IMAGING GENOMICS

Imaging genomics is an emerging research field, where integrative analysis of imaging and omics data is performed to provide new insights into the phenotypic characteristics and genetic mechanisms of normal and/or disordered biological structures and functions, and to impact the development of new diagnostic, therapeutic and preventive approaches. The Imaging Genomics Session at PSB 2017 aims to encourage discussion on fundamental concepts, new methods and innovative applications in this young and rapidly evolving field

Machine Learning Models for Deciphering Regulatory Mechanisms and Morphological Variations in Cancer

The exponential growth of multi-omics biological datasets is resulting in an emerging paradigm shift in fundamental biological research. In recent years, imaging and transcriptomics datasets are increasingly incorporated into biological studies, pushing biology further into the domain of data-intensive-sciences. New approaches and tools from statistics, computer science, and data engineering are profoundly influencing biological research. Harnessing this ever-growing deluge of multi-omics biological data requires the development of novel and creative computational approaches. In parallel, fundamental research in data sciences and Artificial Intelligence (AI) has advanced tremendously, allowing the scientific community to generate a massive amount of knowledge from data. Advances in Deep Learning (DL), in particular, are transforming many branches of engineering, science, and technology. Several of these methodologies have already been adapted for harnessing biological datasets; however, there is still a need to further adapt and tailor these techniques to new and emerging technologies. In this dissertation, we present computational algorithms and tools that we have developed to study gene-regulation and cellular morphology in cancer. The models and platforms that we have developed are general and widely applicable to several problems relating to dysregulation of gene expression in diseases. Our pipelines and software packages are disseminated in public repositories for larger scientific community use. This dissertation is organized in three main projects. In the first project, we present Causal Inference Engine (CIE), an integrated platform for the identification and interpretation of active regulators of transcriptional response. The platform offers visualization tools and pathway enrichment analysis to map predicted regulators to Reactome pathways. We provide a parallelized R-package for fast and flexible directional enrichment analysis to run the inference on custom regulatory networks. Next, we designed and developed MODEX, a fully automated text-mining system to extract and annotate causal regulatory interaction between Transcription Factors (TFs) and genes from the biomedical literature. MODEX uses putative TF-gene interactions derived from high-throughput ChIP-Seq or other experiments and seeks to collect evidence and meta-data in the biomedical literature to validate and annotate the interactions. MODEX is a complementary platform to CIE that provides auxiliary information on CIE inferred interactions by mining the literature. In the second project, we present a Convolutional Neural Network (CNN) classifier to perform a pan-cancer analysis of tumor morphology, and predict mutations in key genes. The main challenges were to determine morphological features underlying a genetic status and assess whether these features were common in other cancer types. We trained an Inception-v3 based model to predict TP53 mutation in five cancer types with the highest rate of TP53 mutations. We also performed a cross-classification analysis to assess shared morphological features across multiple cancer types. Further, we applied a similar methodology to classify HER2 status in breast cancer and predict response to treatment in HER2 positive samples. For this study, our training slides were manually annotated by expert pathologists to highlight Regions of Interest (ROIs) associated with HER2+/- tumor microenvironment. Our results indicated that there are strong morphological features associated with each tumor type. Moreover, our predictions highly agree with manual annotations in the test set, indicating the feasibility of our approach in devising an image-based diagnostic tool for HER2 status and treatment response prediction. We have validated our model using samples from an independent cohort, which demonstrates the generalizability of our approach. Finally, in the third project, we present an approach to use spatial transcriptomics data to predict spatially-resolved active gene regulatory mechanisms in tissues. Using spatial transcriptomics, we identified tissue regions with differentially expressed genes and applied our CIE methodology to predict active TFs that can potentially regulate the marker genes in the region. This project bridged the gap between inference of active regulators using molecular data and morphological studies using images. The results demonstrate a significant local pattern in TF activity across the tissue, indicating differential spatial-regulation in tissues. The results suggest that the integrative analysis of spatial transcriptomics data with CIE can capture discriminant features and identify localized TF-target links in the tissue

Morphological, immunohistochemical and genetic aspects of acinar and ductal adenocarcinoma of the prostate

Prostate cancer is one of the most common causes of cancer-related death in developed countries. Acinar adenocarcinoma is by far the most common subtype of prostate cancer, with ductal adenocarcinoma being the second most common subtype. Biobanking of prostate cancer tissue is important for basic research, development of new biomarkers and a move towards personalized medicine. Various biobanking techniques have been described but harvesting of tissue is still often based on macroscopic identification of cancer in radical prostatectomy specimens. In the literature, the macroscopic features of prostate cancer in unfixed prostatectomy specimens are incompletely described. In our first study, we investigated the macroscopic features of identifiable tumors and their zonal distribution in 514 radical prostatectomy specimens. Grossly detected findings conclusive for cancer were seen in 52% of cases and suspicious for cancer in 24%. Macroscopic findings conclusive for cancer predicted microscopic identification of prostate cancer on microscopic examination in most cases. Cancers ≥2 mm were present somewhere on the cut surface in the majority of cases even when no suspicious or conclusive cancers had been identified macroscopically. Tumors in the transition zone of the prostate were more difficult to identify macroscopically. In our second study, we report a novel biobanking protocol for harvesting a full horizontal slice of unfixed prostate tissue from 20 radical prostatectomy specimens. In 18 of 20 cases, cancer was found in the biobanked tissue material. The biobanking protocol facilitated harvesting of a large slice of prostatic tissue, allowing studies of multifocal tumors and tumor heterogeneity. Clinical histopathological parameters could be reported from frozen sections of the biobanked material. The morphological quality, using cryogel, and the RNA quality, measured by RNA integrity number (RIN), were excellent. Ductal adenocarcinoma is a high-grade neoplasm with an adverse prognosis compared to acinar adenocarcinoma. The definition of ductal adenocarcinoma is based on histological features. Ductal adenocarcinoma usually presents in mixed tumors together with acinar adenocarcinoma. For a long time, the histogenesis and definition of ductal adenocarcinoma has been controversial. Some studies have suggested that acinar and ductal adenocarcinoma components may have a common clonal background. Expression of Programmed Death Ligand-1 (PD-L1) is a predictive biomarker for a new group of oncological drugs, immune checkpoint inhibitors. The frequency of PD-L1 expression in ductal adenocarcinoma is not well described. Deficient mismatch repair (dMMR) results in an accumulation of mutations in cancer cells. dMMR has been reported to be uncommon in prostate cancer. In our third study, we investigated the expression of PD-L1, dMMR and tumor infiltrating immune cells in acinar and ductal adenocarcinoma using a tissue microarray (TMA). PD-L1 expression in tumor cells was rare but more common in tumor infiltrating immune cells. PD-L1 expression was identified in tumor infiltrating immune cells in 29% of ductal adenocarcinomas. dMMR was rare, identified in only 5% of cases. There was a statistically significant increase in the number of CD8+ lymphocytes in ductal adenocarcinoma compared to acinar adenocarcinoma. In our final study, we investigated the clonal relationship between acinar and ductal adenocarcinoma components in mixed prostate cancers. Targeted sequencing was performed in 15 cases, followed by bioinformatic processing and manual curation of data. A common somatic denominator for both tumor components could be identified in 12 out of 15 cases indicating a common clonal origin. Increased ploidy, which is associated with advanced prostate cancer, was seen in more than half (53%) of ductal adenocarcinomas but not in any acinar adenocarcinoma. PTEN and CTNNB1 mutations were common in ductal adenocarcinoma (40%) but not seen in any acinar adenocarcinoma. In both acinar and ductal adenocarcinomas, ERG gene fusions were detected in 47%. No cases showed microsatellite instability or high tumor mutation burden. The genetic signature of ductal adenocarcinoma was consistent with its characterization of ductal adenocarcinoma as an aggressive form of prostate cance

Stroma and vessel characteristics in cancer : impact on prognosis and response to treatment

A series of pre-clinical and clinical studies imply vessel and pericyte status as determinants of tumor growth, metastasis and response to treatment. These studies thus imply biomarkerpotential of these features. Earlier studies of vessels and pericytes have largely applied semiquantitative approaches. In the studies of this thesis, novel tools for quantification of vesseland tumor stroma-related features were developed and applied to different sets of clinically well-annotated tumor collections. Analyses of perivascular status in ovarian cancer and colorectal cancer identified independently expressed marker-defined subsets of perivascular cells with differential associations with survival. These studies also identified novel significant associations between specific oncogenic mutations and vascular phenotypes. Studies analyzing stage II/III colon cancer samples, derived from a randomized adjuvant study, identified two novel stroma-related “metrics” that acted as markers for benefit of adjuvant chemotherapy. Firstly, high vessel density in the invasive region, but not tumor center, was identified as a marker that characterized patients benefiting from adjuvant treatment. Secondly, a digital-image-analyses-derived “metric”, related to high complexity of the tumor stroma interface, also defined a group showing benefit of adjuvant treatment. Notably, both novel markers showed statistically significant interactions with treatment supporting their relevance as response-predictive markers. Furthermore, cell-type-specific analyses of claudin-2 expression in colorectal cancer indicated that CAF-expression of this marker was specifically associated with benefit of oxaliplatin-based treatment for metastatic colorectal cancer. Taken together, our findings suggest continued exploration and validation of stroma-derived features, for development of clinically meaningful prognostic and response-predictive tissuebased biomarkers

Beyond molecular tumor heterogeneity : protein synthesis takes control

Altres ajuts: 245 SRYC acknowledges support from Fondo de Investigaciones Sanitarias (P1170185 and PI 14/01320), Redes Temáticas de Investigación Cooperativa en Salud (RTICC, RD 12/0036/0057), and CIBERONC 2017.One of the daunting challenges facing modern medicine lies in the understanding and treatment of tumor heterogeneity. Most tumors show intra-tumor heterogeneity at both genomic and proteomic levels, with marked impacts on the responses of therapeutic targets. Therapeutic target-related gene expression pathways are affected by hypoxia and cellular stress. However, the finding that targets such as eukaryotic initiation factor (eIF) 4E (and its phosphorylated form, p-eIF4E) are generally homogenously expressed throughout tumors, regardless of the presence of hypoxia or other cellular stress conditions, opens the exciting possibility that malignancies could be treated with therapies that combine targeting of eIF4E phosphorylation with immune checkpoint inhibitors or chemotherapy

Computational Image Analysis Identifies Histopathological Image Features Associated With Somatic Mutations and Patient Survival in Gastric Adenocarcinoma

Computational analysis of histopathological images can identify sub-visual objective image features that may not be visually distinguishable by human eyes, and hence provides better modeling of disease phenotypes. This study aims to investigate whether specific image features are associated with somatic mutations and patient survival in gastric adenocarcinoma (sample size = 310). An automated image analysis pipeline was developed to extract quantitative morphological features from H&E stained whole-slide images. We found that four frequently somatically mutated genes (TP53, ARID1A, OBSCN, and PIK3CA) were significantly associated with tumor morphological changes. A prognostic model built on the image features significantly stratified patients into low-risk and high-risk groups (log-rank test p-value = 2.6e-4). Multivariable Cox regression showed the model predicted risk index was an additional prognostic factor besides tumor grade and stage. Gene ontology enrichment analysis showed that the genes whose expressions mostly correlated with the contributing features in the prognostic model were enriched on biological processes such as cell cycle and muscle contraction. These results demonstrate that histopathological image features can reflect underlying somatic mutations and identify high-risk patients that may benefit from more precise treatment regimens. Both the image features and pipeline are highly interpretable to enable translational applications

СВЯЗЬ МЕЖДУ РЕГУЛИРУЮЩИМИ БЕЛКАМИ АУТОФАГИИ M-TOR И BECLIN-1 И ПАРАМЕТРАМИ ЛИМФОГЕННОГО МЕТАСТАЗИРОВАНИЯ ПРИ КОЛОРЕКТАЛЬНОМ РАКЕ

Currently the impact of autophagy on carcinogenesis remains understudied. On the one hand, autophagy acts as a tumor suppressor, as it activates degradation of oncoproteins,  toxic proteins, and damaged cell organelles, that may be aggressive and lead to DNA  damage. On the other hand, autophagy may promote tumor cell survival under hypoxia and  in the presence of reactive oxygen species, which occurs primarily due to blocking of  apoptosis mechanisms, raising the chances for maintaining tumor clone dynamics.  Autophagy regulation is a complicated and multi-stage process. The main regulator here is a  signaling pathway that activates serine/threonine protein kinase m-TOR (the mammalian  target of rapamycin). Data on the impact of autophagic proteins ATG5, LC3A, LC3B, and  Beclin-1 on malignant cell survival as well as on tumor growth and progression have been  reported in literature. However, studies aimed at seeking possible relationships between autophagy and pathogenetic mechanisms of carcinogenesis are of great interest.The aim of the study is to investigate a relationship between the expression parameters of  autophagy regulatory proteins m-TOR and Beclin-1 and the features of lymphogenic metastasis in colorectal cancer.Materials and methods. The study included 105 patients with T1-4N0-3M0 colorectal cancer treated in the Thoracic and Abdominal Department of Cancer Research Institute of  Tomsk Research Medical Center from 2012 to 2015. The average age of patients was  59.7±4.3 years. Morphological verification of the diagnosis was performed on the biopsy  samples of primary tumor tissue. Staging of colorectal cancer was determined according to  the TNM classification of malignant tumors (2002).Results. Analysis of the frequency of lymphogenic metastasis depending on the presence or absence of m-Tor and Beclin-1 expression in tumor cell cytoplasm revealed a statistically significant link between these variables.Conclusion. The obtained findings clearly exhibit that deceleration or loss of autophagic  activity in the tumor is accompanied by implementation of lymphogenic dissemination, which is a predictor of an unfavorable outcome of the disease.На сегодняшний день влияние процессов аутофагии на канцерогенез остается не до конца изученным. С  одной стороны, аутофагия является опухолевым супрессором за счет активации разрушения онкогенных  протеинов, токсичных белков и дефектных органелл, которые могут обладать агрессивными свойствами и  способствовать повреждению ДНК клетки. C другой стороны, аутофагия может способствовать выживанию  опухолевых клеток в условиях гипоксии и присутствия активных форм кислорода, что происходит  преимущественно за счет блокировки механизмов апоптоза, увеличивая шансы на поддержание циркуляции  опухолевого клона. Регуляция аутофагии является сложным, многоэтапным и комплексным  процессом. Основным его регулятором является сигнальный путь, который активирует белок  протеинкиназосерин-треониновой специфичности m-Tor (мишень рапамицина у млекопитающих). В  литературе имеются данные о влиянии белков аутофагии ATG5, LC3A и LC3B, Beclin-1 на способность  злокачественно трансформированных клеток к выживанию, а также на развитие опухоли и ее прогрессирование. Крайне актуальными являются исследования, направленные на поиск возможных взаимосвязей между процессами аутофагии и патогенетическими механизмами канцерогенеза.Цель исследования – изучить взаимосвязь экспрессионных параметров белков-регуляторов аутофагии m-TOR и Beclin-1 с параметрами лимфогенного метастазирования при колоректальном раке.Материал и методы. В исследование были включены 105 пациентов с колоректальным раком T1–4N0–3M0,находившихся на лечении в отделении торако-абдоминальной онкологии НИИ онкологии Томского НИМЦ в  период с 2012 по 2015 г. Средний возраст больных составил 59,7±4,3 года. Морфологическая верификация  диагноза колоректального рака проводилась на биопсийном материале фрагментов ткани первичной  опухоли. Распространенность онкологического заболевания определялась согласно международной классификации по системе TNM (2002).Результаты. Анализ частоты лимфогенного метастазирования в зависимости от наличия или отсутствия  экспрессии белков m-Tor и Beclin-1 в цитоплазме опухолевых клеток выявил статистически значимую связь между этими параметрами.Заключение. Полученные данные отчетливо демонстрируют тот факт, что снижение или утрата активности  процессов аутофагии в опухоли сопровождается реализацией механизмов лимфогенной диссеминации, которая является предиктором неблагоприятного прогноза заболевания

Diagnostic, Prognostic and Therapeutic Value of Gene Signatures

Gene expression studies have revealed diagnostic profiles and upregulation of specific pathways in many solid tumors. Some gene-expression signatures are already used as predictors of relapse in early breast cancer patients. The explosion of new information in gene expression profiling could potentially lead to the development of tailored treatments in many solid tumors. In addition, many studies are ongoing to validate these signatures also in predicting response to hormonal, chemotherapeutic, and targeted agents in breast cancer as well as in other tumors. This book has been carried out with the aim of providing readers a useful and comprehensive resource about the range of applications of microarray technology on oncological diseases. The book is principally addressed to resident and fellow physicians, medical oncologists, molecular biologists, biotechnologists, and those who study oncological diseases. The chapters have been written by leading international researchers on these topics who have prepared their manuscripts according to current literature and field experience with microarray technology

Self-supervised learning in non-small cell lung cancer discovers novel morphological clusters linked to patient outcome and molecular phenotypes

Histopathological images provide the definitive source of cancer diagnosis, containing information used by pathologists to identify and subclassify malignant disease, and to guide therapeutic choices. These images contain vast amounts of information, much of which is currently unavailable to human interpretation. Supervised deep learning approaches have been powerful for classification tasks, but they are inherently limited by the cost and quality of annotations. Therefore, we developed Histomorphological Phenotype Learning, an unsupervised methodology, which requires no annotations and operates via the self-discovery of discriminatory image features in small image tiles. Tiles are grouped into morphologically similar clusters which appear to represent recurrent modes of tumor growth emerging under natural selection. These clusters have distinct features which can be identified using orthogonal methods. Applied to lung cancer tissues, we show that they align closely with patient outcomes, with histopathologically recognised tumor types and growth patterns, and with transcriptomic measures of immunophenotype

Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review

Molecular and genomic properties are critical in selecting cancer treatments to target individual tumors, particularly for immunotherapy. However, the methods to assess such properties are expensive, time-consuming, and often not routinely performed. Applying machine learning to H&E images can provide a more cost-effective screening method. Dozens of studies over the last few years have demonstrated that a variety of molecular biomarkers can be predicted from H&E alone using the advancements of deep learning: molecular alterations, genomic subtypes, protein biomarkers, and even the presence of viruses. This article reviews the diverse applications across cancer types and the methodology to train and validate these models on whole slide images. From bottom-up to pathologist-driven to hybrid approaches, the leading trends include a variety of weakly supervised deep learning-based approaches, as well as mechanisms for training strongly supervised models in select situations. While results of these algorithms look promising, some challenges still persist, including small training sets, rigorous validation, and model explainability. Biomarker prediction models may yield a screening method to determine when to run molecular tests or an alternative when molecular tests are not possible. They also create new opportunities in quantifying intratumoral heterogeneity and predicting patient outcomes.Comment: 20 pages, 2 figure