Cambridge Healthtech Instituteの第20回年次
Genomics-Driven Drug Discovery
PLENARY KEYNOTE PROGRAM
Plenary Keynote Introduction (Sponsorship Opportunity Available)10:45 am
PLENARY: The New Science of Therapeutics
I will share reflections on how new paradigms in the science of therapeutics are creating opportunities to approach historic challenges in medicine. Specifically, I will share approaches to targeting transcription factors and discuss how modularity is a paradigm for next-generation low-molecular weight and biological therapeutics. Finally, I will offer reflections on drug development and the fitness, opportunities, and challenges of the biomedical ecosystem.
PLENARY: Accelerating Drug Discovery Using Machine Learning and Cell Painting Images
Microscopy images can reveal whether a cell is diseased, is responding to a drug treatment, or whether a pathway has been disrupted by a genetic mutation. In a strategy called image-based profiling, often using the Cell Painting assay, we extract hundreds of features of cells from images. Just like transcriptional profiling, the similarities and differences in the patterns of extracted features reveal connections among diseases, drugs, and genes.
Enjoy Lunch on Your Own12:25 pm
Welcome Remarks1:45 pm
SYNTHETIC BIOLOGY & PROGRAMMABLE REGULATION
FEATURED PRESENTATION: Mammalian Synthetic Biology and Programmable Organoids
Based on programmed differentiation into synthetic mammalian tissues having multiple cell type architectures that are similar to human organs, Programmable Organoids mimic the response of a target organ to both positive and negative effects of drug candidates. Programmable Organoids can host a large array of live-cell biosensors, built-in to one or more cell types, providing a rapid and real-time spatial readout of pathway-specific biomarkers including miRNAs, mRNAs, proteins, and other metabolites. Organoids programmed with both general and disease-specific sensors then provide detailed information that can be used to identify candidates for further analysis.
Programmable Regulation of Mammalian Transcription and Therapeutic Protein Expression
Mammalian transgene expression relies on a limited collection of natural promoters that drive discrete levels of transcription and consequent protein expression. However, the behaviors of natural promoters could be challenging to predict in different cell types, making it difficult to precisely control transcription. To address these obstacles, using synthetic biology toolkits, we have developed versatile, scalable platforms and programmable genetic components to transform regulation of mammalian transcription and transgene expression.
Sponsored Presentation (Opportunity Available)2:55 pm
Refreshment Break in the Exhibit Hall with Poster Viewing3:25 pm
A Transcription Factor Atlas of Directed Differentiation
To comprehensively understand transcription factors (TFs), we created a barcoded library of all human TF splice isoforms and applied it to build a TF Atlas charting single-cell expression profiles of pluripotent stem cells overexpressing each TF. We validated TFs for generation of diverse cell types, spanning all three germ layers and trophoblasts. We further developed a strategy for predicting TF combinations that produce target cell types to accelerate cellular engineering.
Synthetic Transcription-Factor Activity Responsive (STAR) Gene Circuits for Cancer Immunotherapy
We have developed synthetic cancer-targeting gene circuits that specifically target cancer cells. Once the circuits enter cells, they will sense the activity of several cancer-associated transcription factors and get activated in tumor cells to trigger tumor-localized combinatorial immunotherapy. Circuits mediate robust therapeutic efficacy in ovarian cancer mouse models. This platform can be adjusted to treat multiple cancer types and can potentially trigger genetically encodable immunomodulators as therapeutic outputs.
Close of Day8:00 pm
Registration and Morning Coffee7:30 am
LEVERAGING CRISPR & FUNCTIONAL GENOMICS
The Power of Partnerships in Functional Genomics
I will be talking on how industry/public partnership can enable the development of functional genomics capabilities for target discovery. I will present a few case studies to exemplify this concept and discuss the challenges and opportunities we face in this field.
Confounding of CRISPR-Based Target Discovery by Genomic Proximity
CRISPR-Cas9 has been reported to cause a variety of undesired large-scale structural changes to the genome. We performed an arrayed CRISPR-Cas9 scan of the genome in primary human cells targeting 17,065 genes and revealed a proximity bias in which knockouts bear unexpected phenotypic similarity to unrelated genes on the same chromosome arm. Transcriptomics connects this effect to chromosome-arm truncations, and analysis of published large-scale experiments confirms that it is general across cell types, labs, Cas9 delivery mechanisms, and assay modalities. Finally, we demonstrate a simple correction for large-scale CRISPR screens to mitigate this pervasive bias while preserving biological relationships.
Having Fun (at Last) with Functional Genomics
Abstract: We will cover CRISPR technologies that allow genome exploration at both broad scale and fine resolution. For the former, Cas12a allows for facile multiplexing of guide RNAs, simplifying combinatorial perturbations; we will share work mapping synthetic lethal interactions with knockout screens and progress in developing Cas12a for CRISPR activation screens. Additionally, base editor technology enables nucleotide-level manipulation, and we will present screens mapping cancer-relevant genes and pathways.
A Drug’s Most Potent Target Is Not Necessarily the Source of Its Anti-Cancer Activity
The small-molecule drug ralimetinib was developed as an inhibitor of the kinase p38α. We describe a multi-modal approach that demonstrates that ralimetinib’s anti-cancer activity occurs due to its ability to inhibit EGFR, rather than p38α. Our results demonstrate that a compound’s anti-cancer effects should not necessarily be attributed to the protein that it inhibits most strongly, and instead, comprehensive cellular and genetic profiling is required to understand a drug’s mechanism-of-action.
In-Person Group Discussions10:05 am
Coffee Break in the Exhibit Hall with Poster Viewing10:50 am
Opportunity for 3D Cellular Models to Address Gaps in Drug Development
Topics to be covered:
- Understanding the goals of the FDA Modernization Act - What are the gaps in preclinical research that can now be addressed by 3D models?
- Using the right 3D Models to help the development of genomically-targeted therapeutics
- Improving the predictive power of 3D Models
We will introduce a number of Cellecta technologies along with relevant data for drug & biomarker discovery and validation, including CRISPR functional screening, cell tracking tools, as well as the recently launched DriverMap™ Adaptive Immune Receptor (AIR) Profiling Assay that enables the identification of more clonotypes and their activation levels with greater sensitivity and reproducibility than other assays on the market.
Transition to Lunch1:00 pm
Dessert Break in the Exhibit Hall with Last Chance for Poster Viewing1:35 pm
AI/ML & MULTI-OMICS FOR TARGET DISCOVERY
Leveraging Single-Cell Genomics & Machine Learning for Novel Target Identification
In recent years, we have seen significant advancements in the field of precision medicine. New genomic technologies hold great promise for the identification of actionable drug targets and associated biomarkers for several complex diseases, such as autoimmunity. Our approach uses single-cell RNAseq and machine learning to elucidate the precise cell types and gene expression programs involved in the progression of complex diseases in order to identify novel therapeutic targets.
From Data to Discovery: The Role of AI in Omics-Based New Target Discovery
Predicting Onset Age of Tumors from Inflammatory and Metabolic Measurements on Skin Biopsy-derived Fibroblasts
Cornerstone to the development of cancer and other age-dependent disease are the downstream influences of systemic metabolism and inflam-ageing. Relying on skin biopsy-derived fibroblasts, we developed multivariate phenotypic readouts that quantify metabolic and inflammatory rates that are specific to the individual biopsy donor at the single cell level. We found that these cell assay readouts correlate with pharmacological (rapamycin) or life-style (weight gain) interventions that had occurred in the individuals past and predict the onset age of cancers in the individuals future. Skin fibroblasts derived from cancer-free individuals from Li Fraumeni patients that were subject to years-long clinical surveillance, predicted the specific (personalized) onset age of cancers that developed years post-biopsy.
Using CRISPR/AI to Uncover Disease-Driving RNA Messages for Therapeutics Discovery
Algen is a platform therapeutics and drug discovery company using the world’s leading CRISPR and AI to uncover disease-driving RNA messages to find treatments for cancer, inflammation, and other diseases. Spun out from Nobel Laureate Professor Jennifer Doudna's Lab, Algen aims to develop the world’s smartest drug discovery decision platform and data universe to create next-generation therapeutics.
Close of Conference4:20 pm