A Decision-tree Approach to Stratify DLBCL Risk Based on Stromal and Immune Microenvironment Determinants

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In diffuse large B-cell lymphoma (DLBCL), the heterogeneity of response to standard first-line therapy1 largely relies on the tremendous biological complexity of the disease, claiming for an urgent improvement of our capacity to characterize it at diagnosis to guide treatments. Among emerging prognosticators, peculiar cytotypes of tumor microenvironment (TME) and relative gene sets were shown to predict patients’ risk.2–5 We previously recognized prognostic genes reflecting patterns of stromal (myofibroblasts [Myo]) and immune elements (CD4+ T and dendritic cells) intriguingly associated with macrophages (Mo).6 These latter as well as the choice of reproducible biomarkers to capture their functional heterogeneity remain an object of intense debate.7 Starting from the notion that Mo polarization and inflammatory response may be influenced by oxysterol levels via nuclear “Liver X receptors” (LXRs),8,9 we recently recognized the prognostic role of LXRα (NR1H3) in DLBCL, prompting future investigation on new LXR agonists for therapeutic purposes.10 Here, we sought to corroborate our previous observations using a decision-tree approach on large independent DLBCL cohorts to refine risk stratification integrating TME and clinical features. In doing so, we validated NR1H3 as an intriguing M1-Mo-related prognosticator, envisioning its potential as a molecular predictor toward future approaches of Mo-targeting drugs. We applied the deconvolution algorithm CIBERSORT4 in combination with a decision tree–based approach on 2 large independent DLBCL cohorts to recognize the most relevant features among clinical and TME prognosticators. The whole methodological pipeline is detailed in Figure 1A.Figure 1.: Decision tree–based model combining IPI and TME cells in DLBCL. (A) Schematic description of the pipeline used for data processing. (B) Histograms showing the relative percentages of each cell type in the M24 customized signature as detected by CIBERSORT analysis deconvolution of the GSE117556 dataset (n = 928). Decision-tree depicting results of recursive models applied on clinical and biological features built on PFS (C) and OS (D) in the training set. The most relevant groups are shown along with survival plots (log-rank test, P < 10−3). Decision-tree showing results of recursive models applied on clinical features (Rev-IPI variables), ACTA2 and NR1H3 surrogating Myofibroblasts and M1 macrophages, respectively, built on PFS (E) and OS (F). Most relevant groups are shown along with survival plots (log-rank test, P < 10−3). Kaplan-Meier survival plots for PFS (G–H) and OS (I–J) of ACTA2 and NR1H3 dichotomized expression combined with Rev-IPI in the validation cohort (GSE98588, n = 98). Adjusted P values as derived from pairwise comparisons using log-rank test are shown. ***P ≤ 0.001; **P ≤ 0.01; *P ≤ 0.05; ns (not significant) P > 0.05. AAstage = Ann Arbor Stage; ABC = activated B cell; ECOGps = eastern cooperative oncology group performance status; GBC = germinal B center-like; IPI or Rev-IPI = Revised International Prognostic Index; LDH>/≤ULN = lactate dehydrogenase >/≤ upper level of normal; NK = natural killer; OS = overall survival; PFS = progression-free survival; TME = tumor microenvironment.Briefly, GEP data from 928 DLBCL cases (training set; GSE117556, Table 1) were deconvoluted to quantify the relative percentages of 24 TME cytotypes (Suppl. Table S1, Figure 1B) and dichotomized according to maximally selected rank statistics (surv_cutpoint function implemented in surminer R package) based on both progression-free survival (PFS) and overall survival (OS). The resulting variables in keeping with known clinical prognosticators (cell of origin [COO] subtypes and revised International Prognostic Index [Rev-IPI]) and other clinical features (gender, age at diagnosis, lactate dehydrogenase > upper limit of normal, Eastern Cooperative Group performance status, Ann Arbor stage, extranodal involvements, and treatment arm) underwent univariate feature selection by applying log-rank and Cox proportional hazard test on PFS and OS (Suppl. Table S2). Then, the selected significant features were included in a recursive decision-tree model (partykit package implemented in R software).11 To increase the translational power of the model, it was reapplied by substituting prognostic cell types with consistent molecular surrogates (smooth muscle alpha-2 actin encoded by ACTA2 gene for Myo and NR1H3 for M1-Mo). The expression values of these 2 genes were dichotomized according to a cutoff identified by maximally selected rank statistics in 2 groups (high or low) before reapplying the recursive model. Finally, we validated the results on an independent case set of 137 DLBCL (GSE98588)12 (Suppl. material) using Rev-IPI, ACTA2, and NR1H3 expression value dichotomized according to the cutoff previously identified in the training set. To overcome the batch effect on normalization derived from 2 different array platforms, the expression values were properly scaled. To validate our methods, we used the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis (TRIPOD) criteria (Suppl. TRIPOD). Table 1 - Patients’ Characteristics Training Set Validation Set No. of patients 928 137 P Value Gender, N of males (%) 517 (55.7) 76 (55.5) 1 Age, y: median (IQR) 44.0 (35.0–50.0) 45.5 (36.0–55.0) 0.018 LDH > ULN, N (%) 388 (41.8) 52 (50.0) 0.134 ECOGps 3/4, N (%) 105 (11.3) 21 (19.4) 0.022 AAStage III/IV, N (%) 638 (68.8) 69 (63.9) 0.35 Extranodal involvement, N (%) 521 (56.1) 28 (25.9) <0.001 Rev IPI (%) 0.029 Poor 446 (48.1) 44 (42.3) Good 428 (46.1) 47 (45.2) Very good 54 (5.8) 13 (12.5) NA 0 33 COO (%) <0.001 ABC 255 (27.5) 63 (46.0) GCB 543 (58.5) 54 (39.4) UNC 130 (14.0) 20 (14.6) Molecular high grade (%) NA Double hit/triple hit 35 (9.7) NA MYC-normal 309 (85.8) NA MYC-rearranged 14 (3.9) NA TME cytotypes Tumor cells ABC lowa, N (%) 552 (59.5) 109 (79.6) <0.001 Tumor cells ABC lowb, N (%) 552 (59.5) 109 (79.6) <0.001 Tumor cells GCB lowa, N (%) 834 (89.9) 29 (21.2) <0.001 Tumor cells GCB lowb, N (%) 831 (89.5) 29 (21.2) <0.001 Naive B-cells lowa, N (%) 792 (85.3) 87 (63.5) <0.001 Naive B-cells lowb, N (%) 777 (83.7) 106 (77.4) 0.085 Memory B-cells lowa, N (%) 107 (11.5) 94 (68.6) <0.001 Memory B-cells lowb, N (%) 107 (11.5) 19 (13.9) 0.516 Plasma cells lowa, N (%) 237 (25.5) 107 (78.1) <0.001 Plasma cells lowb, N (%) 699 (75.3) 107 (78.1) 0.548 CD8+ cells lowa, N (%) 699 (75.3) 55 (40.1) <0.001 CD8+ cells lowb, N (%) 383 (41.3) 74 (54.0) 0.007 CD4+ cells lowa, N (%) 417 (44.9) 77 (56.2) 0.017 CD4+ cells lowb, N (%) 680 (73.3) 100 (73.0) 1 Gamma delta TC lowa, N (%) 765 (82.4) 124 (90.5) 0.024 Gamma delta TC lowb, N (%) 763 (82.2) 124 (90.5) 0.021 Follicular helper TC lowa, N (%) 413 (44.5) 35 (25.5) <0.001 Follicular helper TC lowb, N (%) 504 (54.3) 35 (25.5) <0.001 Regulatory cells lowa, N (%) 518 (55.8) 137 (100.0) <0.001 Regulatory cells lowb, N (%) 808 (87.1) 137 (100.0) <0.001 NK resting lowa, N Low (%) 830 (89.4) 15 (10.9) <0.001 NK resting lowb, N Low (%) 223 (24.0) 28 (20.4) 0.414 NK activated lowa, N (%) 217 (23.4) 0 (0.0) <0.001 NK activated lowb, N (%) 203 (21.9) 40 (29.2) 0.072 Monocytes lowa, N (%) 187 (20.2) 11 (8.0) 0.001 Monocytes lowb, N (%) 187 (20.2) 28 (20.4) 1 M1-Mo lowa, N (%) 198 (21.3) 112 (81.8) <0.001 M1-Mo lowb, N (%) 790 (85.1) 113 (82.5) 0.498 M2-Mo lowa, N (%) 807 (87.0) 56 (40.9) <0.001 M2-Mo lowb, N (%) 0 (0.0) 29 (21.2) <0.001 Dendritic cell lowa, N (%) 0 (0.0) 121 (88.3) <0.001 Dendritic cell lowb, N (%) 0 (0.0) 119 (86.9) <0.001 Eosinophils lowa, N (%) 0 (0) 0 (0) NA Eosinophils lowb, N (%) 307 (33.1) 0 (0.0) <0.001 Neutrophils lowa, N (%) 371 (40.0) 0 (0.0) <0.001 Neutrophils lowb, N (%) 824 (88.8) 0 (0.0) <0.001 Myofibroblasts lowa, N (%) 703 (75.8) 113 (82.5) 0.103 Myofibroblasts lowb, N (%) 665 (71.7) 0 (0.0) <0.001 Lymphatic endothelial cells low a, N (%) 223 (24.0) 0 (0.0) <0.001 Lymphatic endothelial cel

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