Second-line treatment strategies for RAS wild-type colorectal cancer: A systematic review and Network Meta-analysis (NMA).

BACKGROUND: The optimal strategy for second-line (IIL) treatment in KRAS wt metastatic colorectal cancer (mCRC) is not determined yet. METHODS: A random-effect NMA of phase II/III RCTs was conducted to evaluate IIL treatment for all-RAS wt mCRC, comparing anti-EGFR or anti-VEGF, and chemotherapy (CT). RESULTS: Overall, 11 RCTs (3613 patients) were included. In KRAS wt patients, PFS was improved with anti-VEGF (HR 0.43) and anti-EGFR (HR 0.63) vs CT. However, anti-VEGF based therapy had the highest likelihood of being ranked as the best treatment in terms of PFS (SUCRA 99.3%) and OS (SUCRA 99.4%). Bevacizumab-based treatment is most likely to be the best treatment in terms of PFS (SUCRA 89.1%) and OS (SUCRA 86.7%). CONCLUSIONS: Second line treatment with anti-VEGF and anti-EGFR improved PFS in mCRC patients, however, anti-VEGF based therapy, particularly CT plus bevacizumab, is the best treatment according to SUCRA in terms of PFS and OS.

Analysis of workload generated in the two years following first consultation by each new cancer patient: studying the past to plan the future of cancer care.

INTRODUCTION: Prevalence of cancer patients is dramatically increasing. We aimed at quantifying the oncology workload generated by each new cancer patient in the two years following first consultation. METHODS: In this record-based retrospective study, we retrieved data of all newly diagnosed patients treated at the Oncology Department of Udine Academic Hospital between 01.01.2012 and 31.12.2017. We calculated mean number and standard deviation of the activity type generated by each new cancer patient during the following 2 years. RESULTS: Seven thousand four hundred fifty-two cancer patients generated a total of 85,338 clinical episodes. The two-years mean number of oncology episodes generated was 11.31 (i.e., for every 1,000 new cancer patients, 11,310 oncology activities are generated overall in the following two-year lapse). Patients with advanced disease generated the highest workload (24.3; SD 18.8) with a statistically significant difference compared to adjuvant and follow-up patients (p < 0.001). The workload generated in the period 0-6 and 0-12 months was significantly higher than in the following months (p < 0.001) and it was also higher for patients initially designated to treatment (p < 0.001). CONCLUSION: This is the first study reporting on the mean oncology workload generated during the 2 years following first consultation. Workload is the highest for patient with advanced disease, especially in the first months and in patients in active treatment. A detailed analysis of workloads in oncology is feasible and could be crucial for planning a sustainable framework for cancer care in the next future.

What to do in an oncology department to face the new COVID-19 era challenges?

Italy was the first European country to be hit by COVID-19 pandemic. As a consequence, Italian oncologists had to guarantee essential treatments although minimizing exposure to the virus, and accidental infection, of patients and healthcare professionals. As Department of Medical Oncology of the University Hospital of Udine, in this short report, we describe the measures that we have taken, and gradually updated, since February 26, 2020. All accesses to our Oncology facilities are currently regulated by entrance check-points where patients are screened for infections following dedicated algorithms. Up to date, after 6 weeks of systematic execution of swabs no physician, nurse or other individual of the staff has been found positive to COVID-19. Only one patient admitted for therapy has been identified as COVID-19 positive. The aim of our work is to propose a model, made up of a set of operative procedures, that may be adopted by all the oncologists that daily struggle to guarantee safety and care in Oncology during this COVID-19 emergency.

Network-guided modeling allows tumor-type independent prediction of sensitivity to all-trans-retinoic acid.

Background: All-trans-retinoic acid (ATRA) is a differentiating agent used in the treatment of acute-promyelocytic-leukemia (APL) and it is under-exploited in other malignancies despite its low systemic toxicity. A rational/personalized use of ATRA requires the development of predictive tools allowing identification of sensitive cancer types and responsive individuals. Materials and methods: RNA-sequencing data for 10 080 patients and 33 different tumor types were derived from the TCGA and Leucegene datasets and completely re-processed. The study was carried out using machine learning methods and network analysis. Results: We profiled a large panel of breast-cancer cell-lines for in vitro sensitivity to ATRA and exploited the associated basal gene-expression data to initially generate a model predicting ATRA-sensitivity in this disease. Starting from these results and using a network-guided approach, we developed a generalized model (ATRA-21) whose validity extends to tumor types other than breast cancer. ATRA-21 predictions correlate with experimentally determined sensitivity in a large panel of cell-lines representative of numerous tumor types. In patients, ATRA-21 correctly identifies APL as the most sensitive acute-myelogenous-leukemia subtype and indicates that uveal-melanoma and low-grade glioma are top-ranking diseases as for average predicted responsiveness to ATRA. There is a consistent number of tumor types for which higher ATRA-21 predictions are associated with better outcomes. Conclusions: In summary, we generated a tumor-type independent ATRA-sensitivity predictor which consists of a restricted number of genes and has the potential to be applied in the clinics. Identification of the tumor types that are likely to be generally sensitive to the action of ATRA paves the way to the design of clinical studies in the context of these diseases. In addition, ATRA-21 may represent an important diagnostic tool for the selection of individual patients who may benefit from ATRA-based therapeutic strategies also in tumors characterized by lower average sensitivity.