Supplementary Material: The file contains supplementary text, figures and tables in the following order. 1. Supplementary Note 1: ChIP-seq data pre-processing 2. Supplementary Note 2: eQTL evaluation 3. Supplementary Note 3: Literature Validation of Significant TF-Drug pairs 4. Supplementary Note 4: Literature Validation of Mediator 5. Supplementary Figure 1: ASB evaluation with entire data set (genotyped and imputed SNPs) 6. Supplementary Table 1: Number of Motifs for each TF 7. Supplementary Table 2: AUROC and AUPRC Comparisons 8. Supplementary Table 3: TF-Drug Analysis using Delta-MOP 9. Supplementary Table 4: TF-Drug Analysis using Delta-gkm-SVM 10. Supplementary Table 5: Full Results of TF-Drug Analysis 11. Supplementary Table 6: Mediator Genes in ELF1-Doxorubicin Analysis 12. Supplementary File 1: Transcription factor binding motifs 13. References

Supplementary Note 1: Here we describe how we pre-process ChIP-seq data for training purposes.

We used clustered ChIP (version 3) track from ENCODE UCSC as experimental evidence of TF binding. Signal scores, used as measurement of experimental TF binding strength, are collected from ChIP-seq signal files from ENCODE project. We selected 1000 TFBS for each TF as positive sequences using following steps:

1. TF ChIP peaks of GM12878 cell line from TFBS clusters were used. 2. A TF’s peak in the TFBS clusters is defined as a High Occupancy Target Region (HOT Region) if there are 6 or more TFs having TFBS overlapping over half of the peak. 3. For each TF, we defined a subset of ChIP peaks from TFBS clusters as function peaks with two criterions. 1) overlap with a coding ’s 50kb upstream regulatory region; 2) not a HOT region. 4. We selected the top 1500 peaks with the largest ChIP score for each TF. 5. For each selected peak, signal scores for each in the window were obtained from ChIP-seq signal file. The maximum signal score was used as a measurement of TF binding strength at this window. The window was then modified to be a 501bp window around the position with the maximum score (250bp upstream and 250 bp downstream). Top 1000 peaks with the largest signal score were selected to be positive windows where the corresponding TF binds. 6. Negative windows were defined as open region that are able to be bound by other TFs and show no evidence of binding of the target TF. Negative windows for �� were selected as follows: 1) Take a union of all positive windows of all �� (� ≠ �). 2) Remove windows with signal scores greater than the mean of the 1000 positive window signal scores of ��. 3) Randomly select 1000 windows from this union as negative windows of ��.

ENCODE clustered ChIP track contains ChIP-seq data for 62 TFs for GM12878. After the pre- processing steps mentioned above, the number of TFs having enough data decreased to 37. A dataset with sequences and signal scores of these 2000 windows were obtained for each TF and used for training and testing.

Supplementary Note 2: eQTL evaluation Besides ASB evaluation, we also evaluated TFBS-SNP impact prediction of four methods by asking if the ‘binding-change SNPs’ are enriched for eQTLs using hypergeometric tests. We evaluated ‘binding-change SNPs’ predicted by four methods: Delta-MOP, Delta-gkmSVM, Delta- STAP and Delta-LLR and compared them with a set of same number of randomly selected SNPs that covered by the TF’s ChIP peak. The scatter plots show pairwise comparison of each method (Delta-MOP, Delta-gkm-SVM, Delta-STAP and Delta-LLR) and its corresponding random test. For all four methods, the overall enrichment were similar no matter the TF binding predictors were used or not.

Supplementary Note 3: Here we list and discuss the literature evidence found for the 38 significant TF-Drug pairs identified in the TF-Drug analysis. For each TF-Drug pair, we provide an evidence code and a brief summary of the evidence. We considered five types of evidence code. 1. Direct: A direct evidence is usually exhibited by TF knockdown or impact on drug response through TF binding or expression. 2. Regulation of Drug Action: Target TF has regulatory impact on drug action. 3. Regulatory Target Direct: Target TF is a regulator of a gene with direct evidence. 4. Sibling Drug Evidence: Target TF has direct evidence for a sibling drug. 5. Complex Partner Direct: Target TF is a complex partner of a protein with direct evidence. 6. Sibling of Direct: Target TF is a sibling of a protein with direct evidence. 7. Indirect and No Evidence: Other types of evidence than the first five are classified as indirect evidence. Indirect evidences are not considered as support for the TF-Drug pair.

1 ELF1 – Rapamycin Evidence Code: Indirect Evidence: The TF ELF4 is a part of the mTOR pathway and its expression is regulated by mTOR in CDH8+ cells; rapamycin inhibits mTOR. ELF4 is a paralog of ELF1 (1).

2 ELF1 – Epirubicin Evidence Code: Direct Evidence: TF Knockdown (2).

3 MAX – CDDP Evidence Code: Complex Partner Direct Evidence: MXD1, a member of the Myc-Max-Mxd family, has been shown to induce CDDP (cisplatin) resistance in hypoxic U 2OS and MG 63 cells (3). Mxd1 (MAD) is a well-known dimerization partner of MAX (4,5), and the reported association of MAX with CDDP is thus likely to reflect the previously demonstrated Mxd1-CDDP relationship.

4 SIN3A – CDDP Evidence Code: Complex Partner Direct Evidence: SIN3A is part of the SIN3A-HDAC complex that functions as a repressor and is associated with diseases including cancer (6), and HDAC inhibitors are known to potentiate CDDP activity (7-9).

5 SP1 – Carboplatin Evidence Code: Direct Evidence: PMID 24122978 (10).

6 ELF1 – Doxorubicin Evidence Code: Direct Evidence: TF knockdown (2)

7 NFYB – 6TG Evidence: [no TF in database for this drug] NFYB, when induced by E2F1, attenuates . Nothing specific to 6TG.

8 MAX – 6TG Evidence: [no TF in database for this drug] MAX is a heterodimeric partner of c-Myc, which is known to affect mutation rate in the HGPRT gene; the latter in turn is a critical component of 6-TG sensitivity (11).

9 SIN3A – MPA Evidence: [no TF in database for this drug]

10 ELF1 – Carboplatin Evidence Code: Regulatory Target Direct; Sibling Drug Evidence Evidence: ELF1 is one of the top 20 molecular features associated with response to the decitabine:carboplatin combination, as per CTRP database (PharmacoDB). Also, ELF1 is an ETS family transcription factor and other TFs of this family, such as ETS-1, have been shown to be involved in resistance to platinum drugs (12). ELF1 is also believed to regulate BRCA2, which in turn is a key determinant of response to platinum therapy (12).

11 NFYB – 6MP Evidence Code: No evidence

12 MXI1 – CDDP Evidence Code: Sibling of Direct Evidence: MXI1, also known as MXD2, is a member of the MXD1 family, and a closely related member of this family – MXD1 – has been shown, via knock-down assays, to affect CDDP resistance in hypoxic conditions via repression of PTEN (3).

13 YY1 – CDDP Evidence Code: Direct Evidence: YY1 knock-down in non-small cell lung cancer (NSCLC) cells increased sensitivity to CDDP (13) though no effect was seen in ovarian cancer cell lines (14).

14 SP1 – CDDP Evidence Code: Direct Evidence: SP1 inhibition by tolfenamic acid (TA) was found to increase CDDP response in epithelial ovarian cancer (EOC) cell lines (15).

15 ELF1 – CDDP Evidence Code: Direct Evidence: As noted above, ELF1 is believed to regulate BRCA2, which in turn is a key determinant of response to platinum therapy (12). Also, ELF1 binding to DNA was found to increase DNA damage upon cisplatin treatment (16), suggesting that the TF may modulate cisplatin activity.

16 SIN3A – 6TG Evidence Code: No Evidence

17 PML – Epirubicin Evidence Code: Sibling Drug Evidence Evidence: (17) See PML-Doxorubicin association below.

18 REST – Rapamycin Evidence Code: Regulatory Target Direct Evidence: A previous study showed that REST exerts regulatory control over the ‘mammalian Target Of Rapamycin’ (mTOR) signaling pathway (18), which is the pathway mediating Rapamycin induced apoptosis. mTOR-Rapamycin is a ‘direct’ evidence pair (19).

19 REST – 6MP Evidence Code: No Evidence

20 NFYB – Epirubicin Evidence Code: No Evidence

21 SP1 – Epirubicin Evidence Code: Sibling Drug Evidence Evidence: SP1 has been found in various studies to be associated with the activity of Doxorubicin (see below), a drug closely related to Epirubicin (of the anthracycline family). The association between SP1 and Doxorubicin was also reported as significant by our analysis, see below.

22 BHLHE40 – TMZ Evidence Code: Indirect Evidence: The apoptosis inhibitor BHLHE40, also known as DEC1, has been shown to be predictive of TMZ response in glioma patients (20).

23 PBX3 – Epirubicin Evidence Code: No Evidence

24 PML – Doxorubicin Evidence Code: Direct Evidence: The promyelocytic leukemia (PML) protein was shown to regulate DNA modification in response to Doxorubicin and knockout of PML was found to abolish Doxorubicin-induced DNA modification and to regulated inhibition of cell proliferation by the drug (21).

25 MXI1 – 6MP Evidence Code: No Evidence

26 SP1 – Oxaliplatin Evidence Code: Regulation of Drug Action Evidence: A study of oxaliplatin response in esophageal cancer cell lines showed that survivin may be a key target of the drug, and that downregulation of survivin by the drug is mediated by SP1, thus arguing in favor of a major role for SP1 in oxaliplatin response (22).

27 MAX – 6MP Evidence Code: Indirect Evidence: Evidence Code MAX is a heterodimeric partner of c-Myc, which is known to affect mutation rate in the HGPRT gene (11); the latter in turn is a critical component of 6-MP resistance (23).

28 REST – Carboplatin Evidence Code: No Evidence

29 NFYB – Doxorubicin Evidence Code: No Evidence

30 MAX – Epirubicin Evidence Code: Complex Partner Direct Evidence: We note that various homo- and hetero-dimers of the MAX family including MAX-MAX and MYC-MAX have similar binding preferences and compete for each other’s sites, so our report of an association with MAX may equally well represent an association with MYC-MAX. Another study showed that cMyc, an important binding partner of MAX, attenuates the effect of Doxorubicin (24), a drug closely related to Epirubicin. Also, Doxorubicin was shown to disrupt MYC-MAX interaction (25) and thus affect DNA binding by MYC-MAX, suggesting that regulatory effects of MYC-MAX may underlie Doxorubicin response variation.

31 ATF2 – Fludarabine Evidence Code: Indirect Evidence: The drug Fludarabine, a purine anti-metabolite, shares properties similar to cladribine and gemcitabine. A gemcitabine combination therapy downregulated ATF2 phosphorylation and activated SAPK/JUNK activation, which in certain cell types yields ATF2- cJun heterodimerization (26). In another study, fludarabine treatment, in a cocktail of therapies, led to the Inhibition of the AKT pro-survival pathway and activation of the JNK-ATF2 pathway; fludarabine therapies seemingly increased the concentration of P-ATF2 (27).

32 BHLHE40 – Epirubicin Evidence Code: Direct Evidence: BHLHE40, also known as DEC1, is has been shown to induce cell cycle arrest and premature senescence on Doxorubicin treatment in MCF7 cells (28). The mechanism for this was further explored in (29).

33 SP1 – Doxorubicin Evidence Code: Regulation of Drug Action Evidence: SP1 activity is increased in Doxorubicin-resistant HL 60 leukemia cells (30), and SP1 expression levels are predictive of long term prognosis in triple negative breast cancer patients under Doxorubicin treatment (31). Moreover, a recent study (32) showed that insulin pretreatment protects against Doxorubicin induced apoptosis by SP1-mediated transactivation of survivin.

34 MXI1 – Carboplatin Evidence Code: Indirect Evidence: MXI1, also known as MXD2, is a member of the MXD1 family, and a closely related member of this family – MXD1 – has been shown, via knock-down assays, to affect cisplatin resistance in hypoxic conditions via repression of PTEN. Cisplatin is a platinum drug closely related to carboplatin, believed to have similar resistance mechanisms (33).

35 ATF2 – Everolimus Evidence Code: No Evidence

36 PAX5 – Hypoxia Evidence Code: Indirect Evidence: Hypoxic conditions increase Pax5 expression in KMS-12BM cells (34).

37 SIN3A – MTX Evidence Code: Indirect Evidence: The SIN3A repressor complex is a master regulator of the transcriptional activity of STAT3 (35), and STAT3 has been shown to be directly involved in MTX sensitivity (36).

38 PBX3 – Doxorubicin Evidence Code: No Evidence

Supplementary Note 4: Here we list and discuss the literature evidence we found to show the impact of mediator genes on Drug-induced apoptosis pathway. We looked at the mediators genes identified in the ELF1-Doxorubicin enrichment test.

Gene Evidence ANKRD52 ANKRD52, interacting with PP6, lead to the dephosphorylation of PAK1 (37), which can protect cell from apoptosis through bcl-2 involved pathway (38). ATM ATM is part of the ATM/TP53 pathway and plays an important role in the activation of TP53 (39). ATP6V0D1 ATP6V0D1 is a subunit of V-ATPase (NCBI Gene: ATP6V0D1). Inhibition of V-ATPase activity can lead to cell death (40-42). B4GALT2 In HeLa cells, B4GALT2 is identified as a target gene of p53-mediated apoptosis (43) and its overexpression can induce cell apoptosis (44). BAK1 COMMD9 Decreasing the expression of COMMD9 leads to the decreasing of E2F1 activation which enhances p53-mediated apoptosis (45). EIF4A3 EIF4A3 plays an important role in the alternative splicing of BCL2L1/BCL-X and probably also other apoptotic genes (46). Knocking down of EIF4A3 increases the expression of BCL-Xs in HeLa cells (46). IPO13 IPO13 mediates the nuclear translocation of PDCD5 (47), which promotes apoptosis through HDAC3/p53 pathway (48). miR6734 miR6734 up-regulates the expression of p21 and induces cell arrest and apoptosis (49). NABP2 NABP2 plays an important role in the DNA damage response (50). It can also regulate the stability and transcriptional activity of p53 (51). NEURL4 NEURL4 regulates the activity of p53 (52). REXO4 REXO4 is also known as hPMC2, which regulates the expression of p21 (53). TFIP11 TFIP11 is also known as TFIP-1 (NCBI Gene: TFIP11). Knocking down TFIP induces apoptosis through caspases 3 and caspase 9 (54). TMEM86B TMEM86B has protein-protein interaction with BCL2L13 (STRING). URI1 URI1 deficiency is associated with p53-mediated apoptosis (55).

SUPPLEMENTARY TABLES NOTE: All Supplementary Tables are included as sheets in a separate Excel file. Legends for these tables are provided below.

Supplemental Table 1: The number of motifs for each TF. See worksheet “Table 1 Number of Motifs”.

Supplementary Table 2: The AUROCs and AUPRCs of TFs in the ASB enrichment using four different TF binding predictors. See worksheet “Table 2 AUROCs and AUPRCs”.

Supplementary Table 3: TFs discovered regulating drug response using Delta-MOP alone to predict binding-change SNPs. See worksheet “Table 3 Delta-MOP”.

Supplementary Table 4: TFs discovered regulating drug response using Delta-SVM alone to predict binding-change SNPs. See worksheet “Table 4 Delta-gkmSVM”

Supplementary Table 5: The results of drug associated enrichment tests for all 888 TF-Drug pairs. See worksheet “Table 5 full results”.

Supplementary Table 6: Mediator Genes in ELF1-Doxorubicin Analysis. SNPs in the intersection of TF-Drug hypergeometric tests are: 1) eQTLs associated to Drug Response Genes; 2) ‘binding- change SNPs’ of the TF. DRGs associated with those SNPs are potentially mediators regulated by the TF. Here we list all mediator genes in ELF1-Docorubicin analysis. See worksheet “Table 6 DRGs in ELF1-DOX”.

Supplementary File 1: Transcription factor binding motifs. We obtained 239 PWMs from three different data sources: 1) factor book motifs processed by ENCODE UCSC; 2) motifs downloaded from Factorbook; 3) HOCOMOCO Human (v10) motifs. STAP models are trained separately for each motifs. We excluded the motifs if the maximum prediction of the corresponding STAP model is too small (<0.5). The remaining 225 PWMs, which were used in following analysis, are included in Supplementary File 1.

References

1. Yamada, T., Gierach, K., Lee, P.H., Wang, X. and Lacorazza, H.D. (2010) Cutting edge: Expression of the transcription factor E74-like factor 4 is regulated by the mammalian target of rapamycin pathway in CD8+ T cells. J Immunol, 185, 3824-3828. 2. Hanson, C., Cairns, J., Wang, L. and Sinha, S. (2016) Computational discovery of transcription factors associated with drug response. Pharmacogenomics J, 16, 573-582. 3. Zheng, D., Wu, W., Dong, N., Jiang, X., Xu, J., Zhan, X., Zhang, Z. and Hu, Z. (2017) Mxd1 mediates hypoxia-induced cisplatin resistance in osteosarcoma cells by repression of the PTEN tumor suppressor gene. Mol Carcinog, 56, 2234-2244. 4. Gupta, K., Anand, G., Yin, X., Grove, L. and Prochownik, E.V. (1998) Mmip1: a novel leucine zipper protein that reverses the suppressive effects of Mad family members on c-myc. Oncogene, 16, 1149-1159. 5. Luscher, B. (2001) Function and regulation of the transcription factors of the Myc/Max/Mad network. Gene, 277, 1-14. 6. Yang, Y., Huang, W., Qiu, R., Liu, R., Zeng, Y., Gao, J., Zheng, Y., Hou, Y., Wang, S., Yu, W. et al. (2018) LSD1 coordinates with the SIN3A/HDAC complex and maintains sensitivity to chemotherapy in breast cancer. J Mol Cell Biol, 10, 285-301. 7. Jin, K.L., Park, J.Y., Noh, E.J., Hoe, K.L., Lee, J.H., Kim, J.H. and Nam, J.H. (2010) The effect of combined treatment with cisplatin and histone deacetylase inhibitors on HeLa cells. J Gynecol Oncol, 21, 262-268. 8. Beyer, U., Kronung, S.K., Leha, A., Walter, L. and Dobbelstein, M. (2016) Comprehensive identification of genes driven by ERV9-LTRs reveals TNFRSF10B as a re-activatable mediator of testicular cancer cell death. Cell Death Differ, 23, 64-75. 9. Gueugnon, F., Cartron, P.F., Charrier, C., Bertrand, P., Fonteneau, J.F., Gregoire, M. and Blanquart, C. (2014) New histone deacetylase inhibitors improve cisplatin antitumor properties against thoracic cancer cells. Oncotarget, 5, 4504-4515. 10. Chen, H.H. and Kuo, M.T. (2013) Overcoming platinum drug resistance with copper- lowering agents. Anticancer Res, 33, 4157-4161. 11. Mac Partlin, M., Homer, E., Robinson, H., McCormick, C.J., Crouch, D.H., Durant, S.T., Matheson, E.C., Hall, A.G., Gillespie, D.A. and Brown, R. (2003) Interactions of the DNA mismatch repair proteins MLH1 and MSH2 with c-MYC and MAX. Oncogene, 22, 819- 825. 12. Wiedemeyer, W.R., Beach, J.A. and Karlan, B.Y. (2014) Reversing Platinum Resistance in High-Grade Serous Ovarian Carcinoma: Targeting BRCA and the Homologous Recombination System. Front Oncol, 4, 34. 13. Boucherat, O., Chabot, S., Bourgeois, A., Provencher, S., Paulin, R., Maltais, F. and Bonnet, S. (2016) Role of the transcription factor Yin Yang 1 in non-small cell lung cancer. The FASEB Journal, 30, 699.698-699.698. 14. Matsumura, N., Huang, Z., Baba, T., Lee, P.S., Barnett, J.C., Mori, S., Chang, J.T., Kuo, W.L., Gusberg, A.H., Whitaker, R.S. et al. (2009) Yin yang 1 modulates taxane response in epithelial ovarian cancer. Mol Cancer Res, 7, 210-220. 15. Sankpal, U.T., Ingersoll, S.B., Ahmad, S., Holloway, R.W., Bhat, V.B., Simecka, J.W., Daniel, L., Kariali, E., Vishwanatha, J.K. and Basha, R. (2016) Association of Sp1 and survivin in epithelial ovarian cancer: Sp1 inhibitor and cisplatin, a novel combination for inhibiting epithelial ovarian cancer cell proliferation. Tumour Biol, 37, 14259-14269. 16. Hu, J., Adebali, O., Adar, S. and Sancar, A. (2017) Dynamic maps of UV damage formation and repair for the . Proc Natl Acad Sci U S A, 114, 6758-6763. 17. Joannides, M., Mays, A.N., Mistry, A.R., Hasan, S.K., Reiter, A., Wiemels, J.L., Felix, C.A., Coco, F.L., Osheroff, N., Solomon, E. et al. (2011) Molecular pathogenesis of secondary acute promyelocytic leukemia. Mediterr J Hematol Infect Dis, 3, e2011045. 18. Cho, E., Moon, S.M., Park, B.R., Kim, D.K., Lee, B.K. and Kim, C.S. (2015) NRSF/REST regulates the mTOR signaling pathway in oral cancer cells. Oncol Rep, 33, 1459-1464. 19. Ballou, L.M. and Lin, R.Z. (2008) Rapamycin and mTOR kinase inhibitors. J Chem Biol, 1, 27-36. 20. Li, X.M., Lin, W., Wang, J., Zhang, W., Yin, A.A., Huang, Y., Zhang, J., Yao, L., Bian, H., Zhang, J. et al. (2016) Dec1 expression predicts prognosis and the response to temozolomide chemotherapy in patients with glioma. Mol Med Rep, 14, 5626-5636. 21. Song, C., Wang, L., Wu, X., Wang, K., Xie, D., Xiao, Q., Li, S., Jiang, K., Liao, L., Yates, J.R., 3rd et al. (2018) PML Recruits TET2 to Regulate DNA Modification and Cell Proliferation in Response to Chemotherapeutic Agent. Cancer Res, 78, 2475-2489. 22. Ngan, C.Y., Yamamoto, H., Takagi, A., Fujie, Y., Takemasa, I., Ikeda, M., Takahashi- Yanaga, F., Sasaguri, T., Sekimoto, M., Matsuura, N. et al. (2008) Oxaliplatin induces mitotic catastrophe and apoptosis in esophageal cancer cells. Cancer Sci, 99, 129-139. 23. Prendergast, G.C. and Jaffee, E.M. (2013) Cancer immunotherapy: immune suppression and tumor growth. Academic Press. 24. SHI, H.-Q., HUANG, H., ZHANG, X.-T. and ZHANG, J.-C. (2010) c-Myc Attenuates The Ability of Doxorubicin to Reduce The Colony Formation Partially Through Regulating Nbs1 in U2OS Cells. Progress in Biochemistry and Biophysics, 37, 855-863. 25. Frenzel, A., Zirath, H., Vita, M., Albihn, A. and Henriksson, M.A. (2011) Identification of cytotoxic drugs that selectively target tumor cells with MYC overexpression. PLoS One, 6, e27988. 26. Valdez, B.C., Zander, A.R., Song, G., Murray, D., Nieto, Y., Li, Y., Champlin, R.E. and Andersson, B.S. (2014) Synergistic cytotoxicity of gemcitabine, clofarabine and edelfosine in lymphoma cell lines. Blood Cancer J, 4, e171. 27. Valdez, B.C., Li, Y., Murray, D., Ji, J., Liu, Y., Popat, U., Champlin, R.E. and Andersson, B.S. (2015) Comparison of the cytotoxicity of cladribine and clofarabine when combined with fludarabine and busulfan in AML cells: Enhancement of cytotoxicity with epigenetic modulators. Exp Hematol, 43, 448-461 e442. 28. Qian, Y., Zhang, J., Yan, B. and Chen, X. (2008) DEC1, a basic helix-loop-helix transcription factor and a novel target gene of the p53 family, mediates p53-dependent premature senescence. J Biol Chem, 283, 2896-2905. 29. Qian, Y. and Chen, X. (2008) ID1, inhibitor of differentiation/DNA binding, is an effector of the p53-dependent DNA damage response pathway. J Biol Chem, 283, 22410-22416. 30. Borellini, F., Aquino, A., Josephs, S.F. and Glazer, R.I. (1990) Increased expression and DNA-binding activity of transcription factor Sp1 in doxorubicin-resistant HL-60 leukemia cells. Mol Cell Biol, 10, 5541-5547. 31. Kim, J.Y., Jung, H.H., Ahn, S., Bae, S., Lee, S.K., Kim, S.W., Lee, J.E., Nam, S.J., Ahn, J.S., Im, Y.H. et al. (2016) The relationship between nuclear factor (NF)-kappaB family and prognosis in triple-negative breast cancer (TNBC) patients receiving adjuvant doxorubicin treatment. Sci Rep, 6, 31804. 32. Lee, B.S., Oh, J., Kang, S.K., Park, S., Lee, S.H., Choi, D., Chung, J.H., Chung, Y.W. and Kang, S.M. (2015) Insulin Protects Cardiac Myocytes from Doxorubicin Toxicity by Sp1- Mediated Transactivation of Survivin. PLoS One, 10, e0135438. 33. Stewart, D.J. (2007) Mechanisms of resistance to cisplatin and carboplatin. Crit Rev Oncol Hematol, 63, 12-31. 34. Kawano, Y., Kikukawa, Y., Fujiwara, S., Wada, N., Okuno, Y., Mitsuya, H. and Hata, H. (2013) Hypoxia reduces CD138 expression and induces an immature and stem cell-like transcriptional program in myeloma cells. Int J Oncol, 43, 1809-1816. 35. Icardi, L., Mori, R., Gesellchen, V., Eyckerman, S., De Cauwer, L., Verhelst, J., Vercauteren, K., Saelens, X., Meuleman, P., Leroux-Roels, G. et al. (2012) The Sin3a repressor complex is a master regulator of STAT transcriptional activity. Proc Natl Acad Sci U S A, 109, 12058-12063. 36. Yang, H., Yamazaki, T., Pietrocola, F., Zhou, H., Zitvogel, L., Ma, Y. and Kroemer, G. (2016) Improvement of immunogenic chemotherapy by STAT3 inhibition. Oncoimmunology, 5, e1078061. 37. Lee, T.-F., Lin, Y.-F., Liu, Y.-P. and Wu, C.-W. (2018). AACR. 38. Kichina, J.V., Goc, A., Al-Husein, B., Somanath, P.R. and Kandel, E.S. (2010) PAK1 as a therapeutic target. Expert Opin Ther Targets, 14, 703-725. 39. Steelman, L.S., Franklin, R.A., Abrams, S.L., Chappell, W., Kempf, C.R., Basecke, J., Stivala, F., Donia, M., Fagone, P., Nicoletti, F. et al. (2011) Roles of the Ras/Raf/MEK/ERK pathway in leukemia therapy. Leukemia, 25, 1080-1094. 40. Hernandez, A., Serrano-Bueno, G., Perez-Castineira, J.R. and Serrano, A. (2012) Intracellular proton pumps as targets in chemotherapy: V-ATPases and cancer. Curr Pharm Des, 18, 1383-1394. 41. Stransky, L., Cotter, K. and Forgac, M. (2016) The Function of V-ATPases in Cancer. Physiol Rev, 96, 1071-1091. 42. von Schwarzenberg, K., Wiedmann, R.M., Oak, P., Schulz, S., Zischka, H., Wanner, G., Efferth, T., Trauner, D. and Vollmar, A.M. (2013) Mode of cell death induction by pharmacological vacuolar H+-ATPase (V-ATPase) inhibition. J Biol Chem, 288, 1385-1396. 43. Zhou, J., Wei, Y., Liu, D., Ge, X., Zhou, F., Jiang, X.Y. and Gu, J. (2008) Identification of beta1,4GalT II as a target gene of p53-mediated HeLa cell apoptosis. J Biochem, 143, 547-554. 44. Zhou, H., Ma, H., Wei, W., Ji, D., Song, X., Sun, J., Zhang, J. and Jia, L. (2013) B4GALT family mediates the multidrug resistance of human leukemia cells by regulating the hedgehog pathway and the expression of p-glycoprotein and multidrug resistance- associated protein 1. Cell Death Dis, 4, e654. 45. Zhan, W., Wang, W., Han, T., Xie, C., Zhang, T., Gan, M. and Wang, J.B. (2017) COMMD9 promotes TFDP1/E2F1 transcriptional activity via interaction with TFDP1 in non-small cell lung cancer. Cell Signal, 30, 59-66. 46. Michelle, L., Cloutier, A., Toutant, J., Shkreta, L., Thibault, P., Durand, M., Garneau, D., Gendron, D., Lapointe, E., Couture, S. et al. (2012) Proteins associated with the exon junction complex also control the alternative splicing of apoptotic regulators. Mol Cell Biol, 32, 954-967. 47. Choi, H.K., Choi, Y., Park, E.S., Park, S.Y., Lee, S.H., Seo, J., Jeong, M.H., Jeong, J.W., Jeong, J.H., Lee, P.C. et al. (2015) Programmed cell death 5 mediates HDAC3 decay to promote genotoxic stress response. Nat Commun, 6, 7390. 48. Li, G., Ma, D. and Chen, Y. (2016) Cellular functions of programmed cell death 5. Biochim Biophys Acta, 1863, 572-580. 49. Kang, M.R., Park, K.H., Yang, J.O., Lee, C.W., Oh, S.J., Yun, J., Lee, M.Y., Han, S.B. and Kang, J.S. (2016) miR-6734 Up-Regulates p21 Gene Expression and Induces Cell Cycle Arrest and Apoptosis in Colon Cancer Cells. PLoS One, 11, e0160961. 50. Paquet, N., Adams, M.N., Ashton, N.W., Touma, C., Gamsjaeger, R., Cubeddu, L., Leong, V., Beard, S., Bolderson, E., Botting, C.H. et al. (2016) hSSB1 (NABP2/OBFC2B) is regulated by oxidative stress. Sci Rep, 6, 27446. 51. Xu, S., Wu, Y., Chen, Q., Cao, J., Hu, K., Tang, J., Sang, Y., Lai, F., Wang, L., Zhang, R. et al. (2013) hSSB1 regulates both the stability and the transcriptional activity of p53. Cell Res, 23, 423-435. 52. Cubillos-Rojas, M., Schneider, T., Bartrons, R., Ventura, F. and Rosa, J.L. (2017) NEURL4 regulates the transcriptional activity of tumor suppressor protein p53 by modulating its oligomerization. Oncotarget, 8, 61824-61836. 53. Lau, K.M. and To, K.F. (2016) Importance of Estrogenic Signaling and Its Mediated Receptors in Prostate Cancer. Int J Mol Sci, 17. 54. Tang, Y., Yan, G., Song, X., Wu, K., Li, Z., Yang, C., Deng, T., Sun, Y., Hu, X., Yang, C. et al. (2015) STIP overexpression confers oncogenic potential to human non-small cell lung cancer cells by regulating cell cycle and apoptosis. J Cell Mol Med, 19, 2806-2817. 55. Lipinski, K.A., Britschgi, C., Schrader, K., Christinat, Y., Frischknecht, L. and Krek, W. (2016) Colorectal cancer cells display chaperone dependency for the unconventional prefoldin URI1. Oncotarget, 7, 29635-29647.