Abstract
Introduction: Previously, we reported that CD30 expression was not acquired at time of relapse in patients with diffuse large B-cell lymphoma (DLBCL) utilizing conventional immunohistochemistry (IHC; Calzada et al., ASH 2016). However, novel approaches to detection of CD30 expression may improve the detection of low levels of expression and thus may have implications regarding the use of CD30-directed therapies in DLBCL and other lymphomas. We utilized a computational tissue analysis mechanism to evaluate CD30 expression using patient samples that had previously been considered CD30 negative by IHC.
Methods: We included patients with relapsed DLBCL with available archived tissue samples from the time of relapse (and when feasible, from the time of diagnosis) that was sufficient to prepare a slide for staining. CD30 IHC staining was completed for all involved tissue at Emory using our standard antibody (Ber-H2). Samples were manually annotated to indicate regions for inclusion and exclusion in image analysis. CD30 status was assessed by Flagship Biosciences using their proprietary CD30/DLBCL computational tissue analysis (cTA) algorithm. The primary endpoint for this analysis was the number and percentage of CD30+ cells in each tissue sample. Clinical, demographic and laboratory variables were also collected for each case, and overall survival (OS) was estimated using the Kaplan-Meier method. We divided the cohort into CD30 high vs. low expression using the median percentage of CD30+ cells and also performed a cut point analysis to identify a clinically significant cut-off for high vs. low using an outcome-oriented approach (Contal 1999).
Results: We included 25 patients with relapsed/refractory DLBCL (including 9 with available paired tissue from the time of initial diagnosis). Among all patients, the median age at diagnosis was 58 years (range 34-76), 72% presented with elevated LDH, 56% were Stage III/IV, 62% had B-symptoms, and 83% had extranodal disease (not including bone marrow). The median time from diagnosis to relapse was 9.4 months. Eighty-eight percent of patients received R-CHOP as frontline therapy. Cell of origin by the Hans algorithm was germinal center B-cell-like (GCB) for 9 patients, non-GCB for 5 patients, and unknown for 11 patients. Utilizing conventional IHC, none of the diagnostic or relapsed samples had detectable CD30 expression. Upon image analysis, 11/19 available samples were CD30-positive at relapse using ≥0.1% as a cutoff. Range of percentage of CD30 positive cells was 0.1 to 23.6%, and the median percentage of CD30 positive cells was 0.25%. Among the 9 patients with paired samples, 5 were positive at diagnosis and 5 were positive at relapse. One patient went from negative to positive and 1 patient transitioned from positive to negative. Most patients who were positive at baseline were still positive at relapse, and the median change in percentage of CD30+ cells between diagnosis and relapse was 0.35%. There were no statistical differences between the CD30 high vs low patients with regards to baseline patient- or disease-related characteristics or time to relapse using the median value of 0.25 as the cut point. In addition, CD30 expression was not associated with OS from the date of relapse or from diagnosis (p=0.406 & p=0.316, respectively). None of the included patients were treated with brentuximab vedotin or any other CD30-directed therapy.
Conclusions: This analysis identifies a novel way to detect CD30 expression in patients with DLBCL and suggests that more patients may be CD30+ than previously thought. Prior studies in T-cell lymphoma suggest that even patients with a very low percentage of CD30+ cells may respond to CD30-directed treatment (Kim 2017), although expression using this novel method was not part of those studies. Assaying the CD30 status of relapsed/refractory DLBCL using this novel platform in a larger cohort is warranted as patients with such low level CD30 expression may benefit from future evaluation of CD30-directed treatments.
Calzada:Seattle Genetics: Research Funding. Flowers:Genentech/Roche: Research Funding; Janssen Pharmaceutical: Research Funding; Acerta: Research Funding; BeiGene: Research Funding; Gilead: Consultancy; Pharmacyclics: Research Funding; OptumRx: Consultancy; Karyopharm: Consultancy; V Foundation: Research Funding; Abbvie: Research Funding; Eastern Cooperative Oncology Group: Research Funding; Gilead: Research Funding; Millennium/Takeda: Research Funding; Pharmacyclics/ Janssen: Consultancy; National Cancer Institute: Research Funding; Burroughs Wellcome Fund: Research Funding; TG Therapeutics: Research Funding; Genentech/Roche: Consultancy; Celgene: Research Funding; Spectrum: Consultancy; Abbvie: Consultancy, Research Funding; Bayer: Consultancy; Denovo Biopharma: Consultancy. Cohen:Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Research Funding; BioInvent: Consultancy; BioInvent: Consultancy; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Research Funding; Infinity Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Millennium: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Research Funding.
Author notes
Asterisk with author names denotes non-ASH members.
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