INPP4B and RAD50 have an interactive effect on survival after breast cancer

Abstract

Genes sharing similar genomic landscape have the potential to interactively orchestrate certain clinicopathological features of a disease. Deletion of the RAD50 gene is a common event particularly in basal-like breast cancer, and often occurs together with deletions of BRCA1, RB1, TP53, PTEN, and INPP4B. In this study, we investigate whether these co-deleted genes have interactive effects on survival in breast cancer. Using publicly available TCGA data, we employed Cox’s proportional hazards models to test whether genomic deletions of these genes, or reduced protein or transcript levels associate with breast cancer patient survival in an interactive manner. Further validation was obtained at the transcriptional level by including 1,596 additional cases from 13 publicly available gene expression data sets from the KM-plotter database. Our results indicate that RAD50 and INPP4B associate interactively with breast cancer survival at the transcriptional, translational, and genomic levels in the TCGA data set (p (interaction) < 0.05). While neither of the genes was independently prognostic on its own, low INPP4B levels in combination with above median RAD50 abundance associated with increased hazard, both at the mRNA (HR 2.39, 95 % CI 1.20–4.76) and protein (HR 2.92, 95 % CI 1.42–6.00) levels, whereas concomitant deletion or low expression of both genes associated with unexpectedly improved survival. A similar pattern was observed in the KM-plotter data set (p (interaction) = 0.0067). We find that RAD50 and INPP4B expression levels have a synergistic influence on breast cancer survival, possibly through their effects on treatment response.

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Fig. 1

Abbreviations

TCGA:

The Cancer Genome Atlas

CNV:

Copy number variation

HR:

Hazard ratio

CI:

Confidence interval

RPPA:

Reverse-phase protein microarray

OS:

Overall survival

RFS:

Relapse-free survival

ER:

Estrogen receptor

T:

Tumor size

N:

Lymph node metastasis

References

  1. 1.

    Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, Thorsen T, Quist H et al (2001) Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 98:10869–10874

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  2. 2.

    Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, Fluge O, Pergamenschikov A et al (2000) Molecular portraits of human breast tumours. Nature 406:747–752

    CAS  PubMed  Article  Google Scholar 

  3. 3.

    Chin K, DeVries S, Fridlyand J, Spellman PT, Roydasgupta R, Kuo WL, Lapuk A, Neve RM, Qian Z, Ryder T, Chen F, Feiler H et al (2006) Genomic and transcriptional aberrations linked to breast cancer pathophysiologies. Cancer Cell 10:529–541

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Wood LD, Parsons DW, Jones S, Lin J, Sjoblom T, Leary RJ, Shen D, Boca SM, Barber T, Ptak J, Silliman N, Szabo S et al (2007) The genomic landscapes of human breast and colorectal cancers. Science 318:1108–1113

    CAS  PubMed  Article  Google Scholar 

  5. 5.

    Bergamaschi A, Kim YH, Wang P, Sorlie T, Hernandez-Boussard T, Lonning PE, Tibshirani R, Borresen-Dale AL, Pollack JR (2006) Distinct patterns of DNA copy number alteration are associated with different clinicopathological features and gene-expression subtypes of breast cancer. Genes Chromosomes Cancer 45:1033–1040

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Van Loo P, Nordgard SH, Lingjaerde OC, Russnes HG, Rye IH, Sun W, Weigman VJ, Marynen P, Zetterberg A, Naume B, Perou CM, Borresen-Dale AL et al (2010) Allele-specific copy number analysis of tumors. Proc Natl Acad Sci U S A 107:16910–16915

    PubMed Central  PubMed  Article  Google Scholar 

  7. 7.

    Russnes HG, Vollan HK, Lingjaerde OC, Krasnitz A, Lundin P, Naume B, Sorlie T, Borgen E, Rye IH, Langerod A, Chin SF, Teschendorff AE et al (2010) Genomic architecture characterizes tumor progression paths and fate in breast cancer patients. Sci Transl Med 2:38–47

    Article  Google Scholar 

  8. 8.

    Johannsdottir HK, Jonsson G, Johannesdottir G, Agnarsson BA, Eerola H, Arason A, Heikkila P, Egilsson V, Olsson H, Johannsson OT, Nevanlinna H, Borg A et al (2006) Chromosome 5 imbalance mapping in breast tumors from BRCA1 and BRCA2 mutation carriers and sporadic breast tumors. Int J Cancer 119:1052–1060

    CAS  PubMed  Article  Google Scholar 

  9. 9.

    Heikkinen K, Rapakko K, Karppinen SM, Erkko H, Knuutila S, Lundan T, Mannermaa A, Borresen-Dale AL, Borg A, Barkardottir RB, Petrini J, Winqvist R (2006) RAD50 and NBS1 are breast cancer susceptibility genes associated with genomic instability. Carcinogenesis 27:1593–1599

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  10. 10.

    Tommiska J, Seal S, Renwick A, Barfoot R, Baskcomb L, Jayatilake H, Bartkova J, Tallila J, Kaare M, Tamminen A, Heikkila P, Evans DG et al (2006) Evaluation of RAD50 in familial breast cancer predisposition. Int J Cancer 118:2911–2916

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Weigman VJ, Chao HH, Shabalin AA, He X, Parker JS, Nordgard SH, Grushko T, Huo D, Nwachukwu C, Nobel A, Kristensen VN, Borresen-Dale AL et al (2012) Basal-like Breast cancer DNA copy number losses identify genes involved in genomic instability, response to therapy, and patient survival. Breast Cancer Res Treat 133:865–880

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  12. 12.

    Miki Y, Swensen J, Shattuck-Eidens D, Futreal PA, Harshman K, Tavtigian S, Liu Q, Cochran C, Bennett LM, Ding W (1994) A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science 266:66–71

    CAS  PubMed  Article  Google Scholar 

  13. 13.

    Zhong Q, Chen CF, Li S, Chen Y, Wang CC, Xiao J, Chen PL, Sharp ZD, Lee WH (1999) Association of BRCA1 with the hRad50-hMre11-p95 complex and the DNA damage response. Science 285:747–750

    CAS  PubMed  Article  Google Scholar 

  14. 14.

    Herschkowitz JI, Simin K, Weigman VJ, Mikaelian I, Usary J, Hu Z, Rasmussen KE, Jones LP, Assefnia S, Chandrasekharan S, Backlund MG, Yin Y et al (2007) Identification of conserved gene expression features between murine mammary carcinoma models and human breast tumors. Genome Biol 8:R76

    PubMed Central  PubMed  Article  Google Scholar 

  15. 15.

    Cressman VL, Backlund DC, Hicks EM, Gowen LC, Godfrey V, Koller BH (1999) Mammary tumor formation in p53- and BRCA1-deficient mice. Cell Growth Differ 10:1–10

    CAS  PubMed  Google Scholar 

  16. 16.

    Jiang Z, Deng T, Jones R, Li H, Herschkowitz JI, Liu JC, Weigman VJ, Tsao MS, Lane TF, Perou CM, Zacksenhaus E (2010) Rb deletion in mouse mammary progenitors induces luminal-B or basal-like/EMT tumor subtypes depending on p53 status. J Clin Investg 120:3296–3309

    CAS  Article  Google Scholar 

  17. 17.

    Lopez-Knowles E, O’Toole SA, McNeil CM, Millar EK, Qiu MR, Crea P, Daly RJ, Musgrove EA, Sutherland RL (2010) PI3K pathway activation in breast cancer is associated with the basal-like phenotype and cancer-specific mortality. Int J Cancer 126:1121–1131

    CAS  PubMed  Google Scholar 

  18. 18.

    Jackson SP, Bartek J (2009) The DNA-damage response in human biology and disease. Nature 461:1071–1078

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  19. 19.

    Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, Antipin Y, Reva B et al (2012) The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2:401–404

    PubMed  Article  Google Scholar 

  20. 20.

    Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E, Cerami E, Sander C et al (2013) Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6:pl1

    PubMed Central  PubMed  Article  Google Scholar 

  21. 21.

    Beroukhim R, Getz G, Nghiemphu L, Barretina J, Hsueh T, Linhart D, Vivanco I, Lee JC, Huang JH, Alexander S, Du J, Kau T et al (2007) Assessing the significance of chromosomal aberrations in cancer: methodology and application to glioma. Proc Natl Acad Sci U S A 104:20007–20012

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  22. 22.

    Hennessy BT, Lu Y, Gonzalez-Angulo AM, Carey MS, Myhre S, Ju Z, Davies MA, Liu W, Coombes K, Meric-Bernstam F, Bedrosian I, McGahren M et al (2010) A technical assessment of the utility of reverse phase protein arrays for the study of the functional proteome in non-microdissected human breast cancers. Clin Proteomics 6:129–151

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  23. 23.

    The Cancer Genome Atlas Research Network (2012) Comprehensive molecular portraits of human breast tumours. Nature 490:61–70

    Article  Google Scholar 

  24. 24.

    Gyorffy B, Lanczky A, Eklund AC, Denkert C, Budczies J, Li Q, Szallasi Z (2010) An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients. Breast Cancer Res Treat 123:725–731

    PubMed  Article  Google Scholar 

  25. 25.

    The Cancer Genome Atlas Research Network (2011) Integrated genomic analyses of ovarian carcinoma. Nature 474:609–615

    PubMed Central  Article  Google Scholar 

  26. 26.

    Smit AFA, Hubley R, Green P. RepeatMasker Open-4.0. Available at: http://www.repeatmasker.org. Accessed 15 Aug 2014

  27. 27.

    Gewinner C, Wang ZC, Richardson A, Teruya-Feldstein J, Etemadmoghadam D, Bowtell D, Barretina J, Lin WM, Rameh L, Salmena L, Pandolfi PP, Cantley LC (2009) Evidence that inositol polyphosphate 4-phosphatase type II is a tumor suppressor that inhibits PI3K signaling. Cancer Cell 16:115–125

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  28. 28.

    Fedele CG, Ooms LM, Ho M, Vieusseux J, O’Toole SA, Millar EK, Lopez-Knowles E, Sriratana A, Gurung R, Baglietto L, Giles GG, Bailey CG et al (2010) Inositol polyphosphate 4-phosphatase II regulates PI3K/Akt signaling and is lost in human basal-like breast cancers. Proc Natl Acad Sci U S A 107:22231–22236

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  29. 29.

    Bassi C, Ho J, Srikumar T, Dowling RJ, Gorrini C, Miller SJ, Mak TW, Neel BG, Raught B, Stambolic V (2013) Nuclear PTEN controls DNA repair and sensitivity to genotoxic stress. Science 341:395–399

    CAS  PubMed  Article  Google Scholar 

  30. 30.

    Min JW, Kim KI, Kim HA, Kim EK, Noh WC, Jeon HB, Cho DH, Oh JS, Park IC, Hwang SG, Kim JS (2013) INPP4B-mediated tumor resistance is associated with modulation of glucose metabolism via hexokinase 2 regulation in laryngeal cancer cells. Biochem Biophys Res Commun 440:137–142

    CAS  PubMed  Article  Google Scholar 

  31. 31.

    Kim JS, Yun HS, Um HD, Park JK, Lee KH, Kang CM, Lee SJ, Hwang SG (2012) Identification of inositol polyphosphate 4-phosphatase type II as a novel tumor resistance biomarker in human laryngeal cancer HEp-2 cells. Cancer Biol Ther 13:1307–1318

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  32. 32.

    Ewald B, Sampath D, Plunkett W (2008) ATM and the Mre11-Rad50-Nbs1 complex respond to nucleoside analogue-induced stalled replication forks and contribute to drug resistance. Cancer Res 68:7947–7955

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  33. 33.

    Abuzeid WM, Jiang X, Shi G, Wang H, Paulson D, Araki K, Jungreis D, Carney J, O’Malley BW Jr, Li D (2009) Molecular disruption of RAD50 sensitizes human tumor cells to cisplatin-based chemotherapy. J Clin Investg 119:1974–1985

    CAS  Article  Google Scholar 

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Acknowledgments

This work was supported by the Helsinki University Central Hospital Research Fund, the Finnish Cancer Society, the Sigrid Juselius Foundation, and the Academy of Finland (266528). The results shown in this study are in part based upon data generated by the TCGA Research Network (http://cancergenome.nih.gov/) and the Kaplan–Meier Plotter database (http://kmplot.com/). We thank the specimen donors and research groups who have provided these data and made this study possible.

Conflicts of interest

All authors declare no conflicts of interest.

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Correspondence to Heli Nevanlinna.

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Xiaofeng Dai and Rainer Fagerholm have contributed equally to this work.

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Dai, X., Fagerholm, R., Khan, S. et al. INPP4B and RAD50 have an interactive effect on survival after breast cancer. Breast Cancer Res Treat 149, 363–371 (2015). https://doi.org/10.1007/s10549-014-3241-y

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Keywords

  • Breast cancer
  • RAD50
  • INPP4B
  • Survival
  • Interaction