Aim:We aimed to characterize the safety profile of pembrolizumab in advanced melanoma patients at our center to better reflect ‘real-world’ data on anti-PD-1 inhibitors.Materials & methods:At our institution, 58 ipilimumab-naive and 30 ipilimumab-treated patients with advanced melanoma who have received pembrolizumab between June 2014 and June 2017 were included for analysis.Results:Incidence of any-grade and grade 3/4 toxicities were 81.8% (n = 72) and 12.5% (n = 11), respectively. The most common side effects were skin-related (61.4%, n = 54) and gastrointestinal-related (51.1%, n = 45) events. In total, 25% of patients required oral steroids to manage immune-related adverse events with a median cumulative prednisolone dose of 683 mg (range: 40–3745 mg).Conclusion:Pembrolizumab is well tolerated in ‘real-world’ patients and severe toxicities can be effectively managed with systemic steroids.
Immunotherapy is a novel type of anti-cancer treatment that works by upregulating the host's immune system to fight against cancer cells. Landmark immunotherapy trials have demonstrated improvements in response rates and survival compared to cytotoxic chemotherapy. Specific immunotherapies known as checkpoint inhibitors are now routinely used in a range of cancers including melanoma, lung, renal and urological cancers. Immunotherapies are associated with immune-related adverse events which are very different to those seen with traditional cytotoxic chemotherapies. This can present a new challenge to oncologists, acute physicians and the wider team of health-care professionals who look after patients receiving immunotherapy. Generally, these side effects are easily managed but some, if untreated, can be subtle and potentially life-threatening. Patients on immunotherapy may present to a wide variety of medical professionals including the emergency department, primary care and general medical admissions units. It is therefore vital that there is increased awareness and education to identify and manage side effects of immunotherapy effectively.
Background: Cancer types eligible for treatment with immune checkpoint inhibitors (ICI) have increased over the past decade thus simultaneously growing the number of patients presenting to emergency departments (ED) with immune mediated toxicities. Objectives: The objective of this study was to ascertain information regarding the knowledge and management of immune checkpoint inhibitor mediated toxicities amongst emergency department physicians. Methods: A multiple-choice questionnaire was developed assessing the understanding of ICI usage and management of immune mediated toxicities, amongst ED physicians in 6 major ED departments in London. Participating clinicians included all levels of trainees and ED physicians. Questionnaires were distributed during weekly ED educational sessions, followed by training on immune-mediated toxicities. Results: Between March 2019 and September 2019, the questionnaire was delivered to 126 participants (80% junior grade, 20% specialist ED consultants). There was no significant association between clinician's seniority and overall score reached on the questionnaire. Amongst all participants, 56, 49, and 36% identified correctly ICIs as the first-line treatment regimen for melanoma, renal cell carcinoma, and non-small cell lung cancer, respectively. Overall, 90% of the participants recognized correctly cisplatin as a chemotherapy agent and 77% pembrolizumab as an ICI agent. Generally, there was a good understanding of chemotherapy related toxicities, however, the participants scored less well on questions relating to ICIs. Ten months following the initial audit and educational intervention, a single site re-audit was performed. The total average correct score was similar pre- and post-intervention (8, 13%, respectively). Conclusions: Knowledge and management of immune mediated toxicities is inferior compared to chemotherapy across physicians working in major ED departments in London. This survey highlights the need for increased education on ICI amongst ED clinicians.
5074 Background: Existing clinicopathological tools are unable to accurately identify renal cell carcinoma (RCC) patients who will develop metastases after surgery. As a result, it is unclear how long and how often to follow-up patients post-operatively. Tumor macropathology, as assayed by CT scanning, represents the sum product of tumor biology and microenvironment. We hypothesized that quantitative tumor features extracted from CT scans (termed radiomics) could discriminate between metastatic and non-metastatic RCCs. Methods: This retrospective study incorporated three cohorts of clear-cell RCC patients (n = 279, from TCGA, CPTAC and KiTS19 datasets) treated with nephrectomy. The study cohort was sub-divided into metastatic (n = 54, M1 at diagnosis or recurrence after surgery), high metastatic risk/HMR (n = 85, N1, T3-4, T2G3/4, T1G4) or low metastatic risk/LMR (n = 140, absence of these features) subsets. 3D primary tumor segmentation of arterial contrast CT scans was performed by trained investigators. Features were extracted using pyRadiomics 2.2.0 (n = 839) with gray value and voxel size normalization. For random forest (RF) model training, the cohort was randomly split into training (75%) and validation (25%) sets. Results: Multidimensional clustering of radiomic features by t-SNE analysis showed that metastatic and HMR tumors predominantly cluster together, while LMR tumors cluster separately. Consistent with this, there were no differentially regulated radiomic features (DR-features) between HMR and metastatic tumors. In contrast, we identified 26 DR-features (adjusted p-value < 0.05) between presumed-metastatic (n = 139, HMR and metastatic tumors) and LMR tumors, which were then used as input to a RF binary classifier. In the training set, the trained classifier discriminated between presumed-metastatic and LMR tumors with bootstrapped AUC = 0.81. In the validation set, the classifier discriminated subsets with AUC = 0.80. Conclusions: High-risk and metastatic tumors have similar radiomic properties, suggesting common biology driving metastasis in RCC. We propose a novel radiomic classifier that accurately distinguishes between presumed-metastatic and low-risk tumors. Further work will assess whether this tool can identify patients with micrometastatic disease at diagnosis, who may benefit from adjuvant therapy or closer, long-term surveillance.
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