Brachytherapy: An emblematic example of extreme hypofractionated regimen

In order to provide more convenient irradiation regimens for patient comfort, radiation facility organization and health expenses, new hypofractionated protocols have been evaluated. Moderately (dose/fraction: 2.3 to 3 Gy), then ultra (dose/fraction: 5.2 to 6.1 Gy) hypofractionated irradiations were first validated. The current question is: is it possible to go forward using extreme hypofractionated regimens (EHR) based on 1 to 3 fractions. Different irradiation techniques are under investigation. However, brachytherapy remains the smartest way to deliver a high dose in a small volume. We report prospective and retrospective study results which evaluated EHR for breast and prostate brachytherapy. While oncological outcome and toxicity profile appear extremely encouraging for low-risk breast cancer after a 1 to 4 fractions (6.25 to 20 Gy/fraction), the use of a single fraction of 19 to 23 Gy appears debatable for prostate cancer. Brachytherapy represents an emblematic example of EHR but longer follow-up and more mature results are awaited in order to specify the right indications and refine the EQD2 calculation method including new biological and technical factors.     

Effect of gender on the reliability of COVID-19 rapid antigen test among elderly

Defining con-founders that affect the reliability of diagnostic tests for coronavirus disease 2019 is vital to breaking the chain of infection. The elderly population is a higher risk group for the emerging virus. However, gender seems to exert a critical role in modifying the infection risk among women owing to hormonal changes. The menopause transition is an exceptional period for older women where the protective and immunomodulatory effects of the estrogen hormone are lost. Accordingly, attention should be given to postmenopausal women since they will have an increased risk compared to their pre-menopausal peers.     

SARS-CoV-2 and cancer: the intriguing and informative cross-talk

The COVID-19 pandemic caused by the SARS-CoV-2 virus has significantly disrupted and burdened the diagnostic workup and delivery of care, including transfusion, to cancer patients across the globe. Furthermore, cancer patients suffering from solid tumors or hematologic malignancies were more prone to the infection and had higher morbidity and mortality than the rest of the population. Major signaling pathways have been identified at the intersection of SARS-CoV-2 and cancer cells, often leading to tumor progression or alteration of the tumor response to therapy. The reactivation of oncogenic viruses has also been alluded to in the context and following COVID-19. Paradoxically, certain tumors responded better following the profound infection-induced immune modulation. Unveiling the mechanisms of the virus-tumor cell interactions will lead to a better understanding of the pathophysiology of both cancer progression and virus propagation. It would be challenging to monitor, through the different cancer registries, retrospectively, the response of patients who have been previously exposed to the virus in contrast to those who have not contracted the infection.     

Predicting Survival for Chimeric Antigen Receptor T-Cell Therapy: A Validation of Survival Models Using Follow-Up Data From ZUMA-1

ObjectivesSurvival extrapolation for chimeric antigen receptor T-cell therapies is challenging, owing to their unique mechanistic properties that translate to complex hazard functions. Axicabtagene ciloleucel is indicated for the treatment of relapse or refractory diffuse large B-cell lymphoma after 2 or more lines of therapy based on the ZUMA-1 trial. Four data snapshots are available, with minimum follow-up of 12, 24, 36, and 48 months. This analysis explores how survival extrapolations for axicabtagene ciloleucel using ZUMA-1 data can be validated and compared.MethodsThree different parametric modeling approaches were applied: standard parametric, spline-based, and cure-based models. Models were compared using a range of metrics, across the 4 data snapshot, including visual fit, plausibility of long-term estimates, statistical goodness of fit, inspection of hazard plots, point-estimate accuracy, and conditional survival estimates.ResultsStandard and spline-based parametric extrapolations were generally incapable of fitting the ZUMA-1 data well. Cure-based models provided the best fit based on the earliest data snapshot, with extrapolations remaining consistent as data matured. At 48 months, the maximum survival overestimate was 8.3% (Gompertz mixture-cure model) versus the maximum underestimate of 33.5% (Weibull standard parametric model).ConclusionsWhere a plateau in the survival curve is clinically plausible, cure-based models may be helpful in making accurate predictions based on immature data. The ability to reliably extrapolate from maturing data may reduce delays in patient access to potentially lifesaving treatments. Additional research is required to understand how models compare in broader contexts, including different treatments and therapeutic areas.