Prognostic Model to Predict Post-Autologous Stem-Cell Transplantation Outcomes in Classical Hodgkin Lymphoma

Abstract

Our aim was to capture the biology of classical Hodgkin lymphoma (cHL) at the time of relapse and discover novel and robust biomarkers that predict outcomes after autologous stem-cell transplantation (ASCT). Materials and MethodsWe performed digital gene expression profiling on a cohort of 245 formalin-fixed, paraffin-embedded tumor specimens from 174 patients with cHL, including 71 with biopsies taken at both primary diagnosis and relapse, to investigate temporal gene expression differences and associations with post-ASCT outcomes. Relapse biopsies from a training cohort of 65 patients were used to build a gene expression-based prognostic model of post-ASCT outcomes (RHL30), and two independent cohorts were used for validation. ResultsGene expression profiling revealed that 24% of patients exhibited poorly correlated expression patterns between their biopsies taken at initial diagnosis and relapse, indicating biologic divergence. Comparative analysis of the prognostic power of gene expression measurements in primary versus relapse specimens demonstrated that the biology captured at the time of relapse contained superior properties for post-ASCT outcome prediction. We developed RHL30, using relapse specimens, which identified a subset of high-risk patients with inferior post-ASCT outcomes in two independent external validation cohorts. The prognostic power of RHL30 was independent of reported clinical prognostic markers (both at initial diagnosis and at relapse) and microenvironmental components as assessed by immunohistochemistry. ConclusionWe have developed and validated a novel clinically applicable prognostic assay that at the time of first relapse identifies patients with unfavorable post-ASCT outcomes. Moving forward, it will be critical to evaluate the clinical use of RHL30 in the context of positron emission tomography-guided response assessment and the evolving cHL treatment landscape.

Biology of classical Hodgkin lymphoma: implications for prognosis and novel therapies

“…The development of technologies that allow robust gene expression profiling from archival formalin-fixed, paraffin-embedded tissue has significantly advanced the field and led to the creation of gene expression-based prognostic models. [123][124][125][126] Because of the paucity of HRS cells, gene expression profiles derived from wholetissue sections largely reflect the cellular composition of the TME, 104,[123][124][125] whereas only a few studies have investigated the gene expression profiling of isolated tumor cells. 112,127 From most of these studies, macrophages emerged as the most promising and potent cellular compartment 104,125 to predict treatment outcomes in cHL, a concept that was proposed .30 years ago.…”

“…129 Of note, the prognostic properties of macrophage content were also evaluated in the relapse/refractory setting, where a high number of macrophages in relapse biopsies was associated with inferior post-ASCT survival. 126,130 To integrate prognostic information from a multitude of different cell types and individual genes, a number of groups have developed prognostic models using quantitative reverse transcription polymerase chain reaction assays or digital gene expression platforms in cohorts of adult patients with cHL. However, early studies in childhood and adolescent/young adult cHL indicate that the 23-gene predictor developed by Scott et al 123 is not predictive of poor outcome in this patient population, 131 supporting the concept that cHL, despite similar morphology, represents different biology across the age spectrum.…”

“…135 More recently, a gene expressionbased predictor (RHL30) was developed using the biological information from tissue biopsies obtained at relapse to provide information on post-ASCT outcomes. 126 This predictor is built on the rationale that in a proportion of cases, the biology at relapse, as measured by digital gene expression profiling, is substantially different from the biology at first diagnosis and that relapse biopsies are the superior specimens to predict post-ASCT survival. RHL30 identified a high-risk group of patients with significantly inferior post-ASCT failure-free survival (5-year failure-free survival: 23.8% high risk vs 77.5% low risk) as well as inferior post-ASCT overall survival (5-year overall survival: 28.7% high risk vs 85.4% low risk) compared with the low-risk group.…”

Novel insights into the disease dynamics of B‐cell lymphomas in the Genomics Era

“…As discussed in this review, the biology of lymphomas associated with relapsed/refractory disease can be significantly distinct from that at initial diagnosis [10,30,34,35,42,44,78]. These observed disease dynamics have major implications for biomarker assay development and highlight the need for accurate profiling of biology at disease progression.…”

Validation of the RHL30 digital gene expression assay as a prognostic biomarker for relapsed Hodgkin lymphoma

“…Biological disease properties have historically not been well studied at the time of relapse. In 2017, we reported in Chan et al 7 . that gene expression changes, identified at the time of relapse, capture the risk of subsequent relapse.…”