Breast cancer prognostic classifiers: combining clinical with molecular profiles

Muftah, Abir A. Abdelhadi (2017) Breast cancer prognostic classifiers: combining clinical with molecular profiles. PhD thesis, University of Nottingham.

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Background and aims

While current prognostic tools for breast cancer (BC) provide valuable information on behaviour and outcome, there are growing concerns that these parameters are not sufficient to reflect the degree of heterogeneity or to guide management decisions at individual patient level. Therefore, further refinement of the existing prognostic tools is needed. The advent of multi-parameter gene signatures has increased our hope of refining BC prognostic classification; however, their cost and restricted application to certain subgroups of BC limit their clinical usefulness. Molecular taxonomy of BC using intrinsic gene sets has not only improved our understanding of BC biology, but has also provided important prognostic information. Yet, integration of this molecular classification with the clinical parameters remains a challenging task.

Our group has recently developed a prognostic tool that incorporates the molecular features of BC with the well-established prognostic morphological variables; the Nottingham Prognostic Index Plus (NPI+) that aimed at overcoming the limitations of using different molecular and clinicopathological prognostic parameters separately. However, NPI+ currently has limitations and needs further refinement to be applicable to BC management in routine practice. Therefore, this study aimed to investigate some relevant potential prognostic markers that can improve BC prognostic classification, in the context of combined molecular and morphological prognostic BC taxonomy, and to further refine the NPI+ to improve its prognostic value, while addressing some issues related to its components and performance. The study included four main objectives. The first was the integration of the proliferation biomarker Ki67 and addressing some technical issues related to its prognostic value and application in routine practice. The second was the evaluation of a high-throughput proteomic technique, reverse phase protein array (RPPA), for its potential use as a single-step quantification method of multiple proteins. The third aim proposed to determine the biological relevance of BC expressing low levels of oestrogen receptor (ER) using techniques at both transcriptome and protein levels. The fourth aim was to investigate the incorporation of a novel cancer stem cell (CSC) biomarker as a potential prognostic variable that can refine BC classification.


In this study, five large primary invasive BC cohorts were investigated at both transcriptome and protein levels. Tissue microarray (TMA) and whole tissue section (WTS) were used. Molecular techniques used in this study included immunohistochemistry (IHC), western blot (WB), different protein and RNA extraction techniques, laser capture microdissection (LCM), RPPA, real time-polymerase chain reaction (RT-PCR) and RNAscope.


Although Ki67 expression can be examined using both WTS and TMA, the assessment of Ki67 in whole sections is preferred and using multiple or larger TMA cores has to be explored as an alternative to WTS. When assessing Ki67 in TMAs in BC, a cut-point of 20% appears to be optimum in concordance with WTS and patients outcome. However, our results support the use of Ki67 as a continuous variable, particularly in the stratification of patients into prognostic groups, either using TMA or WTS assessment (Muftah et al., 2017). Using the MIB-1 clone with different optimisation conditions is associated with cytoplasmic/membranous reactivity. In this regard, it is recommended that different anti-Ki67 clones could be used for clearer staining. The results show that Ki67 can successfully replace mitotic frequency in the updated prognostic index, NPI+.

Unlocking FFPE tissue lysates utilising RPPA is a reliable method for protein quantification. Data produced by this high-throughput technique could be used in concurrent analyses of protein profiles in a large number of clinical cases. Accordingly, RPPA could be consistently used in molecular classification of BC, such as the NPI+ (Negm et al., 2016). Adding Ki67 to the cluster using the RPPA technique improved molecular classification with reassignment of 16% of the unclassified patients.

Investigating ER in BC at both levels (transcriptome and protein) shows that its expression is essentially bimodal (Muftah et al., 2016). Additionally, our results question the advantage of hormonal therapy in the low ER (<10%) subgroup; where, in the study cohort, nearly half (42.2%) of the Core Needle Biopsy (CNB) cases showed WTS negative. There is strong agreement between the IHC and in situ RNAscope results, particularly in focal heterogeneous ER staining areas. This study provides scientific evidence that the actual ER cut-off seems to be 10%. Furthermore, this research supports the clinical importance of B-cell specific Moloney leukaemia virus insertion site-1 (Bmi-1) as a favourable prognostic biomarker in BC and its ability to refine the CD44/CD24 phenotypes as well as slow proliferating tumours into prognostically relevant subgroups.


This study presented data enabling the updating and evaluation of existing prognostic parameters and indices using promising biomarkers and high-throughput techniques, while combining molecular and clinical variables to stratify BC patients into relevant prognostic subgroups. Further investigation of the potential and refinement of the existing BC prognostic parameters is needed in order to allow more precise BC classifications that can predict patient outcomes and potential response to therapy.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Ellis, Ian
Rakha, Emad
Green, Andrew
Keywords: Breast cancer prognosis; Breast cancer classification; Biomarkers; Prognostic tools; Molecular classification
Subjects: W Medicine and related subjects (NLM Classification) > WP Gynecology
Faculties/Schools: UK Campuses > Faculty of Medicine and Health Sciences > School of Medicine
Item ID: 45547
Depositing User: Muftah, Abir A Abdelhadi
Date Deposited: 25 Apr 2018 13:02
Last Modified: 15 Oct 2021 04:30

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