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Cure Probability Models for Evaluation of Patients with a Previous Cancer Diagnosis for Solid Organ Transplantation

Evaluation and referral of patients with cancer based on cure probability could increase safety and overall population-level benefit of transplantation
26 Oct 2021
Cancer in Special Situations / Population

Findings from a first study that applied a formal statistical framework to inform the evaluation of transplant candidates with a previous cancer diagnosis were published on 22 October 2021 in the Journal of Clinical Oncology. Dr Eric A. Engels of the Division of Cancer Epidemiology and Genetics, National Cancer Institute in Bethesda, US and colleagues used general population cancer registry data on 10.5 million patients with cancer to model statistical cure and applied these models to calculate individual patients' cure probabilities for 17 common cancer types. They demonstrated that these cure probabilities offer prognostic information. After transplantation, patients in the low-cure probability tertile had twice the mortality from cancer as patients in the other two tertiles, which translated into an increase in overall mortality. Cure probability was not predictive of non-cancer mortality among transplanted patients.

The authors wrote that with improvements in cancer survival, the prevalence of cancer has increased among people evaluated for organ transplantation and among those who eventually receive a transplant. Because of the medical complexity of transplantation, including the need to administer lifelong immunosuppression, transplant programmes must make complex decisions that weigh benefits and risks in determining who should be referred for transplantation.

Cure probabilities may be useful for evaluating which patients with a previous cancer diagnosis could safely be listed for solid organ transplantation. However, no previous study has derived and applied cure models for this purpose.

The study team fitted statistical cure models for patients with cancer in the US general population using data from 13 cancer registries. Patients subsequently undergoing solid organ transplantation were identified through the Scientific Registry of Transplant Recipients. The team applied these models to individuals with a previous cancer who underwent solid organ transplantation to estimate the probability that those patients had been cured of their cancer at the time of transplantation. They then evaluated the extent to which the cure probabilities for these patients predicted cancer-specific mortality after transplantation. Finally, they assessed associations of several transplant-related factors, that is, the transplanted organ and use of specific immunosuppressive medications, with cancer-specific mortality among this population.

Among 10,524,326 patients with 17 cancer types in the general population, the median cure probability at diagnosis was 62%. Of these patients, 5,425 (0.05%) subsequently underwent solid organ transplantation and their median cure probability at transplantation was 94% (interquartile range, 86-98%).

Compared with the tertile of transplanted patients with highest cure probability, those in the lowest tertile more frequently had lung or breast cancers and less frequently colorectal, testicular, or thyroid cancers, more frequently had advanced-stage cancer, were older (median 57 versus 51 years), and were transplanted sooner after cancer diagnosis (median 3.6 versus 8.6 years).

Patients in the low-cure probability tertile had increased cancer-specific mortality after transplantation (adjusted hazard ratio, 2.08; 95% confidence interval 1.48 to 2.93; versus the high tertile), whereas those in the middle tertile did not differ.

The authors commented that statistical cure probability may provide a useful framework to inform transplant guidelines and evaluate individual patients. Before application of this approach in a real-world clinical setting, their results should be replicated by using additional data, ideally incorporating detailed tumour and treatment information. If validated, cure models could be readily translated into the form of an application programme for use in clinical settings and a patient's updated cure probability and its trajectory could then be calculated and assessed in real time.

Although these probabilities can serve as an appropriate benchmark, it would be reasonable for clinicians to use additional patient data, including tests regarding the presence or absence of residual disease, to modify their estimate of a patient's cure probability. This approach might result in some low-risk patients being offered transplantation earlier than under the current approach, whereas other high-risk patients who are currently offered a transplant would be deferred.

The authors concluded that their results suggest that evaluation and referral of patients with cancer on the basis of cure probability could help increase the safety and overall population-level benefit of transplantation.

Reference

Engels EA, Haber G, Hart A, et al. Predicted Cure and Survival Among Transplant Recipients With a Previous Cancer Diagnosis. JCO; Published online 22 October 2021. DOI:10.1200/JCO.21.01195

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