TP53 alterations enrich within the GCB-like subset of rrDLBCL patients and associate with EZB alterations
We integrated patient and targeted sequencing panel data from the Rushton analysis into 127 profiles (Supplementary Fig. 1). Unsupervised NMF clustering was applied to the cases, producing the highest cophenetic values when tumors were grouped as 2 clusters (0.9215) (Fig. 1A, Supplementary Fig. 2) (Supplementary Table 1). The 2 clusters were categorized as RR1 (N=58) and RR2 (N=69). Differential Marker Selection revealed that 13 of the 91 gene alterations were significantly enriched within either group (FDR <0.05) (Fig. 1B, Supplementary Tables 2, 3). Patients within the RR1 family were associated with MYD88, PIM1, IGLL5, HIST1HC, SOCS1, HIST1H1E, and CD79B alterations. Patients within the RR2 family were associated with CREBBP, EZH2, STAT6, BCL2, TNFRSF14, and TP53 alterations. Double/Triple-hit structural variations were also associated with RR2 family tumors (P=0.0391) (Supplementary Fig. 3). RR1 was heterogenous in its composition, harboring MCD/ABC or BN2/Unclassified tumors, while RR2 was more homogenous, composed primarily of EZB/GCB classified tumors (Supplementary Fig. 4).
Figure 1. TP53 alterations enrich within GCB-like DNA subclassification alterations and rrDLBCL patients compared to de-novo cohorts. (A) K2 (2-cluster) NMF clustering. rrDLBCL patients (N=127) were analyzed for the best fit when measuring the association patterns of DNA alterations (N=91). Patient similarity is designated by color, with red representing the most co-association and blue the least. The RR1 and RR2 subsets that emerged from clustering are designated by light grey and dark grey coloring, respectively. (B) A volcano plot displays differentially enriched DNA alterations between RR1 and RR2. Comparative marker selection between the groups resulted in 2-sided T and FDR values. Greater T values were associated with RR1 and lesser values with RR2. The dotted line represents the 0.05 FDR threshold to be met for significant association with one family over the other. Significant alterations are color coded for their corresponding LymphGen cluster, if designated. (C) TP53 alterations are significantly enriched towards EZB tumors in rrDLBCL but not in de-novo DLBCL. Stacked bar graphs denote the presence of TP53 alterations within EZB and non-EZB tumors. The pre-treatment Schmitz et al. 2018  cohort is compared to the Rushton et al.  rrDLBCL cohort. Significance was determined with a Fisher’s Exact test within both groups. (D) TP53 Alterations significantly co-occur with EZB alterations and significantly occlude MCD alterations. A volcano plot displays TP53 Pearson distance for measured rrDLBCL genes. FDR-corrected correlation similarity values are plotted on the Y-axis, with FDR <0.05 noted with a dotted line. Genes are labelled and noted for LymphGen subclassification. (E) RR2 driver genes increased association with TP53 alterations in comparison to pre-treatment association measurements. Z-score normalized Pearson Distance values are plotted on the Y-axis against RR1 and RR2 genes on the X-axis. rrDLBCL associations (Rushton) are compared to 3 separate de-novo cohort values (Lacy, Reddy, and Wright) based on TP53 co-association. Two-way ANOVA analysis was used to measure significance between pre-treatment and rrDLBCL values after Bonferroni multiple comparison correction.
TP53 alterations were significantly enriched within EZB-designated rrDLBCL cases compared to de-novo EZB cases (P=0.0018) (Fig. 1C). TNFRSF14, EZH2, CREBBP, BCL2, and KMT2D alterations significantly co-occurred in tumors bearing altered TP53 (Fig. 1D) after Pearson similarity matrix analysis (Supplementary Fig. 5, 6, Supplementary Table 4). In contrast, CD79B, PIM1, MYD88, GRHPR, and SOCS1 alterations were significantly exclusionary of TP53. Z-scored Pearson distance values from TP53 also displayed collective shifts away from RR1 genes (ABC-like) and trends towards RR2 genes (GCB-like) when compared individually (Fig. 1E) and collectively to de-novo levels (Supplementary Fig. 7, Supplementary Table 5). Specifically, rrDLBCL TP53 associations significantly associated with EZH2 (P=0.0156) and TNFRSRF14 (P=0.0073).
TP53 rrDLBCL enriches towards EZB/GCB-like alterations and cases in multiple rrDLBCL cohorts and is associated with inferior RCHOP response within EZB and BCL2 subsets
We next isolated EZB-associated genes to compare TP53-impairment differences between de-novo analyses and rrDLBCL cases. Collective EZB Z-score association with TP53 alterations was significantly greater in the rrDLBCL cohort compared to de-novo cohorts (P=0.0166) (Fig. 2A). TP53 co-associations were next compared across all LymphGen-subsets. EZB genes harbored a significantly greater TP53 co-association than genes associated with the MCD (P=0.0026), BN2, (P=0.0237) or unclassified (P=0.0017) classifications in rrDLBCL (Fig. 2B). This significant trend was observed once more within a second population of rrDLBCL patients (N=44) (Fig. 2C) . We added cases to this rrDLBCL validation population, integrating Greenawalt, Morin (N=25), Juskevicius (N=21), and Jain (N=24) (Supplementary Table 6) [10-13]. Significant enrichment of TP53 alterations within GCB cases were noted for both rrDLBCL populations (Fig. 2D, Supplementary Fig. 8). As a final measure of TP53-impariment’s role driving refractory cases of GCB-like subclassified tumors, Kaplan-Meier survival analyses revealed significantly inferior patient survival within both when TP53 alterations are present before RCHOP treatment (Fig. 2E). These data inform that the detrimental role of TP53 impairment at diagnosis rises within GCB-related subclassifications of rrDLBCL.
Figure 2. TP53 rrDLBCL enriches towards GCB-like subclassification alterations and cases in multiple rrDLBCL cohorts and is associated with inferior RCHOP response within de-novo EZB and BCL2 Subset patients. (A) A dot plot summarizes the Z-score normalized TP53 alteration Pearson Distance values of EZB classifier genes in the rrDLBCL population vs. combined de-novo DLBCL cohort values. A Welch’s t-test was used to measure significance. Mean±SD is designated by error bars. (B) TP53 alterations are significantly associated with EZB alterations in rrDLBCL. TP53 Pearson distance values are displayed for 90 rrDLBCL cohort genes (Z-score normalized). One-way ANOVA analysis was used to measure significance and was corrected by a Holm-Šídák's multiple comparisons test. Gene color is matched to LymphGen subclassification. Mean±SD is designated by error bars. (C) Gene Pearson Distance from TP53 Alterations within the Greenawalt rrDLBCL cohort (N=44). A dot plot summarizes the Z-score normalized TP53 alteration Pearson Distance values of LymphGen classifier genes of each subset. One-way ANOVA analysis was used to measure significance and was corrected by a Holm-Šídák's multiple comparisons test. Gene color is matched to LymphGen subclassification. Mean±SD is designated by error bars. Alterations present in the cohort but not included in LymphGen classification are displayed in white. (D) Percentage of EZB (or corresponding) cases with non-altered (WT) vs. altered TP53 in DLBCL vs. rrDLBCL populations. A gradient denotes percent composition of each population. Significance was determined through a Fisher’s Exact test between TP53 alteration enrichment within EZB or GCB cases vs. Non-GCB cases. The number of GCB/corresponding cases examined is listed on the right. (E) The presence of TP53 alterations at diagnosis in the GCB-like subset EZB (Wright et al. ) and BCL2 (Lacy et al. ) patients is associated with inferior overall survival. Kaplan-Meier survival curves assess the overall survival impact of TP53 alteration within EZB or BCL2 populations when treated with RCHOP. Significance was measured with Logrank analysis. The 95% confidence interval for each alteration is designated by dotted lines.