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Radiosensitivity is associated with antitumor immunity in estrogen receptor-negative breast cancer

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Abstract

Purpose

This study evaluated radiosensitivity and the tumor microenvironment (TME) to identify characteristics of breast cancer patients who would benefit most from radiation therapy.

Methods

We analyzed 1903 records from the Molecular Taxonomy of Breast Cancer International Consortium cohort using the radiosensitivity index and gene expression deconvolution algorithms, CIBERSORT and xCell, that estimates the TME composition of tumor samples. In this study, patients were stratified according to TME and radiosensitivity. We performed integrative analyses of clinical and immuno-genomic data to characterize molecular features associated with radiosensitivity.

Results

Radiosensitivity was significantly associated with activation of antitumor immunity. In contrast, radioresistance was associated with a reactive stromal microenvironment. The immuno-genomic analysis revealed that estrogen receptor (ER) pathway activity was correlated with suppression of antitumor immunity. In ER-negative disease, the best prognosis was shown in the immune-high and radiosensitive group patients, and the lowest was in the immune-low and radioresistant group patients. In ER-positive disease, immune signature and radiosensitivity had no prognostic significance.

Conclusion

Taken together, these results suggest that tumor radiosensitivity is associated with activation of antitumor immunity and a better prognosis, particularly in patients with ER-negative breast cancer.

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Data availability

The data generated in this study are publicly available.

References

  1. Sharma P, Wagner K, Wolchok JD, Allison JP (2011) Novel cancer immunotherapy agents with survival benefit: recent successes and next steps. Nat Rev Cancer 11:805–812. https://doi.org/10.1038/nrc3153

    Article  CAS  Google Scholar 

  2. Weichselbaum RR, Liang H, Deng L, Fu Y-X (2017) Radiotherapy and immunotherapy: a beneficial liaison? Nat Rev Clin Oncol 14:365–379. https://doi.org/10.1038/nrclinonc.2016.211

    Article  CAS  Google Scholar 

  3. Galluzzi L, Vitale I, Warren S et al (2020) Consensus guidelines for the definition, detection and interpretation of immunogenic cell death. J Immunother Cancer 8:1–21. https://doi.org/10.1136/jitc-2019-000337

    Article  Google Scholar 

  4. Tramm T, Mohammed H, Myhre S et al (2014) Development and validation of a gene profile predicting benefit of postmastectomy radiotherapy in patients with high-risk breast cancer: a study of gene expression in the DBCG82bc cohort. Clin Cancer Res 20:5272–5280. https://doi.org/10.1158/1078-0432.CCR-14-0458

    Article  CAS  Google Scholar 

  5. Cui Y, Li B, Pollom EL et al (2018) Integrating radiosensitivity and immune gene signatures for predicting benefit of radiotherapy in breast cancer. Clin Cancer Res 24:4754–4762. https://doi.org/10.1158/1078-0432.CCR-18-0825

    Article  Google Scholar 

  6. Sjöström M, Laura Chang S, Fishbane N et al (2019) Clinicogenomic radiotherapy classifier predicting the need for intensified locoregional treatment after breast-conserving surgery for early-stage breast cancer. J Clin Oncol 37:3340–3349. https://doi.org/10.1200/JCO.19.00761

    Article  Google Scholar 

  7. Speers C, Zhao S, Liu M et al (2015) Development and validation of a novel radiosensitivity signature in human breast cancer. Clin Cancer Res 21:3667–3677. https://doi.org/10.1158/1078-0432.CCR-14-2898

    Article  CAS  Google Scholar 

  8. Liveringhouse CL, Washington IR, Diaz R et al (2021) Genomically guided breast radiation therapy: a review of the current data and future directions. Adv Radiat Oncol 6:100731. https://doi.org/10.1016/j.adro.2021.100731

    Article  Google Scholar 

  9. Eschrich S, Zhang H, Zhao H et al (2009) Systems biology modeling of the radiation sensitivity network: a biomarker discovery platform. Int J Radiat Oncol Biol Phys 75:497–505. https://doi.org/10.1016/j.ijrobp.2009.05.056

    Article  CAS  Google Scholar 

  10. Eschrich SA, Pramana J, Zhang H et al (2009) A gene expression model of intrinsic tumor radiosensitivity: prediction of response and prognosis after chemoradiation. Int J Radiat Oncol Biol Phys 75:489–496. https://doi.org/10.1016/j.ijrobp.2009.06.014

    Article  CAS  Google Scholar 

  11. Eschrich SA, Fulp WJ, Pawitan Y et al (2012) Validation of a radiosensitivity molecular signature in breast cancer. Clin Cancer Res 18:5134–5143. https://doi.org/10.1158/1078-0432.CCR-12-0891

    Article  Google Scholar 

  12. Scott JG, Sedor G, Ellsworth P et al (2021) Pan-cancer prediction of radiotherapy benefit using genomic-adjusted radiation dose (GARD): a cohort-based pooled analysis. Lancet Oncol 22:1221–1229. https://doi.org/10.1016/S1470-2045(21)00347-8

    Article  Google Scholar 

  13. Torres-Roca JF, Fulp WJ, Caudell JJ et al (2015) Integration of a radiosensitivity molecular signature into the assessment of local recurrence risk in breast cancer. Int J Radiat Oncol 93:631–638. https://doi.org/10.1016/j.ijrobp.2015.06.021

    Article  Google Scholar 

  14. Schmid P, Cortes J, Pusztai L et al (2020) Pembrolizumab for early triple-negative breast cancer. N Engl J Med 382:810–821. https://doi.org/10.1056/nejmoa1910549

    Article  CAS  Google Scholar 

  15. Cortes J, Cescon DW, Rugo HS et al (2020) Pembrolizumab plus chemotherapy versus placebo plus chemotherapy for previously untreated locally recurrent inoperable or metastatic triple-negative breast cancer (KEYNOTE-355): a randomised, placebo-controlled, double-blind, phase 3 clinical trial. Lancet 396:1817–1828. https://doi.org/10.1016/S0140-6736(20)32531-9

    Article  Google Scholar 

  16. Stanton SE, Disis ML (2016) Clinical significance of tumor-infiltrating lymphocytes in breast cancer. J Immunother Cancer 4:1–7. https://doi.org/10.1186/s40425-016-0165-6

    Article  Google Scholar 

  17. Ali HR, Chlon L, Pharoah PDP et al (2016) Patterns of immune infiltration in breast cancer and their clinical implications: a gene-expression-based retrospective study. PLoS Med 13:e1002194. https://doi.org/10.1371/journal.pmed.1002194

    Article  CAS  Google Scholar 

  18. Ali HR, Dariush A, Thomas J et al (2017) Lymphocyte density determined by computational pathology validated as a predictor of response to neoadjuvant chemotherapy in breast cancer: secondary analysis of the ARTemis trial. Ann Oncol Off J Eur Soc Med Oncol 28:1832–1835. https://doi.org/10.1093/annonc/mdx266

    Article  CAS  Google Scholar 

  19. Hamy AS, Bonsang-Kitzis H, De Croze D et al (2019) Interaction between molecular subtypes and stromal immune infiltration before and after treatment in breast cancer patients treated with neoadjuvant chemotherapy. Clin Cancer Res 25:6731–6741. https://doi.org/10.1158/1078-0432.CCR-18-3017

    Article  CAS  Google Scholar 

  20. Savas P, Salgado R, Denkert C et al (2016) Clinical relevance of host immunity in breast cancer: from TILs to the clinic. Nat Rev Clin Oncol 13:228–241. https://doi.org/10.1038/nrclinonc.2015.215

    Article  CAS  Google Scholar 

  21. Goldberg J, Pastorello RG, Vallius T et al (2021) The immunology of hormone receptor positive breast cancer. Front Immunol 12:1–22. https://doi.org/10.3389/fimmu.2021.674192

    Article  CAS  Google Scholar 

  22. Strom T, Harrison LB, Giuliano AR et al (2017) Tumour radiosensitivity is associated with immune activation in solid tumours. Eur J Cancer 84:304–314. https://doi.org/10.1016/j.ejca.2017.08.001

    Article  CAS  Google Scholar 

  23. Jang BS, Han W, Kim IA (2020) Tumor mutation burden, immune checkpoint crosstalk and radiosensitivity in single-cell RNA sequencing data of breast cancer. Radiother Oncol 142:202–209. https://doi.org/10.1016/j.radonc.2019.11.003

    Article  CAS  Google Scholar 

  24. Dai YH, Wang YF, Shen PC et al (2021) Radiosensitivity index emerges as a potential biomarker for combined radiotherapy and immunotherapy. NPJ Genomic Med 6:1–10. https://doi.org/10.1038/s41525-021-00200-0

    Article  CAS  Google Scholar 

  25. Curtis C, Shah SP, Chin SF et al (2012) The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486:346–352. https://doi.org/10.1038/nature10983

    Article  CAS  Google Scholar 

  26. Cerami E, Gao J, Dogrusoz U et al (2012) The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2:401–404. https://doi.org/10.1158/2159-8290.CD-12-0095

    Article  Google Scholar 

  27. Prat A, Parker JS, Karginova O et al (2010) Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Breast Cancer Res. https://doi.org/10.1186/bcr2635

    Article  Google Scholar 

  28. Bernard PS, Parker JS, Mullins M et al (2009) Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 27:1160–1167. https://doi.org/10.1200/JCO.2008.18.1370

    Article  Google Scholar 

  29. Aran D, Hu Z, Butte AJ (2017) xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol 18:220. https://doi.org/10.1186/s13059-017-1349-1

    Article  CAS  Google Scholar 

  30. Saldanha AJ (2004) Java Treeview—extensible visualization of microarray data. Bioinformatics 20:3246–3248. https://doi.org/10.1093/bioinformatics/bth349

    Article  CAS  Google Scholar 

  31. Newman AM, Liu CL, Green MR et al (2015) Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 12:453–457. https://doi.org/10.1038/nmeth.3337

    Article  CAS  Google Scholar 

  32. Ritchie ME, Phipson B, Wu D et al (2015) Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47. https://doi.org/10.1093/nar/gkv007

    Article  CAS  Google Scholar 

  33. Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44–57. https://doi.org/10.1038/nprot.2008.211

    Article  CAS  Google Scholar 

  34. Hänzelmann S, Castelo R, Guinney J (2013) GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics 14:7. https://doi.org/10.1186/1471-2105-14-7

    Article  Google Scholar 

  35. Ahmed KA, Liveringhouse CL, Mills MN et al (2019) Utilizing the genomically adjusted radiation dose (GARD) to personalize adjuvant radiotherapy in triple negative breast cancer management. EBioMedicine 47:163–169. https://doi.org/10.1016/j.ebiom.2019.08.019

    Article  Google Scholar 

  36. Gough MJ, Melcher AA, Crittenden MR et al (2001) Macrophages orchestrate the immune response to tumor cell death. Cancer Res 61:7240–7247

    CAS  Google Scholar 

  37. Sica A, Larghi P, Mancino A et al (2008) Macrophage polarization in tumour progression. Semin Cancer Biol 18:349–355. https://doi.org/10.1016/j.semcancer.2008.03.004

    Article  CAS  Google Scholar 

  38. Hao NB, Lü MH, Fan YH et al (2012) Macrophages in tumor microenvironments and the progression of tumors. Clin Dev Immunol. https://doi.org/10.1155/2012/948098

    Article  Google Scholar 

  39. Wennerberg E, Lhuillier C, Vanpouille-Box C et al (2017) Barriers to radiation-induced in situ tumor vaccination. Front Immunol. https://doi.org/10.3389/fimmu.2017.00229

    Article  Google Scholar 

  40. Mehta AK, Kadel S, Townsend MG et al (2021) Macrophage biology and mechanisms of immune suppression in breast cancer. Front Immunol 12:1–17. https://doi.org/10.3389/fimmu.2021.643771

    Article  CAS  Google Scholar 

  41. Wang T, Jin J, Qian C et al (2021) Estrogen/ER in anti-tumor immunity regulation to tumor cell and tumor microenvironment. Cancer Cell Int 21:1–13. https://doi.org/10.1186/s12935-021-02003-w

    Article  CAS  Google Scholar 

  42. Binnewies M, Roberts EW, Kersten K et al (2018) microenvironment (TIME) for effective therapy. Nat Med 24:541–550. https://doi.org/10.1038/s41591-018-0014-x

    Article  CAS  Google Scholar 

  43. Hegde PS, Chen DS (2020) Top 10 challenges in cancer immunotherapy. Immunity 52:17–35. https://doi.org/10.1016/j.immuni.2019.12.011

    Article  CAS  Google Scholar 

  44. Mariathasan S, Turley SJ, Nickles D et al (2018) TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554:544–548. https://doi.org/10.1038/nature25501

    Article  CAS  Google Scholar 

  45. Hegde PS, Karanikas V, Evers S (2016) The where, the when, and the how of immune monitoring for cancer immunotherapies in the era of checkpoint inhibition. Clin Cancer Res 22:1865–1874. https://doi.org/10.1158/1078-0432.CCR-15-1507

    Article  CAS  Google Scholar 

  46. Galon J, Bruni D (2019) Approaches to treat immune hot, altered and cold tumours with combination immunotherapies. Nat Rev Drug Discov 18:197–218. https://doi.org/10.1038/s41573-018-0007-y

    Article  CAS  Google Scholar 

  47. Yeo SK, Guan JL (2017) Breast cancer: multiple subtypes within a tumor? Trends in Cancer 3:753–760. https://doi.org/10.1016/j.trecan.2017.09.001

    Article  CAS  Google Scholar 

  48. Wu SZ, Al-Eryani G, Roden DL et al (2021) A single-cell and spatially resolved atlas of human breast cancers. Nat Genet 53:1334–1347. https://doi.org/10.1038/s41588-021-00911-1

    Article  CAS  Google Scholar 

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Funding

This work was supported by grants from the Ministry of Science and Information & Communication Technology (NRF#2020R1A2C2005141).

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Authors

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by B-HK. The first draft of the manuscript was written by B-HK and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to In Ah Kim.

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The authors declare no competing interests.

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Institutional review board (IRB) approval at Seoul National University Bundang Hospital was waived as publicly available databases were used.

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Cite this article

Kang, BH., Jang, BS. & Kim, I.A. Radiosensitivity is associated with antitumor immunity in estrogen receptor-negative breast cancer. Breast Cancer Res Treat 197, 479–488 (2023). https://doi.org/10.1007/s10549-022-06818-7

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  • DOI: https://doi.org/10.1007/s10549-022-06818-7

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