334 research outputs found
Raman Spectroscopy Techniques for the Detection and Management of Breast Cancer
Breast cancer has recently become the most common cancer worldwide, and with increased incidence, there is increased pressure on health services to diagnose and treat many more patients. Mortality and survival rates for this particular disease are better than other cancer types, and part of this is due to the facilitation of early diagnosis provided by screening programmes, including the National Health Service breast screening programme in the UK. Despite the benefits of the programme, some patients undergo negative experiences in the form of false negative mammograms, overdiagnosis and subsequent overtreatment, and even a small number of cancers are induced by the use of ionising radiation. In addition to this, false positive mammograms cause a large number of unnecessary biopsies, which means significant costs, both financially and in terms of clinicians' time, and discourages patients from attending further screening. Improvement in areas of the treatment pathway is also needed. Surgery is usually the first line of treatment for early breast cancer, with breast conserving surgery being the preferred option compared to mastectomy. This type of operation achieves the same outcome as mastectomy - removal of the tumour - while allowing the patient to retain the majority of their normal breast tissue for improved aesthetic and psychological results. Yet, re-excision operations are often required when clear margins are not achieved, i.e. not all of the tumour is removed. This again has implications on cost and time, and increases the risk to the patient through additional surgery.
Currently lacking in both the screening and surgical contexts is the ability to discern specific chemicals present in the breast tissue being assessed/removed. Specifically relevant to mammography is the presence of calcifications, the chemistry of which holds information indicative of pathology that cannot be accessed through x-rays. In addition, the chemical composition of breast tumour tissue has been shown to be different to normal tissue in a variety of ways, with one particular difference being a significant increase in water content. Raman spectroscopy is a rapid, non-ionising, non-destructive technique based on light scattering. It has been proven to discern between chemical types of calcification and subtleties within their spectra that indicate the malignancy status of the surrounding tissue, and differentiate between cancerous and normal breast tissue based on the relative water contents.
Furthermore, this thesis presents work aimed at exploring deep Raman techniques to probe breast calcifications at depth within tissue, and using a high wavenumber Raman probe to discriminate tumour from normal tissue predominantly via changes in tissue water content. The ability of transmission Raman spectroscopy to detect different masses and distributions of calcified powder inclusions within tissue phantoms was tested, as well as elucidating a signal profile of a similar inclusion through a tissue phantom of clinically relevant thickness. The technique was then applied to the measurement of clinically active samples of bulk breast tissue from informed and consented patients to try to measure calcifications. Ex vivo specimens were also measured with a high wavenumber Raman probe, which found significant differences between tumour and normal tissue, largely due to water content, resulting in a classification model that achieved 77.1% sensitivity and 90.8% specificity. While calcifications were harder to detect in the ex vivo specimens, promising results were still achieved, potentially indicating a much more widespread influence of calcification in breast tissue, and to obtain useful signal from bulk human tissue is encouraging in itself. Consequently, this work demonstrates the potential value of both deep Raman techniques and high wavenumber Raman for future breast screening and tumour margin assessment methods
Self-supervised deep learning for highly efficient spatial immunophenotyping
Background: Efficient biomarker discovery and clinical translation depend on the fast and accurate analytical output from crucial technologies such as multiplex imaging. However, reliable cell classification often requires extensive annotations. Label-efficient strategies are urgently needed to reveal diverse cell distribution and spatial interactions in large-scale multiplex datasets. / Methods: This study proposed Self-supervised Learning for Antigen Detection (SANDI) for accurate cell phenotyping while mitigating the annotation burden. The model first learns intrinsic pairwise similarities in unlabelled cell images, followed by a classification step to map learnt features to cell labels using a small set of annotated references. We acquired four multiplex immunohistochemistry datasets and one imaging mass cytometry dataset, comprising 2825 to 15,258 single-cell images to train and test the model. / Findings: With 1% annotations (18–114 cells), SANDI achieved weighted F1-scores ranging from 0.82 to 0.98 across the five datasets, which was comparable to the fully supervised classifier trained on 1828–11,459 annotated cells (−0.002 to −0.053 of averaged weighted F1-score, Wilcoxon rank-sum test, P = 0.31). Leveraging the immune checkpoint markers stained in ovarian cancer slides, SANDI-based cell identification reveals spatial expulsion between PD1-expressing T helper cells and T regulatory cells, suggesting an interplay between PD1 expression and T regulatory cell-mediated immunosuppression. / Interpretation: By striking a fine balance between minimal expert guidance and the power of deep learning to learn similarity within abundant data, SANDI presents new opportunities for efficient, large-scale learning for histology multiplex imaging data. / Funding: This study was funded by the Royal Marsden/ ICR National Institute of Health Research Biomedical Research Centre
Artificial intelligence in histopathology image analysis for cancer precision medicine
In recent years, there have been rapid advancements in the field of computational
pathology. This has been enabled through the adoption of digital pathology
workflows that generate digital images of histopathological slides, the publication
of large data sets of these images and improvements in computing infrastructure.
Objectives in computational pathology can be subdivided into two categories,
first the automation of routine workflows that would otherwise be performed by
pathologists and second the addition of novel capabilities. This thesis focuses on
the development, application, and evaluation of methods in this second category,
specifically the prediction of gene expression from pathology images and the
registration of pathology images among each other.
In Study I, we developed a computationally efficient cluster-based technique to
perform transcriptome-wide predictions of gene expression in prostate cancer
from H&E-stained whole-slide-images (WSIs). The suggested method
outperforms several baseline methods and is non-inferior to single-gene CNN
predictions, while reducing the computational cost with a factor of approximately
300. We included 15,586 transcripts that encode proteins in the analysis and
predicted their expression with different modelling approaches from the WSIs. In
a cross-validation, 6,618 of these predictions were significantly associated with
the RNA-seq expression estimates with FDR-adjusted p-values <0.001. Upon
validation of these 6,618 expression predictions in a held-out test set, the
association could be confirmed for 5,419 (81.9%). Furthermore, we demonstrated
that it is feasible to predict the prognostic cell-cycle progression score with a
Spearman correlation to the RNA-seq score of 0.527 [0.357, 0.665].
The objective of Study II is the investigation of attention layers in the context of
multiple-instance-learning for regression tasks, exemplified by a simulation study
and gene expression prediction. We find that for gene expression prediction, the
compared methods are not distinguishable regarding their performance, which
indicates that attention mechanisms may not be superior to weakly supervised
learning in this context.
Study III describes the results of the ACROBAT 2022 WSI registration challenge,
which we organised in conjunction with the MICCAI 2022 conference. Participating
teams were ranked on the median 90th percentile of distances between
registered and annotated target landmarks. Median 90th percentiles for eight
teams that were eligible for ranking in the test set consisting of 303 WSI pairs
ranged from 60.1 µm to 15,938.0 µm. The best performing method therefore has a
score slightly below the median 90th percentile of distances between first and
second annotator of 67.0 µm.
Study IV describes the data set that we published to facilitate the ACROBAT
challenge. The data set is available publicly through the Swedish National Data
Service SND and consists of 4,212 WSIs from 1,153 breast cancer patients.
Study V is an example of the application of WSI registration for computational
pathology. In this study, we investigate the possibility to register invasive cancer
annotations from H&E to KI67 WSIs and then subsequently train cancer detection
models. To this end, we compare the performance of models optimised with
registered annotations to the performance of models that were optimised with
annotations generated for the KI67 WSIs. The data set consists of 272 female
breast cancer cases, including an internal test set of 54 cases. We find that in this
test set, the performance of both models is not distinguishable regarding
performance, while there are small differences in model calibration
AI-enabled routine H&E image based prognostic marker for early-stage luminal breast cancer
Breast cancer (BC) grade is a well-established subjective prognostic indicator of tumour aggressiveness. Tumour heterogeneity and subjective assessment result in high degree of variability among observers in BC grading. Here we propose an objective Haematoxylin & Eosin (H&E) image-based prognostic marker for early-stage luminal/Her2-negative BReAst CancEr that we term as the BRACE marker. The proposed BRACE marker is derived from AI based assessment of heterogeneity in BC at a detailed level using the power of deep learning. The prognostic ability of the marker is validated in two well-annotated cohorts (Cohort-A/Nottingham: n = 2122 and Cohort-B/Coventry: n = 311) on early-stage luminal/HER2-negative BC patients treated with endocrine therapy and with long-term follow-up. The BRACE marker is able to stratify patients for both distant metastasis free survival (p = 0.001, C-index: 0.73) and BC specific survival (p < 0.0001, C-index: 0.84) showing comparable prediction accuracy to Nottingham Prognostic Index and Magee scores, which are both derived from manual histopathological assessment, to identify luminal BC patients that may be likely to benefit from adjuvant chemotherapy
Tumor biomarkers that identify molecular subtypes and best responders to chemotherapy in patients with PDAC
Das duktale Adenokarzinom der Bauchspeicheldrüse (PDAC) ist eine tödliche Erkrankung, die auf die derzeitigen Therapien nur begrenzt anspricht. Es hat sich gezeigt, dass die molekulare Subtypisierung von PDAC mit dem klinischen Ansprechen auf Medikamente und der Prognose der Patienten zusammenhängt. Der Classical-like Subtyp ist mit einer relativ guten Prognose verbunden, während der Basal-like/QM-PDA-Subtyp mit einer schlechten Prognose und paradoxerweise mit einem guten Ansprechen auf die Chemotherapie korreliert ist. Die Untersuchung repräsentativer Biomarker für jeden molekularen Subtyp befindet sich noch in einem frühen Stadium der Entwicklung. In dieser Studie wurde das Transkriptom Profile von PDAC mit Hilfe der Laser-Capture-Mikroskopie in chemo-naiven Tumoren verbessert und die kanonischen Subtypisierungsschemata von Moffitt, Collisson, Bailey und Notta bestätigt. GATA6, CYP3A5 und HNF1A wurden als Biomarker auf mRNA-Ebene für Tumoren des Classical-like Subtyps identifiziert und erwiesen sich als prognostische Indikatoren. Die Expression von KRT81 auf mRNA-Ebene korrelierte mit dem Moffitt Basal-like Subtyp, war aber in diesem Fall kein signifikanter prognostischer Indikator. Nach einer neoadjuvanten Chemotherapie hatten PDAC Patienten, die viel GATA6 und CYP3A5 Proteine exprimieren, tendenziell ein relativ schlechteres Ansprechen, insbesondere nach einer FFX (FOLFIRINOX) Behandlung. Diese GATA6+ und CYP3A5+ exprimierenden Zellen, die nach der Chemotherapie im Tumorgewebe angereichert waren, könnten persistierende Krebszellen darstellen, die möglicherweise zu einem schlechten Ansprechen auf Medikamente und zu einer Medikamentenresistenz sowie zur Förderung von Tumormetastasen beitragen.
Die Entdeckung weiterer repräsentativer Biomarker für molekulare Phänotypen wird zur einer Verbesserten Bemühungen um eine präzisere und stärker personalisierte Behandlung beitragen. Ein besseres Verständnis der Beschaffenheit der Persistenzzellen des Bauchspeicheldrüsenkrebses nach einer Chemotherapie sollte zur Entwicklung wirksamerer therapeutischer Strategien führen und so dazu beitragen, dass die betroffenen Patienten mit dieser Krankheit länger überleben
Development of a Novel In Vitro Bioengineered Human Breast Ductal Mucosal Model to Investigate the Invasive Properties of Breast Cancer During its Development.
Breast cancer is one of the mostly commonly diagnosed cancers globally, where it poses a significant healthcare burden in both developed and developing countries. Over recent years, it has become increasingly apparent that the tumour microenvironment is a major driver of the adoption of specific migratory phenotypes in breast cancer. However, in vitro models investigating breast cancer invasion often do not recapitulate this important aspect of breast cancer biology, thereby reducing their physiological relevance and predictive power.
This project aimed to develop novel three-dimensional (3D) migration assays, based on available Alvetex® technologies, that account for the tumour microenvironment. Their effectiveness at recapitulating in vivo behaviours was then compared against conventional 2D invasion assays and the literature. Through the use of three immortalised breast cancer cell lines: MCF-10A, MCF-7, and MDA-MB-231’s, the three main stages of ductal breast cancer were able to be simulated, namely: Healthy tissue, Ductal Carcinoma In Situ (DCIS), and Invasive Ductal Carcinoma (IDC), respectively.
Initially the impact of a 3D geometric space on breast cancer invasion characteristics was investigated using Alvetex® Strata. Despite the increase in in vivo-like characteristics of each cell line in this platform, the physiological relevance of these models was limited due to the lack of presence of Extracellular Matrix (ECM) constituents and stromal cells. Using Co-culture techniques, optimised in the Przyborski lab, Human Neonatal Dermal Fibroblasts (HDFn’s) were cultured in Alvetex® Scaffold with the immortalised cell lines to create and optimise a complex 3D breast cancer invasion model. The physiological relevance of these models was then assessed using immunostaining and histological analysis to confirm the presence of in vivo characteristics and reproducibility of this platform. This led to the creation of a novel reproducible 3D invasion assay for breast cancer that accounts for a physiologically relevant mammary microenvironment. The modular nature of this model was then explored, testing its compatibility with primary mammary fibroblast and epithelial cells to further increase physiological relevance, while also exploring the potential for patient personalised Alvetex® models.
Although physiological relevance is important in invasion models, so is the compatibility of a platform with anti-migratory compounds, as treatments of in vitro models with known inhibitors is a cornerstone for increasing our understanding of invasive processes, as well as identifying novel compounds. Thus, each model platform (2D, Alvetex® Strata, Alvetex® Scaffold) was treated with a known migration inhibitor, Caffeic Acid Phenyl-Ethyl Ester (CAPE), to demonstrate their compatibility with these pipelines.
Together, the data presented in this thesis demonstrates the ability of this novel 3D Co-culture system to recapitulate the migratory behaviour of breast cancer cells in its distinct developmental stages; in a platform that is compatible with both drug treatment protocols and the use of primary cell lines
Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group
The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC
The role of NEDD9 in HER2-driven breast cancer.
Tumor initiation is often driven by unrestricted proliferation. One such driver of proliferation is Human epidermal growth factor receptor 2 (HER2). HER2 is a receptor tyrosine kinase that is part of the epidermal growth factor receptor family (EFGR) that is commonly overexpressed in breast cancer. HER2 positive (+) breast cancers often respond to anti- HER2 therapy, yet many patients eventually develop resistance. Multiple mechanisms contribute to resistance, including activation of HSP90, PI3K/Akt or Src that rely on adaptor molecules (GRB2, p130cas, NEDD9). Neural precursor cell expressed, developmentally downregulated protein 9 (NEDD9) is an adaptor protein that promotes integrin signaling. We found that higher expression of NEDD9 in HER2+ human breast cancer correlates with disease progression, reduced relapse free survival and resistance to anti-HER2 therapy. The central hypothesis is NEDD9 can play a role in mammary gland development, differentiation and proliferation of cells, and response to targeted therapy in HER2 driven breast cancer. To evaluate role of NEDD9 protein in physiologically relevant settings we generated a conditional transgenic mouse model, placing extra copy of human NEDD9 cDNA under the control of cre recombinase. When crossed with mice expressing mammary gland-specific cre recombinase, NEDD9 overexpression promoted early occurrence of benign lesions such as mammary intraepithelial neoplasia (MIN) and ductal carcinoma in situ (DCIS). This phenotype was accelerated by co-expression of HER2 oncogene. The NEDD9 overexpressing mice showed altered development of the mammary gland, with more tertiary and terminal end buds (TEBs), that is indicative of NEDD9’s role in controlling proliferation. The increase in cell proliferation, was further supported by Ki67 staining. The lineage tracing analysis shows that NEDD9 specifically increased the number of luminal progenitor cells, as shown by dual Keratin 5/8 staining. Consistent with these studies, NEDD9 promoted the 2D and 3D cell proliferation of human MCF10A cells. Mechanistically, NEDD9 upregulation resulted in MAPK and AURKA activation inducing cell proliferation. The depletion of NEDD9 in HER2+ cancer cell lines increased their sensitivity to anti-HER2 therapy. These findings support the role of NEDD9 in early stages of HER2-driven tumorigenesis, selectively impacting proliferation of luminal progenitor cells and lay foundation for potential use of NEDD9 expression in early diagnostics of HER2+ BCs and treatment
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