Citations

Chemotherapy-induced peripheral neuropathy (CIPN) is a dose-limiting toxicity affecting 30–40% of patients treated with neurotoxic chemotherapy. Sensory symptoms arise from injury to dorsal root ganglion (DRG) neurons and their axons; yet, the underlying mechanisms remain incompletely understood. While human induced pluripotent stem cell (iPSC)-derived sensory neuron (iSN) monolayers have provided mechanistic insight, they lack the three-dimensional architecture and cellular heterogeneity of native DRG tissue. Here, we generated human iPSC-derived DRG organoids (iDRGOs) containing mixed neuronal and peripheral glial populations and established a quantitative neurite outgrowth assay to model chemotherapy-induced neurotoxicity in a 3D context. iDRGOs from three healthy donors were exposed to bortezomib, vincristine, or paclitaxel. All three drugs caused dose-dependent neurite outgrowth impairment without significant short-term changes in organoid size, consistent with early axonal injury. Vincristine reduced MAP2 levels when normalized to total protein, whereas bortezomib and paclitaxel showed divergent microtubule-associated responses compared to monolayer cultures. The developmental stage significantly influenced the baseline neurite outgrowth, highlighting the need for age standardization. These results establish iDRGOs as a physiologically relevant human platform that complements monolayer models for mechanistic studies and therapeutic screening in CIPN.

Neuropathic pain is a debilitating chronic condition that lacks effective treatment. The role of cytokine- and chemokine-mediated neuroinflammation in its pathogenesis has been well documented. Follistatin (FST) is a secreted protein known to antagonize the biological activity of cytokines in the transforming growth factor–β (TGF-β) superfamily. The involvement of FST in neuropathic pain and the underlying mechanism remain largely unknown. Here, we report that FST was up-regulated in A-fiber sensory neurons after spinal nerve ligation (SNL) in mice. Inhibition or deletion of FST alleviated neuropathic pain and reduced the nociceptive neuron hyperexcitability induced by SNL. Conversely, intrathecal or intraplantar injection of recombinant FST, or overexpression of FST in the dorsal root ganglion (DRG) neurons, induced pain hypersensitivity. Furthermore, exogenous FST increased neuronal excitability in nociceptive neurons. The biolayer interferometry (BLI) assay and coimmunoprecipitation (co-IP) demonstrated direct binding of FST to the insulin-like growth factor–1 receptor (IGF1R), and IGF1R inhibition reduced FST-induced activation of extracellular signal–regulated kinase (ERK) and protein kinase B (AKT), as well as neuronal hyperexcitability. Further co-IP analysis revealed that the N-terminal domain of FST exhibits the highest affinity for IGF1R, and blocking this interaction with a peptide derived from FST attenuated Nav1.7-mediated neuronal hyperexcitability and neuropathic pain after SNL. In addition, FST enhanced neuronal excitability in human DRG neurons through IGF1R. Collectively, our findings suggest that FST, released from A-fiber neurons, enhances Nav1.7-mediated hyperexcitability of nociceptive neurons by binding to IGF1R, making it a potential target for neuropathic pain treatment.

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 High grade glioma (HGG) represents a group of devastating diseases with dismal prognosis. Surgical resection of the contrast enhancing (CE) region of HGG remains the mainstay of treatment, but recurrence inevitably arises from the unresected non-contrast enhancing (NE) region, surgically inaccessible due to cancer cell invasion into healthy brain tissue. Due to its critical role in recurrence, understanding of the NE region is central to the improvement of clinical outcomes. We reveal the biological characteristics of this region through image localized multi-regional sampling. We linked microenvironmental characteristics measured by multi-parametric MRI to genomic mutations and transcriptional phenotypes using mixed effect modeling which allowed us to control for individualized patient effects. We first confirmed that T2 is a significant indicator of IDH mutation status in the NE region, being the first description of such a relationship in a HGG cohort. We found the combination of EGFR amplification and CDKN2A homozygous loss was associated with a significantly lower mean diffusivity (MD) compared to double wild type tumors in the NE region, indicating the presence of greater cellular packing and proliferation in EGFR amplification/CDKN2A loss regions. Finally, using single cell pathway based tumor classifications, we showed that nK2, a DSC-MRI metric representing cell size heterogeneity, correlated positively with neuronal signature and negatively with glycolytic/plurimetabolic signature within the NE tumor, indicating that glycolytic/plurimetobolic tumors possessed a high amount of cell size heterogeneity compared to neuronal samples. This hypothesis was supported using digital reference object (DRO) modeling which confirmed that cell size and heterogeneity drove the differential nK2 signal between neuronal and glycolytic/plurimetabolic samples. We identified immune cell infiltrate as one possible mechanism of increased cell size heterogeneity using transcriptomic signature analysis which found more immune cell signatures within glycolytic/plurimetobolic tumors compared to neuronal. Collectively this study demonstrates the central role of multi-parametric MRI as a non-invasive measure of tumor biology and a tool for understanding the clinically critical NE region which can then inform new therapies targeting this region of HGG recurrence. Citation Format: Matthew Flick, Taylor Weiskittel, Kevin Meng-Lin, Fulvio D'Angelo, Francesca Caruso, Shannon Ensign, Mylan Blomquist, Luija Wang, Christopher Sereduk, Gustavo De Leon, Ashley Nespodzany, Javier Urcuyo, Ashlynn Gonzalez, Lee Curtin, Kyle Singleton, Aliya Anil, Natenael Simmineh, Erika Lewis, Teresa Noviello, Reyna Patel, Panwen Wang, Junwen Wang, Jennifer Eschbacher, Andrea Hawkins-Daarud, Pamela Jackson, Kris Smith, Peter Nakaji, Bernard Bendok, Richard Zimmerman, Chandan Krishna, Devi Patra, Naresh Patel, Mark Lyons, Matthew Neal, Kliment Donev, Maciej Mrugala, Alyx Porter, Scott Beeman, Yuxiang Zhou, Leslie Baxter, Christopher Plaisier, Jing Li, Hu Li, Anna Lasorella, Chad Quarles, Kristin Swanson, Michele Ceccarelli, Antonio Iavarone, Nhan Tran, Leland Hu. Multi-parametric MRI maps regional heterogeneity of high grade glioma phenotypes. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5621.