EVALUATION OF CLINICOGENETIC RISK MODELS FOR OUTCOME OF FOLLICULAR LYMPHOMA PATIENTS IN THE PRIMA TRIAL

Genomic landscape of follicular lymphoma across a wide spectrum of clinical behaviors

While some follicular lymphoma (FL) patients do not require treatment or experience prolonged responses, others relapse early, and little is known about genetic alterations specific to patients with a particular clinical behavior. We selected 56 grade 1-3A FL patients according to their need of treatment or timing of relapse: never treated (n = 7), non-relapsed (19), late relapse (14), early relapse or POD24 (11), and primary refractory (5). We analyzed 56 diagnostic and 12 paired relapse lymphoid tissue biopsies and performed copy number alteration (CNA) analysis and next generation sequencing (NGS). We identified six focal driver losses (1p36.32, 6p21.32, 6q14.1, 6q23.3, 9p21.3, 10q23.33) and 1p36.33 copy-neutral loss of heterozygosity (CN-LOH). By integrating CNA and NGS results, the most frequently altered genes/regions were KMT2D (79%), CREBBP (67%), TNFRSF14 (46%) and BCL2 (40%). Although we found that mutations in PIM1, FOXO1 and TMEM30A were associated with an adverse clinical behavior, definitive conclusions cannot be drawn, due to the small sample size. We identified common precursor cells harboring early oncogenic alterations of the KMT2D, CREBBP, TNFRSF14 and EP300 genes and 16p13.3-p13.2 CN-LOH. Finally, we established the functional consequences of mutations by means of protein modeling (CD79B, PLCG2, PIM1, MCL1 and IRF8). These data expand the knowledge on the genomics behind the heterogeneous FL population and, upon replication in larger cohorts, could contribute to risk stratification and the development of targeted therapies.

First external validation of the FLIPI-L score in a single-center series of patients with follicular lymphoma

The FLIPI-L has recently been proposed as a novel prognostic index in follicular lymphoma (FL), combining FLIPI and the presence of lymphopenia. In our single-center validation in 381 FL patients, lymphopenia was less frequent than in the original publication and thus the distribution of risk categories was different. Although it was not able to properly predict time to first treatment, FLIPI-L performed slightly better than FLIPI alone in the prediction of response, early relapse, progression-free and overall survival, and histological transformation. This new tool or others encompassing parameters from the microenvironment might improve upon the prognostic ability of classical scores.

Early Progressing Follicular Lymphoma

PURPOSE OF REVIEW

Follicular lymphoma is an indolent lymphoma which does not limit life expectancy in most patients; however, approximately 20% of patients will experience progression of disease within 24 months (POD24) of diagnosis and have inferior survival outcomes. To date, no clinical, genetic, or tumor microenvironment prediction models have been able to definitively predict which patients will experience POD24 which limits the ability to alter frontline management of patients suspected to be at high risk of early progression. Here, we review recent literature regarding novel prediction models and management recommendations for POD24 patients.

RECENT FINDINGS

Recent studies have revealed novel clinicopathologic prediction models which may be closer to identifying patients at risk of POD24. In addition, several clinical trials utilizing novel therapies such as tazemetostat, obinutuzumab, PI3K inhibitors, and lenalidomide have been performed which help further guide treatment. Ongoing trials seek to identify the optimal management of these patients, and data from bispecific antibodies and CAR T cell therapies is forthcoming. With ongoing research efforts, hope remains that we are closer to being able to predict which patients will experience early progressing follicular lymphoma and have an improved management plan for those who do in order to improve survival outcomes.

Early Relapse in First-Line Follicular Lymphoma: A Review of the Clinical Implications and Available Mitigation and Management Strategies

Chemoimmunotherapy with rituximab (R-chemo) or obinutuzumab (G-chemo) is standard of care for patients with previously untreated symptomatic or high-tumor-burden follicular lymphoma. Median progression-free survival (PFS) with R-chemo plus R maintenance exceeds 10 years, and G-chemo plus G maintenance improves PFS relative to the corresponding R-containing regimen. Despite these positive results, a sizable proportion of patients continue to progress during or shortly after initial treatment. While no single definition of early relapse has been established, progression of disease within 24 months of initial treatment (POD24) is now widely accepted as a critical adverse prognostic factor. Multiple studies have shown increased mortality risk in patients with POD24 versus those without POD24. Unfortunately, tools for the assessment of POD24 risk are suboptimal, and it is not currently possible in clinical practice to identify individual patients who are at increased risk for early relapse. Treatment strategies for patients with POD24 are not well defined. G-chemo regimens appear to reduce the risk of POD24 relative to R-chemo regimens, although the impact on survival outcomes remains unclear. Beyond standard therapy, autologous stem cell transplant and emerging treatment modalities, such as bispecific antibodies and chimeric antigen receptor T-cells, may have a role in future management. Until standard treatments are defined, mitigating the risk of early relapse with effective up-front treatment remains the priority.

Past, present and future of prognostic scores in follicular lymphoma

Although most follicular lymphoma (FL) patients have prolonged survival, the identification of those at risk of early progression, multiple relapses or histological transformation is essential for the improvement of long-term outcomes. In this sense, a plethora of prognostic indexes have been developed in the last decades. However, determining which one is more accurate and clinically meaningful remains a challenge. Key factors for the external validity of available indexes include characteristics of the study population, treatment intervention, and design of the study. While initial risk scores were composed of clinical, biochemical, and hematological variables, genomic and imaging data have been incorporated in recent years. Despite an obvious step forward in the knowledge of the natural history and biology of FL, predictions remain inaccurate. Further research will likely incorporate information from circulating tumor DNA and artificial intelligence models to refine the prognostic classification of the heterogeneous FL population.

Follicular Lymphoma Evaluation Index (FLEX): A new clinical prognostic model that is superior to existing risk scores for predicting progression-free survival and early treatment failure after frontline immunochemotherapy

Patients with advanced‐stage follicular lymphoma (FL) who progress early after receiving first‐line therapy have poor overall survival (OS). Currently applied clinical prognostic models such as FL International Prognostic Index [FLIPI], FLIPI‐2 and PRIMA‐Prognostic Index [PRIMA‐PI] have suboptimal sensitivity and specificity to predict this poor prognosis subgroup. The primary objective was to develop a novel prognostic model, the FL Evaluation Index (FLEX) score, to identify high‐risk patients and compare its performance with FLIPI, FLIPI‐2 and PRIMA‐PI. Progression‐free survival (PFS) after first‐line immunochemotherapy was the key endpoint, while OS and progression of disease within 24 months (POD24) were also assessed. The model, which includes nine clinical variables, was developed using a cohort of patients with previously untreated advanced‐stage FL from the phase 3 GALLIUM trial (NCT01332968). The performance of the model was validated using data from the SABRINA trial (NCT01200758). In GALLIUM (n = 1004; 127 with and 877 without POD24), FLEX increased the intergroup (low‐risk/high‐risk) difference in 2‐year and 3‐year PFS rates and demonstrated superior intergroup differences in 2‐year and 3‐year OS rates compared with FLIPI, FLIPI‐2 and PRIMA‐PI. Sensitivity for a high‐risk score to predict POD24 was 60% using FLEX compared with 53% for FLIPI and FLIPI‐2, and 69% for PRIMA‐PI, while specificity was 68% for FLEX compared with 58% for FLIPI, 59% for FLIPI‐2 and 48% for PRIMA‐PI. The prognostic value of FLEX in SABRINA was similar to FLIPI. Therefore, FLEX appears to perform better than existing prognostic models in previously untreated FL, in particular for the newer treatment regimens.

The promises and challenges of using gene mutations for patient stratification in follicular lymphoma

Follicular lymphoma (FL) is a clinically and molecularly highly heterogeneous disease. Most patients achieve long-lasting remissions and have excellent overall survival (OS) with current treatment. However, ∼20% of patients have early progression of disease and short OS. At present, therapies are not guided by individual risk or disease biology. Reliable tools for patient stratification are urgently needed to avoid overtreatment of low-risk patients and to prioritize alternative approaches in high-risk patients. A rapidly expanding repertoire of promising therapeutic options is available for clinical evaluation; however, the numbers of patients with FL and the resources to conduct adequately powered trials are limited. Recent studies have shown that gene mutations can serve as prognostic and/or predictive biomarkers, in particular when integrated into composite risk models. Before translating these findings into routine clinical practice, however, several challenges loom. We review aspects of “clinicogenetic” risk model development and validation that apply to FL and more generally to other cancers. Finally, we propose a crowdsourcing effort that could expedite the development, validation, refinement, and selection of risk models. A new era of collaboration and harmonization is required if we hope to transition from empiric selection of therapeutics to risk-based, biology-guided treatment of patients with FL.