One of the central tenets of biology is that our genetics—our genotype—influences the physical characteristics we manifest—our phenotype. But with more than 25,000 human genes and more than 6,000,000 common genetic variants mapped in our genome, finding associations between our genotype and phenotype is an ongoing challenge. Indeed, genome-wide association studies have found thousands of small effect size genetic variants that are associated with phenotypic traits and disease. The simplest explanation is that genes and genetic variants work together in complex regulatory networks that help define phenotypes and mediate phenotypic transitions. We have found that the networks, and their structure, provide unique insight into how genetic elements interact with each other and the structure of the network has predictive power for identifying critical processes in health and disease and for identifying potential therapeutic targets. I will touch on multiple examples illustrating the importance of network models, drawing on my work in cancer, in chronic obstructive pulmonary disease, and in the analysis of data from thirty-eight tissues provided by the Genotype-Tissue Expression (GTEx) project. We will use these to explore the development and progression of disease and new ways to identify therapeutics.

Learning Objectives: 

1. Discuss why both single-gene analyses and differential expression comparisons provide an incomplete picture of heahth and disease.
2. Identify the value of networks for exploring subtle but important relationships in biological data.
3. Summarize different types of network modeling (correlation networks vs. gene regulatory networks).

Precision medicine is transforming cancer care, with next-generation sequencing (NGS) enabling the simultaneous analysis of multiple genomic alterations with therapeutic implications. However, due to the sporadic nature of somatic cancers and the increasing uptake of NGS testing, the number of detected variants is exponentially growing, challenging labs to confidently identify meaningful mutations that could influence or improve decisions at the point of care. Approximately 50 percent of the oncologists in the United States find NGS results sometimes or often difficult to interpret, while 31 percent of small to medium-sized labs in Europe view a lack of knowledge and exposure to routine analyses and interpretation as the most critical bottleneck in NGS testing. In this webinar, Ana Krivokuca will present a real-world use case of how the Institute for Oncology and Radiology of Serbia (IORS), a national cancer research center that provides routine NGS testing for cancer patients, uses the Human Somatic Mutation Database (HSMD) in their NGS testing pipeline to validate, assess, and better understand the clinical significance of detected variants. A new somatic database from QIAGEN, HSMD combines two decades of expert-curated content with data from over 300,000 real-world clinical oncology cases to provide deep genomic insight into the molecular characterizations of a patient’s tumor. Easy to search with new content added weekly, HSMD enables users to explore key genes or mutations with driving properties or clinical relevance, search for associated treatment options, evaluate secondary findings, and develop customized gene panels.

Learning Objectives:

1. Express how IORS uses HSMD to identify actionable variants, analyze complex reports, evaluate secondary findings, and design custom gene panels.

2. Visualize HSMD content and features through a virtual demonstration.

3. Identify a complimentary, five-day trial of HSMD.

Variation in gene expression among cell types, tissues, and organisms is commonly examined by reverse transcription quantitative PCR, or RT-qPCR. In this process, RNA is isolated from samples of interest and reverse-transcribed into complementary DNA (cDNA). The levels of gene expression are then determined from the quantity of cDNA amplified by PCR. The initial stages of the RT-qPCR workflow, such as RNA isolation and reverse transcription, can critically impact the accuracy of the quantitation of gene expression.

Learning Objectives:
1. Discuss important considerations for RNA isolation.
2. Review tools for efficient cDNA preparation.
3. Clarify sources of poor RT-qPCR results.

The future of cancer care is contingent on better understanding of molecular, clinical, and outcome data. This can be achieved by leveraging collaborative affiliations and networks locally and internationally. Caris Precision Oncology Alliance (POA) is a vivid example of how this large network takes advantage of collaboration and understanding the nuances of molecular profiling to scale up the research operations that eventually can help all patients diagnosed with cancer. In this presentation, we will highlight how this large network operates and works together to advance the science forward.

Learning Objectives: 

1. Classify the value of broad molecular profiling.
2. Express the value of large collaborative networks.
3. Evaluate the future of precision oncology.

Single-cell RNA sequencing (scRNA-seq) is an important technology that reveals gene expression differences between different cell types. Yet scRNA-seq data can be challenging to analyze and interpret. In this webinar, we’ll show how our QIAGEN Digital Insights bioinformatics tools can help you analyze and interpret whole transcriptome data from a human single-cell sequencing experiment. 

We will analyze and compare the differentially expressed genes between stage III and normal for significant cell types in the tumor microenvironment from patient cases with HGSOC. We will use QIAGEN Ingenuity Pathway Analysis (IPA) to illuminate the underlying biology. We’ll show how IPA’s Land Explorer feature lets us compare and contextualize specific findings in HGSOC to other related or seemingly unrelated datasets.

In this webinar, you’ll learn how to:
- Analyze scRNA-seq samples using QIAGEN CLC Genomic Workbench 
- Analyze and explore the biology of a processed dataset in QIAGEN IPA
- Identify significant cell-specific canonical pathways or function-specific regulators
- Generate hypotheses about novel regulatory networks that suggest drivers of the expression changes seen in the different cell types in stage III HGSOC
- Visualize valuable information about a gene of interest in other contexts (disease, expression)
- Compare gene signatures enriched in clusters of cells versus tens of thousands of gene expression signatures in QIAGEN IPA to pinpoint significant regulator genes associated with HGSOC stage III
- Gain added insights about single-cell biology by comparing the HGSOC dataset in QIAGEN IPA to more than 118,000 public datasets which have been curated and processed in QIAGEN OmicSoft Suite

In real-world evidence (RWE), temporal shifts in clinical knowledge and practice need to be adjusted for treatment effect assessment. We use electronic health records (EHR) data to 1) assess the temporal trends in treatment decisions and patient outcomes; and 2) emulate a randomized controlled trial (RCT) using EHR with temporal trends properly adjusted. We emulated the COST Study Group Trial on open colectomy (OC) vs laparoscopy assisted colectomy (LAC) assessing using EHR data from Mass-General-Brigham. To address confounding in the emulation, we selected and adjusted for pretreatment variables. The temporal trends were adjusted by stratification of the calendar year when the surgeries were performed with co-training across strata. Total 943 met key RCT eligibility criteria. Various temporal trends were observed for which the test indicated significant evidence. The adjusted EHR emulation reached the same conclusion as the RCT: LAC is not inferior to OC in overall survival rate with risk difference (LAC vs. OC) at 5-year -0.007, 95% CI [-0.070,0.057]. The results were consistent for stratified analysis within each temporal period. The study demonstrates that confounding bias from temporal trends needs to be taken into consideration when conducting RWE studies with long time spans. With proper adjustment, RWE may supplement RCT in the assessment of treatment effect over time.

Human papillomavirus (HPV), an 8-kb double stranded DNA virus, infects the mouth and throat and is classified as cancer of the head and neck. Amongst various HPV serotypes, HPV16 has been classified as oncogenic for a various number of cancer sites including head and neck squamous cell carcinoma (HNSCC). Integration of HPV into host genomes is not part of its life cycle. However, the exception is HNSCC wherein E6 and E7 viral proteins predominantly drive its tumor progression. This integration event is a cul-de-sac since it cannot be packaged and transmitted to neighboring cells.

To address oncogenic progression post viral integration, a family of bromodomain and extraterminal domain (BET) proteins that mediates transcription through binding to acetylated histones to recruit chromatin complexes will be investigated. Specifically, the lack of understanding of the distinct roles of BRD2 and BRD4 in regulating viral-host transcription machinery and how it impacts intrinsic mechanisms of impaired DNA repair, cell cycle control and oncogenic signaling pathways. Using a combination of NGS and biochemistry, we will determine the specificity of BRD driven transcription in HPV HNSCC cancer, and discuss transcriptional networks of integrated HPV HNSCC cells and its impact on proliferation.

Learning Objectives:

1. Explore the role of HPV in head and neck squamous cell carcinoma.

2. Explain how HPV transcription impacts cancer proliferation.

3. Discuss how Tecan’s MagicPrep NGS system simplifies library preparation.

Mathematical methods can be used to study evolutionary processes associated with carcinogenesis. Selection, mutation, and drift all play a role in cancer dynamics and cancer treatment. I will present two very general types of evolutionary patterns that occur in the context of many cancers: loss-of-function and gain-of-function mutations. This will be followed by a discussions of different scenarios of cell population dynamics  -- including stochastic tunneling and calculating the rate of cancer evolution. Applications include understanding the role of aspirin in prevention of colorectal cancer.

Learning Objectives:

1. Elucidate the mechanism of aspirin on cellular evolution by studying its impact on cellular kinetics in vitro and in vivo.

2. Examine the effects of aspirin on the epidemiology of advanced adenoma.

3. Discuss mathematical and computational methods of modeling cacrinogenesis.

Breast cancer is the number one cause of cancer in women. Although many risk factors for breast cancer have been identified, only some are modifiable. Since 2004, incidence rates have been rising 0.3% year, highlighting the lack of successful prevention strategies. As a cancer epidemiologist, my goal is to identify risk factors for breast cancer to better predict which women are at the highest risk of breast cancer. Current risk prediction models used in the clinics, do not perform well in discriminating women at high risk of breast cancer. I will discuss three novel risk factors for breast cancer: early life body size, mammographic density and vitamin D supplementation. Being heavier earlier in life is inversely associated with later risk of breast cancer. Understanding the mechanism by which adiposity in early life influences adult breast cancer risk has the opportunity to lead to prevention opportunities. Mammographic density is one of the strongest risk factors for breast cancer. Including mammographic density and other novel risk factors have improved our ability to accurately predict individuals at risk for breast cancer. Several epidemiologic studies have examined circulating vitamin D levels and breast cancer; however, the evidence is inconsistent as to whether vitamin D can reduce risk or improve cancer survival. To address gaps in knowledge and limitations of other studies, we conducted an ancillary study in the context of a randomized trial of high dose vitamin D on mammographic features and breast tissue. Integrating molecular and radiologic features in the context of epidemiologic studies, we can better understand how risk factors influence cancer etiology and progression.
 

Learning Objectives:

1. Clarify the importance of life course and considering timing of exposures.
2. Explain mammographic density as risk factor for breast cancer.
3. Classify how integrative epidemiology can help us to understand how risk factors influence disease.

Gene regulatory networks are representations of the relationship between transcription factors and their target genes. By modeling gene regulatory networks, it is possible to identify changes in the network's activity and structure that result in the emergence of different phenotypes. For that reason, innovative experimental methods and computational approaches for precise post-processing must be developed to characterize the effects of thousands of perturbations on the structure of the gene regulatory network using a multiplexed single-cell level experiment. Here, we showcase the use of LIONESS in combination with SCORPION for the generation of individual-cell gene regulatory networks from single-cell RNA-seq data. Using this approach, we generated 54,798 transcriptome-wide individual-cell gene regulatory networks for MDA-MB-231 cells carrying a random subset of gRNAs from a library containing 20,139 sgRNAs targeting non-coding regions of the human genome. We systematically characterized each perturbation effect on the network structure by comparing the gene regulatory networks generated for cells carrying a given gRNA against all others. Our findings highlight regions actively associated with patterns of gene regulatory network rewiring and others related to changes in the activity of specific transcription factors or over specific target genes. The effect of co-localization between the sgRNA target region and the identified affected set of target genes was also evaluated. Furthermore, we compared the impact of the different non-coding region annotated functional categories to assess the differences in the magnitude of the perturbation caused over the gene regulatory networks.

Learning Objectives:

1. Recognizes the significance of modeling gene regulatory networks in understanding the molecular mechanisms underlying observed phenotypes.
2. Identify the importance of non-coding region perturbations in the rewiring of gene regulatory networks.
3. Appreciates the computational tools available for building and analyzing individual-cell gene regulatory networks.

Successful cancer patient advocacy has many components.  Patients and survivors are in the best position to tell their stories and advocate for better care and treatment.  However, with few survivors to aid in this messaging, family members, healthcare professionals, and an advocacy organization must fill the gap.  The American Cancer Society estimates that in 2022 there will be 62,210 new pancreatic cancer cases and 49,830 deaths. 

Liz will share her personal journey in advocating for future pancreatic cancer patients and the need for better treatment options and early detection methods to improve patient outcomes.   

With the help of healthcare professionals, especially the research community, advocates have been able to secure a dedicated federal pancreatic cancer research program.  Advocates have also seen a 970% increase in additional federal pancreatic cancer research funding.  Teaming with healthcare professionals has made the difference in funding to transform the future for pancreatic cancer patients.

Since 2003, the Pancreatic Cancer Action Network (PanCAN) has awarded 234 grants to 213 scientists at 79 intuitions.  This year alone, PanCAN will invest $25M for a cumulative $174M in research funding.  These efforts have driven the 5-year survival rate from 4% in 2001 to 11% today. PanCAN’s  goal is to improve the rate to 20% by 2030. 

Learning Objectives: 

1. Define year-round efforts to raise awareness and advocate for future patients
2. Review access to grant funding including a focus on early career investigators
3. Describe the free resources and support available for patients and healthcare professionals.

Reliable expression of recombinant proteins in human, CHO, or insect cells is essential for many aspects of biomedical research and drug development, but is often hampered by low expression yields that limit subsequent structural and functional analyses. The full portfolio of Thermo Fisher Scientific, from GeneArt gene synthesis to optimized eukaryotic/mammalian expression systems, offers researchers the unique opportunity to perfect their gene expression results.

In this presentation, we will demonstrate how our proprietary sequence optimization algorithm (Invitrogen GeneArt GeneOptimizer technology) enhances the sequence through a parallel, multi-parameter approach and then how our advanced expressions systems further maximize protein yield. Based solely on a digital sequence, we offer a full workflow solution from gene to protein to support your research ideal for rapid protein production early on during drug development.

Jeff Borgia, Ph.D. is the Director of the Rush Cancer Center Biorepository, where he oversees the collection and annotation of patient specimens in support of ongoing translational research projects and clinical trials. As Director of the Rush Biomarker Development Core, his role permits him to facilitate the administration of ‘state of the art’ services aimed at the discovery and development of novel biomarkers, which could potentially alter the clinical treatment decisions for patients. These decisions range from early cancer detection to personalized treatment selection. His presentation will cover the Development of Custom Immunobead Assays for Neoantigen-Associated Autoantibodies to Prognosticate Response to Immunotherapy in Advanced Lung Cancer.
The severe (1-in-4) mortality rate for lung cancer patients underscores this work’s importance. Moreover, improved assay development can help determine treatments that gain time for patients in this advanced stage of disease for which there is no cure. While recent immunotherapies have revolutionized the treatments for lung cancer patients, not every patient benefits from immunotherapies. The PD-1 and PD-L1 complement was the first successful immunotherapy, with immunohistochemistry assessing the expression of the PD-1 biomarker on tumors.
Custom immunobead assay improvements will be discussed, in addition to the significant challenges of biopsies and immunohistochemistry analysis. Immunobead assays that combine analysis of different immune response targets can be developed with a faster turnaround time and customized for variant proteins. Furthermore, while homebrew or DIY assays involve choosing the relevant biomarker combinations when developing multiplex immunobead assays on the Luminex xMAP platform, this approach has great clinical utility and can help stratify lung cancer patients for immunotherapies.

Learning Objectives: 

1. Discuss criteria for improved development of homebrew/DIY immunobead assays for lung cancer.
2. Research and selection of biomarkers to include in building an immunobead assay.
3. Consideration of analytical characteristics of biomarkers to ensure a compatible multiplex. 

T-cell therapies targeting viral antigens in a range of virus-associated malignancies present challenges to successful manufacturing of virus-antigen-specific T-cells (VSTs) from patients where these VSTs potentially circulate in low-frequency and are rendered dysfunctional in the immunosuppressive tumor microenvironment.  In this study, we investigated and overcame the manufacturing hurdles encountered in the clinical trial NCT01555892 by using Epstein-Barr Virus specific T-cells (EBVSTs) targeting Epstein-Barr virus (EBV) antigens against EBV+ lymphoma. 

We identified manufacturing failures in ~25% of patients resulting from the inability to grow antigen specific T-cells and/or outgrow NK-cells. Since the majority of VSTs reside in the CD45RA negative memory fraction of PBMCs – and the CD45RA positive population includes naïve T-cells, suppressive T-regs, and NK-cells – we hypothesized that the low antigen-specificity results from the outgrowth of non-EBV specific cells and enrichment of memory T-cells prior to stimulation may increase their antigen specificity and hence the clinical potency of our EBVSTs.

Using Miltenyi Biotec CD45RA MicroBeads, human and MACS® columns, we selectively removed CD45RA expressing cells from whole (W-) PBMCs prior to stimulating the resulting CD45RA depleted (RAD)-PBMCs with EBV peptide libraries to generate derivative RAD-EBVSTs. The RAD-EBVSTs demonstrated increased proliferation, enhanced antigen-specificity, and polyfunctionality compared to W-EBVSTs including more rapid EBV+ tumor clearance in our murine model. Most importantly, RAD decreased the frequency of NK cells dominating some of the patients’ EBVSTs and overcoming our manufacturing failures by enabling the outgrowth of these cells from patient PBMCs that previously failed to. Additionally, CD45RA depletion approach enhanced the antigen specificity of VSTs to HPV, VZV, and adenoviral antigens.

Learning Objectives:

1. Classify the obstacles to successful manufacturing of antigen-specific T-cells in immunotherapy.

2. Highlight the effects of CD45RA+ subsets in expansion of antigen-specific memory T-cells and successful application of CD45RA depletion in generating virus-antigen-specific T-cells.

3. Discuss the implications of in-vitro generation and in-vivo infusions of antigen-specific T-cells

Adoptive cell therapy (ACT) is a promising immunotherapy treatment option that uses genetically modified immune cells (T cells) to eliminate tumors. This approach uses the patients’ own immune cells to generate a “living drug” and can avoid the toxic side effects of other common therapies, such as radiation or chemotherapies. However, the tumor microenvironment (TME) can establish several obstacles to protect the tumor from T cells, including delivery of inhibitory and death signals that shut down T cells, usurping metabolic nutrients, and recruiting and/or converting immune cells to inhibitory phenotypes that block the T cell response. Costimulatory signals delivered by surface-bound receptors can initiate gene expression programs that address multiple issues in the TME by mechanisms such as lowering the threshold of activation, altering metabolic programming, and reducing exhaustion. We develop engineering strategies, fusion proteins (FPs), that combine the ectodomain of one receptor with a different intracellular costimulatory signaling domain. Here we describe how intentionally engineered fusion protein can “armor” T cells against multiple obstacles and significantly improve therapeutic efficacy against hematological and solid tumors.

Learning Objectives:

1.     Identify 3 obstacles that inhibit the immune cell response to cancer.
2.     List 3 aspects of T cells antitumor function that can be improved with costimulatory signals.
3.     Explain how fusion proteins can improve T cell therapy.

Antibody-based cancer therapies have clear advantages over chemotherapeutic drugs thanks to their high specificity and low risk of side effects.  However, it can be difficult to identify targetable tumor specific cell surface antigens or to manufacture antibodies that have sufficiently high clinical efficacy on their own.  An antibody drug conjugate (ADC) is a three-part complex composed of a cytotoxic drug bound to a monoclonal antibody via a chemical linker.  ADCs take advantage of the antibody’s ability to recognize and bind to a tumor specific antigen as they deliver their cytotoxic payload to destroy a cancer cell.  In this talk I will compare ADCs to other antibody-based cancer therapies and introduce the structure, action mechanism, and toxicity risk of ADCs.  I will also highlight results from recently published clinical trials that applied ADCs to treat breast cancer patients.

Learning Objectives:
1. Identify the three components of an antibody-drug conjugate.
2. Describe how the "bystander effect" can lead to better efficacy for an antibody-drug conjugate as targeted cancer therapy.
3. Describe how an antibody-drug conjugate is able to limit the "off-target" toxicity of its drug.

The ultimate promise of tumor immunotherapy is dependent on the generation of effective immunity against cancer, including CD4 and CD8 T cell responses. However, activating T cells in cancer types such as pancreatic ductal adenocarcinoma (PDAC) requires overcoming significant hurdles in the tumor microenvironment (TME). Local and systemic suppression of dendritic cell function restrains endogenous T cell immunity against PDAC, which is further exacerbated by tumor cell intrinsic recruitment of suppressive myeloid cells and macrophages to the tumor site. Agonistic anti-CD40 antibody, which mimics the endogenous CD40 ligand to license and mature dendritic cells and re-educate tumor-associated myeloid cells towards a Th1/M1 phenotype, may provide a mechanism by which to bypass the suppressive barriers in the PDAC TME. Using the KrasLSL-G12D/+,Trp53LSL-R172H/+,Pdx1-Cre (KPC) genetically engineered mouse model of pancreatic cancer, we show that combining standard-of-care chemotherapy with CD40 agonism promotes clonal T cell activation and expansion locally in the tumor site, resulting in regressions and cures of established tumors and further enhanced by the addition of dual immune checkpoint blockade (anti-PD-1 and anti-CTLA-4). These studies formed the preclinical rationale for trials investigating the use of agonistic CD40 therapy for patients with resectable or metastatic PDAC, and provided insight for immunological correlates of response in the clinical setting. More recently, we have reported on the negative impact of chemotherapy on the developing anti-tumor T cell response, emphasizing the need for additional investigations into the combination and sequencing of treatment modalities for enhanced outcomes in patients with PDAC. These studies reveal the potential potency of immune interventions in PDAC for patients with the greatest unmet clinical need.

Learning Objectives: 

1. Discuss the impact of CD40 stimulation on T cell activation in mouse models of pancreatic cancer.

2. Identify correlates of response after CD40 therapy in patients.

3. Consider the implications of chemotherapy on immunotherapy-induced T cell responses to tumors.

The microenvironment of many solid tumors impedes many immunotherapeutic treatments by inhibiting the effector functions of many classes of anti-tumor immune cells, including effector T cells. Many components are thought to contribute to the immunosuppressive nature of the tumor microenvironment, but recently abnormal availability of critical nutrients required for immune cell function has emerged as a key immunosuppressive factor. However, precisely which nutrient perturbations and how these tumor nutrient cues impact immune cell function has remained obscure. To identify the tumor nutrients regulating T cell function, we developed techniques to quantify nutrient levels in the microenvironment of solid tumors and cell culture model termed Tumor Interstitial Fluid Medium (TIFM) that enables us to grow and study T cell function under these nutritional constraints. Effector T cells cultured in TIFM exhibit profound dysfunction and we identified the components of TIFM that drive this dysfunction. In this talk, I will describe our work identifying nutrients that are deprived in the tumor microenvironment to impair anti-tumor immunity. I will also discuss our recent findings identifying immunosuppressive metabolites that accumulate in diverse tumors and the molecular mechanisms by which these metabolites impair T cell function.

Learning Objectives:

1. Review the experimental work that suggests the tumor microenvironment in pancreatic cancer limits anti-cancer immune cell effector functions.
2. Identify how nutrient levels in the tumor microenvironment are altered and limit anti-cancer immune cell function.
3. Classify how alterations in Kennedy pathway activity in T cells brought on by the tumor microenvironment impair T cell function.

There are currently several different FDA approved radiopharmaceuticals being used in both large academic and small practice settings to accurately stage and identify local and metastatic prostate cancer, including the recent FDA approval of 68GaPSMA-11 and 18F-DCFPyL for primary and metastatic prostate cancer. Given the widespread application of diagnostic radiopharmaceuticals, and the recent approval of 177Lu PSMA-617 (Pluvicto), there is great promise that the diagnosis and treatment of metastatic prostate cancer will be dramatically improved. This talk covers the scientific rational for PSMA PET and targeted radionuclide therapy and what to expect on the day of therapy.

Learning Objectives: 

1. Discuss next generation PSMA PET imaging agents into clinical practice.

2. Appraise the role that PSMA PET will play in the modern clinical practice and incorporate appropriate imaging protocols, reading criteria, and navigate common pitfalls.

3. Devise a practice with PSMA radiotheranostics including appropriate patient selection, treatment protocols, and methods to evaluate successful therapy.

Despite the immense promise of cancer immunotherapies, significant challenges have been mounting to overcome treatment resistance and to improve efficacy. Strategies such as the combination immunotherapies of existing agents have so far yielded more failures than successes in this effort, highlighting the importance of developing the novel next generation immunotherapy agents.  
Neoantigens are peptides derived from patient-specific and tumor-specific mutations, which can be recognized by the immune system and mediate tumor recognition that leads to immune destruction of the cancer. Neoantigens are emerging as one of the next generation immuno-oncology targets with tremendous potentials. In this talk, I will introduce the definition, identification, prioritization of the neoantigens using tumor sequencing data. I’ll describe the REAL-neo bioinformatics pipeline and several on-going clinical trials of neoantigen vaccine therapies.  

Learning Objectives: 

1. Discuss the source of neoantigen in cancer.

2. Identify the steps of neoantigen identification using bioinformatics tools from sequencing data.

3. Visualize features of "good" neoantigen candidates.

Human solid tumors, like breast cancers, represent a heterogeneous group of diseases that is best demonstrated through many differences in clinical outcomes within homogenously treated patient sets. It is my belief that through a better understanding of the genomics and biology of a given cancer type, we can make important advancements in predicting patient response to therapy, which will ultimately lead to improvements in patient outcomes. Advances in genomic technologies, and drug developments, have already led to improved outcomes for women with breast cancer; nevertheless, there is still a pressing need for additional therapeutic options guided by predictive biomarkers for aggressive tumors; there is also the question of whether patients with good prognoses need aggressive treatments. Resistance to endocrine therapies has been mainly studied in HR+/HER2- BC and includes down-regulation of oestrogen receptor (ER) expression, altered expression of ER co-regulators, presence of ER mutations, ligand-independent activation of ER and co-activators by growth factor receptor kinases. However, those mechanisms might differ between HER2+ and HER2- tumours, in part due to the differential distribution of intrinsic subtypes within each BC subgroup. My research goal is to develop predictive signatures of aromatase inhibitor response and address the clinical challenge of identifying patients who are likely to benefit from each of the specific therapies. In this webinar, I will discuss the current and the potential future clinical implications of molecular subtyping and immune features on HR+ breast cancer tumors to predict treatment response. 

Learning Objectives:

1. Recall how machine learning can play a role in molecular research.
2. Discuss how subtyping can help determine association with clinical outcome.
3. Visualize how multi-omics/spatial genomics heterogeneity can offer new insights into treatment response.

Molecular diagnostic tests are currently used for infectious disease testing due to the superior sensitivity and specificity that can be obtained. Due to the success that has been obtained with this application, molecular diagnostic testing is expected to grow into other areas such as genetic testing, prenatal testing, oncology, and pharmacogenomics. Growth in these areas will undoubtedly come with additional challenges due to the variety of the samples required, the presence of inhibitors, and low sample amounts, all of which contribute to potential false negative results due to poor amplification reactions.

In this presentation, we will demonstrate how a change in assay components can overcome these challenges. Specifically, we will address the issues that the Nucleic Acid Amplification Test (NAAT) (a commonly used diagnostic tool used for its fast turnaround and less invasive sample collection) has faced and how a change in enzyme selection impacted the overall quality and accuracy of the molecular diagnostic assay.

Learning Objectives

1. Classify key challenges in neucleic acid amplification tests.

2. Identify enzyme characteristics important for increased sensitivity, specificity, and speed.

3. Clarify how better PCR enzymes and reverse transcriptases can impact the accuracy and detection of molecular test assays. 

Racial and ethnic minorities often experience cancer disparities in the United States with higher disease prevalence, shorter survival, and higher death rates with many cancer types. These disparities are proposed to reflect the complex interplay of non-biological (socioeconomic, environmental, behavioral) and biological factors (genetic and genomic features).  
Despite the recent efforts of the inclusion of minorities in genetic and genomic studies, data disparities between racial groups, both quantitatively and qualitatively, still exist and are clinically impactful. For example, tumor mutational burden (TMB) is an FDA-approved patient selection biomarker for immune checkpoint inhibitor (ICI) therapy. Last year, our group reported the over-estimation of TMB in patients of African ancestry due to the common clinical practice of tumor-only sequencing without patient-paired germline data and the ineffective germline variant removal resulting from the under-representation of minority groups in public variant databases. This year, we were the first to report the lower sequencing data quality of ancestrally African patients in The Cancer Genome Atlas (TCGA), one of the largest and most widely used cancer sequencing projects, which resulted in lower qualities of both germline variants and somatic mutations in Black patients.  
The clinical impact of the observed data disparity needs to be studied to overcome this major barrier in solving cancer genomics disparities in ancestrally African patients.  

Learning Objectives:

1. Demonstrate the type of therapy for which the tumor mutational burdens (TMB) status is used as a biomarker, per FDA.

2. Discuss the data quality disparity between patients with European and African ancestries in the TCGA.

3. Explain the strategies to overcome inflated TMB estimate in patients of African ancestry.

Technological advances in Artificial Intelligence (AI), particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in various medical fields, propelling it forward at a rapid pace. In this talk, Dr. Aerts will discuss recent developments from his group and collaborators performing research at the intersection of deep learning, radiology, oncology, cardiology, bioinformatics, and data science. Also, he will explore how these methods could impact multiple facets of medicine, with a general focus on applications in radiology, and demonstrate ways in which these methods are advancing the field. The presentation will conclude with a discussion on the need for open-source deep learning frameworks that are transparent and reproducible.  

Tumors invade surrounding tissue as cancer progresses, but the heterogeneity of cell types at the tumor-stroma interface and the complexity of their interactions has hampered mechanistic insight required for efficient therapeutic targeting. In this webinar, I will discuss a recently published study whereby combining single-cell and spatial transcriptomic data taken from human basal cell carcinomas, we defined the cellular contributors to tumor progression. In the invasive niche, tumor cells exhibit a collective migration phenotype, characterized by the expression of cell-cell junction complexes. In close proximity, we identified cancer-associated fibroblasts with extracellular matrix-remodeling features. Tumor cells strongly express the cytokine Activin A, and an increased Activin A-induced gene signature was found in adjacent cancer-associated fibroblast subpopulations. Altogether, our data identifies cell populations and their transcriptional reprogramming that contribute to the spatial organization of the invasive niche of basal cell carcinoma.  In this study, we have demonstrated the power of integrated spatial and single-cell multi-omics to decipher the properties of invasive niches in cancer and develop targeted therapies.
Yerly L et al, Nat Commun (2022)

Learning Objectives: 

1. Describe the characteristics of invasive niches of basal cell carcinoma in terms of cell types and gene expression profiles.

2. Explain how spatial transcriptomics can be used in conjunction with single cell analysis to better understand tissue heterogeneity at the tumor-stroma interface.

3. Discuss how the cytokine Activin A affects the behavior and activity of cancer-associated fibroblasts.

Targeted sequencing has many applications in cancer biomarker research, carrier screening and inherited disorders, drug development, mitochondrial DNA variant detection, human ID and paternity testing and the detection of rare variants in human samples.

With the introduction of our QIAseq NGS Panels in 2016, we set a new standard for targeted amplicon-based genotyping via NGS. New features such as error-correcting molecular barcodes known as Unique Molecular Indices (UMIs) and Single Primer Extension (SPE) allowed the detection of rare variants with high confidence, all from a single tube. Our panels enabled the enrichment of up to 20,000 targets while ensuring high uniformity and complete coverage.

We have now further enhanced QIAseq chemistry to improve your NGS workflow even more. New QIAseq Targeted DNA Pro panels leverage major innovations that provide you with the next level of performance. The panels ensure superior ease of use and increased workflow efficiency while facilitating high-fidelity variant analysis.

This webinar explores how the new QIAseq Targeted DNA Pro workflow fast-tracks and streamlines library preparation so that you can accelerate your time-to-result and get deeper insights faster.

Learning Objectives:

1. Explain NGS applications and challenges in biomarker research.

2. Compare overview and benefits of QIAseq Targeted DNA Pro Panels.

3. Review performance data.

Artificial Intelligence (AI) is a computer program designed to perform operations typical of human intelligence, such as self-learning. AI is taking over different fields in science; we already see it everywhere, including clinical research and precision medicine, driving exponential advances in healthcare. At every stage of clinical research, researchers can rely on AI for developing proposals, recruiting healthy subjects or patients, disseminating results, and discovering new drugs. The global healthcare-focused AI market continues to grow exponentially, reaching an estimated value of $5 billion in 2020 over the last ten years, and is expected to continue to rise to an estimated $44.5 billion in 2026. AI has helped the discovery of new therapies for the treatment and timely detection of difficult-to-treat diseases such as cancer. The application of AI in cancer research can be variable; for example, when specific symptoms are entered into an AI system, it can help in disease prognosis and suggest possible treatment options. Another way AI can help cancer research is by recruiting and selecting subjects or participants in a study. AI speeds up research in drug discovery and increases precision at every step; having drugs faster that are not rejected in the phase 4 clinical trial is crucial for cancer research. Implementing AI in cancer research can be slow and laborious as regulatory hurdles can interfere with integrating AI tools. Privacy issues and ethical dilemmas could decelerate AI-based advancements in cancer research. To bridge this gap, a Laboratory Information Management System (LIMS) plays a critical role. A cloud-based LIMS minimizes challenges by anonymizing sensitive patient data, storing informed consent of participants, and assigning role-based access to users, thereby making implementation of AI in clinical research easy.

Learning Objectives:
 
1. Get an overview of the applications of Artificial Intelligence (AI) in clinical and cancer research.
2. Describe the challenges in implementing AI in cancer research. 
3. Discuss how a LIMS increases the possibility of applying AI to cancer research with ease.

We recently determined that concurrent inhibition of autophagy, using the lysosomal inhibitor chloroquine (CQ), and of ERK, using a small molecule ERK inhibitor (ERKi), synergistically suppressed the growth of pancreatic ductal adenocarcinoma (PDAC) cell lines and patient xenograft-derived (PDX) organoids in vitro and PDX tumors in vivo (Bryant et al., 2019, Nat Med 25:628). Our findings, together with similar observations by McMahon, Kinsey and colleagues (Kinsey et al., 2019, Nat Med 25:620), provided the rationale for our initiation of a Phase I clinical trial evaluating the combination of MEKi (binimetinib; NCT04132505) or ERKi (LY3214996; NCT04386057) with hydroxychloroquine (HCQ) in PDAC. To extend these findings we performed a CRISPR/Cas-9 mediated genetic loss-of-function screen in the presence of CQ to determine additional sensitizers as well as mediators of resistance to autophagy inhibition. Top sensitizers included multiple facilitators of the DNA damage response, mTOR pathway components, and genes involved in the upstream regulation of the autophagy pathway. We validated the receptor tyrosine kinase IGF1R as both a potent sensitizer to HCQ, as well as a protein that exhibited compensatory phosphorylation upon HCQ treatment. Interestingly, while IGF1R inhibition induced autophagy alone, the induction was greatly enhanced in that presence of ERK or MEK inhibition. Accordingly, concurrent inhibition of IGF1R, ERK and autophagy induced cytotoxicity in PDAC cell lines and organoids. Furthermore, we identified PIKFYVE as a potent anti-autophagy target. Ongoing studies are aimed at comparing the effects of PIKFYVE inhibition to that of HCQ treatment in the presence and absence of ERK MAPK pathway inhibition. We conclude that concurrent suppression of multiple metabolic processes, to block compensatory rebound activities, will be needed for effective PDAC treatment.

Learning Objectives: 

1. Define the five current strategies for targeting RAS for cancer treatment.
2. Describe the how the mCherry-EGFP-LC3B reporter construct can be utilized to monitor autophagic flux.
3. Identify the four phases of the autophagic pathway and describe which phase is perturbed by chloroquine and the PIKfyve inhibitor, apilimod.

Prostate cancer (PCa) affects one in nine men and is the most diagnosed solid tumor cancer among men in the United States. The introduction of the prostate-specific antigen (PSA) biomarker blood test in the late 1980s gave rise to a marked increase in PCa cases. Widespread PSA screening led to the earlier detection of aggressive tumors (screening benefit) and increased detection of indolent tumors (screening risk). Unfortunately, the PSA test is unreliable for detecting high-grade PCa (HGPC), leaving a gap for accurate HGPC diagnoses. In 2012, the US Preventive Services Task Force (USPSTF) recommended against PSA screening due to the limited benefits of broad PSA screening. In 2017, to avoid missing HGPC and potentially increasing PCa mortality, the USPSTF promoted age-specific shared decision PSA testing for men aged 55–69 years.

Several assays, including the ExoDx Prostate (IntelliScore) (EPI) test, were developed to make more informed decisions on which patients could likely defer prostate biopsy and which patients are at higher risk for HGPC and should proceed to biopsy. EPI is a noninvasive urine-based gene expression assay. It does not require a pre-collection digital rectal exam (DRE) nor clinical features to provide a result and helps discriminate benign/low-grade PCa (Grade Group, GG1) from HGPC (>GG2) for men aged ≥50 who are in the PSA “gray zone” (2–10 ng/mL).

Learning Objectives:

1. Discuss exosome basics.
2. Review how exosomes can provide value for clinical diagnostics.
3. Classify how exosomes are being used for prostate cancer early detection.

The majority of disease-associated genetic variants are thought to have regulatory effects, including disruption of transcription factor (TF) binding and alteration of downstream gene expression. Identifying the way in which each person’s genotype affects their individual gene regulatory network would provide important insight into disease etiology and enable improved genotype-specific disease risk assessments and treatments. We developed EGRET (Estimating the Genetic Regulatory Effect on TFs), which infers a genotype-specific gene regulatory network (GRN) for each individual in a study population. EGRET begins by constructing a genotype-informed TF-gene prior network derived using TF motif predictions, eQTL data, individual genotypes, and the predicted effects of genetic variants on TF binding. It then uses message passing to integrate this prior network with gene expression and TF protein-protein interaction data to produce a refined, genotype-specific regulatory network. We used EGRET to infer GRNs for two blood-derived cell lines and identified genotype-associated, cell-line specific regulatory differences that we subsequently validated using allele-specific expression, chromatin accessibility QTLs, and differential ChIP-seq TF binding. We also inferred EGRET GRNs for three cell types from each of 119 individuals and identified cell type-specific regulatory differences associated with diseases related to those cell types. Because genotypes are the only individual-level data required, EGRET can be readily applied to disease cohorts where only genetic information is available. 

There are known outcome disparities between Black and White patients with pancreatic neuroendocrine tumors (pNETs). Multiple retrospective series have found that Black patients have more advanced disease and worse survival. Recently, Black patients were shown to have higher rates of lymph node metastasis in smaller tumors than White patients, indicating possible differences in tumor biology. Numerous prognostic gene expression differences between racial groups have been reported in other cancers, but limited analysis has been conducted in pNETs. The most commonly mutated genes in pNETs have not been validated specifically in Black patients. Future multi-omic pNET analyses must be powered to investigate the biologic source of these disparities further.

Individuals with Lynch syndrome (LS) are prone to develop early-onset mismatch repair deficient (dMMR) colorectal- and endometrial cancers due to germline pathogenic variants (PVs) in one of the mismatch repair (MMR) genes MLH1, MSH2, MSH6, PMS2, or deletions affecting the 3’ region of EPCAM. Current germline diagnostics for LS, after the exclusion of somatic MLH1-promoter hypermethylation, include targeted short-read sequencing and multiplex ligation-dependent probe amplification of the coding regions of the MMR genes. In the absence of a germline PV in an MMR gene, the presence of somatic dMMR is investigated. This aforementioned strategy results in a genetic diagnosis of LS in approximately 54% of dMMR cancers before the age of 70 years. In individuals with a dMMR cancer without a germline PV, we detect two somatic nonepigenetic MMR aberrations in about 32% of cancers. Individuals who remain genetically unresolved after germline and somatic analysis (approximately 15% of individuals with dMMR cancers) are considered to have Lynch-like syndrome (LLS). For individuals with LLS and their relatives, treatment options and surveillance are yet unclear. We applied targeted long-read sequencing of the MMR genes to screen for non-coding PVs to explain the observed “missing heritability” in individuals with LLS (n = 32). This approach identified nine noncoding potential PVs in nine patients. Using functional assays, we show that six of these noncoding potential PVs cause splice-site defects. In conclusion, our studies show that 18.8% of individuals with LLS have a germline noncoding PV in an MMR gene that remained undetected by current routine diagnostics. These findings warrant that individual with LLS, who remain unresolved after germline and somatic analyses, should undergo germline sequencing of the complete loci of the MMR genes, preferably by long-read sequencing approaches, to improve LS diagnostics, treatment and cancer surveillance in patients and relatives.
 

Learning Objectives: 

1. Evaluate the diagnostic route for the detection of Lynch syndrome in individuals with a mismatch-repair deficient colorectal- or endometrial cancer.
2. Classify the application of short- and long-read sequencing approaches to detect pathogenic variants in mismatch repair genes.
3. Explain how to use functional assays, such as mini-gene assays, to assess the pathogenicity of noncoding aberrations and their effect on splicing in the mismatch repair genes.

The increasing quantity and quality of spatially resolved omics data provide a unique opportunity to explore the complex molecular mechanisms that take place in biological systems. However, to understand the system, it is not sufficient to only observe and describe it. Mathematical models are needed which are able to holistically integrate all available molecular readouts and their spatial context to elucidate the molecular networks active in an organism, comprising both intra- and intercellular communication.  
The inference of molecular networks using statistical approaches has been a hot topic for many years. Probabilistic graphical models (PGMs) are among the most popular statistical network inference methods and were repeatedly studied for the analysis of high-dimensional omics data, comprising transcriptomic and proteomic data. In this talk, I present an instance of PGMs which takes into account spatial context of molecular variables in order to elucidate the cellular communication that takes place within and between cells.