Citations

Abstract Despite intensive characterization of immune responses after COVID-19 infection and vaccination, research examining protective correlates of vertical transmission in pregnancy are limited. Herein, we profiled humoral and cellular characteristics in pregnant women infected or vaccinated at different trimesters and in their corresponding newborns. We noted a significant correlation between spike S1–specific IgG antibody and its RBD-ACE2 blocking activity (receptor-binding domain–human angiotensin-converting enzyme 2) in maternal and cord plasma (P < .001, R > 0.90). Blocking activity of spike S1–specific IgG was significantly higher in pregnant women infected during the third trimester than the first and second trimesters. Elevated levels of 28 cytokines/chemokines, mainly proinflammatory, were noted in maternal plasma with infection at delivery, while cord plasma with maternal infection 2 weeks before delivery exhibited the emergence of anti-inflammatory cytokines. Our data support vertical transmission of protective SARS-CoV-2–specific antibodies. This vertical antibody transmission and the presence of anti-inflammatory cytokines in cord blood may offset adverse outcomes of inflammation in exposed newborns.

Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The detection of SVGs is analogous to the identification of differentially expressed genes and permits us to understand how genes and associated molecular processes are spatially distributed within cellular niches. However, the expression activities of SVGs fail to encode all information inherent in the spatial distribution of cells. Here, we devised a deep learning model, Spatially Informed Artificial Intelligence (SPIN-AI), to identify spatially predictive genes (SPGs), whose expression can predict how cells are organized in space. We used SPIN-AI on spatial transcriptomic data from squamous cell carcinoma (SCC) as a proof of concept. Our results demonstrate that SPGs not only recapitulate the biology of SCC but also identify genes distinct from SVGs. Moreover, we found a substantial number of ribosomal genes that were SPGs but not SVGs. Since SPGs possess the capability to predict spatial cellular organization, we reason that SPGs capture more biologically relevant information for a given cellular niche than SVGs. Thus, SPIN-AI has broad applications for detecting SPGs and uncovering which biological processes play important roles in governing cellular organization.

Anticipating and understanding cancers’ need for specific gene activities is key for novel therapeutic development. Here we utilized DepMap, a cancer gene dependency screen, to demonstrate that machine learning combined with network biology can produce robust algorithms that both predict what genes a cancer is dependent on and what network features coordinate such gene dependencies. Using network topology and biological annotations, we constructed four groups of novel engineered machine learning features that produced high accuracies when predicting binary gene dependencies. We found that in all examined cancer types, F1 scores were greater than 0.90, and model accuracy remained robust under multiple hyperparameter tests. We then deconstructed these models to identify tumor type-specific coordinators of gene dependency and identified that in certain cancers, such as thyroid and kidney, tumors’ dependencies are highly predicted by gene connectivity. In contrast, other histologies relied on pathway-based features such as lung, where gene dependencies were highly predictive by associations with cell death pathway genes. In sum, we show that biologically informed network features can be a valuable and robust addition to predictive pharmacology models while simultaneously providing mechanistic insights.

Abstract Glioblastoma is the most common and lethal primary brain tumor in adults, with a median survival less than 15 months despite current standard of care. Oncolytic immunovirotherapies (OVs) have shown promise in early clinical trials. OVs are engineered to specifically target cancer cells, trigger autophagic programmed cell death, and release tumor antigens in the context of T-cell activating cytokines. Cellular communication network factor 1 (CCN1) is an extracellular matrix protein expressed in 70% of glioblastomas that has been shown to drastically reduce OV efficacy, particularly herpes simplex virus type 1 (HSV-1). Our objectives were to decode protein-protein interaction (PPI) networks activated by CCN1, identify critical nodes in resistance networks, and use this analysis to design OVs that overcome CCN1-mediated resistance. We used NetDecoder to elucidate phenotype-specific PPI subnetworks in LN229 human glioblastoma cells with tetracycline-inducible CCN1 expression. Publicly available microarray data on CCN1-induced and control samples established differentially expressed genes, which served as sources in our PPI network to derive prioritized context-specific subnetworks. We found that 11 source genes collaborate via 39 deep nodes to confer OV resistance to CCN1-expressing glioblastomas. Of these, a router (IKBKE) and a sink (YBX1) have been previously implicated in glioblastoma pathogenesis. We conclude that autophagy regulator HSP90AA1, a critical node, may be targeted to improve OV efficacy in CCN1-expressing glioblastomas. Furthermore, WebGestalt overrepresentation analysis of subnetwork nodes suggests that oncolytic adenovirus may be more effective than HSV-1 as a synthetic biology chassis.

Abstract Background: Large scale genetic perturbations screens have been instrumental for many key discoveries. In the last decade sequence specific genetic perturbations created RNAi and CRISPR (PMID: 25446316). Recently, specific genome targeting approaches using single guide RNAs (sgRNAs) for the CRISPR-Cas9 system have been repurposed to induce sequence-specific repression or activation of gene expression at a genome-wide scale. Different perturbation approaches have different effects and encompass a trade-off between specificity and duration in establishing a measurable perturbation which can result in different biological readouts. However, bench-side strategies are time consuming and expensive. The method described here aims to build a systems-based computational platform to assess pharmacological and biological perturbations at genome-wide network level.Methods: Our network tool NetDecoder (Nucleic Acids Res. 2016; 44(10):e100) that models genome-wide information flow will be used to build context specific protein-protein interaction networks. These networks will be subsequently subjected to in silico perturbations and their impacts on network properties will be assessed. AVANA and Demeter datasets will be used during method validation procedures. Results: Using publicly available expression datasets we generated context specific Breast Cancer networks and individually manipulated nodes and edges of context-specific networks associated with pharmacological FDA approved targets. Our perturbation networks captured cellular rewiring by engulfing new gene players, alterations of the network size and its connectivity. Conclusions: Our in-silico genome wide perturbation platform provides a flexible approach to fine-tune single and tandem manipulations, allowing for inexpensively monitoring a range of pharmacological interventions using large omics data. Citation Format: Cristina Correia, Choong Yong Ung, Cheng Zhang, Scott H. Kaufmann, Hu Li. A genome wide pharmacological perturbation platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1226.

Abstract Current understandings of individual disease etiology and therapeutics are limited despite great need. To fill the gap, we propose a novel computational pipeline which collects potent disease gene cooperative pathways to envision individualized disease etiology and therapies. Our algorithm constructs individualized disease modules de novo which enable us to elucidate the importance of mutated genes in specific patients and to understand the synthetic penetrance of these genes across patients. We reveal that importance of notorious cancer drivers TP53 and PIK3CA fluctuate widely across breast cancers and peak in tumors with distinct numbers of mutations, and that rarely mutated genes such as XPO1 and PLEKHA1 have high disease module importance in specific individuals. Furthermore, individualized module disruption enables us to devise customized singular and combinatorial target therapies which were highly varied across patients demonstrating the need for precision therapeutics pipelines. As the first analysis of de novo individualized disease modules, we illustrate the power of individualized disease modules for precision medicine by providing deep novel insights on the activity of diseased genes in individuals. Citation Format: Taylor Weiskittel, Cristina Correia, Choong Ung, Cheng Zhang, Hu Li. De novo individualized disease modules reveal the synthetic penetrance of genes and inform personalized treatment regimens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 490.

Together, single-cell technologies and systems biology have been used to investigate previously unanswerable questions in biomedicine with unparalleled detail. Despite these advances, gaps in analytical capacity remain. Machine learning, which has revolutionized biomedical imaging analysis, drug discovery, and systems biology, is an ideal strategy to fill these gaps in single-cell studies. Machine learning additionally has proven to be remarkably synergistic with single-cell data because it remedies unique challenges while capitalizing on the positive aspects of single-cell data. In this review, we describe how systems-biology algorithms have layered machine learning with biological components to provide systems level analyses of single-cell omics data, thus elucidating complex biological mechanisms. Accordingly, we highlight the trifecta of single-cell, systems-biology, and machine-learning approaches and illustrate how this trifecta can significantly contribute to five key areas of scientific research: cell trajectory and identity, individualized medicine, pharmacology, spatial omics, and multi-omics. Given its success to date, the systems-biology, single-cell omics, and machine-learning trifecta has proven to be a potent combination that will further advance biomedical research.

Abstract Glioblastoma multiforme (GBM) is a particularly aggressive primary brain tumor that is relatively common in adults. The disease comes with a dismal prognosis, often with expected survival just over a year when treated with surgery, chemotherapy, and radiation. A promising addition to our GBM treatment arsenal are oncolytic viruses (OVs), a class of immunotherapy now in clinical trials. The most effective OVs home in on tumor cells, killing them and releasing tumor-associated antigens that elicit antitumor immunity. This effectiveness, however, varies depending on the cell state—a property that may be influenced by the composition of the extracellular matrix (ECM) of GBM. Cellular communication network factor 1 (CCN1) is found in the ECM of the majority of GBMs and is predictive of resistance to OVs, particularly those derived from herpes simplex virus type 1 (HSV-1). In this study, our aim is to understand how extracellular CCN1 alters the GBM intracellular state in a manner that confers resistance to OVs. We began by using our NetDecoder platform to build CCN1-specific prioritized protein-protein interaction (PPI) networks from published LN229 human GBM transcriptomic datasets. From this network, we identified 12 PPI edges that dominate the network flow in CCN1-high LN229 cells when compared to CCN1-low controls. The 21 unique nodes that comprise these binary PPI edges were extracted and subjected to downstream biological process overrepresentation analyses using Gene Ontology. We found that the cytokine-mediated signaling pathway (FDR = 4.2 x 10-11; enrichment ratio = 15.8) and the cellular response to cytokine stimulus (FDR = 3.1 x 10-9; enrichment ratio = 10.9) were highly enriched when mapped to these nodes. We conclude that CCN1, acting as a paracrine signal in the GBM tumor microenvironment, primes the cell to inhibit HSV-1 and other candidate OV replication prior to any virus interactions, thus contributing to immunovirotherapy resistance. While CCN1 and its cell surface integrin binding partners are ostensible drug targets for overcoming this resistance, pharmacologic interventions that target CCN1-specific high flow edges in our prioritized PPI network may be more effective. Citation Format: Dileep D. Monie, Cristina Correia, Cheng Zhang, Choong Ung, Richard G. Vile, Hu Li. Innate immunity and cytokine signaling pathways drive resistance to HSV-1 oncolytic immunovirotherapy in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1679.

Abstract One of the greatest barriers to curative treatment of neuroblastoma is its frequent metastatic outgrowth prior to diagnosis, especially in cases driven by amplification of the MYCN oncogene. However, only a limited number of regulatory proteins that contribute to this complex MYCN-mediated process have been elucidated. Here we show that the growth arrest-specific 7 (GAS7) gene, located at chromosome band 17p13.1, is preferentially deleted in high-risk MYCN-driven neuroblastoma. GAS7 expression was also suppressed in MYCN-amplified neuroblastoma lacking 17p deletion. GAS7 deficiency led to accelerated metastasis in both zebrafish and mammalian models of neuroblastoma with overexpression or amplification of MYCN. Analysis of expression profiles and the ultrastructure of zebrafish neuroblastoma tumors with MYCN overexpression identified that GAS7 deficiency led to (i) downregulation of genes involved in cell–cell interaction, (ii) loss of contact among tumor cells as critical determinants of accelerated metastasis, and (iii) increased levels of MYCN protein. These results provide the first genetic evidence that GAS7 depletion is a critical early step in the cascade of events culminating in neuroblastoma metastasis in the context of MYCN overexpression. Significance: Heterozygous deletion or MYCN-mediated repression of GAS7 in neuroblastoma releases an important brake on tumor cell dispersion and migration to distant sites, providing a novel mechanism underlying tumor metastasis in MYCN-driven neuroblastoma. See related commentary by Menard, p. 2815