcinoma

Renal cell carcinoma (RCC) is a common urological tumor, with a poor prognosis, as the result of insensitivity to chemotherapy and radiotherapy. About 20%–30% of patients with RCC have metastasis at the first diagnosis, so only systemic treatment is possible. Due to the heterogeneity of renal tumors, responses to drugs differ from person to person. Consequently, patient-derived organoid, highly recapitulating tumor heterogeneity, becomes a promising model for high-throughput ex vivo drug screening and thus guides the drug choice of patients with RCC. Systemic treatment of RCC mainly targets the tumor microenvironment, including neovasculature and immune cells. We reviewed several methods with which patient-derived organoid models mimic the heterogeneity of not only tumor epithelium but also the tumor microenvironment. We further discuss some new aspects of the development of patient-derived organoids, preserving in vivo conditions in patients with RCC.

Melanoma long non-coding RNA signature predicts prognostic survival and directs clinical risk-specific treatments

BACKGROUND

Various studies have demonstrated that the Breslow thickness, tumor ulceration and mitotic index could serve as prognostic markers in patients with cutaneous melanoma. Recently, however, as these clinicopathological biomarkers lack efficient interpretation of endogenous mechanism of melanoma, the emphasis on the prognosis of melanoma has transformed to molecular tumor markers.

OBJECTIVE

This study was designed to identify survival-related long non-coding RNAs (lncRNAs), and based on the different expressions of these lncRNAs, clinical risk-specific diagnosis and adjuvant therapy could be employed on melanoma patients, especially patients in the early course of disease or patients with a Breslow thickness no more than 2mm.

METHODS

The clinical information and corresponding RNA expression data were obtained from The Cancer Genome Atlas dataset and Gene Expression Omnibus dataset (GSE65904). All samples were categorized into one training dataset and two validation datasets. Cox proportional hazard regression analysis was then used to identify survival-related lncRNAs and risk assessment signature was constructed in training dataset. Kaplan-Meier method was used to estimate the utility of this signature in predicting the duration of survival of patients both in the training dataset and two validation datasets. Meanwhile receiver operating characteristic analyses were used to evaluate the predictive effectiveness of this signature in two validation datasets.

RESULTS

It was found that the signature was effective while used for risk stratification, and Kaplan-Meier analyses indicated that the duration of survival of patients in high-risk groups were significantly shorter than that of low-risk groups. Moreover, areas under the receiver operating characteristic curve were 0.711 (95% confidence interval: 0.618-0.804) and 0.698 (95% confidence interval: 0.614-0.782) when this signature was used to predict the patients' duration of survival in two validation datasets respectively, indicating the superior specificity and sensitivity of this signature.

CONCLUSION

We identified a four-lncRNA prognostic signature with the ability of risk stratification for melanoma patients. Risk score acquired from this signature, combining with differential diagnosis and differential adjuvant therapy, could potentially improve the prognosis quality of life for patients, especially patients in the early course of disease or patients with a Breslow thickness no more than 2mm.

Clinical outcomes and survival of advanced renal cancer patients in phase I clinical trials

BACKGROUND/INTRODUCTION

The purpose of this study was to describe the basic demographic characteristics, and analyze the response and survival experience of advanced renal cancer subjects treated in a phase I trial.

PATIENTS AND METHODS

We conducted a retrospective observational study in 70 renal cancer patients participating in 25 phase I trials. Descriptive statistics, Kaplan-Meier, and multivariate Cox proportional hazards analyses were used to examine factors associated with time from study entry to treatment failure (TTF) and survival.

RESULTS

The median age at diagnosis was 56.50 years. Eastern Cooperative Oncology Group (ECOG) performance status was 0 for 23.19% (n = 16) of the patients; 49.18% (n = 30) had received 2 or more previous lines of systemic therapy; and 84.29% (n = 59) of patients had 2 or more metastatic sites. A median number of 4.00 cycles of treatment was delivered. Four partial responses (6.25%) and 38 cases of stable disease lasting > 4 months (43.75%) were observed. The median TTF was 16.00 weeks. In multivariate analyses, men and patients with lactate dehydrogenase > 1.5 times the upper limit of normal had a shorter TTF. The median overall survival was 45.57 weeks (319.00 days). In multivariate analysis, factors predicting shorter survival were ECOG performance status ≥ 1 (P = .023), age younger than 60 years (P = .015), albumin < 3.4 g/dL (P = .042), and liver metastases (P = .010).

CONCLUSION

Advanced renal cancer patients with select clinical characteristics could consider phase I trials after exhausting standard therapeutic options.