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Supplementary Methods, radiologist's reading of CT images, supplementary figure legends, supplementary references

microCT scans

SFigure 1: Histological examination of mouse lungs; SFigure 2: Tissue sections from human lung adenocarcinoma; SFigure 3: Cabozantinib (CBT) and vandetanib (VDT) resistant cells; SFigure 4: Comparison of in vitro inhibition of RET, RETV804L and RETV804M by ponatinib, cabozantinib, vandetanib, and lenvatinib; SFigure 5: Ponatinib treatment schedule and body weight measurements; SFigure 6: Lung section histology from Dox-induced C/KR mice and treated with vehicle or ponatinib for 1 months; SFigure 7: μCT examination of dox-induced C/KR mice with lung tumors before and after one month treatment with vehicle or ponatinib.

Supplementary Table 1. LD50 concentrations of different agents (determined at 48h) are indicated. Also shown are agents used at 10muM.

Fig. S1. Screening of oncology drugs to identify agents with immuno-stimulatory properties. Fig. S2. Expression of Ccl5, Cxcl9 and Cxcl10 was normalized to ribosomal 18s RNA and is shown as fold change compared to DMSO treated LKR (set at 1). Fig. S3. Treatment regimen of mice receiving i.p. anti-CD4/CD8 and romidepsin. Fig. S4. Percentage of CD4 or CD8 in DAPI- (viable) peripheral blood of mice receiving anti- CD8 or anti-CD4 at 300mug, as indicated. Fig. S5. PD-L1 expression. Cell surface expression of PD-L1 determined by FACS in viable LKR, 393P (393) and 344SQ (344) cells after 24h treatment with 30nM romidepsin or IFNgamma as indicated. UT: untreated cells. Fig. S6. (A) Impact of romidepsin (n=6) and anti-PD-1 (n=7) treatment on LKR tumor growth for 47 days. Fig. S7. 129 mice were inoculated s.c. with 106 344SQ (344) following which they were treated with 2mg/kg romidepsin on days 14,16,18, with or without 300μg/mouse anti-PD-1 antibody on days 15,17,19 as indicated. Fig. S8. (A) Heatmap analysis using conventional red-green scale of microarray results of LKR cells after IFNgamma and/or romidepsin treatment for genes increased after combined vs. single treatments. Fig. S9. (A) 129 mice with day 14 tumors were untreated (UT) or treated with romidepsin on days 14 and 16 and anti-PD-1 antibody on days 15 and 17 (RP) after which FACS was used to determine CD4 (CD3+CD4+) and CD8 (CD3+CD8+) percentages in total viable cells (day 19). Fig. S10. 129 mice were inoculated s.c. with 106 LKR cells. Effect of romidepsin (Rom) treatment (2mg/kg on days 14,16,18) on tumor growth over indicated time periods is shown. Fig. S11. 129 mice were i.p. injected with 2mg/kg romidepsin twice. 48h after the last injection, MDSC cells (CD11b+ Gr1 + Ly- 6G +) from blood of mice (n=3) were analyzed by FACS. Fig. S12. Western blot analysis of LKR cells treated with Romidepsin (Rom) and/ or IFNgamma as indicated. STAT1 levels are shown in whole cell lysates and β-actin levels are indicated as controls

Purpose: A significant limitation of checkpoint blockade immunotherapy is the relatively low response rate (e.g., ∼20% with PD-1 blockade in lung cancer). In this study, we tested whether strategies that increase T-cell infiltration to tumors can be efficacious in enhancing immunotherapy response.Experimental Design: We performed an unbiased screen to identify FDA-approved oncology agents with an ability to enhance T-cell chemokine expression with the goal of identifying agents capable of augmenting immunotherapy response. Identified agents were tested in multiple lung tumor models as single agents and in combination with PD-1 blockade. Additional molecular and cellular analysis of tumors was used to define underlying mechanisms.Results: We found that histone deacetylase (HDAC) inhibitors (HDACi) increased expression of multiple T-cell chemokines in cancer cells, macrophages, and T cells. Using the HDACi romidepsin in vivo, we observed increased chemokine expression, enhanced T-cell infiltration, and T-cell–dependent tumor regression. Importantly, romidepsin significantly enhanced the response to PD-1 blockade immunotherapy in multiple lung tumor models, including nearly complete rejection in two models. Combined romidepsin and PD-1 blockade also significantly enhanced activation of tumor-infiltrating T cells.Conclusions: These results provide evidence for a novel role of HDACs in modulating T-cell chemokine expression in multiple cell types. In addition, our findings indicate that pharmacologic induction of T-cell chemokine expression represents a conceptually novel approach for enhancing immunotherapy response. Finally, these results suggest that combination of HDAC inhibitors with PD-1 blockade represents a promising strategy for lung cancer treatment. Clin Cancer Res; 22(16); 4119–32. ©2016 AACR.

Supplementary Table 2. Probe sets showing 2-fold difference in expression in 2 independent experiments with romidepsin treatment of LKR. Probe sets with very low expression (cutoff of

Supplementary Table Legend

RET fusions have been found in lung adenocarcinoma, of which KIF5B–RET is the most prevalent. We established inducible KIF5B-RET transgenic mice and KIF5B–RET-dependent cell lines for preclinical modeling of KIF5B–RET-associated lung adenocarcinoma. Doxycycline-induced CCSP-rtTA/tetO-KIF5B-RET transgenic mice developed invasive lung adenocarcinoma with desmoplastic reaction. Tumors regressed upon suppression of KIF5B–RET expression. By culturing KIF5B–RET-dependent BaF3 (B/KR) cells with increasing concentrations of cabozantinib or vandetanib, we identified cabozantinib-resistant RETV804L mutation and vandetanib-resistant-RETG810A mutation. Among cabozantinib, lenvatinib, ponatinib, and vandetanib, ponatinib was identified as the most potent inhibitor against KIF5B–RET and its drug-resistant mutants. Interestingly, the vandetanib-resistant KIF5B-RETG810A mutant displayed gain-of-sensitivity (GOS) to ponatinib and lenvatinib. Treatment of doxycycline-induced CCSP-rtTA/tetO-KIF5B-RET bitransgenic mice with ponatinib effectively induced tumor regression. These results indicate that KIF5B-RET–associated lung tumors are addicted to the fusion oncogene and ponatinib is the most effective inhibitor for targeting KIF5B–RET in lung adenocarcinoma. Moreover, this study finds a novel vandetanib-resistant RETG810A mutation and identifies lenvatinib and ponatinib as the secondary drugs to overcome this vandetanib resistance mechanism. Mol Cancer Ther; 15(10); 2521–9. ©2016 AACR.

Purpose: A significant limitation of checkpoint blockade immunotherapy is the relatively low response rate (e.g., ∼20% with PD-1 blockade in lung cancer). In this study, we tested whether strategies that increase T-cell infiltration to tumors can be efficacious in enhancing immunotherapy response. Experimental Design: We performed an unbiased screen to identify FDA-approved oncology agents with an ability to enhance T-cell chemokine expression with the goal of identifying agents capable of augmenting immunotherapy response. Identified agents were tested in multiple lung tumor models as single agents and in combination with PD-1 blockade. Additional molecular and cellular analysis of tumors was used to define underlying mechanisms. Results: We found that histone deacetylase (HDAC) inhibitors (HDACi) increased expression of multiple T-cell chemokines in cancer cells, macrophages, and T cells. Using the HDACi romidepsin in vivo, we observed increased chemokine expression, enhanced T-cell infiltration, and T-cell–dependent tumor regression. Importantly, romidepsin significantly enhanced the response to PD-1 blockade immunotherapy in multiple lung tumor models, including nearly complete rejection in two models. Combined romidepsin and PD-1 blockade also significantly enhanced activation of tumor-infiltrating T cells. Conclusions: These results provide evidence for a novel role of HDACs in modulating T-cell chemokine expression in multiple cell types. In addition, our findings indicate that pharmacologic induction of T-cell chemokine expression represents a conceptually novel approach for enhancing immunotherapy response. Finally, these results suggest that combination of HDAC inhibitors with PD-1 blockade represents a promising strategy for lung cancer treatment. Clin Cancer Res; 22(16); 4119–32. ©2016 AACR.