Page 41 - Read Online

Casadei et al. J Cancer Metastasis Treat 2022;8:21 https://dx.doi.org/10.20517/2394-4722.2022.05 Page 3 of 19

[19]
patients), whereas EZH2 and EP300 are involved in 25% and 15% of cases, respectively . The sum of loss of
function mutations affecting KMT2D, CREBBP, and EP300, together with EZH2 gain of function
mutations, are transcriptionally repressive, leading to lymphoma development via GC cells proliferation,
impairment differentiation, and immune evasion with major histocompatibility complex (MHC)
downregulation . Since most FL carry multiple epigenetic lesions, mutation timing, type, and degree
[20]
become important variables that can help us understand the clinical heterogeneity of FL patients.
Furthermore, other pathways can be disrupted, including immune recognition, nuclear factor kB,
mammalian target of rapamycin (mTOR), and Janus-kinase signal transducers and activators of
transcription (JAK-STAT) signaling: these alterations concur, together with epigenetic mutations, to
lymphomagenesis. In addition to genetic changes, TME is the other leading actor in FL pathophysiology, as
the pabulum of non-neoplastic cells determinate the immune response suppression and facilitate tumor
cells’ growth .
[21]
Beside the longitudinal genetic heterogeneity, another layer of complexity is our understanding of the
existence of spatial genetic heterogeneity within a single patient at different disease sites, posing challenges
for precision medicine approaches. A relatively recent method consists in detecting circulating tumor
cells (CTCs) and circulating cell-free DNA (cfDNA) in the patient’s peripheral blood through polymerase
chain reaction. This allows for an extensive studying of the lymphoma’s genetic characteristics and for the
monitoring of its spatial and temporal heterogeneity without the need for repeated invasive biopsies [7,22] .
This knowledge is important, as some mutations are now included in prognostic models, such as EZH2 and
EP300 that are integrated into the m7-FLIPI score (FL International Prognostic Index), and can be easily
missed with a single sample . Furthermore, in the near future, increasingly free from chemotherapy, the
[23]
failure to detect the corresponding predictive biomarker in a tumor biopsy could prevent some patients
from receiving specific targeted therapies. In this setting, the early identification of patients with high-risk
biology becomes necessary for guiding treatment selection and sequencing.

PROGNOSTIC MODELS IN FOLLICULAR LYMPHOMA: ARE WE READY TO IDENTIFY THE
POOREST RISK SUBSET OF PATIENTS?
Several interesting models have been developed to predict progression-free survival (PFS) and overall
survival (OS) of FL patients. FLIPI, built in the pre-rituximab era, and FLIPI2, developed when chemo-
immunotherapy had become the standard for first-line treatment, both include an evaluation of tumor
burden in terms of Ann Arbor stage, number of nodal sites, lymph node size, and lactatedehidrogenase
(LDH) [24,25] . Recently, Batlevi and colleagues identified the increased FLIPI-score between diagnosis and first-
line treatment as markers of high-risk biology in FL patients. In particular, patients whose FLIPI increased
during observation time had inferior PFS and OS .
[26]

A new model, where gene mutations are included for the first time to predict failure-free survival, is m7-
FLIPI. It was validated in over 100 patients, and it considers FLIPI score, Eastern Cooperative Group
performance status (PS), and the mutational status of seven genes (EZH2, ARID1A, MEF2B, EP300,
FOXO1, CREBBP, and CARD11) to predict a subset of patients at high risk of treatment failure.
Particularly, it divides a low-risk group (78% of patients) with a five-year failure-free survival of 68% versus
25% in a high-risk group (22% of patients) . The high-risk m7-FLIPI is enriched in those patients with
[23]
recurrence or progression of disease within 24 months of frontline treatment (POD24 positive patients),
[23]
introducing the concept of early relapse as poor prognostic factor. More recent studies have highlighted the
role of 23 genes involved in DNA response pathways, immune regulation, cell cycle, and cell migration
pathways as well as tumor microenvironmental components as independent predictors of PFS status at 24
[27]
months and POD24 in FL patients .