Aspects of Endocrine Therapy in Primary Breast Cancer. Risk Profiling and Adherence Perspectives for Improving Tailored Adjuvant Treatment.

Abstract: Breast cancers are heterogeneous tumours. Prognostic markers are needed to better profile the patient´s risk, and predictive markers to indicate the expected benefit of systemic treatment. Using gene expression analyses, breast tumours can be divided into intrinsic subtypes. Immunohistochemical (IHC) markers (oestrogen and progesterone receptors (ER and PR), human epidermal growth factor receptor 2 (HER2), proliferation marker (Ki67)) and histological grade (HG) are used to classify the tumours into the corresponding surrogate subtypes. The ER-positive/HER2-negative (ER+/HER2−) tumours are divided into Luminal A and Luminal B, which are associated with different prognoses. Patients with Luminal B tumours are generally recommended adjuvant chemotherapy in addition to endocrine therapy. The agreement between the subtyping by gene expression and surrogate classification, is not perfect. Prosigna® test provides the tumour subtype and assigns the patient a relapse risk score (Risk of Recurrence (ROR) score) by gene expression analyses, predicting 10 years risk of distant metastases. This test is currently in clinical use for postmenopausal women only, and assumes treatment with endocrine adjuvant drugs. The adherence to these drugs is however known to be poor, mostly because of the side-effects. The immune system and its complex components, such as tumour-infiltrating lymphocytes (TILs) have so far proven to be important markers for certain subtypes of breast cancer, although their value in ER+/HER2− tumours is less known.Study I: Data on the prescribed and collected endocrine drugs from the Swedish Prescribed Drug Register for patients in Region Jönköping County, was retrived. Adherence to the therapy was calculated after 3 (n=445) and 5 years (n=248), defined as collecting over or equal to 80% of the prescribed drugs during the time periods, respectively. The results showed that adherence was over 90% after both 3 and 5 years.Study II: Patient and tumour data from over 2,000 patients included in the SCAN-B project, in which primary tumours were defined by PAM50 subtypes, was retrieved. The ER+/HER2− tumours were divided into Luminal ASurrogate Classification (SC) and Luminal BSC according to three surrogate algorithms. The agreement between luminal subtyping by gene expression and surrogate markers showed poor results. The highest agreement was 70% for the classification mainly based on HG. By combing HG and Ki67, nine subgroups were generated, and among these, six groups (51% of the cohort) were identified having >90% Luminal APAM50 tumours.Study III: Tumour blocks from primary breast cancer tissues were collected from patients that participated in the SBII:2pre study, in which premenopausal women were randomised between 2 years of adjuvant tamoxifen or no systemic treatment. Available follow-up data was over 30 years. TILs were assessed on whole tumour sections. The results showed that a high proportion of TILs (≥50%) was associated with better prognosis in all breast cancer subtypes. Furthermore, the benefit of tamoxifen was higher in patients whose tumours had low infiltration (<50%) of lymphocytes.Study IV: Gene expression analysis (by NanoString Breast Cancer 360™ assay) of the primary tumours from patients in the SBII:2pre study, was conducted. This assigned each tumour a PAM50 intrinsic subtype and the corresponding patient a relapse risk score (ROR score). Surrogate classification according to St. Gallen 2013 was also performed. Both PAM50 and ROR score were prognostic. After 10 years of follow-up, re-classification of Luminal BSC tumours into Luminal APAM50 was associated with improved prognosis as compared to those uniformly classified as Luminal B, and benefit from tamoxifen could only be demonstrated in patients with Luminal APAM50 tumours.In conclusion, the results presented in this thesis showed that risk profiling of patients with primary breast cancer can be performed using a combination of gene expression and IHC markers. These markers and tests are pieces of the puzzle to be put together for each unique patient, and the pieces should be given different weights in order to tailor adjuvant treatment. Moreover, the results indicated that good adherence to endocrine therapy is possible to achieve

Assessment of histopathological methods of evaluating response to neoadjuvant therapy in oesophageal and gastric adenocarcinoma

Upper gastrointestinal tract (GIT) cancers usually receive neoadjuvant therapy prior to surgery. The histological assessment of this response and if this can be predicted on the pre-treatment biopsy are the subject of this thesis. The first study assessed the inter- and intra-observer variation amongst pathologists in evaluating the degree of regression using the Mandard scoring system. The results showed that the reproducibility of this system was only fair to moderate in both cases of inter- and intra-observer testing. The second study examined the levels of expression of selected tumour markers before and after neoadjuvant chemotherapy. These included markers monitoring apoptosis (p53 and bcl-2), proliferation (Ki-67), angio- and lymphangio-genesis (VEGF, CD-31 and LYVE-1). The levels of expression in these markers were measured in the pre-treatment biopsies, to monitor if they could predict the response to neoadjuvant therapy. It was found that when the panel of chosen markers being used together, delivered a much higher power of prediction rather than adopting only one marker, where the collective power of prediction was 80.6%, whereas individually, the power of prediction ranged between 24.6% (VEGF) and 60.7% (Ki-67). The third study explored the use of digital image analysis in assessing the response to neoadjuvant therapy. It was found that while this technique paralleled the Mandard scoring system, it delivered a more objective and reproducible assessment. On the basis of these results I suggest that image analysis should be used to assess tumour regression especially in the context of clinical trials. In this retrospective study it has been shown that the pre-treatment biopsy can predict the degree of regression

The effect of the stromal component of breast tumours on prediction of clinical outcome using gene expression microarray analysis

INTRODUCTION: The aim of this study was to examine the effect of the cellular composition of biopsies on the error rates of multigene predictors of response of breast tumours to neoadjuvant adriamycin and cyclophosphamide (AC) chemotherapy. MATERIALS AND METHODS: Core biopsies were taken from primary breast tumours of 43 patients prior to AC, and subsequent clinical response was recorded. Post-chemotherapy (day 21) samples were available for 16 of these samples. Frozen sections of each core were used to estimate the proportion of invasive cancer and other tissue components at three levels. Transcriptional profiling was performed using a cDNA array containing 4,600 elements. RESULTS: Twenty-three (53%) patients demonstrated a 'good' and 20 (47%) a 'poor' clinical response. The percentage invasive tumour in core biopsies collected from these patients varied markedly. Despite this, agglomerative clustering of sample expression profiles showed that almost all biopsies from the same tumour aggregated as nearest neighbours. SAM (significance analysis of microarrays) regression analysis identified 144 genes which distinguished high- and low-percentage invasive tumour biopsies at a false discovery rate of not more than 5%. The misclassification error of prediction of clinical response using microarray data from pre-treatment biopsies (on leave-one-out cross-validation) was 28%. When prediction was performed on subsets of samples which were more homogeneous in their proportions of malignant and stromal cells, the misclassification error was considerably lower (8%–13%, p < 0.05 on permutation). CONCLUSION: The non-tumour content of breast cancer samples has a significant effect on gene expression profiles. Consideration of this factor improves accuracy of response prediction by expression array profiling. Future gene expression array prediction studies should be planned taking this into account

Improving biomarker assessment in breast pathology

The accuracy of prognostic and therapy-predictive biomarker assessment in breast tumours is crucial for management and therapy decision in patients with breast cancer. In this thesis, biomarkers used in clinical practice with emphasise on Ki67 and HER2 were studied using several methods including immunocytochemistry, in situ hybridisation, gene expression assays and digital image analysis, with the overall aim to improve routine biomarker evaluation and clarify the prognostic potential in early breast cancer. In paper I, we reported discordances in biomarker status from aspiration cytology and paired surgical specimens from breast tumours. The limited prognostic potential of immunocytochemistry-based Ki67 scoring demonstrated that immunohistochemistry on resected specimens is the superior method for Ki67 evaluation. In addition, neither of the methods were sufficient to predict molecular subtype. Following this in paper II, biomarker agreement between core needle biopsies and subsequent specimens was investigated, both in the adjuvant and neoadjuvant setting. Discordances in Ki67 and HER2 status between core biopsies and paired specimens suggested that these biomarkers should be re-tested on all surgical breast cancer specimens. In paper III, digital image analysis using a virtual double staining software was used to compare methods for assessment of proliferative activity, including mitotic counts, Ki67 and the alternative marker PHH3, in different tumour regions (hot spot, invasive edge and whole section). Digital image analysis using virtual double staining of hot spot Ki67 outperformed the alternative markers of proliferation, especially in discriminating luminal B from luminal A tumours. Replacing mitosis in histological grade with hot spot-scored Ki67 added significant prognostic information. Following these findings, the optimal definition of a hot spot for Ki67 scoring using virtual double staining in relation to molecular subtype and outcome was investigated in paper IV. With the growing evidence of global scoring as a superior method to improve reproducibility of Ki67 scoring, a different digital image analysis software (QuPath) was also used for comparison. Altogether, we found that automated global scoring of Ki67 using QuPath had independent prognostic potential compared to even the best virtual double staining hot spot algorithm, and is also a practical method for routine Ki67 scoring in breast pathology. In paper V, the clinical value of HER2 status was investigated in a unique trastuzumab-treated HER2-positive cohort, on the protein, mRNA and DNA levels. The results demonstrated that low levels of ERBB2 mRNA but neither HER2 copy numbers, HER2 ratio nor ER status, was associated with risk of recurrence among anti-HER2 treated breast cancer patients. In conclusion, we have identified important clinical aspects of Ki67 and HER2 evaluation and provided methods to improve the prognostic potential of Ki67 using digital image analysis. In addition to protein expression of routine biomarkers, mRNA levels by targeted gene expression assays may add further prognostic value in early breast cance

Random Forest as a tumour genetic marker extractor

Identifying tumour genetic markers is an essential task for biomedicine. In this thesis, we analyse a dataset of chromosomal rearrangements of cancer samples and present a methodology for extracting genetic markers from this dataset by using a Random Forest as a feature selection tool

Integration of Genome Scale Data for Identifying New Biomarkers in Colon Cancer: Integrated Analysis of Transcriptomics and Epigenomics Data from High Throughput Technologies in Order to Identifying New Biomarkers Genes for Personalised Targeted Therapies for Patients Suffering from Colon Cancer

Colorectal cancer is the third most common cancer and the leading cause of cancer deaths in Western industrialised countries. Despite recent advances in the screening, diagnosis, and treatment of colorectal cancer, an estimated 608,000 people die every year due to colon cancer. Our current knowledge of colorectal carcinogenesis indicates a multifactorial and multi-step process that involves various genetic alterations and several biological pathways. The identification of molecular markers with early diagnostic and precise clinical outcome in colon cancer is a challenging task because of tumour heterogeneity. This Ph.D.-thesis presents the molecular and cellular mechanisms leading to colorectal cancer. A systematical review of the literature is conducted on Microarray Gene expression profiling, gene ontology enrichment analysis, microRNA and system Biology and various bioinformatics tools. We aimed this study to stratify a colon tumour into molecular distinct subtypes, identification of novel diagnostic targets and prediction of reliable prognostic signatures for clinical practice using microarray expression datasets. We performed an integrated analysis of gene expression data based on genetic, epigenetic and extensive clinical information using unsupervised learning, correlation and functional network analysis. As results, we identified 267-gene and 124-gene signatures that can distinguish normal, primary and metastatic tissues, and also involved in important regulatory functions such as immune-response, lipid metabolism and peroxisome proliferator-activated receptors (PPARs) signalling pathways. For the first time, we also identify miRNAs that can differentiate between primary colon from metastatic and a prognostic signature of grade and stage levels, which can be a major contributor to complex transcriptional phenotypes in a colon tumour

Characterising the tumour morphological response to therapeutic intervention:an ex vivo model

In cancer, morphological assessment of histological tissue samples is a fundamental part of both diagnosis and prognosis. Image analysis offers opportunities to support that assessment through quantitative metrics of morphology. Generally, morphometric analysis is carried out on two dimensional tissue section data and so only represents a small fraction of any tumour. We present a novel application of three-dimensional (3D) morphometrics for 3D imaging data obtained from tumours grown in a culture model. Minkowski functionals, a set of measures that characterise geometry and topology in n-dimensional space, are used to quantify tumour topology in the absence of and in response to therapeutic intervention. These measures are used to stratify the morphological response of tumours to therapeutic intervention. Breast tumours are characterised by estrogen receptor (ER) status, human epidermal growth factor receptor (HER)2 status and tumour grade. Previously, we have shown that ER status is associated with tumour volume in response to tamoxifen treatment ex vivo. Here, HER2 status is found to predict the changes in morphology other than volume as a result of tamoxifen treatment ex vivo. Finally, we show the extent to which Minkowski functionals might be used to predict tumour grade.Minkowski functionals are generalisable to any 3D data set, including in vivo and cellular systems. This quantitative topological analysis can provide a valuable link among biomarkers, drug intervention and tumour morphology that is complementary to existing, non-morphological measures of tumour response to intervention and could ultimately inform patient treatment

How many diseases is triple negative breast cancer; the protagonism of the immune microenvironment

Triple negative breast cancer (TNBC) is a type of breast cancer (BC) that does not express the oestrogen and the progesterone receptors and the human epidermal growth factor receptor type 2 (HER2). Since there are no positive markers to reliably classify TNBC, these tumours are not yet treated with targeted therapies. Perhaps for this reason they are the most aggressive form of breast carcinomas. However, the clinical observation that these patients do not carry a uniformly dismal prognosis, coupled with data coming from pathology and epidemiology, suggests that this negative definition is not capturing a single clinical entity, but several. We critically evaluate this evidence in this paper, reviewing clinical and epidemiological data and new studies that aim to subclassify TNBC. Moreover, evidence on the role of tumour infiltrating lymphocytes (TILs) on TNBC progression, response to chemotherapy and patient outcome have been published. The heterogeneity, observed even at TILs level, highlights the idea that TNBC is much more than a single disease with a unique treatment. The exploration of the immune environment present at the tumour site could indeed help in answering the question 'How many diseases is TNBC' and will help to define prognosis and eventually develop new therapies, by stimulating the immune effector cells or by inhibiting immunological repressor molecules. In this review, we focus on the prospect of the patient's diverse immune signatures within the tumour as potential biomarkers and how they could be modulated to fight the disease.publishersversionpublishe

Tamoxifen resistance in early breast cancer: statistical modelling of tissue markers to improve risk prediction

BACKGROUND: For over two decades, the Nottingham Prognostic Index (NPI) has been used in the United Kingdom to calculate risk scores and inform management about breast cancer patients. It is derived using just three clinical variables - nodal involvement, tumour size and grade. New scientific methods now make cost-effective measurement of many biological characteristics of tumour tissue from breast cancer biopsy samples possible. However, the number of potential explanatory variables to be considered presents a statistical challenge. The aim of this study was to investigate whether in ER+ tamoxifen-treated breast cancer patients, biological variables can add value to NPI predictors, to provide improved prognostic stratification in terms of overall recurrence-free survival (RFS) and also in terms of remaining recurrence free while on tamoxifen treatment (RFoT). A particular goal was to enable the discrimination of patients with a very low risk of recurrence. METHODS: Tissue samples of 401 cases were analysed by microarray technology, providing biomarker data for 72 variables in total, from AKT, BAD, HER, MTOR, PgR, MAPK and RAS families. Only biomarkers screened as potentially informative (i.e., exhibiting univariate association with recurrence) were offered to the multivariate model. The multiple imputation method was used to deal with missing values, and bootstrap sampling was used to assess internal validity and refine the model. RESULTS: Neither the RFS nor RFoT models derived included Grade, but both had better predictive and discrimination ability than NPI. A slight difference was observed between models in terms of biomarkers included, and, in particular, the RFoT model alone included HER2. The estimated 7-year RFS rates in the lowest-risk groups by RFS and RFoT models were 95 and 97%, respectively, whereas the corresponding rate for the lowest-risk group of NPI was 89%. CONCLUSION: The findings demonstrate considerable potential for improved prognostic modelling by incorporation of biological variables into risk prediction. In particular, the ability to identify a low-risk group with minimal risk of recurrence is likely to have clinical appeal. With larger data sets and longer follow-up, this modelling approach has the potential to enhance an understanding of the interplay of biological characteristics, treatment and cancer recurrence. British Journal of Cancer (2010) 102, 1503 - 1510. doi:10.1038/sj.bjc.6605627 www.bjcancer.co