Cancer still accounts for nearly 1 in every 4 deaths worldwide. New immunotherapies have the power to induce durable responses in patients with fatal cancers, but only a small percentage of patients are responsive. Not only that but clinicians are also faced with new challenges with managing harmful immune-related adverse events (irAEs). A largely overlooked immune cell type in the context of immunotherapies are B cells, which can exert both anti-tumour and tumour-promoting effects by providing co-stimulatory signals and inhibitory signals for T cell activation, cytokines, and antibodies. B-cells produce anti-tumour antibodies, which can potentially mediate antibody-dependent cellular cytotoxicity (ADCC). It is well established that many cancer types induce an antibody response, which can even be used for early diagnostic purposes. Besides anti-tumour antigen (TAA) antibodies, cancer patients also produce autoantibodies that bind to self-antigens. Breakthrough of tolerance and elevated levels of autoantibodies is also a prominent feature of many autoimmune diseases. Hence, it is particularly relevant to investigate irAEs resembling autoimmune phenotypes.

In this presentation, we will discuss how autoantibody (AAB) profiling using the bead-based multiplex Luminex® Technology combined with machine-learning procedures offers a unique tool for biomarker research. Our SeroTag platform has been developed for quantifying the serum anti-tumour and auto-antibody reactivities towards customized and pre-validated panels of protein and peptide antigens (ImmunoINSIGHT by Oncimmune®). The highly dimensional data can be used to extract predictive models, identify biomarkers, create patient subgroups and also characterize regulatory networks involving cytokine and receptors suggesting that autoantibodies actively participate in immune regulation. Case studies for applications in melanoma CPI treatment and other cancers will be discussed.

Thus, autoantibodies hold the potential to serve as biomarkers of a sustained humoral anti-tumour response in cancer patients treated with immunotherapeutic approaches. Autoantibody measurements can be performed using easily accessible ‘liquid biopsy’ serum samples with minimal sample consumption and high stability. The deployment of an autoantibody immune assay can be easily achieved during clinical development using the identified reactive proteins and standard assay systems.

Learning Objectives:

1. Discuss autoantibodies and how they are relevant immunological biomarkers.

2. Explain how autoantibodies can also target cytokines, receptors they are involved in immune regulation.

3. Explain how autoantibody multiplexing and individual assays can be deployed during clinical research.

Receptors that respond to hormones, neurotransmitters, chemokines and growth factors traffic to the cell surface where they are activated by ligands and signal via coupling to molecules such as G proteins or Grb2. For most G protein-coupled receptors (GPCRs), arrestins then bind and inhibit plasma membrane G protein coupling, facilitate internalization via clathrin-mediated endocystosis, and potentially generate a secondary signalling complex. Signalling potentially continues in endosomes, and receptors are then either recycled to the cell surface or are targeted for degradation. Furthermore, heteromerization can lead to transactivation, transinhibition, or modulation of receptor signalling pathways. Because of the importance of kinetics, being able to measure receptor function in live cells and in real time is highly advantageous, and this can be achieved through the use of technologies such as bioluminescence resonance energy transfer (BRET). BRET uses a donor luciferase enzyme fused to a protein of interest that transfers resonance energy to an acceptor, such as a small fluorescent moiety like BODIPY, or a derivative of green fluorescent protein like Venus. This only occurs if donor and acceptor are within 10 nm of each other. We have utilised BRET to monitor ligand binding, G protein and Grb2 coupling, arrestin recruitment and receptor trafficking. We have done this both with transient transfection and through tagging endogenous receptors with luciferase using CRISPR/Cas9 gene editing. We have also assessed numerous heteromer complexes, and our molecular pharmacology investigations have provided the rationale for both pre-clinical and clinical studies.

Learning Objectives:

1. Explain how the bioluminescence resonance energy transfer (BRET) technology can be used to monitor receptor complexes

2. Explain the implications of receptor heteromerization and how it can be investigated.

3. Explain the benefits of utilizing CRISPR/Cas9 to enable assessment of proteins under endogenous promotion.

What does a quantum leap in drug development look like and what is necessary to make it happen? Our greatest opportunity to deliver significant increases in the speed and effectiveness of drug development won’t come from the lab; it will happen from new, more efficient and equitable clinical trials. What we spend today to get 1 successful NME to market could have covered 90 drugs just 70 years ago, even after we adjust for inflation. R&D is getting more expensive, but less efficient. And looking at representation, 75% of the 32,000 participants in the trials of 53 novel drugs approved in 2020 by the FDA were white. The status quo must be changed to make clinical trials human centric. Only then can medicines serve our broad and diverse humanity. 

We already have the technologies available to transform clinical trials from highly costly, time intensive and centralized undertakings to rapidly scalable, decentralized, collaborative exercises that dramatically increase access to a broader range of participants and improve retention throughout the life of a study and even beyond. We know that rapid change is possible. When COVID 19 vaccines were developed, they were developed in less than 10% of the time it takes for a typical vaccine to be developed, by working together as an industry, with regulatory support and the use of sophisticated digital solutions. 

Now is the time for the entire drug development ecosystem, together, to redefine the effectiveness equation. Solving for ways to improve the rigor, speed and inclusivity of clinical trials will deliver a significant shift in the overall drug development value chain. If we succeed, we will also solve for equity, cost, and unmet health needs. 

Learning Objectives:

1. Identify key barriers to delivering faster drug development.

2. Examine technologies and processes to deliver more responsive, streamlined clinical trials.

3. Define potential outcomes from having decentralized clinical trials.

Flow cytometry is an essential tool for in-process control and release testing of cell therapy products. Standardization of these analytical assays is critical for the success of the manufacturing process and final product quality. However, achieving standardization in flow cytometry can be full of hurdles. In this talk, we will review the common challenges in developing a cell therapy from pre-clinical research to the clinic. Highlights will include details on flow cytometry instrumentation, software reporting and analysis, and top quality recombinant antibody technology - all integrated into seamless, automatic workflows.

Learning Objectives:

1. Explain the challenges of flow analytics in pre-clinical and clinical research.

2. Discuss quality control methods to combat sources of variation during cell analyses.

3. Explain how mitigation strategies improved data quality and time savings.

Integrating external and internal ‘omics data is a key element of hypothesis generation, target identification, and biomarker prioritization when it comes to drug discovery. It is never as straightforward as it seems and usually costs more and takes more time than anticipated, often with poor results. In this talk, Joseph Pearson, PhD, from QIAGEN Digital Insights will discuss approaches and resources to overcome these obstacles and improve outcomes. 

Learning Objectives: 

1. Identify where automated curation approaches can leave you short.

2. Discuss why data scientists spend most of their time not doing data science.

3. Identify where bioinformaticians and data scientist spend most of their time.

4. Explain what is required of high-quality ‘omics data.

5. Analyze the example of high-quality TCGA data.

6. Explain how to extend these practices to internal data structures.

High-throughput sequencing technology such as RNASeq and whole genome sequencing has enabled deep characterization of health and disease states.  Biomarker discovery by these means has only had limited success translating to the clinic.  While these disease specific biomarker discoveries in cancer have been proven actionable, the magnitude of response to the indicated therapy tends to be variable across different patients and a portion of these patients ultimately develop drug resistance.  As such, robust biomarker discovery and development strategies are essential in the drug development process – not only to identify new therapeutic targets and identify patients that are likely to respond to therapy, but also identify patients that are likely to develop drug resistance.  Spatial biology technology, such as NanoString’s GeoMx, presents a unique opportunity for biomarker discovery in the drug development process and this type of biomarker discovery approach will be the focus of this presentation.

For research use only.  Not for use in diagnostic procedures.
 

Learning Objectives:

1. Identify challenges associated with biomarker discovery.

2. Discuss what spatial biology is, and why it is gaining so much attention right now.

3. Explain the unique benefits spatial biology can provide for biomarker discovery.

Genetically engineered cellular models are an integral part of the drug discovery process from early discovery through clinical biomanufacturing. The various applications of these cellular models are seemingly endless, including evaluating efficacy of CAR-T cells, screening drug candidates, and generating potency assays for therapeutic testing. Often research organizations must overcome resource and technical hurdles to generate complex cellular models quickly. Gene editing experts within the Cell Design Studio® team provide premier custom cell line engineering services to create and deliver unique cell-based assays tailored for drug discovery and preclinical manufacturing. We utilize state of the art editing technologies including CRISPR, zinc finger nucleases, lentiviruses, and custom plasmids to generate cell lines suitable for a range of applications. We partner closely with our customers at all stages of the process with regular updates and communication. In this presentation, we will discuss the technology used to generate custom cell lines, for both research use only and pre-clinical workflows, and the advantages to working with the Cell Design Studio® team.

Learning Objectives:

1. Discuss current gene editing technologies and how to apply them.

2. Identify the advantages to working with a research partner for model generation.

3. Discuss applications of cellular models for drug discovery research.

Current drug discovery workflows are inefficient. 90% of therapeutic agents reaching clinical trials fail, predominantly because traditional laboratory experiments fail to adequately predict a drug’s safety or efficacy in human. 

Building more predictive, human-relevant models is key to reducing drug attrition rates and the financial burden to pharmaceutical companies, however, this is not an insignificant task. Microphysiological systems (MPS), also known as organ-on-a-chip (OOC), have been designed to meet this need, filling the gap between preclinical testing and the clinic. MPS enable us to grow 3D organs and tissues from human cells with flow circulating through them to simulate blood and deliver nutrients. These lab-cultured mimics function and respond to drugs in the same way as in humans.

Currently, single-organ models that recapitulate either individual organ functions (such as the liver), or routes of entry into the body (such as gut and lung), are most common. While traditional in vitro approaches fail to recapitulate a human organs’ phenotype, architecture and function for more than seven days, MPSs extend culture longevity up to a month, enabling studies that were not previously possible, such as the ability to profile phase I and II drug metabolism, characterise slowly metabolised compounds, identify potential metabolite-driven toxicity, or detect chronic toxicity. In this presentation, we will discuss the use of liver and lung MPSs for drug safety, disease modelling and efficacy and provide insights into what is coming next to further improve preclinical drug development workflows.

Learning Objectives:

1. Identify preclinical drug development challenges.

2. Introduce MPSs and how/where they fit into the preclinical workflow.

3. Demonstrate how MPSs provide translatable insights into drug safety and efficacy.
 

The quest for alternatives to animal safety testing has fueled the development, characterization, and validation of many new technologies. There have been advances in cell and tissue models as well as the various platforms that utilize these models. Regulatory groups around the world have started to accept the data from these new models and, in some instances, have allowed full replacement of the existing animal tests. Although the use of these single organ models has been successful, the ability to model the more complex systemic multiple organ exposure scenario has not been realized. Early platforms, such as Organ-on-a-Chip and TissUse, show promise but have not yet delivered the data required to understand interactions between organs. Some important challenges with these models have been the plastics used, low fluid volume, a small amount of tissue, high cost, technical difficulty and the ability to link organ types together in a physiologically relevant manner. The success of an in vitro integrated organ model depends upon the presence of a simulated blood system, isolated organ compartments, the presence of key biological barriers, and metabolism. The fluid volumes must be large enough to allow for multiple samples over time, and the tissue mass must be sufficient to allow for the detection of biomarkers. The presentation today will present the development and characterization of a novel platform that addresses these important areas. A human dynamic multiple organ plate (HuDMOP) designed on a meso-scale will be discussed. The platform can accommodate up to six organ chambers each isolated and hence cultured under optimal conditions. The organs are connected via a simulated blood system which keeps the simulated blood separate from the organ culture media. Movement of the test drug or chemical is via passive diffusion across a semi-permeable membrane. The system can be used with most 3D tissue types, as well as 2D cultures already developed and validated. Data will be presented that demonstrate the three primary routes of drug or chemical exposure: oral, dermal, and inhalation. By using well known drugs or chemicals such as diclofenac, acetaminophen, cannabidiols, and cycloheximide it is possible to make comparisons to in vivo data. Pharmacokinetic data obtained with three organ circuits (e.g. Intestine-liver-kidney) will be shown. The HuDMOP platform demonstrates the ability of an in vitro integrated organ system to study systemic exposure by single or multiple doses as well as the development of physiologically based pharmacokinetic models. Ultimately, models like HuDMOP will enable the use of in vitro data to predict drug or chemical behavior in humans.

Learning Objectives:

1. Explain the advantages and disadvantages of microphysiology systems.

2. Discuss the types of questions that can be addressed.

3. Review how to develop drug or chemical kinetic data from an in vitro integrated organ system.

The continued evolution of precision medicine from the early successes seen in Oncology to other areas of medicine, in particular, Chronic Disease Management, is dependent on the efficient translation of biomarker discovery from the research laboratory to clinical practice. This journey can only be achieved through a rigorous process involving multiple stakeholders including clinical research, regulatory agencies, pharma industry, health systems, diagnostics manufacturers, digital and data technologists, payors and patients.  In this talk we will describe how all these elements are applied in a way that enables precision medicine to deliver on the promise of the right information being available at the right time to ensure the optimal treatments are delivered to patients who need them most.

Learning Objectives:

1. Explain what the steps are to translating biomarkers to clinical practice.

2. Describe who are the stakeholders in this journey.

3. Describe what the challenges are and how can these be addressed.

Adhesion G protein-coupled receptors (aGPCRs) are an ancient class of receptors that represent some of the largest transmembrane-integrated proteins in humans. Besides classic features of GPCRs, aGPCRs display a distinct N-terminal structure that contributes essentially to the size of these proteins. Most of the aGPCRs are still considered to be orphan, as detailed information on their signal transduction and physiological function are scarce. The finding of the deletion mutation that renders aGPCRs constitutively active prompted a rapid increase in number of aGPCR signaling studies. This laid ground for the discovery of the tethered agonist sequence, whose role in the complex signaling mechanisms of aGPCRs as well as the characterization of its derived peptide agonists, has been studied extensively. We found that aGPCRs can be activated through various mechanical stimuli, with or without the interaction with an endogenous binding partner. The mechanical force-induced activation depends on the existence of an intact core agonistic region within the tethered agonist sequence and can be modified through the adjacent positioning region. Transferring these observations to synthetic peptides allows for the establishment of agonists with increased specificity and different downstream signaling preferences. As the type of binding partner at the N terminus defines the required mechanical stimuli for receptor activation ligands could be considered allosteric modulators. We propose that aGPCRs ‘sense’ their environment through binding to surrounding proteins to translate this context into a specific signal. This localized signal can be the basis for future therapeutic strategies when targeting this important receptor class. 

Learning Objectives:

1. Discuss how adhesion-type G protein-coupled receptors (aGPCRs) are metabotropic mechanosensors that govern essential physiological functions and that are implicated in a wide range of diseases.

2. Explain how modulation of aGPCRs can be achieved through synthetic peptides, derived from the tethered agonist sequence, extracellular binding partners and a diverse set of mechanical forces. The combination of these different stimuli has the potential to yield efficient and specific therapeutic strategies targeting these receptors.

Cancer is a leading cause of morbidity and mortality in developed countries including the United States. Early detection of various cancers, before they spread and become incurable, has been the best weapon in the war on cancer that began with the signing of the National Cancer Act in 1971. The premise of Precision Medicine is the ability to customize personalized medical care to individual patients through the incorporation of molecular profiles and clinical characteristics in treatment determination utilizing molecular diagnostics, and targeted therapies.

Proteins uniquely produced by tumors and their surrounding microenvironment constitute valuable biomarkers that aid in diagnosis of initial tumors, screening the population, monitoring of tumor progression, metastases, response to treatment and relapse (e.g., the prostate-specific antigen, PSA, is routinely used for screening and monitoring of prostate cancer). Moreover, some biomarkers can stratify patients into sub-groups that differ in their prognosis and response to different treatment options, which allows designing individual treatment plans. For instance, the human epidermal growth factor receptor 2 (Her2-neu) is overexpressed in about 20% of breast cancer patients and can be targeted by a monoclonal antibody called Herceptin in these patients, which enhances the overall survival when combined with chemotherapy.

Recent advances in Genomics and Proteomics allow for the de-novo identification of tumor-related biomarkers through differential expression analyses of the different steps of cancer progression. In our approach to biomarker discovery, we combine the techniques of laser capture microdissection, differential two- dimensional gel electrophoresis, and mass spectrometry to identify novel biomarkers of tumor progression for cervical cancer and other malignancies. These identified molecular markers have promising clinical utility in Precision Medicine Oncology.

Learning Objectives: 

1. Explain the role of Proteomics in Cancer Molecular Diagnostics.
2. Describe the experimental design behind the de-novo biomarker discovery approach combining laser capture microdissection, differential two- dimensional gel electrophoresis, and mass spectrometry to identify novel biomarkers of tumor progression.

Over the last few years, the space of advanced in vitro models has grown dramatically as researchers look to better mimic the in vivo environment within inexpensive and high throughput in vitro models. Of all tissue systems, one of the most modeled is the liver as drug induced liver injury accounts for over 20% of late-stage clinical failures and diseases like NASH and HBV affect millions within the US. The focus of this talk is on describing the current state of advanced liver cell culture models and providing guidance on how best to choose which liver in vitro model system is best for a particular research question. Three separate in vitro model systems will be discussed: 1) HUREL 2D Micro Livers, 2) Spheroid 3D cell culture and 3) Ex vivo tissue slices. The advantages and disadvantages of each model system will be thoroughly described, and researchers will be provided with guidance on how best to choose their models based on cost, in vivo relevancy and throughput.

Learning Objectives:

1. Explain the advantages and disadvantages of various liver in vitro model systems and the types of applications that they should be applied to.

2. Identify a framework for choosing the right liver in vitro model for a specific application based on balancing cost, throughput and in vivo relevancy.

Princess Margaret Living Biobank (PMLB) is a repository for patient-derived organoid models. PMLB has generated over200 organoid models from lung, pancreas, colon, ovary, esophageal and breast tumor tissues obtained from patients at University Health Network, Toronto, Canada. All deposited models have passed quality checks including Short Tandem Repeat (STR) genotyping, mycoplasma testing, growth analysis, and histological identification of organoids using a panel of tissue specific biomarkers. Our long-term organoid establishment rate varied between 10-80% depending on the tumor type. PMLB organoids closely recapitulated the histology, the mutation and copy number landscape of their parental tumors and were tumorigenic in mice. The majority of organoid drug responses were similar to that of the matched patient/PDX. Major challenges in next generation biobanking of patient-derived organoid models include normal organoid outgrowth, mouse-derived organoid contamination and the state of tissue used for organoid generation. The PMLB aims to provide researchers at the academic institutions and industrial collaborators with tools to answer many basic oncology research questions with the hope to positively impact cancer treatment.

Learning Objectives:

1. Discuss processes involved in biobanking of patient-derived organoid models.

2. Explain the advantages and challenges of using patient-derived organoid models for research.

The explosion of biomedical data such as in genomics, structure biology and pharmacology can provide new opportunities to improve our understanding of human physiology and disease. Understanding the magnitude of response to a given treatment at an individual level is one of the most important and long-standing challenges in personalized medicine and pharmacogenomics. A great deal of effort has been placed on understanding variation in drug transport and metabolism. However, little is known about how variability in receptor drug targets affects treatment regimens. G protein-coupled receptors (GPCRs) are the single most prevalent drug target family. Elucidating the spectrum and impact of how a GPCR variant influences ligand binding, signaling and, ultimately, therapeutic effect is vital and could serve as the next important step for understanding variability in drug response.

Learning Objectives:

1. Explain the conceptual principles underlying pharmacogenomics.

2. Identify challenges and opportunities in personalized medicine.

3. Explain current approaches in understanding drug variability originating from GPCR mutations.