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3081 Validation of the CNS-IPI-C Prognostic Model in Patients with Systemic DLBCL in the Real World

Program: Oral and Poster Abstracts
Session: 626. Aggressive Lymphomas: Clinical and Epidemiological: Poster II
Hematology Disease Topics & Pathways:
Clinical trials, Research, Epidemiology, Clinical Research, Real-world evidence, Registries
Sunday, December 8, 2024, 6:00 PM-8:00 PM

Michal Masar, MD1*, Prokop Vodicka1*, Samuel Hricko, MD2*, Andrea Janikova, MD, PhD3*, Katerina Benesova, MD, PhD1*, David Salek, MD2*, Jana Salkova, MD, PhD1*, Martina Filipova, MD2*, Jan Galko, MD4*, Vit Campr, MD5*, Magdalena Klanova, MD, PhD1,6* and Marek Trneny, MD, CSc7

1First Department of Medicine, First Faculty of Medicine, Charles University and General Hospital, Prague, Czech Republic
2Department of Hematology and Oncology, Faculty of Medicine Masaryk University and University Hospital Brno, Brno, Czech Republic
3Department of Hematology and Oncology, Faculty of Medicine Masaryk University and University Hospital, Brno, Czech Republic
4Department of Pathology, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
5Department of Pathology and Molecular Medicine, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic
6Institute of Pathological Physiology, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
7First Faculty of Medicine, Charles University and General Hospital, Prague, Czech Republic

Introduction

CNS relapse in diffuse large B-cell lymphoma (DLBCL) is a rare, but mostly fatal event. The CNS International Prognostic Index (CNS-IPI) represents widely adopted prognostic model. As previously shown, there is a different CNS relapse rate in different subgroups defined by gene expression profiling (GEP; GCB vs ABC; Klanova, 2019), as well as higher frequency of distinct molecular subtypes (MCD) among patients (pts) who developed CNS relapses (Ollila, 2021). Integration of COO by GEP into the CNS-IPI led to a modified prognostic CNS-IPI-C model (one point for high CNS-IPI, and one for ABC/unclass. COO), which identifies pts at high risk of CNS relapse more accurately (Klanova). However, COO assessed by GEP and mutational profile-defined DLBCL subtypes are still not available in daily practice. IHC-assessed COO has been tested as biomarker for CNS relapse as well (Savage, 2016). We aimed to verify if the GEP-defined COO in CNS-IPI-C could be replaced by IHC-defined COO in an CNS-IPI-IHC index in a real-world cohort of DLBCL pts.

Methods

Using the prospective NiHiL project (NCT03199066), we identified 1201 pts with histologically confirmed DLBCL, HG B-NHL, or THRBCL diagnosed between 2010–2021 who received R-CHOP as frontline regimen at two academic centers in the Czech Republic. Pts with CNS involvement at diagnosis were excluded. Out of 1201 pts, 932 (78%) pts had available COO of DLBCL assessed by IHC-based Hans’ algorithm. The COO results were reviewed by two hematopathologists; subsequently 11 pts with unknown COO were excluded resulting in 921 (77%) pts entering the analysis. CNS-IPI-C was approximated using CNS-IPI (1 point for CNS-IPI 4–6) index and COO by IHC-based Hans’ algorithm (1 point for non-GCB), resulting in CNS-IPI-IHC model (low-, LR = 0; intermediate-, IR = 1; high-risk, HiR = 2 points). Univariate analysis (UVA) was performed by log-rank test, cumulative incidence of CNS relapse by the Kaplan-Meier method, and multivariate analysis (MVA) by the Cox regression model.

Results

Out of 921 cases, 486 (53%) were classified as GCB and 435 (47%) as non-GCB DLBCL. According to CNS-IPI, 265 (29%), 398 (43%), and 258 (28%) pts were categorized as LR, IR, and HiR for developing CNS relapse, resp. According to CNS-IPI-IHC, 354 (38%), 441 (48%), and 126 (14%) were categorized as being at LR, IR, and HiR, resp.

With a median follow-up time of 5.4 years (IQR 2.6–8.9 years), 32 (3.5%) pts developed CNS relapse (26 isolated CNS, 6 systemic and CNS relapses). Among these pts, 5 (16%) cases were categorized as LR for both CNS-IPI and CNS-IPI-C, 8 (25%) and 15 (47%) as IR, and 19 (59%) and 12 (38%) as HiR, resp. The CNS relapse rate was 3.0% at 2 years, and 3.7% at 5 years, with a median time to CNS relapse of 1.2 years (range 0.4–9.7 years). The 2-year PFS and OS of these 32 pts was 12.5% and 16.1%.

The 5-year CNS relapse rates in pts with LR, IR, and HiR CNS-IPI vs CNS-IPI-IHC were 1.2% vs 1.0%, 2.2% vs 3.5%, and 9.8% vs 14.7%, resp. In UVA, there was trend for non-GCB DLBCL association with a HiR CNS relapse risk compared to GCB (5.5% vs 2.2% at 5 years, HR 1.96, 95% CI0.98 – 3.93, P = 0.059). HiR subgroups of both indexes were associated with higher CNS relapse risk in comparison to LR and IR subgroups (P < 0.001), but statistical significance between IR and LR was retained only in the CNS-IPI-IHC (P = 0.048).

In MVA of factors associated with CNS relapse, HiR CNS-IPI was significantly associated with CNS relapse (HR 5.82, 95% CI 2.16–15.7, P < 0.001), while association of non-GCB DLBCL with CNS relapse did not reach statistical significance (HR 1.98, 95% CI 0.96–4.05, P = 0.063).

Conclusion

High CNS-IPI was associated with significantly higher risk of CNS relapse in this study. There was a trend towards non-GCB DLBCL to be associated with high CNS relapse risk. The CNS-IPI-IHC model (in which COO was assessed by Hans’ algorithm) in comparison to CNS-IPI identifies a smaller subgroup of HiR pts (14% vs 28% of all DLBCL pts) with cumulative incidence of CNS relapse at 5 years 14.7% vs 9.8%. The size reduction of HiR group led to an incorrect classification of 7 (22%) pts who ultimately developed a CNS relapse to an IR CNS-IPI-IHC group. The implementation of IHC-based COO to a CNS-IPI prognostic model is limited in a daily practice; thus, using GEP or mutational analyses will be needed in the future to better stratify pts at different CNS relapse risk.

First two authors contributed equally. Supported by CU Hem-Onco Cooperatio Program and grant NU21-03-00127.

Disclosures: Vodicka: SwixxBiopharma: Consultancy; AbbVie: Consultancy; Hoffmann-La Roche: Consultancy, Honoraria, Speakers Bureau. Janikova: Hoffmann-La Roche: Honoraria, Other, Speakers Bureau; Takeda: Honoraria; Gilead Sciences: Consultancy; Eli Lilly: Consultancy, Speakers Bureau; Swixx BioPharma: Consultancy. Klanova: Tubulis: Consultancy. Trneny: Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, accommodations, and expenses; Gilead Sciences: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, accommodations, and expenses; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, accommodations, and expenses; MorphoSys: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Honoraria; Genmab: Consultancy; SOBI: Consultancy, Honoraria; Autolus: Consultancy; Caribou Biosciences: Consultancy; Swixx BioPharma: Honoraria; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, accommodations, and expenses; Incyte Corporation: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, accommodations, and expenses; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, accommodations, and expenses.

*signifies non-member of ASH