Fibroblast growth factor receptors in cancer: genetic alterations,diagnostics, therapeutic targets and mechanisms of resistance

Fibroblast growth factor receptors (FGFRs) are aberrantly activated through single-nucleotide variants, gene fusions and copy number amplifications in 5–10% of all human cancers, although this frequency increases to 10–30% in urothelial carcinoma and intrahepatic cholangiocarcinoma. We begin this review by highlighting the diversity of FGFR genomic alterations identified in human cancers and the current challenges associated with the development of clinical-grade molecular diagnostic tests to accurately detect these alterations in the tissue and blood of patients. The past decade has seen significant advancements in the development of FGFR-targeted therapies, which include selective, non-selective and covalent small-molecule inhibitors, as well as monoclonal antibodies against the receptors. We describe the expanding landscape of anti-FGFR therapies that are being assessed in early phase and randomised controlled clinical trials, such as erdafitinib and pemigatinib, which are approved by the Food and Drug Administration for the treatment of FGFR3-mutated urothelial carcinoma and FGFR2-fusion cholangiocarcinoma, respectively. However, despite initial sensitivity to FGFR inhibition, acquired drug resistance leading to cancer progression develops in most patients. This phenomenon underscores the need to clearly delineate tumour-intrinsic and tumour-extrinsic mechanisms of resistance to facilitate the development of second-generation FGFR inhibitors and novel treatment strategies beyond progression on targeted therapy.Subject terms: Cancer, Cancer     

Effectiveness,Patient Satisfaction,and Cost Reduction of Virtual Joint Replacement Clinic Follow-Up of Hip and Knee Arthroplasty

BackgroundTotal hip and knee arthroplasties are increasingly performed operations, and routine follow-up places huge demands on orthopedic services. This study investigates the effectiveness, patients’ satisfaction, and cost reduction of Virtual Joint Replacement Clinic (VJRC) follow-up of total hip arthroplasty and total knee arthroplasty patients in a university hospital. VJRC is especially valuable when in-person appointments are not advised or feasible such as during the COVID-19 pandemic.MethodsA total of 1749 patients who were invited for VJRC follow-up for knee or hip arthroplasty from January 2017 to December 2018 were included in this retrospective study. Patients were referred to VJRC after their 6-week postoperative review. Routine VJRC postoperative review was undertaken at 1 and 7 years and then 3-yearly thereafter. We evaluated the VJRC patient response rate, acceptability, and outcome. Patient satisfaction was measured in a subgroup of patients using a satisfaction survey. VJRC costs were calculated compared to face-to-face follow-up.ResultsThe VJRC had a 92.05% overall response rate. Only 7.22% required further in-person appointments with only 3% being reviewed by an orthopedic consultant. VJRC resulted in an estimated saving of £42,644 per year at our institution. The patients’ satisfaction survey showed that 89.29% of the patients were either satisfied or very satisfied with VJRC follow-up.ConclusionVJRC follow-up for hip and knee arthroplasty patients is an effective alternative to in-person clinic assessment which is accepted by patients, has high patient satisfaction, and can reduce the cost to both health services and patients.     

PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?

Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.