Context-specific gene regulatory networks subdivide intrinsic subtypes of breast cancer

<p>Abstract</p> <p>Background</p> <p>Breast cancer is a highly heterogeneous disease with respect to molecular alterations and cellular composition making therapeutic and clinical outcome unpredictable. This diversity creates a significant challenge in developing tumor classifications that are clinically reliable with respect to prognosis prediction.</p> <p>Results</p> <p>This paper describes an unsupervised context analysis to infer context-specific gene regulatory networks from 1,614 samples obtained from publicly available gene expression data, an extension of a previously published methodology. We use the context-specific gene regulatory networks to classify the tumors into clinically relevant subgroups, and provide candidates for a finer sub-grouping of the previously known intrinsic tumors with a focus on Basal-like tumors. Our analysis of pathway enrichment in the key contexts provides an insight into the biological mechanism underlying the identified subtypes of breast cancer.</p> <p>Conclusions</p> <p>The use of context-specific gene regulatory networks to identify biological contexts from heterogenous breast cancer data set was able to identify genomic drivers for subgroups within the previously reported intrinsic subtypes. These subgroups (contexts) uphold the clinical relevant features for the intrinsic subtypes and were associated with increased survival differences compared to the intrinsic subtypes. We believe our computational approach led to the generation of novel rationalized hypotheses to explain mechanisms of disease progression within sub-contexts of breast cancer that could be therapeutically exploited once validated.</p

Single-Cell Transcriptomics in Cancer Immunobiology: The Future of Precision Oncology.

Cancer is a heterogeneous and complex disease. Tumors are formed by cancer cells and a myriad of non-cancerous cell types that together with the extracellular matrix form the tumor microenvironment. These cancer-associated cells and components contribute to shape the progression of cancer and are deeply involved in patient outcome. The immune system is an essential part of the tumor microenvironment, and induction of cancer immunotolerance is a necessary step involved in tumor formation and growth. Immune mechanisms are intimately associated with cancer progression, invasion, and metastasis; as well as to tumor dormancy and modulation of sensitivity to drug therapy. Transcriptome analyses have been extensively used to understand the heterogeneity of tumors, classifying tumors into molecular subtypes and establishing signatures that predict response to therapy and patient outcomes. However, the classification of the tumor cell diversity and specially the identification of rare populations has been limited in these transcriptomic analyses of bulk tumor cell populations. Massively-parallel single-cell RNAseq analysis has emerged as a powerful method to unravel heterogeneity and to study rare cell populations in cancer, through unsupervised sampling and modeling of transcriptional states in single cells. In this context, the study of the role of the immune system in cancer would benefit from single cell approaches, as it will enable the characterization and/or discovery of the cell types and pathways involved in cancer immunotolerance otherwise missed in bulk transcriptomic information. Thus, the analysis of gene expression patterns at single cell resolution holds the potential to provide key information to develop precise and personalized cancer treatment including immunotherapy. This review is focused on the latest single-cell RNAseq methodologies able to agnostically study thousands of tumor cells as well as targeted single-cell RNAseq to study rare populations within tumors. In particular, we will discuss methods to study the immune system in cancer. We will also discuss the current challenges to the study of cancer at the single cell level and the potential solutions to the current approaches

Single-cell analysis of ER-positive breast cancer treated with letrozole and ribociclib

Breast cancer is the most widespread cancer in the world, accounting for 25% of all female cancers. There is a high inter- and intra-tumor heterogeneity in breast cancer which makes it challenging to optimize the treatment for the individual patient. In recent years, the role of immune infiltration in tumor carcinogenesis and pathophysiology has been increasingly recognized. It has therefore become a priority to understand the interactions and cooperation between immune and cancer cells. Despite a thorough attempt to match treatment options with clinicopathological features such as histological classification, grade, stage, biomarkers, molecular subtypes, and intrinsic subtypes, many patients show resistance to treatment. One attempt to overcome treatment resistance is the emergence of combinatorial treatment, meaning treating patients with two drugs at the same time. CDK4/6 inhibitors are anti-cancer drugs which prohibits cell growth and is shown to have promising results in combination with aromatase inhibitors for breast cancer patients with hormone receptor positive disease. This drug combination is not yet approved in Norway as standard neoadjuvant treatment. The NeoLetRib clinical trial facilitates the access to the combinations of aromatase and CDK4/6 inhibitor to patients. The study also gives the opportunity to investigate potential biomarkers for more personalized treatment, novel predictive biomarkers and assess how the tumor microenvironment changes during treatment. Single cell analysis is the method we used to capture each cells transcriptome in the tumor microenvironment. We performed scRNA-seq of breast cancer biopsies from patients enrolled in the clinical trial NeoLetRib before the neoadjuvant treatment and after 21 days. This study shows that five cellular subtypes including Tregs, and four monocyte subtypes had a significant proportional change. These cell types have been associated with the promotion of a proinflammatory microenvironment and may be associated with tumor progression.M-K

Genetic Ancestry–dependent Differences in Breast Cancer–induced Field Defects in the Tumor-adjacent Normal Breast

Purpose: Genetic ancestry influences evolutionary pathways of cancers. However, whether ancestry influences cancer-induced field defects is unknown. The goal of this study was to utilize ancestry-mapped true normal breast tissues as controls to identify cancer-induced field defects in normal tissue adjacent to breast tumors (NATs) in women of African American (AA) and European (EA) ancestry. Experimental Design: A tissue microarray comprising breast tissues of ancestry-mapped 100 age-matched healthy women from the Komen Tissue Bank (KTB) at Indiana University (Indianapolis, IN) and tumor-NAT pairs from 100 women (300 samples total) was analyzed for the levels of ZEB1, an oncogenic transcription factor that is central to cell fate, mature luminal cell–enriched estrogen receptor alpha (ERα), GATA3, FOXA1, and for immune cell composition. Results: ZEB1+ cells, which were localized surrounding the ductal structures of the normal breast, were enriched in the KTB-normal of AA compared with KTB-normal of EA women. In contrast, in EA women, both NATs and tumors compared with KTB-normal contained higher levels of ZEB1+ cells. FOXA1 levels were lower in NATs compared with KTB-normal in AA but not in EA women. We also noted variations in the levels of GATA3, CD8+ T cells, PD1+ immune cells, and PDL1+ cell but not CD68+ macrophages in NATs of AA and EA women. ERα levels did not change in any of our analyses, pointing to the specificity of ancestry-dependent variations. Conclusions: Genetic ancestry–mapped tissues from healthy individuals are required for proper assessment and development of cancer-induced field defects as early cancer detection markers. This finding is significant in light of recent discoveries of influence of genetic ancestry on both normal biology and tumor evolution

From molecular targets to antitumor immunity

This thesis aimed at the development of strategies that could contribute to the evaluation of the clinical potential of new anticancer therapies. The work was divided into two main sections comprising the development of a therapeutic approach to target cancer genetic vulnerabilities and the development of 3D tumor models incorporating cues from the stromal and immune microenvironments. (...

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

Unsupervised multiple kernel learning approaches for integrating molecular cancer patient data

Cancer is the second leading cause of death worldwide. A characteristic of this disease is its complexity leading to a wide variety of genetic and molecular aberrations in the tumors. This heterogeneity necessitates personalized therapies for the patients. However, currently defined cancer subtypes used in clinical practice for treatment decision-making are based on relatively few selected markers and thus provide only a coarse classifcation of tumors. The increased availability in multi-omics data measured for cancer patients now offers the possibility of defining more informed cancer subtypes. Such a more fine-grained characterization of cancer subtypes harbors the potential of substantially expanding treatment options in personalized cancer therapy. In this thesis, we identify comprehensive cancer subtypes using multidimensional data. For this purpose, we apply and extend unsupervised multiple kernel learning methods. Three challenges of unsupervised multiple kernel learning are addressed: robustness, applicability, and interpretability. First, we show that regularization of the multiple kernel graph embedding framework, which enables the implementation of dimensionality reduction techniques, can increase the stability of the resulting patient subgroups. This improvement is especially beneficial for data sets with a small number of samples. Second, we adapt the objective function of kernel principal component analysis to enable the application of multiple kernel learning in combination with this widely used dimensionality reduction technique. Third, we improve the interpretability of kernel learning procedures by performing feature clustering prior to integrating the data via multiple kernel learning. On the basis of these clusters, we derive a score indicating the impact of a feature cluster on a patient cluster, thereby facilitating further analysis of the cluster-specific biological properties. All three procedures are successfully tested on real-world cancer data. Comparing our newly derived methodologies to established methods provides evidence that our work offers novel and beneficial ways of identifying patient subgroups and gaining insights into medically relevant characteristics of cancer subtypes.Krebs ist eine der häufigsten Todesursachen weltweit. Krebs ist gekennzeichnet durch seine Komplexität, die zu vielen verschiedenen genetischen und molekularen Aberrationen im Tumor führt. Die Unterschiede zwischen Tumoren erfordern personalisierte Therapien für die einzelnen Patienten. Die Krebssubtypen, die derzeit zur Behandlungsplanung in der klinischen Praxis verwendet werden, basieren auf relativ wenigen, genetischen oder molekularen Markern und können daher nur eine grobe Unterteilung der Tumoren liefern. Die zunehmende Verfügbarkeit von Multi-Omics-Daten für Krebspatienten ermöglicht die Neudefinition von fundierteren Krebssubtypen, die wiederum zu spezifischeren Behandlungen für Krebspatienten führen könnten. In dieser Dissertation identifizieren wir neue, potentielle Krebssubtypen basierend auf Multi-Omics-Daten. Hierfür verwenden wir unüberwachtes Multiple Kernel Learning, welches in der Lage ist mehrere Datentypen miteinander zu kombinieren. Drei Herausforderungen des unüberwachten Multiple Kernel Learnings werden adressiert: Robustheit, Anwendbarkeit und Interpretierbarkeit. Zunächst zeigen wir, dass die zusätzliche Regularisierung des Multiple Kernel Learning Frameworks zur Implementierung verschiedener Dimensionsreduktionstechniken die Stabilität der identifizierten Patientengruppen erhöht. Diese Robustheit ist besonders vorteilhaft für Datensätze mit einer geringen Anzahl von Proben. Zweitens passen wir die Zielfunktion der kernbasierten Hauptkomponentenanalyse an, um eine integrative Version dieser weit verbreiteten Dimensionsreduktionstechnik zu ermöglichen. Drittens verbessern wir die Interpretierbarkeit von kernbasierten Lernprozeduren, indem wir verwendete Merkmale in homogene Gruppen unterteilen bevor wir die Daten integrieren. Mit Hilfe dieser Gruppen definieren wir eine Bewertungsfunktion, die die weitere Auswertung der biologischen Eigenschaften von Patientengruppen erleichtert. Alle drei Verfahren werden an realen Krebsdaten getestet. Den Vergleich unserer Methodik mit etablierten Methoden weist nach, dass unsere Arbeit neue und nützliche Möglichkeiten bietet, um integrative Patientengruppen zu identifizieren und Einblicke in medizinisch relevante Eigenschaften von Krebssubtypen zu erhalten

A single-cell atlas of the healthy breast tissues reveals clinically relevant clusters of breast epithelial cells

Single-cell RNA sequencing (scRNA-seq) is an evolving technology used to elucidate the cellular architecture of adult organs. Previous scRNA-seq on breast tissue utilized reduction mammoplasty samples, which are often histologically abnormal. We report a rapid tissue collection/processing protocol to perform scRNA-seq of breast biopsies of healthy women and identify 23 breast epithelial cell clusters. Putative cell-of-origin signatures derived from these clusters are applied to analyze transcriptomes of ~3,000 breast cancers. Gene signatures derived from mature luminal cell clusters are enriched in ~68% of breast cancers, whereas a signature from a luminal progenitor cluster is enriched in ~20% of breast cancers. Overexpression of luminal progenitor cluster-derived signatures in HER2+, but not in other subtypes, is associated with unfavorable outcome. We identify TBX3 and PDK4 as genes co-expressed with estrogen receptor (ER) in the normal breasts, and their expression analyses in >550 breast cancers enable prognostically relevant subclassification of ER+ breast cancers

Cell cycle regulatory proteins and miRNAs in premalignant lesions and breast cancer

Early diagnosis and reliable prognosis and treatment prediction of breast cancer will ultimately lead to a decreased mortality rate. This can be achieved by identification of prognostic and treatment predictive biomarkers, and by understanding the mechanisms behind early changes in the breast. The cell cycle is a closely controlled process, involving multiple components with regulation on several levels. Loss of adequate cell proliferation control and cell cycle regulation is one of the main characteristic of cancer. In this thesis we have found that low level of the cell cycle regulatory protein p27 was associated with impaired response to tamoxifen in premenopausal breast cancer patients, but not with prognosis. We have also observed that the expression of the microRNA (miRNA) miR-92a could provide independent prognostic information in breast cancer patients, and loss of miR-92a was associated with more severe breast cancer traits. The earliest histologically identifiable breast lesion with an increased risk for developing breast cancer is called columnar cell hyperplasia (CCH). We have identified miRNA expression changes in CCH compared to normal mammary gland tissue in both epithelial cells and in the surrounding stroma. In addition we have linked epithelial expression of miR-27a, miR-92a andlet-7c to negative cell proliferation regulation, and stromal miR-I32 expression to alteration of genes associated with extra cellular matrix and actin-motility pathways in fibroblasts, and metabolic genes and pathways in co-cultured CCH epithelial cells. Finally, cyclin Dl associated miR-483-5p affected cell migration in opposite directions depending on estrogen receptor status, highlighting the importance of subdivision of breast cancers in order to correctly understand the biology and subsequently for correct treatment of breast cancer subgroups. In summary, these studies identified two potential biomarkers; one for predicting tamoxifen sensitivity, and one with prognostic value. We also revealed changes in miRNA expression in early premalignant breast lesions involved in cell proliferation, and opposing roles in cell migration for a cell cycle-related miRNA in breast cancer subgroup models