Incidence and survival of soft tissue sarcoma in England between 2013 and 2017, an analysis from the National Cancer Registration and Analysis Service.

There is a paucity of population-based data detailing the incidence and survival of patients with soft tissue sarcoma (STS), in part due to the heterogeneity of disease and changes to classification. Here, the incidence and survival of all STS subtypes registered in England between 2013 and 2017 were analysed using cancer registry data held by the National Cancer Registration and Analysis Service. Age-standardised incidence rates were calculated per 1 000 000 using the 2013 European Standard Population. Net survival was computed using Brenner's alternative method, with the Ederer II estimator. Age-specific overall survival was assessed using Kaplan-Meier. The influence of age, sex, socioeconomic deprivation and diagnostic routes on survival was assessed using Cox proportional hazards modelling. In total, 19 717 patients were diagnosed with STS, an average of 3943 patients per year and representing approximately 0.8% of malignancies. The most common histological diagnoses were Gastrointestinal Stromal Tumours (GIST), leiomyosarcoma and undifferentiated sarcoma, accounting for 20.2%, 13.3% and 12.7% of all sarcomas, respectively. Five-year net survival for all malignant STS was 65.0%; and was lowest for patients with vascular tumours at 39%. Patients from most deprived cohorts had 23% greater chance of dying within 5 years than patients in least deprived areas. This population-based study has allowed us for the first time to define the incidence and survival rates of prevalent STS subtypes in England such as GIST, liposarcoma and leiomyosarcoma, as well as rare entities and groups with inferior outcome. This data is invaluable for service provision, benchmarking and addressing inequality.

Optimizing circadian drug infusion schedules towards personalized cancer chronotherapy.

Precision medicine requires accurate technologies for drug administration and proper systems pharmacology approaches for patient data analysis. Here, plasma pharmacokinetics (PK) data of the OPTILIV trial in which cancer patients received oxaliplatin, 5-fluorouracil and irinotecan via chronomodulated schedules delivered by an infusion pump into the hepatic artery were mathematically investigated. A pump-to-patient model was designed in order to accurately represent the drug solution dynamics from the pump to the patient blood. It was connected to semi-mechanistic PK models to analyse inter-patient variability in PK parameters. Large time delays of up to 1h41 between the actual pump start and the time of drug detection in patient blood was predicted by the model and confirmed by PK data. Sudden delivery spike in the patient artery due to glucose rinse after drug administration accounted for up to 10.7% of the total drug dose. New model-guided delivery profiles were designed to precisely lead to the drug exposure intended by clinicians. Next, the complete mathematical framework achieved a very good fit to individual time-concentration PK profiles and concluded that inter-subject differences in PK parameters was the lowest for irinotecan, intermediate for oxaliplatin and the largest for 5-fluorouracil. Clustering patients according to their PK parameter values revealed patient subgroups for each drug in which inter-patient variability was largely decreased compared to that in the total population. This study provides a complete mathematical framework to optimize drug infusion pumps and inform on inter-patient PK variability, a step towards precise and personalized cancer chronotherapy.