Unveiling breast cancer risk profiles: a survival clustering analysis empowered by an online web application

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References

• 1. Jarman IH, Etchells TA, Martin JD, Lisboa PJ. An integrated framework for risk profiling of breast cancer patients following surgery. Artif. Intell. Med. 42(3), 165–188 (2008).
  MedlineGoogle Scholar
• 2. Milioli HH, Tishchenko I, Riveros C, Berretta R, Moscato P. Basal-like breast cancer: molecular profiles, clinical features and survival outcomes. BMC Med. Genomics 10(1), 19 (2017).
  MedlineGoogle Scholar
• 3. Rosner B, Glynn RJ, Tamimi RM et al. Breast cancer risk prediction with heterogeneous risk profiles according to breast cancer tumor markers. Am. J. Epidemiol. 178(2), 296–308 (2013). •• This study is directly related to our topic and provides a strategy for evaluating heterogeneity of risk factor associations by tumor marker levels while controlling for additional tumor markers.
  MedlineGoogle Scholar
• 4. Caswell-Jin JL, Callahan A, Purington N et al. Treatment and monitoring variability in US metastatic breast cancer care. JCO Clin. Cancer Inform. 5, 600–614 (2021).
  MedlineGoogle Scholar
• 5. Decock J, Long JR, Laxton RC et al. Association of matrix metalloproteinase-8 gene variation with breast cancer prognosis. Cancer Res. 67(21), 10214–10221 (2007).
  Medline CASGoogle Scholar
• 6. Hussain MS, Majami AA, Ali H et al. The complex role of MEG3: an emerging long non-coding RNA in breast cancer. Pathol. Res. Pract. 251, 154850 (2023).
  Medline CASGoogle Scholar
• 7. Nagle PW, Plukker JTM, Muijs CT, Van Luijk P, Coppes RP. Patient-derived tumor organoids for prediction of cancer treatment response. Semin. Cancer Biol. 53, 258–264 (2018).
  Medline CASGoogle Scholar
• 8. Weigelt B, Bissell MJ. Unraveling the microenvironmental influences on the normal mammary gland and breast cancer. Semin. Cancer Biol. 18(5), 311–321 (2008).
  Medline CASGoogle Scholar
• 9. Zeng Y, Li S, Zhang S, Wang L, Yuan H, Hu F. Cell membrane coated-nanoparticles for cancer immunotherapy. Acta Pharm. Sin. B 12(8), 3233–3254 (2022).
  Medline CASGoogle Scholar
• 10. Heng HH, Bremer SW, Stevens JB, Ye KJ, Liu G, Ye CJ. Genetic and epigenetic heterogeneity in cancer: a genome-centric perspective. J. Cell Physiol. 220(3), 538–547 (2009).
  Medline CASGoogle Scholar
• 11. Sariego J. Patterns of breast cancer presentation in the United States: does geography matter? Am. Surg. 75(7), 545–549; discussion 549–550 (2009).
  MedlineGoogle Scholar
• 12. Akechi T, Momino K, Miyashita M, Sakamoto N, Yamashita H, Toyama T. Anxiety in disease-free breast cancer patients might be alleviated by provision of psychological support, not of information. Jpn J. Clin. Oncol. 45(10), 929–933 (2015).
  MedlineGoogle Scholar
• 13. Olopade OI, Grushko TA, Nanda R, Huo D. Advances in breast cancer: pathways to personalized medicine. Clin. Cancer Res. 14(24), 7988–7999 (2008).
  Medline CASGoogle Scholar
• 14. Yang Q, Yu X, Lee HH et al. Single slice thigh CT muscle group segmentation with domain adaptation and self-training. J. Med. Imaging (Bellingham) 10(4), 044001 (2023).
  MedlineGoogle Scholar
• 15. Curtis C, Shah SP, Chin SF et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486(7403), 346–352 (2012).
  Medline CASGoogle Scholar
• 16. Meselhy Eltoukhy M, Faye I, Belhaouari Samir B. A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation. Comput. Biol. Med. 42(1), 123–128 (2012).
  MedlineGoogle Scholar
• 17. Raykov YP, Boukouvalas A, Baig F, Little MA. What to do when K-means clustering fails: a simple yet principled alternative algorithm. PLOS ONE 11(9), e0162259 (2016).
  MedlineGoogle Scholar
• 18. Rich JT, Neely JG, Paniello RC, Voelker CC, Nussenbaum B, Wang EW. A practical guide to understanding Kaplan–Meier curves. Otolaryngol. Head Neck Surg. 143(3), 331–336 (2010). •• A practical guide for the Kaplan–Meier curves in the medical study.
  MedlineGoogle Scholar
• 19. Christensen E. Multivariate survival analysis using Cox's regression model. Hepatology 7(6), 1346–1358 (1987). •• One of the foundation papers for the Cox regression model which we used in our study.
  Medline CASGoogle Scholar
• 20. Zhang Z, Reinikainen J, Adeleke KA, Pieterse ME, Groothuis-Oudshoorn CGM. Time-varying covariates and coefficients in Cox regression models. Ann. Transl. Med. 6(7), 121 (2018).
  MedlineGoogle Scholar
• 21. Bradburn MJ, Clark TG, Love SB, Altman DG. Survival analysis part II: multivariate data analysis – an introduction to concepts and methods. Br. J. Cancer 89(3), 431–436 (2003). •• The bible for multivariate survival analysis, which we used in our study.
  Medline CASGoogle Scholar
• 22. Lanczky A, Gyorffy B. Web-based survival analysis tool tailored for medical research (KMplot): development and implementation. J. Med. Internet Res. 23(7), e27633 (2021). • Another study which developed the web application of Kaplan–Meier plot for survival analysis.
  MedlineGoogle Scholar
• 23. Gu Y, Gong Y, Wang M, Jiang S, Li C, Yuan Z. Enhancing kidney failure analysis: web application development for longitudinal trajectory clustering. medRxiv doi: 10.1101/2023.05.31.23290804 (2023).
  Google Scholar
• 24. Dwivedi B, Mumme H, Satpathy S, Bhasin SS, Bhasin M. Survival Genie, a web platform for survival analysis across pediatric and adult cancers. Sci. Rep. 12(1), 3069 (2022).
  Medline CASGoogle Scholar
• 25. Zhang X, Yu H, Xie Q et al. Design study of a PET detector with 0.5 mm crystal pitch for high-resolution preclinical imaging. Phys. Med. Biol. 66(13), (2021).
  Google Scholar
• 26. Yang J, Zhao S, Wang J, Sheng Q, Liu Q, Shyr Y. Immu-Mela: an open resource for exploring immunotherapy-related multidimensional genomic profiles in melanoma. J. Genet. Genomics 48(5), 361–368 (2021).
  Medline CASGoogle Scholar
• 27. Zhang R, Ma Y, Ren J. Green development performance evaluation based on dual perspectives of level and efficiency: a case study of the Yangtze River economic belt, China. Int. J. Environ Res. Public Health 19(15), (2022).
  Google Scholar
• 28. Lundin M, Lundin J, Burke HB, Toikkanen S, Pylkkanen L, Joensuu H. Artificial neural networks applied to survival prediction in breast cancer. Oncology 57(4), 281–286 (1999). • This interesting paper introduces neural network modeling on survival analysis and also prediction on breast cancer.
  Medline CASGoogle Scholar
• 29. Ganggayah MD, Taib NA, Har YC, Lio P, Dhillon SK. Predicting factors for survival of breast cancer patients using machine learning techniques. BMC Med. Inform. Decis. Mak. 19(1), 48 (2019). • This is a study using machine learning to work on predicting risk factors for survival, they handle the missing data by using multiple imputation.
  MedlineGoogle Scholar
• 30. Feng A, Xue Y, Wang Y et al. Label propagation via random walk for training robust thalamus nuclei parcellation model from noisy annotations. ArXiv (2023). https://arxiv.org/abs/2303.17706
  Google Scholar
• 31. Wang Y, Deng Y, Tan Y, Zhou M, Jiang Y, Liu B. A comparison of random survival forest and Cox regression for prediction of mortality in patients with hemorrhagic stroke. BMC Med. Inform. Decis. Mak. 23(1), 215 (2023).
  MedlineGoogle Scholar
• 32. Mariani L, Coradini D, Biganzoli E et al. Prognostic factors for metachronous contralateral breast cancer: a comparison of the linear Cox regression model and its artificial neural network extension. Breast Cancer Res. Treat. 44(2), 167–178 (1997).
  Medline CASGoogle Scholar
• 33. Vahdaninia M, Montazeri A. Breast cancer in Iran: a survival analysis. Asian Pac. J. Cancer Prev. 5(2), 223–225 (2004).
  MedlineGoogle Scholar
• 34. Hajihosseini M, Faradmal J, Sadighi-Pashaki A. Survival analysis of breast cancer patients after surgery with an intermediate event: application of illness-death model. Iran J. Public Health 44(12), 1677–1684 (2015).
  MedlineGoogle Scholar
• 35. Li Z, Li Z, Chen Q et al. Machine-learning-assisted spontaneous Raman spectroscopy classification and feature extraction for the diagnosis of human laryngeal cancer. Comput. Biol. Med. 146, 105617 (2022).
  Medline CASGoogle Scholar
• 36. Cutler DR, Edwards TC Jr, Beard KH et al. Random forests for classification in ecology. Ecology 88(11), 2783–2792 (2007).
  MedlineGoogle Scholar
• 37. Hopken MW, Gilfillan D, Gilbert AT et al. Biodiversity indices and random forests reveal the potential for striped skunk (Mephitis mephitis) fecal microbial communities to function as a biomarker for oral rabies vaccination. PLOS ONE 18(8), e0285852 (2023).
  Medline CASGoogle Scholar
• 38. Pontil M, Verri A. Properties of support vector machines. Neural Comput. 10(4), 955–974 (1998).
  Medline CASGoogle Scholar
• 39. El Haji H, Souadka A, Patel BN et al. Evolution of breast cancer recurrence risk prediction: a systematic review of statistical and machine learning-based models. JCO Clin. Cancer Inform 7, e2300049 (2023).
  MedlineGoogle Scholar
• 40. Kourounis G, Elmahmudi AA, Thomson B, Hunter J, Ugail H, Wilson C. Computer image analysis with artificial intelligence: a practical introduction to convolutional neural networks for medical professionals. Postgrad. Med. J. 99(1178), 1287–1294 (2023).
  MedlineGoogle Scholar
• 41. Chi CL, Street WN, Wolberg WH. Application of artificial neural network-based survival analysis on two breast cancer datasets. AMIA Annu. Symp. Proc. 2007, 130–134 (2007).
  MedlineGoogle Scholar
• 42. Tong L, Mitchel J, Chatlin K, Wang MD. Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis. BMC Med. Inform. Decis. Mak. 20(1), 225 (2020).
  MedlineGoogle Scholar
• 43. Ye B, Shi J, Kang H et al. Advancing pan-cancer gene expression survial analysis by inclusion of non-coding RNA. RNA Biol. 17(11), 1666–1673 (2020).
  Medline CASGoogle Scholar
• 44. Zhang H, Wang Y, Qi J, Abbaszadeh S. Penalized maximum-likelihood reconstruction for improving limited-angle artifacts in a dedicated head and neck PET system. Phys Med. Biol. 65(16), 165016 (2020).
  Medline CASGoogle Scholar
• 45. Banu A, Ahmed R, Musleh S, Shah Z, Househ M, Alam T. Predicting overall survival in METABRIC cohort using machine learning. Stud. Health Technol. Inform 305, 632–635 (2023).
  MedlineGoogle Scholar
• 46. Huang E, Cheng SH, Dressman H et al. Gene expression predictors of breast cancer outcomes. Lancet 361(9369), 1590–1596 (2003).
  Medline CASGoogle Scholar
• 47. Sorlie T, Tibshirani R, Parker J et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc. Natl Acad. Sci. USA 100(14), 8418–8423 (2003).
  Medline CASGoogle Scholar
• 48. Lu X, Lu X, Wang ZC, Iglehart JD, Zhang X, Richardson AL. Predicting features of breast cancer with gene expression patterns. Breast Cancer Res. Treat 108(2), 191–201 (2008).
  Medline CASGoogle Scholar
• 49. Wodzinski M, Ciepiela I, Kuszewski T, Kedzierawski P, Skalski A. Semi-supervised deep learning-based image registration method with volume penalty for real-time breast tumor bed localization. Sensors (Basel) 21(12), (2021).
  MedlineGoogle Scholar
• 50. Huang Z, Zhan X, Xiang S et al. SALMON: survival analysis learning with multi-omics neural networks on breast cancer. Front. Genet. 10, 166 (2019).
  Medline CASGoogle Scholar
• 51. Zhou P, Liu Z, Wu H, Wang Y, Lei Y, Abbaszadeh S. Automatically detecting bregma and lambda points in rodent skull anatomy images. PLOS ONE 15(12), e0244378 (2020).
  Medline CASGoogle Scholar
• 52. Li Z, Li Z, Chen Q et al. Detection of pancreatic cancer by convolutional-neural-network-assisted spontaneous Raman spectroscopy with critical feature visualization. Neural Netw. 144, 455–464 (2021).
  MedlineGoogle Scholar
• 53. Yang J, Zhao S, Wang J, Sheng Q, Liu Q, Shyr Y. A pan-cancer immunogenomic atlas for immune checkpoint blockade immunotherapy. Cancer Res. 82(4), 539–542 (2021).
  MedlineGoogle Scholar
• 54. Domanski MJ, Tian X, Wu CO et al. Time course of LDL cholesterol exposure and cardiovascular disease event risk. J. Am. Coll. Cardiol. 76(13), 1507–1516 (2020).
  Medline CASGoogle Scholar
• 55. Sachdev V, Tian X, Gu Y et al. A phenotypic risk score for predicting mortality in sickle cell disease. Br. J. Haematol. 192(5), 932–941 (2021).
  MedlineGoogle Scholar
• 56. Wang S, Li C, Wang R et al. Annotation-efficient deep learning for automatic medical image segmentation. Nat. Commun. 12(1), 5915 (2021).
  Medline CASGoogle Scholar
• 57. Wang Y, Li Y, Yi F et al. Two-crossed-polarizers based optical property modulation method for ionizing radiation detection for positron emission tomography. Phys. Med. Biol. 64(13), 135017 (2019).
  Medline CASGoogle Scholar
• 58. Mukherjee A, Russell R, Chin SF et al. Associations between genomic stratification of breast cancer and centrally reviewed tumour pathology in the METABRIC cohort. NPJ Breast Cancer 4, 5 (2018).
  Medline CASGoogle Scholar
• 59. Bennett J, Pomaznoy M, Singhania A, Peters B. A metric for evaluating biological information in gene sets and its application to identify co-expressed gene clusters in PBMC. PLoS Comput. Biol. 17(10), e1009459 (2021).
  Medline CASGoogle Scholar
• 60. Milioli HH, Vimieiro R, Tishchenko I, Riveros C, Berretta R, Moscato P. Iteratively refining breast cancer intrinsic subtypes in the METABRIC dataset. BioData Min. 9, 2 (2016).
  MedlineGoogle Scholar
• 61. Mucaki EJ, Baranova K, Pham HQ et al. Predicting outcomes of hormone and chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) study by biochemically-inspired machine learning. F1000Res 5, 2124 (2016).
  MedlineGoogle Scholar
• 62. Carene D, Tran-Dien A, Lemonnier J et al. Association between FGFR1 copy numbers, MAP3K1 mutations, and survival in axillary node-positive, hormone receptor-positive, and HER2-negative early breast cancer in the PACS04 and METABRIC studies. Breast Cancer Res. Treat 179(2), 387–401 (2020).
  Medline CASGoogle Scholar
• 63. Jordan AM, Weingarten J, Murphy WM. Transitional cell neoplasms of the urinary bladder. Can biologic potential be predicted from histologic grading? Cancer 60(11), 2766–2774 (1987).
  Medline CASGoogle Scholar
• 64. Carter CL, Allen C, Henson DE. Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases. Cancer 63(1), 181–187 (1989).
  Medline CASGoogle Scholar
• 65. Galea MH, Blamey RW, Elston CE, Ellis IO. The Nottingham Prognostic Index in primary breast cancer. Breast Cancer Res. Treat. 22(3), 207–219 (1992).
  Medline CASGoogle Scholar
• 66. Dowsett M, Cooke T, Ellis I et al. Assessment of HER2 status in breast cancer: why, when and how? Eur. J. Cancer 36(2), 170–176 (2000).
  Medline CASGoogle Scholar
• 67. Hess KR, Pusztai L, Buzdar AU, Hortobagyi GN. Estrogen receptors and distinct patterns of breast cancer relapse. Breast Cancer Res. Treat 78(1), 105–118 (2003).
  Medline CASGoogle Scholar
• 68. Glass AG, Lacey JV Jr, Carreon JD, Hoover RN. Breast cancer incidence, 1980–2006: combined roles of menopausal hormone therapy, screening mammography, and estrogen receptor status. J. Natl Cancer Inst. 99(15), 1152–1161 (2007).
  MedlineGoogle Scholar
• 69. Bayati H, Davoudi H, Fatemizadeh E. A heuristic method for finding the optimal number of clusters with application in medical data. Ann. Int. Conf. IEEE Eng. Med. Biol. Soc. 2008, 4684–4687 (2008).
  MedlineGoogle Scholar
• 70. Novak A, Hornyak B, Razso Z et al. The introduction of health behavior profiles in the Hungarian Defense Forces: a cluster analysis of lifestyle factors according to the health screening tests performed in 2011–2015. Int. J. Occup. Med. Environ. Health 32(1), 99–114 (2019).
  MedlineGoogle Scholar
• 71. Negri T, Mantri S, Angelov A et al. A rapid and efficient strategy to identify and recover biosynthetic gene clusters from soil metagenomes. Appl. Microbiol. Biotechnol. 106(8), 3293–3306 (2022).
  Medline CASGoogle Scholar