Contextual AI models for single-cell protein biology.

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here we introduce PINNACLE, a geometric deep learning approach that generates context-aware protein representations.

Phase II Study to Determine the Antitumor Activity and Safety of Simlukafusp Alfa (FAP-IL2v) Combined with Atezolizumab in Esophageal Cancer.

Purpose: In this study, we report the results from the esophageal squamous cell carcinoma (SCC) cohort of a phase II, noncomparative, basket study evaluating the antitumor activity and safety of fibroblast activation protein-IL2 variant (FAP-IL2v) plus atezolizumab in patients with advanced/metastatic solid tumors (NCT03386721).

Patients And Methods: Eligible patients had an Eastern Cooperative Oncology Group performance status of 0 to 1; measurable metastatic, persistent, or recurrent esophageal SCC; progression on ≥1 prior therapy; and were checkpoint inhibitor-naïve. Patients received FAP-IL2v 10 mg plus atezolizumab 1,200 mg intravenously every 3 weeks, or FAP-IL2v weekly for 4 weeks and then every 2 weeks plus atezolizumab 840 mg intravenously every 2 weeks.

Contextual AI models for single-cell protein biology.

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here, we introduce Pinnacle, a geometric deep learning approach that generates context-aware protein representations.

Deciphering molecular and cellular ex vivo responses to bispecific antibodies PD1-TIM3 and PD1-LAG3 in human tumors.

Background: Next-generation cancer immunotherapies are designed to broaden the therapeutic repertoire by targeting new immune checkpoints including lymphocyte-activation gene 3 (LAG-3) and T cell immunoglobulin and mucin-domain containing-3 (TIM-3). Yet, the molecular and cellular mechanisms by which either receptor functions to mediate its inhibitory effects are still poorly understood. Similarly, little is known on the differential effects of dual, compared with single, checkpoint inhibition.

DUSP4 protects BRAF- and NRAS-mutant melanoma from oncogene overdose through modulation of MITF.

MAPK inhibitors (MAPKi) remain an important component of the standard of care for metastatic melanoma. However, acquired resistance to these drugs limits their therapeutic benefit. Tumor cells can become refractory to MAPKi by reactivation of ERK.

BET Inhibition Enhances TNF-Mediated Antitumor Immunity.

Targeting chromatin binding proteins and modifying enzymes can concomitantly affect tumor cell proliferation and survival, as well as enhance antitumor immunity and augment cancer immunotherapies. By screening a small-molecule library of epigenetics-based therapeutics, BET (bromo- and extra-terminal domain) inhibitors (BETi) were identified as agents that sensitize tumor cells to the antitumor activity of CD8 T cells. BETi modulated tumor cells to be sensitized to the cytotoxic effects of the proinflammatory cytokine TNF.

Discoidin Domain Receptors in Melanoma: Potential Therapeutic Targets to Overcome MAPK Inhibitor Resistance.

Melanoma is a highly malignant skin cancer with high propensity to metastasize and develop drug resistance, making it a difficult cancer to treat. Current therapies targeting BRAF (V600) mutations are initially effective, but eventually tumors overcome drug sensitivity and reoccur. This process is accomplished in part by reactivating alternate signaling networks that reinstate melanoma proliferative and survival capacity, mostly through reprogramming of receptor tyrosine kinase (RTK) signaling.

MONET: a toolbox integrating top-performing methods for network modularization.

Summary: We define a disease module as a partition of a molecular network whose components are jointly associated with one or several diseases or risk factors thereof. Identification of such modules, across different types of networks, has great potential for elucidating disease mechanisms and establishing new powerful biomarkers. To this end, we launched the 'Disease Module Identification (DMI) DREAM Challenge', a community effort to build and evaluate unsupervised molecular network modularization algorithms.

Chromatin three-dimensional interactions mediate genetic effects on gene expression.

Studying the genetic basis of gene expression and chromatin organization is key to characterizing the effect of genetic variability on the function and structure of the human genome. Here we unravel how genetic variation perturbs gene regulation using a dataset combining activity of regulatory elements, gene expression, and genetic variants across 317 individuals and two cell types. We show that variability in regulatory activity is structured at the intra- and interchromosomal levels within 12,583 cis-regulatory domains and 30 trans-regulatory hubs that highly reflect the local (that is, topologically associating domains) and global (that is, open and closed chromatin compartments) nuclear chromatin organization.

Eliminating contraction during culture maintains global and local Ca dynamics in cultured rabbit pacemaker cells.

Pacemaker cells residing in the sinoatrial node generate the regular heartbeat. Ca signaling controls the heartbeat rate-directly, through the effect on membrane molecules (NCX exchange, K channel), and indirectly, through activation of calmodulin-AC-cAMP-PKA signaling. Thus, the physiological role of signaling in pacemaker cells can only be assessed if the Ca dynamics are in the physiological range.

A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines.

We report the results of a DREAM challenge designed to predict relative genetic essentialities based on a novel dataset testing 98,000 shRNAs against 149 molecularly characterized cancer cell lines. We analyzed the results of over 3,000 submissions over a period of 4 months. We found that algorithms combining essentiality data across multiple genes demonstrated increased accuracy; gene expression was the most informative molecular data type; the identity of the gene being predicted was far more important than the modeling strategy; well-predicted genes and selected molecular features showed enrichment in functional categories; and frequently selected expression features correlated with survival in primary tumors.

Genome-Wide Association between Transcription Factor Expression and Chromatin Accessibility Reveals Regulators of Chromatin Accessibility.

To better understand genome regulation, it is important to uncover the role of transcription factors in the process of chromatin structure establishment and maintenance. Here we present a data-driven approach to systematically characterise transcription factors that are relevant for this process. Our method uses a linear mixed modelling approach to combine datasets of transcription factor binding motif enrichments in open chromatin and gene expression across the same set of cell lines.

Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases.

Mapping perturbed molecular circuits that underlie complex diseases remains a great challenge. We developed a comprehensive resource of 394 cell type- and tissue-specific gene regulatory networks for human, each specifying the genome-wide connectivity among transcription factors, enhancers, promoters and genes. Integration with 37 genome-wide association studies (GWASs) showed that disease-associated genetic variants--including variants that do not reach genome-wide significance--often perturb regulatory modules that are highly specific to disease-relevant cell types or tissues.

Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics.

Integrating single nucleotide polymorphism (SNP) p-values from genome-wide association studies (GWAS) across genes and pathways is a strategy to improve statistical power and gain biological insight. Here, we present Pascal (Pathway scoring algorithm), a powerful tool for computing gene and pathway scores from SNP-phenotype association summary statistics. For gene score computation, we implemented analytic and efficient numerical solutions to calculate test statistics.

Network deconvolution as a general method to distinguish direct dependencies in networks.

Recognizing direct relationships between variables connected in a network is a pervasive problem in biological, social and information sciences as correlation-based networks contain numerous indirect relationships. Here we present a general method for inferring direct effects from an observed correlation matrix containing both direct and indirect effects. We formulate the problem as the inverse of network convolution, and introduce an algorithm that removes the combined effect of all indirect paths of arbitrary length in a closed-form solution by exploiting eigen-decomposition and infinite-series sums.

Wisdom of crowds for robust gene network inference.

Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development.

Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks.

Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features.

GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods.

Motivation: Over the last decade, numerous methods have been developed for inference of regulatory networks from gene expression data. However, accurate and systematic evaluation of these methods is hampered by the difficulty of constructing adequate benchmarks and the lack of tools for a differentiated analysis of network predictions on such benchmarks.

Results: Here, we describe a novel and comprehensive method for in silico benchmark generation and performance profiling of network inference methods available to the community as an open-source software called GeneNetWeaver (GNW).

Identification of functional elements and regulatory circuits by Drosophila modENCODE.

To gain insight into how genomic information is translated into cellular and developmental programs, the Drosophila model organism Encyclopedia of DNA Elements (modENCODE) project is comprehensively mapping transcripts, histone modifications, chromosomal proteins, transcription factors, replication proteins and intermediates, and nucleosome properties across a developmental time course and in multiple cell lines. We have generated more than 700 data sets and discovered protein-coding, noncoding, RNA regulatory, replication, and chromatin elements, more than tripling the annotated portion of the Drosophila genome. Correlated activity patterns of these elements reveal a functional regulatory network, which predicts putative new functions for genes, reveals stage- and tissue-specific regulators, and enables gene-expression prediction.

Revealing strengths and weaknesses of methods for gene network inference.

Numerous methods have been developed for inferring gene regulatory networks from expression data, however, both their absolute and comparative performance remain poorly understood. In this paper, we introduce a framework for critical performance assessment of methods for gene network inference. We present an in silico benchmark suite that we provided as a blinded, community-wide challenge within the context of the DREAM (Dialogue on Reverse Engineering Assessment and Methods) project.