Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Genetics and Genomics

Bevacizumab-induced hypertension and proteinuria: a genome-wide study of more than 1000 patients



Hypertension and proteinuria are common bevacizumab-induced toxicities. No validated biomarkers are available for identifying patients at risk of these toxicities.


A genome-wide association study (GWAS) meta-analysis was performed in 1039 bevacizumab-treated patients of European ancestry in four clinical trials (CALGB 40502, 40503, 80303, 90401). Grade ≥2 hypertension and proteinuria were recorded (CTCAE v.3.0). Single-nucleotide polymorphism (SNP)-toxicity associations were determined using a cause-specific Cox model adjusting for age and sex.


The most significant SNP associated with hypertension with concordant effect in three out of the four studies (p-value <0.05 for each study) was rs6770663 (A > G) in KCNAB1, with the G allele increasing the risk of hypertension (p-value = 4.16 × 10−6). The effect of the G allele was replicated in ECOG-ACRIN E5103 in 582 patients (p-value = 0.005). The meta-analysis of all five studies for rs6770663 led to p-value = 7.73 × 10−8, close to genome-wide significance. The most significant SNP associated with proteinuria was rs339947 (C > A, between DNAH5 and TRIO), with the A allele increasing the risk of proteinuria (p-value = 1.58 × 10−7).


The results from the largest study of bevacizumab toxicity provide new markers of drug safety for further evaluations. SNP in KCNAB1 validated in an independent dataset provides evidence toward its clinical applicability to predict bevacizumab-induced hypertension. Identifier: NCT00785291 (CALGB 40502); NCT00601900 (CALGB 40503); NCT00088894 (CALGB 80303) and NCT00110214 (CALGB 90401).

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: CONSORT and quality control flowchart for CALGB 80303, 40503, 90401 and 40502, and ECOG-ACRIN E5103.
Fig. 2: Cumulative incidence of grade ≥ 2 hypertension for rs6770663 in KCNAB1.
Fig. 3: Cumulative incidence of grade ≥ 2 proteinuria for rs339947 between DNAH5 and TRIO and grade ≥ 2 composite toxicity for rs16945809 in YWHAE.

Data availability

The datasets generated during and/or analysed during the current study are available in the NHGRI-EBI GWAS Catalog under study accession numbers GCST90026609, GCST90026610 and GCST90026611.


  1. 1.

    Ferrara N, Adamis AP. Ten years of anti-vascular endothelial growth factor therapy. Nat Rev Drug Disov. 2016;15:385–403.

    CAS  Article  Google Scholar 

  2. 2.

    FDA AVASTIN® Prescribing Information. 2020. Accessed on 05 Oct 2020.

  3. 3.

    Ellis LM, Kirkpatrick P. Bevacizumab. Nat Rev Drug Discov. 2005;3:995–996.

    Google Scholar 

  4. 4.

    Zhu X, Wu S, Dahut WL, Parikh CR. Risks of proteinuria and hypertension with bevacizumab, an antibody against vascular endothelial growth factor: systematic review and meta-analysis. Am J Kidney Dis. 2007;49:186–93.

    CAS  Article  Google Scholar 

  5. 5.

    Izzedine H, Ederhy S, Goldwasser F, Soria JC, Milano G, Cohen A, et al. Management of hypertension in angiogenesis inhibitor-treated patients. Ann Oncol. 2009;20:807–15.

    CAS  Article  Google Scholar 

  6. 6.

    Mir O, Coriat R, Cabanes L, Ropert S, Billemont B, Alexandre J, et al. An observational study of bevacizumab-induced hypertension as a clinical biomarker of antitumor activity. Oncologist. 2011;16:1325–32.

    CAS  Article  Google Scholar 

  7. 7.

    Morita S, Uehara K, Nakayama G, Shibata T, Oguri T, Inada-Inoue M, et al. Association between bevacizumab-related hypertension and vascular endothelial growth factor (VEGF) gene polymorphisms in Japanese patients with metastatic colorectal cancer. Cancer Chemother Pharmacol. 2013;71:405–11.

    CAS  Article  Google Scholar 

  8. 8.

    Schneider BP, Wang MM, Radovich M, Sledge GW, Badve S, Thor A, et al. Association of vascular endothelial growth factor and vascular endothelial growth factor receptor-2 genetic polymorphisms with outcome in a trial of paclitaxel compared with paclitaxel plus bevacizumab in advanced breast cancer: ECOG 2100. J Clin Oncol. 2008;28:4672–4678.

    Article  Google Scholar 

  9. 9.

    Schneider BP, Li L, Shen F, Miller KD, Radovich M, O’Neill A, et al. Genetic variant predicts bevacizumab-induced hypertension in ECOG-5103 and ECOG-2100. Br J Cancer. 2014;111:1241–1248.

    CAS  Article  Google Scholar 

  10. 10.

    Kindler HL, Niedzwiecki D, Hollis D, Sutherland S, Schrag D, Hurwitz H, et al. Gemcitabine plus bevacizumab compared with gemcitabine plus placebo in patients with advanced pancreatic cancer: Phase III trial of the Cancer and Leukemia Group B (CALGB 80303). J Clin Oncol. 2010;28:3617–22.

    CAS  Article  Google Scholar 

  11. 11.

    Dickler MN, Barry WT, Cirrincione CT, Ellis MJ, Moynahan ME, Innocenti F, et al. Phase III trial evaluating letrozole as first-line endocrine therapy with or without bevacizumab for the treatment of postmenopausal women with hormone receptor-positive advanced-stage breast cancer: CALGB 40503 (Alliance). J Clin Oncol. 2016;34:2602–2609.

    CAS  Article  Google Scholar 

  12. 12.

    Kelly WK, Halabi S, Carducci M, George D, Mahoney JF, Stadler WM, et al. Randomized, double-blind, placebo-controlled phase III trial comparing docetaxel and prednisone with or without bevacizumab in men with metastatic castration-resistant prostate cancer: CALGB 90401. J Clin Oncol. 2012;30:1534–40.

    CAS  Article  Google Scholar 

  13. 13.

    Rugo HS, Barry WT, Moreno-Aspitia A, Lyss AP, Cirrincione C, Leung E, et al. Randomized phase III trial of paclitaxel once per week compared with nanoparticle albumin-bound nab-paclitaxel once per week or ixabepilone with bevacizumab as first-line chemotherapy for locally recurrent or metastatic breast cancer: CALGB 40502/NCCTG N0. J Clin Oncol. 2015;33:2361–2369.

    CAS  Article  Google Scholar 

  14. 14.

    National Institute of Cancer (NCI) Guidelines for Investigators. 2021. Accessed on 08 Jan 2021.

  15. 15.

    Innocenti F, Jiang C, Sibley AB, Denning S, Etheridge AS, Watson D, et al. An initial genetic analysis of gemcitabine-induced high-grade neutropenia in pancreatic cancer patients in CALGB 80303 (Alliance). Pharmacogenet Genomics. 2019;29:123–31.

    CAS  Article  Google Scholar 

  16. 16.

    Innocenti F, Owzar K, Cox NL, Evans P, Kubo M, Zembutsu H, et al. A genome-wide association study of overall survival in pancreatic cancer patients treated with gemcitabine in CALGB 80303. Clin Cancer Res. 2012;18:577–84.

    CAS  Article  Google Scholar 

  17. 17.

    Rashkin SR, Chua KC, Ho C, Mulkey F, Jiang C, Mushiroda T, et al. A pharmacogenetic prediction model of progression-free survival in breast cancer using genome-wide genotyping data from CALGB 40502 (Alliance). Clin Pharmacol Ther. 2019;105:738–45.

    CAS  Article  Google Scholar 

  18. 18.

    Hertz DL, Owzar K, Lessans S, Wing C, Jiang C, Kelly WK, et al. Pharmacogenetic discovery in CALGB (alliance) 90401 and mechanistic validation of a VAC14 polymorphism that increases risk of docetaxel-induced neuropathy. Clin Cancer Res. 2016;22:4890–4900.

    CAS  Article  Google Scholar 

  19. 19.

    Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38:904–909.

    CAS  Article  Google Scholar 

  20. 20.

    Zhang W, et al. SCAN database: Facilitating integrative analyses of cytosine modification and expression QTL. Database. 2015;27:bav025.

    Article  Google Scholar 

  21. 21.

    Machiela MJ, Chanock SJ. LDlink: A web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics. 2015;31:3555–3557.

    CAS  Article  Google Scholar 

  22. 22.

    James Kent W, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, et al. The human genome browser at UCSC. Genome Res. 2002;12:996–1006.

    Article  Google Scholar 

  23. 23.

    Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski M, et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 2012;22:1790–1797.

    CAS  Article  Google Scholar 

  24. 24.

    Ward LD, Kellis M. HaploReg: A resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 2012;40:D930–D934.

    CAS  Article  Google Scholar 

  25. 25.

    GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348:648–660.

    Article  Google Scholar 

  26. 26.

    Gillies CE, Putler R, Menon R, Otto E, Yasutake K, Nair V, et al. An eQTL landscape of kidney tissue in human nephrotic syndrome. Am J Hum Genet. 2018;103:232–444.

    CAS  Article  Google Scholar 

  27. 27.

    Thul PJ, Akesson L, Wiking M, Mahdessian D, Geladaki A, Ait Blal H, et al. A subcellular map of the human proteome. Science. 2017;356:eaal3321.

    Article  Google Scholar 

  28. 28.

    González C, Baez-Nieto D, Valencia I, Oyarzún I, Rojas P, Naranjo D, et al. K+ channels: Function-structural overview. Compr Physiol. 2012;2:2087–2149.

    Article  Google Scholar 

  29. 29.

    Tipparaju SM, Liu SQ, Barski OA, Bhatnagar A. NADPH binding to β-subunit regulates inactivation of voltage-gated K+ channels. Biochem Biophys Res Commun. 2007;359:269–276.

    CAS  Article  Google Scholar 

  30. 30.

    Sobey CG. Potassium channel function in vascular disease. Arterioscler Thromb Vasc Biol. 2001;21:28–38.

    CAS  Article  Google Scholar 

  31. 31.

    Martens JR, Gelband CH. Alterations in rat interlobar artery membrane potential and K+ channels in genetic and nongenetic hypertension. Circ Res. 1996;79:295–301.

    CAS  Article  Google Scholar 

  32. 32.

    Banerjee B, Peiris DN, Koo SH, Chui P, Lee EJD, Hande MP. Genomic imbalances in key ion channel genes and telomere shortening in sudden cardiac death victims. Cytogenet Genome Res. 2009;122:350–355.

    Article  Google Scholar 

  33. 33.

    Tur J, Chapalamadugu KC, Padawer T, Badole SL, Kilfoil PJ, Bhatnagar A, et al. Deletion of Kvβ1.1 subunit leads to electrical and haemodynamic changes causing cardiac hypertrophy in female murine hearts. Exp Physiol. 2016;101:494–508.

    CAS  Article  Google Scholar 

  34. 34.

    Kulakovskiy IV, Vorontsov IE, Yevshin IS, Sharipov RN, Fedorova AD, Rumynskiy EI, et al. HOCOMOCO: Towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis. Nucleic Acids Res. 2018;46:D252–D259.

    CAS  Article  Google Scholar 

  35. 35.

    Niu Z, Li A, Zhang SX, Schwartz RJ. Serum response factor micromanaging cardiogenesis. Curr Opin Cell Biol. 2007;19:618–627.

    CAS  Article  Google Scholar 

  36. 36.

    Rouillard AD, Gundersen GW, Fernandez NF, Wang Z, Monteiro CD, McDermott MG, et al. The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database (Oxford). 2016;2016:baw100.

  37. 37.

    Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29:308–311.

    CAS  Article  Google Scholar 

  38. 38.

    Leigh MW, Pittman JE, Carson JL, Ferkol TW, Dell SD, Davis SD, et al. Clinical and genetic aspects of primary ciliary dyskinesia/kartagener syndrome. Genet Med. 2009;11:473–487.

    Article  Google Scholar 

  39. 39.

    Maier M, Baldwin C, Aoudjit L, Takano T. The role of trio, a rho guanine nucleotide exchange factor, in glomerular podocytes. Int J Mol Sci. 2018;19:E479.

    Article  Google Scholar 

  40. 40.

    Brahmer JR, Dahlberg SE, Gray RJ, Schiller JH, Perry MC, Sandler A, et al. Sex differences in outcome with bevacizumab therapy: analysis of patients with advanced-stage non-small cell lung cancer treated with or without bevacizumab in combination with paclitaxel and carboplatin in the eastern cooperative oncology group trial 4599. J Thorac Oncol. 2011;6:1031–1038.

    Article  Google Scholar 

  41. 41.

    Schneider BP, Li L, Radovich M, Shen F, Miller KD, Flockhart DA, et al. Genome-wide association studies for taxane-induced peripheral neuropathy in ECOG-5103 and ECOG-1199. Clin Cancer Res. 2015;21:5082–5091.

    CAS  Article  Google Scholar 

  42. 42.

    Baldwin RM, Owzar K, Zembutsu H, Chhibber A, Kubo M, Jiang C, et al. A genome-wide association study identifies novel loci for paclitaxel-induced sensory peripheral neuropathy in CALGB 40101. Clin Cancer Res. 2012;18:5099–5109.

    CAS  Article  Google Scholar 

  43. 43.

    Schirmer MA, Lüske CM, Roppel S, Schaudinn A, Zimmer C, Pflüger R, et al. Relevance of Sp binding site polymorphism in WWOX for treatment outcome in pancreatic cancer. J Natl Cancer Inst. 2016;108:djv387.

    Article  Google Scholar 

Download references


We acknowledge the PI of ECOG-ACRIN E5103, Kathy Miller, MD.


This work was supported by the National Cancer Institute of the National Institutes of Health under Award Numbers U10CA180821 and U10CA180882 (to the Alliance for Clinical Trials in Oncology), U24CA196171, UG1CA233253, UG1CA233327 and UG1CA233373. JCFQ was supported by the São Paulo Research Foundation-FAPESP (2018/04491-2). Also supported in part by funds from Abraxis BioScience, Bristol Meyers Squibb, Celgene and Genentech. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information




JCFQ, ASE and FI wrote the manuscript; FI designed the research; JCFQ, JW, ABS, CJ, ASE, FS, FM, JNP, DLH, ECD, HLM, MB, HR, HLK, WKK, MJR, DLK, KO, BS, DL and F.I. performed the research; JCFQ, JW, ABS, CJ, ASE, GJ, KO and FI analyzed the data; JW, ABS, CJ, ASE and DL contributed new reagents/analytical tools.

Corresponding author

Correspondence to Federico Innocenti.

Ethics declarations

Ethics approval and consent to participate

All participants provided written informed consent for sample collection and pharmacogenetic analysis, and all trials were conducted in accordance with recognised ethical guidelines. The study was performed in accordance with the Declaration of Helsinki and was approved by the local IRB.

Consent for publication

Not applicable.

Competing interests

JCFQ, JW, DL, KO and FI are coinventors of a patent application, serial number 16/932,002. FI is an advisor for Emerald Lake Safety. These relationships have been disclosed to and are under management by UNC-Chapel Hill.

Additional information

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

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Quintanilha, J.C.F., Wang, J., Sibley, A.B. et al. Bevacizumab-induced hypertension and proteinuria: a genome-wide study of more than 1000 patients. Br J Cancer (2021).

Download citation

Further reading


Quick links