The GRCh38 human reference genome contains ~9Mbp of falsely duplicated and collapsed regions. These errors impact the variant calling of 33 protein-coding genes, including 12 with medical relevance. We present a modified GRCh38 reference that corrects errors while maintaining the same coordinates allowing us to leverage the extensive existing annotations of GRCh38, along with an efficient remapping approach, FixItFelix, that enables quick and efficient re-analysis to gain improved insights from existing data. We showcased improvements over multi-ethnic control samples across short and long-read DNA-, and RNA sequencing by using the fixed reference.

Small cell lung cancer (SCLC) is typically detected late, when prognosis is poor.  We assembled a real-world SCLC cohort of >500 US patients, with linked clinical, genetic, transcriptomic and imaging data.  Clinical trial results and published transcriptional subtyping were reproducible when methods were adapted for real-world heterogeneity.  We assess the practicality of biomarker testing in the current patient journey, subtype specificity of molecular targets, prognosis, and signatures of inflammation.

Precision Medicine has made significant strides in using sequencing assays to analyze molecular profiles of patients and inform their targeted treatments, particularly in cancer genomics. A new revolution in healthcare is emerging with technologies such as spatial biology, ubiquitous sensors, and powerful AI tools that can further expand molecular profiling and its interpretation. To harness this revolution, (bio)informatics needs to address the challenges of big data by integrating different data sources to create a comprehensive patient profile. At the Englander Institute for Precision Medicine, we have adopted solutions such as micro-services and mixed reality applications to address these challenges.

This talk will discuss the key strategies that economically successful NGS deploy to establish a sustainable, growing, and profitable business model. Based on his experience working with hundreds of NGS labs globally, Dr. Andreas Scherer will share strategies and implementation examples that are key for NGS labs to be successful long term.

The first two decades of post-genomic biology have highlighted the formidably diverse functions of the two-thirds of human genes that do not encode proteins. Nevertheless, the bulk of drug development has continued to center on protein-based targets and small-molecule drugs. In this session, we integrate cutting-edge computation with genome-scale experimentation to highlight the inexorable progress of sequence-based precision medicine in targeting non-coding RNA causes of human disease with RNA-based drugs in three key systems: the brain, metabolism, and cancer.

Two decades ago, the Human Genome Project revealed that 98% of the human genome was not protein-coding. Long non-coding RNA (lncRNA), the prevalent class of mammalian non-coding genes, is mainly primate-specific in humans. We have characterized estrogen-induced oncogenic lncRNAs in estrogen receptor α positive breast cancer; their suppression points to new cancer treatments. Having discovered, using mass spectrometry in the ENCODE Consortium in 2012, rare translation of short Open Reading Frames in specific lncRNAs, in 2022 we deployed ribosome profiling to show that transcriptional and translational regulation of human estrogen-regulated lncRNAs is unexpectedly discordant. From Genome-Wide Association Studies (GWAS), we have identified a universe of druggable liver-expressed lncRNAs in type 2 diabetes and the metabolic syndrome, which we are pursuing in nonhuman-primate models.

MicroRNAs (miRNAs) and long noncoding RNAs (lncRNAs) are involved in all cellular functions and their dysregulation is implicated in the pathogenesis of cancer and many other diseases. Substantial effort has been invested towards the clinical application of RNA-based therapeutics employing mostly mimics and antisense short RNAs, which is now expanding to encompass the larger class of lncRNAs. Despite these efforts, results have been ambivalent, with some trials showing potent effects while in others RNA-based drugs exhibited limited efficacy or were even harmful. In this talk, I will present innovative “out-of-the box” alternative approaches which aim to address the challenges hampering the success of ncRNA therapeutics.

Human brain evolution has led to the generation of complex behaviors, but also to diseases.  Because of their human-specific evolution, long-non-coding RNAs (lncRNAs) present major challenge in deciphering their functions in health and disease. We have developed a biomedical informatics platform called INTUITION that takes a multi-systems approach to study human brain tissues linking genomic, proteomic, and molecular differences to quantitative electrical activities, brain imaging studies, and clinical data through a relational database.  In this talk, I will summarize our human brain tissue and data platform as well as recent findings implicating lncRNAs as potential therapeutic targets for human epilepsy.

One of the biggest problems for patients with lung cancer is that after radiation treatment a good percentage of these tumors developed resistance to radiation. We recently discovered a group of lncRNAs, known as lincSPRY3 RNAs, that are expressed from the Y chromosome and give radiation sensitivity to male lung cancers. We found that male lung cancer cells that are resistant to radiation therapy have lost their Y chromosome, losing the expression of these lncRNAs. We are currently developing Artificial Intelligence tools that will predict loss of Y chromosome or linc-SPRY3 RNA expression from images of lung cancer patient tumors.

ENDEPUSresearch, Inc.’s technology specialized in data analytics on economic information interferes with medical decision making processes. For instance, one application leads to the development of cost sensitivity indexes at critical decision points in clinical practices, in conjunction with adaptive platforms, where evidence in development can enhance the decisions. Such decision indexes also need to rely on use of various coding systems especially on diseases (ICD9 codes), drugs (NDC codes), procedures (PCT) and expert coding systems.

In clinical research, the gold standard is to use a randomized controlled trial (RCT) to assess the effect of a treatment. Thanks to new technology and the effort of many centers and institutes, the availability of retrospective data about Amyotrophic lateral sclerosis (ALS) is growing. Real World Data and statistics techniques can lead to new ways to assess the effect of medications.

Multiple statistical techniques conducted on publicly available data producing significant findings encompass the intent of bioinformatics. The purpose of our research was to revisit the rad-age associative theory with today’s data availability and computational tools. We explored a radiation-age interaction by combining five different datasets publicly available for secondary analysis by implementing a variety of statistical methods and tests. We used age as a categorical and continuous variable to yield 234 unique genes including 3 that showed statistical differentiation when evaluating lung disease prognosis when including ethnicity and sex. Genes, pathways, and diseases independently associated with radiation exposure and aging processes (such as inflammation, mitochondrial function, and DNA damage/repair) corroborate a dependent association. After attending this presentation, the audience will gain methodology knowledge for employing secondary analysis with varying dataset sizes and platforms, see a genetic foundation for a rad-age association that can indirectly benefit multiple fields of study, and gain statistically significant results that can be used in their own research projects.