Olga Zolotareva

Bio: Olga Zolotareva is an academic researcher from Bielefeld University. The author has contributed to research in topics: Transfer of learning & Comorbidity. The author has an hindex of 8, co-authored 23 publications receiving 350 citations. Previous affiliations of Olga Zolotareva include Moscow State University & University of Hamburg.

Antigen receptor repertoire profiling from RNA-seq data

Abstract: Somatic recombination and accumulation of mutations in V-D-J segments result in vast heterogeneity of T-cell receptor (TCR) and immunoglobulin repertoires1,2. High-throughput profiling of immune receptors has become an important tool for studies of adaptive immunity and for the development of diagnostics, vaccines, and immunotherapies3,4,5,6,7. There are efficient molecular and software tools for the targeted sequencing of TCR and immunoglobulin repertoires6,8, including MiXCR, developed by our team9. However, sufficient amount and quality of tissue or extracted RNA or DNA are not always available for analysis.

MOLI: multi-omics late integration with deep neural networks for drug response prediction.

Abstract: Motivation Historically, gene expression has been shown to be the most informative data for drug response prediction. Recent evidence suggests that integrating additional omics can improve the prediction accuracy which raises the question of how to integrate the additional omics. Regardless of the integration strategy, clinical utility and translatability are crucial. Thus, we reasoned a multi-omics approach combined with clinical datasets would improve drug response prediction and clinical relevance. Results We propose MOLI, a multi-omics late integration method based on deep neural networks. MOLI takes somatic mutation, copy number aberration and gene expression data as input, and integrates them for drug response prediction. MOLI uses type-specific encoding sub-networks to learn features for each omics type, concatenates them into one representation and optimizes this representation via a combined cost function consisting of a triplet loss and a binary cross-entropy loss. The former makes the representations of responder samples more similar to each other and different from the non-responders, and the latter makes this representation predictive of the response values. We validate MOLI on in vitro and in vivo datasets for five chemotherapy agents and two targeted therapeutics. Compared to state-of-the-art single-omics and early integration multi-omics methods, MOLI achieves higher prediction accuracy in external validations. Moreover, a significant improvement in MOLI's performance is observed for targeted drugs when training on a pan-drug input, i.e. using all the drugs with the same target compared to training only on drug-specific inputs. MOLI's high predictive power suggests it may have utility in precision oncology. Availability and implementation https://github.com/hosseinshn/MOLI. Supplementary information Supplementary data are available at Bioinformatics online.

Novel candidate genes important for asthma and hypertension comorbidity revealed from associative gene networks

Abstract: Hypertension and bronchial asthma are a major issue for people’s health. As of 2014, approximately one billion adults, or ~ 22% of the world population, have had hypertension. As of 2011, 235–330 million people globally have been affected by asthma and approximately 250,000–345,000 people have died each year from the disease. The development of the effective treatment therapies against these diseases is complicated by their comorbidity features. This is often a major problem in diagnosis and their treatment. Hence, in this study the bioinformatical methodology for the analysis of the comorbidity of these two diseases have been developed. As such, the search for candidate genes related to the comorbid conditions of asthma and hypertension can help in elucidating the molecular mechanisms underlying the comorbid condition of these two diseases, and can also be useful for genotyping and identifying new drug targets. Using ANDSystem, the reconstruction and analysis of gene networks associated with asthma and hypertension was carried out. The gene network of asthma included 755 genes/proteins and 62,603 interactions, while the gene network of hypertension - 713 genes/proteins and 45,479 interactions. Two hundred and five genes/proteins and 9638 interactions were shared between asthma and hypertension. An approach for ranking genes implicated in the comorbid condition of two diseases was proposed. The approach is based on nine criteria for ranking genes by their importance, including standard methods of gene prioritization (Endeavor, ToppGene) as well as original criteria that take into account the characteristics of an associative gene network and the presence of known polymorphisms in the analysed genes. According to the proposed approach, the genes IL10, TLR4, and CAT had the highest priority in the development of comorbidity of these two diseases. Additionally, it was revealed that the list of top genes is enriched with apoptotic genes and genes involved in biological processes related to the functioning of central nervous system. The application of methods of reconstruction and analysis of gene networks is a productive tool for studying the molecular mechanisms of comorbid conditions. The method put forth to rank genes by their importance to the comorbid condition of asthma and hypertension was employed that resulted in prediction of 10 genes, playing the key role in the development of the comorbid condition. The results can be utilised to plan experiments for identification of novel candidate genes along with searching for novel pharmacological targets.

AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics.

Abstract: Motivation The goal of pharmacogenomics is to predict drug response in patients using their single- or multi-omics data. A major challenge is that clinical data (i.e. patients) with drug response outcome is very limited, creating a need for transfer learning to bridge the gap between large pre-clinical pharmacogenomics datasets (e.g. cancer cell lines), as a source domain, and clinical datasets as a target domain. Two major discrepancies exist between pre-clinical and clinical datasets: (i) in the input space, the gene expression data due to difference in the basic biology, and (ii) in the output space, the different measures of the drug response. Therefore, training a computational model on cell lines and testing it on patients violates the i.i.d assumption that train and test data are from the same distribution. Results We propose Adversarial Inductive Transfer Learning (AITL), a deep neural network method for addressing discrepancies in input and output space between the pre-clinical and clinical datasets. AITL takes gene expression of patients and cell lines as the input, employs adversarial domain adaptation and multi-task learning to address these discrepancies, and predicts the drug response as the output. To the best of our knowledge, AITL is the first adversarial inductive transfer learning method to address both input and output discrepancies. Experimental results indicate that AITL outperforms state-of-the-art pharmacogenomics and transfer learning baselines and may guide precision oncology more accurately. Availability and implementation https://github.com/hosseinshn/AITL. Supplementary information Supplementary data are available at Bioinformatics online.

Whole exome sequencing links dental tumor to an autosomal-dominant mutation in ANO5 gene associated with gnathodiaphyseal dysplasia and muscle dystrophies

Abstract: Tumors of the jaws may represent different human disorders and frequently associate with pathologic bone fractures. In this report, we analyzed two affected siblings from a family of Russian origin, with a history of dental tumors of the jaws, in correspondence to original clinical diagnosis of cementoma consistent with gigantiform cementoma (GC, OMIM: 137575). Whole exome sequencing revealed the heterozygous missense mutation c.1067G > A (p.Cys356Tyr) in ANO5 gene in these patients. To date, autosomal-dominant mutations have been described in the ANO5 gene for gnathodiaphyseal dysplasia (GDD, OMIM: 166260) and multiple recessive mutations have been described in the gene for muscle dystrophies (OMIM: 613319, 611307); the same amino acid (Cys) at the position 356 is mutated in GDD. These genetic data and similar clinical phenotypes demonstrate that the GC and GDD likely represent the same type of bone pathology. Our data illustrate the significance of mutations in single amino-acid position for particular bone tissue pathology. Modifying role of genetic variations in another gene on the severity of the monogenic trait pathology is also suggested. Finally, we propose the model explaining the tissue-specific manifestation of clinically distant bone and muscle diseases linked to mutations in one gene.

Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis

Abstract: The quantity of tumor-infiltrating lymphocytes (TILs) in breast cancer (BC) is a robust prognostic factor for improved patient survival, particularly in triple-negative and HER2-overexpressing BC subtypes1. Although T cells are the predominant TIL population2, the relationship between quantitative and qualitative differences in T cell subpopulations and patient prognosis remains unknown. We performed single-cell RNA sequencing (scRNA-seq) of 6,311 T cells isolated from human BCs and show that significant heterogeneity exists in the infiltrating T cell population. We demonstrate that BCs with a high number of TILs contained CD8+ T cells with features of tissue-resident memory T (TRM) cell differentiation and that these CD8+ TRM cells expressed high levels of immune checkpoint molecules and effector proteins. A CD8+ TRM gene signature developed from the scRNA-seq data was significantly associated with improved patient survival in early-stage triple-negative breast cancer (TNBC) and provided better prognostication than CD8 expression alone. Our data suggest that CD8+ TRM cells contribute to BC immunosurveillance and are the key targets of modulation by immune checkpoint inhibition. Further understanding of the development, maintenance and regulation of TRM cells will be crucial for successful immunotherapeutic development in BC.

B cells, plasma cells and antibody repertoires in the tumour microenvironment

Abstract: Recent data show that B cells and plasma cells located in tumours or in tumour-draining lymph nodes can have important roles in shaping antitumour immune responses. In tumour-associated tertiary lymphoid structures, T cells and B cells interact and undergo cooperative selection, specialization and clonal expansion. Importantly, B cells can present cognate tumour-derived antigens to T cells, with the functional consequences of such interactions being shaped by the B cell phenotype. Furthermore, the isotype and specificity of the antibodies produced by plasma cells can drive distinct immune responses. Here we summarize our current knowledge of the roles of B cells and antibodies in the tumour microenvironment. Moreover, we discuss the potential of using immunoglobulin repertoires as a source of tumour-specific receptors for immunotherapy or as biomarkers to predict the efficacy of immunotherapeutic interventions.

Transient rest restores functionality in exhausted CAR-T cells through epigenetic remodeling.

Abstract: T cell exhaustion limits immune responses against cancer and is a major cause of resistance to chimeric antigen receptor (CAR)-T cell therapeutics. Using murine xenograft models and an in vitro model wherein tonic CAR signaling induces hallmark features of exhaustion, we tested the effect of transient cessation of receptor signaling, or rest, on the development and maintenance of exhaustion. Induction of rest through enforced down-regulation of the CAR protein using a drug-regulatable system or treatment with the multikinase inhibitor dasatinib resulted in the acquisition of a memory-like phenotype, global transcriptional and epigenetic reprogramming, and restored antitumor functionality in exhausted CAR-T cells. This work demonstrates that rest can enhance CAR-T cell efficacy by preventing or reversing exhaustion, and it challenges the notion that exhaustion is an epigenetically fixed state.

Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19

Abstract: The current pandemic has highlighted the need for methodologies that can quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections. In the past decade, network medicine has developed and validated multiple predictive algorithms for drug repurposing, exploiting the sub-cellular network-based relationship between a drug's targets and disease genes. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs that had been experimentally screened in VeroE6 cells, and the list of drugs under clinical trial, that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that while most algorithms offer predictive power for these ground truth data, no single method offers consistently reliable outcomes across all datasets and metrics. This prompted us to develop a multimodal approach that fuses the predictions of all algorithms, showing that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We find that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these drugs rely on network-based actions that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.