A coming of age for immune checkpoint inhibitors in cancer

Psychological aspects in patients with advanced cancer receiving lifelong systemic treatment: protocol for a scoping review

INTRODUCTION

A better understanding of the molecular, genetic and immunological characteristics of cancer and the introduction of new systemic treatment regimens in the last decades, has led to better treatment outcomes and increased survival rates for patients with previously short lived cancers. However, there is no uniform description to refer to this growing group of patients with advanced cancer who now respond to new systemic treatments for longer periods. Furthermore, little is known about the unique psychological challenges these patients face, living with ongoing uncertainty about the course of their disease and life expectancy. The objective of this scoping review is to identify the psychological aspects experienced by, and the definitions used to refer to patients with advanced cancer receiving lifelong systemic treatment.

METHODS AND ANALYSIS

This review will be among the first to summarise literature on the psychological issues in the growing group of advanced cancer patients undergoing long-term systemic treatment. Articles will be retrieved from six databases (MEDLINE, Embase, Web of Science, PsycINFO, CINAHL and the Cochrane Database of Systematic Reviews) and reviewed for eligibility by two investigators independently. Definitions and psychological challenges will be extracted and narratively summarised following a descriptive approach. Furthermore, results will contribute in providing a uniform definition for this patient group, and help to identify knowledge gaps to give direction to further research in this field.

ETHICS AND DISSEMINATION

No ethical approval is required. The results of the scoping review will be submitted for publication to a scientific journal and presented at relevant conferences.

Risk of relapse after anti-PD1 discontinuation in patients with Hodgkin lymphoma

INTRODUCTION

Patients with relapsed/refractory Hodgkin lymphoma (R/R HL) experience high response rates upon anti-PD1 therapy. In these patients, the optimal duration of treatment and the risk of relapse after anti-PD1 discontinuation are unknown.

METHODS

We retrospectively analyzed patients with R/R HL who responded to anti-PD1 monotherapy and discontinued the treatment either because of unacceptable toxicity or prolonged remission. A machine learning algorithm based on 17 candidate variables was trained and validated to predict progression-free survival (PFS) landmarked at the time of discontinuation of anti-PD1 therapy.

RESULTS

Forty patients from 14 centers were randomly assigned to training (n = 25) and validation (n = 15) sets. At the time of anti-PD1 discontinuation, patients had received treatment for a median duration of 11.2 (range, 0-time to best response was not statistically significant in discriminating patients with PFS lesser or greater than 12 months). Considering PFS status as a binary variable (alive or dead) at a specific time point (12 months) is convenient, intuitive and allows for comparing the value of potential predicting variables in these two groups of patients. Nonetheless, this approach has two drawbacks: first, it binarizes outcome; second, it excludes patients alive with a time to last follow up lesser 12 months. Therefore, it is less powerful to demonstrate statistically significant association with PFS even if it exists 5 months. Patients discontinued anti-PD1 treatment either because of prolonged remission (N = 27, 67.5%) or unacceptable toxicity (N = 13, 32.5%). Most patients were in CR (N = 35, 87.5%) at the time of anti-PD1 discontinuation. In the training set, the machine learning algorithm identified that the most important variables to predict PFS were patients' age, time to best response, and presence or absence of CR. The performance observed in the training set was validated in the validation set.

CONCLUSION

In this pilot, proof of concept study using a machine learning algorithm, we identified biomarkers capable of predicting the risk of relapse after anti-PD1 discontinuation (age, time to best response, quality of response). Once confirmed, these simple biomarkers will represent useful tools to guide the management of these patients.