Everolimus after failure of one prior VEGF-targeted therapy in metastatic renal cell carcinoma : Final results of the MARC-2 trial

MARC-2, a prospective, multicenter phase IV trial, aimed to investigate clinical outcomes in patients with metastatic renal cell carcinoma (mRCC) treated with everolimus after failure of one initial vascular endothelial growth factor receptor tyrosine kinase inhibitor (VEGFR-TKI) therapy and to identify subgroups benefiting most, based on clinical characteristics and biomarkers. Patients with clear cell mRCC failing one initial VEGFR-TKI received everolimus until progression or unacceptable toxicity. Primary endpoint was 6-month progression-free survival rate (6moPFS). Secondary endpoints were overall response rate (ORR), PFS, overall survival (OS), and safety. Between 2011 and 2015, 63 patients were enrolled. Median age was 65.4 years (range 43.3-81.1). 6moPFS was 39.3% (95% confidence interval [CI], 27.0-51.3) overall, 54.4% (95% CI, 35.2-70.1) vs 23.7% (95% CI, 10.5-39.9) for patients aged ≥65 vs 25 vs ≤25 kg/m2. A Cox proportional hazards model confirmed a longer PFS for patients aged ≥65 years (hazard ratio [HR] 0.46; 95% CI, 0.26-0.80) and a longer OS for patients with BMI >25 kg/m2 (HR 0.36; 95% CI, 0.18-0.71). Median PFS and median OS were 3.8 months (95% CI, 3.2-6.2) and 16.8 months (95% CI, 14.3-24.3). ORR was 7.9% and disease control rate was 60.3%. No new safety signals emerged. Most common adverse events were stomatitis (31.7%), fatigue (31.7%), and anemia (30.2%). One patient died from treatment-related upper gastrointestinal hemorrhage. Everolimus remains a safe and effective treatment option for mRCC patients after one prior VEGFR-TKI therapy. Patients aged ≥65 years and patients with BMI >25 kg/m2 benefited most

Thrombospondin-2 and LDH Are Putative Predictive Biomarkers for Treatment with Everolimus in Second-Line Metastatic Clear Cell Renal Cell Carcinoma (MARC-2 Study)

There is an unmet need for predictive biomarkers in metastatic renal cell carcinoma (mRCC) therapy. The phase IV MARC-2 trial searched for predictive blood biomarkers in patients with predominant clear cell mRCC who benefit from second-line treatment with everolimus. In an exploratory approach, potential biomarkers were assessed employing proteomics, ELISA, and polymorphism analyses. Lower levels of angiogenesis-related protein thrombospondin-2 (TSP-2) at baseline (≤665 parts per billion, ppb) identified therapy responders with longer median progressionfree survival (PFS; ≤665 ppb at baseline: 6.9 months vs. 1.8, p = 0.005). Responders had higher lactate dehydrogenase (LDH) levels in serum two weeks after therapy initiation (>27.14 nmol/L), associated with a longer median PFS (3.8 months vs. 2.2, p = 0.013) and improved overall survival (OS; 31.0 months vs. 14.0 months, p < 0.001). Baseline TSP-2 levels had a stronger relation to PFS (HR 0.36, p = 0.008) than baseline patient parameters, including IMDC score. Increased serum LDH levels two weeks after therapy initiation were the best predictor for OS (HR 0.21, p < 0.001). mTOR polymorphisms appeared to be associated with therapy response but were not significant. Hence, we identified TSP-2 and LDH as promising predictive biomarkers for therapy response on everolimus after failure of one VEGF-targeted therapy in patients with clear cell mRCC

Improving Chemotherapy-Induced Peripheral Neuropathy in Patients with Breast or Colon Cancer after End of (Neo)adjuvant Therapy: Results from the Observational Study STEFANO

Introduction: Chemotherapy-induced peripheral neuropathy (CIPN) is a common side effect persisting after completion of neurotoxic chemotherapies. This observational study was designed to evaluate the effectiveness of the dietary supplement OnLife (R) (patented mixture of specific fatty acids and palmitoylethanolamide) in improving symptoms of CIPN in breast and colon cancer patients. Methods: Improvement of CIPN was evaluated in adult patients, previously treated with (neo)adjuvant paclitaxel- (breast cancer) or oxaliplatin-based (colon cancer) therapies, receiving OnLife (R) for 3 months after completion of chemotherapy. The primary endpoint was to compare the severity of peripheral sensory neuropathy (PSN) and peripheral motor neuropathy (PMN) before and at the end of OnLife (R) treatment. Secondary endpoints included the assessment of patient-reported quality of life and CIPN symptoms as assessed by questionnaires. Results: 146 patients (n = 75 breast cancer patients and n = 71 colon cancer patients) qualified for analysis; 31.1% and 37.5% of breast cancer patients had an improvement of PSN and PMN, respectively. In colon cancer patients, PSN and PMN improved in 16.9% and 20.0% of patients, respectively. According to patient-reported outcomes, 45.9% and 37.5% of patients with paclitaxel-induced PSN and PMN, and 23.9% and 22.0% of patients with oxaliplatin-induced PSN and PMN experienced a reduction of CIPN symptoms, respectively. Conclusion: OnLife (R) treatment confirmed to be beneficial in reducing CIPN severity and in limiting the progression of neuropathy, more markedly in paclitaxel-treated patients and also in patients with oxaliplatin-induced CIPN

A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task

Background: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measured to date. This study compares the performance of a convolutional neuronal network trained with dermoscopic images exclusively for identifying melanoma in clinical photographs with the manual grading of the same images by dermatologists. Methods: We compared automatic digital melanoma classification with the performance of 145 dermatologists of 12 German university hospitals. We used methods from enhanced deep learning to train a CNN with 12,378 open-source dermoscopic images. We used 100 clinical images to compare the performance of the CNN to that of the dermatologists. Dermatologists were compared with the deep neural network in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with clinical images was 89.4% (range: 55.0%-100%) and 64.4% (range: 22.5%-92.5%). At the same sensitivity, the CNN exhibited a mean specificity of 68.2% (range 47.5%-86.25%). Among the dermatologists, the attendings showed the highest mean sensitivity of 92.8% at a mean specificity of 57.7%. With the same high sensitivity of 92.8%, the CNN had a mean specificity of 61.1%. Interpretation: For the first time, dermatologist-level image classification was achieved on a clinical image classification task without training on clinical images. The CNN had a smaller variance of results indicating a higher robustness of computer vision compared with human assessment for dermatologic image classification tasks. (C) 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

Background: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy. Methods: We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany. Outperformance of dermatologists by the deep neural network was measured in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with dermoscopic images was 74.1% (range 40.0%-100%) and 60% (range 21.3%-91.3%), respectively. At a mean sensitivity of 74.1%, the CNN exhibited a mean specificity of 86.5% (range 70.8%-91.3%). At a mean specificity of 60%, a mean sensitivity of 87.5% (range 80%-95%) was achieved by our algorithm. Among the dermatologists, the chief physicians showed the highest mean specificity of 69.2% at a mean sensitivity of 73.3%. With the same high specificity of 69.2%, the CNN had a mean sensitivity of 84.5%. Interpretation: A CNN trained by open-source images exclusively outperformed 136 of the 157 dermatologists and all the different levels of experience (from junior to chief physicians) in terms of average specificity and sensitivity. (C) 2019 The Author(s). Published by Elsevier Ltd