Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Novel technologies and emerging biomarkers for personalized cancer immunotherapy

The culmination of over a century's work to understand the role of the immune system in tumor control has led to the recent advances in cancer immunotherapies that have resulted in durable clinical responses in patients with a variety of malignancies. Cancer immunotherapies are rapidly changing traditional treatment paradigms and expanding the therapeutic landscape for cancer patients. However, despite the current success of these therapies, not all patients respond to immunotherapy and even those that do often experience toxicities. Thus, there is a growing need to identify predictive and prognostic biomarkers that enhance our understanding of the mechanisms underlying the complex interactions between the immune system and cancer. Therefore, the Society for Immunotherapy of Cancer (SITC) reconvened an Immune Biomarkers Task Force to review state of the art technologies, identify current hurdlers, and make recommendations for the field. As a product of this task force, Working Group 2 (WG2), consisting of international experts from academia and industry, assembled to identify and discuss promising technologies for biomarker discovery and validation. Thus, this WG2 consensus paper will focus on the current status of emerging biomarkers for immune checkpoint blockade therapy and discuss novel technologies as well as high dimensional data analysis platforms that will be pivotal for future biomarker research. In addition, this paper will include a brief overview of the current challenges with recommendations for future biomarker discovery

The immune microenvironment in mantle cell lymphoma : Targeted liquid and spatial proteomic analyses

The complex interplay of the tumour and immune cells affects tumour growth, progression, and response to treatment. Restorationof effective immune response forms the basis of onco-immunology, which further enabled the development of immunotherapy. Inthe era of precision medicine, pin-pointing patient biological heterogeneity especially in relation to patient-specific immunemicroenvironment is a necessity for the discovery of novel biomarkers and for development of patient stratification tools for targetedtherapeutics. Mantle cell lymphoma (MCL) is a rare and aggressive subtype of B-cell lymphoma with poor survival and high relapserates. Previous investigations of MCL have largely focused on the tumour itself and explorations of the immune microenvironmenthave been limited. This thesis and the included five papers, investigates multiple aspects of the immune microenvironment withrespect to proteomic analysis performed on tissue and liquid biopsies of diagnostic and relapsed/refractory (R/R) MCL cohorts.Analyses based on liquid biopsies (serum) in particular are relevant for aggressive cases such as in relapse, where invasiveprocedures for extracting tissues is not recommended. Thus, paper I-II probes the possibility of using serum for treatment andoutcome-associated biomarker discovery in R/R MCL, using a targeted affinity-based protein microarray platform quantifyingimmune-regulatory and tumor-secretory proteins in sera. Analysis performed in paper I using pre-treatment samples, identifies 11-plex biomarker signature (RIS – relapsed immune signature) associated with overall survival. Further integration of RIS with mantlecell lymphoma international prognostic index (MIPI) led to the development of MIPIris index for the stratification of R/R MCL intothree risk groups. Moreover, longitudinal analysis can be important in understanding how patient respond to treatment and thiscan further guide therapeutic interventions. Thus, paper II is a follow-up study wherein longitudinal analyses was performed onpaired samples collected at pre-treatment (baseline) and after three months of chemo-immunotherapy (on-treatment). We showhow genetic aberrations can influence systemic profiles and thus integrating genetic information can be crucial for treatmentselection. Furthermore, we observe that the inter-patient heterogeneity associated with absolute values can be circumvented byusing velocity of change to capture general changes over time in groups of patients. Thus, using velocity of change in serumproteins between pre- and on-treatment samples identified response biomarkers associated with minimal residual disease andprogression. While exploratory analysis using high dimensional omics-based data can be important for accelerating discovery,translating such information for clinical utility is a necessity. Thus, in paper III, we show how serum quantification can be usedcomplementary tissue-identified prognostic biomarkers and this can enable faster clinical implementation. Presence of CD163+M2-like macrophages has shown to be associated with poor outcome in MCL tissues. We show that higher expression of sCD163levels in sera quantified using ELISA, is also associated with poor outcome in diagnostic and relapsed MCL. Furthermore, wesuggest a cut-off for sCD163 levels that can be used for clinical utility. Further exploration of the dynamic interplay of tumourimmunemicroenvironment is now possible using spatial resolved omics for tissue-based analysis. Thus, in paper IV and V, weanalyse cell-type specific proteomic data collected from tumour and immune cells using GeoMx™ digital spatial profiler. In paperIV, we show that presence as well as spatial localization of CD163+ macrophage with respect to tumour regions impactsmacrophage phenotypic profiles. Further modulation in the profile of surrounding tumour and T-cells is observed whenmacrophages are present in the vicinity. Based on this analysis, we suggest MAPK pathway as a potential therapeutic target intumours with CD163+ macrophages. Immune composition can be defined not just by the type of cells, but also with respect tofrequency and spatial localization and this is explored in paper V with respect to T-cell subtypes. Thus, in paper V, we optimizeda workflow of multiplexed immunofluorescence image segmentation that allowed us to extract cell metrics for four subtypes ofCD3+ T-cells. Using this data, we show that higher infiltration of T-cells is associated with a positive outcome in MCL. Moreover,by combining image derived metrics to cell specific spatial omics data, we were able to identify immunosuppressivemicroenvironment associated with highly infiltrated tumours and suggests new potential targets of immunotherapy with respect toIDO1, GITR and STING. In conclusion, this thesis explores systemic and tumor-associated immune microenvironment in MCL, fordefining patient heterogeneity, developing methods of patient stratification and for identifying novel and actionable biomarkers

A Systematic Analysis of miRNA Transcriptome in Marek’s Disease Virus-Induced Lymphoma Reveals Novel and Differentially Expressed miRNAs

Marek’s disease is a lymphoproliferative neoplastic disease of the chicken, which poses a serious threat to poultry health. Marek’s disease virus (MDV)-induced T-cell lymphoma is also an excellent biomedical model for neoplasia research. Recently, miRNAs have been demonstrated to play crucial roles in mediating neoplastic transformation. To investigate host miRNA expression profiles in the tumor transformation phase of MDV infection, we performed deep sequencing in two MDVinfected samples (tumorous spleen and MD lymphoma from liver), and two non-infected controls (non-infected spleen and lymphocytes). In total, 187 and 16 known miRNAs were identified in chicken and MDV, respectively, and 17 novel chicken miRNAs were further confirmed by qPCR. We identified 28 down-regulated miRNAs and 11 up-regulated miRNAs in MDVinfected samples by bioinformatic analysis. Of nine further tested by qPCR, seven were verified. The gga-miR-181a, gga-miR- 26a, gga-miR-221, gga-miR-222, gga-miR-199*, and gga-miR-140* were down-regulated, and gga-miR-146c was upregulated in MDV-infected tumorous spleens and MD lymphomas. In addition, 189 putative target genes for seven differentially expressed miRNAs were predicted. The luciferase reporter gene assay showed interactions of gga-miR-181a with MYBL1, gga-miR-181a with IGF2BP3, and gga-miR-26a with EIF3A. Differential expression of miRNAs and the predicted targets strongly suggest that they contribute to MDV-induced lymphomagenesis

SITC cancer immunotherapy resource document: a compass in the land of biomarker discovery.

Since the publication of the Society for Immunotherapy of Cancer\u27s (SITC) original cancer immunotherapy biomarkers resource document, there have been remarkable breakthroughs in cancer immunotherapy, in particular the development and approval of immune checkpoint inhibitors, engineered cellular therapies, and tumor vaccines to unleash antitumor immune activity. The most notable feature of these breakthroughs is the achievement of durable clinical responses in some patients, enabling long-term survival. These durable responses have been noted in tumor types that were not previously considered immunotherapy-sensitive, suggesting that all patients with cancer may have the potential to benefit from immunotherapy. However, a persistent challenge in the field is the fact that only a minority of patients respond to immunotherapy, especially those therapies that rely on endogenous immune activation such as checkpoint inhibitors and vaccination due to the complex and heterogeneous immune escape mechanisms which can develop in each patient. Therefore, the development of robust biomarkers for each immunotherapy strategy, enabling rational patient selection and the design of precise combination therapies, is key for the continued success and improvement of immunotherapy. In this document, we summarize and update established biomarkers, guidelines, and regulatory considerations for clinical immune biomarker development, discuss well-known and novel technologies for biomarker discovery and validation, and provide tools and resources that can be used by the biomarker research community to facilitate the continued development of immuno-oncology and aid in the goal of durable responses in all patients

Biomarker-Based Targeted Therapeutics

Cancer biomarkers are emerging as important tools for disease diagnosis, prediction and prognosis. A significant number of studies have been reported in the field of biomarker discovery due to their potential as personalized targeted therapy. With the converging gap about their utilization as specific targets, studies have focused on identifying disease-specific biomarkers in different cancer types. This chapter provides a comprehensive overview about different cancer-associated biomarkers, their prevalence in different cancer types and their use as targeted therapy. Additionally, we provide an in-sight on the therapeutic and diagnostic potential of different noncoding RNAs as cancer biomarkers

Circulating non-coding RNAs in head and neck cancer : roles in diagnosis, prognosis, and therapy monitoring

Head and neck cancer (HNC), the seventh most common form of cancer worldwide, is a group of epithelial malignancies affecting sites in the upper aerodigestive tract. The 5-year overall survival for patients with HNC has stayed around 40-50% for decades, with mortality being attributable mainly to late diagnosis and recurrence. Recently, non-coding RNAs, including tRNA halves, YRNA fragments, microRNAs (miRNAs), and long non-coding RNAs (lncRNAs), have been identified in the blood and saliva of patients diagnosed with HNC. These observations have recently fueled the study of their potential use in early detection, diagnosis, and risk assessment. The present review focuses on recent insights and the potential impact that circulating non-coding RNA evaluation may have on clinical decision-making in the management of HNC

Ultrasensitive Molecular Monitoring of Breast Cancer and Acute Myeloid Leukemia

Cancer is the common name to a group of biologically diverse malignant neoplastic diseases. Approximately 18 million people are diagnosed with cancer annually and 8.8 million patients die from it. Tumorigenesis and progression of cancer are driven by alterations in the cancer cell genome. These alterations lead to gain of oncogenic functions, loss of tumor suppressor functions, or may be chromosomal rearrangements without obvious function, and these alterations themselves can serve as tumor-specific biomarkers that may have diagnostic and clinical utility.In this thesis, we investigated oncogenic and tumor suppressive genes in breast cancers and leukemias, with a focus on the PTEN/PIK3CA pathway as well as minimally-invasive monitoring of cancer patients using “liquid biopsies.” We studied the underlying mechanism of PTEN protein loss in breast cancer, and showed how various types of tumor-specific mutations, including those in PIK3CA, can be used as biomarkers to monitor the dynamics of occult tumor burden, evaluate the degree of tumor content dissemination into the bloodstream with mammographic compression, and detect minimal residual disease in breast cancer and acute myeloid leukemia.In Paper I, we found that the frequent loss of PTEN protein in human breast cancer is not attributable to the overexpression of the E3 ubiquitin ligase NEDD4, and thus NEDD4 is unlikely to be a regulator of the oncogenic PI3K/PTEN signaling pathway. In Paper II, we showed that serial monitoring of tumor specific chromosomal rearrangements, identified with low coverage whole genome sequencing and then measured in blood samples by digital PCR (dPCR), is a highly sensitive and specific approach to detect occult breast cancer disease prior to the onset of symptoms and clinical detection. Detected plasma ctDNA level was a quantitative predictor of poor relapse-free and overall survival. In Paper III, we confirmed the general safety of mammography, using FDA approved CellSearch® and our ultrasensitive mutation detection dPCR technology IBSAFE, that mammographic compression of the breast with a breast tumor does not appear to lead to significant additional dissemination of CTCs and ctDNA into the bloodstream. In Paper IV, we showed that acute myeloid leukemia specific mutations can be serially monitored in follow-up bone marrow samples by IBSAFE, providing an insight in subclonal evolution of the leukemia and the status of minimal residual disease.These results and our mutation detection technology suggest they have high potential to be utilized in assessing treatment response, monitoring the disease course, detecting remnant tumor deposits with targetable mutations, and helping to speed the development of new drugs in in the future

High-throughput analysis and functional interpretation of extracellular vesicle content in hematological malignancies

Extracellular vesicles (EVs) are membrane-coated particles secreted by virtually all cell types in response to different stimuli, both in physiological and pathological conditions. Their content generally reflects their biological functions and includes a variety of molecules, such as nucleic acids, proteins and cellular components. The role of EVs as signaling vehicles has been widely demonstrated. In particular, they are actively involved in the pathogenesis of several hematological malignancies (HM), mainly interacting with a number of target cells and inducing functional and epigenetic changes. In this regard, by releasing their cargo, EVs play a pivotal role in the bilateral cross-talk between tumor microenvironment and cancer cells, thus facilitating mechanisms of immune escape and supporting tumor growth and progression. Recent advances in high-throughput technologies have allowed the deep characterization and functional interpretation of EV content. In this review, the current knowledge on the high-throughput technology-based characterization of EV cargo in HM is summarized