Markers of Progression in Early-Stage Invasive Breast Cancer: a Predictive Immunohistochemical Panel Algorithm for Distant Recurrence Risk Stratification

Aleskandarany, M.A., Soria, D., Green, A.R., Nolan, C., Diez-Rodriguez, M., Ellis, I.O. and Rakha, E.A. 2015. Markers of Progression in Early-Stage Invasive Breast Cancer: a Predictive Immunohistochemical Panel Algorithm for Distant Recurrence Risk Stratification. Breast Cancer Research and Treatment. 151 (2), pp. 325-333. doi:10.1007/s10549-015-3406-3

TitleMarkers of Progression in Early-Stage Invasive Breast Cancer: a Predictive Immunohistochemical Panel Algorithm for Distant Recurrence Risk Stratification
AuthorsAleskandarany, M.A., Soria, D., Green, A.R., Nolan, C., Diez-Rodriguez, M., Ellis, I.O. and Rakha, E.A.
Abstract

Accurate distant metastasis (DM) prediction is critical for risk stratification and effective treatment decisions in breast cancer (BC). Many prognostic markers/models based on tissue marker studies are continually emerging using conventional statistical approaches analysing complex/dimensional data association with DM/poor prognosis. However, few of them have fulfilled satisfactory evidences for clinical application. This study aimed at building DM risk assessment algorithm for BC patients. A well-characterised series of early invasive primary operable BC (n = 1902), with immunohistochemical expression of a panel of biomarkers (n = 31) formed the material of this study. Decision tree algorithm was computed using WEKA software, utilising quantitative biomarkers’ expression and the absence/presence of distant metastases. Fifteen biomarkers were significantly associated with DM, with six temporal subgroups characterised based on time to development of DM ranging from <1 to >15 years of follow-up. Of these 15 biomarkers, 10 had a significant expression pattern where Ki67LI, HER2, p53, N-cadherin, P-cadherin, PIK3CA and TOMM34 showed significantly higher expressions with earlier development of DM. In contrast, higher expressions of ER, PR and BCL2 were associated with delayed occurrence of DM. DM prediction algorithm was built utilising cases informative for the 15 significant markers. Four risk groups of patients were characterised. Three markers p53, HER2 and BCL2 predicted the probability of DM, based on software-generated cut-offs, with a precision rate of 81.1 % for positive predictive value and 77.3 %, for the negative predictive value. This algorithm reiterates the reported prognostic values of these three markers and underscores their central biological role in BC progression. Further independent validation of this pruned panel of biomarkers is therefore warranted.

JournalBreast Cancer Research and Treatment
Journal citation151 (2), pp. 325-333
ISSN0167-6806
Year2015
PublisherSpringer
Digital Object Identifier (DOI)doi:10.1007/s10549-015-3406-3
Publication dates
Published08 May 2015

Related outputs

Combining Clustering and Classification Ensembles: A Novel Pipeline to Identify Breast Cancer Profiles
Agrawal, U., Soria, D., Wagner, C., Garibaldi, J.M., Ellis, I.O., Bartlett, J.M.S., Cameron, D., Rakha, E.A. and Green, A.R. 2019. Combining Clustering and Classification Ensembles: A Novel Pipeline to Identify Breast Cancer Profiles. Artificial Intelligence in Medicine. 97, pp. 27-37. doi:10.1016/j.artmed.2019.05.002

Fuzzy Integral Driven Ensemble Classification using A Priori Fuzzy Measures
Agrawal, U., Wagner, C., Garibaldi, J.M. and Soria, D. 2019. Fuzzy Integral Driven Ensemble Classification using A Priori Fuzzy Measures. International Conference on Fuzzy Systems (FUZZ-IEEE 2019). 23 - 26 Jun 2019 IEEE .

The combined expression of solute carriers is associated with a poor prognosis in highly proliferative ER+ breast cancer
El Ansari, R., Craze, M.L., Alfarsi, L., Soria, D., Diez-Rodriguez, M., Nolan, C.C., Ellis, I.O., Rakha, E.A. and Green, A.R. 2019. The combined expression of solute carriers is associated with a poor prognosis in highly proliferative ER+ breast cancer. Breast Cancer Research and Treatment. 175 (1), pp. 27-38. doi:10.1007/s10549-018-05111-w

An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA
Liu, J., Xu, B., Zheng, C., Gong, Y., Garibaldi, J.M., Soria, D., Green, A., Ellis, I.O., Zou, W. and Qiu, G. 2019. An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA. IEEE Transactions on Medical Imaging. 38 (2), pp. 617-628. doi:10.1109/TMI.2018.2868333

Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom
Figueredo, G.P., Agrawal, U., Mase, J.M.M., Mesgarpour, M., Wagner, C., Soria, D., Garibaldi, J.M., Siebers, P.O. and John, R.I. 2018. Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom. IEEE Transactions on Intelligent Transportation Systems. Advanced online publication. doi:10.1109/TITS.2018.2875343

Interpretability and Complexity of Design in the Creation of Fuzzy Logic Systems - A User Study
Razak, T.R., Garibaldi, J.M., Wagner, C., Pourabdollah, A. and Soria, D. 2018. Interpretability and Complexity of Design in the Creation of Fuzzy Logic Systems - A User Study. 2018 IEEE Symposium Series on Computational Intelligence. Bengaluru, India 18 - 21 Nov 2018 IEEE . doi:10.1109/SSCI.2018.8628924

Comparison of Fuzzy Integral-Fuzzy Measure based Ensemble Algorithms with the State-of-the-art Ensemble Algorithms
Agrawal, U., Pinar, A.J., Wagner, C., Havens, T.C., Soria, D. and Garibaldi, J.M. 2018. Comparison of Fuzzy Integral-Fuzzy Measure based Ensemble Algorithms with the State-of-the-art Ensemble Algorithms. Medina, J., Ojeda-Aciego, M., Verdegay, J.L., Perfilieva, I., Bouchon-Meunier, B. and Yager, R.R. (ed.) 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems. Cadiz, Spain 11 - 15 Jun 2018 Springer. doi:10.1007/978-3-319-91479-4

Development of a pre-operative scoring system for predicting risk of post-operative paediatric cerebellar mutism syndrome
Liu, J.-F., Dineen, R.A., Avula, S., Chambers, T., Dutta, M., Jaspan, T., MacArthur, D.C., Howarth, S., Soria, D., Quinlan, P., Harave, S., Ong, C.C., Mallucci, C.L., Kumar, R., Pizer, B. and Walker, D.A. 2018. Development of a pre-operative scoring system for predicting risk of post-operative paediatric cerebellar mutism syndrome. British Journal of Neurosurgery. 32 (1), pp. 18-27. doi:10.1080/02688697.2018.1431204

A multicentre integration of a computer-led follow-up of prostate cancer is valid and safe
Salem, H., Caddeo, G., McFarlane, J., Patel, K., Cochrane, L., Soria, D., Henley, M. and Lund, J. 2018. A multicentre integration of a computer-led follow-up of prostate cancer is valid and safe. BJU international. 122 (3), pp. 418-426. doi:10.1111/bju.14157

MYC regulation of Glutamine-Proline regulatory axis is key in Luminal B breast cancer
Craze, M.L., Cheung, H., Jewa, N., Coimbra, N.D.M., Soria, D., El-Ansari, R., Aleskandarany, M.A., Cheng, K.W., Diez-Rodriguez, M., Nolan, C.C., Ellis, I.O., Rakha, E. and Green, A.R. 2018. MYC regulation of Glutamine-Proline regulatory axis is key in Luminal B breast cancer. British Journal of Cancer. 118 (2), pp. 258-265. doi:10.1038/bjc.2017.387

Interpretability indices for hierarchical fuzzy systems
Razak, T.R., Garibaldi, J.M., Wagner, C., Pourabdollah, A. and Soria, D. 2017. Interpretability indices for hierarchical fuzzy systems. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017). Naples, Italy 09 - 12 Jul 2017 IEEE . doi:10.1109/FUZZ-IEEE.2017.8015616

Validation of a quantifier-based fuzzy classification system for breast cancer patients on external independent cohorts
Soria, D. and Garibaldi, J.M. 2016. Validation of a quantifier-based fuzzy classification system for breast cancer patients on external independent cohorts. IEEE International Conference on Machine Learning and Applications (ICMLA2016). Anaheim, California, USA 18 - 20 Dec 2016 IEEE . doi:10.1109/ICMLA.2016.0101

Nottingham Prognostic Index Plus: Validation of a clinical decision making tool in breast cancer in an independent series
Green, A.R., Soria, D., Stephen, J., Powe, D.G., Nolan, C.C., Kunkler, I., Thomas, J., Kerr, G.R., Jack, W., Cameron, D., Piper, T., Ball, G.R., Garibaldi, J.M., Rakha, E.A., Bartlett, J.M.S. and Ellis, I.O. 2016. Nottingham Prognostic Index Plus: Validation of a clinical decision making tool in breast cancer in an independent series. The Journal of Pathology: Clinical Research. 1 (2), pp. 32-40. doi:10.1002/cjp2.32

Illness Beliefs Predict Mortality in Patients with Diabetic Foot Ulcers
Vedhara, K., Dawe, K., Miles, J.N.V., Wetherell, M.A., Cullum, N., Dayan, C., Drake, N., Price, P., Tarlton, J., Weinman, J., Day, A., Campbell, R., Reps, J. and Soria, D. 2016. Illness Beliefs Predict Mortality in Patients with Diabetic Foot Ulcers. PLoS ONE. 11 (4), p. e0153315. doi:10.1371/journal.pone.0153315

Nottingham prognostic index plus (NPI+) predicts risk of distant metastases in primary breast cancer
Green, A.R., Soria, D., Powe, G., Nolan, C.C., Aleskandarany, N.M., Szász, M.A., Tőkés, A.M., Ball, G.R., Garibaldi, J.M., Rakha, E.A., Kulka, J. and Ellis, I.O. 2016. Nottingham prognostic index plus (NPI+) predicts risk of distant metastases in primary breast cancer. Breast Cancer Research and Treatment. 157 (1), pp. 65-75. doi:10.1007/s10549-016-3804-1

KI67 and DLX2 predict increased risk of metastasis formation in prostate cancer–a targeted molecular approach
Green, W.J.F., Ball, G., Hulman, G., Johnson, C., Van Schalwyk, G., Ratan, H.L., Soria, D., Garibaldi, J.M., Parkinson, R., Hulman, J., Rees, R. and Powe, D.G. 2016. KI67 and DLX2 predict increased risk of metastasis formation in prostate cancer–a targeted molecular approach. British Journal of Cancer. 115 (2), pp. 236-242. doi:10.1038/bjc.2016.169

Cancer subtype identification pipeline: A classifusion approach
Agrawal, U., Soria, D. and Wagner, C. 2016. Cancer subtype identification pipeline: A classifusion approach. Evolutionary Computation (CEC), 2016 IEEE Congress on. 24 - 29 Jul 2016 IEEE . doi:10.1109/CEC.2016.7744150

Practical detection of a definitive biomarker panel for Alzheimer’s Disease; comparisons between plasma and cerebrospinal fluid
Richens, J.L., Vere, K.-A., Light, R.A., Soria, D., Garibaldi, J.M., Smith, A.D., Warden, D., Wilcock, G., Bajaj, N., Morgan, K. and O’Shea, P. 2014. Practical detection of a definitive biomarker panel for Alzheimer’s Disease; comparisons between plasma and cerebrospinal fluid. International Journal of Molecular Epidemiology and Genetics. 5 (2), pp. 53-70.

Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs
Reps, J.M., Aickelin, U., Garibaldi, J.M., Soria, D., Gibson, J.E. and Hubbard, R.B. 2014. Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs. Drug Safety. 37 (3), pp. 163-170. doi:10.1007/s40264-014-0137-z

A Novel Semisupervised Algorithm for Rare Prescription Side Effect Discovery
Reps, J.M., Garibaldi, J.M., Aickelin, U., Soria, D., Gibson, J.E. and Hubbard, R.B. 2014. A Novel Semisupervised Algorithm for Rare Prescription Side Effect Discovery. IEEE Journal of Biomedical and Health Informatics. 18 (2), pp. 537-547. doi:10.1109/JBHI.2013.2281505

Guest Editorial: Data Mining in Bioinformatics
Reps, J.M., Garibaldi, J.M., Aickelin, U., Soria, D., Gibson, J.E. and Hubbard, R.B. 2014. Guest Editorial: Data Mining in Bioinformatics. IEEE Journal of Biomedical and Health Informatics. 18 (2), p. 483. doi:10.1109/JBHI.2014.2306988

Nottingham Prognostic Index Plus (NPI+): A Modern Clinical Decision Making Tool in Breast Cancer
Rakha, E., Soria, D., Green, A.R., Lemetre, C., Powe, D.G., Nolan, C.C., Garibaldi, J.M., Ball, G.R. and Ellis, I.O. 2014. Nottingham Prognostic Index Plus (NPI+): A Modern Clinical Decision Making Tool in Breast Cancer. British Journal of Cancer. 110 (7), pp. 1688-1697. doi:10.1038/bjc.2014.120

A methodology for automatic classification of breast cancer immunohistochemical data using semi-supervised Fuzzy c-Means
Lai, D.T.C., Garibaldi, J.M., Soria, D. and Roadknight, C.M. 2014. A methodology for automatic classification of breast cancer immunohistochemical data using semi-supervised Fuzzy c-Means. Central European Journal of Operations Research. 22 (3), pp. 475-499. doi:10.1007/s10100-013-0318-3

Permalink - https://westminsterresearch.westminster.ac.uk/item/9z882/markers-of-progression-in-early-stage-invasive-breast-cancer-a-predictive-immunohistochemical-panel-algorithm-for-distant-recurrence-risk-stratification


Share this
Tweet
Email