Jonathan Warrell’s research while affiliated with NEC Laboratories America and other places

Levels of selection and multilevel evolutionary processes are essential concepts in evolutionary theory, and yet there is a lack of common mathematical models for these core ideas. Here, we propose a unified mathematical framework for formulating and optimizing multilevel evolutionary processes and genetic algorithms over arbitrarily many levels based on concepts from category theory and population genetics. We formulate a multilevel version of the Wright-Fisher process using this approach, and we show that this model can be analyzed to clarify key features of multilevel selection. Particularly, we derive an extended multilevel probabilistic version of Price's Equation via the Kantorovich Monad, and we use this to characterize regimes of parameter space within which selection acts antagonistically or cooperatively across levels. Finally, we show how our framework can provide a unified setting for learning genetic algorithms (GAs), and we show how we can use a Variational Optimization and a multi-level analogue of coalescent analysis to fit multilevel GAs to simulated data.

Background Naturally occurring TCR targeting cancer antigens are associated with relatively low affinity comparatively to TCR targeting external pathogens. This might be explained by the proximity of cancer specific sequences to self. Engineering of modified affinity enhanced TCR constitutes a possible solution, however, TCR binding remains challenging to model using structural biology approaches because of the conformational flexibility of the TCR complex. The use of machine learning based methods constitutes a promising approach to design TCR of higher affinity. Herein, we report enhanced affinity TCR sequences against cancer antigens designed using TCRPPO, a proprietary pipeline for TCR sequence optimization. Methods TCRPPO is a new reinforcement-learning framework based on proximal policy optimization to optimize TCRs through a mutation policy. Briefly after training the system on a series of TCR sequences known to bind a given target, TCRPPO introduces mutations on existing sequence to achieve higher affinity guided by a reward function factoring in affinity of the new sequence and the likelihood for this sequence to be a valid TCRs. To validate our approach, we designed a series of candidate TCR sequences against known clinically relevant cancer antigens (KRAS G12V and MART-1) and evaluated their biological functional potency. To do so, genes encoding variable regions of the original and optimized TCRα and β chains were assembled into plasmid vectors containing a constant region of a TCRα or TCRβ chain. TAP fragments of TCRα and TCRβ together with a NFAT-Luc reporter plasmid were transfected into the ΔTCR Jurkat cell line. The cells were cultured in the presence of antigen presenting cells with or without target peptide, and then the activation of the reporter gene was measured by luciferase assay. Results Our AI-based TCR engineering approach generated valid enhanced TCR sequences against the selected epitopes. Engineered TCR transfected cells showed higher activity in the functional assay and demonstrated that TCR generated using a mutation policy can achieve higher biological activity than endogenous TCR. Enhanced TCR generated against KRAS G12V and MART-1 are dissimilar from already described TCR.¹ Conclusions We successfully engineered TCRs to have better antigen recognition. The enhanced TCRs warrant further characterization to evaluate their therapeutic potential. Beyond this case, our approach constitutes a pipeline that might be applied to other targets for which alternative TCRs are required. Reference • Chen Z, Min MR, Guo H, Cheng C, Clancy T, Ning X. T-Cell receptor optimization with reinforcement learning and mutation polices for precision immunotherapy. In: Tang, H. (eds) Research in Computational Molecular Biology. RECOMB. 2023;Lecture Notes in Computer Science(), vol 13976. Springer, Cham. https://doi.org/10.1007/978-3-031-29119-7_11.

Graph neural networks (GNNs) have emerged as powerful tools for representation learning. Their efficacy depends on their having an optimal underlying graph. In many cases, the most relevant information comes from specific subgraphs. In this work, we introduce a GNN-based framework (graph-partitioned GNN [GP-GNN]) to partition the GNN graph to focus on the most relevant subgraphs. Our approach jointly learns task-dependent graph partitions and node representations, making it particularly effective when critical features reside within initially unidentified subgraphs. Protein liquid-liquid phase separation (LLPS) is a problem especially well-suited to GP-GNNs because intrinsically disordered regions (IDRs) are known to function as protein subdomains in it, playing a key role in the phase separation process. In this study, we demonstrate how GP-GNN accurately predicts LLPS by partitioning protein graphs into task-relevant subgraphs consistent with known IDRs. Our model achieves state-of-the-art accuracy in predicting LLPS and offers biological insights valuable for downstream investigation.

Non-small cell lung cancer (NSCLC) has an increasing number of targeted and systemic therapies where subsets of patients have long-term durable benefit. Fundamental to understanding responses to a given therapy is comprehensive molecular characterization of the underlying tumor immune microenvironment (TIME). The TIME may inform models to predict immunotherapy outcomes and features to delineate therapeutic responses and clinical endpoints. We hypothesize that an integrated multi-omics approach will uncover interactions within the NSCLC TIME and identify novel biomarkers that are predictive of immunotherapy responses, thus aiding precision oncology. To develop a spatially resolved TIME model for NSCLC immunotherapy using unbiased spatial proteomic and whole transcriptome profiling. We utilized a multi-omics approach, combining spatial mapping of protein expression at the single-cell resolution by Phenocycler Fusion (PCF) and multi-cellular readout whole transcriptome profiling at cellular compartment resolution by Digital Spatial Profiling (DSP-GeoMx-WTA).) This approach facilitated a detailed examination of the TIME in NSCLC samples from patients undergoing first-line immunotherapy. We studied two independent cohorts of advanced NSCLC tissue samples, treated with PD-1-based immunotherapies. We derived gene signatures from cell type signatures to predict treatment outcomes using a Least Absolute Shrinkage and Selection Operator (LASSO) model. Our spatial proteomic analysis identified three distinct cell types, proliferating tumor cells, granulocytes, and vessels, associated with resistance to immunotherapy. A high proportion of these cell types demonstrated a hazard ratio (HR) of 3.8 (p = 0.004) in the training cohort and 1.8 (p = 0.05) in the validation cohort. In the response cell-type model, higher levels of M1 macrophages, M2 macrophages, and CD4 T cells had an HR of 0.4 (p = 0.019) in the training cohort and 0.49 (p = 0.036) in the validation. In the transcriptomic analysis, gene signatures derived from these cell types predicted outcomes with high accuracy. The resistance gene model, which included 8 genes associated with epithelial-mesenchymal transition (EMT) and cell migration, showed an HR of 5.3 (p < 0.001) in the training cohort and 2.2 (p = 0.036) in the validation cohort. Conversely, the response gene model, consisting of 8 genes associated with immunomodulation, had an HR of 0.22 (p = 0.005) in the training cohort and 0.38 (p = 0.034) in the validation cohort. This research highlights the potential of a multi-omics cell typing and gene expression profiling approach in advancing NSCLC treatment toward precision oncology. By offering insights into the TIME and unveiling novel biomarkers, our model seeks to define resistance and to improve the prediction of response to treatment.

Motivation Spatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologies difficult to use in practice. Histological images co-registered with targeted tissues are more affordable and routinely generated in many research and clinical studies. Hence, predicting spatial gene expression from the morphological clues embedded in tissue histological images provides a scalable alternative approach to decoding tissue complexity. Results Here, we present a graph neural network based framework to predict the spatial expression of highly expressed genes from tissue histological images. Extensive experiments on two separate breast cancer data cohorts demonstrate that our method improves the prediction performance compared to the state-of-the-art, and that our model can be used to better delineate spatial domains of biological interest. Availability and implementation https://github.com/song0309/asGNN/

Purpose We aim to improve the prediction of response or resistance to immunotherapies in patients with melanoma. This goal is based on the hypothesis that current gene signatures predicting immunotherapy outcomes show only modest accuracy due to the lack of spatial information about cellular functions and molecular processes within tumors and their microenvironment. Experimental Design We collected gene expression data spatially from three cellular compartments defined by CD68+ macrophages, CD45+ leukocytes, and S100B+ tumor cells in 55 immunotherapy-treated melanoma specimens using Digital Spatial Profiling–Whole Transcriptome Atlas. We developed a computational pipeline to discover compartment-specific gene signatures and determine if adding spatial information can improve patient stratification. Results We achieved robust performance of compartment-specific signatures in predicting the outcome of immune checkpoint inhibitors in the discovery cohort. Of the three signatures, the S100B signature showed the best performance in the validation cohort (N = 45). We also compared our compartment-specific signatures with published bulk signatures and found the S100B tumor spatial signature outperformed previous signatures. Within the eight-gene S100B signature, five genes (PSMB8, TAX1BP3, NOTCH3, LCP2, and NQO1) with positive coefficients predict the response, and three genes (KMT2C, OVCA2, and MGRN1) with negative coefficients predict the resistance to treatment. Conclusions We conclude that the spatially defined compartment signatures utilize tumor and tumor microenvironment–specific information, leading to more accurate prediction of treatment outcome, and thus merit prospective clinical assessment.

Motivation: Spatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologies difficult to use in practice. Histological images co-registered with targeted tissues are more affordable and routinely generated in many research and clinical studies. Hence, predicting spatial gene expression from the morphological clues embedded in tissue histological images, provides a scalable alternative approach to decoding tissue complexity. Results: Here, we present a graph neural network based framework to predict the spatial expression of highly expressed genes from tissue histological images. Extensive experiments on two separate breast cancer data cohorts demonstrate that our method improves the prediction performance compared to the state-of-the-art, and that our model can be used to better delineate spatial domains of biological interest. Availability: https://github.com/song0309/asGNN/

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type–specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.

The widespread use of immunotherapy in lung cancer, and its more recent approval for early stages, underscores the need for biomarkers that can identify the most responsive patients. Spatial transcriptomics, which maps gene expression in its spatial tissue context, offers a unique approach over bulk transcriptomics. By incorporating spatial information, the predictive power of the signature can be enhanced. Here, we aim to develop spatially informed gene signatures that could be translated into clinical RNA in situ assays, distinguishing patients unlikely to benefit from immunotherapy and thereby sparing them unnecessary side effects. We utilized the NanoString GeoMX Whole Transcriptome Atlas for spatially resolved transcriptomic profiling of retrospectively collected lung cancer tissue samples from patients treated with immunotherapy in an advanced-stage setting (N=60). By targeting 18,190 genes within distinct areas of interest (AOIs)—including stromal (macrophages/CD68+ and leukocytes/CD45+) and tumor (cytokeratin, CK+) cells—we developed AOI-specific gene signatures to predict objective responses. These were derived from a robust computational framework employing LASSO logistic regression on a split-sample approach, yielding predictive models for treatment outcome.We achieved high predictive accuracy on the training set, with the area under the curve (AUC) exceeding 0.86 for all AOI-specific spatial signatures, indicating strong potential for clinical application. Validation against an independent cohort (N=42) corroborated the efficacy of these signatures. Our 6-gene tumor signature was validated with an AUC of 0.73 (95% CI: 0.67-0.89, p = 0.009**), while the CD45 5-gene signature showed an AUC of 0.75 (95% CI: 0.53-0.97, p = 0.022*). Our 18-gene CD68 signature trended towards validation but lacked statistical significance. A combined CD68 and CD45 signature predicted outcomes with greater accuracy, achieving an AUC of 0.79 (95% CI: 0.62-0.98, p = 0.0088*). Following gene set enrichment analysis on our differentially expressed and signature genes, we identified genes with positive coefficients in both tumor and stroma signatures that were associated with glucocorticoid response and glycolytic processes linked to T-cell homeostasis, while genes with negative coefficients were associated with epithelial cell differentiation and cytokine production. These associations are concordant with the observation that genes with positive coefficients are predictors of treatment response, while those with negative coefficients indicate resistance.Our findings indicate that AOI-specific signatures predict the immunotherapy outcome in lung cancer with high accuracy, suggesting that spatial assessment can provide substantial predictive information. The high performance of these signatures indicates their potential for prospective clinical applications. Citation Format: Thazin N. Aung, Myrto Moutafi, Ioannis Trontzas, Arutha Kulasinghe, James Monkman, Niki Gavrielatou, Ioannis Vathiotis, Jonathan H. Warrell, David L. Rimm. Spatial-specific gene signatures to predict immunotherapy outcomes in lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1141.