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Kinome Profiling of Primary Endometrial Tumors Using Multiplexed Inhibitor Beads and Mass Spectrometry Identifies SRPK1 as Candidate Therapeutic Target

2020; Elsevier BV; Volume: 19; Issue: 12 Linguagem: Inglês

10.1074/mcp.ra120.002012

ISSN

1535-9484

Autores

Alison Kurimchak, Vikas Kumar, Carlos Herrera-Montávez, Katherine J. Johnson, Nishi Srivastava, Karthik Davarajan, Suraj Peri, Kathy Q. Cai, Gina Mantia-Smaldone, James S. Duncan,

Tópico(s)

Genetic factors in colorectal cancer

Resumo

Endometrial carcinoma (EC) is the most common gynecologic malignancy in the United States, with limited effective targeted therapies. Endometrial tumors exhibit frequent alterations in protein kinases, yet only a small fraction of the kinome has been therapeutically explored. To identify kinase therapeutic avenues for EC, we profiled the kinome of endometrial tumors and normal endometrial tissues using Multiplexed Inhibitor Beads and Mass Spectrometry (MIB-MS). Our proteomics analysis identified a network of kinases overexpressed in tumors, including Serine/Arginine-Rich Splicing Factor Kinase 1 (SRPK1). Immunohistochemical (IHC) analysis of endometrial tumors confirmed MIB-MS findings and showed SRPK1 protein levels were highly expressed in endometrioid and uterine serous cancer (USC) histological subtypes. Moreover, querying large-scale genomics studies of EC tumors revealed high expression of SRPK1 correlated with poor survival. Loss-of-function studies targeting SRPK1 in an established USC cell line demonstrated SRPK1 was integral for RNA splicing, as well as cell cycle progression and survival under nutrient deficient conditions. Profiling of USC cells identified a compensatory response to SRPK1 inhibition that involved EGFR and the up-regulation of IGF1R and downstream AKT signaling. Co-targeting SRPK1 and EGFR or IGF1R synergistically enhanced growth inhibition in serous and endometrioid cell lines, representing a promising combination therapy for EC. Endometrial carcinoma (EC) is the most common gynecologic malignancy in the United States, with limited effective targeted therapies. Endometrial tumors exhibit frequent alterations in protein kinases, yet only a small fraction of the kinome has been therapeutically explored. To identify kinase therapeutic avenues for EC, we profiled the kinome of endometrial tumors and normal endometrial tissues using Multiplexed Inhibitor Beads and Mass Spectrometry (MIB-MS). Our proteomics analysis identified a network of kinases overexpressed in tumors, including Serine/Arginine-Rich Splicing Factor Kinase 1 (SRPK1). Immunohistochemical (IHC) analysis of endometrial tumors confirmed MIB-MS findings and showed SRPK1 protein levels were highly expressed in endometrioid and uterine serous cancer (USC) histological subtypes. Moreover, querying large-scale genomics studies of EC tumors revealed high expression of SRPK1 correlated with poor survival. Loss-of-function studies targeting SRPK1 in an established USC cell line demonstrated SRPK1 was integral for RNA splicing, as well as cell cycle progression and survival under nutrient deficient conditions. Profiling of USC cells identified a compensatory response to SRPK1 inhibition that involved EGFR and the up-regulation of IGF1R and downstream AKT signaling. Co-targeting SRPK1 and EGFR or IGF1R synergistically enhanced growth inhibition in serous and endometrioid cell lines, representing a promising combination therapy for EC. Endometrial carcinoma (EC) is the most common gynecologic malignancy in the United States with 60,050 new cases and 10,470 deaths expected in 2020 (1Siegel R.L. Miller K.D. Jemal A. Cancer statistics, 2016.CA Cancer J. Clin. 2016; 66: 7-30Crossref PubMed Scopus (21988) Google Scholar). There are two major histological types of EC, Type I and Type II, each displaying distinctive overall prognosis and survival outcomes (2Remmerie M. Janssens V. Targeted Therapies in Type II Endometrial Cancers: Too Little, but Not Too Late.Int. J. Mol. Sci. 2018; 19Crossref PubMed Scopus (28) Google Scholar). Type 1 ECs are composed of low-grade endometrioid tumors representing the majority of EC (80%) with early stage detection and favorable prognosis. In contrast, Type II ECs are high-grade, display poor prognosis, and consist of 3 distinct histologies: serous adenocarcinomas, clear cell adenocarcinomas, and carcinosarcomas (2Remmerie M. Janssens V. Targeted Therapies in Type II Endometrial Cancers: Too Little, but Not Too Late.Int. J. Mol. Sci. 2018; 19Crossref PubMed Scopus (28) Google Scholar). Uterine serous carcinoma (USC) is the most lethal form of Type II EC because of late stage detection and high recurrence rates with current treatments only modestly impacting survival (3Black J.D. English D.P. Roque D.M. Santin A.D. Targeted therapy in uterine serous carcinoma: an aggressive variant of endometrial cancer.Womens Health (Lond). 2014; 10: 45-57Crossref PubMed Google Scholar). Moreover, there has been a steady increase in the mortality rate for EC that has been attributed to higher proportions of patients presenting with USC (4Ueda S.M. Kapp D.S. Cheung M.K. Shin J.Y. Osann K. Husain A. Teng N.N. Berek J.S. Chan J.K. Trends in demographic and clinical characteristics in women diagnosed with corpus cancer and their potential impact on the increasing number of deaths.Am. J. Obstet. Gynecol. 2008; 198: e211-e216Google Scholar). EC tumors are characterized by alterations in TP53, PPP2R1A, FBXW7, CDKN2A, PTEN, EGFR, ERBB2, PIK3CA, and KRAS (5Hecht J.L. Mutter G.L. Molecular and pathologic aspects of endometrial carcinogenesis.J. Clin. Oncol. 2006; 24: 4783-4791Crossref PubMed Scopus (432) Google Scholar, 6Kuhn E. Wu R.C. Guan B. Wu G. Zhang J. Wang Y. Song L. Yuan X. Wei L. Roden R.B. Kuo K.T. Nakayama K. Clarke B. Shaw P. Olvera N. Kurman R.J. Levine D.A. Wang T.L. Shih Ie M. Identification of molecular pathway aberrations in uterine serous carcinoma by genome-wide analyses.J. Natl. Cancer Inst. 2012; 104: 1503-1513Crossref PubMed Scopus (187) Google Scholar), and consequently inhibitors targeting EGFR, ERBB2, PI3K/AKT, and MAPK pathway components have been extensively explored as molecular targeted therapies (2Remmerie M. Janssens V. Targeted Therapies in Type II Endometrial Cancers: Too Little, but Not Too Late.Int. J. Mol. Sci. 2018; 19Crossref PubMed Scopus (28) Google Scholar). However, inhibiting these kinases has shown limited therapeutic benefit in EC because of drug resistance (7Mitamura T. Dong P. Ihira K. Kudo M. Watari H. Molecular-targeted therapies and precision medicine for endometrial cancer.Jap. J. Clin. Oncol. 2019; 49: 108-120Crossref PubMed Scopus (30) Google Scholar), prompting the search for new therapeutic avenues with a strong focus on combination therapies. Protein kinases are a family of ∼535 enzymes that collectively are termed the kinome (8Wilson L.J. Linley A. Hammond D.E. Hood F.E. Coulson J.M. MacEwan D.J. Ross S.J. Slupsky J.R. Smith P.D. Eyers P.A. Prior I.A. New perspectives, opportunities, and challenges in exploring the human protein kinome.Cancer Res. 2018; 78: 15-29Crossref PubMed Scopus (90) Google Scholar). Uncontrolled protein kinase activity has been linked to the development of nearly 25% of all cancers; consequently, protein kinases represent one of the most promising avenues for cancer therapy (9Metz J.T. Johnson E.F. Soni N.B. Merta P.J. Kifle L. Hajduk P.J. Navigating the kinome.Nat. Chem. Biol. 2011; 7: 200-202Crossref PubMed Scopus (203) Google Scholar, 10Knight Z.A. Lin H. Shokat K.M. Targeting the cancer kinome through polypharmacology.Nat. Rev. Cancer. 2010; 10: 130-137Crossref PubMed Scopus (554) Google Scholar). Indeed, ∼60 kinase-specific inhibitors are currently approved for therapy of various cancer types, with several hundred kinase inhibitors in Phase 1-3 clinical trials across all diseases (8Wilson L.J. Linley A. Hammond D.E. Hood F.E. Coulson J.M. MacEwan D.J. Ross S.J. Slupsky J.R. Smith P.D. Eyers P.A. Prior I.A. New perspectives, opportunities, and challenges in exploring the human protein kinome.Cancer Res. 2018; 78: 15-29Crossref PubMed Scopus (90) Google Scholar, 11Roskoski R. Properties of FDA-approved small molecule protein kinase inhibitors: A 2020 update.Pharmacol. Res. 2020; 152104609 Crossref PubMed Scopus (322) Google Scholar). However, most of these kinase-specific inhibitors target a relatively small fraction of the human kinome with the majority untargeted for cancer therapy (12Knapp S. Arruda P. Blagg J. Burley S. Drewry D.H. Edwards A. Fabbro D. Gillespie P. Gray N.S. Kuster B. Lackey K.E. Mazzafera P. Tomkinson N.C.O. Willson T.M. Workman P. Zuercher W.J. A public-private partnership to unlock the untargeted kinome.Nat. Chem. Biol. 2013; 9: 3-6Crossref PubMed Scopus (114) Google Scholar, 13Fedorov O. Muller S. Knapp S. The (un)targeted cancer kinome.Nat. Chem. Biol. 2010; 6: 166-169Crossref PubMed Scopus (238) Google Scholar, 14Drewry D.H. Wells C.I. Andrews D.M. Angell R. Al-Ali H. Axtman A.D. Capuzzi S.J. Elkins J.M. Ettmayer P. Frederiksen M. Gileadi O. Gray N. Hooper A. Knapp S. Laufer S. Luecking U. Michaelides M. Müller S. Muratov E. Denny R.A. Saikatendu K.S. Treiber D.K. Zuercher W.J. Willson T.M. Progress towards a public chemogenomic set for protein kinases and a call for contributions.PLoS ONE. 2017; 12e0181585 Crossref PubMed Scopus (85) Google Scholar). Moreover, a significant proportion of the kinome is largely uncharacterized with respect to the function of these kinases in cancer (12Knapp S. Arruda P. Blagg J. Burley S. Drewry D.H. Edwards A. Fabbro D. Gillespie P. Gray N.S. Kuster B. Lackey K.E. Mazzafera P. Tomkinson N.C.O. Willson T.M. Workman P. Zuercher W.J. A public-private partnership to unlock the untargeted kinome.Nat. Chem. Biol. 2013; 9: 3-6Crossref PubMed Scopus (114) Google Scholar, 13Fedorov O. Muller S. Knapp S. The (un)targeted cancer kinome.Nat. Chem. Biol. 2010; 6: 166-169Crossref PubMed Scopus (238) Google Scholar, 14Drewry D.H. Wells C.I. Andrews D.M. Angell R. Al-Ali H. Axtman A.D. Capuzzi S.J. Elkins J.M. Ettmayer P. Frederiksen M. Gileadi O. Gray N. Hooper A. Knapp S. Laufer S. Luecking U. Michaelides M. Müller S. Muratov E. Denny R.A. Saikatendu K.S. Treiber D.K. Zuercher W.J. Willson T.M. Progress towards a public chemogenomic set for protein kinases and a call for contributions.PLoS ONE. 2017; 12e0181585 Crossref PubMed Scopus (85) Google Scholar). Notably, several CRISPR/Cas9 and/or RNAi loss-of-function studies have shown that many understudied kinases are essential for cancer cell viability, highlighting the therapeutic potential of the understudied kinome for the treatment of cancer (15Scholl C. Fröhling S. Dunn I.F. Schinzel A.C. Barbie D.A. Kim S.Y. Silver S.J. Tamayo P. Wadlow R.C. Ramaswamy S. Döhner K. Bullinger L. Sandy P. Boehm J.S. Root D.E. Jacks T. Hahn W.C. Gilliland D.G. Synthetic lethal interaction between oncogenic KRAS dependency and STK33 suppression in human cancer cells.Cell. 2009; 137: 821-834Abstract Full Text Full Text PDF PubMed Scopus (474) Google Scholar, 16Barbie D.A. Tamayo P. Boehm J.S. Kim S.Y. Moody S.E. Dunn I.F. Schinzel A.C. Sandy P. Meylan E. Scholl C. Frohling S. Chan E.M. Sos M.L. Michel K. Mermel C. Silver S.J. Weir B.A. Reiling J.H. Sheng Q. Gupta P.B. Wadlow R.C. Le H. Hoersch S. Wittner B.S. Ramaswamy S. Livingston D.M. Sabatini D.M. Meyerson M. Thomas R.K. Lander E.S. Mesirov J.P. Root D.E. Gilliland D.G. Jacks T. Hahn W.C. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1.Nature. 2009; 462: 108-112Crossref PubMed Scopus (1857) Google Scholar). Chemical proteomics strategies, such as KinobeadsTM, Kinativ, and Multiplexed Inhibitor Beads (MIBs) have been widely utilized to measure kinase abundance in cancer cell lines and tissues (17Bantscheff M. Eberhard D. Abraham Y. Bastuck S. Boesche M. Hobson S. Mathieson T. Perrin J. Raida M. Rau C. Reader V. Sweetman G. Bauer A. Bouwmeester T. Hopf C. Kruse U. Neubauer G. Ramsden N. Rick J. Kuster B. Drewes G. Quantitative chemical proteomics reveals mechanisms of action of clinical ABL kinase inhibitors.Nat. Biotechnol. 2007; 25: 1035-1044Crossref PubMed Scopus (860) Google Scholar, 18Duncan J.S. Whittle M.C. Nakamura K. Abell A.N. Midland A.A. Zawistowski J.S. Johnson N.L. Granger D.A. Jordan N.V. Darr D.B. Usary J. Kuan P.-F. Smalley D.M. Major B. He X. Hoadley K.A. Zhou B. Sharpless N.E. Perou C.M. Kim W.Y. Gomez S.M. Chen X. Jin J. Frye Stephen V. Earp H.S. Graves L.M. Johnson G.L. Dynamic reprogramming of the kinome in response to targeted MEK inhibition in triple-negative breast cancer.Cell. 2012; 149: 307-321Abstract Full Text Full Text PDF PubMed Scopus (547) Google Scholar, 19Franks C.E. Hsu K.-L. Activity-based kinome profiling using chemical proteomics and ATP acyl phosphates.Curr. Protoc. Chem. Biol. 2019; 11: e72Crossref PubMed Scopus (4) Google Scholar). Endogenous kinases are enriched from lysates by immobilized kinase inhibitors and subsequently identified by quantitative MS (17Bantscheff M. Eberhard D. Abraham Y. Bastuck S. Boesche M. Hobson S. Mathieson T. Perrin J. Raida M. Rau C. Reader V. Sweetman G. Bauer A. Bouwmeester T. Hopf C. Kruse U. Neubauer G. Ramsden N. Rick J. Kuster B. Drewes G. Quantitative chemical proteomics reveals mechanisms of action of clinical ABL kinase inhibitors.Nat. Biotechnol. 2007; 25: 1035-1044Crossref PubMed Scopus (860) Google Scholar, 18Duncan J.S. Whittle M.C. Nakamura K. Abell A.N. Midland A.A. Zawistowski J.S. Johnson N.L. Granger D.A. Jordan N.V. Darr D.B. Usary J. Kuan P.-F. Smalley D.M. Major B. He X. Hoadley K.A. Zhou B. Sharpless N.E. Perou C.M. Kim W.Y. Gomez S.M. Chen X. Jin J. Frye Stephen V. Earp H.S. Graves L.M. Johnson G.L. Dynamic reprogramming of the kinome in response to targeted MEK inhibition in triple-negative breast cancer.Cell. 2012; 149: 307-321Abstract Full Text Full Text PDF PubMed Scopus (547) Google Scholar). Importantly, these proteomics approaches can measure the levels of numerous understudied kinases, providing a strategy to interrogate the unexplored cancer kinome. Several methods for quantitating kinases enriched by MIBs or kinobeads have been explored, including isobaric tagging (i.e. iTRAQ or TMT), label-free quantitation (LFQ) approaches, and labeled methods such as stable isotope labeling with amino acids in cell culture (SILAC) (17Bantscheff M. Eberhard D. Abraham Y. Bastuck S. Boesche M. Hobson S. Mathieson T. Perrin J. Raida M. Rau C. Reader V. Sweetman G. Bauer A. Bouwmeester T. Hopf C. Kruse U. Neubauer G. Ramsden N. Rick J. Kuster B. Drewes G. Quantitative chemical proteomics reveals mechanisms of action of clinical ABL kinase inhibitors.Nat. Biotechnol. 2007; 25: 1035-1044Crossref PubMed Scopus (860) Google Scholar, 18Duncan J.S. Whittle M.C. Nakamura K. Abell A.N. Midland A.A. Zawistowski J.S. Johnson N.L. Granger D.A. Jordan N.V. Darr D.B. Usary J. Kuan P.-F. Smalley D.M. Major B. He X. Hoadley K.A. Zhou B. Sharpless N.E. Perou C.M. Kim W.Y. Gomez S.M. Chen X. Jin J. Frye Stephen V. Earp H.S. Graves L.M. Johnson G.L. Dynamic reprogramming of the kinome in response to targeted MEK inhibition in triple-negative breast cancer.Cell. 2012; 149: 307-321Abstract Full Text Full Text PDF PubMed Scopus (547) Google Scholar, 20Klaeger S. Heinzlmeir S. Wilhelm M. Polzer H. Vick B. Koenig P.-A. Reinecke M. Ruprecht B. Petzoldt S. Meng C. Zecha J. Reiter K. Qiao H. Helm D. Koch H. Schoof M. Canevari G. Casale E. Depaolini S.R. Feuchtinger A. Wu Z. Schmidt T. Rueckert L. Becker W. Huenges J. Garz A.-K. Gohlke B.-O. Zolg D.P. Kayser G. Vooder T. Preissner R. Hahne H. Tõnisson N. Kramer K. Götze K. Bassermann F. Schlegl J. Ehrlich H.-C. Aiche S. Walch A. Greif P.A. Schneider S. Felder E.R. Ruland J. Médard G. Jeremias I. Spiekermann K. Kuster B. The target landscape of clinical kinase drugs.Science. 2017; 358eaan4368 Crossref PubMed Scopus (423) Google Scholar). Super-SILAC (s-SILAC) approaches (21Geiger T. Cox J. Ostasiewicz P. Wisniewski J.R. Mann M. Super-SILAC mix for quantitative proteomics of human tumor tissue.Nat. Methods. 2010; 7: 383-385Crossref PubMed Scopus (432) Google Scholar) that spike in a mixture of SILAC-labeled cell lines with tissues have also been used to measure kinases in tumors (22Deeb S.J. D'Souza R.C.J. Cox J. Schmidt-Supprian M. Mann M. Super-SILAC allows classification of diffuse large B-cell lymphoma subtypes by their protein expression profiles.Mol. Cell. Proteomics. 2012; 11: 77-89Abstract Full Text Full Text PDF PubMed Scopus (138) Google Scholar, 23Zhang W. Wei Y. Ignatchenko V. Li L. Sakashita S. Pham N.-A. Taylor P. Tsao M.S. Kislinger T. Moran M.F. Proteomic profiles of human lung adeno and squamous cell carcinoma using super-SILAC and label-free quantification approaches.Proteomics. 2014; 14: 795-803Crossref PubMed Scopus (27) Google Scholar). In our prior work, we utilized a s-SILAC reference mixed with tumor tissues to measure kinase abundance in ovarian tumors (24Kurimchak A.M. Herrera-Montávez C. Brown J. Johnson K.J. Sodi V. Srivastava N. Kumar V. Deihimi S. O'Brien S. Peri S. Mantia-Smaldone G.M. Jain A. Winters R.M. Cai K.Q. Chernoff J. Connolly D.C. Duncan J.S. Functional proteomics interrogation of the kinome identifies MRCKA as a therapeutic target in high-grade serous ovarian carcinoma.Sci. Signal. 2020; 13eaax8238 Crossref PubMed Scopus (9) Google Scholar). Additionally, combining multiple quantitative approaches such as LFQ and labeling techniques has been shown to increase coverage and quantitative precision for tissue proteomics (25Gunawardena H.P. O'Brien J. Wrobel J.A. Xie L. Davies S.R. Li S. Ellis M.J. Qaqish B.F. Chen X. QuantFusion: novel unified methodology for enhanced coverage and precision in quantifying global proteomic changes in whole tissues.Mol. Cell. Proteomics. 2016; 15: 740-751Abstract Full Text Full Text PDF PubMed Scopus (5) Google Scholar). Here, we employed MIB-MS kinome profiling using LFQ and s-SILAC quantitation to measure the abundance of kinases in patient endometrial carcinoma (EC) tumors and normal endometrial (NE) tissues. MIB-MS profiling identified several kinases overexpressed in endometrial tumors including Serine/Arginine-Rich Splicing Factor Kinase 1 (SRPK1). Inhibition of SRPK1 in USC cells using SPHINX31 altered RNA splicing, blocked cell growth, and induced apoptosis under nutrient-deficient conditions. Moreover, SRPK1 inhibitors had minimal impact on EC cell growth in the presence of serum because of compensatory EGFR, IGF1R, and AKT signaling. Together, our findings nominate SRPK1 as a putative target in combination with EGFR, IGF1R, or AKT inhibitors for the treatment of EC. For proteomic measurement of kinase abundance in tissues, we used MIB-MS profiling and quantitated kinase levels using a combination of Label-Free Quantitation (LFQ) and super-SILAC (s-SILAC) (26Cox J. Hein M.Y. Luber C.A. Paron I. Nagaraj N. Mann M. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ.Mol. Cell. Proteomics. 2014; 13: 2513-2526Abstract Full Text Full Text PDF PubMed Scopus (2688) Google Scholar, 27Neubert T.A. Tempst P. Super-SILAC for tumors and tissues.Nat. Meth. 2010; 7: 361-362Crossref PubMed Scopus (25) Google Scholar). Briefly, an equal amount of s-SILAC reference (5 mg) was spiked into each primary tissue sample (5 mg) and kinases purified from tissues using MIB-resins, kinases were eluted, digested, and peptides analyzed by LC–MS/MS as previously described (24Kurimchak A.M. Herrera-Montávez C. Brown J. Johnson K.J. Sodi V. Srivastava N. Kumar V. Deihimi S. O'Brien S. Peri S. Mantia-Smaldone G.M. Jain A. Winters R.M. Cai K.Q. Chernoff J. Connolly D.C. Duncan J.S. Functional proteomics interrogation of the kinome identifies MRCKA as a therapeutic target in high-grade serous ovarian carcinoma.Sci. Signal. 2020; 13eaax8238 Crossref PubMed Scopus (9) Google Scholar). To identify kinases elevated in endometrial tumors relative to normal endometrial tissues, we performed MIB-MS analysis on 20 distinct primary endometrial tumors and 16 distinct normal endometrial tissues. Measurement of MIB-enriched kinase abundance in tissues was performed by LFQ and s-SILAC quantitation using MaxQuant software version 1.6.1.0. POWER analysis of MIB-MS profiling of tissues: Measuring changes in kinase abundance among 20 tumors and 16 normal tissues using LFQ or s-SILAC was sufficiently powered. With 16 normal and 20 tumor samples, we would be able to detect a standardized effect size of 1.15 between these groups (in terms of log2 LFQ z-scores, this corresponds to a 2.22-fold difference between these groups), and achieve 80% power at the 1% significance level. A Type I Error of 1% has been used to account for the large number of kinases. With 16 normal and 20 tumor samples, we would be able to detect a difference of 1.03 between these groups (in terms of log2 s-SILAC ratios, this corresponds to a 2.04-fold difference between these groups), and achieve 80% power at the 1% significance level. A Type I Error of 1% has been used to account for the large number of kinases. Data analysis of MIB-MS profiling of tissues: MaxQuant normalized LFQ values or SILAC ratios (H/L) were filtered for human protein kinases in excel and then imported into Perseus software (1.6.2.3) for quantitation. LFQ data processing: Kinase LFQ intensity values were filtered in the following manner: kinases identified by site only were removed, reverse or potential contaminants were removed then filtered for kinases identified by >1 unique peptide. Kinase LFQ intensity values were then log2 transformed, technical replicates averaged, and rows filtered for minimum valid kinases measured (n= >70% of runs). No imputation of missing values was performed. s-SILAC data processing: Kinase s-SILAC ratios were transformed 1/(x) to generate light/heavy ratios, log2 transformed, technical replicates averaged, and rows filtered for minimum valid kinases measured (n= >70% of runs). No imputation of missing values was performed. Student's t test analysis of tumors versus normal tissues: Kinase log2 LFQ intensity values or s-SILAC ratios were subjected to a Student's t test comparing tumors (n = 20) versus normal tissues (n = 16) using Perseus Software. Parameters for the Student's t test for LFQ data were the following: S0 = 2, side both using Permutation-based FDR <0.05. Parameters for the Student's t test for s-SILAC ratios were the following: S0 = 0.1, side both using Permutation-based FDR <0.05. Volcano plots depicting differences in kinases abundance determined by LFQ or s-SILAC analysis were generated using R studio software. For PCA analysis of kinase log2 LFQ intensities, rows were filtered for kinases measured in 100% of MIB-MS runs and principal component analysis (PC1 versus PC2, PC2 versus PC3 and PC1 versus PC3) performed to visualize kinome profiles among tissue samples. For hierarchical clustering (Euclidean) of kinase levels among tissues samples, all MIB-enriched protein log2 LFQ intensities were z-score normalized in Perseus, then filtered for kinases, followed by row filtering for minimum valid kinases measured (n= >70% of runs). Scatter plots depicting differences in kinases abundance were generated using R studio software and bar graphs generated in excel or Prism. Experiments using MIB-MS were performed as previously described (24Kurimchak A.M. Herrera-Montávez C. Brown J. Johnson K.J. Sodi V. Srivastava N. Kumar V. Deihimi S. O'Brien S. Peri S. Mantia-Smaldone G.M. Jain A. Winters R.M. Cai K.Q. Chernoff J. Connolly D.C. Duncan J.S. Functional proteomics interrogation of the kinome identifies MRCKA as a therapeutic target in high-grade serous ovarian carcinoma.Sci. Signal. 2020; 13eaax8238 Crossref PubMed Scopus (9) Google Scholar). Briefly, cells or tumors were lysed and an equal amount of the s-SILAC reference (5 mg) lysate was added to nonlabeled (5 mg) lysate (cell, or tumor tissue) and endogenous kinases isolated by flowing lysates over kinase inhibitor-conjugated Sepharose beads (purvalanol B, VI16832, PP58 and CTx-0294885 beads) in 10 ml gravity-flow columns. Eluted kinases were reduced by incubation with 5 mm DTT at 65 °C for 25 min, alkylated with 20 mm iodoacetamide at room temperature for 30 min in the dark, and alkylation was quenched with DTT for 10 min, followed by digestion with sequencing-grade modified trypsin (Promega) overnight at 37 °C. C-18 purified peptides were dried in a speed vac, and subsequent LC-/MS/MS analysis was performed. Reproducibility analysis of MIB-enriched kinases using LFQ or s-SILAC quantitation-Reproducibility of MIB-MS paired with LFQ or s-SILAC quantitation was assessed as previously described (28Anastassiadis T. Deacon S.W. Devarajan K. Ma H. Peterson J.R. Comprehensive assay of kinase catalytic activity reveals features of kinase inhibitor selectivity.Nat. Biotech. 2011; 29: 1039-1045Crossref PubMed Scopus (655) Google Scholar). Briefly, MIB-MS-determined kinase expression data (log2 ratios) were transformed onto the nonnegative scale using 2^x transformation. In the case of triplicate or quadruplicate measurements, all pairs of replicates were considered in this analysis. Pairs of replicates with either missing observations or identical values across duplicates were removed from further analysis. The primary measures utilized in this analysis include the coefficient of variation (Z) and the difference (Δ) in duplicate observations computed for each pair of replicates. Using this method, a small number of outliers were identified from SILAC-based data and no outliers were identified from LFQ-based data, verifying the reproducibility of MIB-MS paired with either LFQ or s-SILAC. MIB-MS analysis of SPHINX31, BMS754807 or the combination of SPHINX31 and BMS754807 treatment in SPEC-2 cells was performed as previously described (24Kurimchak A.M. Herrera-Montávez C. Brown J. Johnson K.J. Sodi V. Srivastava N. Kumar V. Deihimi S. O'Brien S. Peri S. Mantia-Smaldone G.M. Jain A. Winters R.M. Cai K.Q. Chernoff J. Connolly D.C. Duncan J.S. Functional proteomics interrogation of the kinome identifies MRCKA as a therapeutic target in high-grade serous ovarian carcinoma.Sci. Signal. 2020; 13eaax8238 Crossref PubMed Scopus (9) Google Scholar). Briefly, serum-competent SPEC-2 cells were treated with 5 μm SPHINX31 (n = 3 biological replicates), 2 μm BMS754807 (n = 3 biological replicates), or 5 μm SPHINX31 + 2 μm BMS754807 (n = 3 biological replicates) for 48 h and lysates incubated with MIB-beads. Measurement of MIB-enriched kinase abundance in cell lines was performed by LFQ using MaxQuant software version 1.6.1.0. MaxQuant normalized LFQ values were filtered for human protein kinases in excel and then imported into Perseus software (1.6.2.3) for quantitation. LFQ values were filtered in the following manner: kinases identified by site only were removed, reverse or potential contaminants were removed, then filtered for kinases identified by >1 unique peptide. Kinase LFQ intensity values were then log2 transformed and rows filtered for minimum valid kinases measured (n= >70% of runs). No imputation of missing values was performed. Log2 LFQ intensity values were subjected to a Student's t test comparing treatment versus DMSO using Perseus Software. Parameters for the Student's t test for LFQ data were the following: S0 = 2, side both using Permutation-based FDR 1 unique peptide. Protein LFQ values were log2 transformed, filtered for a minimum valid number of 3, normalized using Z-scores, annotated, and subjected to a Student's t test with comparing SPHINX31 versus DMSO in serum-competent or starved SPEC-2 cells. Parameters for the Student's t test were the following: S0 = 0.1, side both using Benjamini-Hochberg FDR <0.05. Volcano plots depicting differences in protein abundance were generated using R studio software. Proteins induced or repressed by SPHINX31 treatment (FDR < 0.05) were imported into Metascape for pathway analysis (30Zhou Y. Zhou B. Pache L. Chang M. Khodabakhshi A.H. Tanaseichuk O. Benner C. Chanda S.K. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets.Nat. Commun. 2019; 101523 Crossref PubMed Scopus (4196) Google Scholar). Proteolytic peptides were resuspended in 0.1% formic acid and separated with a Thermo Scientific RSLC Ultimate 3000 on a Thermo Scientific Easy-Spray C18 PepMap 75 μm × 50 cm C-18 2 μm column. For MIB runs, a 240 min gradient of 4–25% acetonitrile with 0.1% formic acid was used. For total proteome runs, a 305 min gradient of 2–20% (180 min) 20-28% (45 min) 28–48% (20 min) acetonitrile with 0.1% formic acid was used. Both gradients were run at 300 nL/min at 50 °C. Eluted peptides were analyzed by Thermo Scientific Q Exactive or Q Exactive plus mass spectrometer using a top 15 methodology in which the 15 most intense peptide precursor ions were subjected to fragmentation. The AGC for MS1 was set to 3 × 106 with a max injection time of 120 ms, the AGC for MS2 ions was set to 1 × 105 with a max injection time of 15

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