Biomedical information extraction for matching patients to clinical trials

Digital Medical information had an astonishing growth on the last decades, driven by an unprecedented number of medical writers, which lead to a complete revolution in what and how much information is available to the health professionals. The problem with this wave of information is that performing a precise selection of the information retrieved by medical information repositories is very exhaustive and time consuming for physicians. This is one of the biggest challenges for physicians with the new digital era: how to reduce the time spent finding the perfect matching document for a patient (e.g. intervention articles, clinical trial, prescriptions). Precision Medicine (PM) 2017 is the track by the Text REtrieval Conference (TREC), that is focused on this type of challenges exclusively for oncology. Using a dataset with a large amount of clinical trials, this track is a good real life example on how information retrieval solutions can be used to solve this types of problems. This track can be a very good starting point for applying information extraction and retrieval methods, in a very complex domain. The purpose of this thesis is to improve a system designed by the NovaSearch team for TREC PM 2017 Clinical Trials task, which got ranked on the top-5 systems of 2017. The NovaSearch team also participated on the 2018 track and got a 15% increase on precision compared to the 2017 one. It was used multiple IR techniques for information extraction and processing of data, including rank fusion, query expansion (e.g. Pseudo relevance feedback, Mesh terms expansion) and experiments with Learning to Rank (LETOR) algorithms. Our goal is to retrieve the best possible set of trials for a given patient, using precise documents filters to exclude the unwanted clinical trials. This work can open doors in what can be done for searching and perceiving the criteria to exclude or include the trials, helping physicians even on the more complex and difficult information retrieval tasks

Neural Representations of Concepts and Texts for Biomedical Information Retrieval

Information retrieval (IR) methods are an indispensable tool in the current landscape of exponentially increasing textual data, especially on the Web. A typical IR task involves fetching and ranking a set of documents (from a large corpus) in terms of relevance to a user\u27s query, which is often expressed as a short phrase. IR methods are the backbone of modern search engines where additional system-level aspects including fault tolerance, scale, user interfaces, and session maintenance are also addressed. In addition to fetching documents, modern search systems may also identify snippets within the documents that are potentially most relevant to the input query. Furthermore, current systems may also maintain preprocessed structured knowledge derived from textual data as so called knowledge graphs, so certain types of queries that are posed as questions can be parsed as such; a response can be an output of one or more named entities instead of a ranked list of documents (e.g., what diseases are associated with EGFR mutations? ). This refined setup is often termed as question answering (QA) in the IR and natural language processing (NLP) communities. In biomedicine and healthcare, specialized corpora are often at play including research articles by scientists, clinical notes generated by healthcare professionals, consumer forums for specific conditions (e.g., cancer survivors network), and clinical trial protocols (e.g., www.clinicaltrials.gov). Biomedical IR is specialized given the types of queries and the variations in the texts are different from that of general Web documents. For example, scientific articles are more formal with longer sentences but clinical notes tend to have less grammatical conformity and are rife with abbreviations. There is also a mismatch between the vocabulary of consumers and the lingo of domain experts and professionals. Queries are also different and can range from simple phrases (e.g., COVID-19 symptoms ) to more complex implicitly fielded queries (e.g., chemotherapy regimens for stage IV lung cancer patients with ALK mutations ). Hence, developing methods for different configurations (corpus, query type, user type) needs more deliberate attention in biomedical IR. Representations of documents and queries are at the core of IR methods and retrieval methodology involves coming up with these representations and matching queries with documents based on them. Traditional IR systems follow the approach of keyword based indexing of documents (the so called inverted index) and matching query phrases against the document index. It is not difficult to see that this keyword based matching ignores the semantics of texts (synonymy at the lexeme level and entailment at phrase/clause/sentence levels) and this has lead to dimensionality reduction methods such as latent semantic indexing that generally have scale-related concerns; such methods also do not address similarity at the sentence level. Since the resurgence of neural network methods in NLP, the IR field has also moved to incorporate advances in neural networks into current IR methods. This dissertation presents four specific methodological efforts toward improving biomedical IR. Neural methods always begin with dense embeddings for words and concepts to overcome the limitations of one-hot encoding in traditional NLP/IR. In the first effort, we present a new neural pre-training approach to jointly learn word and concept embeddings for downstream use in applications. In the second study, we present a joint neural model for two essential subtasks of information extraction (IE): named entity recognition (NER) and entity normalization (EN). Our method detects biomedical concept phrases in texts and links them to the corresponding semantic types and entity codes. These first two studies provide essential tools to model textual representations as compositions of both surface forms (lexical units) and high level concepts with potential downstream use in QA. In the third effort, we present a document reranking model that can help surface documents that are likely to contain answers (e.g, factoids, lists) to a question in a QA task. The model is essentially a sentence matching neural network that learns the relevance of a candidate answer sentence to the given question parametrized with a bilinear map. In the fourth effort, we present another document reranking approach that is tailored for precision medicine use-cases. It combines neural query-document matching and faceted text summarization. The main distinction of this effort from previous efforts is to pivot from a query manipulation setup to transforming candidate documents into pseudo-queries via neural text summarization. Overall, our contributions constitute nontrivial advances in biomedical IR using neural representations of concepts and texts

Systematising and scaling literature curation for genetically determined developmental disorders

The widespread availability of genomic sequencing has transformed the diagnosis of genetically-determined developmental disorders (GDD). However, this type of test often generates a number of genetic variants, which have to be reviewed and related back to the clinical features (phenotype) of the individual being tested. This frequently entails a time-consuming review of the peer-reviewed literature to look for case reports describing variants in the gene(s) of interest. This is particularly true for newly described and/or very rare disorders not covered in phenotype databases. Therefore, there is a need for scalable, automated literature curation to increase the efficiency of this process. This should lead to improvements in the speed in which diagnosis is made, and an increase in the number of individuals who are diagnosed through genomic testing. Phenotypic data in case reports/case series is not usually recorded in a standardised, computationally-tractable format. Plain text descriptions of similar clinical features may be recorded in several different ways. For example, a technical term such as ‘hypertelorism’, may be recorded as its synonym ‘widely spaced eyes’. In addition, case reports are found across a wide range of journals, with different structures and file formats for each publication. The Human Phenotype Ontology (HPO) was developed to store phenotypic data in a computationally-accessible format. Several initiatives have been developed to link diseases to phenotype data, in the form of HPO terms. However, these rely on manual expert curation and therefore are not inherently scalable, and cannot be updated automatically. Methods of extracting phenotype data from text at scale developed to date have relied on abstracts or open access papers. At the time of writing, Europe PubMed Central (EPMC, https://europepmc.org/) contained approximately 39.5 million articles, of which only 3.8 million were open access. Therefore, there is likely a significant volume of phenotypic data which has not been used previously at scale, due to difficulties accessing non-open access manuscripts. In this thesis, I present a method for literature curation which can utilise all relevant published full text through a newly developed package which can download almost all manuscripts licenced by a university or other institution. This is scalable to the full spectrum of GDD. Using manuscripts identified through manual literature review, I use a full text download pipeline and NLP (natural language processing) based methods to generate disease models. These are comprised of HPO terms weighted according to their frequency in the literature. I demonstrate iterative refinement of these models, and use a custom annotated corpus of 50 papers to show the text mining process has high precision and recall. I demonstrate that these models clinically reflect true disease expressivity, as defined by manual comparison with expert literature reviews, for three well-characterised GDD. I compare these disease models to those in the most commonly used genetic disease phenotype databases. I show that the automated disease models have increased depth of phenotyping, i.e. there are more terms than those which are manually-generated. I show that, in comparison to ‘real life’ prospectively gathered phenotypic data, automated disease models outperform existing phenotype databases in predicting diagnosis, as defined by increased area under the curve (by 0.05 and 0.08 using different similarity measures) on ROC curve plots. I present a method for automated PubMed search at scale, to use as input for disease model generation. I annotated a corpus of 6500 abstracts. Using this corpus I show a high precision (up to 0.80) and recall (up to 1.00) for machine learning classifiers used to identify manuscripts relevant to GDD. These use hand-picked domain-specific features, for example utilising specific MeSH terms. This method can be used to scale automated literature curation to the full spectrum of GDD. I also present an analysis of the phenotypic terms used in one year of GDD-relevant papers in a prominent journal. This shows that use of supplemental data and parsing clinical report sections from manuscripts is likely to result in more patient-specific phenotype extraction in future. In summary, I present a method for automated curation of full text from the peer-reviewed literature in the context of GDD. I demonstrate that this method is robust, reflects clinical disease expressivity, outperforms existing manual literature curation, and is scalable. Applying this process to clinical testing in future should improve the efficiency and accuracy of diagnosis

Pathway and Network Approaches for Identification of Cancer Signature Markers from Omics Data

The advancement of high throughput omic technologies during the past few years has made it possible to perform many complex assays in a much shorter time than the traditional approaches. The rapid accumulation and wide availability of omic data generated by these technologies offer great opportunities to unravel disease mechanisms, but also presents significant challenges to extract knowledge from such massive data and to evaluate the findings. To address these challenges, a number of pathway and network based approaches have been introduced. This review article evaluates these methods and discusses their application in cancer biomarker discovery using hepatocellular carcinoma (HCC) as an example

Knowledge Management Approaches for predicting Biomarker and Assessing its Impact on Clinical Trials

The recent success of companion diagnostics along with the increasing regulatory pressure for better identification of the target population has created an unprecedented incentive for the drug discovery companies to invest into novel strategies for stratified biomarker discovery. Catching with this trend, trials with stratified biomarker in drug development have quadrupled in the last decade but represent a small part of all Interventional trials reflecting multiple co-developmental challenges of therapeutic compounds and companion diagnostics. To overcome the challenge, varied knowledge management and system biology approaches are adopted in the clinics to analyze/interpret an ever increasing collection of OMICS data. By semi-automatic screening of more than 150,000 trials, we filtered trials with stratified biomarker to analyse their therapeutic focus, major drivers and elucidated the impact of stratified biomarker programs on trial duration and completion. The analysis clearly shows that cancer is the major focus for trials with stratified biomarker. But targeted therapies in cancer require more accurate stratification of patient population. This can be augmented by a fresh approach of selecting a new class of biomolecules i.e. miRNA as candidate stratification biomarker. miRNA plays an important role in tumorgenesis in regulating expression of oncogenes and tumor suppressors; thus affecting cell proliferation, differentiation, apoptosis, invasion, angiogenesis. miRNAs are potential biomarkers in different cancer. However, the relationship between response of cancer patients towards targeted therapy and resulting modifications of the miRNA transcriptome in pathway regulation is poorly understood. With ever-increasing pathways and miRNA-mRNA interaction databases, freely available mRNA and miRNA expression data in multiple cancer therapy have created an unprecedented opportunity to decipher the role of miRNAs in early prediction of therapeutic efficacy in diseases. We present a novel SMARTmiR algorithm to predict the role of miRNA as therapeutic biomarker for an anti-EGFR monoclonal antibody i.e. cetuximab treatment in colorectal cancer. The application of an optimised and fully automated version of the algorithm has the potential to be used as clinical decision support tool. Moreover this research will also provide a comprehensive and valuable knowledge map demonstrating functional bimolecular interactions in colorectal cancer to scientific community. This research also detected seven miRNA i.e. hsa-miR-145, has-miR-27a, has- miR-155, hsa-miR-182, hsa-miR-15a, hsa-miR-96 and hsa-miR-106a as top stratified biomarker candidate for cetuximab therapy in CRC which were not reported previously. Finally a prospective plan on future scenario of biomarker research in cancer drug development has been drawn focusing to reduce the risk of most expensive phase III drug failures

INTEROPERABILITY IN TOXICOLOGY: CONNECTING CHEMICAL, BIOLOGICAL, AND COMPLEX DISEASE DATA

The current regulatory framework in toxicology is expanding beyond traditional animal toxicity testing to include new approach methodologies (NAMs) like computational models built using rapidly generated dose-response information like US Environmental Protection Agency’s Toxicity Forecaster (ToxCast) and the interagency collaborative Tox21 initiative. These programs have provided new opportunities for research but also introduced challenges in application of this information to current regulatory needs. One such challenge is linking in vitro chemical bioactivity to adverse outcomes like cancer or other complex diseases. To utilize NAMs in prediction of complex disease, information from traditional and new sources must be interoperable for easy integration. The work presented here describes the development of a bioinformatic tool, a database of traditional toxicity information with improved interoperability, and efforts to use these new tools together to inform prediction of cancer and complex disease. First, a bioinformatic tool was developed to provide a ranked list of Medical Subject Heading (MeSH) to gene associations based on literature support, enabling connection of complex diseases to genes potentially involved. Second, a seminal resource of traditional toxicity information, Toxicity Reference Database (ToxRefDB), was redeveloped, including a controlled vocabulary for adverse events used to map identifiers in the Unified Medical Language System (UMLS), thus enabling a connection to MeSH terms. Finally, gene to MeSH associations were used to evaluate the biological coverage of ToxCast for cancer to understand the capacity to use ToxCast to identify chemical hazard potential. ToxCast covers many gene targets putatively linked to cancer; however, more information on pathways in cancer progression is needed to identify robust associations between chemical exposure and risk of complex disease. The findings herein demonstrate that increased interoperability between data resources is necessary to leverage the large amount of data currently available to understand the role environmental exposures play in etiologies of complex diseases.Doctor of Philosoph

Identification of novel miRNAs as diagnostic and prognostic biomarkers for prostate cancer using an in silico approach

Magister Scientiae - MSc (Biotechnology)Cancer is known as uncontrollable cell growth which results in the formation of tumours in the areas that are affected by the cancer. There are two types of tumours: benign and malignant. This study focus is on prostate cancer (PCa) as one of the most common cancers in men around the world. A previous study has reported that there were 27,132 new cases of cancer in South Africa in 2010. Out of those, 4652 were prostate cancer cases, which make it a considerable issue. The prostate is a gland that forms part of the male reproductive system. Prostate cancer is more apparent in men over the age of 65 years however it can be present in men of a lower age. However it is rare in men under 45 years of age. Prostate cancer start as a small group of cancer cells that can grow into a mature tumour. In the advanced stages, the tumour cells can spread to other tissue by metastases and can lead to death. Current diagnostic tools include Digital Rectal Examination (DRE), the Prostate-Specific Antigen test (PSA) ultra sound, and biopsy

MicroRNAs as predictive biomarkers for diagnosis and prognosis of colorectal cancer using in silico approaches

Philosophiae Doctor - PhDColorectal cancer (CRC) is referred to as cancers that arise in the colon or rectum. Rectal cancer is most often defined as cancers originating within 15 cm from the anal verge. The crude incidence of CRC in sub-Sahara African populations has been found to be 4.04/100,000 (4.38 for men and 3.69 for women). CRC stage correlates well with survival/cure rates with the majority of patients diagnosed with CRC presenting with advanced disease and a low survival/cure rate.2022-04-3

Elucidating the constitutional genetic basis of multiple primary tumours

Cancer predisposition syndromes are responsible for a significant minority of neoplasm occurrences and beget opportunities to mitigate the associated risks with clinical intervention. They are caused by constitutional genetic variation affecting tumour suppressor genes or proto-oncogenes and recent improvements in sequencing technology have led to greater capability to detect more affected individuals through diagnostic testing. Predisposition syndromes can be suggested by a variety of clinical indicators that can be used to prioritise cases for diagnostic assessment or research. These include the occurrence of multiple primary tumours (MPT) in the same individual, a phenomenon which forms the basis of this project and is observed more frequently in populations as cancer survivorship increases. A series of MPT cases was assembled through recruitment from clinical genetics centres. These had previously been assessed without a molecular diagnosis being identified. A heterogenous tumour profile was observed, apparently influenced by population frequency and pattern of referral to cancer genetics services. Comparison with a population based series emphasised these influences. Constitutional DNA samples from participants were subject to whole genome sequencing (WGS) and panel based sequencing to elicit causative variants in known cancer predisposition genes (CPGs) or at novel loci. Analysis of WGS data for variants affecting known CPGs showed that a pathogenic or likely pathogenic (P/LP) variant was detected in around 15% of previously undiagnosed cases. Just over 1% of these were structural variants, which would unlikely have been detected with nonWGS approaches. Over 40% of probands who harboured a P/LP variant had been diagnosed with a tumour that is not typical of P/LP variants in the relevant CPG, which may represent phenotypic expansion or coincidental tumours. Three individuals harboured two P/LP variants in two CPGs, a phenomenon which is reviewed in the context of cases previously reported in the literature. A clinical scoring system was devised and tested to predict which individuals had P/LP variants but this showed little potential for clinical utility. Panel data was interrogated for possible mosaic CPG variants but few were identified that were likely to be genuinely post-zygotic and causative. Various case control based analyses to identify novel loci associated with cancer predisposition were undertaken using single nucleotide variant calls and/or structural variant calls from WGS data. These focused on a number of putative coding and non-coding regions including genes recurrently mutated in somatic cancer studies, known or putative gain of function CPGs, telomere related genes, enhancers/promoters, ultra-conserved elements and expression quantitative trait loci observed in normal and cancer tissues. The potential efficacy of this approach was demonstrated by the over-representation of variants affecting known CPGs (CHEK2, PALB2, MAX, NF1) in some phenotypic subgroups. However, there was little evidence for novel loci relevant to cancer predisposition.Cambridge Cancer Centre Clinical Research Training Fellowshi