Page 230 - 2022_01-Haematologica-web

C.Rossi et al.
Introduction
Although some patients exhibit long-term remission after anti-CD20-based therapy, 20-30% patients with fol- licular lymphoma (FL) experience early progression,1 and FL transforms into aggressive lymphoma in 2-3% patients per year.2 Determining the prognostic factors that could be used in the early stages to identify patients with a high risk of treatment failure has become a central challenge in the management of FL.3 Follicular Lymphoma International Prognostic Index (FLIPI) and FLIPI2 scores, which include baseline clinical and standard biological parameters, are not able to accurately identify early relapse associated with an increased risk of death. Because FL is fluorodeoxyglu- cose (FDG) avid, FDG positron emission tomography (PET) is used in routine practice to stage pretreatment dis- ease and to identify sites with high FDG uptake that are at risk of transformation.4 The predictive power of post- induction PET status on outcome appears to be much stronger than FLIPI or FLIPI2 scores and computed tomog- raphy (CT)-based response, and most patients who achieve PET negativity can expect their first remission to last several years.5 However, the critical challenge is to identify patients who have a high risk of standard treat- ment failure before initiating therapy.
Among baseline PET metrics, total metabolic tumor vol- ume (TMTV) is a strong predictor of outcome in FL, inde- pendent from FLIPI2.6 Baseline whole-body maximum standardized uptake (SUVmax) has been used to predict out- comes in mantle cell lymphoma patients,7 but to our knowledge the value of pre-treatment SUVmax for prognosis in FL patients treated with rituximab (R)-chemotherapy followed by R maintenance (Rm) remains unclear. Very recently, a retrospective study8 reported that pre-treatment SUVmax >18 was associated with lower overall survival (OS) in a cohort of 346 advanced stage FL patients, but only a few these patients received heterogeneous induction treat- ments with maintenance therapy.
The biological basis of high SUVmax at baseline remains unclear, and the cell populations, either in tumors or their microenvironment, that mediate a high uptake of FDG remain unknown. However, a pro-tumor microenviron- ment and its interactions with cancer cells could play a key role in promoting tumor cell growth and invasion.9 Recently, we developed a set of 33 genes involved in tumor immune escape (Immune Escape Gene Set [IEGS33)]. IEGS33 includes the genes encoding for immune check- points (ICP) (e.g., CTLA4, PDCD1, LAG3, HAVCR2), for their ligands (e.g., CD80, CD86, CD274, PDCD1LG2, LGALS9), for enzymes producing immunosuppressive metabolites (e.g., IDO1, ARG1, ENTPD1), and for immunosuppressive cytokines and chemokines (e.g., IL10, HGF, GDF15). We discovered that the whole IEGS33 gene set is significantly up-regulated in all non-Hodgkin lym- phoma (NHL) samples.10 Although immune escape strate- gies in lymphoma may vary between individuals, our for- mer analysis of 1446 B-NHL transcriptomes evidenced the consistent up-regulation of IEGS33 in B-cell lymphomas.10 Furthermore, because the activation of immune effectors represents the ‘substrate’ of immune escape (IE), our meta- analysis of both ‘T-cell activation’ (44 T-cell genes such as IL2, CD28, ZAP70, LCK) and IEGS33 outlined four differ- ent stages of IE. These stages are seen in NHL patients in whom immune activation and escape are not observed, those in whom both are observed, those with mostly
immune activation, and those with mostly immune escape. Patients from these four classes displayed corre- spondingly different rates of overall survival (OS). Along the same lines, another study identified a signature of 23 genes involved in tumor cell cycle events (B-cell develop- ment, DNA damage response, cell migration and cell cycle) and immune regulation, which was predictive of progres- sion-free survival (PFS) and progression of disease within 24 months (POD24) in FL patients with a high tumor bur- den treated with R-chemotherapy plus maintenance.11 The tumor cell mutation profile may also influence patient out- comes. For instance, in combination with the FLIPI score, the mutation profile of seven genes (m7-FLIPI) involved in B-cell lymphomagenesis has been used to define a clinico- genetic risk index in FL patients receiving R-chemotherapy combined with cyclophosphamide, doxorubicin, vin- cristine and prednisone (R-CHOP).12 So far, however, none of these expression and mutational signatures has been analyzed with regard to baseline PET metrics.
PET has become an important tool for staging and response assessment in FL patients. For this reason, the relationships between the molecular and functional imag- ing parameters need to be explored in order to ascertain whether they could improve FL patient risk stratification in the initial staging. The objective of our study was therefore to determine the prognostic value of PET metrics at base- line in FL patients, and to establish links to the molecular signatures of FL tumor cells and their immune cell infiltra- tion.
Methods
Patients
The training cohort consisted of 48 FL patients (diagnosed by CL, CS or SP according to the World Health Oragnization classi- fication13) treated in the Department of Hematology of the IUCT Oncopole (Toulouse, France) between 2011 and 2016. The valida- tion cohort included 84 additional FL patients (diagnosed by LM or SR) treated in the Department of Hematology of the Dijon Bourgogne University Hospital (France) between 2012 and 2016. Patient characteristics are detailed in the Online Supplementary Table S1.
Tissue samples were collected and processed at the CRB Cancer des Hôpitaux de Toulouse and CRB Cancer du CHU de Dijon follow- ing ethics guidelines (Declaration of Helsinki), and written informed consent was obtained from all patients. CRB collections were declared to the Ministry of Research (DC-2009-989 for Toulouse, and DC-2008-508 for Dijon) and a transfer agreement (AC-2008-820) was obtained after approval from the appropriate ethicscommittees.
Positron emission tomography/computed tomography acquisition and analysis
Baseline PET acquisition was performed before any treatment and detailed in the Online Supplementary Methods.
PET images at baseline were centrally reviewed by one experi- enced reader (SK), who was blinded to any medical information, and analyzed using the free open-source software, Beth Israel Plugin for Fiji (http://petctviewer.org). PET and CT images (single modality and fused images) were displayed in three axes with multi-planar reconstruction along with maximum intensity pro- jection.
Pathological uptake was defined as an increased uptake of 18FDG over the physiological background. For each PET, whole-
222
haematologica | 2022; 107(1)