Efficacy analyses of axitinib and nivolumab in metastatic renal cell carcinoma after failure of targeted therapy: which is better?

PURPOSE: The objective of the present study was to compare the efficacy of axitinib and nivolumab in metastatic renal cell carcinoma (mRCC) previously treated with targeted therapy. METHODS: A total of 79 patients were enrolled (39 patients in axitinib group, 40 patients in nivolumab group). Survival outcomes of patients, progression-free survival (PFS), and overall survival (OS) were estimated using the Kaplan-Meier method and compared with the log-rank test. The associations between potential prognostic variables and OS were evaluated in univariate and multivariate Cox regression analyses. RESULTS: The median PFS and OS of all cohort were 8.1 and 36.6 months, respectively. Higher PFS and OS were evaluated in axitinib group than nivolumab group (PFS: 9.4 months vs 6.3 months, p=0.386; OS: 38.2 months vs 36.6 months, p=0.671, respectively). Patients treated with axitinib had numerically higher objective response rate (ORR) and disease control rate (DCR) than those treated with nivolumab (ORR: 43.6% vs 27.6%, p=0.157, DCR: 74.4% vs 62.5%, p=0.157, respectively). Multivariate analysis revealed that the independent predictors of OS were higher tumor grade (hazard ratio [HR]: 6.178, p=0.004), worse response to axitinib and nivolumab (HR:4.902, p=0.011), the presence of lung metastasis (HR:15.637, p=0.002) and the presence of liver metastasis (HR:12.010, p=0.001). CONCLUSION: Comparable survival outcomes were detected in the axitinib and nivolumab groups. However, head to head comparisons are needed to highlight the relative efficacy of these therapies in mRCC.

Artificial intelligence method to predict overall survival of hepatocellular carcinoma.

Background and Aim: Hepatocellular carcinoma (HCC) is a complex disease with heterogenous outcomes influenced by disease- and patient-related factors. The prediction of outcomes requires a comprehensive approach, and artificial intelligence could provide a feasible means of estimating HCC outcomes. This study was designed to assess the viability of a machine learning model to predict survival in HCC patients. Materials and Methods: HCC patient data with at least 5 years of follow-up were retrospectively reviewed. Patients with accessible data on the primary liver disease, tumor and laboratory values at the time of diagnosis, and length of survival were included. A gradient boosting machine learning algorithm was constructed to predict patient survival at 6 time points. Results: A total of 100 HCC patients (80% male) with a median overall survival of 43 months (range: 0.7-256 months) were included. The survival rate for 6, 12, 24, 36, 60, and 120 months was 88%, 81%, 67%, 60%, 40%, and 11%, respectively. The mean area under the curve of the model prediction was 0.92 (0.061) for >6 months, 0.81 (0.107) for >1 year, 0.78 (0.11) for >2 years, 0.81 (0.083) for >3 years, 0.82 (0.079) for >5 years, 0.81 (0.96) for >8 years, and 0.66 (0.14) for >10 years. Conclusion: The machine learning model successfully predicted short- and long-term survival of patients with HCC.

Efficacy of regorafenib in the second-and third-line setting for patients with advanced hepatocellular carcinoma: A real life data of multicenter study from Turkey.

PURPOSE: After failure of the first-line sorafenib treatment in advanced or metastatic stage hepatocellular carcinoma (HCC), regorafenib is one of the newly-approved targeted agents. We aimed to evaluate the efficacy of regorafenib in patients with advanced HCC treated in the second- or third-line setting. METHODS: In this retrospective and multicenter study, advanced HCC patients not eligible for local therapies, who received a second- or third-line regorafenib therapy after progression on the first-line sorafenib or sequential therapy with chemotherapy (CT) followed by sorafenib, were included. RESULTS: In the first-line setting, 28 (28.9%) patients received CT and 69 (71.1%) patients received sorafenib. There were 24 (24.7%) patients who were intolerant to sorafenib. Disease control rate (DCR) was 53.6% for all patients treated with regorafenib, 62.3% in patients who received regorafenib in the second-line, and 32.1% for those receiving regorafenib in the third-line (p=0.007). Median progression-free survival (PFS) and overall survival (OS) were 5.6 (range; 4.3-6.9) and 8.8 (range, 6.3-11.3) months for all patients treated with regorafenib vs. 7.1 months and 10.3 months for patients who received regorafenib in the second-line vs. 5.1 and 8.7 months for patients who received regorafenib in the third-line, respectively; however, there was no statistically significant difference (pPFS=0.22 and pOS=0.85). CONCLUSION: Although receiving CT as a first-line therapy in advanced HCC patients did not affect the survival rates of subsequent regorafenib therapy, it might diminish the DCR of regorafenib.

Efficacy of Pazopanib in patients with metastatic uterine sarcoma: A multi-institutional study.

PURPOSE: Uterine sarcoma accounts for 3-9% of uterine malignant tumors and has poor prognosis. Pazopanib is an oral multi-kinase inhibitor and the only tyrosine kinase inhibitor which has been approved for metastatic soft tissue sarcoma. In the present study we aimed to evaluate the efficacy of pazopanib in metastatic uterine sarcoma. METHODS: The data of 28 metastatic uterine sarcoma patients receiving pazopanib therapy, who were followed in four oncology centers in Ankara, Turkey between May 2013 and June 2018, were retrospectively analyzed. Patients over 18 years, ECOG performance status ≤ 2, receiving at least one line of chemotherapy for metastatic disease, measurable disease at diagnosis, and histologically proven uterine high grade sarcoma were the inclusion criteria. Progression-free survival (PFS), overall survival (OS), and response rates to pazopanib were retrospectively evaluated. RESULTS: The median age was 53 years (range, 26-76). The majority of the patients had uterine leiomyosarcoma (LMS) (n=25, 89.3%), 2 (7.1%) had undifferentiated uterine sarcoma (UUS), and 1(3.6%) had high grade endometrial stromal sarcoma (ESS). The most common site of metastasis was lung (n: 21, 75%). The median time for pazopanib therapy was 5 months (0.6-28.3). In 22 patients (78.5%), pazopanib was discontinued due to disease progression, while 2 patients (7.1%) quitted therapy owing to toxicity. Partial response was achieved in 4 patients (14.3%), while 17 (60.7%) had stable disease. Median PFS was 5.2 months (95% CI 2.8-7.5) and median OS was 11.4 months (95% CI 3.4-19.5). CONCLUSION: In the present study aiming to assess the real-life outcome of pazopanib-treated patients, we found that pazopanib is efficient in metastatic uterine sarcoma, and our results correspond to the literature.

A rare cause of proximal muscle weakness: immune necrotising myopathy.

Background Immune-mediated necrotising myopathies are characterised clinically by the subacute onset of proximal limb weakness, accompanied by elevated creatinine kinase levels. They are distinguished from other myopathies by the absence of prominent infiltration of the muscle with inflammatory cells in the biopsies. Case presentation A 44-year-old man presented with upper extremity weakness and dysphagia. Laboratory tests included a creatinine kinase level of 4362 U/L (normal: 52-336 U/L). Rheumatological markers were all negative. A muscle biopsy showed multiple necrotic fibres with minimal inflammatory infiltration. One gram of methylprednisolone (IV) was given, followed by 1 mg/kg of methylprednisolone daily by the oral route. Intravenous immunoglobulin (0.4 mg/kg/day) was given for five days. Muscle weakness regressed and dysphagia disappeared with treatment. The patient remains well in the 23rd month of treatment, taking 5 mg/day prednisolone and monthly intravenous immunoglobulin. Conclusion Treatment of immune-mediated necrotising myopathy can be challenging as evidence-based therapeutic options are limited. It is generally accepted that early and extensive immunosuppression, including glucocorticoids as first-line agents, may be required.