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From High-Throughput Assay to Systems Biology: New Tools for Drug Discovery

From High-Throughput Assay to Systems Biology: New Tools for Drug Discovery

Curator: Stephen J. Williams, PhD

Marc W. Kirschner*

Department of Systems Biology
Harvard Medical School

Boston, Massachusetts 02115

With the new excitement about systems biology, there is understandable interest in a definition. This has proven somewhat difficult. Scientific fields, like species, arise by descent with modification, so in their earliest forms even the founders of great dynasties are only marginally different than their sister fields and species. It is only in retrospect that we can recognize the significant founding events. Before embarking on a definition of systems biology, it may be worth remembering that confusion and controversy surrounded the introduction of the term “molecular biology,” with claims that it hardly differed from biochemistry. Yet in retrospect molecular biology was new and different. It introduced both new subject matter and new technological approaches, in addition to a new style.

As a point of departure for systems biology, consider the quintessential experiment in the founding of molecular biology, the one gene one enzyme hypothesis of Beadle and Tatum. This experiment first connected the genotype directly to the phenotype on a molecular level, although efforts in that direction can certainly be found in the work of Archibald Garrod, Sewell Wright, and others. Here a protein (in this case an enzyme) is seen to be a product of a single gene, and a single function; the completion of a specific step in amino acid biosynthesis is the direct result. It took the next 30 years to fill in the gaps in this process. Yet the one gene one enzyme hypothesis looks very different to us today. What is the function of tubulin, of PI-3 kinase or of rac? Could we accurately predict the phenotype of a nonlethal mutation in these genes in a multicellular organism? Although we can connect structure to the gene, we can no longer infer its larger purpose in the cell or in the organism. There are too many purposes; what the protein does is defined by context. The context also includes a history, either developmental or physiological. Thus the behavior of the Wnt signaling pathway depends on the previous lineage, the “where and when” questions of embryonic development. Similarly the behavior of the immune system depends on previous experience in a variable environment. All of these features stress how inadequate an explanation for function we can achieve solely by trying to identify genes (by annotating them!) and characterizing their transcriptional control circuits.

That we are at a crossroads in how to explore biology is not at all clear to many. Biology is hardly in its dotage; the process of discovery seems to have been perfected, accelerated, and made universally applicable to all fields of biology. With the completion of the human genome and the genomes of other species, we have a glimpse of many more genes than we ever had before to study. We are like naturalists discovering a new continent, enthralled with the diversity itself. But we have also at the same time glimpsed the finiteness of this list of genes, a disturbingly small list. We have seen that the diversity of genes cannot approximate the diversity of functions within an organism. In response, we have argued that combinatorial use of small numbers of components can generate all the diversity that is needed. This has had its recent incarnation in the simplistic view that the rules of cis-regulatory control on DNA can directly lead to an understanding of organisms and their evolution. Yet this assumes that the gene products can be linked together in arbitrary combinations, something that is not assured in chemistry. It also downplays the significant regulatory features that involve interactions between gene products, their localization, binding, posttranslational modification, degradation, etc. The big question to understand in biology is not regulatory linkage but the nature of biological systems that allows them to be linked together in many nonlethal and even useful combinations. More and more we come to realize that understanding the conserved genes and their conserved circuits will require an understanding of their special properties that allow them to function together to generate different phenotypes in different tissues of metazoan organisms. These circuits may have certain robustness, but more important they have adaptability and versatility. The ease of putting conserved processes under regulatory control is an inherent design feature of the processes themselves. Among other things it loads the deck in evolutionary variation and makes it more feasible to generate useful phenotypes upon which selection can act.

Systems biology offers an opportunity to study how the phenotype is generated from the genotype and with it a glimpse of how evolution has crafted the phenotype. One aspect of systems biology is the development of techniques to examine broadly the level of protein, RNA, and DNA on a gene by gene basis and even the posttranslational modification and localization of proteins. In a very short time we have witnessed the development of high-throughput biology, forcing us to consider cellular processes in toto. Even though much of the data is noisy and today partially inconsistent and incomplete, this has been a radical shift in the way we tear apart problems one interaction at a time. When coupled with gene deletions by RNAi and classical methods, and with the use of chemical tools tailored to proteins and protein domains, these high-throughput techniques become still more powerful.

High-throughput biology has opened up another important area of systems biology: it has brought us out into the field again or at least made us aware that there is a world outside our laboratories. Our model systems have been chosen intentionally to be of limited genetic diversity and examined in a highly controlled and reproducible environment. The real world of ecology, evolution, and human disease is a very different place. When genetics separated from the rest of biology in the early part of the 20^th century, most geneticists sought to understand heredity and chose to study traits in the organism that could be easily scored and could be used to reveal genetic mechanisms. This was later extended to powerful effect to use genetics to study cell biological and developmental mechanisms. Some geneticists, including a large school in Russia in the early 20^th century, continued to study the genetics of natural populations, focusing on traits important for survival. That branch of genetics is coming back strongly with the power of phenotypic assays on the RNA and protein level. As human beings we are most concerned not with using our genetic misfortunes to unravel biology’s complexity (important as that is) but with the role of our genetics in our individual survival. The context for understanding this is still not available, even though the data are now coming in torrents, for many of the genes that will contribute to our survival will have small quantitative effects, partially masked or accentuated by other genetic and environmental conditions. To understand the genetic basis of disease will require not just mapping these genes but an understanding of how the phenotype is created in the first place and the messy interactions between genetic variation and environmental variation.

Extracts and explants are relatively accessible to synthetic manipulation. Next there is the explicit reconstruction of circuits within cells or the deliberate modification of those circuits. This has occurred for a while in biology, but the difference is that now we wish to construct or intervene with the explicit purpose of describing the dynamical features of these synthetic or partially synthetic systems. There are more and more tools to intervene and more and more tools to measure. Although these fall short of total descriptions of cells and organisms, the detailed information will give us a sense of the special life-like processes of circuits, proteins, cells in tissues, and whole organisms in their environment. This meso-scale systems biology will help establish the correspondence between molecules and large-scale physiology.

You are probably running out of patience for some definition of systems biology. In any case, I do not think the explicit definition of systems biology should come from me but should await the words of the first great modern systems biologist. She or he is probably among us now. However, if forced to provide some kind of label for systems biology, I would simply say that systems biology is the study of the behavior of complex biological organization and processes in terms of the molecular constituents. It is built on molecular biology in its special concern for information transfer, on physiology for its special concern with adaptive states of the cell and organism, on developmental biology for the importance of defining a succession of physiological states in that process, and on evolutionary biology and ecology for the appreciation that all aspects of the organism are products of selection, a selection we rarely understand on a molecular level. Systems biology attempts all of this through quantitative measurement, modeling, reconstruction, and theory. Systems biology is not a branch of physics but differs from physics in that the primary task is to understand how biology generates variation. No such imperative to create variation exists in the physical world. It is a new principle that Darwin understood and upon which all of life hinges. That sounds different enough for me to justify a new field and a new name. Furthermore, the success of systems biology is essential if we are to understand life; its success is far from assured—a good field for those seeking risk and adventure.

Source: “Meaning of Systems Biology” Cell, Vol. 121, 503–504, May 20, 2005, DOI 10.1016/j.cell.2005.05.005

Old High-throughput Screening, Once the Gold Standard in Drug Development, Gets a Systems Biology Facelift

From Phenotypic Hit to Chemical Probe: Chemical Biology Approaches to Elucidate Small Molecule Action in Complex Biological Systems

Quentin T. L. Pasquer, Ioannis A. Tsakoumagkos and Sascha Hoogendoorn

Molecules 2020, 25(23), 5702; https://doi.org/10.3390/molecules25235702

Abstract

Biologically active small molecules have a central role in drug development, and as chemical probes and tool compounds to perturb and elucidate biological processes. Small molecules can be rationally designed for a given target, or a library of molecules can be screened against a target or phenotype of interest. Especially in the case of phenotypic screening approaches, a major challenge is to translate the compound-induced phenotype into a well-defined cellular target and mode of action of the hit compound. There is no “one size fits all” approach, and recent years have seen an increase in available target deconvolution strategies, rooted in organic chemistry, proteomics, and genetics. This review provides an overview of advances in target identification and mechanism of action studies, describes the strengths and weaknesses of the different approaches, and illustrates the need for chemical biologists to integrate and expand the existing tools to increase the probability of evolving screen hits to robust chemical probes.

5.1.5. Large-Scale Proteomics

While FITExP is based on protein expression regulation during apoptosis, a study of Ruprecht et al. showed that proteomic changes are induced both by cytotoxic and non-cytotoxic compounds, which can be detected by mass spectrometry to give information on a compound’s mechanism of action. They developed a large-scale proteome-wide mass spectrometry analysis platform for MOA studies, profiling five lung cancer cell lines with over 50 drugs. Aggregation analysis over the different cell lines and the different compounds showed that one-quarter of the drugs changed the abundance of their protein target. This approach allowed target confirmation of molecular degraders such as PROTACs or molecular glues. Finally, this method yielded unexpected off-target mechanisms for the MAP2K1/2 inhibitor PD184352 and the ALK inhibitor ceritinib [97]. While such a mapping approach clearly provides a wealth of information, it might not be easily attainable for groups that are not equipped for high-throughput endeavors.

All-in-all, mass spectrometry methods have gained a lot of traction in recent years and have been successfully applied for target deconvolution and MOA studies of small molecules. As with all high-throughput methods, challenges lie in the accessibility of the instruments (both from a time and cost perspective) and data analysis of complex and extensive data sets.

5.2. Genetic Approaches

Both label-based and mass spectrometry proteomic approaches are based on the physical interaction between a small molecule and a protein target, and focus on the proteome for target deconvolution. It has been long realized that genetics provides an alternative avenue to understand a compound’s action, either through precise modification of protein levels, or by inducing protein mutations. First realized in yeast as a genetically tractable organism over 20 years ago, recent advances in genetic manipulation of mammalian cells have opened up important opportunities for target identification and MOA studies through genetic screening in relevant cell types [98]. Genetic approaches can be roughly divided into two main areas, with the first centering on the identification of mutations that confer compound resistance (Figure 3a), and the second on genome-wide perturbation of gene function and the concomitant changes in sensitivity to the compound (Figure 3b). While both methods can be used to identify or confirm drug targets, the latter category often provides many additional insights in the compound’s mode of action.

Figure 3. Genetic methods for target identification and mode of action studies. Schematic representations of (a) resistance cloning, and (b) chemogenetic interaction screens.

5.2.1. Resistance Cloning

The “gold standard” in drug target confirmation is to identify mutations in the presumed target protein that render it insensitive to drug treatment. Conversely, different groups have sought to use this principle as a target identification method based on the concept that cells grown in the presence of a cytotoxic drug will either die or develop mutations that will make them resistant to the compound. With recent advances in deep sequencing it is now possible to then scan the transcriptome [99] or genome [100] of the cells for resistance-inducing mutations. Genes that are mutated are then hypothesized to encode the protein target. For this approach to be successful, there are two initial requirements: (1) the compound needs to be cytotoxic for resistant clones to arise, and (2) the cell line needs to be genetically unstable for mutations to occur in a reasonable timeframe.

In 2012, the Kapoor group demonstrated in a proof-of-concept study that resistance cloning in mammalian cells, coupled to transcriptome sequencing (RNA-seq), yields the known polo-like kinase 1 (PLK1) target of the small molecule BI 2536. For this, they used the cancer cell line HCT-116, which is deficient in mismatch repair and consequently prone to mutations. They generated and sequenced multiple resistant clones, and clustered the clones based on similarity. PLK1 was the only gene that was mutated in multiple groups. Of note, one of the groups did not contain PLK1 mutations, but rather developed resistance through upregulation of ABCBA1, a drug efflux transporter, which is a general and non-specific resistance mechanism [101]. In a following study, they optimized their pipeline “DrugTargetSeqR”, by counter-screening for these types of multidrug resistance mechanisms so that these clones were excluded from further analysis (Figure 3a). Furthermore, they used CRISPR/Cas9-mediated gene editing to determine which mutations were sufficient to confer drug resistance, and as independent validation of the biochemical relevance of the obtained hits [102].

While HCT-116 cells are a useful model cell line for resistance cloning because of their genomic instability, they may not always be the cell line of choice, depending on the compound and process that is studied. Povedana et al. used CRISPR/Cas9 to engineer mismatch repair deficiencies in Ewing sarcoma cells and small cell lung cancer cells. They found that deletion of MSH2 results in hypermutations in these normally mutationally silent cells, resulting in the formation of resistant clones in the presence of bortezomib, MLN4924, and CD437, which are all cytotoxic compounds [103]. Recently, Neggers et al. reasoned that CRISPR/Cas9-induced non-homologous end-joining repair could be a viable strategy to create a wide variety of functional mutants of essential genes through in-frame mutations. Using a tiled sgRNA library targeting 75 target genes of investigational neoplastic drugs in HAP1 and K562 cells, they generated several KPT-9274 (an anticancer agent with unknown target)-resistant clones, and subsequent deep sequencing showed that the resistant clones were enriched in NAMPT sgRNAs. Direct target engagement was confirmed by co-crystallizing the compound with NAMPT [104]. In addition to these genetic mutation strategies, an alternative method is to grow the cells in the presence of a mutagenic chemical to induce higher mutagenesis rates [105,106].

When there is already a hypothesis on the pathway involved in compound action, the resistance cloning methodology can be extended to non-cytotoxic compounds. Sekine et al. developed a fluorescent reporter model for the integrated stress response, and used this cell line for target deconvolution of a small molecule inhibitor towards this pathway (ISRIB). Reporter cells were chemically mutagenized, and ISRIB-resistant clones were isolated by flow cytometry, yielding clones with various mutations in the delta subunit of guanine nucleotide exchange factor eIF2B [107].

While there are certainly successful examples of resistance cloning yielding a compound’s direct target as discussed above, resistance could also be caused by mutations or copy number alterations in downstream components of a signaling pathway. This is illustrated by clinical examples of acquired resistance to small molecules, nature’s way of “resistance cloning”. For example, resistance mechanisms in Hedgehog pathway-driven cancers towards the Smoothened inhibitor vismodegib include compound-resistant mutations in Smoothened, but also copy number changes in downstream activators SUFU and GLI2 [108]. It is, therefore, essential to conduct follow-up studies to confirm a direct interaction between a compound and the hit protein, as well as a lack of interaction with the mutated protein.

5.2.3. “Chemogenomics”: Examples of Gene-Drug Interaction Screens

When genetic perturbations are combined with small molecule drugs in a chemogenetic interaction screen, the effect of a gene’s perturbation on compound action is studied. Gene perturbation can render the cells resistant to the compound (suppressor interaction), or conversely, result in hypersensitivity and enhanced compound potency (synergistic interaction) [5,117,121]. Typically, cells are treated with the compound at a sublethal dose, to ascertain that both types of interactions can be found in the final dataset, and often it is necessary to use a variety of compound doses (i.e., LD₂₀, LD₃₀, LD₅₀) and timepoints to obtain reliable insights (Figure 3b).

An early example of successful coupling of a phenotypic screen and downstream genetic screening for target identification is the study of Matheny et al. They identified STF-118804 as a compound with antileukemic properties. Treatment of MV411 cells, stably transduced with a high complexity, genome-wide shRNA library, with STF-118804 (4 rounds of increasing concentration) or DMSO control resulted in a marked depletion of cells containing shRNAs against nicotinamide phosphoribosyl transferase (NAMPT) [122].

The Bassik lab subsequently directly compared the performance of shRNA-mediated knockdown versus CRISPR/Cas9-knockout screens for the target elucidation of the antiviral drug GSK983. The data coming out of both screens were complementary, with the shRNA screen resulting in hits leading to the direct compound target and the CRISPR screen giving information on cellular mechanisms of action of the compound. A reason for this is likely the level of protein depletion that is reached by these methods: shRNAs lead to decreased protein levels, which is advantageous when studying essential genes. However, knockdown may not result in a phenotype for non-essential genes, in which case a full CRISPR-mediated knockout is necessary to observe effects [123].

Another NAMPT inhibitor was identified in a CRISPR/Cas9 “haplo-insufficiency (HIP)”-like approach [124]. Haploinsuffiency profiling is a well-established system in yeast which is performed in a ~50% protein background by heterozygous deletions [125]. As there is no control over CRISPR-mediated loss of alleles, compound treatment was performed at several timepoints after addition of the sgRNA library to HCT116 cells stably expressing Cas9, in the hope that editing would be incomplete at early timepoints, resulting in residual protein levels. Indeed, NAMPT was found to be the target of phenotypic hit LB-60-OF61, especially at earlier timepoints, confirming the hypothesis that some level of protein needs to be present to identify a compound’s direct target [124]. This approach was confirmed in another study, thereby showing that direct target identification through CRISPR-knockout screens is indeed possible [126].

An alternative strategy was employed by the Weissman lab, where they combined genome-wide CRISPR-interference and -activation screens to identify the target of the phase 3 drug rigosertib. They focused on hits that had opposite action in both screens, as in sensitizing in one but protective in the other, which were related to microtubule stability. In a next step, they created chemical-genetic profiles of a variety of microtubule destabilizing agents, rationalizing that compounds with the same target will have similar drug-gene interactions. For this, they made a focused library of sgRNAs, based on the most high-ranking hits in the rigosertib genome-wide CRISPRi screen, and compared the focused screen results of the different compounds. The profile for rigosertib clustered well with that of ABT-571, and rigorous target validation studies confirmed rigosertib binding to the colchicine binding site of tubulin—the same site as occupied by ABT-571 [127].

From the above examples, it is clear that genetic screens hold a lot of promise for target identification and MOA studies for small molecules. The CRISPR screening field is rapidly evolving, sgRNA libraries are continuously improving and increasingly commercially available, and new tools for data analysis are being developed [128]. The challenge lies in applying these screens to study compounds that are not cytotoxic, where finding the right dosage regimen will not be trivial.

SYSTEMS BIOLOGY AND CANCER RESEARCH & DRUG DISCOVERY

Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence

Youngjun Park, Dominik Heider and Anne-Christin Hauschild. Cancers 2021, 13(13), 3148; https://doi.org/10.3390/cancers13133148

Abstract

The rapid improvement of next-generation sequencing (NGS) technologies and their application in large-scale cohorts in cancer research led to common challenges of big data. It opened a new research area incorporating systems biology and machine learning. As large-scale NGS data accumulated, sophisticated data analysis methods became indispensable. In addition, NGS data have been integrated with systems biology to build better predictive models to determine the characteristics of tumors and tumor subtypes. Therefore, various machine learning algorithms were introduced to identify underlying biological mechanisms. In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems biology and omics data. Subsequently, we discuss how deep neural networks outperform other approaches, the potential of graph neural networks (GNN) in systems biology, and the limitations in NGS biomedical research. To reflect on the various challenges and corresponding computational solutions, we will discuss the following three topics: (i) molecular characteristics, (ii) tumor heterogeneity, and (iii) drug discovery. We conclude that machine learning and network-based approaches can add valuable insights and build highly accurate models. However, a well-informed choice of learning algorithm and biological network information is crucial for the success of each specific research question

1. Introduction

The development and widespread use of high-throughput technologies founded the era of big data in biology and medicine. In particular, it led to an accumulation of large-scale data sets that opened a vast amount of possible applications for data-driven methodologies. In cancer, these applications range from fundamental research to clinical applications: molecular characteristics of tumors, tumor heterogeneity, drug discovery and potential treatments strategy. Therefore, data-driven bioinformatics research areas have tailored data mining technologies such as systems biology, machine learning, and deep learning, elaborated in this review paper (see Figure 1 and Figure 2). For example, in systems biology, data-driven approaches are applied to identify vital signaling pathways [1]. This pathway-centric analysis is particularly crucial in cancer research to understand the characteristics and heterogeneity of the tumor and tumor subtypes. Consequently, this high-throughput data-based analysis enables us to explore characteristics of cancers with a systems biology and a systems medicine point of view [2].Combining high-throughput techniques, especially next-generation sequencing (NGS), with appropriate analytical tools has allowed researchers to gain a deeper systematic understanding of cancer at various biological levels, most importantly genomics, transcriptomics, and epigenetics [3,4]. Furthermore, more sophisticated analysis tools based on computational modeling are introduced to decipher underlying molecular mechanisms in various cancer types. The increasing size and complexity of the data required the adaptation of bioinformatics processing pipelines for higher efficiency and sophisticated data mining methodologies, particularly for large-scale, NGS datasets [5]. Nowadays, more and more NGS studies integrate a systems biology approach and combine sequencing data with other types of information, for instance, protein family information, pathway, or protein–protein interaction (PPI) networks, in an integrative analysis. Experimentally validated knowledge in systems biology may enhance analysis models and guides them to uncover novel findings. Such integrated analyses have been useful to extract essential information from high-dimensional NGS data [6,7]. In order to deal with the increasing size and complexity, the application of machine learning, and specifically deep learning methodologies, have become state-of-the-art in NGS data analysis.

Figure 1. Next-generation sequencing data can originate from various experimental and technological conditions. Depending on the purpose of the experiment, one or more of the depicted omics types (Genomics, Transcriptomics, Epigenomics, or Single-Cell Omics) are analyzed. These approaches led to an accumulation of large-scale NGS datasets to solve various challenges of cancer research, molecular characterization, tumor heterogeneity, and drug target discovery. For instance, The Cancer Genome Atlas (TCGA) dataset contains multi-omics data from ten-thousands of patients. This dataset facilitates a variety of cancer researches for decades. Additionally, there are also independent tumor datasets, and, frequently, they are analyzed and compared with the TCGA dataset. As the large scale of omics data accumulated, various machine learning techniques are applied, e.g., graph algorithms and deep neural networks, for dimensionality reduction, clustering, or classification. (Created with BioRender.com.)

Figure 2. (a) A multitude of different types of data is produced by next-generation sequencing, for instance, in the fields of genomics, transcriptomics, and epigenomics. (b) Biological networks for biomarker validation: The in vivo or in vitro experiment results are considered ground truth. Statistical analysis on next-generation sequencing data produces candidate genes. Biological networks can validate these candidate genes and highlight the underlying biological mechanisms (Section 2.1). (c) De novo construction of Biological Networks: Machine learning models that aim to reconstruct biological networks can incorporate prior knowledge from different omics data. Subsequently, the model will predict new unknown interactions based on new omics information (Section 2.2). (d) Network-based machine learning: Machine learning models integrating biological networks as prior knowledge to improve predictive performance when applied to different NGS data (Section 2.3). (Created with BioRender.com).

Therefore, a large number of studies integrate NGS data with machine learning and propose a novel data-driven methodology in systems biology [8]. In particular, many network-based machine learning models have been developed to analyze cancer data and help to understand novel mechanisms in cancer development [9,10]. Moreover, deep neural networks (DNN) applied for large-scale data analysis improved the accuracy of computational models for mutation prediction [11,12], molecular subtyping [13,14], and drug repurposing [15,16].

2. Systems Biology in Cancer Research

Genes and their functions have been classified into gene sets based on experimental data. Our understandings of cancer concentrated into cancer hallmarks that define the characteristics of a tumor. This collective knowledge is used for the functional analysis of unseen data.. Furthermore, the regulatory relationships among genes were investigated, and, based on that, a pathway can be composed. In this manner, the accumulation of public high-throughput sequencing data raised many big-data challenges and opened new opportunities and areas of application for computer science. Two of the most vibrantly evolving areas are systems biology and machine learning which tackle different tasks such as understanding the cancer pathways [9], finding crucial genes in pathways [22,53], or predicting functions of unidentified or understudied genes [54]. Essentially, those models include prior knowledge to develop an analysis and enhance interpretability for high-dimensional data [2]. In addition to understanding cancer pathways with in silico analysis, pathway activity analysis incorporating two different types of data, pathways and omics data, is developed to understand heterogeneous characteristics of the tumor and cancer molecular subtyping. Due to its advantage in interpretability, various pathway-oriented methods are introduced and become a useful tool to understand a complex diseases such as cancer [55,56,57].

In this section, we will discuss how two related research fields, namely, systems biology and machine learning, can be integrated with three different approaches (see Figure 2), namely, biological network analysis for biomarker validation, the use of machine learning with systems biology, and network-based models.

2.1. Biological Network Analysis for Biomarker Validation

The detection of potential biomarkers indicative of specific cancer types or subtypes is a frequent goal of NGS data analysis in cancer research. For instance, a variety of bioinformatics tools and machine learning models aim at identify lists of genes that are significantly altered on a genomic, transcriptomic, or epigenomic level in cancer cells. Typically, statistical and machine learning methods are employed to find an optimal set of biomarkers, such as single nucleotide polymorphisms (SNPs), mutations, or differentially expressed genes crucial in cancer progression. Traditionally, resource-intensive in vitro analysis was required to discover or validate those markers. Therefore, systems biology offers in silico solutions to validate such findings using biological pathways or gene ontology information (Figure 2b) [58]. Subsequently, gene set enrichment analysis (GSEA) [50] or gene set analysis (GSA) [59] can be used to evaluate whether these lists of genes are significantly associated with cancer types and their specific characteristics. GSA, for instance, is available via web services like DAVID [60] and g:Profiler [61]. Moreover, other applications use gene ontology directly [62,63]. In addition to gene-set-based analysis, there are other methods that focuse on the topology of biological networks. These approaches evaluate various network structure parameters and analyze the connectivity of two genes or the size and interconnection of their neighbors [64,65]. According to the underlying idea, the mutated gene will show dysfunction and can affect its neighboring genes. Thus, the goal is to find abnormalities in a specific set of genes linked with an edge in a biological network. For instance, KeyPathwayMiner can extract informative network modules in various omics data [66]. In summary, these approaches aim at predicting the effect of dysfunctional genes among neighbors according to their connectivity or distances from specific genes such as hubs [67,68]. During the past few decades, the focus of cancer systems biology extended towards the analysis of cancer-related pathways since those pathways tend to carry more information than a gene set. Such analysis is called Pathway Enrichment Analysis (PEA) [69,70]. The use of PEA incorporates the topology of biological networks. However, simultaneously, the lack of coverage issue in pathway data needs to be considered. Because pathway data does not cover all known genes yet, an integration analysis on omics data can significantly drop in genes when incorporated with pathways. Genes that can not be mapped to any pathway are called ‘pathway orphan.’ In this manner, Rahmati et al. introduced a possible solution to overcome the ‘pathway orphan’ issue [71]. At the bottom line, regardless of whether researchers consider gene-set or pathway-based enrichment analysis, the performance and accuracy of both methods are highly dependent on the quality of the external gene-set and pathway data [72].

2.2. De Novo Construction of Biological Networks

While the known fraction of existing biological networks barely scratches the surface of the whole system of mechanisms occurring in each organism, machine learning models can improve on known network structures and can guide potential new findings [73,74]. This area of research is called de novo network construction (Figure 2c), and its predictive models can accelerate experimental validation by lowering time costs [75,76]. This interplay between in silico biological networks building and mining contributes to expanding our knowledge in a biological system. For instance, a gene co-expression network helps discover gene modules having similar functions [77]. Because gene co-expression networks are based on expressional changes under specific conditions, commonly, inferring a co-expression network requires many samples. The WGCNA package implements a representative model using weighted correlation for network construction that leads the development of the network biology field [78]. Due to NGS developments, the analysis of gene co-expression networks subsequently moved from microarray-based to RNA-seq based experimental data [79]. However, integration of these two types of data remains tricky. Ballouz et al. compared microarray and NGS-based co-expression networks and found the existence of a bias originating from batch effects between the two technologies [80]. Nevertheless, such approaches are suited to find disease-specific co-expressional gene modules. Thus, various studies based on the TCGA cancer co-expression network discovered characteristics of prognostic genes in the network [81]. Accordingly, a gene co-expression network is a condition-specific network rather than a general network for an organism. Gene regulatory networks can be inferred from the gene co-expression network when various data from different conditions in the same organism are available. Additionally, with various NGS applications, we can obtain multi-modal datasets about regulatory elements and their effects, such as epigenomic mechanisms on transcription and chromatin structure. Consequently, a gene regulatory network can consist of solely protein-coding genes or different regulatory node types such as transcription factors, inhibitors, promoter interactions, DNA methylations, and histone modifications affecting the gene expression system [82,83]. More recently, researchers were able to build networks based on a particular experimental setup. For instance, functional genomics or CRISPR technology enables the high-resolution regulatory networks in an organism [84]. Other than gene co-expression or regulatory networks, drug target, and drug repurposing studies are active research areas focusing on the de novo construction of drug-to-target networks to allow the potential repurposing of drugs [76,85].

2.3. Network Based Machine Learning

A network-based machine learning model directly integrates the insights of biological networks within the algorithm (Figure 2d) to ultimately improve predictive performance concerning cancer subtyping or susceptibility to therapy. Following the establishment of high-quality biological networks based on NGS technologies, these biological networks were suited to be integrated into advanced predictive models. In this manner, Zhang et al., categorized network-based machine learning approaches upon their usage into three groups: (i) model-based integration, (ii) pre-processing integration, and (iii) post-analysis integration [7]. Network-based models map the omics data onto a biological network, and proper algorithms travel the network while considering both values of nodes and edges and network topology. In the pre-processing integration, pathway or other network information is commonly processed based on its topological importance. Meanwhile, in the post-analysis integration, omics data is processed solely before integration with a network. Subsequently, omics data and networks are merged and interpreted. The network-based model has advantages in multi-omics integrative analysis. Due to the different sensitivity and coverage of various omics data types, a multi-omics integrative analysis is challenging. However, focusing on gene-level or protein-level information enables a straightforward integration [86,87]. Consequently, when different machine learning approaches tried to integrate two or more different data types to find novel biological insights, one of the solutions is reducing the search space to gene or protein level and integrated heterogeneous datatypes [25,88].

In summary, using network information opens new possibilities for interpretation. However, as mentioned earlier, several challenges remain, such as the coverage issue. Current databases for biological networks do not cover the entire set of genes, transcripts, and interactions. Therefore, the use of networks can lead to loss of information for gene or transcript orphans. The following section will focus on network-based machine learning models and their application in cancer genomics. We will put network-based machine learning into the perspective of the three main areas of application, namely, molecular characterization, tumor heterogeneity analysis, and cancer drug discovery.

3. Network-Based Learning in Cancer Research

As introduced previously, the integration of machine learning with the insights of biological networks (Figure 2d) ultimately aims at improving predictive performance and interpretability concerning cancer subtyping or treatment susceptibility.

3.1. Molecular Characterization with Network Information

Various network-based algorithms are used in genomics and focus on quantifying the impact of genomic alteration. By employing prior knowledge in biological network algorithms, performance compared to non-network models can be improved. A prominent example is HotNet. The algorithm uses a thermodynamics model on a biological network and identifies driver genes, or prognostic genes, in pan-cancer data [89]. Another study introduced a network-based stratification method to integrate somatic alterations and expression signatures with network information [90]. These approaches use network topology and network-propagation-like algorithms. Network propagation presumes that genomic alterations can affect the function of neighboring genes. Two genes will show an exclusive pattern if two genes complement each other, and the function carried by those two genes is essential to an organism [91]. This unique exclusive pattern among genomic alteration is further investigated in cancer-related pathways. Recently, Ku et al. developed network-centric approaches and tackled robustness issues while studying synthetic lethality [92]. Although synthetic lethality was initially discovered in model organisms of genetics, it helps us to understand cancer-specific mutations and their functions in tumor characteristics [91].

Furthermore, in transcriptome research, network information is used to measure pathway activity and its application in cancer subtyping. For instance, when comparing the data of two or more conditions such as cancer types, GSEA as introduced in Section 2 is a useful approach to get an overview of systematic changes [50]. It is typically used at the beginning of a data evaluation [93]. An experimentally validated gene set can provide information about how different conditions affect molecular systems in an organism. In addition to the gene sets, different approaches integrate complex interaction information into GSEA and build network-based models [70]. In contrast to GSEA, pathway activity analysis considers transcriptome data and other omics data and structural information of a biological network. For example, PARADIGM uses pathway topology and integrates various omics in the analysis to infer a patient-specific status of pathways [94]. A benchmark study with pan-cancer data recently reveals that using network structure can show better performance [57]. In conclusion, while the loss of data is due to the incompleteness of biological networks, their integration improved performance and increased interpretability in many cases.

3.2. Tumor Heterogeneity Study with Network Information

The tumor heterogeneity can originate from two directions, clonal heterogeneity and tumor impurity. Clonal heterogeneity covers genomic alterations within the tumor [95]. While de novo mutations accumulate, the tumor obtains genomic alterations with an exclusive pattern. When these genomic alterations are projected on the pathway, it is possible to observe exclusive relationships among disease-related genes. For instance, the CoMEt and MEMo algorithms examine mutual exclusivity on protein–protein interaction networks [96,97]. Moreover, the relationship between genes can be essential for an organism. Therefore, models analyzing such alterations integrate network-based analysis [98].

In contrast, tumor purity is dependent on the tumor microenvironment, including immune-cell infiltration and stromal cells [99]. In tumor microenvironment studies, network-based models are applied, for instance, to find immune-related gene modules. Although the importance of the interaction between tumors and immune cells is well known, detailed mechanisms are still unclear. Thus, many recent NGS studies employ network-based models to investigate the underlying mechanism in tumor and immune reactions. For example, McGrail et al. identified a relationship between the DNA damage response protein and immune cell infiltration in cancer. The analysis is based on curated interaction pairs in a protein–protein interaction network [100]. Most recently, Darzi et al. discovered a prognostic gene module related to immune cell infiltration by using network-centric approaches [101]. Tu et al. presented a network-centric model for mining subnetworks of genes other than immune cell infiltration by considering tumor purity [102].

3.3. Drug Target Identification with Network Information

In drug target studies, network biology is integrated into pharmacology [103]. For instance, Yamanishi et al. developed novel computational methods to investigate the pharmacological space by integrating a drug-target protein network with genomics and chemical information. The proposed approaches investigated such drug-target network information to identify potential novel drug targets [104]. Since then, the field has continued to develop methods to study drug target and drug response integrating networks with chemical and multi-omic datasets. In a recent survey study by Chen et al., the authors compared 13 computational methods for drug response prediction. It turned out that gene expression profiles are crucial information for drug response prediction [105].

Moreover, drug-target studies are often extended to drug-repurposing studies. In cancer research, drug-repurposing studies aim to find novel interactions between non-cancer drugs and molecular features in cancer. Drug-repurposing (or repositioning) studies apply computational approaches and pathway-based models and aim at discovering potential new cancer drugs with a higher probability than de novo drug design [16,106]. Specifically, drug-repurposing studies can consider various areas of cancer research, such as tumor heterogeneity and synthetic lethality. As an example, Lee et al. found clinically relevant synthetic lethality interactions by integrating multiple screening NGS datasets [107]. This synthetic lethality and related-drug datasets can be integrated for an effective combination of anticancer therapeutic strategy with non-cancer drug repurposing.

4. Deep Learning in Cancer Research

DNN models develop rapidly and become more sophisticated. They have been frequently used in all areas of biomedical research. Initially, its development was facilitated by large-scale imaging and video data. While most data sets in the biomedical field would not typically be considered big data, the rapid data accumulation enabled by NGS made it suitable for the application of DNN models requiring a large amount of training data [108]. For instance, in 2019, Samiei et al. used TCGA-based large-scale cancer data as benchmark datasets for bioinformatics machine learning research such as Image-Net in the computer vision field [109]. Subsequently, large-scale public cancer data sets such as TCGA encouraged the wide usage of DNNs in the cancer domain [110]. Over the last decade, these state-of-the-art machine learning methods have been incorporated in many different biological questions [111].

In addition to public cancer databases such as TCGA, the genetic information of normal tissues is stored in well-curated databases such as GTEx [112] and 1000Genomes [113]. These databases are frequently used as control or baseline training data for deep learning [114]. Moreover, other non-curated large-scale data sources such as GEO (https://www.ncbi.nlm.nih.gov/geo/, accessed on 20 May 2021) can be leveraged to tackle critical aspects in cancer research. They store a large-scale of biological data produced under various experimental setups (Figure 1). Therefore, an integration of GEO data and other data requires careful preprocessing. Overall, an increasing amount of datasets facilitate the development of current deep learning in bioinformatics research [115].

4.1. Challenges for Deep Learning in Cancer Research

Many studies in biology and medicine used NGS and produced large amounts of data during the past few decades, moving the field to the big data era. Nevertheless, researchers still face a lack of data in particular when investigating rare diseases or disease states. Researchers have developed a manifold of potential solutions to overcome this lack of data challenges, such as imputation, augmentation, and transfer learning (Figure 3b). Data imputation aims at handling data sets with missing values [116]. It has been studied on various NGS omics data types to recover missing information [117]. It is known that gene expression levels can be altered by different regulatory elements, such as DNA-binding proteins, epigenomic modifications, and post-transcriptional modifications. Therefore, various models integrating such regulatory schemes have been introduced to impute missing omics data [118,119]. Some DNN-based models aim to predict gene expression changes based on genomics or epigenomics alteration. For instance, TDimpute aims at generating missing RNA-seq data by training a DNN on methylation data. They used TCGA and TARGET (https://ocg.cancer.gov/programs/target/data-matrix, accessed on 20 May 2021) data as proof of concept of the applicability of DNN for data imputation in a multi-omics integration study [120]. Because this integrative model can exploit information in different levels of regulatory mechanisms, it can build a more detailed model and achieve better performance than a model build on a single-omics dataset [117,121]. The generative adversarial network (GAN) is a DNN structure for generating simulated data that is different from the original data but shows the same characteristics [122]. GANs can impute missing omics data from other multi-omics sources. Recently, the GAN algorithm is getting more attention in single-cell transcriptomics because it has been recognized as a complementary technique to overcome the limitation of scRNA-seq [123]. In contrast to data imputation and generation, other machine learning approaches aim to cope with a limited dataset in different ways. Transfer learning or few-shot learning, for instance, aims to reduce the search space with similar but unrelated datasets and guide the model to solve a specific set of problems [124]. These approaches train models with data of similar characteristics and types but different data to the problem set. After pre-training the model, it can be fine-tuned with the dataset of interest [125,126]. Thus, researchers are trying to introduce few-shot learning models and meta-learning approaches to omics and translational medicine. For example, Select-ProtoNet applied the ProtoTypical Network [127] model to TCGA transcriptome data and classified patients into two groups according to their clinical status [128]. AffinityNet predicts kidney and uterus cancer subtypes with gene expression profiles [129].

Figure 3. (a) In various studies, NGS data transformed into different forms. The 2-D transformed form is for the convolution layer. Omics data is transformed into pathway level, GO enrichment score, or Functional spectra. (b) DNN application on different ways to handle lack of data. Imputation for missing data in multi-omics datasets. GAN for data imputation and in silico data simulation. Transfer learning pre-trained the model with other datasets and fine-tune. (c) Various types of information in biology. (d) Graph neural network examples. GCN is applied to aggregate neighbor information. (Created with BioRender.com).

4.2. Molecular Charactization with Network and DNN Model

DNNs have been applied in multiple areas of cancer research. For instance, a DNN model trained on TCGA cancer data can aid molecular characterization by identifying cancer driver genes. At the very early stage, Yuan et al. build DeepGene, a cancer-type classifier. They implemented data sparsity reduction methods and trained the DNN model with somatic point mutations [130]. Lyu et al. [131] and DeepGx [132] embedded a 1-D gene expression profile to a 2-D array by chromosome order to implement the convolution layer (Figure 3a). Other algorithms, such as the deepDriver, use k-nearest neighbors for the convolution layer. A predefined number of neighboring gene mutation profiles was the input for the convolution layer. It employed this convolution layer in a DNN by aggregating mutation information of the k-nearest neighboring genes [11]. Instead of embedding to a 2-D image, DeepCC transformed gene expression data into functional spectra. The resulting model was able to capture molecular characteristics by training cancer subtypes [14].

Another DNN model was trained to infer the origin of tissue from single-nucleotide variant (SNV) information of metastatic tumor. The authors built a model by using the TCGA/ICGC data and analyzed SNV patterns and corresponding pathways to predict the origin of cancer. They discovered that metastatic tumors retained their original cancer’s signature mutation pattern. In this context, their DNN model obtained even better accuracy than a random forest model [133] and, even more important, better accuracy than human pathologists [12].

4.3. Tumor Heterogeneity with Network and DNN Model

As described in Section 4.1, there are several issues because of cancer heterogeneity, e.g., tumor microenvironment. Thus, there are only a few applications of DNN in intratumoral heterogeneity research. For instance, Menden et al. developed ’Scaden’ to deconvolve cell types in bulk-cell sequencing data. ’Scaden’ is a DNN model for the investigation of intratumor heterogeneity. To overcome the lack of training datasets, researchers need to generate in silico simulated bulk-cell sequencing data based on single-cell sequencing data [134]. It is presumed that deconvolving cell types can be achieved by knowing all possible expressional profiles of the cell [36]. However, this information is typically not available. Recently, to tackle this problem, single-cell sequencing-based studies were conducted. Because of technical limitations, we need to handle lots of missing data, noises, and batch effects in single-cell sequencing data [135]. Thus, various machine learning methods were developed to process single-cell sequencing data. They aim at mapping single-cell data onto the latent space. For example, scDeepCluster implemented an autoencoder and trained it on gene-expression levels from single-cell sequencing. During the training phase, the encoder and decoder work as denoiser. At the same time, they can embed high-dimensional gene-expression profiles to lower-dimensional vectors [136]. This autoencoder-based method can produce biologically meaningful feature vectors in various contexts, from tissue cell types [137] to different cancer types [138,139].

4.4. Drug Target Identification with Networks and DNN Models

In addition to NGS datasets, large-scale anticancer drug assays enabled the training train of DNNs. Moreover, non-cancer drug response assay datasets can also be incorporated with cancer genomic data. In cancer research, a multidisciplinary approach was widely applied for repurposing non-oncology drugs to cancer treatment. This drug repurposing is faster than de novo drug discovery. Furthermore, combination therapy with a non-oncology drug can be beneficial to overcome the heterogeneous properties of tumors [85]. The deepDR algorithm integrated ten drug-related networks and trained deep autoencoders. It used a random-walk-based algorithm to represent graph information into feature vectors. This approach integrated network analysis with a DNN model validated with an independent drug-disease dataset [15].

The authors of CDRscan did an integrative analysis of cell-line-based assay datasets and other drug and genomics datasets. It shows that DNN models can enhance the computational model for improved drug sensitivity predictions [140]. Additionally, similar to previous network-based models, the multi-omics application of drug-targeted DNN studies can show higher prediction accuracy than the single-omics method. MOLI integrated genomic data and transcriptomic data to predict the drug responses of TCGA patients [141].

4.5. Graph Neural Network Model

In general, the advantage of using a biological network is that it can produce more comprehensive and interpretable results from high-dimensional omics data. Furthermore, in an integrative multi-omics data analysis, network-based integration can improve interpretability over traditional approaches. Instead of pre-/post-integration of a network, recently developed graph neural networks use biological networks as the base structure for the learning network itself. For instance, various pathways or interactome information can be integrated as a learning structure of a DNN and can be aggregated as heterogeneous information. In a GNN study, a convolution process can be done on the provided network structure of data. Therefore, the convolution on a biological network made it possible for the GNN to focus on the relationship among neighbor genes. In the graph convolution layer, the convolution process integrates information of neighbor genes and learns topological information (Figure 3d). Consequently, this model can aggregate information from far-distant neighbors, and thus can outperform other machine learning models [142].

In the context of the inference problem of gene expression, the main question is whether the gene expression level can be explained by aggregating the neighboring genes. A single gene inference study by Dutil et al. showed that the GNN model outperformed other DNN models [143]. Moreover, in cancer research, such GNN models can identify cancer-related genes with better performance than other network-based models, such as HotNet2 and MutSigCV [144]. A recent GNN study with a multi-omics integrative analysis identified 165 new cancer genes as an interactive partner for known cancer genes [145]. Additionally, in the synthetic lethality area, dual-dropout GNN outperformed previous bioinformatics tools for predicting synthetic lethality in tumors [146]. GNNs were also able to classify cancer subtypes based on pathway activity measures with RNA-seq data. Lee et al. implemented a GNN for cancer subtyping and tested five cancer types. Thus, the informative pathway was selected and used for subtype classification [147]. Furthermore, GNNs are also getting more attention in drug repositioning studies. As described in Section 3.3, drug discovery requires integrating various networks in both chemical and genomic spaces (Figure 3d). Chemical structures, protein structures, pathways, and other multi-omics data were used in drug-target identification and repurposing studies (Figure 3c). Each of the proposed applications has a specialty in the different purposes of drug-related tasks. Sun et al. summarized GNN-based drug discovery studies and categorized them into four classes: molecular property and activity prediction, interaction prediction, synthesis prediction, and de novo drug design. The authors also point out four challenges in the GNN-mediated drug discovery. At first, as we described before, there is a lack of drug-related datasets. Secondly, the current GNN models can not fully represent 3-D structures of chemical molecules and protein structures. The third challenge is integrating heterogeneous network information. Drug discovery usually requires a multi-modal integrative analysis with various networks, and GNNs can improve this integrative analysis. Lastly, although GNNs use graphs, stacked layers still make it hard to interpret the model [148].

4.6. Shortcomings in AI and Revisiting Validity of Biological Networks as Prior Knowledge

The previous sections reviewed a variety of DNN-based approaches that present a good performance on numerous applications. However, it is hardly a panacea for all research questions. In the following, we will discuss potential limitations of the DNN models. In general, DNN models with NGS data have two significant issues: (i) data requirements and (ii) interpretability. Usually, deep learning needs a large proportion of training data for reasonable performance which is more difficult to achieve in biomedical omics data compared to, for instance, image data. Today, there are not many NGS datasets that are well-curated and -annotated for deep learning. This can be an answer to the question of why most DNN studies are in cancer research [110,149]. Moreover, the deep learning models are hard to interpret and are typically considered as black-boxes. Highly stacked layers in the deep learning model make it hard to interpret its decision-making rationale. Although the methodology to understand and interpret deep learning models has been improved, the ambiguity in the DNN models’ decision-making hindered the transition between the deep learning model and translational medicine [149,150].

As described before, biological networks are employed in various computational analyses for cancer research. The studies applying DNNs demonstrated many different approaches to use prior knowledge for systematic analyses. Before discussing GNN application, the validity of biological networks in a DNN model needs to be shown. The LINCS program analyzed data of ’The Connectivity Map (CMap) project’ to understand the regulatory mechanism in gene expression by inferring the whole gene expression profiles from a small set of genes (https://lincsproject.org/, accessed on 20 May 2021) [151,152]. This LINCS program found that the gene expression level is inferrable with only nearly 1000 genes. They called this gene list ’landmark genes’. Subsequently, Chen et al. started with these 978 landmark genes and tried to predict other gene expression levels with DNN models. Integrating public large-scale NGS data showed better performance than the linear regression model. The authors conclude that the performance advantage originates from the DNN’s ability to model non-linear relationships between genes [153].

Following this study, Beltin et al. extensively investigated various biological networks in the same context of the inference of gene expression level. They set up a simplified representation of gene expression status and tried to solve a binary classification task. To show the relevance of a biological network, they compared various gene expression levels inferred from a different set of genes, neighboring genes in PPI, random genes, and all genes. However, in the study incorporating TCGA and GTEx datasets, the random network model outperformed the model build on a known biological network, such as StringDB [154]. While network-based approaches can add valuable insights to analysis, this study shows that it cannot be seen as the panacea, and a careful evaluation is required for each data set and task. In particular, this result may not represent biological complexity because of the oversimplified problem setup, which did not consider the relative gene-expressional changes. Additionally, the incorporated biological networks may not be suitable for inferring gene expression profiles because they consist of expression-regulating interactions, non-expression-regulating interactions, and various in vivo and in vitro interactions.

“ However, although recently sophisticated applications of deep learning showed improved accuracy, it does not reflect a general advancement. Depending on the type of NGS data, the experimental design, and the question to be answered, a proper approach and specific deep learning algorithms need to be considered. Deep learning is not a panacea. In general, to employ machine learning and systems biology methodology for a specific type of NGS data, a certain experimental design, a particular research question, the technology, and network data have to be chosen carefully.”

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Use of Systems Biology in Anti-Microbial Drug Development

Genomics, Computational Biology and Drug Discovery for Mycobacterial Infections: Fighting the Emergence of Resistance. Asma Munir, Sundeep Chaitanya Vedithi, Amanda K. Chaplin and Tom L. Blundell. Front. Genet., 04 September 2020 | https://doi.org/10.3389/fgene.2020.00965

In an earlier review article (Waman et al., 2019), we discussed various computational approaches and experimental strategies for drug target identification and structure-guided drug discovery. In this review we discuss the impact of the era of precision medicine, where the genome sequences of pathogens can give clues about the choice of existing drugs, and repurposing of others. Our focus is directed toward combatting antimicrobial drug resistance with emphasis on tuberculosis and leprosy. We describe structure-guided approaches to understanding the impacts of mutations that give rise to antimycobacterial resistance and the use of this information in the design of new medicines.

Genome Sequences and Proteomic Structural Databases

In recent years, there have been many focused efforts to define the amino-acid sequences of the M. tuberculosis pan-genome and then to define the three-dimensional structures and functional interactions of these gene products. This work has led to essential genes of the bacteria being revealed and to a better understanding of the genetic diversity in different strains that might lead to a selective advantage (Coll et al., 2018). This will help with our understanding of the mode of antibiotic resistance within these strains and aid structure-guided drug discovery. However, only ∼10% of the ∼4128 proteins have structures determined experimentally.

Several databases have been developed to integrate the genomic and/or structural information linked to drug resistance in Mycobacteria (Table 1). These invaluable resources can contribute to better understanding of molecular mechanisms involved in drug resistance and improvement in the selection of potential drug targets.

There is a dearth of information related to structural aspects of proteins from M. leprae and their oligomeric and hetero-oligomeric organization, which has limited the understanding of physiological processes of the bacillus. The structures of only 12 proteins have been solved and deposited in the protein data bank (PDB). However, the high sequence similarity in protein coding genes between M. leprae and M. tuberculosis allows computational methods to be used for comparative modeling of the proteins of M. leprae. Mainly monomeric models using single template modeling have been defined and deposited in the Swiss Model repository (Bienert et al., 2017), in Modbase (Pieper et al., 2014), and in a collection with other infectious disease agents (Sosa et al., 2018). There is a need for multi-template modeling and building homo- and hetero-oligomeric complexes to better understand the interfaces, druggability and impacts of mutations.

We are now exploiting Vivace, a multi-template modeling pipeline developed in our lab for modeling the proteomes of M. tuberculosis (CHOPIN, see above) and M. abscessus [Mabellini Database (Skwark et al., 2019)], to model the proteome of M. leprae. We emphasize the need for understanding the protein interfaces that are critical to function. An example of this is that of the RNA-polymerase holoenzyme complex from M. leprae. We first modeled the structure of this hetero-hexamer complex and later deciphered the binding patterns of rifampin (Vedithi et al., 2018; Figures 1A,B). Rifampin is a known drug to treat tuberculosis and leprosy. Owing to high rifampin resistance in tuberculosis and emerging resistance in leprosy, we used an approach known as “Computational Saturation Mutagenesis”, to identify sites on the protein that are less impacted by mutations. In this study, we were able to understand the association between predicted impacts of mutations on the structure and phenotypic rifampin-resistance outcomes in leprosy.

FIGURE 2

Figure 2. (A) Stability changes predicted by mCSM for systematic mutations in the ß-subunit of RNA polymerase in M. leprae. The maximum destabilizing effect from among all 19 possible mutations at each residue position is considered as a weighting factor for the color map that gradients from red (high destabilizing effects) to white (neutral to stabilizing effects) (Vedithi et al., 2020). (B) One of the known mutations in the ß-subunit of RNA polymerase, the S437H substitution which resulted in a maximum destabilizing effect [-1.701 kcal/mol (mCSM)] among all 19 possibilities this position. In the mutant, histidine (residue in green) forms hydrogen bonds with S434 and Q438, aromatic interactions with F431, and other ring-ring and π interactions with the surrounding residues which can impact the shape of the rifampin binding pocket and rifampin affinity to the ß-subunit [-0.826 log(affinity fold change) (mCSM-lig)]. Orange dotted lines represent weak hydrogen bond interactions. Ring-ring and intergroup interactions are depicted in cyan. Aromatic interactions are represented in sky-blue and carbonyl interactions in pink dotted lines. Green dotted lines represent hydrophobic interactions (Vedithi et al., 2020).

Examples of Understanding and Combatting Resistance

The availability of whole genome sequences in the present era has greatly enhanced the understanding of emergence of drug resistance in infectious diseases like tuberculosis. The data generated by the whole genome sequencing of clinical isolates can be screened for the presence of drug-resistant mutations. A preliminary in silico analysis of mutations can then be used to prioritize experimental work to identify the nature of these mutations.

FIGURE 3

Figure 3. (A) Mechanism of isoniazid activation and INH-NAD adduct formation. (B) Mutations mapped (Munir et al., 2019) on the structure of KatG (PDB ID:1SJ2; Bertrand et al., 2004).

e-Proceedings for 2021 World Medical Innovation Forum, Mass General Brigham, Gene and Cell Therapy, VIRTUAL May 19–21, 2021

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2021 Virtual World Medical Innovation Forum, Mass General Brigham, Gene and Cell Therapy, VIRTUAL May 19–21, 2021

The 2021 Virtual World Medical Innovation Forum will focus on the growing impact of gene and cell therapy. Senior healthcare leaders from all over look to shape and debate the area of gene and cell therapy. Our shared belief: no matter the magnitude of change, responsible healthcare is centered on a shared commitment to collaborative innovation–industry, academia, and practitioners working together to improve patients’ lives.

About the World Medical Innovation Forum

Mass General Brigham is pleased to present the World Medical Innovation Forum (WMIF) virtual event Wednesday, May 19 – Friday, May 21. This interactive web event features expert discussions of gene and cell therapy (GCT) and its potential to change the future of medicine through its disease-treating and potentially curative properties. The agenda features 150+ executive speakers from the healthcare industry, venture, startups, life sciences manufacturing, consumer health and the front lines of care, including many Harvard Medical School-affiliated researchers and clinicians. The annual in-person Forum will resume live in Boston in 2022. The World Medical Innovation Forum is presented by Mass General Brigham Innovation, the global business development unit supporting the research requirements of 7,200 Harvard Medical School faculty and research hospitals including Massachusetts General, Brigham and Women’s, Massachusetts Eye and Ear, Spaulding Rehab and McLean Hospital. Follow us on Twitter: twitter.com/@MGBInnovation

Accelerating the Future of Medicine with Gene and Cell Therapy What Comes Next

Click to access 2021-WMIF-White-Paper-1.0.pdf

https://worldmedicalinnovation.org/agenda/

Virtual | May 19–21, 2021

#WMIF2021

@MGBInnovation

Leaders in Pharmaceutical Business Intelligence (LPBI) Group

will cover the event in Real Time

Aviva Lev-Ari, PhD, RN

Founder LPBI 1.0 & LPBI 2.0

will be in virtual attendance producing the e-Proceedings

and the Tweet Collection of this Global event expecting +15,000 attendees

@pharma_BI

@AVIVA1950

LPBI’s Eighteen Books in Medicine

https://lnkd.in/ekWGNqA

Among them, books on Gene and Cell Therapy include the following:

Topics for May 19 – 21 include:

Impact on Patient Care – Therapeutic and Potentially Curative GCT Developments

GCT Delivery, Manufacturing – What’s Next

GCT Platform Development

Oncolytic Viruses – Cancer applications, start-ups

Regenerative Medicine/Stem Cells

Future of CAR-T

M&A Shaping GCT’s Future

Market Priorities

Venture Investing in GCT

China’s GCT Juggernaut

Disease and Patient Focus: Benign blood disorders, diabetes, neurodegenerative diseases

Click here for the current WMIF agenda

Plus:

Fireside Chats: 1:1 interviews with industry CEOs/C-Suite leaders including Novartis Gene Therapies, ThermoFisher, Bayer AG, FDA

First Look: 18 briefings on emerging GCT research from Mass General Brigham scientists

Virtual Poster Session: 40 research posters and presenters on potential GCT discoveries from Mass General Brigham

Announcement of the Disruptive Dozen, 12 GCT technologies likely to break through in the next few years

AGENDA

Wednesday, May 19, 2021

8:00 AM – 8:10 AM

Opening Remarks

Welcome and the vision for Gene and Cell Therapy and why it is a top Mass General Brigham priority. Introducer: Scott Sperling

Co-President, Thomas H. Lee Partners
Chairman of the Board of Directors, PHS

Presenter: Anne Klibanski, MD

CEO, Mass General Brigham

3,000 people joined 5/19 morning

30 sessions: Lab to Clinic, academia, industry, investment community

May 22,23,24, 2022 – in Boston, in-person 2022 WMIF on CGT 8:10 AM – 8:30 AM

The Grand Challenge of Widespread GCT Patient Benefits

Co-Chairs identify the key themes of the Forum – set the stage for top GCT opportunities, challenges, and where the field might take medicine in the future. Moderator: Susan Hockfield, PhD

President Emerita and Professor of Neuroscience, MIT

GCT – poised to deliver therapies

Inflection point as Panel will present

Doctors and Patients – Promise for some patients

Barriers for Cell & Gene

Access for patients to therapies like CGT Speakers: Nino Chiocca, MD, PhD

Neurosurgeon-in-Chief and Chairman, Neurosurgery, BWH
Harvey W. Cushing Professor of Neurosurgery, HMS

Oncolytic virus triple threat: Toxic, immunological, combine with anti cancer therapies

Polygenic therapy – multiple genes involved, plug-play, Susan Slaugenhaupt, PhD

Scientific Director and Elizabeth G. Riley and Daniel E. Smith Jr., Endowed Chair, Mass General Research Institute
Professor, Neurology, HMS

Ravi Thadhani, MD

CAO, Mass General Brigham
Professor, Medicine and Faculty Dean, HMS

Role of academia special to spear head the Polygenic therapy – multiple genes involved, plug-play,

Access critical, relations with IndustryLuk Vandenberghe, PhD

Grousbeck Family Chair, Gene Therapy, MEE
Associate Professor, Ophthalmology, HMS

Pharmacology Gene-Drug, Interface academic centers and industry

many CGT drugs emerged in Academic center 8:35 AM – 8:50 AM FIRESIDE

Gene and Cell Therapy 2.0 – What’s Next as We Realize their Potential for Patients

Dave Lennon, PhD

President, Novartis Gene Therapies

Hope that CGT emerging, how the therapies work, neuro, muscular, ocular, genetic diseases of liver and of heart revolution for the industry 900 IND application 25 approvals Economic driver Skilled works, VC disease. Modality one time intervention, long duration of impart, reimbursement, ecosystem to be built around CGT

FDA works by indications and risks involved, Standards and expectations for streamlining manufacturing, understanding of process and products

payments over time payers and Innovators relations Moderator: Julian Harris, MD

Partner, Deerfield

Promise of CGT realized, what part?

FDA role and interaction in CGT

Manufacturing aspects which is critical Speaker: Dave Lennon, PhD

President, Novartis Gene Therapies

FDA works by indications and risks involved, Standards and expectations for streamlining manufacturing, understanding of process and products

payments over time payers and Innovators relations

Q&A 8:55 AM – 9:10 AM

8:55 AM – 9:20 AM

The Patient and GCT

GCT development for rare diseases is driven by patient and patient-advocate communities. Understanding their needs and perspectives enables biomarker research, the development of value-driving clinical trial endpoints and successful clinical trials. Industry works with patient communities that help identify unmet needs and collaborate with researchers to conduct disease natural history studies that inform the development of biomarkers and trial endpoints. This panel includes patients who have received cutting-edge GCT therapy as well as caregivers and patient advocates. Moderator: Patricia Musolino, MD, PhD

Co-Director Pediatric Stroke and Cerebrovascular Program, MGH
Assistant Professor of Neurology, HMS

What is the Power of One – the impact that a patient can have on their own destiny by participating in Clinical Trials Contacting other participants in same trial can be beneficial Speakers: Jack Hogan

Patient, MEE

Jeanette Hogan

Parent of Patient, MEE

Jim Holland

CEO, Backcountry.com

Parkinson patient Constraints by regulatory on participation in clinical trial advance stage is approved participation Patients to determine the level of risk they wish to take Information dissemination is critical Barbara Lavery

Chief Program Officer, ACGT Foundation

Advocacy agency beginning of work Global Genes educational content and out reach to access the information

Patient has the knowledge of the symptoms and recording all input needed for diagnosis by multiple clinicians Early application for CGTDan Tesler

Clinical Trial Patient, BWH/DFCC

Experimental Drug clinical trial patient participation in clinical trial is very important to advance the state of scienceSarah Beth Thomas, RN

Professional Development Manager, BWH

Outcome is unknown, hope for good, support with resources all advocacy groups,

Q&A 9:25 AM – 9:40 AM

9:25 AM – 9:45 AM FIRESIDE

GCT Regulatory Framework | Why Different?

Moderator: Vicki Sato, PhD

Chairman of the Board, Vir Biotechnology

Diversity of approaches

Process at FDA generalize from 1st entry to rules more generalizable Speaker: Peter Marks, MD, PhD

Director, Center for Biologics Evaluation and Research, FDA

Last Spring it became clear that something will work a vaccine by June 2020 belief that enough candidates the challenge manufacture enough and scaling up FDA did not predicted the efficacy of mRNA vaccine vs other approaches expected to work

Recover Work load for the pandemic will wean & clear, Gene Therapies IND application remained flat in the face of the pandemic Rare diseases urgency remains Consensus with industry advisory to get input gene therapy Guidance T-Cell therapy vs Regulation best thinking CGT evolve speedily flexible gained by Guidance

Immune modulators, Immunotherapy Genome editing can make use of viral vectors future technologies nanoparticles and liposome encapsulation

Q&A 9:50 AM – 10:05 AM

9:50 AM – 10:15 AM

Building a GCT Platform for Mainstream Success

This panel of GCT executives, innovators and investors explore how to best shape a successful GCT strategy. Among the questions to be addressed:

How are GCT approaches set around defining and building a platform?
Is AAV the leading modality and what are the remaining challenges?
What are the alternatives?
Is it just a matter of matching modalities to the right indications?

Moderator: Jean-François Formela, MD

Partner, Atlas Venture

Established core components of the Platform Speakers: Katherine High, MD

President, Therapeutics, AskBio

Three drugs approved in Europe in the Gene therapy space

Regulatory Infrastructure exists for CGT drug approval – as new class of therapeutics

Participants investigators, regulators, patients i. e., MDM

Hemophilia in male most challenging

Human are natural hosts for AV safety signals Dave Lennon, PhD

President, Novartis Gene Therapies

big pharma has portfolios of therapeutics not one drug across Tx areas: cell, gene iodine therapy

collective learning infrastructure features manufacturing at scale early in development Acquisitions strategy for growth # applications for scaling Rick Modi

CEO, Affinia Therapeutics

Copy, paste EDIT from product A to B novel vectors leverage knowledge varient of vector, coder optimization choice of indication is critical exploration on larger populations Speed to R&D and Speed to better gene construct get to clinic with better design vs ASAP

Data sharing clinical experience with vectors strategies patients selection, vector selection, mitigation, patient type specific Louise Rodino-Klapac, PhD

EVP, Chief Scientific Officer, Sarepta Therapeutics

AAV based platform 15 years in development same disease indication vs more than one indication stereotype, analytics as hurdle 1st was 10 years 2nd was 3 years

Safety to clinic vs speed to clinic, difference of vectors to trust

Q&A 10:20 AM – 10:35 AM

10:20 AM – 10:45 AM

AAV Success Studies | Retinal Dystrophy | Spinal Muscular Atrophy

Recent AAV gene therapy product approvals have catalyzed the field. This new class of therapies has shown the potential to bring transformative benefit to patients. With dozens of AAV treatments in clinical studies, all eyes are on the field to gauge its disruptive impact.

The panel assesses the largest challenges of the first two products, the lessons learned for the broader CGT field, and the extent to which they serve as a precedent to broaden the AAV modality.

Is AAV gene therapy restricted to genetically defined disorders, or will it be able to address common diseases in the near term?
Lessons learned from these first-in-class approvals.
Challenges to broaden this modality to similar indications.
Reflections on safety signals in the clinical studies?

Moderator: Joan Miller, MD

Chief, Ophthalmology, MEE
Cogan Professor & Chair of Ophthalmology, HMS

Retina specialist, Luxturna success FMA condition cell therapy as solution

Lessons learned

Safety Speakers: Ken Mills

CEO, RegenXBio

Tissue types additional administrations, tech and science, address additional diseases, more science for photoreceptors a different tissue type underlying pathology novelties in last 10 years

Cell therapy vs transplant therapy no immunosuppressionEric Pierce, MD, PhD

Director, Ocular Genomics Institute, MEE
Professor of Ophthalmology, HMS

Laxterna success to be replicated platform, paradigms measurement visual improved

More science is needed to continue develop vectors reduce toxicity,

AAV can deliver different cargos reduce adverse events improve vectorsRon Philip

Chief Operating Officer, Spark Therapeutics

The first retinal gene therapy, voretigene neparvovec-rzyl (Luxturna, Spark Therapeutics), was approved by the FDA in 2017.Meredith Schultz, MD

Executive Medical Director, Lead TME, Novartis Gene Therapies

Impact of cell therapy beyond muscular dystrophy, translational medicine, each indication, each disease, each group of patients build platform unlock the promise

Monitoring for Safety signals real world evidence remote markers, home visits, clinical trial made safer, better communication of information

Q&A 10:50 AM – 11:05 AM

10:45 AM – 10:55 AM

Break

10:55 AM – 11:05 AM FIRST LOOK

Control of AAV pharmacology by Rational Capsid Design

Luk Vandenberghe, PhD

Grousbeck Family Chair, Gene Therapy, MEE
Associate Professor, Ophthalmology, HMS

AAV a complex driver in Pharmacology durable, vector of choice, administer in vitro, gene editing tissue specificity, pharmacokinetics side effects and adverse events manufacturability site variation diversify portfolios,

Pathway for rational AAV rational design, curated smart variant libraries, AAV sequence screen multiparametric , data enable liver (de-) targeting unlock therapeutics areas: cochlea

Q&A 11:05 AM – 11:25 AM

11:05 AM – 11:15 AM FIRST LOOK

Enhanced gene delivery and immunoevasion of AAV vectors without capsid modification

Casey Maguire, PhD

Associate Professor of Neurology, MGH & HMS

Virus Biology: Enveloped (e) or not

enveloped for gene therapy eAAV platform technology: tissue targets and Indications commercialization of eAAV

Q&A 11:15 AM – 11:35 AM

11:20 AM – 11:45 AM HOT TOPICS

AAV Delivery

This panel will address the advances in the area of AAV gene therapy delivery looking out the next five years. Questions that loom large are: How can biodistribution of AAV be improved? What solutions are in the wings to address immunogenicity of AAV? Will patients be able to receive systemic redosing of AAV-based gene therapies in the future? What technical advances are there for payload size? Will the cost of manufacturing ever become affordable for ultra-rare conditions? Will non-viral delivery completely supplant viral delivery within the next five years?What are the safety concerns and how will they be addressed? Moderators: Xandra Breakefield, PhD

Geneticist, MGH, MGH
Professor, Neurology, HMS

Florian Eichler, MD

Director, Center for Rare Neurological Diseases, MGH
Associate Professor, Neurology, HMS

Speakers: Jennifer Farmer

CEO, Friedreich’s Ataxia Research Alliance

Ataxia requires therapy targeting multiple organ with one therapy, brain, spinal cord, heart several IND, clinical trials in 2022Mathew Pletcher, PhD

SVP, Head of Gene Therapy Research and Technical Operations, Astellas

Work with diseases poorly understood, collaborations needs example of existing: DMD is a great example explain dystrophin share placedo data

Continue to explore large animal guinea pig not the mice, not primates (ethical issues) for understanding immunogenicity and immune response Manny Simons, PhD

CEO, Akouos

AAV Therapy for the fluid of the inner ear, CGT for the ear vector accessible to surgeons translational work on the inner ear for gene therapy right animal model

Biology across species nerve ending in the cochlea

engineer out of the caspid, lowest dose possible, get desired effect by vector use, 2022 new milestones

Q&A 11:50 AM – 12:05 PM

11:50 AM – 12:15 PM

M&A | Shaping GCT Innovation

The GCT M&A market is booming – many large pharmas have made at least one significant acquisition. How should we view the current GCT M&A market? What is its impact of the current M&A market on technology development? Are these M&A trends new are just another cycle? Has pharma strategy shifted and, if so, what does it mean for GCT companies? What does it mean for patients? What are the long-term prospects – can valuations hold up? Moderator: Adam Koppel, MD, PhD

Managing Director, Bain Capital Life Sciences

What acquirers are looking for??

What is the next generation vs what is real where is the industry going? Speakers:

Debby Baron,

Worldwide Business Development, Pfizer

CGT is an important area Pfizer is active looking for innovators, advancing forward programs of innovation with the experience Pfizer has internally

Scalability and manufacturing regulatory conversations, clinical programs safety in parallel to planning getting drug to patients

Kenneth Custer, PhD

Vice President, Business Development and Lilly New Ventures, Eli Lilly and Company

Marianne De Backer, PhD

Head of Strategy, Business Development & Licensing, and Member of the Executive Committee, Bayer

Absolute Leadership in Gene editing, gene therapy, via acquisition and strategic alliance

Operating model of the acquired company discussed , company continue independence

Sean Nolan

Board Chairman, Encoded Therapeutics & Affinia

Executive Chairman, Jaguar Gene Therapy & Istari Oncology

As acquiree multiple M&A: How the acquirer looks at integration and cultures of the two companies

Traditional integration vs jump start by external acquisition

AAV – epilepsy, next generation of vectors

Q&A 12:20 PM – 12:35 PM

12:15 PM – 12:25 PM FIRST LOOK

Gene Therapies for Neurological Disorders: Insights from Motor Neuron Disorders

Merit Cudkowicz, MD

Chief of Neurology, MGH

ALS – Man 1in 300, Women 1 in 400, next decade increase 7%

10% ALS is heredity 160 pharma in ALS space, diagnosis is late 1/3 of people are not diagnosed, active community for clinical trials Challenges: disease heterogeneity cases of 10 years late in diagnosis. Clinical Trials for ALS in Gene Therapy targeting ASO1 protein therapies FUS gene struck youngsters

Patricia Musolino, MD, PhD

12:25 PM – 12:45 PM

12:25 PM – 12:35 PM FIRST LOOK

Gene Therapy for Neurologic Diseases

Co-Director Pediatric Stroke and Cerebrovascular Program, MGH
Assistant Professor of Neurology, HMS

Cerebral Vascular disease – ACTA2 179H gene smooth muscle cell proliferation disorder

no surgery or drug exist –

Cell therapy for ACTA2 Vasculopathy in the brain and control the BP and stroke – smooth muscle intima proliferation. Viral vector deliver aiming to change platform to non-viral delivery rare disease , gene editing, other mutations of ACTA2 gene target other pathway for atherosclerosis

Q&A 12:35 PM – 12:55 PM

12:35 PM – 1:15 PM

Lunch

1:15 PM – 1:40 PM

Oncolytic Viruses in Cancer | Curing Melanoma and Beyond

Oncolytic viruses represent a powerful new technology, but so far an FDA-approved oncolytic (Imlygic) has only occurred in one area – melanoma and that what is in 2015. This panel involves some of the protagonists of this early success story. They will explore why and how Imlygic became approved and its path to commercialization. Yet, no other cancer indications exist for Imlygic, unlike the expansion of FDA-approved indication for immune checkpoint inhibitors to multiple cancers. Why? Is there a limitation to what and which cancers can target? Is the mode of administration a problem?

No other oncolytic virus therapy has been approved since 2015. Where will the next success story come from and why? Will these therapies only be beneficial for skin cancers or other easily accessible cancers based on intratumoral delivery?

The panel will examine whether the preclinical models that have been developed for other cancer treatment modalities will be useful for oncolytic viruses. It will also assess the extent pre-clinical development challenges have slowed the development of OVs. Moderator: Nino Chiocca, MD, PhD

Neurosurgeon-in-Chief and Chairman, Neurosurgery, BWH
Harvey W. Cushing Professor of Neurosurgery, HMS

Challenges of manufacturing at Amgen what are they? Speakers: Robert Coffin, PhD

Chief Research & Development Officer, Replimune

2002 in UK promise in oncolytic therapy GNCSF

Phase III melanoma 2015 M&A with Amgen

oncolytic therapy remains non effecting on immune response

data is key for commercialization

do not belief in systemic therapy achieve maximum immune response possible from a tumor by localized injection Roger Perlmutter, MD, PhD

Chairman, Merck & Co.

response rates systemic therapy like PD1, Keytruda, OPTIVA well tolerated combination of Oncolytic with systemic

GMP critical for manufacturing David Reese, MD

Executive Vice President, Research and Development, Amgen

Inter lesion injection of agent vs systemic therapeutics

cold tumors immune resistant render them immune susceptible

Oncolytic virus is a Mono therapy

addressing the unknown Ann Silk, MD

Physician, Dana Farber-Brigham and Women’s Cancer Center
Assistant Professor of Medicine, HMS

Which person gets oncolytics virus if patient has immune suppression due to other indications

Safety of oncolytic virus greater than Systemic treatment

series biopsies for injected and non injected tissue and compare Suspect of hot tumor and cold tumors likely to have sme response to agent unknown all potential

Q&A 1:45 PM – 2:00 PM

1:45 PM – 2:10 PM

Market Interest in Oncolytic Viruses | Calibrating

There are currently two oncolytic virus products on the market, one in the USA and one in China. As of late 2020, there were 86 clinical trials 60 of which were in phase I with just 2 in Phase III the rest in Phase I/II or Phase II. Although global sales of OVs are still in the ramp-up phase, some projections forecast OVs will be a $700 million market by 2026. This panel will address some of the major questions in this area:

What regulatory challenges will keep OVs from realizing their potential? Despite the promise of OVs for treating cancer only one has been approved in the US. Why has this been the case? Reasons such have viral tropism, viral species selection and delivery challenges have all been cited. However, these are also true of other modalities. Why then have oncolytic virus approaches not advanced faster and what are the primary challenges to be overcome?

Will these need to be combined with other agents to realize their full efficacy and how will that impact the market?
Why are these companies pursuing OVs while several others are taking a pass?

Moderators: Martine Lamfers, PhD

Visiting Scientist, BWH

Challenged in development of strategies

Demonstrate efficacyRobert Martuza, MD

Consultant in Neurosurgery, MGH
William and Elizabeth Sweet Distinguished Professor of Neurosurgery, HMS

Modulation mechanism Speakers: Anlong Li, MD, PhD

Clinical Director, Oncology Clinical Development, Merck Research Laboratories

IV delivery preferred – delivery alternative are less aggereable Jeffrey Infante, MD

Early development Oncolytic viruses, Oncology, Janssen Research & Development

oncologic virus if it will generate systemic effects the adoption will accelerate

What areas are the best efficacious

Direct effect with intra-tumor single injection with right payload

Platform approach Prime with 1 and Boost with 2 – not yet experimented with

Do not have the data at trial design for stratification of patients

Turn off strategy not existing yetLoic Vincent, PhD

Head of Oncology Drug Discovery Unit, Takeda

R&D in collaboration with Academic

Vaccine platform to explore different payload

IV administration may not bring sufficient concentration to the tumor is administer in the blood stream

Classification of Patients by prospective response type id UNKNOWN yet, population of patients require stratification

Q&A 2:15 PM – 2:30 PM

2:10 PM – 2:20 PM FIRST LOOK

Oncolytic viruses: turning pathogens into anticancer agents

Nino Chiocca, MD, PhD

Neurosurgeon-in-Chief and Chairman, Neurosurgery, BWH
Harvey W. Cushing Professor of Neurosurgery, HMS

Oncolytic therapy DID NOT WORK Pancreatic Cancer and Glioblastoma

Intra- tumoral heterogeniety hinders success

Solution: Oncolytic VIRUSES – Immunological “coldness”

GADD-34 20,000 GBM 40,000 pancreatic cancer

Q&A 2:25 PM – 2:40 PM

2:20 PM – 2:45 PM

Entrepreneurial Growth | Oncolytic Virus

In 2020 there were a total of 60 phase I trials for Oncolytic Viruses. There are now dozens of companies pursuing some aspect of OV technology. This panel will address:

How are small companies equipped to address the challenges of developing OV therapies better than large pharma or biotech?
Will the success of COVID vaccines based on Adenovirus help the regulatory environment for small companies developing OV products in Europe and the USA?
Is there a place for non-viral delivery and other immunotherapy companies to engage in the OV space? Would they bring any real advantages?

Moderator: Reid Huber, PhD

Partner, Third Rock Ventures

Critical milestones to observe Speakers: Caroline Breitbach, PhD

VP, R&D Programs and Strategy, Turnstone Biologics

Trying Intra-tumor delivery and IV infusion delivery oncolytic vaccine pushing dose

translation biomarkers program

transformation tumor microenvironment Brett Ewald, PhD

SVP, Development & Corporate Strategy, DNAtrix

Studies gets larger, kicking off Phase III multiple tumors Paul Hallenbeck, PhD

President and Chief Scientific Officer, Seneca Therapeutics

Translation: Stephen Russell, MD, PhD

CEO, Vyriad

Systemic delivery Oncolytic Virus IV delivery woman in remission

Collaboration with Regeneron

Data collection: Imageable reporter secretable reporter, gene expression

Field is intense systemic oncolytic delivery is exciting in mice and in human, response rates are encouraging combination immune stimulant, check inhibitors

Q&A 2:50 PM – 3:05 PM

2:45 PM – 3:00 PM

Break

3:00 PM – 3:25 PM

CAR-T | Lessons Learned | What’s Next

Few areas of potential cancer therapy have had the attention and excitement of CAR-T. This panel of leading executives, developers, and clinician-scientists will explore the current state of CAR-T and its future prospects. Among the questions to be addressed are:

Is CAR-T still an industry priority – i.e. are new investments being made by large companies? Are new companies being financed? What are the trends?
What have we learned from first-generation products, what can we expect from CAR-T going forward in novel targets, combinations, armored CAR’s and allogeneic treatment adoption?
Early trials showed remarkable overall survival and progression-free survival. What has been observed regarding how enduring these responses are?
Most of the approvals to date have targeted CD19, and most recently BCMA. What are the most common forms of relapses that have been observed?
Is there a consensus about what comes after these CD19 and BCMA trials as to additional targets in liquid tumors? How have dual-targeted approaches fared?

Moderator:
Marcela Maus, MD, PhD
- Director, Cellular Immunotherapy Program, Cancer Center, MGH
- Associate Professor, Medicine, HMSIs CAR-T Industry priority
Speakers:
- Jakob Dupont, MD
Head of R&D, Atara BioTherapeutics
Phyno-type of the cells for hematologic cancers
solid tumor
inventory of Therapeutics for treating patients in the future
Progressive MS program
EBBT platform B-Cells and T-Cells
- Stefan Hendriks
  - Gobal Head, Cell & Gene, Novartis
  - yes, CGT is a strategy in the present and future
  - Journey started years ago
  - Confirmation the effectiveness of CAR-T therapies, 1 year response prolonged to 5 years 26 months
  - Patient not responding – a lot to learn
  - Patient after 8 months of chemo can be helped by CAR-T
- Christi Shaw
  - CEO, Kite
  - CAR-T is priority 120 companies in the space
  - Manufacturing consistency
  - Patients respond with better quality of life
  - Blood cancer – more work to be done

3:30 PM – 3:45 PM

3:30 PM – 3:55 PM HOT TOPICS

CAR-T | Solid Tumors Success | When?

The potential application of CAR-T in solid tumors will be a game-changer if it occurs. The panel explores the prospects of solid tumor success and what the barriers have been. Questions include:

How would industry and investor strategy for CAR-T and solid tumors be characterized? Has it changed in the last couple of years?
Does the lack of tumor antigen specificity in solid tumors mean that lessons from liquid tumor CAR-T constructs will not translate well and we have to start over?
Whether due to antigen heterogeneity, a hostile tumor micro-environment, or other factors are some specific solid tumors more attractive opportunities than others for CAR-T therapy development?
Given the many challenges that CAR-T faces in solid tumors, does the use of combination therapies from the start, for example, to mitigate TME effects, offer a more compelling opportunity.

Moderator: Oladapo Yeku, MD, PhD

Clinical Assistant in Medicine, MGH

window of opportunities studies Speakers: Jennifer Brogdon

Executive Director, Head of Cell Therapy Research, Exploratory Immuno-Oncology, NIBR

2017 CAR-T first approval

M&A and research collaborations

TCR tumor specific antigens avoid tissue toxicity Knut Niss, PhD

CTO, Mustang Bio

tumor hot start in 12 month clinical trial solid tumors , theraties not ready yet. Combination therapy will be an experimental treatment long journey checkpoint inhibitors to be used in combination maintenance Lipid tumor Barbra Sasu, PhD

CSO, Allogene

T cell response at prostate cancer

tumor specific

cytokine tumor specific signals move from solid to metastatic cell type for easier infiltration

Where we might go: safety autologous and allogeneic Jay Short, PhD

Chairman, CEO, Cofounder, BioAlta, Inc.

Tumor type is not enough for development of therapeutics other organs are involved in the periphery

difficult to penetrate solid tumors biologics activated in the tumor only, positive changes surrounding all charges, water molecules inside the tissue acidic environment target the cells inside the tumor and not outside

Combination staggered key is try combination

Q&A 4:00 PM – 4:15 PM

4:00 PM – 4:25 PM

GCT Manufacturing | Vector Production | Autologous and Allogeneic | Stem Cells | Supply Chain | Scalability & Management

The modes of GCT manufacturing have the potential of fundamentally reordering long-established roles and pathways. While complexity goes up the distance from discovery to deployment shrinks. With the likelihood of a total market for cell therapies to be over $48 billion by 2027, groups of products are emerging. Stem cell therapies are projected to be $28 billion by 2027 and non-stem cell therapies such as CAR-T are projected be $20 billion by 2027. The manufacturing challenges for these two large buckets are very different. Within the CAR-T realm there are diverging trends of autologous and allogeneic therapies and the demands on manufacturing infrastructure are very different. Questions for the panelists are:

Help us all understand the different manufacturing challenges for cell therapies. What are the trade-offs among storage cost, batch size, line changes in terms of production cost and what is the current state of scaling naïve and stem cell therapy treatment vs engineered cell therapies?
For cell and gene therapy what is the cost of Quality Assurance/Quality Control vs. production and how do you think this will trend over time based on your perspective on learning curves today?
Will point of care production become a reality? How will that change product development strategy for pharma and venture investors? What would be the regulatory implications for such products?
How close are allogeneic CAR-T cell therapies? If successful what are the market implications of allogenic CAR-T? What are the cost implications and rewards for developing allogeneic cell therapy treatments?

Moderator: Michael Paglia

VP, ElevateBio

Speakers:

Dannielle Appelhans
- SVP TechOps and Chief Technical Officer, Novartis Gene Therapies
Thomas Page, PhD
- VP, Engineering and Asset Development, FUJIFILM Diosynth Biotechnologies
Rahul Singhvi, ScD
- CEO and Co-Founder, National Resilience, Inc.
Thomas VanCott, PhD
- Global Head of Product Development, Gene & Cell Therapy, Catalent
- 2/3 autologous 1/3 allogeneic CAR-T high doses and high populations scale up is not done today quality maintain required the timing logistics issues centralized vs decentralized allogeneic are health donors innovations in cell types in use improvements in manufacturing

Ropa Pike, Director, Enterprise Science & Partnerships, Thermo Fisher Scientific

Centralized biopharma industry is moving to decentralized models site specific license

Q&A 4:30 PM – 4:45 PM

4:30 PM – 4:40 PM FIRST LOOK

CAR-T

Marcela Maus, MD, PhD

Director, Cellular Immunotherapy Program, Cancer Center, MGH
Assistant Professor, Medicine, HMS

Fit-to-purpose CAR-T cells: 3 lead programs

Tr-fill

CAR-T induce response myeloma and multiple myeloma GBM

27 patents on CAR-T

+400 patients treaded 40 Clinical Trials

Q&A 4:40 PM – 5:00 PM

4:40 PM – 4:50 PM FIRST LOOK

Repurposed Tumor Cells as Killers and Immunomodulators for Cancer Therapy

Khalid Shah, PhD

Vice Chair, Neurosurgery Research, BWH
Director, Center for Stem Cell Therapeutics and Imaging, HMS

Solid tumors are the hardest to treat because: immunosuppressive, hypoxic, Acidic Use of autologous tumor cells self homing ThTC self targeting therapeutic cells Therapeutic tumor cells efficacy pre-clinical models GBM 95% metastesis ThTC translation to patient settings

Q&A 4:50 PM – 5:10 PM

4:50 PM – 5:00 PM FIRST LOOK

Other Cell Therapies for Cancer

David Scadden, MD

Director, Center for Regenerative Medicine; Co-Director, Harvard Stem Cell Institute, Director, Hematologic Malignancies & Experimental Hematology, MGH
Jordan Professor of Medicine, HMS

T-cell are made in bone marrow create cryogel can be an off-the-shelf product repertoire on T Receptor CCL19+ mesenchymal cells mimic Tymus cells –

inter-tymic injection. Non human primate validation

Q&A

5:00 PM – 5:20 PM 5:00 PM – 5:20 PM FIRESIDE

Fireside with Mikael Dolsten, MD, PhD

Introducer: Jonathan Kraft Moderator: Daniel Haber, MD, PhD

Chair, Cancer Center, MGH
Isselbacher Professor of Oncology, HMS

Vaccine Status Mikael Dolsten, MD, PhD

Chief Scientific Officer and President, Worldwide Research, Development and Medical, Pfizer

Deliver vaccine around the Globe, Israel, US, Europe.

3BIL vaccine in 2022 for all Global vaccination

Bio Ntech in Germany

Experience with Biologics immuneoncology & allogeneic antibody cells – new field for drug discovery

mRNA curative effort and cancer vaccine

Access to drugs developed by Pfizer to underdeveloped countries

Q&A 5:25 PM – 5:40 AM

5:20 PM – 5:30 PM

Closing Remarks

Thursday, May 20, 2021

8:00 AM – 8:25 AM

GCT | The China Juggernaut

China embraced gene and cell therapies early. The first China gene therapy clinical trial was in 1991. China approved the world’s first gene therapy product in 2003—Gendicine—an oncolytic adenovirus for the treatment of advanced head and neck cancer. Driven by broad national strategy, China has become a hotbed of GCT development, ranking second in the world with more than 1,000 clinical trials either conducted or underway and thousands of related patents. It has a booming GCT biotech sector, led by more than 45 local companies with growing IND pipelines.

In late 1990, a T cell-based immunotherapy, cytokine-induced killer (CIK) therapy became a popular modality in the clinic in China for tumor treatment. In early 2010, Chinese researchers started to carry out domestic CAR T trials inspired by several important reports suggested the great antitumor function of CAR T cells. Now, China became the country with the most registered CAR T trials, CAR T therapy is flourishing in China.

The Chinese GCT ecosystem has increasingly rich local innovation and growing complement of development and investment partnerships – and also many subtleties.

This panel, consisting of leaders from the China GCT corporate, investor, research and entrepreneurial communities, will consider strategic questions on the growth of the gene and cell therapy industry in China, areas of greatest strength, evolving regulatory framework, early successes and products expected to reach the US and world market. Moderator: Min Wu, PhD

Managing Director, Fosun Health Fund

What are the area of CGT in China, regulatory similar to the US Speakers: Alvin Luk, PhD

CEO, Neuropath Therapeutics

Monogenic rare disease with clear genomic target

Increase of 30% in patient enrollment

Regulatory reform approval is 60 days no delayPin Wang, PhD

CSO, Jiangsu Simcere Pharmaceutical Co., Ltd.

Similar starting point in CGT as the rest of the World unlike a later starting point in other biologicalRichard Wang, PhD

CEO, Fosun Kite Biotechnology Co., Ltd

Possibilities to be creative and capitalize the new technologies for innovating drug

Support of the ecosystem by funding new companie allowing the industry to be developed in China

Autologous in patients differences cost challengeTian Xu, PhD

Vice President, Westlake University

ICH committee and Chinese FDA -r regulation similar to the US

Difference is the population recruitment, in China patients are active participants in skin disease

Active in development of transposome

Development of non-viral methods, CRISPR still in D and transposome

In China price of drugs regulatory are sensitive Shunfei Yan, PhD

Investment Manager, InnoStar Capital

Indication driven: Hymophilia,

Allogogenic efficiency therapies

Licensing opportunities

Q&A 8:30 AM – 8:45 AM

8:30 AM – 8:55 AM

Impact of mRNA Vaccines | Global Success Lessons

The COVID vaccine race has propelled mRNA to the forefront of biomedicine. Long considered as a compelling modality for therapeutic gene transfer, the technology may have found its most impactful application as a vaccine platform. Given the transformative industrialization, the massive human experience, and the fast development that has taken place in this industry, where is the horizon? Does the success of the vaccine application, benefit or limit its use as a therapeutic for CGT?

How will the COVID success impact the rest of the industry both in therapeutic and prophylactic vaccines and broader mRNA lessons?
How will the COVID success impact the rest of the industry both on therapeutic and prophylactic vaccines and broader mRNA lessons?
Beyond from speed of development, what aspects make mRNA so well suited as a vaccine platform?
Will cost-of-goods be reduced as the industry matures?
How does mRNA technology seek to compete with AAV and other gene therapy approaches?

Moderator: Lindsey Baden, MD

Director, Clinical Research, Division of Infectious Diseases, BWH
Associate Professor, HMS

In vivo delivery process regulatory cooperation new opportunities for same platform for new indication Speakers:

Melissa Moore
- Chief Scientific Officer, Moderna

Many years of mRNA pivoting for new diseases, DARPA, nucleic Acids global deployment of a manufacturing unit on site where the need arise Elan Musk funds new directions at Moderna

How many mRNA can be put in one vaccine: Dose and tolerance to achieve efficacy

45 days for Personalized cancer vaccine one per patient

Ron Renaud
- CEO, Translate Bio

1.6 Billion doses produced rare disease monogenic correct mRNA like CF multiple mutation infection disease and oncology applications

Platform allowing to swap cargo reusing same nanoparticles address disease beyond Big Pharma options for biotech

WHat strain of Flu vaccine will come back in the future when people do not use masks

Kate Bingham, UK Vaccine Taskforce

July 2020, AAV vs mRNA delivery across UK local centers administered both types supply and delivery uplift

Q&A 9:00 AM – 9:15 AM

9:00 AM – 9:25 AM HOT TOPICS

Benign Blood Disorders

Hemophilia has been and remains a hallmark indication for the CGT. Given its well-defined biology, larger market, and limited need for gene transfer to provide therapeutic benefit, it has been at the forefront of clinical development for years, however, product approval remains elusive. What are the main hurdles to this success? Contrary to many indications that CGT pursues no therapeutic options are available to patients, hemophiliacs have an increasing number of highly efficacious treatment options. How does the competitive landscape impact this field differently than other CGT fields? With many different players pursuing a gene therapy option for hemophilia, what are the main differentiators? Gene therapy for hemophilia seems compelling for low and middle-income countries, given the cost of currently available treatments; does your company see opportunities in this market? Moderator: Nancy Berliner, MD

Chief, Division of Hematology, BWH
H. Franklin Bunn Professor of Medicine, HMS

Speakers: Theresa Heggie

CEO, Freeline Therapeutics

Safety concerns, high burden of treatment CGT has record of safety and risk/benefit adoption of Tx functional cure CGT is potent Tx relative small quantity of protein needs be delivered

Potency and quality less quantity drug and greater potency

risk of delivery unwanted DNA, capsules are critical

analytics is critical regulator involvement in potency definition

Close of collaboration is excitingGallia Levy, MD, PhD

Chief Medical Officer, Spark Therapeutics

Hemophilia CGT is the highest potential for Global access logistics in underdeveloped countries working with NGOs practicality of the Tx

Roche reached 120 Counties great to be part of the Roche GroupAmir Nashat, PhD

Managing Partner, Polaris Ventures

Suneet Varma

Global President of Rare Disease, Pfizer

Gene therapy at Pfizer small molecule, large molecule and CGT – spectrum of choice allowing Hemophilia patients to marry

1/3 internal 1/3 partnership 1/3 acquisitions

Learning from COVID-19 is applied for other vaccine development

review of protocols and CGT for Hemophelia

You can’t buy Time

With MIT Pfizer is developing a model for Hemopilia CGT treatment

Q&A 9:30 AM – 9:45 AM

9:25 AM – 9:35 AM FIRST LOOK

Treating Rett Syndrome through X-reactivation

Jeannie Lee, MD, PhD

Molecular Biologist, MGH
Professor of Genetics, HMS

200 disease X chromosome unlock for neurological genetic diseases: Rett Syndromeand other autism spectrum disorders female model vs male mice model

deliver protein to the brain

restore own missing or dysfunctional protein

Epigenetic not CGT – no exogent intervention Xist ASO drug

Female model

Q&A 9:35 AM – 9:55 AM

9:35 AM – 9:45 AM FIRST LOOK

Rare but mighty: scaling up success in single gene disorders

Florian Eichler, MD

Director, Center for Rare Neurological Diseases, MGH
Associate Professor, Neurology, HMS

Single gene disorder NGS enable diagnosis, DIagnosis to Treatment How to know whar cell to target, make it available and scale up Address gap: missing components Biomarkers to cell types lipid chemistry cell animal biology

crosswalk from bone marrow matter

New gene discovered that causes neurodevelopment of stagnant genes Examining new Biology cell type specific biomarkers

Q&A 9:45 AM – 10:05 AM

9:50 AM – 10:15 AM HOT TOPICS

Diabetes | Grand Challenge

The American Diabetes Association estimates 30 million Americans have diabetes and 1.5 million are diagnosed annually. GCT offers the prospect of long-sought treatment for this enormous cohort and their chronic requirements. The complexity of the disease and its management constitute a grand challenge and highlight both the potential of GCT and its current limitations.

Islet transplantation for type 1 diabetes has been attempted for decades. Problems like loss of transplanted islet cells due to autoimmunity and graft site factors have been difficult to address. Is there anything different on the horizon for gene and cell therapies to help this be successful?
How is the durability of response for gene or cell therapies for diabetes being addressed? For example, what would the profile of an acceptable (vs. optimal) cell therapy look like?

Moderator: Marie McDonnell, MD

Chief, Diabetes Section and Director, Diabetes Program, BWH
Lecturer on Medicine, HMS

Type 1 Diabetes cost of insulin for continuous delivery of drug

alternative treatments:

The Future: neuropotent stem cells

What keeps you up at night Speakers: Tom Bollenbach, PhD

Chief Technology Officer, Advanced Regenerative Manufacturing Institute

Data managment sterility sensors, cell survival after implantation, stem cells manufacturing, process development in manufacturing of complex cells

Data and instrumentation the Process is the Product

Manufacturing tight schedules Manasi Jaiman, MD

Vice President, Clinical Development, ViaCyte
Pediatric Endocrinologist

continous glucose monitoring Bastiano Sanna, PhD

EVP, Chief of Cell & Gene Therapies and VCGT Site Head, Vertex Pharmaceuticals

100 years from discovering Insulin, Insulin is not a cure in 2021 – asking patients to partner more

Produce large quantities of the Islet cells encapsulation technology been developed

Scaling up is a challengeRogerio Vivaldi, MD

CEO, Sigilon Therapeutics

Advanced made, Patient of Type 1 Outer and Inner compartments of spheres (not capsule) no immune suppression continuous secretion of enzyme Insulin independence without immune suppression

Volume to have of-the-shelf inventory oxegenation in location lymphatic and vascularization conrol the whole process modular platform learning from others

Q&A 10:20 AM – 10:35 AM

10:20 AM – 10:40 AM FIRESIDE

Building A Unified GCT Strategy

Introducer: John Fish

CEO, Suffolk
Chairman of Board Trustees, Brigham Health

Moderator: Meg Tirrell

Senior Health and Science Reporter, CNBC

Last year, what was it at Novartis Speaker: Jay Bradner, MD

President, NIBR

Keep eyes open, waiting the Pandemic to end and enable working back on all the indications

Portfolio of MET, Mimi Emerging Therapies

Learning from the Pandemic – operationalize the practice science, R&D leaders, new collaboratives at NIH, FDA, Novartis

Pursue programs that will yield growth, tropic diseases with Gates Foundation, Rising Tide pods for access CGT within Novartis Partnership with UPenn in Cell Therapy

Cost to access to IP from Academia to a Biotech CRISPR accessing few translations to Clinic

Protein degradation organization constraint valuation by parties in a partnership

Novartis: nuclear protein lipid nuclear particles, tamplate for Biotech to collaborate

Game changing: 10% of the Portfolio, New frontiers human genetics in Ophthalmology, CAR-T, CRISPR, Gene Therapy Neurological and payloads of different matter

Q&A 10:45 AM – 11:00 AM

10:40 AM – 10:50 AM

Break

10:50 AM – 11:00 AM FIRST LOOK

Getting to the Heart of the Matter: Curing Genetic Cardiomyopathy

Christine Seidman, MD

Director, Cardiovascular Genetics Center, BWH
Smith Professor of Medicine & Genetics, HMS

The Voice of Dr. Seidman – Her abstract is cited below

The ultimate opportunity presented by discovering the genetic basis of human disease is accurate prediction and disease prevention. To enable this achievement, genetic insights must enable the identification of at-risk

individuals prior to end-stage disease manifestations and strategies that delay or prevent clinical expression. Genetic cardiomyopathies provide a paradigm for fulfilling these opportunities. Hypertrophic cardiomyopathy (HCM) is characterized by left ventricular hypertrophy, diastolic dysfunction with normal or enhanced systolic performance and a unique histopathology: myocyte hypertrophy, disarray and fibrosis. Dilated cardiomyopathy (DCM) exhibits enlarged ventricular volumes with depressed systolic performance and nonspecific histopathology. Both HCM and DCM are prevalent clinical conditions that increase risk for arrhythmias, sudden death, and heart failure. Today treatments for HCM and DCM focus on symptoms, but none prevent disease progression. Human molecular genetic studies demonstrated that these pathologies often result from dominant mutations in genes that encode protein components of the sarcomere, the contractile unit in striated muscles. These data combined with the emergence of molecular strategies to specifically modulate gene expression provide unparalleled opportunities to silence or correct mutant genes and to boost healthy gene expression in patients with genetic HCM and DCM. Many challenges remain, but the active and vital efforts of physicians, researchers, and patients are poised to ensure success.

Hypertrophic and Dilated Cardiomyopaies ‘

10% receive heart transplant 12 years survival

Mutation puterb function

TTN: contribute 20% of dilated cardiomyopaty

Silence gene

pleuripotential cells deliver therapies

Q&A 11:00 AM – 11:20 AM

11:00 AM – 11:10 AM FIRST LOOK

Unlocking the secret lives of proteins in health and disease

Anna Greka, MD, PhD

Medicine, BWH
Associate Professor, Medicine, HMS

Cyprus Island, kidney disease by mutation causing MUC1 accumulation and death BRD4780 molecule that will clear the misfolding proteins from the kidney organoids: pleuripotent stem cells small molecule developed for applications in the other cell types in brain, eye, gene mutation build mechnism for therapy clinical models transition from Academia to biotech

Laura Sepp-Lorenzino, PhD

11:10 AM – 11:30 AM

11:10 AM – 11:35 AM

Rare and Ultra Rare Diseases | GCT Breaks Through

One of the most innovative segments in all of healthcare is the development of GCT driven therapies for rare and ultra-rare diseases. Driven by a series of insights and tools and funded in part by disease focused foundations, philanthropists and abundant venture funding disease after disease is yielding to new GCT technology. These often become platforms to address more prevalent diseases. The goal of making these breakthroughs routine and affordable is challenged by a range of issues including clinical trial design and pricing.

What is driving the interest in rare diseases?
What are the biggest barriers to making breakthroughs ‘routine and affordable?’
What is the role of retrospective and prospective natural history studies in rare disease? When does the expected value of retrospective disease history studies justify the cost?
Related to the first question, what is the FDA expecting as far as controls in clinical trials for rare diseases? How does this impact the collection of natural history data?

Moderator: Susan Slaugenhaupt, PhD

Scientific Director and Elizabeth G. Riley and Daniel E. Smith Jr., Endowed Chair, Mass General Research Institute
Professor, Neurology, HMS

Speakers: Leah Bloom, PhD

SVP, External Innovation and Strategic Alliances, Novartis Gene Therapies

Ultra rare (less than 100) vs rare difficulty to recruit patients and to follow up after treatment Bobby Gaspar, MD, PhD

CEO, Orchard Therapeutics

Study of rare condition have transfer to other larger diseases – delivery of therapeutics genes, like immune disorders

Patient testimonials just to hear what a treatment can make Emil Kakkis, MD, PhD

CEO, Ultragenyx

Do 100 patient study then have information on natural history to develop a clinical trial Stuart Peltz, PhD

CEO, PTC Therapeutics

Rare disease, challenge for FDA approval and after market commercialization follow ups

Justification of cost for Rare disease – demonstration of Change is IP in value patients advocacy is helpful

Q&A 11:40 AM – 11:55 AM

11:40 AM – 12:00 PM FIRESIDE

Partnering Across the GCT Spectrum

Moderator: Erin Harris

Chief Editor, Cell & Gene

Perspective & professional tenure

Partnership in manufacturing what are the recommendations?

Hospital systems: Partnership Challenges Speaker: Marc Casper

CEO, ThermoFisher

25 years in Diagnostics last 20 years at ThermoFisher

products used in the Lab for CAR-T research and manufacture

CGT Innovations: FDA will have a high level of approval each year

How move from research to clinical trials to manufacturing Quicker process

Best practices in Partnerships: the root cause if acceleration to market service providers to deliver highest standards

Building capacity by acquisition to avoid the waiting time

Accelerate new products been manufactured

Collaborations with Academic Medical center i.e., UCSF in CGT joint funding to accelerate CGT to clinics’

Customers are extremely knowledgable, scale the capital investment made investment

150MIL a year to improve the Workflow

Q&A 12:05 PM – 12:20 PM

12:05 PM – 12:30 PM

12:05 PM – 12:20 PM

12:05 PM – 12:30 PM

CEO Panel | Anticipating Disruption | Planning for Widespread GCT

The power of GCT to cure disease has the prospect of profoundly improving the lives of patients who respond. Planning for a disruption of this magnitude is complex and challenging as it will change care across the spectrum. Leading chief executives shares perspectives on how the industry will change and how this change should be anticipated. Moderator: Meg Tirrell

Senior Health and Science Reporter, CNBC

CGT becoming staple therapy what are the disruptors emerging Speakers: Lisa Dechamps

SVP & Chief Business Officer, Novartis Gene Therapies

Reimagine medicine with collaboration at MGH, MDM condition in children

The Science is there, sustainable processes and systems impact is transformational

Value based pricing, risk sharing Payers and Pharma for one time therapy with life span effect

Collaboration with FDAKieran Murphy

CEO, GE Healthcare

Diagnosis of disease to be used in CGT

2021 investment in CAR-T platform

Investment in several CGT frontier

Investment in AI, ML in system design new technologies

GE: Scale and Global distributions, sponsor companies in software

Waste in Industry – Healthcare % of GDP, work with MGH to smooth the workflow faster entry into hospital and out of Hospital

Telemedicine during is Pandemic: Radiologist needs to read remotely

Supply chain disruptions slow down all ecosystem

Production of ventilators by collaboration with GM – ingenuity

Scan patients outside of hospital a scanner in a Box Christian Rommel, PhD

Head, Pharmaceuticals Research & Development, Bayer AG

CGT – 2016 and in 2020 new leadership and capability

Disease Biology and therapeutics

Regenerative Medicine: CGT vs repair building pipeline in ophthalmology and cardiovascular

During Pandemic: Deliver Medicines like Moderna, Pfizer – collaborations between competitors with Government Bayer entered into Vaccines in 5 days, all processes had to change access innovations developed over decades for medical solutions

Q&A 12:35 PM – 12:50 PM

12:35 PM – 12:55 PM FIRESIDE

Building a GCT Portfolio

GCT represents a large and growing market for novel therapeutics that has several segments. These include Cardiovascular Disease, Cancer, Neurological Diseases, Infectious Disease, Ophthalmology, Benign Blood Disorders, and many others; Manufacturing and Supply Chain including CDMO’s and CMO’s; Stem Cells and Regenerative Medicine; Tools and Platforms (viral vectors, nano delivery, gene editing, etc.). Bayer’s pharma business participates in virtually all of these segments. How does a Company like Bayer approach the development of a portfolio in a space as large and as diverse as this one? How does Bayer approach the support of the production infrastructure with unique demands and significant differences from its historical requirements? Moderator:

Shinichiro Fuse, PhD

Managing Partner, MPM Capital

Speaker: Wolfram Carius, PhD

EVP, Pharmaceuticals, Head of Cell & Gene Therapy, Bayer AG

CGT will bring treatment to cure, delivery of therapies

Be a Leader repair, regenerate, cure

Technology and Science for CGT – building a portfolio vs single asset decision criteria development of IP market access patients access acceleration of new products

Bayer strategy: build platform for use by four domains

Gener augmentation

Autologeneic therapy, analytics

Gene editing

Oncology Cell therapy tumor treatment: What kind of cells – the jury is out

Of 23 product launch at Bayer no prediction is possible some high some lows

Q&A 1:00 PM – 1:15 PM

12:55 PM – 1:35 PM

Lunch

1:40 PM – 2:05 PM

GCT Delivery | Perfecting the Technology

Gene delivery uses physical, chemical, or viral means to introduce genetic material into cells. As more genetically modified therapies move closer to the market, challenges involving safety, efficacy, and manufacturing have emerged. Optimizing lipidic and polymer nanoparticles and exosomal delivery is a short-term priority. This panel will examine how the short-term and long-term challenges are being tackled particularly for non-viral delivery modalities. Moderator: Natalie Artzi, PhD

Assistant Professor, BWH

Speakers: Geoff McDonough, MD

CEO, Generation Bio

Sonya Montgomery

CMO, Evox Therapeutics

Chief Scientific Officer, Executive Vice President, Intellia Therapeutics

Doug Williams, PhD

CEO, Codiak BioSciences

Q&A 2:10 PM – 2:25 PM

2:05 PM – 2:10 PM

Invention Discovery Grant Announcement

2:10 PM – 2:20 PM FIRST LOOK

Enhancing vesicles for therapeutic delivery of bioproducts

Xandra Breakefield, PhD

Geneticist, MGH, MGH
Professor, Neurology, HMS

Q&A 2:20 PM – 2:35 PM

2:20 PM – 2:30 PM FIRST LOOK

Versatile polymer-based nanocarriers for targeted therapy and immunomodulation

Natalie Artzi, PhD

Assistant Professor, BWH

Q&A 2:30 PM – 2:45 PM

2:55 PM – 3:20 PM HOT TOPICS

Gene Editing | Achieving Therapeutic Mainstream

Gene editing was recognized by the Nobel Committee as “one of gene technology’s sharpest tools, having a revolutionary impact on life sciences.” Introduced in 2011, gene editing is used to modify DNA. It has applications across almost all categories of disease and is also being used in agriculture and public health.

Today’s panel is made up of pioneers who represent foundational aspects of gene editing. They will discuss the movement of the technology into the therapeutic mainstream.

Successes in gene editing – lessons learned from late-stage assets (sickle cell, ophthalmology)
When to use what editing tool – pros and cons of traditional gene-editing v. base editing. Is prime editing the future? Specific use cases for epigenetic editing.
When we reach widespread clinical use – role of off-target editing – is the risk real? How will we mitigate? How practical is patient-specific off-target evaluation?

Moderator: J. Keith Joung, MD, PhD

Robert B. Colvin, M.D. Endowed Chair in Pathology & Pathologist, MGH
Professor of Pathology, HMS

Speakers: John Evans

CEO, Beam Therapeutics

Lisa Michaels

EVP & CMO, Editas Medicine

Q&A 3:25 PM – 3:50 PM

3:25 PM – 3:50 PM HOT TOPICS

Common Blood Disorders | Gene Therapy

There are several dozen companies working to develop gene or cell therapies for Sickle Cell Disease, Beta Thalassemia, and Fanconi Anemia. In some cases, there are enzyme replacement therapies that are deemed effective and safe. In other cases, the disease is only managed at best. This panel will address a number of questions that are particular to this class of genetic diseases:

What are the pros and cons of various strategies for treatment? There are AAV-based editing, non-viral delivery even oligonucleotide recruitment of endogenous editing/repair mechanisms. Which approaches are most appropriate for which disease?
How can companies increase the speed of recruitment for clinical trials when other treatments are available? What is the best approach to educate patients on a novel therapeutic?
How do we best address ethnic and socio-economic diversity to be more representative of the target patient population?
How long do we have to follow up with the patients from the scientific, patient’s community, and payer points of view? What are the current FDA and EMA guidelines for long-term follow-up?
Where are we with regards to surrogate endpoints and their application to clinically meaningful endpoints?
What are the emerging ethical dilemmas in pediatric gene therapy research? Are there challenges with informed consent and pediatric assent for trial participation?
Are there differences in reimbursement policies for these different blood disorders? Clearly durability of response is a big factor. Are there other considerations?

Moderator: David Scadden, MD

Director, Center for Regenerative Medicine; Co-Director, Harvard Stem Cell Institute, Director, Hematologic Malignancies & Experimental Hematology, MGH
Jordan Professor of Medicine, HMS

Speakers: Samarth Kukarni, PhD Nick Leschly

Chief Bluebird, Bluebird Bio

Mike McCune, MD, PhD

Head, HIV Frontiers, Global Health Innovative Technology Solutions, Bill & Melinda Gates Foundation

Q&A 3:55 PM – 4:15 PM

3:50 PM – 4:00 PM FIRST LOOK

Gene Editing

J. Keith Joung, MD, PhD

Robert B. Colvin, M.D. Endowed Chair in Pathology & Pathologist, MGH
Professor of Pathology, HMS

Q&A 4:00 PM – 4:20 PM

4:20 PM – 4:45 PM HOT TOPICS

Gene Expression | Modulating with Oligonucleotide-Based Therapies

Oligonucleotide drugs have recently come into their own with approvals from companies such as Biogen, Alnylam, Novartis and others. This panel will address several questions:

How important is the delivery challenge for oligonucleotides? Are technological advancements emerging that will improve the delivery of oligonucleotides to the CNS or skeletal muscle after systemic administration?

Will oligonucleotides improve as a class that will make them even more effective? Are further advancements in backbone chemistry anticipated, for example.
Will oligonucleotide based therapies blaze trails for follow-on gene therapy products?
Are small molecules a threat to oligonucleotide-based therapies?
Beyond exon skipping and knock-down mechanisms, what other roles will oligonucleotide-based therapies take mechanistically — can genes be activating oligonucleotides? Is there a place for multiple mechanism oligonucleotide medicines?
Are there any advantages of RNAi-based oligonucleotides over ASOs, and if so for what use?

Moderator: Jeannie Lee, MD, PhD

Molecular Biologist, MGH
Professor of Genetics, HMS

Speakers: Bob Brown, PhD

CSO, EVP of R&D, Dicerna

Brett Monia, PhD

CEO, Ionis

Alfred Sandrock, MD, PhD

EVP, R&D and CMO, Biogen

Q&A 4:50 PM – 5:05 PM

4:45 PM – 4:55 PM FIRST LOOK

RNA therapy for brain cancer

Pierpaolo Peruzzi, MD, PhD

Nuerosurgery, BWH
Assistant Professor of Neurosurgery, HMS

Q&A 4:55 PM – 5:15 PM

Friday, May 21, 2021

8:30 AM – 8:55 AM

Venture Investing | Shaping GCT Translation

What is occurring in the GCT venture capital segment? Which elements are seeing the most activity? Which areas have cooled? How is the investment market segmented between gene therapy, cell therapy and gene editing? What makes a hot GCT company? How long will the market stay frothy? Some review of demographics — # of investments, sizes, etc. Why is the market hot and how long do we expect it to stay that way? Rank the top 5 geographic markets for GCT company creation and investing? Are there academic centers that have been especially adept at accelerating GCT outcomes? Do the business models for the rapid development of coronavirus vaccine have any lessons for how GCT technology can be brought to market more quickly? Moderator: Meredith Fisher, PhD

Partner, Mass General Brigham Innovation Fund

Speakers: David Berry, MD, PhD

CEO, Valo Health
General Partner, Flagship Pioneering

Robert Nelsen

Managing Director, Co-founder, ARCH Venture Partners

Kush Parmar, MD, PhD

Managing Partner, 5AM Ventures

Q&A 9:00 AM – 9:15 AM

9:00 AM – 9:25 AM

Regenerative Medicine | Stem Cells

The promise of stem cells has been a highlight in the realm of regenerative medicine. Unfortunately, that promise remains largely in the future. Recent breakthroughs have accelerated these potential interventions in particular for treating neurological disease. Among the topics the panel will consider are:

Stem cell sourcing
Therapeutic indication growth
Genetic and other modification in cell production
Cell production to final product optimization and challenges
How to optimize the final product

Moderator: Ole Isacson, MD, PhD

Director, Neuroregeneration Research Institute, McLean
Professor, Neurology and Neuroscience, HMS

Speakers: Kapil Bharti, PhD

Senior Investigator, Ocular and Stem Cell Translational Research Section, NIH

Joe Burns, PhD

VP, Head of Biology, Decibel Therapeutics

Erin Kimbrel, PhD

Executive Director, Regenerative Medicine, Astellas

Nabiha Saklayen, PhD

CEO and Co-Founder, Cellino

Q&A 9:30 AM – 9:45 AM

9:25 AM – 9:35 AM FIRST LOOK

Stem Cells

Bob Carter, MD, PhD

Chairman, Department of Neurosurgery, MGH
William and Elizabeth Sweet, Professor of Neurosurgery, HMS

Q&A 9:35 AM – 9:55 AM

9:35 AM – 10:00 AM

Capital Formation ’21-30 | Investing Modes Driving GCT Technology and Timing

The dynamics of venture/PE investing and IPOs are fast evolving. What are the drivers – will the number of investors grow will the size of early rounds continue to grow? How is this reflected in GCT target areas, company design, and biotech overall? Do patients benefit from these trends? Is crossover investing a distinct class or a little of both? Why did it emerge and what are the characteristics of the players? Will SPACs play a role in the growth of the gene and cell therapy industry. What is the role of corporate investment arms eg NVS, Bayer, GV, etc. – has a category killer emerged? Are we nearing the limit of what the GCT market can absorb or will investment capital continue to grow unabated? Moderator: Roger Kitterman

VP, Venture, Mass General Brigham

Speakers: Ellen Hukkelhoven, PhD

Managing Director, Perceptive Advisors

Peter Kolchinsky, PhD

Founder and Managing Partner, RA Capital Management

Deep Nishar

Senior Managing Partner, SoftBank Investment Advisors

Oleg Nodelman

Founder & Managing Partner, EcoR1 Capital

Q&A 10:05 AM – 10:20 AM

10:00 AM – 10:10 AM FIRST LOOK

New scientific and clinical developments for autologous stem cell therapy for Parkinson’s disease patients

Penelope Hallett, PhD

NRL, McLean
Assistant Professor Psychiatry, HMS

Q&A 10:10 AM – 10:30 AM

10:10 AM – 10:35 AM HOT TOPICS

Neurodegenerative Clinical Outcomes | Achieving GCT Success

Can stem cell-based platforms become successful treatments for neurodegenerative diseases?

What are the commonalities driving GCT success in neurodegenerative disease and non-neurologic disease, what are the key differences?
Overcoming treatment administration challenges
GCT impact on degenerative stage of disease
How difficult will it be to titrate the size of the cell therapy effect in different neurological disorders and for different patients?
Demonstrating clinical value to patients and payers
Revised clinical trial models to address issues and concerns specific to GCT

Moderator: Bob Carter, MD, PhD

Chairman, Department of Neurosurgery, MGH
William and Elizabeth Sweet, Professor of Neurosurgery, HMS

Speakers: Erwan Bezard, PhD

INSERM Research Director, Institute of Neurodegenerative Diseases

Nikola Kojic, PhD

CEO and Co-Founder, Oryon Cell Therapies

Geoff MacKay

President & CEO, AVROBIO

Viviane Tabar, MD

Founding Investigator, BlueRock Therapeutics
Chair of Neurosurgery, Memorial Sloan Kettering

Q&A 10:40 AM – 10:55 AM

10:35 AM – 11:35 AM

Disruptive Dozen: 12 Technologies that Will Reinvent GCT

Nearly one hundred senior Mass General Brigham Harvard faculty contributed to the creation of this group of twelve GCT technologies that they believe will breakthrough in the next two years. The Disruptive Dozen identifies and ranks the GCT technologies that will be available on at least an experimental basis to have the chance of significantly improving health care. 11:35 AM – 11:45 AM

Concluding Remarks

Friday, May 21, 2021

Computer connection to the iCloud of WordPress.com FROZE completely at 10:30AM EST and no file update was possible. COVERAGE OF MAY 21, 2021 IS RECORDED BELOW FOLLOWING THE AGENDA BY COPY AN DPASTE OF ALL THE TWEETS I PRODUCED ON MAY 21, 2021 8:30 AM – 8:55 AM

Venture Investing | Shaping GCT Translation

Partner, Mass General Brigham Innovation Fund

Speakers: David Berry, MD, PhD

CEO, Valo Health
General Partner, Flagship Pioneering

Robert Nelsen

Managing Director, Co-founder, ARCH Venture Partners

Kush Parmar, MD, PhD

Managing Partner, 5AM Ventures

Q&A 9:00 AM – 9:15 AM

9:00 AM – 9:25 AM

Regenerative Medicine | Stem Cells

Stem cell sourcing
Therapeutic indication growth
Genetic and other modification in cell production
Cell production to final product optimization and challenges
How to optimize the final product

Moderator: Ole Isacson, MD, PhD

Director, Neuroregeneration Research Institute, McLean
Professor, Neurology and Neuroscience, HMS

Speakers: Kapil Bharti, PhD

Senior Investigator, Ocular and Stem Cell Translational Research Section, NIH

Joe Burns, PhD

VP, Head of Biology, Decibel Therapeutics

Erin Kimbrel, PhD

Executive Director, Regenerative Medicine, Astellas

Nabiha Saklayen, PhD

CEO and Co-Founder, Cellino

Q&A 9:30 AM – 9:45 AM

9:25 AM – 9:35 AM FIRST LOOK

Stem Cells

Bob Carter, MD, PhD

Chairman, Department of Neurosurgery, MGH
William and Elizabeth Sweet, Professor of Neurosurgery, HMS

Q&A 9:35 AM – 9:55 AM

9:35 AM – 10:00 AM

Capital Formation ’21-30 | Investing Modes Driving GCT Technology and Timing

VP, Venture, Mass General Brigham

Speakers: Ellen Hukkelhoven, PhD

Managing Director, Perceptive Advisors

Peter Kolchinsky, PhD

Founder and Managing Partner, RA Capital Management

Deep Nishar

Senior Managing Partner, SoftBank Investment Advisors

Oleg Nodelman

Founder & Managing Partner, EcoR1 Capital

Q&A 10:05 AM – 10:20 AM

10:00 AM – 10:10 AM FIRST LOOK

New scientific and clinical developments for autologous stem cell therapy for Parkinson’s disease patients

Penelope Hallett, PhD

NRL, McLean
Assistant Professor Psychiatry, HMS

Q&A 10:10 AM – 10:30 AM

10:10 AM – 10:35 AM HOT TOPICS

Neurodegenerative Clinical Outcomes | Achieving GCT Success

Can stem cell-based platforms become successful treatments for neurodegenerative diseases?

What are the commonalities driving GCT success in neurodegenerative disease and non-neurologic disease, what are the key differences?
Overcoming treatment administration challenges
GCT impact on degenerative stage of disease
How difficult will it be to titrate the size of the cell therapy effect in different neurological disorders and for different patients?
Demonstrating clinical value to patients and payers
Revised clinical trial models to address issues and concerns specific to GCT

Moderator: Bob Carter, MD, PhD

Chairman, Department of Neurosurgery, MGH
William and Elizabeth Sweet, Professor of Neurosurgery, HMS

Speakers: Erwan Bezard, PhD

INSERM Research Director, Institute of Neurodegenerative Diseases

Nikola Kojic, PhD

CEO and Co-Founder, Oryon Cell Therapies

Geoff MacKay

President & CEO, AVROBIO

Viviane Tabar, MD

Founding Investigator, BlueRock Therapeutics
Chair of Neurosurgery, Memorial Sloan Kettering

Q&A 10:40 AM – 10:55 AM

10:35 AM – 11:35 AM

Disruptive Dozen: 12 Technologies that Will Reinvent GCT

Concluding Remarks

The co-chairs convene to reflect on the insights shared over the three days. They will discuss what to expect at the in-person GCT focused May 2-4, 2022 World Medical Innovation Forum.

The co-chairs convene to reflect on the insights shared over the three days. They will discuss what to expect at the in-person GCT focused May 2-4, 2022 World Medical Innovation Forum.Christine Seidman, MD

Hypertrophic and Dilated Cardiomyopaies ‘

10% receive heart transplant 12 years survival

Mutation puterb function

TTN: contribute 20% of dilated cardiomyopaty

Silence gene

pleuripotential cells deliver therapies

Q&A 11:00 AM – 11:20 AM

11:00 AM – 11:10 AM FIRST LOOK

Unlocking the secret lives of proteins in health and disease

Anna Greka, MD, PhD

Medicine, BWH
Associate Professor, Medicine, HMS

Laura Sepp-Lorenzino, PhD

11:10 AM – 11:30 AM

11:10 AM – 11:35 AM

Rare and Ultra Rare Diseases | GCT Breaks Through

What is driving the interest in rare diseases?
What are the biggest barriers to making breakthroughs ‘routine and affordable?’
What is the role of retrospective and prospective natural history studies in rare disease? When does the expected value of retrospective disease history studies justify the cost?
Related to the first question, what is the FDA expecting as far as controls in clinical trials for rare diseases? How does this impact the collection of natural history data?

Moderator: Susan Slaugenhaupt, PhD

Scientific Director and Elizabeth G. Riley and Daniel E. Smith Jr., Endowed Chair, Mass General Research Institute
Professor, Neurology, HMS

Speakers: Leah Bloom, PhD

SVP, External Innovation and Strategic Alliances, Novartis Gene Therapies

Ultra rare (less than 100) vs rare difficulty to recruit patients and to follow up after treatment Bobby Gaspar, MD, PhD

CEO, Orchard Therapeutics

Study of rare condition have transfer to other larger diseases – delivery of therapeutics genes, like immune disorders

Patient testimonials just to hear what a treatment can make Emil Kakkis, MD, PhD

CEO, Ultragenyx

Do 100 patient study then have information on natural history to develop a clinical trial Stuart Peltz, PhD

CEO, PTC Therapeutics

Rare disease, challenge for FDA approval and after market commercialization follow ups

Justification of cost for Rare disease – demonstration of Change is IP in value patients advocacy is helpful

Q&A 11:40 AM – 11:55 AM

11:40 AM – 12:00 PM FIRESIDE

Partnering Across the GCT Spectrum

Moderator: Erin Harris

Chief Editor, Cell & Gene

Perspective & professional tenure

Partnership in manufacturing what are the recommendations?

Hospital systems: Partnership Challenges Speaker: Marc Casper

CEO, ThermoFisher

25 years in Diagnostics last 20 years at ThermoFisher

products used in the Lab for CAR-T research and manufacture

CGT Innovations: FDA will have a high level of approval each year

How move from research to clinical trials to manufacturing Quicker process

Best practices in Partnerships: the root cause if acceleration to market service providers to deliver highest standards

Building capacity by acquisition to avoid the waiting time

Accelerate new products been manufactured

Collaborations with Academic Medical center i.e., UCSF in CGT joint funding to accelerate CGT to clinics’

Customers are extremely knowledgable, scale the capital investment made investment

150MIL a year to improve the Workflow

Q&A 12:05 PM – 12:20 PM

12:05 PM – 12:30 PM

CEO Panel | Anticipating Disruption | Planning for Widespread GCT

Senior Health and Science Reporter, CNBC

CGT becoming staple therapy what are the disruptors emerging Speakers: Lisa Dechamps

SVP & Chief Business Officer, Novartis Gene Therapies

Reimagine medicine with collaboration at MGH, MDM condition in children

The Science is there, sustainable processes and systems impact is transformational

Value based pricing, risk sharing Payers and Pharma for one time therapy with life span effect

Collaboration with FDAKieran Murphy

CEO, GE Healthcare

Diagnosis of disease to be used in CGT

2021 investment in CAR-T platform

Investment in several CGT frontier

Investment in AI, ML in system design new technologies

GE: Scale and Global distributions, sponsor companies in software

Waste in Industry – Healthcare % of GDP, work with MGH to smooth the workflow faster entry into hospital and out of Hospital

Telemedicine during is Pandemic: Radiologist needs to read remotely

Supply chain disruptions slow down all ecosystem

Production of ventilators by collaboration with GM – ingenuity

Scan patients outside of hospital a scanner in a Box Christian Rommel, PhD

Head, Pharmaceuticals Research & Development, Bayer AG

CGT – 2016 and in 2020 new leadership and capability

Disease Biology and therapeutics

Regenerative Medicine: CGT vs repair building pipeline in ophthalmology and cardiovascular

Q&A 12:35 PM – 12:50 PM

12:35 PM – 12:55 PM FIRESIDE

Building a GCT Portfolio

Shinichiro Fuse, PhD

Managing Partner, MPM Capital

Speaker: Wolfram Carius, PhD

EVP, Pharmaceuticals, Head of Cell & Gene Therapy, Bayer AG

CGT will bring treatment to cure, delivery of therapies

Be a Leader repair, regenerate, cure

Technology and Science for CGT – building a portfolio vs single asset decision criteria development of IP market access patients access acceleration of new products

Bayer strategy: build platform for use by four domains

Gener augmentation

Autologeneic therapy, analytics

Gene editing

Oncology Cell therapy tumor treatment: What kind of cells – the jury is out

Of 23 product launch at Bayer no prediction is possible some high some lows

Q&A 1:00 PM – 1:15 PM

12:55 PM – 1:35 PM

Lunch

1:40 PM – 2:05 PM

GCT Delivery | Perfecting the Technology

Assistant Professor, BWH

Speakers: Geoff McDonough, MD

CEO, Generation Bio

Sonya Montgomery

CMO, Evox Therapeutics

Chief Scientific Officer, Executive Vice President, Intellia Therapeutics

Doug Williams, PhD

CEO, Codiak BioSciences

Q&A 2:10 PM – 2:25 PM

2:05 PM – 2:10 PM

Invention Discovery Grant Announcement

2:10 PM – 2:20 PM FIRST LOOK

Enhancing vesicles for therapeutic delivery of bioproducts

Xandra Breakefield, PhD

Geneticist, MGH, MGH
Professor, Neurology, HMS

Q&A 2:20 PM – 2:35 PM

2:20 PM – 2:30 PM FIRST LOOK

Versatile polymer-based nanocarriers for targeted therapy and immunomodulation

Natalie Artzi, PhD

Assistant Professor, BWH

Q&A 2:30 PM – 2:45 PM

2:55 PM – 3:20 PM HOT TOPICS

Gene Editing | Achieving Therapeutic Mainstream

Today’s panel is made up of pioneers who represent foundational aspects of gene editing. They will discuss the movement of the technology into the therapeutic mainstream.

Successes in gene editing – lessons learned from late-stage assets (sickle cell, ophthalmology)
When to use what editing tool – pros and cons of traditional gene-editing v. base editing. Is prime editing the future? Specific use cases for epigenetic editing.
When we reach widespread clinical use – role of off-target editing – is the risk real? How will we mitigate? How practical is patient-specific off-target evaluation?

Moderator: J. Keith Joung, MD, PhD

Robert B. Colvin, M.D. Endowed Chair in Pathology & Pathologist, MGH
Professor of Pathology, HMS

Speakers: John Evans

CEO, Beam Therapeutics

Lisa Michaels

EVP & CMO, Editas Medicine

Q&A 3:25 PM – 3:50 PM

3:25 PM – 3:50 PM HOT TOPICS

Common Blood Disorders | Gene Therapy

What are the pros and cons of various strategies for treatment? There are AAV-based editing, non-viral delivery even oligonucleotide recruitment of endogenous editing/repair mechanisms. Which approaches are most appropriate for which disease?
How can companies increase the speed of recruitment for clinical trials when other treatments are available? What is the best approach to educate patients on a novel therapeutic?
How do we best address ethnic and socio-economic diversity to be more representative of the target patient population?
How long do we have to follow up with the patients from the scientific, patient’s community, and payer points of view? What are the current FDA and EMA guidelines for long-term follow-up?
Where are we with regards to surrogate endpoints and their application to clinically meaningful endpoints?
What are the emerging ethical dilemmas in pediatric gene therapy research? Are there challenges with informed consent and pediatric assent for trial participation?
Are there differences in reimbursement policies for these different blood disorders? Clearly durability of response is a big factor. Are there other considerations?

Moderator: David Scadden, MD

Director, Center for Regenerative Medicine; Co-Director, Harvard Stem Cell Institute, Director, Hematologic Malignancies & Experimental Hematology, MGH
Jordan Professor of Medicine, HMS

Speakers: Samarth Kukarni, PhD Nick Leschly

Chief Bluebird, Bluebird Bio

Mike McCune, MD, PhD

Head, HIV Frontiers, Global Health Innovative Technology Solutions, Bill & Melinda Gates Foundation

Q&A 3:55 PM – 4:15 PM

3:50 PM – 4:00 PM FIRST LOOK

Gene Editing

J. Keith Joung, MD, PhD

Robert B. Colvin, M.D. Endowed Chair in Pathology & Pathologist, MGH
Professor of Pathology, HMS

Q&A 4:00 PM – 4:20 PM

4:20 PM – 4:45 PM HOT TOPICS

Gene Expression | Modulating with Oligonucleotide-Based Therapies

Oligonucleotide drugs have recently come into their own with approvals from companies such as Biogen, Alnylam, Novartis and others. This panel will address several questions:

Will oligonucleotides improve as a class that will make them even more effective? Are further advancements in backbone chemistry anticipated, for example.
Will oligonucleotide based therapies blaze trails for follow-on gene therapy products?
Are small molecules a threat to oligonucleotide-based therapies?
Beyond exon skipping and knock-down mechanisms, what other roles will oligonucleotide-based therapies take mechanistically — can genes be activating oligonucleotides? Is there a place for multiple mechanism oligonucleotide medicines?
Are there any advantages of RNAi-based oligonucleotides over ASOs, and if so for what use?

Moderator: Jeannie Lee, MD, PhD

Molecular Biologist, MGH
Professor of Genetics, HMS

Speakers: Bob Brown, PhD

CSO, EVP of R&D, Dicerna

Brett Monia, PhD

CEO, Ionis

Alfred Sandrock, MD, PhD

EVP, R&D and CMO, Biogen

Q&A 4:50 PM – 5:05 PM

4:45 PM – 4:55 PM FIRST LOOK

RNA therapy for brain cancer

Pierpaolo Peruzzi, MD, PhD

Nuerosurgery, BWH
Assistant Professor of Neurosurgery, HMS

Q&A 4:55 PM – 5:15 PM

Friday, May 21, 2021

8:30 AM – 8:55 AM

Venture Investing | Shaping GCT Translation

Partner, Mass General Brigham Innovation Fund

Strategies, success what changes are needed in the drug discovery process Speakers:

David Berry, MD, PhD
- CEO, Valo Health
- General Partner, Flagship Pioneering

Bring disruptive frontier as a platform with reliable delivery CGT double knock out disease cure all change efficiency and scope human centric vs mice centered right scale of data converted into therapeutics acceleratetion

Innovation in drugs 60% fails in trial because of Toxicology system of the future deal with big diseases

Moderna is an example in unlocking what is inside us Microbiome and beyond discover new drugs epigenetics

Robert Nelsen
- Managing Director, Co-founder, ARCH Venture Partners

Manufacturing change is not a new clinical trial FDA need to be presented with new rethinking for big innovations Drug pricing cheaper requires systematization How to systematically scaling up systematize the discovery and the production regulatory innovations

Kush Parmar, MD, PhD
- Managing Partner, 5AM Ventures

Responsibility mismatch should be and what is “are”

Long term diseases Stack holders and modalities risk benefir for populations

Q&A 9:00 AM – 9:15 AM

9:00 AM – 9:25 AM

Regenerative Medicine | Stem Cells

Stem cell sourcing
Therapeutic indication growth
Genetic and other modification in cell production
Cell production to final product optimization and challenges
How to optimize the final product

Moderator:
- Ole Isacson, MD, PhD
  - Director, Neuroregeneration Research Institute, McLean
  - Professor, Neurology and Neuroscience, MGH, HMS

Opportunities in the next generation of the tactical level Welcome the oprimism and energy level of all Translational medicine funding stem cells enormous opportunities

Speakers:
Kapil Bharti, PhD
- Senior Investigator, Ocular and Stem Cell Translational Research Section, NIH
- first drug required to establish the process for that innovations design of animal studies not done before
- Off-th-shelf one time treatment becoming cure
- Intact tissue in a dish is fragile to maintain metabolism
Joe Burns, PhD
- VP, Head of Biology, Decibel Therapeutics
- Ear inside the scall compartments and receptors responsible for hearing highly differentiated tall ask to identify cell for anticipated differentiation
- multiple cell types and tissue to follow
Erin Kimbrel, PhD
- Executive Director, Regenerative Medicine, Astellas
- In the ocular space immunogenecity
- regulatory communication
- use gene editing for immunogenecity Cas1 and Cas2 autologous cells
- gene editing and programming big opportunities
Nabiha Saklayen, PhD
- CEO and Co-Founder, Cellino
- scale production of autologous cells foundry using semiconductor process in building cassettes
- solution for autologous cells
Q&A 9:30 AM – 9:45 AM

9:25 AM – 9:35 AM FIRST LOOK

Stem Cells

Bob Carter, MD, PhD

Chairman, Department of Neurosurgery, MGH
William and Elizabeth Sweet, Professor of Neurosurgery, HMS
Cell therapy for Parkinson to replace dopamine producing cells lost ability to produce dopamin
skin cell to become autologous cells reprograms to become cells producing dopamine
transplantation fibroblast cells metabolic driven process lower mutation burden
Quercetin inhibition elimination undifferentiated cells graft survival oxygenation increased

Q&A 9:35 AM – 9:55 AM

9:35 AM – 10:00 AM

Capital Formation ’21-30 | Investing Modes Driving GCT Technology and Timing

VP, Venture, Mass General Brigham
Saturation reached or more investment is coming in CGT

Speakers: Ellen Hukkelhoven, PhD

Managing Director, Perceptive Advisors
Cardiac area transduct cells
matching tools
10% success of phase 1 in drug development next phase matters more

Peter Kolchinsky, PhD

Founder and Managing Partner, RA Capital Management
Future proof for new comers disruptors
Ex Vivo gene therapy to improve funding products what tool kit belongs to
company insulation from next instability vs comapny stabilizing themselves along few years
Company interested in SPAC
cross over investment vs SPAC
Multi Omics in cancer early screening metastatic diseas will be wiped out

Deep Nishar

Senior Managing Partner, SoftBank Investment Advisors
Young field vs CGT started in the 80s
high payloads is a challenge
cost effective fast delivery to large populations
Mission oriented by the team and management
Multi Omics disease modality

Oleg Nodelman

Founder & Managing Partner, EcoR1 Capital
Invest in company next round of investment will be IPO
Help company raise money cross over investment vs SPAC
Innovating ideas from academia in need for funding

Q&A 10:05 AM – 10:20 AM

10:00 AM – 10:10 AM FIRST LOOK

New scientific and clinical developments for autologous stem cell therapy for Parkinson’s disease patients

Penelope Hallett, PhD

NRL, McLean
Assistant Professor Psychiatry, HMS
Pharmacologic agent in existing cause another disorders locomo-movement related
efficacy Autologous cell therapy transplantation approach program T cells into dopamine generating neurons greater than Allogeneic cell transplantation

Q&A 10:10 AM – 10:30 AM

10:10 AM – 10:35 AM HOT TOPICS

Neurodegenerative Clinical Outcomes | Achieving GCT Success

Can stem cell-based platforms become successful treatments for neurodegenerative diseases?

What are the commonalities driving GCT success in neurodegenerative disease and non-neurologic disease, what are the key differences?
Overcoming treatment administration challenges
GCT impact on degenerative stage of disease
How difficult will it be to titrate the size of the cell therapy effect in different neurological disorders and for different patients?
Demonstrating clinical value to patients and payers
Revised clinical trial models to address issues and concerns specific to GCT

Moderator: Bob Carter, MD, PhD

Chairman, Department of Neurosurgery, MGH
William and Elizabeth Sweet, Professor of Neurosurgery, HMS
Neurogeneration REVERSAL or slowing down

Speakers: Erwan Bezard, PhD

INSERM Research Director, Institute of Neurodegenerative Diseases
Cautious on reversal
Early intervantion versus late

Nikola Kojic, PhD

CEO and Co-Founder, Oryon Cell Therapies
Autologus cell therapy placed focal replacing missing synapses reestablishment of neural circuitary

Geoff MacKay

President & CEO, AVROBIO
Prevent condition to be manifested in the first place
clinical effect durable single infusion preventions of symptoms to manifest
Cerebral edema – stabilization
Gene therapy know which is the abnormal gene grafting the corrected one
More than biomarker as end point functional benefit not yet established

Viviane Tabar, MD

Founding Investigator, BlueRock Therapeutics
Chair of Neurosurgery, Memorial Sloan Kettering
Current market does not have delivery mechanism that a drug-delivery is the solution Trials would fail on DELIVERY
Immune suppressed patients during one year to avoid graft rejection Autologous approach of Parkinson patient genetically mutated reprogramed as dopamine generating neuron – unknowns are present
Circuitry restoration
Microenvironment disease ameliorate symptoms – education of patients on the treatment

Q&A 10:40 AM – 10:55 AM

10:35 AM – 11:35 AM

Disruptive Dozen: 12 Technologies that Will Reinvent GCT

Concluding Remarks

The co-chairs convene to reflect on the insights shared over the three days. They will discuss what to expect at the in-person GCT focused May 2-4, 2022 World Medical Innovation Forum.

ALL THE TWEETS PRODUCED ON MAY 21, 2021 INCLUDE THE FOLLOWING:

@AVIVA1950

#WMIF2021

@MGBInnovation

Erwan Bezard, PhD INSERM Research Director, Institute of Neurodegenerative Diseases Cautious on reversal

Nikola Kojic, PhD CEO and Co-Founder, Oryon Cell Therapies Autologus cell therapy placed focal replacing missing synapses reestablishment of neural circutary

Bob Carter, MD, PhD Chairman, Department of Neurosurgery, MGH William and Elizabeth Sweet, Professor of Neurosurgery, HMS Neurogeneration REVERSAL or slowing down?

Penelope Hallett, PhD NRL, McLean Assistant Professor Psychiatry, HMS efficacy Autologous cell therapy transplantation approach program T cells into dopamine genetating cells greater than Allogeneic cell transplantation

Penelope Hallett, PhD NRL, McLean Assistant Professor Psychiatry, HMS Pharmacologic agent in existing cause another disorders locomo-movement related

Roger Kitterman VP, Venture, Mass General Brigham Saturation reached or more investment is coming in CGT Multi OMICS and academia originated innovations are the most attractive areas

Roger Kitterman VP, Venture, Mass General Brigham Saturation reached or more investment is coming in CGT

Oleg Nodelman Founder & Managing Partner, EcoR1 Capital Invest in company next round of investment will be IPO 20% discount

Peter Kolchinsky, PhD Founder and Managing Partner, RA Capital Management Future proof for new comers disruptors Ex Vivo gene therapy to improve funding products what tool kit belongs to

Deep Nishar Senior Managing Partner, SoftBank Investment Advisors Young field vs CGT started in the 80s high payloads is a challenge

Bob Carter, MD, PhD MGH, HMS cells producing dopamine transplantation fibroblast cells metabolic driven process lower mutation burden Quercetin inhibition elimination undifferentiated cells graft survival oxygenation increased

Chairman, Department of Neurosurgery, MGH, Professor of Neurosurgery, HMS Cell therapy for Parkinson to replace dopamine producing cells lost ability to produce dopamine skin cell to become autologous cells reprogramed

Kapil Bharti, PhD Senior Investigator, Ocular and Stem Cell Translational Research Section, NIH Off-th-shelf one time treatment becoming cure Intact tissue in a dish is fragile to maintain metabolism to become like semiconductors

Ole Isacson, MD, PhD Director, Neuroregeneration Research Institute, McLean Professor, Neurology and Neuroscience, MGH, HMS Opportunities in the next generation of the tactical level Welcome the oprimism and energy level of all

Erin Kimbrel, PhD Executive Director, Regenerative Medicine, Astellas In the ocular space immunogenecity regulatory communication use gene editing for immunogenecity Cas1 and Cas2 autologous cells

Nabiha Saklayen, PhD CEO and Co-Founder, Cellino scale production of autologous cells foundry using semiconductor process in building cassettes by optic physicists

Joe Burns, PhD VP, Head of Biology, Decibel Therapeutics Ear inside the scall compartments and receptors responsible for hearing highly differentiated tall ask to identify cell for anticipated differentiation control by genomics

Kapil Bharti, PhD Senior Investigator, Ocular and Stem Cell Translational Research Section, NIH first drug required to establish the process for that innovations design of animal studies not done before

Meredith Fisher, PhD Partner, Mass General Brigham Innovation Fund Strategies, success what changes are needed in the drug discovery process@pharma_BI

Robert Nelsen Managing Director, Co-founder, ARCH Venture Partners Manufacturing change is not a new clinical trial FDA need to be presented with new rethinking for big innovations Drug pricing cheaper requires systematization

Kush Parmar, MD, PhD Managing Partner, 5AM Ventures Responsibility mismatch should be and what is “are”

David Berry, MD, PhD CEO, Valo Health GP, Flagship Pioneering Bring disruptive frontier platform reliable delivery CGT double knockout disease cure all change efficiency scope human centric vs mice centered right scale acceleration

Kush Parmar, MD, PhD Managing Partner, 5AM Ventures build it yourself, benefit for patients FIrst Look at MGB shows MEE innovation on inner ear worthy investment

Robert Nelsen Managing Director, Co-founder, ARCH Venture Partners Frustration with supply chain during the Pandemic, GMC anticipation in advance CGT rapidly prototype rethink and invest proactive investor .edu and Pharma

@pharma_BI

@AVIVA1950

https://advances.sciencemag.org/content/6/46/eaba6784

Machine Learning (ML) in cancer prognosis prediction helps the researcher to identify multiple known as well as candidate cancer diver genes

Posted in Artificial Intelligence in CANCER, Cancer - General, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Genomics, Cancer Screening, Clinical Genomics, CRISPR/Cas9 & Gene Editing, Genetics & Innovations in Treatment, Machine Learning, Mutant Gene Expression, Personalized and Precision Medicine & Genomic Research, Precision Cancer Medicine, tagged Artificial intelligence, Cancer, Cancer prognosis prediction, cancer progression, computational algorithm, driver gene, Machine Learning on May 4, 2021| Leave a Comment »

Machine Learning (ML) in cancer prognosis prediction helps the researcher to identify multiple known as well as candidate cancer diver genes

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

This image has an empty alt attribute; its file name is morethanthes.jpg — **Seeing “through” the cancer with the power of data analysis — possible with the help of artificial intelligence. Credit: MPI f. Molecular Genetics/ Ella Maru Studio**
**Image Source: https://medicalxpress.com/news/2021-04-sum-mutations-cancer-genes-machine.html**

Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low-risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) and Artificial Intelligence (AI) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions by predicting new algorithms.

In the majority of human cancers, heritable loss of gene function through cell division may be mediated as often by epigenetic as by genetic abnormalities. Epigenetic modification occurs through a process of interrelated changes in CpG island methylation and histone modifications. Candidate gene approaches of cell cycle, growth regulatory and apoptotic genes have shown epigenetic modification associated with loss of cognate proteins in sporadic pituitary tumors.

On 11th November 2020, researchers from the University of California, Irvine, has established the understanding of epigenetic mechanisms in tumorigenesis and publicized a previously undetected repertoire of cancer driver genes. The study was published in “Science Advances”

Researchers were able to identify novel tumor suppressor genes (TSGs) and oncogenes (OGs), particularly those with rare mutations by using a new prediction algorithm, called DORGE (Discovery of Oncogenes and tumor suppressor genes using Genetic and Epigenetic features) by integrating the most comprehensive collection of genetic and epigenetic data.

The senior author Wei Li, Ph.D., the Grace B. Bell chair and professor of bioinformatics in the Department of Biological Chemistry at the UCI School of Medicine said

Existing bioinformatics algorithms do not sufficiently leverage epigenetic features to predict cancer driver genes, even though epigenetic alterations are known to be associated with cancer driver genes.

The Study

This study demonstrated how cancer driver genes, predicted by DORGE, included both known cancer driver genes and novel driver genes not reported in current literature. In addition, researchers found that the novel dual-functional genes, which DORGE predicted as both TSGs and OGs, are highly enriched at hubs in protein-protein interaction (PPI) and drug/compound-gene networks.

**Flowchart for the illustration of the DORGE method**
**Image Source**: https://advances.sciencemag.org/content/suppl/2020/11/09/6.46.eaba6784.DC1/aba6784_SM.pdf

Prof. Li explained that the DORGE algorithm, successfully leveraged public data to discover the genetic and epigenetic alterations that play significant roles in cancer driver gene dysregulation and could be instrumental in improving cancer prevention, diagnosis and treatment efforts in the future.

Another new algorithmic prediction for the identification of cancer genes by Machine Learning has been carried out by a team of researchers at the Max Planck Institute for Molecular Genetics (MPIMG) in Berlin and the Institute of Computational Biology of Helmholtz Zentrum München combining a wide variety of data analyzed it with “Artificial Intelligence” and identified numerous cancer genes. They termed the algorithm as EMOGI (Explainable Multi-Omics Graph Integration). EMOGI can predict which genes cause cancer, even if their DNA sequence is not changed. This opens up new perspectives for targeted cancer therapy in personalized medicine and the development of biomarkers. The research was published in Nature Machine Intelligence on 12th April 2021.

In cancer, cells get out of control. They proliferate and push their way into tissues, destroying organs and thereby impairing essential vital functions. This unrestricted growth is usually induced by an accumulation of DNA changes in cancer genes—i.e. mutations in these genes that govern the development of the cell. But some cancers have only very few mutated genes, which means that other causes lead to the disease in these cases.

The Study

**Overlap of EMOGI’s positive predictions with known cancer genes (KCGs) and candidate cancer genes**
**Image Source**: https://static-content.springer.com/esm/art%3A10.1038%2Fs42256-021-00325-y/MediaObjects/42256_2021_325_MOESM1_ESM.pdf

The aim of the study has been represented in 4 main headings

Additional targets for personalized medicine
Better results by combination
In search of hints for further studies
Suitable for other types of diseases as well

The team was headed by Annalisa Marsico. The team used the algorithm to identify 165 previously unknown cancer genes. The sequences of these genes are not necessarily altered-apparently, already a dysregulation of these genes can lead to cancer. All of the newly identified genes interact closely with well-known cancer genes and be essential for the survival of tumor cells in cell culture experiments. The EMOGI can also explain the relationships in the cell’s machinery that make a gene a cancer gene. The software integrates tens of thousands of data sets generated from patient samples. These contain information about DNA methylations, the activity of individual genes and the interactions of proteins within cellular pathways in addition to sequence data with mutations. In these data, a deep-learning algorithm detects the patterns and molecular principles that lead to the development of cancer.

Marsico says

Ideally, we obtain a complete picture of all cancer genes at some point, which can have a different impact on cancer progression for different patients

Unlike traditional cancer treatments such as chemotherapy, personalized treatments are tailored to the exact type of tumor. “The goal is to choose the best treatment for each patient, the most effective treatment with the fewest side effects. In addition, molecular properties can be used to identify cancers that are already in the early stages.

Roman Schulte-Sasse, a doctoral student on Marsico’s team and the first author of the publication says

To date, most studies have focused on pathogenic changes in sequence, or cell blueprints, at the same time, it has recently become clear that epigenetic perturbation or dysregulation gene activity can also lead to cancer.

This is the reason, researchers merged sequence data that reflects blueprint failures with information that represents events in cells. Initially, scientists confirmed that mutations, or proliferation of genomic segments, were the leading cause of cancer. Then, in the second step, they identified gene candidates that are not very directly related to the genes that cause cancer.

Clues for future directions

The researcher’s new program adds a considerable number of new entries to the list of suspected cancer genes, which has grown to between 700 and 1,000 in recent years. It was only through a combination of bioinformatics analysis and the newest Artificial Intelligence (AI) methods that the researchers were able to track down the hidden genes.

Schulte-Sasse says “The interactions of proteins and genes can be mapped as a mathematical network, known as a graph.” He explained by giving an example of a railroad network; each station corresponds to a protein or gene, and each interaction among them is the train connection. With the help of deep learning—the very algorithms that have helped artificial intelligence make a breakthrough in recent years – the researchers were able to discover even those train connections that had previously gone unnoticed. Schulte-Sasse had the computer analyze tens of thousands of different network maps from 16 different cancer types, each containing between 12,000 and 19,000 data points.

Many more interesting details are hidden in the data. Patterns that are dependent on particular cancer and tissue were seen. The researchers were also observed this as evidence that tumors are triggered by different molecular mechanisms in different organs.

Marsico explains

The EMOGI program is not limited to cancer, the researchers emphasize. In theory, it can be used to integrate diverse sets of biological data and find patterns there. It could be useful to apply our algorithm for similarly complex diseases for which multifaceted data are collected and where genes play an important role. An example might be complex metabolic diseases such as diabetes.

Main Source

New prediction algorithm identifies previously undetected cancer driver genes

Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms

https://www.nature.com/articles/s42256-021-00325-y#citeas

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VIDEOS on Cancer Biology, Cancer Genetics, Cancer Immunotherapy

Inhibitory CD161 receptor recognized as a potential immunotherapy target in glioma-infiltrating T cells by single-cell analysis

Posted in Anticancer Resistance, “Antibody–enzyme conjugates”, Biochemical pathways, Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Genomics, Cancer Screening, Cancer Vaccines: Targeting Cancer Genes for Immunotherapy, CRISPR alternative for editing genes without cutting, Etiology, Gene Therapy & Gene Editing Development, Genomic Expression, Glioblastoma, Immunology, Immunotherapy, Innovation in Immunology Diagnostics, Metastasis Process, mRNA Therapeutics, NK Cell-Based Cancer Immunotherapy, tagged antibody-drug conjugate, cancer biology, CD161, gene expression, Glioblastoma, IDH-mutant gliomas, immune checkpoint, Immunotherapy, RNA sequencing, single-cell RNA-seq, T cells on February 20, 2021| Leave a Comment »

Inhibitory CD161 receptor recognized as a potential immunotherapy target in glioma-infiltrating T cells by single-cell analysis

Reporter: Dr. Premalata Pati, Ph.D., Postdoc

Brain tumors, especially the diffused Gliomas are of the most devastating forms of cancer and have so-far been resistant to immunotherapy. It is comprehended that T cells can penetrate the glioma cells, but it still remains unknown why infiltrating cells miscarry to mount a resistant reaction or stop the tumor development.

Gliomas are brain tumors that begin from neuroglial begetter cells. The conventional therapeutic methods including, surgery, chemotherapy, and radiotherapy, have accomplished restricted changes inside glioma patients. Immunotherapy, a compliance in cancer treatment, has introduced a promising strategy with the capacity to penetrate the blood-brain barrier. This has been recognized since the spearheading revelation of lymphatics within the central nervous system. Glioma is not generally carcinogenic. As observed in a number of cases, the tumor cells viably reproduce and assault the adjoining tissues, by and large, gliomas are malignant in nature and tend to metastasize. There are four grades in glioma, and each grade has distinctive cell features and different treatment strategies. Glioblastoma is a grade IV glioma, which is the crucial aggravated form. This infers that all glioblastomas are gliomas, however, not all gliomas are glioblastomas.

Decades of investigations on infiltrating gliomas still take off vital questions with respect to the etiology, cellular lineage, and function of various cell types inside glial malignancies. In spite of the available treatment options such as surgical resection, radiotherapy, and chemotherapy, the average survival rate for high-grade glioma patients remains 1–3 years (1).

A recent in vitro study performed by the researchers of Dana-Farber Cancer Institute, Massachusetts General Hospital, and the Broad Institute of MIT and Harvard, USA, has recognized that CD161 is identified as a potential new target for immunotherapy of malignant brain tumors. The scientific team depicted their work in the Cell Journal, in a paper entitled, “Inhibitory CD161 receptor recognized in glioma-infiltrating T cells by single-cell analysis.” on 15th February 2021.

To further expand their research and findings, Dr. Kai Wucherpfennig, MD, PhD, Chief of the Center for Cancer Immunotherapy, at Dana-Farber stated that their research is additionally important in a number of other major human cancer types such as

melanoma,
lung,
colon, and
liver cancer.

Dr. Wucherpfennig has praised the other authors of the report Mario Suva, MD, PhD, of Massachusetts Common Clinic; Aviv Regev, PhD, of the Klarman Cell Observatory at Broad Institute of MIT and Harvard, and David Reardon, MD, clinical executive of the Center for Neuro-Oncology at Dana-Farber.

Hence, this new study elaborates the effectiveness of the potential effectors of anti-tumor immunity in subsets of T cells that co-express cytotoxic programs and several natural killer (NK) cell genes.

The Study-

**IMAGE SOURCE: Experimental Strategy (Mathewson ***et al***., 2021)**

The group utilized single-cell RNA sequencing (RNA-seq) to mull over gene expression and the clonal picture of tumor-infiltrating T cells. It involved the participation of 31 patients suffering from diffused gliomas and glioblastoma. Their work illustrated that the ligand molecule CLEC2D activates CD161, which is an immune cell surface receptor that restrains the development of cancer combating activity of immune T cells and tumor cells in the brain. The study reveals that the activation of CD161 weakens the T cell response against tumor cells.

Based on the study, the facts suggest that the analysis of clonally expanded tumor-infiltrating T cells further identifies the NK gene KLRB1 that codes for CD161 as a candidate inhibitory receptor. This was followed by the use of

CRISPR/Cas9 gene-editing technology to inactivate the KLRB1 gene in T cells and showed that CD161 inhibits the tumor cell-killing function of T cells. Accordingly,
genetic inactivation of KLRB1 or
antibody-mediated CD161 blockade

enhances T cell-mediated killing of glioma cells in vitro and their anti-tumor function in vivo. KLRB1 and its associated transcriptional program are also expressed by substantial T cell populations in other forms of human cancers. The work provides an atlas of T cells in gliomas and highlights CD161 and other NK cell receptors as immune checkpoint targets.

Further, it has been identified that many cancer patients are being treated with immunotherapy drugs that disable their “immune checkpoints” and their molecular brakes are exploited by the cancer cells to suppress the body’s defensive response induced by T cells against tumors. Disabling these checkpoints lead the immune system to attack the cancer cells. One of the most frequently targeted checkpoints is PD-1. However, recent trials of drugs that target PD-1 in glioblastomas have failed to benefit the patients.

In the current study, the researchers found that fewer T cells from gliomas contained PD-1 than CD161. As a result, they said, “CD161 may represent an attractive target, as it is a cell surface molecule expressed by both CD8 and CD4 T cell subsets [the two types of T cells engaged in response against tumor cells] and a larger fraction of T cells express CD161 than the PD-1 protein.”

However, potential side effects of antibody-mediated blockade of the CLEC2D-CD161 pathway remain unknown and will need to be examined in a non-human primate model. The group hopes to use this finding in their future work by

utilizing their outline by expression of KLRB1 gene in tumor-infiltrating T cells in diffuse gliomas to make a remarkable contribution in therapeutics related to immunosuppression in brain tumors along with four other common human cancers ( Viz. melanoma, non-small cell lung cancer (NSCLC), hepatocellular carcinoma, and colorectal cancer) and how this may be manipulated for prevalent survival of the patients.

References

(1) Anders I. Persson, QiWen Fan, Joanna J. Phillips, William A. Weiss, 39 – Glioma, Editor(s): Sid Gilman, Neurobiology of Disease, Academic Press, 2007, Pages 433-444, ISBN 9780120885923, https://doi.org/10.1016/B978-012088592-3/50041-4.

Main Source

Mathewson ND, Ashenberg O, Tirosh I, Gritsch S, Perez EM, Marx S, et al. 2021. Inhibitory CD161 receptor identified in glioma-infiltrating T cells by single-cell analysis. Cell.https://www.cell.com/cell/fulltext/S0092-8674(21)00065-9?elqTrackId=c3dd8ff1d51f4aea87edd0153b4f2dc7

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First single-course ‘curative’ CRISPR Shot by Intellia rivals Alnylam, Ionis and Pfizer

Posted in Biological Networks, Gene Regulation and Evolution, Cardiomyopathy, Cell Biology, Cerebrovascular and Neurodegenerative Diseases, Chemical Biology and its relations to Metabolic Disease, Chemical Genetics, CRISPR alternative for editing genes without cutting, CRISPR/Cas9 & Gene Editing, Frontiers in Cardiology and Cardiovascular Disorders, Genetics & Pharmaceutical, Genome Biology, Liver & Digestive Diseases Research, Population Health Management, Genetics & Pharmaceutical, Proteomics, RNA interference (RNAi) therapeutic, tagged polyneuropathy on February 19, 2021| Leave a Comment »

First single-course ‘curative’ CRISPR Shot by Intellia rivals Alnylam, Ionis and Pfizer

Reporter: Aviva Lev-Ari, PhD, RN

UPDATED on 2/23/2023

As Novartis pulls back from legacy sickle cell program, Intellia CEO calls the market ‘wide open’

By Annalee ArmstrongFeb 23, 2023 10:10am

Intellia announced in its fourth-quarter earnings report that Novartis had ended development of sickle cell treatment OTQ923/HIX763. (Getty Images)

Novartis will no longer develop an ex vivo sickle cell disease program that was part of an older deal with Intellia, and the gene editing biotech’s CEO John Leonard, M.D., thinks he knows why.

“We’ve always believed that the future lies with the in vivo approaches, and that’s been a focus of the work that we do,” Leonard said. “I’m sure they looked at the ex vivo space and may have had some of the same realizations that we had some years ago.”

Leonard, of course, said he wasn’t completely sure why Novartis opted to cut the program, but noted that the Big Pharma is undergoing a broad pipeline reorganization.

Novartis confirmed just that in an emailed statement to Fierce Biotech, saying that the program was discontinued for strategic reasons. The overall partnership with Intellia remains intact, however, the spokesperson said.

Intellia announced in its fourth-quarter earnings report Thursday that the Swiss pharma ended development of OTQ923/HIX763 this month.

The therapy uses autologous, ex vivo, CRISPR-edited hematopoietic stem cells to target fetal hemoglobin for treating sickle cell disease. Novartis initiated dosing on a phase 1/2 trial for the Intellia-partnered program in 2021.

Intellia has both types of candidate in its pipeline, but the in vivo list is longer and more advanced, with NTLA-2001 in transthyretin (ATTR) amyloidosis leading the pack.

Novartis and Intellia have had a cell therapy partnership since January 2015, which was three months after Intellia launched from Atlas Venture and Caribou Biosciences. The agreement was revised in 2018 to expand to ex vivo development of cell therapies using certain ocular stem cells. At that time, Intellia received a $10 million payment, but other financial details of the agreement have not been disclosed. Novartis gained the rights to opt in on one or more programs, while Intellia earned the right to use the pharma’s lipid nanoparticle technology for all genome editing applications in both in vivo and ex vivo settings.

SOURCE

Novartis plots Precision attack on sickle cell, paying $75M and putting up $1.4B in biobucks to form in vivo gene editing pact

By Nick Paul TaylorJun 22, 2022 03:40am

Intellia, working with its partner Regeneron, has shown over the past year that CRISPR/Cas9 in vivo gene editing can cause high, seemingly durable levels of gene knockdown in humans. While questions about the Intellia data, and the concept more broadly, remain unanswered, there is now early evidence that the approach may be effective and, as importantly, safe. Precision is one of a clutch of companies barreling toward the clinic in the wake of Intellia, and the potential of its Arcus platform to provide greater precision and versatility than CRISPR/Cas9 and zinc finger nuclease has now attracted a suitor.

To add to its in vivo capabilities, Novartis is set to pay $50 million in cash to partner with Precision. The deal also features a $25 million equity investment priced at $2.01 per share, a 20% premium over the recent average for the stock, as well as up to $1.4 billion in milestones, research funding and royalties ranging from the mid-single-digit to low-double-digit percentages.

SOURCE

https://www.fiercebiotech.com/biotech/novartis-plots-precision-attack-sickle-cell-paying-50m-and-putting-14b-biobucks-form-vivo

@@@@

Intellia to kick-start first single-course ‘curative’ CRISPR shot, as it hopes to beat rivals Alnylam, Ionis and Pfizer

Cancer treatment using CRISPR-based Genome Editing System

Posted in CRISPR/Cas9 & Gene Editing, Gene Therapy & Gene Editing Development, Genome Biology, tagged cancer metastasis, CRISPR-Cas9, CRISPR-LNP, gene editing on January 9, 2021| Leave a Comment »

Cancer treatment using CRISPR-based Genome Editing System

Reporter: Irina Robu, PhD

CRISPR, stands for “clusters of regularly interspaced short palindromic repeats” is one of the biggest accomplishments in science of this decade and it is the simplest tool for altering DNA sequences and modifying gene functions. The technology is adapted form the natural defense mechanism of bacteria. Bacteria uses CRISPR-derived RNA and different Cas proteins to foil attacks by viruses and foreign bodies.

Scientists in the laboratory of Prof. Dan Peer, VP for R&D and Head of the Laboratory of Precision Nanomedicine at the Shmunis School of Biomedicine and Cancer Research at TAU have shown that CRISPR/Cas9 system is efficient in treating metastatic cancer. They developed a novel lipid nanoparticle-based delivery system that targets cancer cells and ends them by genetic manipulation, called CRISPR-LNPs, which were published in published in November 2020 in Science Advances.

Professor Peer and his team of scientists chose two of the deadliest cancers: glioblastoma and metastatic ovarian cancer to prove that CRISPR genome editing system can be used to treat cancer effectively in a living animal. Since, glioblastoma is the most aggressive type of brain cancer with a life expectancy of 15 months after diagnosis, the researchers showed that the single treatment with CRISPR-LNPs doubled the average life expectancy of mice with glioblastoma tumors. At the same time, ovarian cancer is the most lethal cancer of female reproductive system and many patients are usually diagnosed at the advance stage of the disease. Treatment with CRISPR-LNPs in a metastatic ovarian cancer mice model increased their overall survival rate by 80%.

Despite CRISPR genome editing technology being capable of identifying and altering any genetic segment, clinical implementation is still in its infancy because the inability to accurately deliver the CRISPR to the target cells. In order to solve the issue, Professor Peer developed a delivery system that targets the DNA responsible for the cancer cells.

By demonstrating that the technology can treat two aggressive cancers, researchers open the technology to numerous new possibilities for treating other types of cancer. They intend to go on to experiments with blood cancers which are very interesting genetically.

SOURCE

Revolutionary CRISPR-based genome editing system treatment destroys cancer cells