Silva, Alvin

Abstract Background: Sorafenib (Sor) remains a first-line option for hepatocellular carcinoma (HCC) or refractory renal cell carcinomas (RCC). PLC/PRF/5 HCC model showed upregulation of hypoxia with enhanced efficacy when Sor is combined with hypoxia-activated prodrug evofosfamide (Evo). Methods: This phase IB 3 + 3 design investigated 3 Evo dose levels (240, 340, 480 mg/m2 on days 8, 15, 22), combined with Sor 200 mg orally twice daily (po bid) on days 1-28 of a 28-day cycle. Primary objectives included determining maximum tolerated dose (MTD) and recommended phase II dose (RP2D) of Sor + Evo. Results: Eighteen patients were enrolled (median age 62.5 years; 17 male /1 female; 12 HCC/6 RCC) across three dose levels (DL0: Sor 200 mg bid/Evo 240 mg/m2 [n=6], DL1:Sor 200 mg bid/Evo 480 mg/m2 [n=5], DL1a: Sor 200 mg bid/Evo 340 mg/m2 [n=7]). Two dose-limiting toxicities (DLTs) were reported with Evo 480 mg/m2 (grade 3 mucositis, grade 4 hepatic failure). Grade 3 rash DLT was observed in one patient at Evo 240 mg/m2. No DLTs were observed at Evo 340 mg/m2. MTD and RP2D were established as Sor 200 mg/Evo 340 mg/m2 and Sor 200/Evo 240 mg/m2, respectively. The most common treatment-related adverse events included fatigue, hand-foot syndrome, hypertension, and nausea/vomiting. Two partial responses were observed, one each at DL0 and DL1a.; disease control rate was 55%.Conclusions: RP2D was established as sorafenib 200 mg bid + Evo 240 mg/m2. While preliminary anti-tumor activity was observed, future development must account for advances in immunotherapy in HCC/RCC. NCT01497444Date of registration December 20, 2011

Abstract Prostate cancer is the second most common new cancer diagnosis in the United States. The prostate gland sits beneath the urinary bladder and surrounds the first part of the urethra. Usually, prostate cancer is slow-growing; stays confined to the prostate gland; and can be treated conservatively (active surveillance) or with surgery. However, if the cancer has spread beyond the prostate, such as to the lymph nodes, then that suggests the cancer is more aggressive and surgery is not adequate. In those cases, radiation and/or systemic therapies (e.g., chemotherapy, immunotherapy) are required. The challenge is that it is often difficult for radiologists to differentiate malignant lymph nodes from non-malignant ones with current medical imaging technology. In this study, we design a scalable hybrid approach utilizing a deep learning model to extract features into a machine learning classifier to automatically identify malignant lymph nodes in patients with prostate cancer.