A genetic predictive model for precision treatment of diffuse large B-cell lymphoma with early progression

Abstract

Background: Early progression after the first-line R-CHOP treatment leads to a very dismal outcome and necessitates alternative treatment for patients with diffuse large B-cell lymphoma (DLBCL). This study aimed to develop a genetic predictive model for early progression and evaluate its potential in advancing alternative treatment.

Methods: Thirty-two hotspot driver genes were examined in 145 DLBCL patients and 5 DLBCL cell lines using next-generation sequencing. The association of clinical features, cell-of-origin, double expression, positive p53 protein, and gene alterations with early progression was analyzed, and the genetic predictive model was developed based on the related independent variables and assessed by the area under receiver operating characteristic. The potential of novel treatment based on the modeling was investigated in in-vitro DLBCL cell lines and in vivo xenograft mouse models.

Results: The frequency of CD79B (42.86% vs 9.38%, p = 0.000) and PIM1 mutations (38.78% vs 17.71%, p = 0.005) showed a significant increase in patients with early progression. CD79B and PIM1 mutations were associated with complex genetic events, double expression, non-GCB subtype, advance stage and unfavorable prognosis. A powerful genetic predictive model (AUROC = 0.771, 95% CI: 0.689-0.853) incorporating lactate dehydrogenase levels (OR = 2.990, p = 0.018), CD79B mutations (OR = 5.970, p = 0.001), and PIM1 mutations (OR = 3.021, p = 0.026) was created and verified in the other cohort. This modeling for early progression outperformed the prediction accuracy of conventional International Prognostic Index, and new molecular subtypes of MCD and Cluster 5. CD79B and PIM1 mutations indicated a better response to inhibitors of BTK (ibrutinib) and pan-PIM kinase (AZD 1208) through repressing activated oncogenic signaling. Since the two inhibitors failed to decrease BCL2 level, BCL2 inhibitor (venetoclax) was added and demonstrated to enhance their apoptosis-inducing activity in mutant cells with double expression.

Conclusions: The genetic predictive model provides a robust tool to identify early progression and determine precision treatment. These findings warrant the development of optimal alternative treatment in clinical trials.

Keywords: CD79B; Diffuse large B-cell lymphoma; Early progression; PIM1.

Fig. 1

The diagram on frequencies of hotspot gene mutations

Fig. 2

The genetic features of CD79B and PIM1 mutations and their association with survival. (a) Complex genetic events were involved in the CD79B and PIM1 mutations. (b) PIM1- (n = 36, p = 0.004) and CD79B-mutant (n = 30, p = 0.000) patients had poorer PFS than wild-type patients. CD79B-mutant patients displayed poorer OS (n = 30, p = 0.001), while those with PIM1 mutation were indifferent (n = 36, p = 0.862). (c) PIM1- (n = 166, p = 0.002) and CD79B-mutant (n = 47, p = 0.028) patients were validated to have worse OS in a larger DLBCL cohort

Fig. 3

The new genetic predictive model for POD12 including CD79B mutation, PIM1 mutation, and LDH levels. (a) The incidence of POD12 was significantly different between patients with scores of 0–1 (n = 104) and 2–4 (n = 41) based on the genetic predictive model (21.15% vs 65.85%, p = 0.000). (b) There was an inverse correlation on PFS (p = 0.000) and OS (p = 0.018) between patients with score of 0–1 (n = 104) and 2–4 (n = 41). (c) The genetic predictive model was validated in a cohort of 84 DLBCL cases. (d) The association of scores of 2–4 (n = 119) with poorer survival was confirmed in a larger cohort (p = 0.000). (e) The genetic predictive model for POD12 outperformed the IPI score and MCD subtype

Fig. 4

Correlation of CD79B and PIM1 mutations with BTK and pan-PIM inhibitors response. (a) BTK inhibitor (Ibrutinib) and pan-PIM inhibitor (AZD 1208) showed a dose- and time-dependent growth inhibition in DLBCL cell lines. (b) CD79B-mutant Val cells and PIM1-mutant OCI-Ly8 cells were more susceptible to Ibrutinib (10 μM)- and AZD 1208 (40 μM)-induced growth inhibition (p < 0.01). (c) and (d) CD79B-mutant Val cells and PIM1-mutant OCI-Ly8 cells were more sensitive to Ibrutinib (10 μM)- and AZD 1208 (40 μM)-induced apoptosis (p < 0.01)

Fig. 5

Xenograft mouse models and mechanisms of BTK and pan-PIM Inhibitors. (a) Tumor growth was significantly slowed down in PIM1-mutant OCI-Ly8 xenograft mice compared with PIM1-wildtype Val xenografts (p < 0.01). (b) Ibrutinib (10 μM) and AZD 1208 (40 μM) decreased the expression of key molecules in the related oncogenic pathways in CD79B- and PIM1-mutant cells. (c) Both Val and OCI-Ly8 cells expressed c-MYC and BCL2 proteins. Ibrutinib (10 μM) and AZD1208 (40 μM) induced the downregulation of c-MYC, but not BCL2 expression

Fig. 6

The effect of other key drugs for DLBCL therapy on BTK and pan-PIM inhibitors. (a) Venetoclax (0.1 μM) showed the most synergistic effect on Ibrutinib (10 μM)- and AZD 1208 (40 μM)-induced apoptosis (p < 0.01). (b) These drugs did not significantly decrease the BCL2 levels, although some of them exerted notable inhibitory on BCL-X_L and MCL1 levels