Joint analysis of histopathology image features and gene expression in breast cancer

BACKGROUND Genomics and proteomics are nowadays the dominant techniques for novel biomarker discovery. However, histopathology images contain a wealth of information related to the tumor histology, morphology and tumor-host interactions that is not accessible through these techniques. Thus, integrating the histopathology images in the biomarker discovery workflow could potentially lead to the identification of new image-based biomarkers and the refinement or even replacement of the existing genomic and proteomic signatures. However, extracting meaningful and robust image features to be mined jointly with genomic (and clinical, etc.) data represents a real challenge due to the complexity of the images. RESULTS We developed a framework for integrating the histopathology images in the biomarker discovery workflow based on the bag-of-features approach - a method that has the advantage of being assumption-free and data-driven. The images were reduced to a set of salient patterns and additional measurements of their spatial distribution, with the resulting features being directly used in a standard biomarker discovery application. We demonstrated this framework in a search for prognostic biomarkers in breast cancer which resulted in the identification of several prognostic image features and a promising multimodal (imaging and genomic) prognostic signature. The source code for the image analysis procedures is freely available. CONCLUSIONS The framework proposed allows for a joint analysis of images and gene expression data. Its application to a set of breast cancer cases resulted in image-based and combined (image and genomic) prognostic scores for relapse-free survival

Breast cancer outcome prediction with tumour tissue images and machine learning

PurposeRecent advances in machine learning have enabled better understanding of large and complex visual data. Here, we aim to investigate patient outcome prediction with a machine learning method using only an image of tumour sample as an input.MethodsUtilising tissue microarray (TMA) samples obtained from the primary tumour of patients (N=1299) within a nationwide breast cancer series with long-term-follow-up, we train and validate a machine learning method for patient outcome prediction. The prediction is performed by classifying samples into low or high digital risk score (DRS) groups. The outcome classifier is trained using sample images of 868 patients and evaluated and compared with human expert classification in a test set of 431 patients.ResultsIn univariate survival analysis, the DRS classification resulted in a hazard ratio of 2.10 (95% CI 1.33-3.32, p=0.001) for breast cancer-specific survival. The DRS classification remained as an independent predictor of breast cancer-specific survival in a multivariate Cox model with a hazard ratio of 2.04 (95% CI 1.20-3.44, p=0.007). The accuracy (C-index) of the DRS grouping was 0.60 (95% CI 0.55-0.65), as compared to 0.58 (95% CI 0.53-0.63) for human expert predictions based on the same TMA samples.ConclusionsOur findings demonstrate the feasibility of learning prognostic signals in tumour tissue images without domain knowledge. Although further validation is needed, our study suggests that machine learning algorithms can extract prognostically relevant information from tumour histology complementing the currently used prognostic factors in breast cancer.Peer reviewe

Integrative analysis of histopathological images and chromatin accessibility data for estrogen receptor-positive breast cancer

Background: Existing studies have demonstrated that the integrative analysis of histopathological images and genomic data can be used to better understand the onset and progression of many diseases, as well as identify new diagnostic and prognostic biomarkers. However, since the development of pathological phenotypes are influenced by a variety of complex biological processes, complete understanding of the underlying gene regulatory mechanisms for the cell and tissue morphology is still a challenge. In this study, we explored the relationship between the chromatin accessibility changes and the epithelial tissue proportion in histopathological images of estrogen receptor (ER) positive breast cancer. Methods: An established whole slide image processing pipeline based on deep learning was used to perform global segmentation of epithelial and stromal tissues. We then used canonical correlation analysis to detect the epithelial tissue proportion-associated regulatory regions. By integrating ATAC-seq data with matched RNA-seq data, we found the potential target genes that associated with these regulatory regions. Then we used these genes to perform the following pathway and survival analysis. Results: Using canonical correlation analysis, we detected 436 potential regulatory regions that exhibited significant correlation between quantitative chromatin accessibility changes and the epithelial tissue proportion in tumors from 54 patients (FDR < 0.05). We then found that these 436 regulatory regions were associated with 74 potential target genes. After functional enrichment analysis, we observed that these potential target genes were enriched in cancer-associated pathways. We further demonstrated that using the gene expression signals and the epithelial tissue proportion extracted from this integration framework could stratify patient prognoses more accurately, outperforming predictions based on only omics or image features. Conclusion: This integrative analysis is a useful strategy for identifying potential regulatory regions in the human genome that are associated with tumor tissue quantification. This study will enable efficient prioritization of genomic regulatory regions identified by ATAC-seq data for further studies to validate their causal regulatory function. Ultimately, identifying epithelial tissue proportion-associated regulatory regions will further our understanding of the underlying molecular mechanisms of disease and inform the development of potential therapeutic targets

Computer Vision for Tissue Characterization and Outcome Prediction in Cancer

The aim of this dissertation was to investigate the use of computer vision for tissue characterization and patient outcome prediction in cancer. This work focused on analysis of digitized tissue specimens, which were stained only for basic morphology (i.e. hematoxylin and eosin). The applicability of texture analysis and convolutional neural networks was evaluated for detection of biologically and clinically relevant features. Moreover, novel approaches to guide ground-truth annotation and outcome-supervised learning for prediction of patient survival directly from the tumor tissue images without expert guidance was investigated. We first studied quantification of tumor viability through segmentation of necrotic and viable tissue compartments. We developed a regional texture analysis method, which was trained and tested on whole sections of mouse xenograft models of human lung cancer. Our experiments showed that the proposed segmentation was able to discriminate between viable and non-viable tissue regions with high accuracy when compared to human expert assessment. We next investigated the feasibility of pre-trained convolutional neural networks in analysis of breast cancer tissue, aiming to quantify tumor-infiltrating lymphocytes in the specimens. Interestingly, our results showed that pre-trained convolutional neural networks can be adapted for analysis of histological image data, outperforming texture analysis. The results also indicated that the computerized assessment was on par with pathologist assessments. Moreover, the study presented an image annotation technique guided by specific antibody staining for improved ground-truth labeling. Direct outcome prediction in breast cancer was then studied using a nationwide patient cohort. A computerized pipeline, which incorporated orderless feature aggregation and convolutional image descriptors for outcome-supervised classification, resulted in a risk grouping that was predictive of both disease-specific and overall survival. Surprisingly, further analysis suggested that the computerized risk prediction was also an independent prognostic factor that provided information complementary to the standard clinicopathological factors. This doctoral thesis demonstrated how computer-vision methods can be powerful tools in analysis of cancer tissue samples, highlighting strategies for supervised characterization of tissue entities and an approach for identification of novel prognostic morphological features.Kudosnäytteiden mikroskooppisten piirteiden visuaalinen tarkastelu on yksi tärkeimmistä määrityksistä syöpäpotilaiden diagnosoinnissa ja hoidon suunnittelussa. Edistyneet kuvantamisteknologiat ovat mahdollistaneet histologisten kasvainkudosnäytteiden digitalisoinnin tarkalla resoluutiolla. Näytteiden digitalisoinnin seurauksena niiden analysointiin voidaan soveltaa edistyneitä koneoppimiseen perustuvia konenäön menetelmiä. Tämä väitöskirja tutkii konenäön menetelmien soveltamista syöpäkudosnäytteiden laskennalliseen analyysiin. Työssä tutkitaan yksittäisten histologisten entiteettien, kuten nekroottisen kudoksen ja immuunisolujen automaattista kvantifiointia. Lisäksi työssä esitellään menetelmä potilaan selviytymisen ennustamiseen pelkkään kudosmorfologiaan perustuen

Impacto nos custos e no financiamento da utilização de Terapias Biológicas para o cancro da mama feminino

Trabalho Final do Curso de Especialização em Administração HospitalarRESUMO - Enquadramento: Num contexto de gestão de serviços de saúde, em que as necessidades ilimitadas se contrapõem com uma escassez de recursos, as análises custo-efetividade a cada inovação terapêutica não se revelam suficientes. É importante uma análise aos custos totais de cada tratamento, permitindo a adoção de estratégias de racionalização dos recursos. Desta feita, para o presente trabalho foram definidos como objetivos o apuramento do impacto nos custos das terapias biológicas para o cancro da mama, a verificação da adequação do modelo de financiamento atual à utilização destas e, ainda, a comparação da utilização do Trastuzumab subcutâneo (SC) com o Trastuzumab intravenoso (IV). Metodologia: Utilizaram-se os sistemas de informação internos do IPOLFG para recolha dos dados com os procedimentos realizados, e o Sistema de Custeio para a imputação dos custos. Foi analisada uma amostra representativa das utentes que entraram para o programa de financiamento da patologia da mama em 2013, durante os dois primeiros anos de tratamento. Adicionalmente, com recurso a entrevistas semiestruturadas a profissionais de saúde, foram apurados os consumos de recursos decorrentes da utilização do Trastuzumab IV e do Trastuzumab SC, para o ano de 2015. Resultados: Em termos médios, cada utente com cancro da mama custa 21.748,35€ no 1º ano de tratamento com Trastuzumab, e 15.342,22€ no 2º ano com o mesmo tratamento. Estes valores mostraram-se bastante superiores ao montante de financiamento contratualizado, em 10.599,39€ no 1º ano de tratamento e 10.520,38€ no 2º ano. Quando se analisaram em detalhe os custos com a utilização da inovação terapêutica associada ao Trastuzumab, administração por via subcutânea, foram apuradas poupanças na ordem dos 237,46€ a 314,50€ por utente e por ano de tratamento. Discussão: A gestão de serviços de saúde encontra grandes entraves à definição de estratégias a longo prazo, decorrentes da constante introdução de inovações terapêuticas com custos bastante elevados e superiores aos montantes disponíveis para financiamento. Os benefícios apresentados para estas inovações, principalmente na área oncológica, inferem uma grande pressão às instituições, levando a que, frequentemente, o seu poder de decisão seja reduzido.ABSTRACT - Background: Within a health management service context, in which the unlimited needs are opposed to a scarcity of resources, the conduction of cost-effectiveness analysis to each new therapeutic innovation does not reveal to be sufficient. In fact, it is relevant to perform an analysis of the total costs of each treatment, enabling the adoption of resources streamlining strategies. For purposes of the current study the following objectives were defined, assessment of the impact of biologic treatment costs before breast cancer, suitability analysis of those costs current funding structure and, as well, the comparison of the use of Subcutaneous Trastuzumab (SC) with Intravenous Trastuzumab (IV). Methodology: The internal information systems of the IPOLFG were used for data collection with the performed procedures, and the Costing System for cost allocation. A representative sample of patients who entered for the breast pathology funding program within 2013 was analyzed, during the first two years of treatment. Additionally, with the performance of semi-structured interviews to health professionals, the consumption of resources arising from the use of Trastuzumab IV and Trastuzumab SC was gathered, for the year of 2015. Results: On average, each patient with breast cancer costs 21.748,35€ in the first year of treatment with Trastuzumab, and 15.342,22€ in the second year with the same treatment. Please note that these amounts are considerable higher than the established funding, in 10.599,39€ in the first year of treatment and 10.520,38€ in the second year. When the costs of the use of the therapeutic innovation were analyzed in detail with reference to Trastuzumab, subcutaneous administration, savings of approximately 237,46€ to 314,50€ for patient and for year of treatment were computed. Discussion: The management of health services faces significant constraints on the definition of long-term strategies, arising from the permanent emergence of therapeutic innovations with costs that are not only considerable but also higher than those available for funding. The introduced benefits for these innovations, mainly with regard to the field of oncology, induce a significant pressure for institutions, leading to, on a regular manner, a reduction of their decision-making power.N/