data have been provided as Source Data files. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

## Code availability

The source code used for SPHINKS and the GBM-specific kinome phosphorylome network are available at GitHub at https://github.com/miccec/MAKINA. The Shiny app of the frozen and FFPE classification tools is available at https://lucgar88.shinyapps.io/GBMclassifier.

## References

1. Simon, R. & Roychowdhury, S. Implementing personalized cancer genomics in clinical trials. *Nat. Rev. Drug Discov.* **12**, 358–369 (2013).

2. Kundra, R. et al. OncoTree: a cancer classification system for precision oncology. *JCO Clin. Cancer Inform.* **5**, 221–230 (2021).

3. Mertins, P. et al. Proteogenomics connects somatic mutations to signalling in breast cancer. *Nature* **534**, 55–62 (2016).

4. Zhang, B. et al. Proteogenomic characterization of human colon and rectal cancer. *Nature* **513**, 382–387 (2014).

5. Garofano, L. et al. Pathway-based classification of glioblastoma uncovers a mitochondrial subtype with therapeutic vulnerabilities. *Nat. Cancer* **2**, 141–156 (2021).

6. Wang, L. B. et al. Proteogenomic and metabolomic characterization of human glioblastoma. *Cancer Cell* **39**, 509–528 (2021).

7. Wang, B. et al. Similarity network fusion for aggregating data types on a genomic scale. *Nat. Methods* **11**, 333–337 (2014).

8. Wang, Q. et al. Tumor evolution of glioma-intrinsic gene expression subtypes associates with immunological changes in the microenvironment. *Cancer Cell* **32**, 42–56 (2017).

9. Capper, D. et al. DNA methylation-based classification of central nervous system tumours. *Nature* **555**, 469–474 (2018).

10. Frattini, V. et al. A metabolic function of FGFR3-TACC3 gene fusions in cancer. *Nature* **553**, 222–227 (2018).

11. Bielle, F. et al. Diffuse gliomas with FGFR3-TACC3 fusion have characteristic histopathological and molecular features. *Brain Pathol.* **28**, 674–683 (2018).

12. Gutman, D. A. et al. MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. *Radiology* **267**, 560–569 (2013).

13. Jain, R. et al. Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor. *Radiology* **272**, 484–493 (2014).

14. Molenaar, M. R. et al. LION/web: a web-based ontology enrichment tool for lipidomic data analysis. *Gigascience* **8**, giz061 (2019).

15. Park, M. et al. A role for ceramides, but not sphingomyelins, as antagonists of insulin signaling and mitochondrial metabolism in C2C12 myotubes. *J. Biol. Chem.* **291**, 23978–23988 (2016).

16. Petan, T., Jarc, E. & Jusovic, M. Lipid droplets in cancer: guardians of fat in a stressful world. *Molecules* **23**, 1941 (2018).

17. Zigdon, H. et al. Ablation of ceramide synthase 2 causes chronic oxidative stress due to disruption of the mitochondrial respiratory chain. *J. Biol. Chem.* **288**, 4947–4956 (2013).

18. Carrasco, S. & Merida, I. Diacylglycerol, when simplicity becomes complex. *Trends Biochem. Sci.* **32**, 27–36 (2007).

19. Terce, F., Brun, H. & Vance, D. E. Requirement of phosphatidylcholine for normal progression through the cell cycle in C3H/10T1/2 fibroblasts. *J. Lipid Res.* **35**, 2130–2142 (1994).

20. Kim, H. Y., Huang, B. X. & Spector, A. A. Phosphatidylserine in the brain: metabolism and function. *Prog. Lipid Res.* **56**, 1–18 (2014).

21. Hussain, G. et al. Role of cholesterol and sphingolipids in brain development and neurological diseases. *Lipids Health Dis.* **18**, 26 (2019).

22. Tanguy, E., Wang, Q., Moine, H. & Vitale, N. Phosphatidic acid: from pleiotropic functions to neuronal pathology. *Front. Cell Neurosci.* **13**, 2 (2019).

23. Harachi, M., Masui, K., Cavenee, W. K., Mischel, P. S. & Shibata, N. Protein acetylation at the interface of genetics, epigenetics and environment in cancer. *Metabolites* **11**, 216 (2021).

24. Blumenberg, L. et al. BlackSheep: a bioconductor and bioconda package for differential extreme value analysis. *J. Proteome Res.* **20**, 3767–3773 (2021).

25. Guarente, L. The logic linking protein acetylation and metabolism. *Cell Metab.* **14**, 151–153 (2011).

26. Karanam, B., Jiang, L., Wang, L., Kelleher, N. L. & Cole, P. A. Kinetic and mass spectrometric analysis of p300 histone acetyltransferase domain autoacetylation. *J. Biol. Chem.* **281**, 40292–40301 (2006).

27. Yuan, H. et al. MYST protein acetyltransferase activity requires active site lysine autoacetylation. *EMBO J.* **31**, 58–70 (2012).

28. Thomas, T. & Voss, A. K. The diverse biological roles of MYST histone acetyltransferase family proteins. *Cell Cycle* **6**, 696–704 (2007).

29. Zhang, Y. & Hunter, T. Roles of Chk1 in cell biology and cancer therapy. *Int. J. Cancer* **134**, 1013–1023 (2014).

30. Hornbeck, P. V. et al. PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. *Nucleic Acids Res.* **43**, D512–D520 (2015).

31. Wiredja, D. D., Koyuturk, M. & Chance, M. R. The KSEA app: a web-based tool for kinase activity inference from quantitative phosphoproteomics. *Bioinformatics* **33**, 3489–3491 (2017).

32. Kuleshov, M. V. et al. KEA3: improved kinase enrichment analysis via data integration. *Nucleic Acids Res.* **49**, W304–W316 (2021).

33. Ochoa, D. et al. An atlas of human kinase regulation. *Mol. Syst. Biol.* **12**, 888 (2016).

34. Yilmaz, S. et al. Robust inference of kinase activity using functional networks. *Nat. Commun.* **12**, 1177 (2021).

35. Bezy, O. et al. PKCδ regulates hepatic insulin sensitivity and hepatosteatosis in mice and humans. *J. Clin. Invest.* **121**, 2504–2517 (2011).

36. Gibbs, P. E., Miralem, T., Lerner-Marmarosh, N., Tudor, C. & Maines, M. D. Formation of ternary complex of human biliverdin reductase-protein kinase Cδ-ERK2 protein is essential for ERK2-mediated activation of Elk1 protein, nuclear factor-κB, and inducible nitric-oxidase synthase (iNOS). *J. Biol. Chem.* **287**, 1066–1079 (2012).

37. Li, W. et al. Protein kinase C-δ is an important signaling molecule in insulin-like growth factor I receptor-mediated cell transformation. *Mol. Cell Biol.* **18**, 5888–5898 (1998).

38. Zhan, J., Chitta, R. K., Harwood, F. C. & Grosveld, G. C. Phosphorylation of TSC2 by PKC-δ reveals a novel signaling pathway that couples protein synthesis to mTORC1 activity. *Mol. Cell Biochem.* **456**, 123–134 (2019).

39. Kim, M. J. et al. Importance of PKCδ signaling in fractionated-radiation-induced expansion of glioma-initiating cells and resistance to cancer treatment. *J. Cell Sci.* **124**, 3084–3094 (2011).

40. Steinberg, S. F. Distinctive activation mechanisms and functions for protein kinase Cδ. *Biochem. J.* **384**, 449–459 (2004).

41. Takashima, A. et al. Protein kinase Cδ is a therapeutic target in malignant melanoma with NRAS mutation. *ACS Chem. Biol.* **9**, 1003–1014 (2014).

42. Buisson, R., Boisvert, J. L., Benes, C. H. & Zou, L. Distinct but concerted roles of ATR, DNA-PK, and Chk1 in countering replication stress during S phase. *Mol. Cell* **59**, 1011–1024 (2015).

43. Yue, X., Bai, C., Xie, D., Ma, T. & Zhou, P. K. DNA-PKcs: a multi-faceted player in DNA damage response. *Front. Genet.* **11**, 607428 (2020).
data have been provided as Source Data files. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

## Code availability

The source code used for SPHINKS and the GBM-specific kinome phosphorylome network are available at GitHub at https://github.com/miccec/MAKINA. The Shiny app of the frozen and FFPE classification tools is available at https://lucgar88.shinyapps.io/GBMclassifier.

## References

1. Simon, R. & Roychowdhury, S. Implementing personalized cancer genomics in clinical trials. *Nat. Rev. Drug Discov.* **12**, 358–369 (2013).

2. Kundra, R. et al. OncoTree: a cancer classification system for precision oncology. *JCO Clin. Cancer Inform.* **5**, 221–230 (2021).

3. Mertins, P. et al. Proteogenomics connects somatic mutations to signalling in breast cancer. *Nature* **534**, 55–62 (2016).

4. Zhang, B. et al. Proteogenomic characterization of human colon and rectal cancer. *Nature* **513**, 382–387 (2014).

5. Garofano, L. et al. Pathway-based classification of glioblastoma uncovers a mitochondrial subtype with therapeutic vulnerabilities. *Nat. Cancer* **2**, 141–156 (2021).

6. Wang, L. B. et al. Proteogenomic and metabolomic characterization of human glioblastoma. *Cancer Cell* **39**, 509–528 (2021).

7. Wang, B. et al. Similarity network fusion for aggregating data types on a genomic scale. *Nat. Methods* **11**, 333–337 (2014).

8. Wang, Q. et al. Tumor evolution of glioma-intrinsic gene expression subtypes associates with immunological changes in the microenvironment. *Cancer Cell* **32**, 42–56 (2017).

9. Capper, D. et al. DNA methylation-based classification of central nervous system tumours. *Nature* **555**, 469–474 (2018).

10. Frattini, V. et al. A metabolic function of FGFR3-TACC3 gene fusions in cancer. *Nature* **553**, 222–227 (2018).

11. Bielle, F. et al. Diffuse gliomas with FGFR3-TACC3 fusion have characteristic histopathological and molecular features. *Brain Pathol.* **28**, 674–683 (2018).

12. Gutman, D. A. et al. MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. *Radiology* **267**, 560–569 (2013).

13. Jain, R. et al. Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor. *Radiology* **272**, 484–493 (2014).

14. Molenaar, M. R. et al. LION/web: a web-based ontology enrichment tool for lipidomic data analysis. *Gigascience* **8**, giz061 (2019).

15. Park, M. et al. A role for ceramides, but not sphingomyelins, as antagonists of insulin signaling and mitochondrial metabolism in C2C12 myotubes. *J. Biol. Chem.* **291**, 23978–23988 (2016).

16. Petan, T., Jarc, E. & Jusovic, M. Lipid droplets in cancer: guardians of fat in a stressful world. *Molecules* **23**, 1941 (2018).

17. Zigdon, H. et al. Ablation of ceramide synthase 2 causes chronic oxidative stress due to disruption of the mitochondrial respiratory chain. *J. Biol. Chem.* **288**, 4947–4956 (2013).

18. Carrasco, S. & Merida, I. Diacylglycerol, when simplicity becomes complex. *Trends Biochem. Sci.* **32**, 27–36 (2007).

19. Terce, F., Brun, H. & Vance, D. E. Requirement of phosphatidylcholine for normal progression through the cell cycle in C3H/10T1/2 fibroblasts. *J. Lipid Res.* **35**, 2130–2142 (1994).

20. Kim, H. Y., Huang, B. X. & Spector, A. A. Phosphatidylserine in the brain: metabolism and function. *Prog. Lipid Res.* **56**, 1–18 (2014).

21. Hussain, G. et al. Role of cholesterol and sphingolipids in brain development and neurological diseases. *Lipids Health Dis.* **18**, 26 (2019).

22. Tanguy, E., Wang, Q., Moine, H. & Vitale, N. Phosphatidic acid: from pleiotropic functions to neuronal pathology. *Front. Cell Neurosci.* **13**, 2 (2019).

23. Harachi, M., Masui, K., Cavenee, W. K., Mischel, P. S. & Shibata, N. Protein acetylation at the interface of genetics, epigenetics and environment in cancer. *Metabolites* **11**, 216 (2021).

24. Blumenberg, L. et al. BlackSheep: a bioconductor and bioconda package for differential extreme value analysis. *J. Proteome Res.* **20**, 3767–3773 (2021).

25. Guarente, L. The logic linking protein acetylation and metabolism. *Cell Metab.* **14**, 151–153 (2011).

26. Karanam, B., Jiang, L., Wang, L., Kelleher, N. L. & Cole, P. A. Kinetic and mass spectrometric analysis of p300 histone acetyltransferase domain autoacetylation. *J. Biol. Chem.* **281**, 40292–40301 (2006).

27. Yuan, H. et al. MYST protein acetyltransferase activity requires active site lysine autoacetylation. *EMBO J.* **31**, 58–70 (2012).

28. Thomas, T. & Voss, A. K. The diverse biological roles of MYST histone acetyltransferase family proteins. *Cell Cycle* **6**, 696–704 (2007).

29. Zhang, Y. & Hunter, T. Roles of Chk1 in cell biology and cancer therapy. *Int. J. Cancer* **134**, 1013–1023 (2014).

30. Hornbeck, P. V. et al. PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. *Nucleic Acids Res.* **43**, D512–D520 (2015).

31. Wiredja, D. D., Koyuturk, M. & Chance, M. R. The KSEA app: a web-based tool for kinase activity inference from quantitative phosphoproteomics. *Bioinformatics* **33**, 3489–3491 (2017).

32. Kuleshov, M. V. et al. KEA3: improved kinase enrichment analysis via data integration. *Nucleic Acids Res.* **49**, W304–W316 (2021).

33. Ochoa, D. et al. An atlas of human kinase regulation. *Mol. Syst. Biol.* **12**, 888 (2016).

34. Yilmaz, S. et al. Robust inference of kinase activity using functional networks. *Nat. Commun.* **12**, 1177 (2021).

35. Bezy, O. et al. PKCδ regulates hepatic insulin sensitivity and hepatosteatosis in mice and humans. *J. Clin. Invest.* **121**, 2504–2517 (2011).

36. Gibbs, P. E., Miralem, T., Lerner-Marmarosh, N., Tudor, C. & Maines, M. D. Formation of ternary complex of human biliverdin reductase-protein kinase Cδ-ERK2 protein is essential for ERK2-mediated activation of Elk1 protein, nuclear factor-κB, and inducible nitric-oxidase synthase (iNOS). *J. Biol. Chem.* **287**, 1066–1079 (2012).

37. Li, W. et al. Protein kinase C-δ is an important signaling molecule in insulin-like growth factor I receptor-mediated cell transformation. *Mol. Cell Biol.* **18**, 5888–5898 (1998).

38. Zhan, J., Chitta, R. K., Harwood, F. C. & Grosveld, G. C. Phosphorylation of TSC2 by PKC-δ reveals a novel signaling pathway that couples protein synthesis to mTORC1 activity. *Mol. Cell Biochem.* **456**, 123–134 (2019).

39. Kim, M. J. et al. Importance of PKCδ signaling in fractionated-radiation-induced expansion of glioma-initiating cells and resistance to cancer treatment. *J. Cell Sci.* **124**, 3084–3094 (2011).

40. Steinberg, S. F. Distinctive activation mechanisms and functions for protein kinase Cδ. *Biochem. J.* **384**, 449–459 (2004).

41. Takashima, A. et al. Protein kinase Cδ is a therapeutic target in malignant melanoma with NRAS mutation. *ACS Chem. Biol.* **9**, 1003–1014 (2014).

42. Buisson, R., Boisvert, J. L., Benes, C. H. & Zou, L. Distinct but concerted roles of ATR, DNA-PK, and Chk1 in countering replication stress during S phase. *Mol. Cell* **59**, 1011–1024 (2015).

43. Yue, X., Bai, C., Xie, D., Ma, T. & Zhou, P. K. DNA-PKcs: a multi-faceted player in DNA damage response. *Front. Genet.* **11**, 607428 (2020).
44. Majd, N. K. et al. The promise of DNA damage response inhibitors for the treatment of glioblastoma. *Neurooncol. Adv.* **3**, vdab015 (2021).

45. Chan, D. W. et al. Autophosphorylation of the DNA-dependent protein kinase catalytic subunit is required for rejoining of DNA double-strand breaks. *Genes Dev.* **16**, 2333–2338 (2002).

46. Krug, K. et al. Proteogenomic landscape of breast cancer tumorigenesis and targeted therapy. *Cell* **183**, 1436–1456 (2020).

47. Petralia, F. et al. Integrated proteogenomic characterization across major histological types of pediatric brain cancer. *Cell* **183**, 1962–1985 (2020).

48. Satpathy, S. et al. A proteogenomic portrait of lung squamous cell carcinoma. *Cell* **184**, 4348–4371 (2021).

49. Behling, F. & Schittenhelm, J. Oncogenic BRAF alterations and their role in brain tumors. *Cancers* **11**, 794 (2019).

50. Holloway, R. W. & Marignani, P. A. Targeting mTOR and glycolysis in HER2-positive breast cancer. *Cancers* **13**, 2922 (2021).

51. Wilkerson, M. D. et al. Lung squamous cell carcinoma mRNA expression subtypes are reproducible, clinically important, and correspond to normal cell types. *Clin. Cancer Res.* **16**, 4864–4875 (2010).

52. Corsello, S. M. et al. Discovering the anti-cancer potential of non-oncology drugs by systematic viability profiling. *Nat. Cancer* **1**, 235–248 (2020).

53. Lee, G. H. et al. FYN promotes mesenchymal phenotypes of basal type breast cancer cells through STAT5/NOTCH2 signaling node. *Oncogene* **37**, 1857–1868 (2018).

54. Xie, Y. G. et al. FYN promotes breast cancer progression through epithelial-mesenchymal transition. *Oncol. Rep.* **36**, 1000–1006 (2016).

55. Campillo-Marcos, I., Garcia-Gonzalez, R., Navarro-Carrasco, E. & Lazo, P. A. The human VRK1 chromatin kinase in cancer biology. *Cancer Lett.* **503**, 117–128 (2021).

56. Salzano, M. et al. Vaccinia-related kinase 1 (VRK1) confers resistance to DNA-damaging agents in human breast cancer by affecting DNA damage response. *Oncotarget* **5**, 1770–1778 (2014).

57. Turnbull, A. K. et al. Unlocking the transcriptomic potential of formalin-fixed paraffin-embedded clinical tissues: comparison of gene expression profiling approaches. *BMC Bioinform.* **21**, 30 (2020).

58. Wright, G. W. et al. A probabilistic classification tool for genetic subtypes of diffuse large B cell lymphoma with therapeutic implications. *Cancer Cell* **37**, 551–568 (2020).

59. Roos, W. P. & Krumm, A. The multifaceted influence of histone deacetylases on DNA damage signalling and DNA repair. *Nucleic Acids Res.* **44**, 10017–10030 (2016).

60. Cleary, J. M., Aguirre, A. J., Shapiro, G. I. & D'Andrea, A. D. Biomarker-guided development of DNA repair inhibitors. *Mol. Cell* **78**, 1070–1085 (2020).

61. Berger, A. C. et al. A comprehensive pan-cancer molecular study of gynecologic and breast cancers. *Cancer Cell* **33**, 690–705 (2018).

62. Cancer Genome Atlas Research Network. Comprehensive genomic characterization of squamous cell lung cancers. *Nature* **489**, 519–525 (2012).

63. Mukherjee, A. et al. Associations between genomic stratification of breast cancer and centrally reviewed tumour pathology in the METABRIC cohort. *NPJ Breast Cancer* **4**, 5 (2018).

64. Aryee, M. J. et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. *Bioinformatics* **30**, 1363–1369 (2014).

65. Saghafinia, S., Mina, M., Riggi, N., Hanahan, D. & Ciriello, G. Pan-cancer landscape of aberrant DNA methylation across human tumors. *Cell Rep.* **25**, 1066–1080 (2018).

66. Ma, W. et al. DreamAI: algorithm for the imputation of proteomics data. Preprint at bioRxiv https://doi.org/10.1101/2020.07.21.214205 (2021).

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69. Binder, J. X. et al. COMPARTMENTS: unification and visualization of protein subcellular localization evidence. *Database* **2014**, bau012 (2014).

70. Beli, P. et al. Proteomic investigations reveal a role for RNA processing factor THRAP3 in the DNA damage response. *Mol. Cell* **46**, 212–225 (2012).

71. Bensimon, A. et al. ATM-dependent and -independent dynamics of the nuclear phosphoproteome after DNA damage. *Sci. Signal.* **3**, rs3 (2010).

72. Elia, A. E. et al. Quantitative proteomic atlas of ubiquitination and acetylation in the DNA damage response. *Mol. Cell* **59**, 867–881 (2015).

73. Matsuoka, S. et al. ATM and ATR substrate analysis reveals extensive protein networks responsive to DNA damage. *Science* **316**, 1160–1166 (2007).

74. Stokes, M. P. et al. Profiling of UV-induced ATM/ATR signaling pathways. *Proc. Natl Acad. Sci. USA* **104**, 19855–19860 (2007).

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## Acknowledgements

This work was supported by National Institutes of Health grant nos. U54CA193313, R01CA239721 and R01CA268592 (to A.L.); U54CA193313, R01CA190891, R01CA268592, R01CA239698 and R35CA253183; NCI P30 Supplement GBM CARE-HOPE; the Chemotherapy Foundation (to A.I.); and the Italian Association for Cancer Research Project IDs 21846 (IG) and 21073 (5 per mille) (to M.C.). S.M. is recipient of a fellowship from the Italian Association for Cancer Research. INCa-DGOS-INSERM_12560 (SiRIC CURAMUS) and the Ligue Nationale contre le Cancer (LNCC; Equipe labellisée) to M.S.

## Author contributions

A.L. and A.I. conceived and coordinated the studies and provided overall supervision. S.M. and L.G. developed and performed bioinformatics analyses of multi-omics data, with the assistance of F.D. and M.C.; Y.T.O. and H.M. performed cell, molecular biology and metabolic assays, with the collaboration of R.D.N.; M.S., F.B., A.L.D.S., J.L., A.P. and J.N.S. provided GBM samples and data. L.G., S.M. and M.C. wrote the computational sections. A.L. and A.I. wrote, edited and revised the manuscript with input from all authors.
44. Majd, N. K. et al. The promise of DNA damage response inhibitors for the treatment of glioblastoma. *Neurooncol. Adv.* **3**, vdab015 (2021).

45. Chan, D. W. et al. Autophosphorylation of the DNA-dependent protein kinase catalytic subunit is required for rejoining of DNA double-strand breaks. *Genes Dev.* **16**, 2333–2338 (2002).

46. Krug, K. et al. Proteogenomic landscape of breast cancer tumorigenesis and targeted therapy. *Cell* **183**, 1436–1456 (2020).

47. Petralia, F. et al. Integrated proteogenomic characterization across major histological types of pediatric brain cancer. *Cell* **183**, 1962–1985 (2020).

48. Satpathy, S. et al. A proteogenomic portrait of lung squamous cell carcinoma. *Cell* **184**, 4348–4371 (2021).

49. Behling, F. & Schittenhelm, J. Oncogenic BRAF alterations and their role in brain tumors. *Cancers* **11**, 794 (2019).

50. Holloway, R. W. & Marignani, P. A. Targeting mTOR and glycolysis in HER2-positive breast cancer. *Cancers* **13**, 2922 (2021).

51. Wilkerson, M. D. et al. Lung squamous cell carcinoma mRNA expression subtypes are reproducible, clinically important, and correspond to normal cell types. *Clin. Cancer Res.* **16**, 4864–4875 (2010).

52. Corsello, S. M. et al. Discovering the anti-cancer potential of non-oncology drugs by systematic viability profiling. *Nat. Cancer* **1**, 235–248 (2020).

53. Lee, G. H. et al. FYN promotes mesenchymal phenotypes of basal type breast cancer cells through STAT5/NOTCH2 signaling node. *Oncogene* **37**, 1857–1868 (2018).

54. Xie, Y. G. et al. FYN promotes breast cancer progression through epithelial-mesenchymal transition. *Oncol. Rep.* **36**, 1000–1006 (2016).

55. Campillo-Marcos, I., Garcia-Gonzalez, R., Navarro-Carrasco, E. & Lazo, P. A. The human VRK1 chromatin kinase in cancer biology. *Cancer Lett.* **503**, 117–128 (2021).

56. Salzano, M. et al. Vaccinia-related kinase 1 (VRK1) confers resistance to DNA-damaging agents in human breast cancer by affecting DNA damage response. *Oncotarget* **5**, 1770–1778 (2014).

57. Turnbull, A. K. et al. Unlocking the transcriptomic potential of formalin-fixed paraffin-embedded clinical tissues: comparison of gene expression profiling approaches. *BMC Bioinform.* **21**, 30 (2020).

58. Wright, G. W. et al. A probabilistic classification tool for genetic subtypes of diffuse large B cell lymphoma with therapeutic implications. *Cancer Cell* **37**, 551–568 (2020).

59. Roos, W. P. & Krumm, A. The multifaceted influence of histone deacetylases on DNA damage signalling and DNA repair. *Nucleic Acids Res.* **44**, 10017–10030 (2016).

60. Cleary, J. M., Aguirre, A. J., Shapiro, G. I. & D'Andrea, A. D. Biomarker-guided development of DNA repair inhibitors. *Mol. Cell* **78**, 1070–1085 (2020).

61. Berger, A. C. et al. A comprehensive pan-cancer molecular study of gynecologic and breast cancers. *Cancer Cell* **33**, 690–705 (2018).

62. Cancer Genome Atlas Research Network. Comprehensive genomic characterization of squamous cell lung cancers. *Nature* **489**, 519–525 (2012).

63. Mukherjee, A. et al. Associations between genomic stratification of breast cancer and centrally reviewed tumour pathology in the METABRIC cohort. *NPJ Breast Cancer* **4**, 5 (2018).

64. Aryee, M. J. et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. *Bioinformatics* **30**, 1363–1369 (2014).

65. Saghafinia, S., Mina, M., Riggi, N., Hanahan, D. & Ciriello, G. Pan-cancer landscape of aberrant DNA methylation across human tumors. *Cell Rep.* **25**, 1066–1080 (2018).

66. Ma, W. et al. DreamAI: algorithm for the imputation of proteomics data. Preprint at bioRxiv https://doi.org/10.1101/2020.07.21.214205 (2021).

67. Stoney, R. A., Schwartz, J. M., Robertson, D. L. & Nenadic, G. Using set theory to reduce redundancy in pathway sets. *BMC Bioinform.* **19**, 386 (2018).

68. Kramer, A., Green, J., Pollard, J. Jr. & Tugendreich, S. Causal analysis approaches in Ingenuity Pathway Analysis. *Bioinformatics* **30**, 523–530 (2014).

69. Binder, J. X. et al. COMPARTMENTS: unification and visualization of protein subcellular localization evidence. *Database* **2014**, bau012 (2014).

70. Beli, P. et al. Proteomic investigations reveal a role for RNA processing factor THRAP3 in the DNA damage response. *Mol. Cell* **46**, 212–225 (2012).

71. Bensimon, A. et al. ATM-dependent and -independent dynamics of the nuclear phosphoproteome after DNA damage. *Sci. Signal.* **3**, rs3 (2010).

72. Elia, A. E. et al. Quantitative proteomic atlas of ubiquitination and acetylation in the DNA damage response. *Mol. Cell* **59**, 867–881 (2015).

73. Matsuoka, S. et al. ATM and ATR substrate analysis reveals extensive protein networks responsive to DNA damage. *Science* **316**, 1160–1166 (2007).

74. Stokes, M. P. et al. Profiling of UV-induced ATM/ATR signaling pathways. *Proc. Natl Acad. Sci. USA* **104**, 19855–19860 (2007).

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## Acknowledgements

This work was supported by National Institutes of Health grant nos. U54CA193313, R01CA239721 and R01CA268592 (to A.L.); U54CA193313, R01CA190891, R01CA268592, R01CA239698 and R35CA253183; NCI P30 Supplement GBM CARE-HOPE; the Chemotherapy Foundation (to A.I.); and the Italian Association for Cancer Research Project IDs 21846 (IG) and 21073 (5 per mille) (to M.C.). S.M. is recipient of a fellowship from the Italian Association for Cancer Research. INCa-DGOS-INSERM_12560 (SiRIC CURAMUS) and the Ligue Nationale contre le Cancer (LNCC; Equipe labellisée) to M.S.

## Author contributions

A.L. and A.I. conceived and coordinated the studies and provided overall supervision. S.M. and L.G. developed and performed bioinformatics analyses of multi-omics data, with the assistance of F.D. and M.C.; Y.T.O. and H.M. performed cell, molecular biology and metabolic assays, with the collaboration of R.D.N.; M.S., F.B., A.L.D.S., J.L., A.P. and J.N.S. provided GBM samples and data. L.G., S.M. and M.C. wrote the computational sections. A.L. and A.I. wrote, edited and revised the manuscript with input from all authors.
**Competing interests**

A.L. and A.I. are inventors of a biomarker technology that has been licensed to QIAGEN. A.I. received sponsored research funding from AstraZeneca and Taiho Pharmaceutical and has served as a paid consultant/advisor to AIMEDBIO. A.L. received sponsored research funding from Celgene. A.L. and A.I. are inventors of a patent application based on this work. All other authors declare no competing interests.

**Additional information**

**Extended data** is available for this paper at https://doi.org/10.1038/s43018-022-00510-x.

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s43018-022-00510-x.

**Correspondence and requests for materials** should be addressed to Anna Lasorella or Antonio Iavarone.

**Peer review information** *Nature Cancer* thanks Bing Zhao and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

**Reprints and permissions information** is available at www.nature.com/reprints.

**Publisher's note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

© The Author(s) 2023

<footnote n="1">¹Institute for Cancer Genetics, Columbia University Medical Center, New York, NY, USA. ²Sylvester Comprehensive Cancer Center, University of Miami, Miller School of Medicine, Miami, FL, USA. ³AP-HP, Hôpital de la Pitié-Salpêtrière, Service de Neurologie 2, Paris, France. ⁴Sorbonne Université, INSERM Unité 1127, CNRS UMR 7225, Paris Brain Institute, Equipe labellissée LNCC, Paris, France. ⁵Department of Neuropathology, Pitié-Salpêtrière-Charles Foix, AP-HP, Paris, France. ⁶Department of Neurology, Foch Hospital, Suresnes, Paris, France. ⁷Neurosurgery Unit, Spedali Riuniti, Livorno, Italy. ⁸Department of Radiation Oncology, Mayo Clinic, Rochester, MN, USA. ⁹Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Napoli, Italy. ¹⁰BIOGEM Institute of Molecular Biology and Genetics, Via Camporeale, Ariano Irpino, Italy. ¹¹Onconeurotek Tumor Bank, Paris Brain Institute ICM, Paris, France. ¹²Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, USA. ¹³Department of Pediatrics, Columbia University Medical Center, New York, NY, USA. ¹⁴Department of Biochemistry and Molecular Biology, University of Miami, Miller School of Medicine, Miami, FL, USA. ¹⁵Department of Neurology, Columbia University Medical Center, New York, NY, USA. ¹⁶Department of Neurological Surgery, University of Miami, Miller School of Medicine, Miami, FL, USA. ¹⁷These authors contributed equally: Simona Migliozzi, Young Taek Oh, Mohammad Hasanain, Luciano Garofano. ¹⁸These authors jointly supervised this work: Anna Lasorella, Antonio Iavarone. ✉e-mail: axl4252@miami.edu; axi435@miami.edu</footnote>
**Competing interests**

A.L. and A.I. are inventors of a biomarker technology that has been licensed to QIAGEN. A.I. received sponsored research funding from AstraZeneca and Taiho Pharmaceutical and has served as a paid consultant/advisor to AIMEDBIO. A.L. received sponsored research funding from Celgene. A.L. and A.I. are inventors of a patent application based on this work. All other authors declare no competing interests.

**Additional information**

**Extended data** is available for this paper at https://doi.org/10.1038/s43018-022-00510-x.

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s43018-022-00510-x.

**Correspondence and requests for materials** should be addressed to Anna Lasorella or Antonio Iavarone.

**Peer review information** *Nature Cancer* thanks Bing Zhao and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

**Reprints and permissions information** is available at www.nature.com/reprints.

**Publisher's note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

© The Author(s) 2023

<footnote n="1">¹Institute for Cancer Genetics, Columbia University Medical Center, New York, NY, USA. ²Sylvester Comprehensive Cancer Center, University of Miami, Miller School of Medicine, Miami, FL, USA. ³AP-HP, Hôpital de la Pitié-Salpêtrière, Service de Neurologie 2, Paris, France. ⁴Sorbonne Université, INSERM Unité 1127, CNRS UMR 7225, Paris Brain Institute, Equipe labellissée LNCC, Paris, France. ⁵Department of Neuropathology, Pitié-Salpêtrière-Charles Foix, AP-HP, Paris, France. ⁶Department of Neurology, Foch Hospital, Suresnes, Paris, France. ⁷Neurosurgery Unit, Spedali Riuniti, Livorno, Italy. ⁸Department of Radiation Oncology, Mayo Clinic, Rochester, MN, USA. ⁹Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Napoli, Italy. ¹⁰BIOGEM Institute of Molecular Biology and Genetics, Via Camporeale, Ariano Irpino, Italy. ¹¹Onconeurotek Tumor Bank, Paris Brain Institute ICM, Paris, France. ¹²Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, USA. ¹³Department of Pediatrics, Columbia University Medical Center, New York, NY, USA. ¹⁴Department of Biochemistry and Molecular Biology, University of Miami, Miller School of Medicine, Miami, FL, USA. ¹⁵Department of Neurology, Columbia University Medical Center, New York, NY, USA. ¹⁶Department of Neurological Surgery, University of Miami, Miller School of Medicine, Miami, FL, USA. ¹⁷These authors contributed equally: Simona Migliozzi, Young Taek Oh, Mohammad Hasanain, Luciano Garofano. ¹⁸These authors jointly supervised this work: Anna Lasorella, Antonio Iavarone. ✉e-mail: axl4252@miami.edu; axi435@miami.edu</footnote>
a

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**Extended Data Fig. 1** | Definition of functional subtypes of GBM by SNF and relationship to prior GBM classifiers. **a**, Circular plot indicating the annotation of data available for each platform and individual tumors of CPTAC-GBM cohort (n = 93 GBM samples). The number (n) of GBM samples for each platform is indicated. **b**, Integrative clustering of GBM tumors by SNF (n = 89 GBM samples). Heat map of patient-to-patient similarity coefficients generated by the integration of subtype-specific gene expression of the highest 50 genes in the ranked lists of the functional subtypes of 52 GBM samples classified as anchors and fCNVs associated with the four GBM subtypes from TCGA. Yellow-to-orange scale represents low to high similarity coefficient. **c**, Dot plot showing the genes

harboring fCNV<sup>prot</sup> gain or loss and relative pathway enrichment for each GBM subtype (n = 85 GBM samples). Dot size indicates significance of the pathway enrichment (P < 0.05, Fisher's exact test) and color the log<sub>2</sub>(FC) of the protein abundance in tumors harboring the fCNV<sup>prot</sup> alteration compared to wild-type tumors (blue to red scale indicate fCNV<sup>prot</sup> gain, red scale; fCNV<sup>prot</sup> loss, blue scale; two-sided MWW test). **d-e**, Chord diagram of GBM subtype assignment of the indicated classifiers in each individual tumor from TCGA (n = 199 GBM samples) (d) and CPTAC (n = 83 GBM samples) (e) datasets. **f**, Chord diagram of GBM subtype assignment according to the indicated classifiers in each individual tumor from the CPTAC dataset (n = 85 GBM samples).
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**Extended Data Fig. 1** | Definition of functional subtypes of GBM by SNF and relationship to prior GBM classifiers. **a**, Circular plot indicating the annotation of data available for each platform and individual tumors of CPTAC-GBM cohort (n = 93 GBM samples). The number (n) of GBM samples for each platform is indicated. **b**, Integrative clustering of GBM tumors by SNF (n = 89 GBM samples). Heat map of patient-to-patient similarity coefficients generated by the integration of subtype-specific gene expression of the highest 50 genes in the ranked lists of the functional subtypes of 52 GBM samples classified as anchors and fCNVs associated with the four GBM subtypes from TCGA. Yellow-to-orange scale represents low to high similarity coefficient. **c**, Dot plot showing the genes

harboring fCNV<sup>prot</sup> gain or loss and relative pathway enrichment for each GBM subtype (n = 85 GBM samples). Dot size indicates significance of the pathway enrichment (P < 0.05, Fisher's exact test) and color the log<sub>2</sub>(FC) of the protein abundance in tumors harboring the fCNV<sup>prot</sup> alteration compared to wild-type tumors (blue to red scale indicate fCNV<sup>prot</sup> gain, red scale; fCNV<sup>prot</sup> loss, blue scale; two-sided MWW test). **d-e**, Chord diagram of GBM subtype assignment of the indicated classifiers in each individual tumor from TCGA (n = 199 GBM samples) (d) and CPTAC (n = 83 GBM samples) (e) datasets. **f**, Chord diagram of GBM subtype assignment according to the indicated classifiers in each individual tumor from the CPTAC dataset (n = 85 GBM samples).
a

fCNV amp vs Subtype NES

Subtype assignment vs Protein abundance

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Subtype assignment vs fusion status

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fCNV del vs Subtype NES X (-1)

Subtype assignment vs Protein abundance

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**Extended Data Fig. 2 | Association between fCNV status of GBM driver genes and pathway-based subtypes.** a, Forest plots showing the association between fCNV amplification/mutation status of GBM driver oncogenes and subtype transcriptomic activity (magenta) or abundance of protein of the corresponding gene (light blue) in the CPTAC-GBM cohort (n = 84 GBM samples; univariate logistic regression). log(odd ratio) estimates (OR), 95% confidence intervals (CI) and *P* values are reported. log(OR) estimates higher/lower than 0 represent positive/negative association. b, FGFR3-TACC3 fusion analysis was

performed using a cohort of GBM profiled by FFPE tissue RNA-Seq (n = 170 GBM samples; univariate logistic regression). log(OR) estimates, 95% CI and *P* values are reported. c, Forest plots showing the association between fCNV deletion/ mutation status in GBM tumor suppressor genes and subtype transcriptomic activity (blue) or protein abundance of the corresponding gene (light blue; n = 84 GBM samples; univariate logistic regression). log(OR) estimates, 95% CI and *P* values are reported. For tumor suppressor genes, subtype activity values (NES) were multiplied by -1 for visualization purposes.
a

fCNV amp vs Subtype NES

Subtype assignment vs Protein abundance

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b

Subtype assignment vs fusion status

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fCNV del vs Subtype NES X (-1)

Subtype assignment vs Protein abundance

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**Extended Data Fig. 2** | Association between fCNV status of GBM driver genes and pathway-based subtypes. **a**, Forest plots showing the association between fCNV amplification/mutation status of GBM driver oncogenes and subtype transcriptomic activity (magenta) or abundance of protein of the corresponding gene (light blue) in the CPTAC-GBM cohort (n = 84 GBM samples; univariate logistic regression). log(odd ratio) estimates (OR), 95% confidence intervals (CI) and *P* values are reported. log(OR) estimates higher/lower than 0 represent positive/negative association. **b**, FGFR3-TACC3 fusion analysis was

performed using a cohort of GBM profiled by FFPE tissue RNA-Seq (n = 170 GBM samples; univariate logistic regression). log(OR) estimates, 95% CI and *P* values are reported. **c**, Forest plots showing the association between fCNV deletion/ mutation status in GBM tumor suppressor genes and subtype transcriptomic activity (blue) or protein abundance of the corresponding gene (light blue; n = 84 GBM samples; univariate logistic regression). log(OR) estimates, 95% CI and *P* values are reported. For tumor suppressor genes, subtype activity values (NES) were multiplied by -1 for visualization purposes.
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Cellular components

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Extended Data Fig. 3 | See next page for caption.
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Extended Data Fig. 3 | See next page for caption.
**Extended Data Fig. 3** | Multiplatform validation of the metabolic axis of the GBM subtypes. a, Comparative analysis of the interactome network including intermediate metabolites and enzymes of the indicated metabolic activities in GPM versus MTC tumors (GPM GBM samples: *n* = 16; MTC GBM samples: *n* = 10 for metabolites; GPM GBM samples: *n* = 22; MTC GBM samples: *n* = 12 for proteins; two-sided MWW test). Orange to green scale indicates metabolite/protein increase to decrease in GPM versus MTC samples; [glycolytic intermediates: logit(NES) = 1.76, *P* = 0.0007, mitochondrial intermediates: logit(NES) = -1.65, *P* = 0.018; glycolytic proteins: logit(NES) = 1.27, *P* = 0.017, mitochondrial proteins: logit(NES) = -1.19, *P* = 5.93e-13; two-sided MWW-GST]. **b-d**, Enrichment analysis of **b**, lipid subclasses and **c**, LION terms, grouped according to cellular components and **d**, lipid functions. Lipid subclasses and LION terms significantly enriched in at least one GBM subtype are reported (*n* = 64 GBM samples; log

odds ratio > 0, *P* < 0.05; Fisher's exact test). Circles are color-coded and their size reflect the log odds ratio. Asterisks: \* *P* < 0.05, \*\* *P* < 0.005, \*\*\* *P* < 0.001. e, Heat map showing unsupervised clustering of metabolic proteins differentially expressed between MTC and GPM samples [log₂(FC) > 0.3, *P* < 0.05; two-sided MWW test]. Biological pathways significantly enriched in metabolic proteins are reported on the right (log odds ratio > 0, *P* < 0.05; Fisher's exact test). *n*, number of GBM samples in GPM and MTC subtypes. f, Heat map depicting the outlier fraction of acetylated metabolic protein in GPM and MTC tumors (*P* < 0.05; BlackSheep). Representative outlier acetylated proteins are listed on the left according to decreasing *P* value. Biological pathways significantly enriched in outlier acetylated proteins are reported on the right (*P* < 0.0005; Fisher's exact test). *n*, number of GBM samples in GPM and MTC subtypes.
**Extended Data Fig. 3** | Multiplatform validation of the metabolic axis of the GBM subtypes. a, Comparative analysis of the interactome network including intermediate metabolites and enzymes of the indicated metabolic activities in GPM versus MTC tumors (GPM GBM samples: *n* = 16; MTC GBM samples: *n* = 10 for metabolites; GPM GBM samples: *n* = 22; MTC GBM samples: *n* = 12 for proteins; two-sided MWW test). Orange to green scale indicates metabolite/protein increase to decrease in GPM versus MTC samples; [glycolytic intermediates: logit(NES) = 1.76, *P* = 0.0007, mitochondrial intermediates: logit(NES) = -1.65, *P* = 0.018; glycolytic proteins: logit(NES) = 1.27, *P* = 0.017, mitochondrial proteins: logit(NES) = -1.19, *P* = 5.93e-13; two-sided MWW-GST]. **b-d**, Enrichment analysis of **b**, lipid subclasses and **c**, LION terms, grouped according to cellular components and **d**, lipid functions. Lipid subclasses and LION terms significantly enriched in at least one GBM subtype are reported (*n* = 64 GBM samples; log

odds ratio > 0, *P* < 0.05; Fisher's exact test). Circles are color-coded and their size reflect the log odds ratio. Asterisks: \* *P* < 0.05, \*\* *P* < 0.005, \*\*\* *P* < 0.001. **e**, Heat map showing unsupervised clustering of metabolic proteins differentially expressed between MTC and GPM samples [log₂(FC) > 0.3, *P* < 0.05; two-sided MWW test]. Biological pathways significantly enriched in metabolic proteins are reported on the right (log odds ratio > 0, *P* < 0.05; Fisher's exact test). **n**, number of GBM samples in GPM and MTC subtypes. **f**, Heat map depicting the outlier fraction of acetylated metabolic protein in GPM and MTC tumors (*P* < 0.05; BlackSheep). Representative outlier acetylated proteins are listed on the left according to decreasing *P* value. Biological pathways significantly enriched in outlier acetylated proteins are reported on the right (*P* < 0.0005; Fisher's exact test). **n**, number of GBM samples in GPM and MTC subtypes.
a

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**Extended Data Fig. 4** | Computational strategy for the identification of MKs in functional GBM subtypes and benchmarking of SPHINKS approach. **a.** The reconstruction of an unbiased kinome network combines SVM classifiers trained on different instances of the negative set as follows: (step i) train SVM classifier on validated kinase-substrate interactions (green arrows, positive training set) and a subset of randomly selected unknown interactions (red dotted arrow, negative set) using kinase abundance from proteomics and substrate abundance from phosho-proteomics; (step ii) compute a score for all the interactions in the network according to the SVM classifier; (step iii) perform bagging and obtain the average SVM scores; (step iv) retain only interactions whose average score was above the average SVM score threshold (50% of the known interactions) and whose Spearman's correlation was positive; (step v) calculate MKs activity as the difference of two terms, the weighted average of the predicted substrate's abundances using the SPHINKS score as weight (left), and the weighted average of randomly selected control substrate-set (right). **b.** ROC curves of the predictions of the interactions by SPHINKS derived from simulated phospho-proteomic matrix with different rates of missing values. The top-left side of plot was magnified for accurate visualization. **c.** ROC curves of the interactions by SPHINKS for each of the 10 cross-validation iterations of experimentally

validated interactions. **d.** Box plots of the average kinase $\Delta$-activity (percentage) from unperturbed versus 100 networks perturbed with random phosphosites interactions for each kinase replacing true interactions in the network ($p = 5\%$, 10%, 15%, 20%, 50%). In the upper plot, each dot represents the average $\Delta$-activity for each kinase across all runs at each perturbation percentage; in the lower plot, each dot represents the average $\Delta$-activity for each run across all kinases at each ratio of perturbation. Box plots span the first to third quartiles and whiskers show the 1.5 × interquartile range. **e.** Kinase-substrate interactome from SPHINKS highlighting MKs for each functional subtype indicated by colors: red, green, blue and cyan, MKs in GPM, MTC, NEU, and PPR, respectively (effect size > 0.3, $P < 0.01$; two-sided MWW test; $n = 85$ GBM samples). Nodes represent kinases and substrates, and lines their interactions. Gray nodes are subtype non-specific kinases; purple nodes are kinase-targeted phosphosites substrates. Orange lines indicate kinase-phosphosite interactions from PhosphoSitePlus; cyan lines represent novel kinase-substrate interactions inferred by the SPHINKS. **f.** MKs significantly active in each functional GBM subtype were mapped onto the human kinome tree. Red, green, blue and cyan, MKs in GPM, MTC, NEU, and PPR, respectively. The size of the circles is proportional to the kinase activity. The number of GBM samples is as in e.
**Extended Data Fig. 4** | Computational strategy for the identification of MKs in functional GBM subtypes and benchmarking of SPHINKS approach. **a.** The reconstruction of an unbiased kinome network combines SVM classifiers trained on different instances of the negative set as follows: (step i) train SVM classifier on validated kinase-substrate interactions (green arrows, positive training set) and a subset of randomly selected unknown interactions (red dotted arrow, negative set) using kinase abundance from proteomics and substrate abundance from phosho-proteomics; (step ii) compute a score for all the interactions in the network according to the SVM classifier; (step iii) perform bagging and obtain the average SVM scores; (step iv) retain only interactions whose average score was above the average SVM score threshold (50% of the known interactions) and whose Spearman's correlation was positive; (step v) calculate MKs activity as the difference of two terms, the weighted average of the predicted substrate's abundances using the SPHINKS score as weight (left), and the weighted average of randomly selected control substrate-set (right). **b.** ROC curves of the predictions of the interactions by SPHINKS derived from simulated phospho-proteomic matrix with different rates of missing values. The top-left side of plot was magnified for accurate visualization. **c.** ROC curves of the interactions by SPHINKS for each of the 10 cross-validation iterations of experimentally

validated interactions. **d.** Box plots of the average kinase Δ-activity (percentage) from unperturbed versus 100 networks perturbed with random phosphosites interactions for each kinase replacing true interactions in the network (p = 5%, 10%, 15%, 20%, 50%). In the upper plot, each dot represents the average Δ-activity for each kinase across all runs at each perturbation percentage; in the lower plot, each dot represents the average Δ-activity for each run across all kinases at each ratio of perturbation. Box plots span the first to third quartiles and whiskers show the 1.5 × interquartile range. **e.** Kinase-substrate interactome from SPHINKS highlighting MKs for each functional subtype indicated by colors: red, green, blue and cyan, MKs in GPM, MTC, NEU, and PPR, respectively (effect size > 0.3, P < 0.01; two-sided MWW test; n = 85 GBM samples). Nodes represent kinases and substrates, and lines their interactions. Gray nodes are subtype non-specific kinases; purple nodes are kinase-targeted phosphosites substrates. Orange lines indicate kinase-phosphosite interactions from PhosphoSitePlus; cyan lines represent novel kinase-substrate interactions inferred by the SPHINKS. **f.** MKs significantly active in each functional GBM subtype were mapped onto the human kinome tree. Red, green, blue and cyan, MKs in GPM, MTC, NEU, and PPR, respectively. The size of the circles is proportional to the kinase activity. The number of GBM samples is as in e.
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**Extended Data Fig. 5** | Benchmarking of SPHINKS against previously published kinase-substrate inference methods. **a**, Bar plot showing the probability of correctly identifying upregulated or downregulated kinases by the analysis of the 'top-10-hit' using the indicated inference methods (n = 103 kinase perturbations). **b**, Bar plot of the differential rank (Δ-rank) of activity between

SPHINKS and the indicated inference methods for the kinases significantly active in each GBM subtype by SPHINKS and common to the networks of all five approaches (n = 85 GBM samples). Kinases are ordered according to the rank of activity by SPHINKS.
<figure><img src="image_78.png" /><figcaption>**Extended Data Fig. 5** | Benchmarking of SPHINKS against previously published kinase-substrate inference methods. a, Bar plot showing the probability of correctly identifying upregulated or downregulated kinases by the analysis of the 'top-10-hit' using the indicated inference methods (n = 103 kinase perturbations). b, Bar plot of the differential rank (Δ-rank) of activity between</figcaption></figure>

SPHINKS and the indicated inference methods for the kinases significantly active in each GBM subtype by SPHINKS and common to the networks of all five approaches (n = 85 GBM samples). Kinases are ordered according to the rank of activity by SPHINKS.
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Extended Data Fig. 6 | See next page for caption.
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<figure><img src="image_80.png" /></figure>

b

<figure><img src="image_81.png" /></figure>

c

<figure><img src="image_82.png" /></figure>

d

<figure><img src="image_83.png" /></figure>

e

<figure><img src="image_84.png" /></figure>

Extended Data Fig. 6 | See next page for caption.
**Extended Data Fig. 6** | Global and phospho-proteomics events in insulin receptor/IGF-PKCδ pathway in GPM GBM and enrichment of DDR and RS phospho-proteins as a specific feature of PPR GBM. **a**, Signaling network highlighting the molecules and proteins involved in IGF-I/insulin signaling of GPM GBM tumors. Orange or red scale indicates the MWW score derived from the proteomic or phosphosite ranked list of GPM tumors when compared to the others, respectively (two-sided MWW test, *n* = 85 GBM samples). Molecules in white are proteins not profiled or whose abundance was not significantly higher in GPM when compared to the other subtypes. **b–c**, Western blot analysis of GPM PDO cells incubated with b, IGF-I (10 ng/ml), IGF-II (10 ng/ml) and c, insulin (100 ng/ml) for the indicated times using the indicated antibodies. GAPDH is shown as a loading control. Each experiment was repeated independently 2 times

with similar results. **d**, Viability curves of *n* = 8 PPR PDOs each derived from an independent patient and *n* = 8 GPM PDOs, each derived from an independent patient treated with increasing concentration of Nedisertib. Data are mean ± s.d. of *n* = 4 technical replicates for each PDO from one representative experiment. Experiments were repeated 2 times with similar results. **e**, Quantification of clonogenic assay of 2 PPR PDOs (PDO 015 and PDO 044, top panels) each derived from an independent patient and 2 GPM PDOs (PDO 021 and PDO 062, bottom panels) each derived from an independent patient treated with IR or IR plus Nedisertib (1667nM). Data are mean of *n* = 3 technical replicates from one representative experiment. Experiments were repeated 2 times with similar results.
**Extended Data Fig. 6** | Global and phospho-proteomics events in insulin receptor/IGF-PKCδ pathway in GPM GBM and enrichment of DDR and RS phospho-proteins as a specific feature of PPR GBM. **a**, Signaling network highlighting the molecules and proteins involved in IGF-I/insulin signaling of GPM GBM tumors. Orange or red scale indicates the MWW score derived from the proteomic or phosphosite ranked list of GPM tumors when compared to the others, respectively (two-sided MWW test, *n* = 85 GBM samples). Molecules in white are proteins not profiled or whose abundance was not significantly higher in GPM when compared to the other subtypes. **b–c**, Western blot analysis of GPM PDO cells incubated with b, IGF-I (10 ng/ml), IGF-II (10 ng/ml) and c, insulin (100 ng/ml) for the indicated times using the indicated antibodies. GAPDH is shown as a loading control. Each experiment was repeated independently 2 times

with similar results. d, Viability curves of *n* = 8 PPR PDOs each derived from an independent patient and *n* = 8 GPM PDOs, each derived from an independent patient treated with increasing concentration of Nedisertib. Data are mean ± s.d. of *n* = 4 technical replicates for each PDO from one representative experiment. Experiments were repeated 2 times with similar results. e, Quantification of clonogenic assay of 2 PPR PDOs (PDO 015 and PDO 044, top panels) each derived from an independent patient and 2 GPM PDOs (PDO 021 and PDO 062, bottom panels) each derived from an independent patient treated with IR or IR plus Nedisertib (1667nM). Data are mean of *n* = 3 technical replicates from one representative experiment. Experiments were repeated 2 times with similar results.
<figure><img src="image_90.png" /><figcaption>a</figcaption></figure>

**Extended Data Fig. 7** | Proteomics characterization and clinical outcome of PG stratified according to functional subtypes. a-b, Heat map showing the median abundance of the 150 highest scoring proteins of the ranked lists (two-sided MWW test) of the four functional subtypes in a, PG-LGG and b, PG-HGG (two-sided MWW test). Rows are proteins and columns are functional subtypes (n = 82 PG-LGG samples; n = 22 PG-HGG samples). Left and top color tracks indicate functional subtypes. Unsupervised clustering was performed for

<figure><img src="image_91.png" /><figcaption>b</figcaption></figure>

each subtype-specific protein signature. For each subtype, biological pathways significantly enriched by each gene subcluster are reported on the left (P < 0.05, Fisher's exact test). c, Kaplan-Meier curves of PG (n = 94 patients) stratified by SNF combining gene and protein signatures obtained from the functional GBM subtypes. Patients in the PPR subgroup exhibit significantly worse survival (log-rank test).

<figure><img src="image_92.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_93.png" /></figure>

p = 1.01e-05
<figure><img src="image_85.png" /><figcaption>a</figcaption></figure>

**Extended Data Fig. 7** | Proteomics characterization and clinical outcome of PG stratified according to functional subtypes. a-b, Heat map showing the median abundance of the 150 highest scoring proteins of the ranked lists (two-sided MWW test) of the four functional subtypes in a, PG-LGG and b, PG-HGG (two-sided MWW test). Rows are proteins and columns are functional subtypes (n = 82 PG-LGG samples; n = 22 PG-HGG samples). Left and top color tracks indicate functional subtypes. Unsupervised clustering was performed for

<figure><img src="image_86.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_87.png" /></figure>

<figure><img src="image_88.png" /></figure>

each subtype-specific protein signature. For each subtype, biological pathways significantly enriched by each gene subcluster are reported on the left (P < 0.05, Fisher's exact test). c, Kaplan-Meier curves of PG (n = 94 patients) stratified by SNF combining gene and protein signatures obtained from the functional GBM subtypes. Patients in the PPR subgroup exhibit significantly worse survival (log-rank test).
<figure><img src="image_94.png" /><figcaption>a</figcaption></figure>

<figure><img src="image_95.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_96.png" /></figure>

<figure><img src="image_97.png" /></figure>

$p=1.01e-05$

<figure><img src="image_98.png" /></figure>

$p=0.01106$

<figure><img src="image_99.png" /></figure>

$p=0.11$

<figure><img src="image_100.png" /></figure>

$p=2.079e-06$

Time (days)

Time (days)

Time (days)

Time (days)

<figure><img src="image_101.png" /><figcaption>e</figcaption></figure>

<figure><img src="image_102.png" /></figure>

$p=0.0041$

<figure><img src="image_103.png" /></figure>

$p=0.0055$

<figure><img src="image_104.png" /></figure>

$p=0.1458$

<figure><img src="image_105.png" /></figure>

$p=0.2017$

Time (days)

Time (days)

<figure><img src="image_106.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_107.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_108.png" /></figure>

<figure><img src="image_109.png" /><figcaption>f</figcaption></figure>

<figure><img src="image_110.png" /></figure>

<figure><img src="image_111.png" /></figure>

<figure><img src="image_112.png" /></figure>

<figure><img src="image_113.png" /></figure>

Extended Data Fig. 8 | See next page for caption.
<figure><img src="image_89.png" /><figcaption>a</figcaption></figure>

<figure><img src="image_90.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_91.png" /></figure>

<figure><img src="image_92.png" /></figure>

$p=1.01e-05$

<figure><img src="image_93.png" /></figure>

$p=0.01106$

<figure><img src="image_94.png" /></figure>

$p=0.11$

<figure><img src="image_95.png" /></figure>

$p=2.079e-06$

<figure><img src="image_96.png" /><figcaption>e</figcaption></figure>

<figure><img src="image_97.png" /></figure>

$p=0.0041$

<figure><img src="image_98.png" /></figure>

$p=0.0055$

<figure><img src="image_99.png" /></figure>

$p=0.1458$

<figure><img src="image_100.png" /></figure>

$p=0.2017$

<figure><img src="image_101.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_102.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_103.png" /></figure>

<figure><img src="image_104.png" /><figcaption>f</figcaption></figure>

<figure><img src="image_105.png" /></figure>

<figure><img src="image_106.png" /></figure>

<figure><img src="image_107.png" /></figure>

<figure><img src="image_108.png" /></figure>

Extended Data Fig. 8 | See next page for caption.
**Extended Data Fig. 8** | **Functional classification of BRCA and LSCC and prognostic implications.** a–b, Heat map showing the 150 highest scoring genes of the ranked lists of the four functional subtypes obtained from tumors classified in a, TCGA- (n = 810 BRCA samples) and b, METABRIC-BRCA (n = 1,088 BRCA samples) datasets (two-sided MWW test). Rows are genes and columns are tumors. Horizontal top and left tracks indicate functional subtypes; horizontal middle track indicates PAM50 classification of BRCA by TCGA; horizontal lower track indicates tumor grade. Unsupervised clustering was performed for each subtype-specific gene signature. Biological pathways significantly enriched by each gene subcluster are reported on the left (P < 0.05; Fisher's exact test). c, Kaplan–Meier curves and log-rank test analysis of 1,897 BRCA patients from the combined TCGA (n = 809 patients) and METABRIC datasets (n = 1,088 patients), stratified according to the four functional subclasses (log-rank test). d, Heat map showing the 150 highest scoring genes of the ranked lists of the

four functional subtypes in LUSC from TCGA database (n = 360 LUSC samples; two-sided MWW test). Rows are genes and columns are tumors. Horizontal top and left tracks indicate functional subtypes; horizontal lower track indicates tumor grade. Unsupervised clustering was performed for each subtype-specific gene signature. For each subtype, biological pathways significantly enriched by each gene subcluster are reported on the left (P < 0.05; Fisher's exact test). e, Kaplan–Meier curves of 356 patients with LUSC from the TCGA dataset stratified according to the four functional subclasses. f, Mitochondrial activity (NES) and menadione survival ratio (log₂) for 26 BRCA (upper plot) and 71 LUSC (lower plot) cell lines from DepMap. Upper track, functional classification; middle track, mitochondrial activity; lower track, menadione survival ratio. Survival ratio: difference between mitochondrial cell lines versus the others; log₂(FC) = −1.31, *p* = 0.008 for BRCA; log₂(FC) = −0.63, *p* = 0.076 for LUSC; two-sided *t*-test, unequal variance.
**Extended Data Fig. 8** | **Functional classification of BRCA and LSCC and prognostic implications.** a-b, Heat map showing the 150 highest scoring genes of the ranked lists of the four functional subtypes obtained from tumors classified in a, TCGA- (n = 810 BRCA samples) and b, METABRIC-BRCA (n = 1,088 BRCA samples) datasets (two-sided MWW test). Rows are genes and columns are tumors. Horizontal top and left tracks indicate functional subtypes; horizontal middle track indicates PAM50 classification of BRCA by TCGA; horizontal lower track indicates tumor grade. Unsupervised clustering was performed for each subtype-specific gene signature. Biological pathways significantly enriched by each gene subcluster are reported on the left (P < 0.05; Fisher's exact test). c, Kaplan-Meier curves and log-rank test analysis of 1,897 BRCA patients from the combined TCGA (n = 809 patients) and METABRIC datasets (n = 1,088 patients), stratified according to the four functional subclasses (log-rank test). d, Heat map showing the 150 highest scoring genes of the ranked lists of the

four functional subtypes in LUSC from TCGA database (n = 360 LUSC samples; two-sided MWW test). Rows are genes and columns are tumors. Horizontal top and left tracks indicate functional subtypes; horizontal lower track indicates tumor grade. Unsupervised clustering was performed for each subtype-specific gene signature. For each subtype, biological pathways significantly enriched by each gene subcluster are reported on the left (P < 0.05; Fisher's exact test). e, Kaplan-Meier curves of 356 patients with LUSC from the TCGA dataset stratified according to the four functional subclasses. f, Mitochondrial activity (NES) and menadione survival ratio (log₂) for 26 BRCA (upper plot) and 71 LUSC (lower plot) cell lines from DepMap. Upper track, functional classification; middle track, mitochondrial activity; lower track, menadione survival ratio. Survival ratio: difference between mitochondrial cell lines versus the others; log₂(FC) = -1.31, *p* = 0.008 for BRCA; log₂(FC) = -0.63, *p* = 0.076 for LUSC; two-sided *t*-test, unequal variance.
<figure><img src="image_114.png" /><figcaption>**Extended Data Fig. 9** | Common and specific Master Kinases across CPTAC-GBM, -PG, -BRCA, and -LSCC. Venn diagrams reporting the common and specific master kinases of each functional subtype resulted significantly activated in CPTAC-GBM, -PG, -BRCA, and -LSCC (GBM: *n* = 85 samples; PG: *n* = 104 samples; BRCA: *n* = 118 samples; LSCC: *n* = 106 samples).</figcaption></figure>
<figure><img src="image_109.png" /><figcaption>**Extended Data Fig. 9** | Common and specific Master Kinases across CPTAC-GBM, -PG, -BRCA, and -LSCC. Venn diagrams reporting the common and specific master kinases of each functional subtype resulted significantly activated in CPTAC-GBM, -PG, -BRCA, and -LSCC (GBM: *n* = 85 samples; PG: *n* = 104 samples; BRCA: *n* = 118 samples; LSCC: *n* = 106 samples).</figcaption></figure>
a

<figure><img src="image_115.png" /></figure>

$$P(K = k | X = x) = \frac{e^{\beta_{0k} + \beta_k^T x}}{\sum_l e^{\beta_{0l} + \beta_l^T x}}$$

b

<figure><img src="image_116.png" /></figure>

c

<figure><img src="image_117.png" /></figure>

<figure><img src="image_118.png" /></figure>

d

<figure><img src="image_119.png" /></figure>

**Extended Data Fig. 10 | Clinical-grade probabilistic tool for the classification of frozen and FFPE IDH wild-type GBM.** a, Schematics of the approach for calculating the probability of a GBM sample of belonging to one of the four defined functional subtypes. The Agilent expression data of 506 samples from the TCGA cohort of GBM were classified into one of the four functional subtypes (top left). The standardized expression of all the genes from the subtype-specific gene signatures (bottom left) was used to train a multinomial regression model with lasso penalty using glmnet (middle part). Each sample (input) was used to build a multi-class logistic regression model that returns four probabilities $P_{ik}$, one for each functional GBM subtype. We classified a tumor into one subtype if the fitted probability of the particular subtype was the highest ($P_{ikhigh}$) and the sample showed a simplicity score (SS) above a defined threshold (δ). Tumors that did not comply with the defined thresholds remained unclassified.

b, Comparison bar plot of sensitivity, specificity, and precision in each GBM

subtype of the multinomial regression model using RNA-Seq data from 45 matched frozen samples. c, Consensus clustering generated from the 178 FFPE GBM samples using the expression of the 200 genes from the FFPE-specific gene signatures. Columns and rows represent FFPE samples. Color bar on the top defines four subgroups according consensus clustering. Track at bottom indicates the functional classification of the corresponding 45 matched frozen samples. The number (n) of samples in each cluster and subtype is indicated. Yellow-to-blue scale indicates low to high similarity. d, Comparison bar plot of sensitivity, specificity, and precision in each GBM subtype of the multinomial regression model using RNA-Seq data from 45 matched FFPE samples. Dashed lines and corresponding values indicate the average of each performance measure (blue: sensitivity; orange: specificity; purple: precision) in each GBM subgroup.
a

<figure><img src="image_110.png" /></figure>

$$P(K = k | X = x) = \frac{e^{\beta_{0k} + \beta_k^T x}}{\sum_l e^{\beta_{0l} + \beta_l^T x}}$$

b

<figure><img src="image_111.png" /></figure>

c

<figure><img src="image_112.png" /></figure>

d

<figure><img src="image_113.png" /></figure>

**Extended Data Fig. 10 | Clinical-grade probabilistic tool for the classification of frozen and FFPE IDH wild-type GBM.** a, Schematics of the approach for calculating the probability of a GBM sample of belonging to one of the four defined functional subtypes. The Agilent expression data of 506 samples from the TCGA cohort of GBM were classified into one of the four functional subtypes (top left). The standardized expression of all the genes from the subtype-specific gene signatures (bottom left) was used to train a multinomial regression model with lasso penalty using glmnet (middle part). Each sample (input) was used to build a multi-class logistic regression model that returns four probabilities $P_{ik}$, one for each functional GBM subtype. We classified a tumor into one subtype if the fitted probability of the particular subtype was the highest ($P_{ikhigh}$) and the sample showed a simplicity score (SS) above a defined threshold (δ). Tumors that did not comply with the defined thresholds remained unclassified.

b, Comparison bar plot of sensitivity, specificity, and precision in each GBM

subtype of the multinomial regression model using RNA-Seq data from 45 matched frozen samples. c, Consensus clustering generated from the 178 FFPE GBM samples using the expression of the 200 genes from the FFPE-specific gene signatures. Columns and rows represent FFPE samples. Color bar on the top defines four subgroups according consensus clustering. Track at bottom indicates the functional classification of the corresponding 45 matched frozen samples. The number (n) of samples in each cluster and subtype is indicated. Yellow-to-blue scale indicates low to high similarity. d, Comparison bar plot of sensitivity, specificity, and precision in each GBM subtype of the multinomial regression model using RNA-Seq data from 45 matched FFPE samples. Dashed lines and corresponding values indicate the average of each performance measure (blue: sensitivity; orange: specificity; purple: precision) in each GBM subgroup.
## Reporting Summary

Nature Portfolio wishes to improve the reproducibility of the work that we publish. This form provides structure for consistency and transparency in reporting. For further information on Nature Portfolio policies, see our *Editorial Policies* and the *Editorial Policy Checklist*.

## Statistics

For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.

<figure><img src="image_120.png" /></figure>

Our web collection on *statistics for biologists* contains articles on many of the points above.

## Software and code

Policy information about *availability of computer code*

Data collection No software was used

Data analysis Blacksheep R package v.1.2.0

glmnet R package v.4.1-2

gprofiler2 R package v.0.2.1

GraphPad Prism v.8.0

ImageJ (Fiji) v.2.3.1

Ingenuity Pathways Knowledge Base QIAGEN IPA 2020 Release.

KinMap beta

MWW-GST v.2017.08.25.

R v.4.0.2

Set cover pathway redundancy v.1.

SNFtool R package v.2.3.1

TCGAbiolinks R package v.2.14.0.

DreamAl v.0.1.0

The source code used for SPHINKS and the GBM-specific kinome phosphorylome network are available at GitHub: https://github.com/miccec/MAKINA.

The Shiny app of the frozen and FFPE classification tools is available at https://lucgar88.shinyapps.io/GBMclassifier.

For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Portfolio guidelines for submitting code & software for further information.
<figure><img src="image_114.png" /></figure>

# Reporting Summary

Nature Portfolio wishes to improve the reproducibility of the work that we publish. This form provides structure for consistency and transparency in reporting. For further information on Nature Portfolio policies, see our *Editorial Policies* and the *Editorial Policy Checklist*.

## Statistics

For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.

<figure><img src="image_115.png" /></figure>

Our web collection on *statistics for biologists* contains articles on many of the points above.

## Software and code

Policy information about *availability of computer code*

Data collection No software was used

Data analysis
Blacksheepr R package v.1.2.0
glmnet R package v.4.1-2
gprofiler2 R package v.0.2.1
GraphPad Prism v.8.0.
ImageJ (Fiji) v.2.3.1
Ingenuity Pathways Knowledge Base QIAGEN IPA 2020 Release.
KinMap beta
MWW-GST v.2017.08.25.
R v.4.0.2
Set cover pathway redundancy v.1.
SNFtool R package v.2.3.1
TCGAbiolinks R package v.2.14.0.
DreamAl v.0.1.0

The source code used for SPHINKS and the GBM-specific kinome phosphorylome network are available at GitHub: https://github.com/miccec/MAKINA.

The Shiny app of the frozen and FFPE classification tools is available at https://lucgar88.shinyapps.io/GBMclassifier.

For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Portfolio guidelines for submitting code & software for further information.
# Data

Policy information about [availability of data](#)

All manuscripts must include a [data availability statement](#). This statement should provide the following information, where applicable:

- Accession codes, unique identifiers, or web links for publicly available datasets

- A description of any restrictions on data availability

- For clinical datasets or third party data, please ensure that the statement adheres to our [policy](#).

RNA-Seq expression data of the 178 FFPE-derived and 45 frozen GBM IDH-wt tumors have been submitted to Synapse (http://synapse.org, accession no. syn27042663). Previously published multi-omics data from CPTAC that were re-analysed here are available from Ref 6, 46-48. The human GBM transcriptomic, genomic, methylation and clinical data, BRCA and LUSC transcriptomic and clinical data were derived from the TCGA Research Network: http://cancergenome.nih.gov/ using TCGAbiolinks. BRCA transcriptomic data from METABRIC has been derived from Ref 63. MolecularNeuroPathology (MNP) GBM methylation data were derived from GEO (accession no. GSE90496). Source data have been provided as Source Data files. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

The list of figures that have associated biological raw data are:

Figure 4 d

Figure 6 a, b, c, d, e, f, g, h, i, j, k, l, m;

Extended Data Figure 6 b, c, d, e.

# Human research participants

Policy information about [studies involving human research participants and Sex and Gender in Research](#).

## Reporting on sex and gender

Gender information is available in Supplementary Table 1 and 12. Analysis of breast carcinoma apply to female patients.

Gender-based analysis were performed and reported in Figure 2.

## Population characteristics

Studies include patient diagnosis with glioblastoma multiforme (adult), glioma (pediatric), breast carcinoma, lung adenocarcinoma. Age and diagnosis is reported in sSupplementary Table 1.

178 FFPE-derived and 45 frozen GBM IDH-wt tumors are from Onconeurotek tumor bank certification (NF S96 900) and authorization from Ethics committee (CPP Ile de France VI, ref A39II), and the French Ministry for research (AC 2013-1962) and were de-identified before reaching the research lab. Age and gender is reported in Supplementary Table 12.

## Recruitment

Study does not involve recruitment procedures; this is exempt research.

## Ethics oversight

Work with these materials was designated as IRB exempt under paragraph 4 and it is covered under IRB protocol and Onconeurotek tumor bank certification (NF S96 900) and authorization from Ethics committee (CPP Ile de France VI, ref A39II), and the French Ministry for research (AC 2013-1962).

Note that full information on the approval of the study protocol must also be provided in the manuscript.

# Field-specific reporting

Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection.

<figure><img src="image_121.png" /></figure>

Life sciences

<figure><img src="image_122.png" /></figure>

Behavioural & social sciences

<figure><img src="image_123.png" /></figure>

Ecological, evolutionary & environmental sciences

For a reference copy of the document with all sections, see [nature.com/documents/nr-reporting-summary-flat.pdf](https://nature.com/documents/nr-reporting-summary-flat.pdf)

# Life sciences study design

All studies must disclose on these points even when the disclosure is negative.

## Sample size

Sample sizes were chosen based on data availability and on previous studies that showed robust statistical power. All available samples passing the quality control were included.

## Data exclusions

No data were excluded.

## Replication

At least three technical replicates were performed and experiments were repeated at least two times with similar results. All attempts at replication were successful.

## Randomization

Our work does not include clinical or biospecimen-based studies and therefore there was no requirement for randomization in any of the experiments performed. For in-vitro experiments, randomization of cell lines was not possible. All cell lines were treated in same manner and when shRNA experiments were performed, all comparisons were between shRNA-PKCdelta and shRNA-NT to control the covariates.

## Blinding

Molecular classification was performed independent of and blinded to the clinical features. Investigators were blinded to the clinical and
# Data

Policy information about [availability of data](#)

All manuscripts must include a [data availability statement](#). This statement should provide the following information, where applicable:

- Accession codes, unique identifiers, or web links for publicly available datasets

- A description of any restrictions on data availability

- For clinical datasets or third party data, please ensure that the statement adheres to our [policy](#).

RNA-Seq expression data of the 178 FFPE-derived and 45 frozen GBM IDH-wt tumors have been submitted to Synapse (http://synapse.org, accession no. syn27042663). Previously published multi-omics data from CPTAC that were re-analysed here are available from Ref 6, 46-48. The human GBM transcriptomic, genomic, methylation and clinical data, BRCA and LUSC transcriptomic and clinical data were derived from the TCGA Research Network: http://cancergenome.nih.gov/ using TCGAbiolinks. BRCA transcriptomic data from METABRIC has been derived from Ref 63. MolecularNeuroPathology (MNP) GBM methylation data were derived from GEO (accession no. GSE90496). Source data have been provided as Source Data files. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

The list of figures that have associated biological raw data are:
Figure 4 d

Figure 6 a, b, c, d, e, f, g, h, i, j, k, l, m;

Extended Data Figure 6 b, c, d, e.

# Human research participants

Policy information about [studies involving human research participants and Sex and Gender in Research.](#)

## Reporting on sex and gender

Gender information is available in Supplementary Table 1 and 12. Analysis of breast carcinoma apply to female patients. Gender-based analysis were performed and reported in Figure 2.

## Population characteristics

Studies include patient diagnosis with glioblastoma multiforme (adult), glioma (pediatric), breast carcinoma, lung adenocarcinoma. Age and diagnosis is reported in sSupplementary Table 1.

178 FFPE-derived and 45 frozen GBM IDH-wt tumors are from Onconeurotek tumor bank certification (NF S96 900) and authorization from Ethics committee (CPP Ile de France VI, ref A39II), and the French Ministry for research (AC 2013-1962) and were de-identified before reaching the research lab. Age and gender is reported in Supplementary Table 12.

## Recruitment

Study does not involve recruitment procedures; this is exempt research.

## Ethics oversight

Work with these materials was designated as IRB exempt under paragraph 4 and it is covered under IRB protocol and Onconeurotek tumor bank certification (NF S96 900) and authorization from Ethics committee (CPP Ile de France VI, ref A39II), and the French Ministry for research (AC 2013-1962).

Note that full information on the approval of the study protocol must also be provided in the manuscript.

# Field-specific reporting

Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection.

<figure><img src="image_116.png" /></figure>

Life sciences

<figure><img src="image_117.png" /></figure>

Behavioural & social sciences

<figure><img src="image_118.png" /></figure>

Ecological, evolutionary & environmental sciences

For a reference copy of the document with all sections, see [nature.com/documents/nr-reporting-summary-flat.pdf](https://nature.com/documents/nr-reporting-summary-flat.pdf)

# Life sciences study design

All studies must disclose on these points even when the disclosure is negative.

## Sample size

Sample sizes were chosen based on data availability and on previous studies that showed robust statistical power. All available samples passing the quality control were included.

## Data exclusions

No data were excluded.

## Replication

At least three technical replicates were performed and experiments were repeated at least two times with similar results. All attempts at replication were successful.

## Randomization

Our work does not include clinical or biospecimen-based studies and therefore there was no requirement for randomization in any of the experiments performed. For in-vitro experiments, randomization of cell lines was not possible. All cell lines were treated in same manner and when shRNA experiments were performed, all comparisons were between shRNA-PKCdelta and shRNA-NT to control the covariates.

## Blinding

Molecular classification was performed independent of and blinded to the clinical features. Investigators were blinded to the clinical and
# Reporting for specific materials, systems and methods

We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.

## Materials & experimental systems

<table><thead><tr><th>n/a</th><th>Involved in the study</th></tr></thead><tbody><tr><td>☐</td><td>☑ Antibodies</td></tr><tr><td>☐</td><td>☑ Eukaryotic cell lines</td></tr><tr><td>☑</td><td>☐ Palaeontology and archaeology</td></tr><tr><td>☑</td><td>☐ Animals and other organisms</td></tr><tr><td>☑</td><td>☐ Clinical data</td></tr><tr><td>☑</td><td>☐ Dual use research of concern</td></tr></tbody></table>

## Methods

<table><thead><tr><th>n/a</th><th>Involved in the study</th></tr></thead><tbody><tr><td>☑</td><td>☐ ChIP-seq</td></tr><tr><td>☑</td><td>☐ Flow cytometry</td></tr><tr><td>☑</td><td>☐ MRI-based neuroimaging</td></tr></tbody></table>

## Antibodies

### Antibodies used

Anti-Akt (pan) (Cell Signaling Technology, #4691, C67E7, rabbit monoclonal, 1:1,000)
Anti-phospho-Akt (Cell Signaling Technology, #4060, Ser-473, D9E, rabbit monoclonal, 1:1,000)
Anti-phospho-Akt (Cell Signaling Technology, #13038, Thr-308, D25E6, rabbit monoclonal, 1:1,000)
Anti-Stat3 (Cell Signaling Technology, #4904, 79D7, rabbit monoclonal, 1:1,000)
Anti-phospho-Stat3 (Cell Signaling Technology, #9145, Tyr-705, D3A7, rabbit monoclonal, 1:1,000)
Anti-phospho-PKC Delta (Cell Signaling Technology, #2055, Tyr-311, rabbit polyclonal, 1:1,000)
Anti-PKC Delta (Abcam, #ab182126, EPR17075, rabbit monoclonal, 1:1,000)
Anti-PKC Delta (Cell Signaling Technology, #9616, D10E2, rabbit monoclonal, 1:1,000)
Anti-p44/42 MAPK (Erk1/2) (Cell Signaling Technology, #9102, rabbit polyclonal, 1:1,000)
Anti-phospho-p44/42 MAPK (Erk1/2) (Cell Signaling Technology, #4370, Thr202/Tyr204, D13.14.4E, rabbit monoclonal, 1:1,000)
Anti-phospho-DNA-PKcs (Cell Signaling Technology, #68716, Ser-2056, E9J4G, rabbit monoclonal, 1:1,000)
Anti-DNA-PKcs (Cell Signaling Technology, #38168, E6U3A, rabbit monoclonal, 1:1,000)
Anti-phospho-p95/NBS1 (Cell Signaling Technology, #3001, Ser-343, rabbit polyclonal, 1:1,000)
Anti-p95/NBS1 (Cell Signaling Technology, #14956, D6JS1, rabbit monoclonal, 1:1,000)
Anti-phospho-Histone H2A.X (Cell Signaling Technology, #2577, Ser-139, rabbit polyclonal, 1:1,000)
Anti-phospho-KAP1 (Abcam, #ab133440, Ser-824, EPR5248, rabbit monoclonal, 1:1,000)
Anti-KAP1 (Abcam, #ab109287, EPR5216, rabbit monoclonal, 1:1,000)
Anti-Chk1 (Cell Signaling Technology, #2360, 2G1D5, mouse monoclonal, 1:1,000)
Anti-phospho-Chk1 (Cell Signaling Technology, #12302, Ser-317, D12H3, rabbit monoclonal, 1:1,000)
Anti-β-actin (Sigma-Aldrich, #A5441, clone AC-15, mouse monoclonal, 1:10,000)
Anti-vinculin (Sigma-Aldrich, #V9131, clone hVIN-1, mouse monoclonal, 1:10,000)
Anti-GAPDH (Abcam, #ab9484, mAbcam 9484, mouse monoclonal, 1:10,000)
Anti-Mouse IgG (H+L) Cross-Adsorbed Secondary Antibody, HRP (Invitrogen, #31438, 1:10,000)
Anti-Rabbit IgG (H+L) Cross-Adsorbed Secondary Antibody, HRP (Invitrogen, #31458, 1:10,000)
Anti-Rabbit IgG (H+L) Cross-Adsorbed Secondary Antibody, Cyanine3 (Invitrogen, #A10520, 1:500)

### Validation

Anti-Akt (pan) (Cell Signaling Technology, #4691, C67E7)
Reactivity: H/M/R/Mk/Dm, Sensitivity: endogenous, MW (kDa): 60, Source: rabbit monoclonal, Application-dilution: Western Blot-1:1,000/Immunoprecipitation- 1:50/Immunohistochemistry-1:150-1:600/Immunofluorescence-1:200-1:800/Flow Cytometry-1:100-1:400, Citation (PMID): 35415308, Akt (pan) (C67E7) Rabbit mAb detects endogenous levels of total Akt protein.
This antibody does not cross-react with other related proteins. Monoclonal antibody is produced by immunizing animals with a synthetic peptide corresponding to residues in the carboxy-terminal sequence of mouse Akt. (https://www cellsignal com/products/ primary-antibodies/akt-pan-c67e7-rabbit-mab/4691)
Anti-phospho-Akt (Cell Signaling Technology, #4060, Ser-473, D9E)
Reactivity: H/M/R/Hm/Mk/Dm/Z/B, Sensitivity: endogenous, MW (kDa): 60, Source: rabbit monoclonal, Application-dilution: Western Blot-1:2,000/Immunoprecipitation- 1:50/Immunohistochemistry-1:50-1:200/Immunofluorescence-1:400-1:800/Flow Cytometry-1:100-1:400, Citation (PMID): 35855640, Akt (pan) (C67E7) Rabbit mAb detects endogenous levels of total Akt protein.
This antibody does not cross-react with other related proteins. Monoclonal antibody is produced by immunizing animals with a synthetic peptide corresponding to residues in the carboxy-terminal sequence of mouse Akt. (https://www cellsignal com/products/ primary-antibodies/akt-pan-c67e7-rabbit-mab/4691)
Anti-Phospho-Akt (Cell Signaling Technology, #13038, Thr-308, D25E6)
Reactivity: H/M/R/Mk, Sensitivity: endogenous, MW (kDa): 60, Source: rabbit, Application-dilution: Western Blot-1:1,000/ Immunoprecipitation- 1:50/Immunofluorescence-1:800 - 1:1600/Flow Cytometry-1:1600 - 1:6400, Citation (PMID): 36207295, Phospho-Akt (Thr308) (D25E6) XP® Rabbit mAb recognizes endogenous levels of Akt1 protein only when phosphorylated at Thr308.
This antibody also recognizes endogenous levels of Akt2 protein when phosphorylated at Thr309 or Akt3 protein when phosphorylated at Thr305. Monoclonal antibody is produced by immunizing animals with a synthetic peptide corresponding to residues surrounding Thr308 of human Akt1 protein. (https://www cellsignal com/products/primary-antibodies/phospho-akt-thr308- d25e6-xp-rabbit-mab/13038).
# Reporting for specific materials, systems and methods

We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.

## Materials & experimental systems

<table><thead><tr><th>n/a</th><th>Involved in the study</th></tr></thead><tbody><tr><td>☐</td><td>☑ Antibodies</td></tr><tr><td>☐</td><td>☑ Eukaryotic cell lines</td></tr><tr><td>☑</td><td>☐ Palaeontology and archaeology</td></tr><tr><td>☑</td><td>☐ Animals and other organisms</td></tr><tr><td>☑</td><td>☐ Clinical data</td></tr><tr><td>☑</td><td>☐ Dual use research of concern</td></tr></tbody></table>

## Methods

<table><thead><tr><th>n/a</th><th>Involved in the study</th></tr></thead><tbody><tr><td>☑</td><td>☐ ChIP-seq</td></tr><tr><td>☑</td><td>☐ Flow cytometry</td></tr><tr><td>☑</td><td>☐ MRI-based neuroimaging</td></tr></tbody></table>

## Antibodies

### Antibodies used

Anti-Akt (pan) (Cell Signaling Technology, #4691, C67E7, rabbit monoclonal, 1:1,000)

Anti-phospho-Akt (Cell Signaling Technology, #4060, Ser-473, D9E, rabbit monoclonal, 1:1,000)

Anti-phospho-Akt (Cell Signaling Technology, #13038, Thr-308, D25E6, rabbit monoclonal, 1:1,000)

Anti-Stat3 (Cell Signaling Technology, #4904, 79D7, rabbit monoclonal 1:1,000)

Anti-phospho-Stat3 (Cell Signaling Technology, #9145, Tyr-705, D3A7, rabbit monoclonal, 1:1,000)

Anti-phospho-PKC Delta (Cell Signaling Technology, #2055, Tyr-311, rabbit polyclonal, 1:1,000)

Anti-PKC Delta (Abcam, #ab182126, EPR17075, rabbit monoclonal, 1:1,000)

Anti-PKC Delta (Cell Signaling Technology, #9616, D10E2, rabbit monoclonal, 1:1,000)

Anti-p44/42 MAPK (Erk1/2) (Cell Signaling Technology, #9102, rabbit polyclonal, 1:1,000)

Anti-phospho-p44/42 MAPK (Erk1/2) (Cell Signaling Technology, #4370, Thr202/Tyr204, D13.14.4E, rabbit monoclonal, 1:1,000)

Anti-phospho-DNA-PKcs (Cell Signaling Technology, #68716, Ser-2056, E9J4G, rabbit monoclonal, 1:1,000)

Anti-DNA-PKcs (Cell Signaling Technology, #38168, E6U3A, rabbit monoclonal, 1:1,000)

Anti-phospho-p95/NBS1 (Cell Signaling Technology, #3001, Ser-343, rabbit polyclonal, 1:1,000)

Anti-p95/NBS1 (Cell Signaling Technology, #14956, D6J5I, rabbit monoclonal, 1:1,000)

Anti-phospho-Histone H2A.X (Cell Signaling Technology, #2577, Ser-139, rabbit polyclonal, 1:1,000)

Anti-phospho-KAP1 (Abcam, #ab133440, Ser-824, EPR5248, rabbit monoclonal, 1:1,000)

Anti-KAP1 (Abcam, #ab109287, EPR5216, rabbit monoclonal, 1:1,000)

Anti-Chk1 (Cell Signaling Technology, #2360, 2G1D5, mouse monoclonal, 1:1,000)

Anti-phospho-Chk1 (Cell Signaling Technology, #12302, Ser-317, D12H3, rabbit monoclonal, 1:1,000)

Anti-β-actin (Sigma-Aldrich, #A5441, clone AC-15, mouse monoclonal, 1:10,000)

Anti-vinculin (Sigma-Aldrich, #V9131, clone hVIN-1, mouse monoclonal, 1:10,000)

Anti-GAPDH (Abcam, #ab9484, mAbcam 9484, mouse monoclonal, 1:10,000)

Anti-Mouse IgG (H+L) Cross-Adsorbed Secondary Antibody, HRP (Invitrogen, #31438, 1:10,000)

Anti-Rabbit IgG (H+L) Cross-Adsorbed Secondary Antibody, HRP (Invitrogen, #31458, 1:10,000)

Anti-Rabbit IgG (H+L) Cross-Adsorbed Secondary Antibody, Cyanine3 (Invitrogen, #A10520, 1:500)

### Validation

Anti-Akt (pan) (Cell Signaling Technology, #4691, C67E7)

Reactivity: H/M/R/Mk/Dm, Sensitivity: endogenous, MW (kDa): 60, Source: rabbit monoclonal, Application-dilution: Western

Blot-1:1,000/Immunoprecipitation- 1:50/Immunohistochemistry-1:150-1:600/Immunofluorescence-1:200-1:800/Flow

Cytometry-1:100-1:400, Citation (PMID): 35415308, Akt (pan) (C67E7) Rabbit mAb detects endogenous levels of total Akt protein.

This antibody does not cross-react with other related proteins. Monoclonal antibody is produced by immunizing animals with a synthetic peptide corresponding to residues in the carboxy-terminal sequence of mouse Akt. (https://www cellsignal com/products/ primary-antibodies/akt-pan-c67e7-rabbit-mab/4691)

Anti-phospho-Akt (Cell Signaling Technology, #4060, Ser-473, D9E)

Reactivity: H/M/R/Hm/Mk/Dm/Z/B, Sensitivity: endogenous, MW (kDa): 60, Source: rabbit monoclonal, Application-dilution: Western

Blot-1:2,000/Immunoprecipitation- 1:50/Immunohistochemistry-1:50-1:200/Immunofluorescence-1:400-1:800/Flow

Cytometry-1:100-1:400, Citation (PMID): 35855640, Akt (pan) (C67E7) Rabbit mAb detects endogenous levels of total Akt protein.

This antibody does not cross-react with other related proteins. Monoclonal antibody is produced by immunizing animals with a synthetic peptide corresponding to residues in the carboxy-terminal sequence of mouse Akt. (https://www cellsignal com/products/ primary-antibodies/akt-pan-c67e7-rabbit-mab/4691)

Anti-Phospho-Akt (Cell Signaling Technology, #13038, Thr-308, D25E6)

Reactivity: H/M/R/Mk, Sensitivity: endogenous, MW (kDa): 60, Source: rabbit, Application-dilution: Western Blot-1:1,000/
Immunoprecipitation- 1:50/Immunofluorescence-1:800 - 1:1600/Flow Cytometry-1:1600 - 1:6400, Citation (PMID): 36207295,
Phospho-Akt (Thr308) (D25E6) XP® Rabbit mAb recognizes endogenous levels of Akt1 protein only when phosphorylated at Thr308.

This antibody also recognizes endogenous levels of Akt2 protein when phosphorylated at Thr309 or Akt3 protein when phosphorylated at Thr305. Monoclonal antibody is produced by immunizing animals with a synthetic peptide corresponding to residues surrounding Thr308 of human Akt1 protein. (https://www cellsignal com/products/primary-antibodies/phospho-akt-thr308- d25e6-xp-rabbit-mab/13038).
### Anti-Stat3 (Cell Signaling Technology, #4904, 79D7)

Reactivity: H/M/R/Mk, Sensitivity: endogenous, MW (kDa): 79/86, Source: rabbit monoclonal, Application-dilution: Western Blot-1:1,000/Immunoprecipitation-1:100/Chomatin IP: 1:50, Citation (PMID): 36289850, Stat3 (79D7) Rabbit mAb detects endogenous levels of total Stat3 protein. Monoclonal antibody is produced by immunizing animals with a Stat3 fusion protein corresponding to the carboxy-terminal sequence of mouse Stat3 protein. (https://www cellsignal com/products primary-antibodies stat3 79d7-rabbit mab 4904).

### Anti-Phospho-Stat3 (Cell Signaling Technology, #9145, Tyr-705, D3A7)

Reactivity: H/M/R/Mk, Sensitivity: endogenous, MW (kDa): 78/86, Source: rabbit, Application-dilution: Western Blot-1:1,000/Immunoprecipitation-1:100/IHC-Leica BOND-1:100 – 1:400/Immuohistochemistry-1:100 – 1:400/Immunofluorescence-1:100 – 1:200/Flow Cytometry-1:100 – 1:400/Chromatin IP-1:100/Chromatin IP-seq-1:100, Citation (PMID): 35896788, Phospho-Stat3 (Tyr705) (D3A7) XP® Rabbit mAb detects endogenous levels of Stat3 only when phosphorylated at tyrosine 705. This antibody does not cross-react with phospho-EGFR or the corresponding phospho-tyrosines of other Stat proteins. Monoclonal antibody is produced by immunizing animals with a synthetic phosphopeptide corresponding to residues surrounding Tyr705 of mouse Stat3. (https://www cellsignal com/products primary-antibodies phospho-stat3 tyr705 d3a7-xp-rabbit-mab 9145).

### Anti-phospho-PKCδ (Cell Signaling Technology, #2055, Tyr-311)

Reactivity: H/M/R, Sensitivity: endogenous, MW (kDa): 80, Source: rabbit, Application-dilution: Western Blot-1:1,000, Citation (PMID): 35166238, Phospho-PKCdelta (Tyr311) Antibody detects endogenous levels of PKCdelta only when phosphorylated at tyrosine 311. This antibody does not cross-react with other phosphorylated PKC isoforms. Polyclonal antibodies are produced by immunizing animals with a synthetic phosphopeptide corresponding to residues surrounding Tyr313 of human PKCdelta (which is equivalent to Tyr311 in mouse and rat). Antibodies are purified by protein A and peptide affinity chromatography. (https://www cellsignal com/products primary-antibodies phospho-pkcdelta tyr311-antibody 2055).

### Anti-PKC Delta (Abcam, #ab182126, EPR17075)

Reactivity: Mouse/Rat/Human, MW (kDa): 78, Source: rabbit monoclonal, Application-dilution: Flow Cyt-1:250/IHC-P-1:2,000/WB-1:5,000/ICC/IF-5 µg/ml, Citation (PMID): 33688230, Recombinant fragment. This information is proprietary to Abcam and/or its suppliers. (https://www abcam com/pkc-delta-antibody-epr17075 ab182126.html).

### Anti-PKC Delta (Cell Signaling Technology, #9616, D10E2)

Reactivity: H/M/R/Mk Sensitivity: endogenous, MW (kDa): 78, Source: rabbit monoclonal, Application-dilution: Western Blot-1:1,000/Immunoprecipitation-1:50, Citation (PMID): 30979895, PKCδ (D10E2) Rabbit mAb recognizes endogenous levels of total PKCδ protein. This antibody does not cross-react with other PKC isoforms. Monoclonal antibody is produced by immunizing animals with a synthetic peptide corresponding to residues surrounding Arg216 of human PKCδ protein. (https://www cellsignal com/products primary-antibodies pkcd d10e2-rabbit-mab 9616).

### Anti-p44/42 MAPK (Erk1/2) (Cell Signaling Technology, #9102)

Reactivity: H/M/R/Mk/Mi/Z/B/Pg/Sc Sensitivity: endogenous, MW (kDa): 42/44, Source: rabbit, Application-dilution: Western Blot-1:1,000/Immunoprecipitation-1:50/Immunohistochemistry-1:50 – 1:200, Citation (PMID): 36336784, p44/42 MAPK (Erk1/2) Antibody detects endogenous levels of total p44/42 MAP kinase (Erk1/Erk2) protein. In some cell types, this antibody recognizes p44 MAPK more readily than p42 MAPK. The antibody does not recognize either JNK/SAPK or p38 MAP kinase. Polyclonal antibodies are produced by immunizing animals with a synthetic peptide corresponding to a sequence in the C-terminus of rat p44 MAP Kinase.

Antibodies are purified by protein A and peptide affinity chromatography. (https://www cellsignal com/products primary-antibodies p44-42-mapk-erk1-2-antibody 9102).

### Anti-phospho-p44/42 MAPK (Erk1/2) (Cell Signaling Technology, #4370, Thr202/Tyr204, D13.14.4E)

Reactivity: H/M/R/Mk/Mi/Dm/Z/B/Dg/Pg/Sc, Sensitivity: endogenous, MW (kDa): 42/44, Source: rabbit monoclonal, Application-dilution: Western Blot-1:2,000/Immunoprecipitation-1:200 – 1:400/Immunohistochemistry-1:200 – 1:400/Flow Cytometry-1:800 – 1:1600, Citation (PMID): 36376983, Phospho-p44/42 MAPK (Erk1/2) (Thr202/Tyr204) (D13.14.4E) XP® Rabbit mAb detects endogenous levels of p44 and p42 MAP Kinase (Erk1 and Erk2) when dually phosphorylated at Thr202 and Tyr204 of Erk1 (Thr185 and Tyr187 of Erk2), and singly phosphorylated at Thr202. This antibody does not cross-react with the corresponding phosphorylated residues of either JNK/SAPK or p38 MAP kinases. Monoclonal antibody is produced by immunizing animals with a synthetic phosphopeptide corresponding to residues surrounding Thr202/Tyr204 of human p44 MAP kinase. (https://www cellsignal com/products primary-antibodies phospho-p44-42-mapk-erk1-2-thr202-tyr204 d13-14-4e-xp-rabbit-mab 4370).

### Anti-phospho-DNA-PKcs (Cell Signaling Technology, #68716, Ser2056, E9J4G)

Reactivity: H, Sensitivity: endogenous, MW (kDa): 450, Source: rabbit monoclonal, Application-dilution: Western Blot-1:1,000, Citation (PMID): 34644577, Phospho-DNA-PKcs (Ser2056) (E9J4G) Rabbit mAb recognizes endogenous levels of DNA-PKcs protein only when phosphorylated at Ser2056. Monoclonal antibody is produced by immunizing animals with a synthetic peptide corresponding to residues surrounding Ser2056 of human DNA-PKcs protein. (https://www cellsignal com/products primary-antibodies phospho-dna-pkcs ser2056-e9j4g-rabbit-mab 68716)

### Anti-DNA-PKcs (Cell Signaling Technology, #38168, E6U3A)

Reactivity: H, Sensitivity: endogenous, MW (kDa): 450, Source: rabbit monoclonal, Application-dilution: Western Blot-1:1,000/Immunohistochemistry-1:800/Immunofluorescence-1:100/Flow Cytometry-1:100, Citation (PMID): 35173610, DNA-PKcs (E6U3A) Rabbit mAb recognizes endogenous levels of total DNA-PKcs protein. Monoclonal antibody is produced by immunizing animals with a synthetic peptide corresponding to residues surrounding Pro608 of human DNA-PKcs protein. (https://www cellsignal com/products primary-antibodies dna-pkcs e6u3a-rabbit-mab 38168)

### Anti-phospho-p95/NBS1 (Cell Signaling Technology, #3001, Ser-343)

Reactivity: H, Sensitivity: endogenous, MW (kDa): 95, Source: rabbit, Application-dilution: Western Blot-1:1,000, Citation (PMID): 36242003, Phospho-p95/NBS1 (Ser343) Antibody detects endogenous levels of p95/NBS1 only when phosphorylated at serine 343. Polyclonal antibodies are produced by immunizing animals with a synthetic phosphopeptide corresponding to residues surrounding Ser343 of human p95/NBS1. Antibodies are purified by protein A and peptide affinity chromatography. (https://www cellsignal com/products primary-antibodies phospho-p95-nbs1 ser343-antibody 3001)

### Anti-p95/NBS1 (Cell Signaling Technology, #14956, D6J5I)

Reactivity: H/M/R, Sensitivity: endogenous, MW (kDa): 95, Source: rabbit monoclonal, Application-dilution: Western Blot-1:1,000/Immunoprecipitation-1:100/Immunofluorescence-1:100, Citation (PMID): 35551189, p95/NBS1 (D6J5I) Rabbit mAb recognizes endogenous levels of total p95/NBS1 protein. This antibody also cross-reacts with an unidentified protein of 180 kDa in some cell lines. Monoclonal antibody is produced by immunizing animals with a synthetic peptide corresponding to residues surrounding Ala740 of human p95/NBS1 protein. (https://www cellsignal com/products primary-antibodies p95-nbs1-d6j5i-rabbit-mab 14956)

### Anti-phospho-Histone H2A.X (Cell Signaling Technology, #2577, Ser-139)
Reactivity: H/M/R/Mk, Sensitivity: endogenous, MW (kDa): 15, Source: rabbit, Application-dilution: Western Blot-1:1,000/Immunofluorescence-1:400 – 1:1600/Flow Cytometry-1:200, Citation (PMID): 36092604, Phospho-H2A.X (Ser139) Antibody detects endogenous levels of H2A.X only when phosphorylated at Ser139. Antibodies are produced by immunizing animals with a synthetic
### Anti-Stat3 (Cell Signaling Technology, #4904, 79D7)

Reactivity: H/M/R/Mk, Sensitivity: endogenous, MW (kDa): 79/86, Source: rabbit monoclonal, Application-dilution: Western Blot-1:1,000/Immunoprecipitation-1:100/Chomatin IP: 1:50, Citation (PMID): 36289850, Stat3 (79D7) Rabbit mAb detects endogenous levels of total Stat3 protein. Monoclonal antibody is produced by immunizing animals with a Stat3 fusion protein corresponding to the carboxy-terminal sequence of mouse Stat3 protein. (https://www cellsignal com/products primary-antibodies stat3 79d7-rabbit mab 4904).

### Anti-Phospho-Stat3 (Cell Signaling Technology, #9145, Tyr-705, D3A7)

Reactivity: H/M/R/Mk, Sensitivity: endogenous, MW (kDa): 78/86, Source: rabbit, Application-dilution: Western Blot-1:1,000/Immunoprecipitation-1:100/IHC-Leica BOND-1:100 – 1:400/Immuohistochemistry-1:100 – 1:400/Immunofluorescence-1:100 – 1:200/Flow Cytometry-1:100 – 1:400/Chromatin IP-1:100/Chromatin IP-seq-1:100, Citation (PMID): 35896788, Phospho-Stat3 (Tyr705) (D3A7) XP® Rabbit mAb detects endogenous levels of Stat3 only when phosphorylated at tyrosine 705. This antibody does not cross-react with phospho-EGFR or the corresponding phospho-tyrosines of other Stat proteins. Monoclonal antibody is produced by immunizing animals with a synthetic phosphopeptide corresponding to residues surrounding Tyr705 of mouse Stat3. (https://www cellsignal com/products primary-antibodies phospho-stat3 tyr705 d3a7-xp-rabbit-mab 9145).

### Anti-phospho-PKCδ (Cell Signaling Technology, #2055, Tyr-311)

Reactivity: H/M/R, Sensitivity: endogenous, MW (kDa): 80, Source: rabbit, Application-dilution: Western Blot-1:1,000, Citation (PMID): 35166238, Phospho-PKCdelta (Tyr311) Antibody detects endogenous levels of PKCdelta only when phosphorylated at tyrosine 311. This antibody does not cross-react with other phosphorylated PKC isoforms. Polyclonal antibodies are produced by immunizing animals with a synthetic phosphopeptide corresponding to residues surrounding Tyr313 of human PKCdelta (which is equivalent to Tyr311 in mouse and rat). Antibodies are purified by protein A and peptide affinity chromatography. (https://www cellsignal com/products primary-antibodies phospho-pkcdelta tyr311-antibody 2055).

### Anti-PKC Delta (Abcam, #ab182126, EPR17075)

Reactivity: Mouse/Rat/Human, MW (kDa): 78, Source: rabbit monoclonal, Application-dilution: Flow Cyt-1:250/IHC-P-1:2,000/WB-1:5,000/ICC/IF-5 µg/ml, Citation (PMID): 33688230, Recombinant fragment. This information is proprietary to Abcam and/or its suppliers. (https://www abcam com/pkc-delta-antibody-epr17075-ab182126.html).

### Anti-PKC Delta (Cell Signaling Technology, #9616, D10E2)

Reactivity: H/M/R/Mk Sensitivity: endogenous, MW (kDa): 78, Source: rabbit monoclonal, Application-dilution: Western Blot-1:1,000/Immunoprecipitation-1:50, Citation (PMID): 30979895, PKCδ (D10E2) Rabbit mAb recognizes endogenous levels of total PKCδ protein. This antibody does not cross-react with other PKC isoforms. Monoclonal antibody is produced by immunizing animals with a synthetic peptide corresponding to residues surrounding Arg216 of human PKCδ protein. (https://www cellsignal com/products primary-antibodies pkcd d10e2-rabbit-mab 9616).

### Anti-p44/42 MAPK (Erk1/2) (Cell Signaling Technology, #9102)

Reactivity: H/M/R/Mk/Mi/Z/B/Pg/Sc Sensitivity: endogenous, MW (kDa): 42/44, Source: rabbit, Application-dilution: Western Blot-1:1,000/Immunoprecipitation-1:50/Immunohistochemistry-1:50 – 1:200, Citation (PMID): 36336784, p44/42 MAPK (Erk1/2) Antibody detects endogenous levels of total p44/42 MAP kinase (Erk1/Erk2) protein. In some cell types, this antibody recognizes p44 MAPK more readily than p42 MAPK. The antibody does not recognize either JNK/SAPK or p38 MAP kinase. Polyclonal antibodies are produced by immunizing animals with a synthetic peptide corresponding to a sequence in the C-terminus of rat p44 MAP Kinase.

Antibodies are purified by protein A and peptide affinity chromatography. (https://www cellsignal com/products primary-antibodies p44-42-mapk-erk1-2-antibody 9102).

### Anti-phospho-p44/42 MAPK (Erk1/2) (Cell Signaling Technology, #4370, Thr202/Tyr204, D13.14.4E)

Reactivity: H/M/R/Mk/Mi/Dm/Z/B/Dg/Pg/Sc, Sensitivity: endogenous, MW (kDa): 42/44, Source: rabbit monoclonal, Application-dilution: Western Blot-1:2,000/Immunoprecipitation-1:200 – 1:400/Immunohistochemistry-1:200 – 1:400/Flow Cytometry-1:800 – 1:1600, Citation (PMID): 36376983, Phospho-p44/42 MAPK (Erk1/2) (Thr202/Tyr204) (D13.14.4E) XP® Rabbit mAb detects endogenous levels of p44 and p42 MAP Kinase (Erk1 and Erk2) when dually phosphorylated at Thr202 and Tyr204 of Erk1 (Thr185 and Tyr187 of Erk2), and singly phosphorylated at Thr202. This antibody does not cross-react with the corresponding phosphorylated residues of either JNK/SAPK or p38 MAP kinases. Monoclonal antibody is produced by immunizing animals with a synthetic phosphopeptide corresponding to residues surrounding Thr202/Tyr204 of human p44 MAP kinase. (https://www cellsignal com/products primary-antibodies phospho-p44-42-mapk-erk1-2-thr202-tyr204 d13-14-4e-xp-rabbit-mab 4370).

### Anti-phospho-DNA-PKcs (Cell Signaling Technology, #68716, Ser2056, E9J4G)

Reactivity: H, Sensitivity: endogenous, MW (kDa): 450, Source: rabbit monoclonal, Application-dilution: Western Blot-1:1,000, Citation (PMID): 34644577, Phospho-DNA-PKcs (Ser2056) (E9J4G) Rabbit mAb recognizes endogenous levels of DNA-PKcs protein only when phosphorylated at Ser2056. Monoclonal antibody is produced by immunizing animals with a synthetic peptide corresponding to residues surrounding Ser2056 of human DNA-PKcs protein. (https://www cellsignal com/products primary-antibodies phospho-dna-pkcs ser2056-e9j4g-rabbit-mab 68716)

### Anti-DNA-PKcs (Cell Signaling Technology, #38168, E6U3A)

Reactivity: H, Sensitivity: endogenous, MW (kDa): 450, Source: rabbit monoclonal, Application-dilution: Western Blot-1:1,000/Immunohistochemistry-1:800/Immunofluorescence-1:100/Flow Cytometry-1:100, Citation (PMID): 35173610, DNA-PKcs (E6U3A) Rabbit mAb recognizes endogenous levels of total DNA-PKcs protein. Monoclonal antibody is produced by immunizing animals with a synthetic peptide corresponding to residues surrounding Pro608 of human DNA-PKcs protein. (https://www cellsignal com/products primary-antibodies dna-pkcs e6u3a-rabbit-mab 38168)

### Anti-phospho-p95/NBS1 (Cell Signaling Technology, #3001, Ser-343)

Reactivity: H, Sensitivity: endogenous, MW (kDa): 95, Source: rabbit, Application-dilution: Western Blot-1:1,000, Citation (PMID): 36242003, Phospho-p95/NBS1 (Ser343) Antibody detects endogenous levels of p95/NBS1 only when phosphorylated at serine 343. Polyclonal antibodies are produced by immunizing animals with a synthetic phosphopeptide corresponding to residues surrounding Ser343 of human p95/NBS1. Antibodies are purified by protein A and peptide affinity chromatography. (https://www cellsignal com/products primary-antibodies phospho-p95-nbs1 ser343-antibody 3001)

### Anti-p95/NBS1 (Cell Signaling Technology, #14956, D6J5I)

Reactivity: H/M/R, Sensitivity: endogenous, MW (kDa): 95, Source: rabbit monoclonal, Application-dilution: Western Blot-1:1,000/Immunoprecipitation-1:100/Immunofluorescence-1:100, Citation (PMID): 35551189, p95/NBS1 (D6J5I) Rabbit mAb recognizes endogenous levels of total p95/NBS1 protein. This antibody also cross-reacts with an unidentified protein of 180 kDa in some cell lines. Monoclonal antibody is produced by immunizing animals with a synthetic peptide corresponding to residues surrounding Ala740 of human p95/NBS1 protein. (https://www cellsignal com/products primary-antibodies p95-nbs1-d6j5i-rabbit-mab 14956)

### Anti-phospho-Histone H2A.X (Cell Signaling Technology, #2577, Ser-139)
Reactivity: H/M/R/Mk, Sensitivity: endogenous, MW (kDa): 15, Source: rabbit, Application-dilution: Western Blot-1:1,000/Immunofluorescence-1:400 – 1:1600/Flow Cytometry-1:200, Citation (PMID): 36092604, Phospho-H2A.X (Ser139) Antibody detects endogenous levels of H2A.X only when phosphorylated at Ser139. Antibodies are produced by immunizing animals with a synthetic
phosphopeptide corresponding to residues surrounding Ser139 of human H2A.X. (https://www cellsignal com/products primary-antibodies/phospho-histone-h2a-x-ser139-antibody/2577)

Anti-phospho-KAP1 (Abcam, #ab133440, Ser-824, EPR5248)

Reactivity: Mouse/Human, MW (kDa): 88, Source: rabbit monoclonal, Application-dilution: WB-1:1,000/IP-1:10-1:100, Citation (PMID): 34108527, Synthetic peptide. This information is proprietary to Abcam and/or its suppliers. (https://www.abcam.com/kap1-phospho-s824-antibody-epr5248-ab133440.html).

Anti-KAP1 (Abcam, #ab109287, EPR5216)

Reactivity: Mouse/Human, MW (kDa): 89, Source: rabbit monoclonal, Application-dilution: WB-1:10,000-1:50,000/IHC-P-1:250-1:500/ICC/IF-1:100-1:250, Citation (PMID): 36198274, Synthetic peptide. This information is proprietary to Abcam and/or its suppliers. (https://www.abcam.com/kap1-antibody-epr5216-ab109287.html).

Anti-Chk1 (Cell Signaling Technology, #2360, 2G1D5)

Reactivity: H/M/R/Mk Sensitivity: endogenous, MW (kDa): 56, Source: mouse monoclonal, Application-dilution: Western Blot-1:1,000, Citation (PMID): 36266721, Chk1 (2G1D5) Mouse mAb recognizes endogenous levels of total Chk1 protein. Monoclonal antibody is produced by immunizing animals with purified recombinant Chk1 protein. (https://www cellsignal com/products primary-antibodies/chk1-2g1d5-mouse-mab/2360)

Anti-phospho-Chk1(Cell Signaling Technology, #12302, Ser-317, D12H3)

Reactivity: H/M/Mk Sensitivity: endogenous, MW (kDa): 56, Source: rabbit monoclonal, Application-dilution: Western Blot-1:1,000/Immunoprecipitation-1:50/Immunofluorescence-1:800-1:1600, Citation (PMID): 36123339, Phospho-Chk1 (Ser317) (D12H3) XP® Rabbit mAb recognizes endogenous levels of Chk1 protein only when phosphorylated at Ser317. This antibody also detects an 80 kDa protein of unknown origin in some cell lines. Monoclonal antibody is produced by immunizing animals with a synthetic phosphopeptide corresponding to residues surrounding Ser317 of human Chk1 protein. (https://www cellsignal com/products/primary-antibodies/phospho-chk1-ser317-d12h3-xp-rabbit-mab/12302)

Anti-β-actin (Sigma-Aldrich, #A5441, clone AC-15)

Reactivity: human/bovine/sheep/pig/rabbit/cat/dog/mouse/rat/guinea pig/chicken/carp/leech tissues, MW (kDa): 42, Source: mouse, Application-dilution: Immunoblotting-1:5,000/Indirect immunofluorescence-1:1,000, Citation (PMID): 8436588, Monoclonal Anti-b-Actin (mouse IgG1 isotype) is derived from the AC-15 hybridoma produced by the fusion of mouse myeloma cells and splenocytes from an immunized mouse. A slightly modified synthetic b-cytoplasmic actin N-terminal peptide Ac-Asp-Asp-Asp-Ile-Ala-Ala-Leu-Val-Ile-Asp-Asn-Gly-Ser-Gly-Lys conjugated to KLH was used as the immunogen. The isotype is determined by a double diffusion immunoassay using Mouse Monoclonal Antibody Isotyping Reagents, Catalog Number ISO2. (https://www.sigmaaldrich.com/US/en/product/sigma/a5441)

Anti-vinculin (Sigma-Aldrich, #V9131, clone hVIN-1)

Reactivity: human/bovine/chicken/dog/rat/mouse/turkey/xenopus,/smooth muscle metavinculin, MW (kDa): 116, Source: mouse, Application-dilution: A minimum antibody titer of 1:400 is determined by indirect immunofluorescent labeling of cultured human fibroblasts. In order to obtain best results in various techniques and preparations, it is recommended that each individual user determines their optimum working dilution by titration, Citation (PMID): 2116004, Monoclonal Anti-Vinculin (mouse IgG1 isotype) is derived from the hVIN-1 hybridoma produced by the fusion of mouse myeloma cells and splenocytes from immunized BALB/c mice. Vinculin, purified from human uterus, was used as the immunogen. The isotype is determined using Sigma ImmunoTypeTM Kit (Product Code ISO-1) and by a double diffusion immunoassay using Mouse Monoclonal Antibody Isotyping Reagents (Product Code ISO-2). (https://www.sigmaaldrich.com/US/en/product/sigma/v9131)

Anti-GAPDH (Abcam, #ab9484, mAbcam 9484)

Reactivity: Mouse/Rat/Chicken/Cow/Human/Pig/Xenopus laevis/Chinese hamster, MW (kDa): 36, Source: mouse, Application-dilution: WB-0.1-1 µg/ml/IHC-P-5 µg/ml, Citation (PMID): 33264494, Full length native protein (purified). This information is proprietary to Abcam and/or its suppliers. (https://www.abcam.com/gapdh-antibody-mabcam-9484-loading-control-ab9484.html).

Anti-Mouse IgG (H+L) Cross-Adsorbed Secondary Antibody, HRP (Invitrogen, #31438)

Reactivity: This antibody reacts with the heavy chains on mouse IgG and with the light chains common to most mouse immunoglobulins. No antibody was detected against non-immunoglobulin serum proteins. The antibody has been tested by ELISA and/or solid-phase adsorbed to ensure minimal cross-reaction with human, bovine and horse serum proteins. However, this antibody may cross-react with immunoglobulins from other species., Application-dilution: Western Blot-1:10,000-1:200,000/Immunohistochemistry-1:500-1:5,000/Immunocytochemistry-1:500-1:5,000, Citation (PMID): 35991835, This antibody has been isolated from antisera by combination of pepsin digestion and immunoaffinity chromatography, using antigen coupled to agarose beads. Fc fragments and whole IgG molecules have been removed. (https://www.thermofisher.com/antibody/product/Goat-anti-Mouse-IgG-H-L-Cross-Adsorbed-Secondary-Antibody-Polyclonal/31438)

Anti-Rabbit IgG (H+L) Cross-Adsorbed Secondary Antibody, HRP (Invitrogen, #31458)

Reactivity: This antibody reacts with the heavy chains of rabbit IgG and with the light chains common to most rabbit immunoglobulins. No antibody was detected against non-immunoglobulin serum proteins. The antibody has been tested by ELISA and/or solid-phase adsorbed to ensure minimal cross-reaction with bovine, chicken, goat, guinea pig, hamster, horse, human, mouse, rat and sheep serum proteins. However, this antibody may cross-react with immunoglobulins from other species., Application-dilution: Western Blot-1:10,000-1:200,000/Immunohistochemistry-1:500-1:5,000/Immunocytochemistry-1:500-1:5,000, Citation (PMID): 36244455. (https://www.thermofisher.com/antibody/product/Donkey-anti-Rabbit-IgG-H-L-Cross-Adsorbed-Secondary-Antibody-Polyclonal/31458)

Anti-Rabbit IgG (H+L) Cross-Adsorbed Secondary Antibody, Cyanine3 (Invitrogen, #A10520)

Application-dilution: Western Blot-1:10,000/Immunocytochemistry-1-10 µg/mL, Citation (PMID): 36424632, Immunogen: Gamma Immunoglobins Heavy and Light chains. (https://www.thermofisher.com/antibody/product/Goat-anti-Rabbit-IgG-H-L-Cross-Adsorbed-Secondary-Antibody-Polyclonal/A10520)

The following are the Research Resource Identifiers (RRIDs) from the Resource Identification Portal, supporting guidelines for Rigor and Transparency in scientific publications.

Anti-Akt (pan), RRID:AB_915783

Anti-phospho-Akt, RRID:AB_331170

Anti-phospho-Akt, RRID:AB_2629447

Anti-Stat3, RRID:AB_331269

Anti-phospho-Stat3, RRID:AB_2491009

Anti-phospho-PKC Delta, RRID:AB_330876

Anti-PKC Delta, RRID:AB_2892154

Anti-PKC Delta, RRID:AB_10949973

Anti-p44/42 MAPK (Erk1/2), RRID:AB_330744
phosphopeptide corresponding to residues surrounding Ser139 of human H2A.X. (https://www cellsignal com/products primary-antibodies/phospho-histone-h2a-x-ser139-antibody/2577)

Anti-phospho-KAP1 (Abcam, #ab133440, Ser-824, EPR5248)

Reactivity: Mouse/Human, MW (kDa): 88, Source: rabbit monoclonal, Application-dilution: WB-1:1,000/IP-1:10-1:100, Citation (PMID): 34108527, Synthetic peptide. This information is proprietary to Abcam and/or its suppliers. (https://www.abcam.com/kap1-phospho-s824-antibody-epr5248-ab133440.html).

Anti-KAP1 (Abcam, #ab109287, EPR5216)

Reactivity: Mouse/Human, MW (kDa): 89, Source: rabbit monoclonal, Application-dilution: WB-1:10,000-1:50,000/IHC-P-1:250-1:500/ICC/IF-1:100-1:250, Citation (PMID): 36198274, Synthetic peptide. This information is proprietary to Abcam and/or its suppliers. (https://www.abcam.com/kap1-antibody-epr5216-ab109287.html).

Anti-Chk1 (Cell Signaling Technology, #2360, 2G1D5)

Reactivity: H/M/R/Mk Sensitivity: endogenous, MW (kDa): 56, Source: mouse monoclonal, Application-dilution: Western Blot-1:1,000, Citation (PMID): 36266721, Chk1 (2G1D5) Mouse mAb recognizes endogenous levels of total Chk1 protein. Monoclonal antibody is produced by immunizing animals with purified recombinant Chk1 protein. (https://www cellsignal com/products primary-antibodies/chk1-2g1d5-mouse-mab/2360)

Anti-phospho-Chk1 (Cell Signaling Technology, #12302, Ser-317, D12H3)

Reactivity: H/M/Mk Sensitivity: endogenous, MW (kDa): 56, Source: rabbit monoclonal, Application-dilution: Western Blot-1:1,000/Immunoprecipitation-1:50/Immunofluorescence-1:800-1:1600, Citation (PMID): 36123339, Phospho-Chk1 (Ser317) (D12H3) XP® Rabbit mAb recognizes endogenous levels of Chk1 protein only when phosphorylated at Ser317. This antibody also detects an 80 kDa protein of unknown origin in some cell lines. Monoclonal antibody is produced by immunizing animals with a synthetic phosphopeptide corresponding to residues surrounding Ser317 of human Chk1 protein. (https://www cellsignal com/products/primary-antibodies/phospho-chk1-ser317-d12h3-xp-rabbit-mab/12302)

Anti-β-actin (Sigma-Aldrich, #A5441, clone AC-15)

Reactivity: human/bovine/sheep/pig/rabbit/cat/dog/mouse/rat/guinea pig/chicken/carp/leech tissues, MW (kDa): 42, Source: mouse, Application-dilution: Immunoblotting-1:5,000/Indirect immunofluorescence-1:1,000, Citation (PMID): 8436588, Monoclonal Anti-b-Actin (mouse IgG1 isotype) is derived from the AC-15 hybridoma produced by the fusion of mouse myeloma cells and splenocytes from an immunized mouse. A slightly modified synthetic b-cytoplasmic actin N-terminal peptide Ac-Asp-Asp-Asp-Ile-Ala-Ala-Leu-Val-Ile-Asp-Asn-Gly-Ser-Gly-Lys conjugated to KLH was used as the immunogen. The isotype is determined by a double diffusion immunoassay using Mouse Monoclonal Antibody Isotyping Reagents, Catalog Number ISO2. (https://www.sigmaaldrich.com/US/en/product/sigma/a5441)

Anti-vinculin (Sigma-Aldrich, #V9131, clone hVIN-1)

Reactivity: human/bovine/chicken/dog/rat/mouse/turkey/xenopus,/smooth muscle metavinculin, MW (kDa): 116, Source: mouse, Application-dilution: A minimum antibody titer of 1:400 is determined by indirect immunofluorescent labeling of cultured human fibroblasts. In order to obtain best results in various techniques and preparations, it is recommended that each individual user determines their optimum working dilution by titration, Citation (PMID): 2116004, Monoclonal Anti-Vinculin (mouse IgG1 isotype) is derived from the hVIN-1 hybridoma produced by the fusion of mouse myeloma cells and splenocytes from immunized BALB/c mice. Vinculin, purified from human uterus, was used as the immunogen. The isotype is determined using Sigma ImmunoTypeTM Kit (Product Code ISO-1) and by a double diffusion immunoassay using Mouse Monoclonal Antibody Isotyping Reagents (Product Code ISO-2). (https://www.sigmaaldrich.com/US/en/product/sigma/v9131)

Anti-GAPDH (Abcam, #ab9484, mAbcam 9484)

Reactivity: Mouse/Rat/Chicken/Cow/Human/Pig/Xenopus laevis/Chinese hamster, MW (kDa): 36, Source: mouse, Application-dilution: WB-0.1-1 µg/ml/IHC-P-5 µg/ml, Citation (PMID): 33264494, Full length native protein (purified). This information is proprietary to Abcam and/or its suppliers. (https://www.abcam.com/gapdh-antibody-mabcam-9484-loading-control-ab9484.html).

Anti-Mouse IgG (H+L) Cross-Adsorbed Secondary Antibody, HRP (Invitrogen, #31438)

Reactivity: This antibody reacts with the heavy chains on mouse IgG and with the light chains common to most mouse immunoglobulins. No antibody was detected against non-immunoglobulin serum proteins. The antibody has been tested by ELISA and/or solid-phase adsorbed to ensure minimal cross-reaction with human, bovine and horse serum proteins. However, this antibody may cross-react with immunoglobulins from other species., Application-dilution: Western Blot-1:10,000-1:200,000/Immunohistochemistry-1:500-1:5,000/Immunocytochemistry-1:500-1:5,000, Citation (PMID): 35991835, This antibody has been isolated from antisera by combination of pepsin digestion and immunoaffinity chromatography, using antigen coupled to agarose beads. Fc fragments and whole IgG molecules have been removed. (https://www.thermofisher.com/antibody/product/Goat-anti-Mouse-IgG-H-L-Cross-Adsorbed-Secondary-Antibody-Polyclonal/31438)

Anti-Rabbit IgG (H+L) Cross-Adsorbed Secondary Antibody, HRP (Invitrogen, #31458)

Reactivity: This antibody reacts with the heavy chains of rabbit IgG and with the light chains common to most rabbit immunoglobulins. No antibody was detected against non-immunoglobulin serum proteins. The antibody has been tested by ELISA and/or solid-phase adsorbed to ensure minimal cross-reaction with bovine, chicken, goat, guinea pig, hamster, horse, human, mouse, rat and sheep serum proteins. However, this antibody may cross-react with immunoglobulins from other species., Application-dilution: Western Blot-1:10,000-1:200,000/Immunohistochemistry-1:500-1:5,000/Immunocytochemistry-1:500-1:5,000, Citation (PMID): 36244455. (https://www.thermofisher.com/antibody/product/Donkey-anti-Rabbit-IgG-H-L-Cross-Adsorbed-Secondary-Antibody-Polyclonal/31458)

Anti-Rabbit IgG (H+L) Cross-Adsorbed Secondary Antibody, Cyanine3 (Invitrogen, #A10520)

Application-dilution: Western Blot-1:10,000/Immunocytochemistry-1-10 µg/mL, Citation (PMID): 36424632, Immunogen: Gamma Immunoglobins Heavy and Light chains. (https://www.thermofisher.com/antibody/product/Goat-anti-Rabbit-IgG-H-L-Cross-Adsorbed-Secondary-Antibody-Polyclonal/A10520)

The following are the Research Resource Identifiers (RRIDs) from the Resource Identification Portal, supporting guidelines for Rigor and Transparency in scientific publications.

Anti-Akt (pan), RRID:AB_915783

Anti-phospho-Akt, RRID:AB_331170

Anti-phospho-Akt, RRID:AB_2629447

Anti-Stat3, RRID:AB_331269

Anti-phospho-Stat3, RRID:AB_2491009

Anti-phospho-PKC Delta, RRID:AB_330876

Anti-PKC Delta, RRID:AB_2892154

Anti-PKC Delta, RRID:AB_10949973

Anti-p44/42 MAPK (Erk1/2), RRID:AB_330744
Anti-phospho-p44/42 MAPK (Erk1/2), RRID:AB_2315112

Anti-DNA-PKcs, RRID:AB_2799128

Anti-phospho-p95/NBS1, RRID:AB_10829154

Anti-p95/NBS1, RRID:AB_2798660

Anti-phospho-Histone H2A.X, RRID:AB_2118010

Anti-KAP1, RRID:AB_10858772

Anti-Chk1, RRID:AB_2080320

Anti-phospho-Chk1, RRID:AB_2783865

Anti-β-actin, RRID:AB_476744

Anti-Vinculin, RRID:AB_477629

Anti-GAPDH, RRID:AB_307274

Anti-Mouse IgG (H+L) Cross-Adsorbed Secondary Antibody, RRID:AB_228217

Anti-Rabbit IgG (H+L) Cross-Adsorbed Secondary Antibody, RRID:AB_228213

Anti-Rabbit IgG (H+L) Cross-Adsorbed Secondary Antibody, Cyanine3, RRID:AB_2534029

Anti-phospho-DNA-PKcs (https://www cellsignal.com/products/primary-antibodies/phospho-dna-pkcs-ser2056-e9j4g-rabbit-mab/68716) and Anti-phospho-KAP1 (https://www abcam.com/kap1-phospho-s824-antibody-epr5248-ab133440.html) were validated by published studies (Anti-phospho-DNA-PKcs, PMID: 34644577; Anti-phospho-KAP1: PMID: 34108527).

## Eukaryotic cell lines

Policy information about **cell lines and Sex and Gender in Research**

<table><tr><td>Cell line source(s)</td><td>HEK293T (ATCC CRL-11268).<br/>Patient-derived organoids (PDOs) were obtained using excess material collected for clinical purposes from de-identified brain tumor specimens. Donors (patients diagnosed with glioblastoma) were anonymous. Progressive numbers were used to label specimens coded in order to preserve the confidentiality of the subjects. Work with these materials was designated as IRB exempt under paragraph 4 and it is covered under IRB protocol #IRB-AAAI7305 and Onconeurotek tumor bank certification (NF S96 900) and authorization from Ethics committee (CPP Ile de France VI, ref A39II), and the French Ministry for research (AC 2013-1962). Of the 23 PDOs utilized in the study 14 were males and 9 were females as assessed by the analysis of chromosome X and Y ratio from whole exome sequencing.</td></tr><tr><td>Authentication</td><td>Cell authentication was performed using short tandem repeats (STR) at the ATCC facility.</td></tr><tr><td>Mycoplasma contamination</td><td>Cells were routinely tested for mycoplasma contamination using the Mycoplasma Plus PCR Primer Set (Agilent Technologies) and were found to be negative.</td></tr><tr><td>Commonly misidentified lines<br/>(See ICLAC register)</td><td>We have not used cell lines listed in the database of commonly misidentified cell lines.</td></tr></table>
Anti-phospho-p44/42 MAPK (Erk1/2), RRID:AB_2315112

Anti-DNA-PKcs, RRID:AB_2799128

Anti-phospho-p95/NBS1, RRID:AB_10829154

Anti-p95/NBS1, RRID:AB_2798660

Anti-phospho-Histone H2A.X, RRID:AB_2118010

Anti-KAP1, RRID:AB_10858772

Anti-Chk1, RRID:AB_2080320

Anti-phospho-Chk1, RRID:AB_2783865

Anti-β-actin, RRID:AB_476744

Anti-Vinculin, RRID:AB_477629

Anti-GAPDH, RRID:AB_307274

Anti-Mouse IgG (H+L) Cross-Adsorbed Secondary Antibody, RRID:AB_228217

Anti-Rabbit IgG (H+L) Cross-Adsorbed Secondary Antibody, RRID:AB_228213

Anti-Rabbit IgG (H+L) Cross-Adsorbed Secondary Antibody, Cyanine3, RRID:AB_2534029

Anti-phospho-DNA-PKcs (https://www cellsignal.com/products/primary-antibodies/phospho-dna-pkcs-ser2056-e9j4g-rabbit-mab/68716) and Anti-phospho-KAP1 (https://www abcam.com/kap1-phospho-s824-antibody-epr5248-ab133440.html) were validated by published studies (Anti-phospho-DNA-PKcs, PMID: 34644577; Anti-phospho-KAP1: PMID: 34108527).

## Eukaryotic cell lines

Policy information about **cell lines and Sex and Gender in Research**

<table><tr><td>Cell line source(s)</td><td>HEK293T (ATCC CRL-11268).<br/>Patient-derived organoids (PDOs) were obtained using excess material collected for clinical purposes from de-identified brain tumor specimens. Donors (patients diagnosed with glioblastoma) were anonymous. Progressive numbers were used to label specimens coded in order to preserve the confidentiality of the subjects. Work with these materials was designated as IRB exempt under paragraph 4 and it is covered under IRB protocol #IRB-AAAI7305 and Onconeurotek tumor bank certification (NF S96 900) and authorization from Ethics committee (CPP Ile de France VI, ref A39II), and the French Ministry for research (AC 2013-1962). Of the 23 PDOs utilized in the study 14 were males and 9 were females as assessed by the analysis of chromosome X and Y ratio from whole exome sequencing.</td></tr><tr><td>Authentication</td><td>Cell authentication was performed using short tandem repeats (STR) at the ATCC facility.</td></tr><tr><td>Mycoplasma contamination</td><td>Cells were routinely tested for mycoplasma contamination using the Mycoplasma Plus PCR Primer Set (Agilent Technologies) and were found to be negative.</td></tr><tr><td>Commonly misidentified lines<br/>(See ICLAC register)</td><td>We have not used cell lines listed in the database of commonly misidentified cell lines.</td></tr></table>