No abstract

Evading immune eradication is a prerequisite for neoplastic progression and one of the hallmarks of cancer. Here, we review the different immune escape strategies of lymphoma and classify them into two main mechanisms. First, lymphoma cells may “hide” to become invisible to the immune system. This can be achieved by losing or downregulating MHC and/or molecules involved in antigen presentation (including antigen processing machinery and adhesion molecules), thereby preventing their recognition by the immune system. Second, lymphoma cells may “defend” themselves to become resistant to immune eradication. This can be achieved in several ways: by becoming resistant to apoptosis, by expressing inhibitory ligands that deactivate immune cells and/or by inducing an immunosuppressive (humoral and cellular) microenvironment. These immune escape mechanisms may have therapeutic implications. Their identification may be used to guide “personalized immunotherapy” for 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.

Patients with hematological malignancies (HMs) have less access to palliative care (PC) than other patients with cancer and benefit from it later on in the course of their disease, although symptom burden is just as heavy. [1][2][3][4] We created a specialized outpatient PC consultation in the hematology department to improve the quality of patient management and enhance cooperation with hematologists. We found that although patient characteristics and survival were extremely variable, they all had in common a need for symptom management and care coordination. As a result of the consultation, hematology teams called upon a specialized PC multidisciplinary team more often to meet patients hospitalized within their departments, and more patients with HMs were hospitalized in PC units.Recent evidence has demonstrated the feasibility, acceptability, and efficacy of integrating PC to improve the quality of life and care of patients with HMs and their caregivers. [5][6][7] Despite clear recommendations to integrate PC in oncology, and in particular, hematooncology, the question of what, when, and how to integrate it has yet to be answered. 8 The constructs of integration plans are needed, adapted to national, regional, and local organizations of oncology and palliative care, as well as to the culture of the organization.