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. 2020 May 26;4(10):2213-2226.
doi: 10.1182/bloodadvances.2020001756.

JAK inhibition synergistically potentiates BCL2, BET, HDAC, and proteasome inhibition in advanced CTCL

Sara Yumeen  1 , Fatima N Mirza  1 , Julia M Lewis  1 , Amber Loren O King  1 , Sa Rang Kim  1 , Kacie R Carlson  1 , Sheila R Umlauf  2 , Yulia V Surovtseva  2 , Francine M Foss  3 , Michael Girardi  1
Affiliations

JAK inhibition synergistically potentiates BCL2, BET, HDAC, and proteasome inhibition in advanced CTCL

Sara Yumeen et al. Blood Adv. .

Abstract

Cutaneous T-cell lymphoma (CTCL) is a malignancy of skin-homing T lymphocytes that is more likely to involve the peripheral blood in advanced stages. For such patients with advanced disease, there are few available systemic treatment options, and prognosis remains poor. Exome sequencing studies of CTCL have suggested therapeutic targets, including within the JAK/STAT pathway, but JAK inhibition strategies may be limited by patient-specific mutational status. Because our recent research has highlighted the potential roles of single and combination approaches specifically using BCL2, bromodomain and extra-terminal domain (BET), and histone deacetylase (HDAC) inhibition, we aimed to investigate the effects of JAK inhibition on CTCL cells and established CTCL cell lines when paired with these and other targeting agents. Peripheral blood malignant CTCL isolates exhibited differential responses to JAK inhibition, with JAK2 expression levels negatively correlating to 50% inhibitory concentration (IC50) values. Regardless of single-agent sensitivity, JAK inhibition potentiated malignant cell cytotoxicity in combination with BCL2, BET, HDAC, or proteasome inhibition. Combination inhibition of JAK and BCL2 showed the strongest potentiation of CTCL cytotoxicity, driven by both intrinsic and extrinsic apoptosis pathways. JAK inhibition decreased expression of BCL2 in the high-responder samples, suggesting a putative mechanism for this combination activity. These results indicate that JAK inhibition may have major effects on CTCL cells, and that combination strategies using JAK inhibition may allow for more generalized cytotoxic effects against the malignant cells from patients with CTCL. Such preclinical assessments help inform prioritization for combination targeted drug approaches for clinical utilization in the treatment of CTCL.

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Conflict of interest statement

Conflict-of-interest disclosure: Yale School of Medicine has received clinical trial funding from AbbVie for an investigator-initiated (M.G.) clinical trial. The remaining authors declare no competing financial interests.

Figures

None
Graphical abstract
Figure 1.
Figure 1.
CTCL patient–isolated malignant cells and CTCL cell lines show variable sensitivity to a panel of targeted therapeutic agents. Isolated malignant cells from patient samples (n = 19), control samples (n = 5), and established CTCL cell lines (n = 5) were incubated with a range of concentrations of ruxolitinib, mivebresib, venetoclax, vorinostat, or bortezomib for 72 hours, from which IC50 values and Hill slopes were calculated. (A) Comparison of IC50 values. CTCL patient samples, control samples, and CTCL cell lines revealed statistically significant differences in response to mivebresib and bortezomib. (Bi) CTCL patient samples in order of IC50 of ruxolitinib. Patients were grouped as high-responders and low-responders to ruxolitinib at an a priori cutoff of 1 µM. The median and mean IC50 for patient samples were 2.16 µM and 79.47 µM, respectively. (Bii) IC50 differences for B1 and B2 stage to ruxolitinib were found to be nonsignificant. (Biii) Hill slope differences of CTCL patient samples and CTCL cell lines to ruxolitinib. (C) Dose–response curves for patient samples (C) and CTCL cell lines (D) for ruxolitinib (i), mivebresib (ii), venetoclax (iii), vorinostat (iv), and bortezomib (v). *P < .05; **P < .001. ns, not significant.
Figure 1.
Figure 1.
CTCL patient–isolated malignant cells and CTCL cell lines show variable sensitivity to a panel of targeted therapeutic agents. Isolated malignant cells from patient samples (n = 19), control samples (n = 5), and established CTCL cell lines (n = 5) were incubated with a range of concentrations of ruxolitinib, mivebresib, venetoclax, vorinostat, or bortezomib for 72 hours, from which IC50 values and Hill slopes were calculated. (A) Comparison of IC50 values. CTCL patient samples, control samples, and CTCL cell lines revealed statistically significant differences in response to mivebresib and bortezomib. (Bi) CTCL patient samples in order of IC50 of ruxolitinib. Patients were grouped as high-responders and low-responders to ruxolitinib at an a priori cutoff of 1 µM. The median and mean IC50 for patient samples were 2.16 µM and 79.47 µM, respectively. (Bii) IC50 differences for B1 and B2 stage to ruxolitinib were found to be nonsignificant. (Biii) Hill slope differences of CTCL patient samples and CTCL cell lines to ruxolitinib. (C) Dose–response curves for patient samples (C) and CTCL cell lines (D) for ruxolitinib (i), mivebresib (ii), venetoclax (iii), vorinostat (iv), and bortezomib (v). *P < .05; **P < .001. ns, not significant.
Figure 2.
Figure 2.
Baseline gene expression of JAK-STAT and BCL2 family members and correlation to ruxolitinib IC50. Selected gene expression was measured in CTCL patient samples (n = 11) and normal control samples (n = 5). Results are expressed as a fold change from the mean of normal control samples. (A) Significantly increased JAK2, STAT5B, and BCL2L2 expression and decreased BCL2 expression were noted in CTCL patient samples relative to control samples. (B) JAK2 expression (fold change in malignant patient-derived cells compared with normal lymphocytes) may predict response to ruxolitinib in vitro, with higher JAK2 expression moderately correlated to sensitivity. *P < .05; ***P < .0001.
Figure 3.
Figure 3.
Preclinical assessment of targeted drug combinations against CTCL patient–derived samples. CTCL patient cells were incubated with each of 5 targeted agents (ruxolitinib, venetoclax, vorinostat, mivebresib, and bortezomib) individually to calculate single-agent IC50 values. Cells were then incubated with combinations of each drug at 3 concentrations, and the combination index (CI) was calculated by using the Chou-Talalay method. Resulting CIs were plotted as heatmaps. (A) Representative heat map of a high-responder to ruxolitinib (i) and a low-responder to ruxolitinib (ii). (Bi) The CI at 1% to 30% viability for patient-derived samples exposed to combinations of ruxolitinib, venetoclax, vorinostat, and mivebresib. Strongest synergy was seen with venetoclax plus ruxolitinib and venetoclax plus mivebresib across patient samples. (Bii) The fold improvement in cytotoxicity for the same concentrations was calculated and plotted for these CTCL patient–derived samples exposed to combinations of ruxolitinib, venetoclax, vorinostat, and mivebresib. The highest fold potentiation was seen with the combination of ruxolitinib and venetoclax. Very strong synergy, CI < 0.1; strong synergy, CI < 0.3; synergy, CI < 0.7; slight to moderate synergy, CI < 0.9; additive effect, 0.9 < CI < 1.1; slight to moderate antagonism, CI < 1.45; antagonism, CI < 3.3; strong antagonism, CI < 10; and very strong antagonism, CI > 10. Adapted from Chou. Very strong potentiation, >10-fold; strong potentiation, two- to 10-fold; potentiation, 1.5- to twofold; moderate antagonism, 0.6- to 0.8-fold; antagonism, 0.3- to 0.6-fold; strong antagonism, <0.3-fold.
Figure 4.
Figure 4.
Synergy and potentiation assessment of targeted agent combinations using ruxolitinib. Patient-derived malignant CTCL samples (n = 14) and CTCL cell lines (n = 5) were incubated in combinations of ruxolitinib with either venetoclax, vorinostat, bortezomib, or mivebresib at 3 concentrations (high [H], medium [M], and low [L]), and combination indices (CIs) were calculated. (A) CIs were plotted as heatmaps for patient-derived samples (i); percent kill plotted for the same concentrations (ii). Very strong synergy was seen with ruxolitinib and venetoclax across all 5 high-responder patients. Strong to very strong potentiation was seen among low-responders. (B) Representative curves of combinations of ruxolitinib with venetoclax (i), vorinostat (ii), bortezomib (iii), and mivebresib (iv) for a high-responder. The most substantial synergy was observed with ruxolitinib and venetoclax.
Figure 5.
Figure 5.
Effects of JAK inhibition and synergy with BCL2 inhibition are mediated in part by induction of apoptosis by caspase-3/7 and caspase-8. (A) Representative curves of caspase 3/7 activity at 4 hours (i), 12 hours (ii), and 24 hours (iii) shown for patient 9 after incubation with ruxolitinib and venetoclax. (B) Caspase-8 activity at 4 hours (i), 12 hours (ii), and 24 hours (iii) shown for patient 9 after incubation with ruxolitinib and venetoclax. Significant increase in caspase-3/7 activity reflecting total apoptosis, and caspase-8 activity reflecting extrinsic apoptosis, was seen with combination therapy compared with either ruxolitinib or venetoclax alone at all concentrations and time points tested. *P < .05; **P < .01; ***P < .001  (P value against ruxolitinib). †P < .05; ††P < .01; †††P < .001 (P value against venetoclax).
Figure 6.
Figure 6.
Alterations in JAK-STAT and BCL2 family member gene expression induced by ruxolitinib and venetoclax. Patient-derived malignant CTCL samples of high-responders and low-responders were incubated with 1 µM ruxolitinib, 0.2 µM venetoclax, or combination for 24 hours. Results expressed as fold change from untreated vehicle controls in high-responders (A) and low-responders (B). Notably, high-responders showed an average of ∼38-fold decrease in BCL2 expression when incubated with ruxolitinib alone or in combination, substantially greater than the fold decrease seen in low-responders (approximately fivefold).
Figure 7.
Figure 7.
Altered pathways and their intersections within CTCL cells suggest multiple opportunities for single and combination therapeutic intervention. Mutations in the JAK/STAT pathway (JAK1, JAK2, JAK3, STAT3, and STAT5B) and the NF-κB pathway (NFKB2) have been previously described in CTCL. The pathways affected all ultimately inhibit both intrinsic and extrinsic apoptosis pathway activation. Inhibition of these pathways (eg, by the targeted agents assessed) overcomes resistance to apoptosis and drives malignant CTCL cell death. (Created with biorender.com.)
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