FL

Predictive gene-profiling model for follicular lymphoma–a fillip for FL beyond FLIPI?

On 22 February 2018, Sarah Huet from the Cancer Research Center of Lyon and Bruno Tesson from the Carnot Institute CALYM, France, and colleagues, published online ahead of print, in The Lancet Oncology, a multicenter international validation analysis for a predictive gene-expression model of follicular lymphoma (FL).

Because of the heterogeneity in clinical outcomes for FL patients, there is a strong need for a predictive model that will facilitate distinctive diagnosis between high- and low-risk disease progression or transformation patients. Despite Follicular Lymphoma International Prognostic Index (FLIPI-1 and FLIPI-2) scores being good pre-treatment clinical outcome predictors, they do not provide realistic long-term profiling for FL patients. Thus, this study sought to build and validate, from gene-expression data on tumor biology and microenvironment, a predictive model for FL patients of all risk types.

The authors designed their FL-predictive model by retrospectively analyzing pre-treatment tumor biopsies from grade 1–3a FL patients participating in PRIMA, a phase III clinical trial (NCT00140582), investigating rituximab maintenance in high-grade FL (training cohort). The model was further validated in pre-treatment biopsies obtained from three independent international ‘validation cohorts’ from the: (a) SPORE project, University of Iowa, Mayo Clinic, (b) Hospital Clinic of Barcelona, and (c) a separate population from PRIMA.

Study design
  • Training cohort (PRIMA): n = 160 fresh-frozen tumor FL biopsies
  • Validation cohorts (all formalin-fixed/paraffin-embedded FL specimens):
    • Cohort 1 (PRIMA): n = 178
    • Cohort 2 (SPORE): n = 201
    • Cohort 3 (Barcelona Hospital Clinic): n = 109
  • Training cohort model development:
    • RNA extraction (n = 149)
    • Affymetrix U133 Plus 2.0 microarrays
    • Multivariate Cox regression analysis to identify progression-free survival (PFS)-associated gene expression changes (n = 134)
    • NanoString expression profiling for 95 genes of interest in formalin-fixed specimens (n = 53)
    • Comparison between Affymetrix and NanoString genes of interest
    • Selection of 23 genes of interest with high PFS correlation
  • Validation cohorts model testing plan:
    • RNA extraction
    • NanoString expression profiling validation for the 23 genes of interest identified in the training cohort
Key results
  • PFS-associated expression changes in n = 395 genes in the training cohort
    • Longer PFS was associated with increased expression of n = 228 genes
    • Shorter PFS was associated with increased expression of n = 167 genes
  • Of those, n = 95 genes were selected by manual curation to integrate technical, statistical and biological aspects and their expression was validated in formalin-fixed/paraffin-embedded samples (n = 53) with NanoString
  • N = 23 genes with correlation coefficient ≥75 were retained after analysis and were mainly involved in:
    • B-cell development
    • DNA damage response
    • Cell migration
    • Immune regulation
    • Other cellular pathways
  • Model predictions in training cohort:
    • The 23-gene expression model was initially tested in the training cohort (n = 134):
    • Signature scores range: 0.621–1.504
    • C-statistic: 0.709 (95% CI [0.644–0.773]) outperforming FLIPI-1 score: 0.578 (95% CI [0.501–0.655])
    • Optimum threshold signature score for separating high- and low-risk FL subgroups: 1.075
      • Patients with high-risk of progression: n = 47 (35%)
      • Patients with low-risk of progression: n = 87 (65%)
    • In a multivariate Cox regression analysis for PFS adjusted by rituximab maintenance treatment and FLIPI-1 score, the model predicted:
    • Progression in the high-risk group compared to the low-risk group (adjusted HR, 3.68; [95% CI (2.19–6.17)]; P < 0.0001)
    • 5-year PFS: 26% (95% CI [16–43]) in the high-risk group
    • 5-year PFS: 73% (95% CI [64–83]) in the low-risk group
  • Model predictions in validation cohorts:
    • The 23-gene expression model was further tested in n = 460 good quality formalin-fixed/paraffin-embedded tumor samples from the three validation cohorts
    • C-statistics:
      • Validation cohort 1: 0.650 (95% CI [0.587–0.712])
      • Validation cohort 2: 0.619 (95% CI [0.558–0.681])
      • Validation cohort 3: 0.614 (95% CI [0.509–0.720])
    • Signature score application (1.075):
      • Validation cohort 1 (n =172):
        • Patients with high-risk of progression: n = 59 (34%)
      • Validation cohort 2 (n =186):
        • Patients with high-risk of progression: n = 42 (23%)
      • Validation cohort 3 (n =102):
        • Patients with high-risk of progression: n = 21 (21%)
      • Median PFS (mPFS):
        • Validation cohort 1 (n =172):
          • mPFS for patients with low-risk of progression: not reached (95% CI [not reached—not reached])
          • mPFS for patients with high-risk of progression: 2.9 years (95% CI [2.4–5.8])
        • Validation cohort 2 (n =186):
          • mPFS for patients with low-risk of progression: not reached (95% CI [not reached–6.6])
          • mPFS for patients with high-risk of progression: 3.1 years (95% CI [1.4–5.6])
        • Validation cohort 3 (n =102):
          • mPFS for patients with low-risk of progression: 10.8 years (95% CI [10.1–not reached])
          • mPFS for patients with high-risk of progression: 4.3 years (95% CI [1.3–not reached])
  • Combined analysis of validation cohorts (n = 460):
    • Median follow-up: 6.6 years [IQR (4.9—7.2)]
    • C-statistic: 0.628 (95% CI [0.587—0.668])
    • Similar performances to FLIPI (n = 453; C-statistic: 0.621 (95% CI [0.583—0.659])
    • mPFS for patients with high-risk of progression: 3.1 years (95% CI [2.4—4.8])
    • mPFS for patients with low-risk of progression: 10.8 years (95% CI [10.1—not reached])
  • Early relapse model prediction (within 2-years after diagnosis [POD24]):
    • 2-year progression probability in the combined validation cohort (n =460)
      • Low-risk group: 19% (95% CI [15—24])
      • High-risk group: 38% (95% CI [29—46])
  • Multivariate prediction analysis in the combined validation cohort:
    • PFS independent of anti-CD20 maintenance treatment and of FLIPI score (adjusted HR, 2.30; [95% CI (1.72—3.07)])

This study developed and validated a 23-gene expression model that predicts clinical outcomes and PFS in high- and low-risk FL patients. Advantages of the model include its application to routinely available formalin-fixed, paraffin-embedded specimens and that it takes into consideration tumor biology and microenvironment heterogeneity. According to the authors, the model accurately identified high-risk FL patients independently of FLIPI score or rituximab maintenance therapy. They state that together with the FLIPI index, this model presents a useful tool for enabling individualized risk group-directed therapy for FL patients.

References
  1. Huet S., Tesson B. et al. A gene-expression profiling score for prediction of outcome in patients with follicular lymphoma: a retrospective training and validation analysis in three international cohorts. Lancet Oncol. 2018 Feb 20. pii: S1470-2045(18)30102-5. doi: 10.1016/S1470-2045(18)30102-5. [Epub ahead of print]