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Tumor Heterogeneity Correlates with Less Immune Response and Worse Survival in Breast Cancer Patients

  • Kerry-Ann McDonald
  • Tsutomu Kawaguchi
  • Qianya Qi
  • Xuan Peng
  • Mariko Asaoka
  • Jessica Young
  • Mateusz Opyrchal
  • Li Yan
  • Santosh Patnaik
  • Eigo Otsuji
  • Kazuaki TakabeEmail author
Breast Oncology

Abstract

Background

Intratumor heterogeneity implies that subpopulations of cancer cells that differ in genetic, phenotypic, or behavioral characteristics coexist in a single tumor (Ma in Breast Cancer Res Treat 162(1):39–48, 2017; Martelotto in Breast Cancer Res 16(3):210, 2014). Tumor heterogeneity drives progression, metastasis and treatment resistance, but its relationship with tumor infiltrating immune cells is a matter of debate, where some argue that tumors with high heterogeneity may generate neoantigens that attract immune cells, and others claim that immune cells provide selection pressure that shapes tumor heterogeneity (McGranahan et al. in Science 351(6280):1463–1469, 2016; McGranahan and Swanton in Cell 168(4):613–628, 2017). We sought to study the association between tumor heterogeneity and immune cells in a real-world cohort utilizing The Cancer Genome Atlas.

Methods

Mutant allele tumor heterogeneity (MATH) was calculated to estimate intratumoral heterogeneity, and immune cell compositions were estimated using CIBERSORT. Survival analyses were demonstrated using Kaplan–Meir curves.

Results

Tumors with high heterogeneity (high MATH) were associated with worse overall survival (p = 0.049), as well as estrogen receptor-positive (p = 0.011) and non-triple-negative tumors (p = 0.01). High MATH tumors were also associated with less infiltration of anti-tumor CD8 (p < 0.013) and CD4 T cells (p < 0.00024), more tumor-promoting regulatory T cells (p < 4e−04), lower expression of T-cell exhaustion markers, specifically PDL-1 (p = 0.0031), IDO2 (p = 0.34), ADORA2A (p = 0.018), VISTA (p = 0.00013), and CCR4 (p < 0.00001), lower expression of cytolytic enzymes granzyme A (p = 0.0056) and perforin 1 (p = 0.053), and low cytolytic activity score (p = 0.0028).

Conclusions

High heterogeneity tumors are associated with less immune cell infiltration, less activation of the immune response, and worse survival in breast cancer. Our results support the notion that tumor heterogeneity is shaped by selection pressure of tumor-infiltrating immune cells.

Notes

Author contributions

Conception and design: KAM, TK, and KT. Development of methodology: TK, LY, and KT. Acquisition of data (acquired and managed patients, provided facilities, etc.): TK, QQ, XP, MA, and LY. Analysis and interpretation of data (e.g. statistical analysis, biostatistics, computational analysis): TK, QQ, XP, MA, and LY. Writing, review, and/or revision of the manuscript: KAM, TK, MO, JY, SP, EO, and KT. Administrative, technical, or material support (i.e. reporting or organizing data, constructing databases): QQ, XP, and LY. Study supervision: KT and TK.

DISCLOSURES

Kazuaki Takabe is funded by the United States National Institute of Health – National Cancer Institute (R01CA160688) and Susan G. Komen Foundation (investigator-initiated research Grant (IIR12222224). This work was also supported by National Cancer Institute Grant P30CA016056 involving the use of Roswell Park Cancer Institute’s Bioinformatics and Biostatistics shared resources. Biospecimens or research pathology services for this study were provided by the Pathology Resource Network. Clinical Data Delivery and Honest Broker services for this study were provided by the Clinical Data Network. Kerry-Ann McDonald, Tsutomu Kawaguchi, Qianya Qi, Xuan Peng, Mariko Asaoka, Jessica Young, Mateusz Opyrchal, Li Yan, Santosh Patnaik, and Eigo Otsuji have no conflicts of interest to declare.

Supplementary material

10434_2019_7338_MOESM1_ESM.tif (289 kb)
FIG. S1 MATH level and patient survival for HER2-positive and -negative breast cancers in the (a) TCGA and (b) PAM50 cohorts. MATH mutant allele tumor heterogeneity, HER2 human epidermal growth factor receptor 2, TCGA The Cancer Genome Atlas (TIFF 288 kb)

References

  1. 1.
    Ma D, Jiang YZ, Liu XY, Liu YR, Shao ZM. Clinical and molecular relevance of mutant-allele tumor heterogeneity in breast cancer. Breast Cancer Res Treat. 2017;162(1):39–48.CrossRefGoogle Scholar
  2. 2.
    Martelotto LG, Ng CK, Piscuoglio S, Weigelt B, Reis-Filho JS. Breast cancer intra-tumor heterogeneity. Breast Cancer Res. 2014;16(3):210.CrossRefGoogle Scholar
  3. 3.
    McGranahan N, Furness AJ, Rosenthal R, et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science. 2016;351(6280):1463–69.CrossRefGoogle Scholar
  4. 4.
    McGranahan N, Swanton C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell. 2017;168(4):613–28.CrossRefGoogle Scholar
  5. 5.
    Marusyk A, Almendro V, Polyak K. Intra-tumour heterogeneity: A looking glass for cancer? Nat Rev Cancer. 2012;12(5):323–34.CrossRefGoogle Scholar
  6. 6.
    Andor N, Graham TA, Jansen M, et al. Pan-cancer analysis of the extent and consequences of intratumor heterogeneity. Nat Med. 2016;22(1):105–13.CrossRefGoogle Scholar
  7. 7.
    Mroz EA, Rocco JW. MATH, a novel measure of intratumor genetic heterogeneity, is high in poor-outcome classes of head and neck squamous cell carcinoma. Oral Oncol. 2013;49(3):211–15.CrossRefGoogle Scholar
  8. 8.
    Rajput A, Bocklage T, Greenbaum A, Lee JH, Ness SA. Mutant-allele tumor heterogeneity scores correlate with risk of metastases in colon cancer. Clin Colorectal Cancer. 2017;16(3):e165–e170.CrossRefGoogle Scholar
  9. 9.
    PLOS Medicine Staff. Correction: intra-tumor genetic heterogeneity and mortality in head and neck cancer: Analysis of data from the cancer genome atlas. PLoS Med. 2015;12(6):e1001844.CrossRefGoogle Scholar
  10. 10.
    10. Burrell RA, McGranahan N, Bartek J, Swanton C. The causes and consequences of genetic heterogeneity in cancer evolution. Nature. 2013;501(7467):338–45.CrossRefGoogle Scholar
  11. 11.
    Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490(7418):61–70.CrossRefGoogle Scholar
  12. 12.
    Cerami E, Gao J, Dogrusoz U, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–404.CrossRefGoogle Scholar
  13. 13.
    Gao J, Aksoy BA, Dogrusoz U, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6(269):pl1.Google Scholar
  14. 14.
    Ramanathan R, Raza A, Sturgill J, et al. Paradoxical association of postoperative plasma sphingosine-1-phosphate with breast cancer aggressiveness and chemotherapy. Mediators Inflamm. 2017;2017:5984819.CrossRefGoogle Scholar
  15. 15.
    Ramanathan R, Olex AL, Dozmorov M, Bear HD, Fernandez LJ, Takabe K. Angiopoietin pathway gene expression associated with poor breast cancer survival. Breast Cancer Res Treat. 2017;162(1):191–98.CrossRefGoogle Scholar
  16. 16.
    Kim SY, Kawaguchi T, Yan L, Young J, Qi Q, Takabe K. Clinical relevance of microRNA expressions in breast cancer validated using the cancer genome atlas (TCGA). Ann Surg Oncol. 2017;24(10):2943–49.CrossRefGoogle Scholar
  17. 17.
    Narayanan S, Kawaguchi T, Yan L, Peng X, Qi Q, Takabe K. Cytolytic activity score to assess anticancer immunity in colorectal cancer. Ann Surg Oncol. 2018;25:2323–31Google Scholar
  18. 18.
    Moro K, Kawaguchi T, Tsuchida J, et al. Ceramide species are elevated in human breast cancer and are associated with less aggressiveness. Oncotarget. 2018;9(28):19874–890.CrossRefGoogle Scholar
  19. 19.
    Kawaguchi T, Yan L, Qi Q, et al. Overexpression of suppressive microRNAs, miR-30a and miR-200c are associated with improved survival of breast cancer patients. Sci Rep. 2017;7(1):15945CrossRefGoogle Scholar
  20. 20.
    Young J, Kawaguchi T, Yan L, Qi Q, Liu S, Takabe K. Tamoxifen sensitivity-related microRNA-342 is a useful biomarker for breast cancer survival. Oncotarget. 2017;8(59):99978–989.CrossRefGoogle Scholar
  21. 21.
    Kawaguchi T, Yan L, Qi Q, et al. Novel MicroRNA-based risk score identified by integrated analyses to predict metastasis and poor prognosis in breast cancer. Ann Surg Oncol. 2018;25(13):4037–46.CrossRefGoogle Scholar
  22. 22.
    Kawaguchi T, Narayanan S, Takabe K. ASO author reflections: “From computer to bedside”: a new translational approach to immunogenomics. Ann Surg Oncol. 2018;25 Suppl 3:846–47.CrossRefGoogle Scholar
  23. 23.
    Rocco JW. Mutant allele tumor heterogeneity (MATH) and head and neck squamous cell carcinoma. Head Neck Pathol. 2015;9(1):1–5.CrossRefGoogle Scholar
  24. 24.
    Budczies J, Klauschen F, Sinn BV, et al. Cutoff finder: A comprehensive and straightforward web application enabling rapid biomarker cutoff optimization. PLoS One. 2012;7(12):e51862.CrossRefGoogle Scholar
  25. 25.
    Chang C, Hsieh MK, Chang WY, Chiang AJ, Chen J. Determining the optimal number and location of cutoff points with application to data of cervical cancer. PLoS ONE. 2017;12(4):e0176231.CrossRefGoogle Scholar
  26. 26.
    26. Mazumdar M, Glassman JR. Categorizing a prognostic variable: Review of methods, code for easy implementation and applications to decision-making about cancer treatments. Stat Med. 2000;19(1):113-132.CrossRefGoogle Scholar
  27. 27.
    27. Brondum L, Eriksen JG, Singers Sorensen B, et al. Plasma proteins as prognostic biomarkers in radiotherapy treated head and neck cancer patients. Clin Transl Radiat Oncol. 2017;2:46-52.CrossRefGoogle Scholar
  28. 28.
    Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453–57Google Scholar
  29. 29.
    Charoentong P, Finotello F, Angelova M, et al. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 2017;18(1):248–62.Google Scholar
  30. 30.
    Turashvili G, Brogi E. Tumor heterogeneity in breast cancer. Front Med (Lausanne). 2017;4:227.CrossRefGoogle Scholar
  31. 31.
    Morris LG, Riaz N, Desrichard A, et al. Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival. Oncotarget. 2016;7(9):10051–063.CrossRefGoogle Scholar
  32. 32.
    Ellsworth RE, Blackburn HL, Shriver CD, Soon-Shiong P, Ellsworth DL. Molecular heterogeneity in breast cancer: state of the science and implications for patient care. Semin Cell Dev Biol. 2017;64:65–72.CrossRefGoogle Scholar
  33. 33.
    Szulwach KE, Chen P, Wang X, et al. Single-cell genetic analysis using automated microfluidics to resolve somatic mosaicism. PLoS One. 2015;10(8):e0135007.CrossRefGoogle Scholar
  34. 34.
    Lindström LS, Yau C, Czene K, et al. Intratumor heterogeneity of the estrogen receptor and the long-term risk of fatal breast cancer. J Natl Cancer Inst. 2018;110(7):726–33.CrossRefGoogle Scholar
  35. 35.
    Roth A, Khattra J, Yap D, et al. PyClone: Statistical inference of clonal population structure in cancer. Nat Methods. 2014;11(4):396–98.CrossRefGoogle Scholar
  36. 36.
    Jiao W, Vembu S, Deshwar AG, Stein L, Morris Q. Inferring clonal evolution of tumors from single nucleotide somatic mutations. BMC Bioinformatics. 2014;15:35–2105-15-35.Google Scholar
  37. 37.
    Oesper L, Mahmoody A, Raphael BJ. THetA: Inferring intra-tumor heterogeneity from high-throughput DNA sequencing data. Genome Biol. 2013;14(7):R80CrossRefGoogle Scholar
  38. 38.
    Carter SL, Cibulskis K, Helman E, et al. Absolute quantification of somatic DNA alterations in human cancer. Nat Biotechnol. 2012;30(5):413–21.CrossRefGoogle Scholar
  39. 39.
    Gu-Trantien C, Loi S, Garaud S, et al. CD4(+) follicular helper T cell infiltration predicts breast cancer survival. J Clin Invest. 2013;123(7):2873–92.CrossRefGoogle Scholar
  40. 40.
    Rajput AB, Turbin DA, Cheang MC, et al. Stromal mast cells in invasive breast cancer are a marker of favourable prognosis: A study of 4,444 cases. Breast Cancer Res Treat. 2008;107(2):249–57.CrossRefGoogle Scholar
  41. 41.
    Bianchini G, Balko JM, Mayer IA, Sanders ME, Gianni L. Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease. Nat Rev Clin Oncol. 2016;13(11):674–90.CrossRefGoogle Scholar
  42. 42.
    Munn DH, Mellor AL. Indoleamine 2,3 dioxygenase and metabolic control of immune responses. Trends Immunol. 2013;34(3):137–43.CrossRefGoogle Scholar
  43. 43.
    Cekic C, Linden J. Adenosine A2A receptors intrinsically regulate CD8+ T cells in the tumor microenvironment. Cancer Res. 2014;74(24):7239–49.CrossRefGoogle Scholar
  44. 44.
    Lines JL, Sempere LF, Broughton T, Wang L, Noelle R. VISTA is a novel broad-spectrum negative checkpoint regulator for cancer immunotherapy. Cancer Immunol Res. 2014;2(6):510–17.CrossRefGoogle Scholar
  45. 45.
    Trapani JA, Smyth MJ. Functional significance of the perforin/granzyme cell death pathway. Nat Rev Immunol. 2002;2(10):735–47.CrossRefGoogle Scholar

Copyright information

© Society of Surgical Oncology 2019

Authors and Affiliations

  • Kerry-Ann McDonald
    • 1
  • Tsutomu Kawaguchi
    • 1
    • 2
  • Qianya Qi
    • 3
  • Xuan Peng
    • 3
  • Mariko Asaoka
    • 1
  • Jessica Young
    • 1
  • Mateusz Opyrchal
    • 4
  • Li Yan
    • 3
  • Santosh Patnaik
    • 5
  • Eigo Otsuji
    • 2
  • Kazuaki Takabe
    • 1
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    Email author
  1. 1.Breast Surgery, Department of Surgical OncologyRoswell Park Comprehensive Cancer CenterBuffaloUSA
  2. 2.Department of SurgeryKyoto Prefectural University of MedicineKyotoJapan
  3. 3.Department of Biostatistics and BioinformaticsRoswell Park Comprehensive Cancer CenterBuffaloUSA
  4. 4.Department of Medical OncologyRoswell Park Comprehensive Cancer CenterBuffaloUSA
  5. 5.Thoracic Surgery, Department of Surgical OncologyRoswell Park Comprehensive Cancer CenterBuffaloUSA
  6. 6.Department of Surgery, University at Buffalo Jacobs School of Medicine and Biomedical SciencesThe State University of New YorkBuffaloUSA
  7. 7.Department of Breast Surgery and OncologyTokyo Medical UniversityTokyoJapan
  8. 8.Department of SurgeryYokohama City UniversityYokohamaJapan
  9. 9.Department of SurgeryNiigata University Graduate School of Medical and Dental SciencesNiigataJapan
  10. 10.Department of Breast Surgery and OncologyFukushima Medical UniversityFukushimaJapan
  11. 11.Breast Oncology and SurgeryRoswell Park Cancer InstituteBuffaloUSA

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