A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients

Soria, Daniele; Garibaldi, Jonathan M.; Ambrogi, Federico; Green, Andrew; Powe, Des G.; Rakha, Emad A.; Macmillan, R. Douglas; Blamey, R.W.; Ball, Graham; Lisboa, Paulo J. G.; Etchells, Terence A.; Boracchi, Patrizia; Biganzoli, Elia; Ellis, Ian O.

doi:10.1016/j.compbiomed.2010.01.003

Cited by 55 publications

(115 citation statements)

References 36 publications

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“…In particular, Abd El-Rehim et al [46] identified and characterised five breast cancer classes, with a sixth group of only four cases also identified but considered too small for further detailed assessment. Subsequently, we investigated the stability of the proposed classification across different case sets, assay methods and data analysis procedures by investigating the effects of multiple hard-clustering methods on a breast cancer dataset [21,51]. This led to a clear definition of cancer classes, but left many patients in a mixed-classified or unclassified group.…”

Section: Discussionmentioning

confidence: 99%

“…Estrogen Receptor (ER) While 25 protein markers were originally used to derive the biological tumour classes presented by Soria et al [21], this was subsequently reduced down to the above mentioned ten as the minimum number of markers compatible with retaining usefulness for clinical decision making [26]. The minimised panel of ten protein biomarkers has been recently used to identify core classes which are clinically meaningful and well-characterised [35].…”

Section: Algorithm Specificationmentioning

confidence: 99%

“…In previous work [21], we applied different clustering algorithms and, through a consensus clustering approach, we identified novel cancer subtypes. This was done at the expense of not classifying a large proportion (38%) of patients.…”

Section: Introductionmentioning

confidence: 99%

“…were derived using clinical expert knowledge, considering patient outcomes and response to treatments. The under-lying classification was firstly proposed by Soria et al [21], where different clustering techniques were combined using a consensus approach and six clinically relevant groups were identified. The limitation of the six-classes approach reported by Soria et al was the high number of patients who presented mixed class characteristics and therefore remained unclassified.…”

Section: Introductionmentioning

confidence: 99%

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A quantifier-based fuzzy classification system for breast cancer patients

Soria

Garibaldi

Green

et al. 2013

Artificial Intelligence in Medicine

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. Consensus between unsupervised classification algorithms has been successfully used to categorise patients into these specific groups, but often at the expenses of not classifying the whole set. It is known that fuzzy methodologies can provide linguistic based classification rules. The objective of this study was to investigate the use of fuzzy methodologies to create an easy to interpret set of classification rules, capable of placing the large majority of patients into one of the specified groups. Methods and materials:In this paper, we extend a data-driven fuzzy rule-based system for classification purposes (called 'fuzzy quantification subsethood-based algorithm') and combine it with a novel class assignment procedure. The whole approach is then applied to a well characterised breast cancer dataset consisting of ten protein markers for over 1,000 patients to refine previously identified groups and to present clinicians with a linguistic ruleset. A range of statistical approaches were used to compare the obtained classes to previously obtained groupings and to assess the proportion of unclassified patients.Results: A rule set was obtained from the algorithm which features one classification rule per class, using labels of High, Low or Omit for each biomarker, to determine the most appropriate class for each patient. When applied to the whole set of patients, the distribution of the obtained classes had an agreement of 0.9 when assessed using Kendall's Tau with the original reference class distribution. In doing so, only 38 patients out of 1,073 remain unclassified, representing a more clinically usable class assignment algorithm. Conclusion:The fuzzy algorithm provides a simple to interpret, linguistic rule set which classifies over 95% of breast cancer patients into one of seven clinical groups.

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Section: Discussionmentioning

confidence: 99%

Section: Algorithm Specificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A quantifier-based fuzzy classification system for breast cancer patients

Soria

Garibaldi

Green

et al. 2013

Artificial Intelligence in Medicine

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“…Structure of clusters is quantified by a variety of methods reported in literature [7], [8], [9]. Typically, the validity of clusters is evaluated by either the dispersion of data each cluster contains, or the data separation between clusters, or both [8].…”

Section: Introductionmentioning

confidence: 99%

Modeling Tumor Growth for Kidney Cancer Based on Nuclei Clusters of Pathology Slides

Saribudak¹,

Dong²,

Hsieh³

et al. 2016

IJET

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Nottingham Prognostic Index Plus: Validation of a clinical decision making tool in breast cancer in an independent series

Green

Soria

Stephen

et al. 2016

The Journal of Pathology CR

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The Nottingham Prognostic Index Plus (NPI+) is a clinical decision making tool in breast cancer (BC) that aims to provide improved patient outcome stratification superior to the traditional NPI. This study aimed to validate the NPI+ in an independent series of BC. Eight hundred and eighty five primary early stage BC cases from Edinburgh were semi‐quantitatively assessed for 10 biomarkers [Estrogen Receptor (ER), Progesterone Receptor (PgR), cytokeratin (CK) 5/6, CK7/8, epidermal growth factor receptor (EGFR), HER2, HER3, HER4, p53, and Mucin 1] using immunohistochemistry and classified into biological classes by fuzzy logic‐derived algorithms previously developed in the Nottingham series. Subsequently, NPI+ Prognostic Groups (PGs) were assigned for each class using bespoke NPI‐like formulae, previously developed in each NPI+ biological class of the Nottingham series, utilising clinicopathological parameters: number of positive nodes, pathological tumour size, stage, tubule formation, nuclear pleomorphism and mitotic counts. Biological classes and PGs were compared between the Edinburgh and Nottingham series using Cramer's V and their role in patient outcome prediction using Kaplan–Meier curves and tested using Log Rank. The NPI+ biomarker panel classified the Edinburgh series into seven biological classes similar to the Nottingham series (p > 0.01). The biological classes were significantly associated with patient outcome (p < 0.001). PGs were comparable in predicting patient outcome between series in Luminal A, Basal p53 altered, HER2+/ER+ tumours (p > 0.01). The good PGs were similarly validated in Luminal B, Basal p53 normal, HER2+/ER− tumours and the poor PG in the Luminal N class (p > 0.01). Due to small patient numbers assigned to the remaining PGs, Luminal N, Luminal B, Basal p53 normal and HER2+/ER− classes could not be validated. This study demonstrates the reproducibility of NPI+ and confirmed its prognostic value in an independent cohort of primary BC. Further validation in large randomised controlled trial material is warranted.

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A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients

Cited by 55 publications

References 36 publications

A quantifier-based fuzzy classification system for breast cancer patients

A quantifier-based fuzzy classification system for breast cancer patients

Modeling Tumor Growth for Kidney Cancer Based on Nuclei Clusters of Pathology Slides

Nottingham Prognostic Index Plus: Validation of a clinical decision making tool in breast cancer in an independent series

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