Squires, J. R. | bioRxiv Science

bioRxiv2025

Rare Subset of T Cells Form Heterotypic Clusters with Circulating Tumor Cells to Foster Cancer Metastasis

The immune ecosystem is central to maintaining effective defensive responses. However, how immune cells in the periphery blood interact with circulating tumor cells (CTCs) - seeds of metastasis - remains largely understudied. Here, our analysis of the blood specimens (N=1,529) from patients with advanced breast cancer revealed that over 75% of the CTC-positive blood specimens contained heterotypic CTC clusters with CD45+ white blood cells (WBCs). Detection of CTC-WBC clusters correlates with breast cancer subtypes (triple negative and luminal B), racial groups (Black), and decreased survival rates. Flow cytometry and ImageStream analyses revealed diverse WBC composition of heterotypic CTC-WBC clusters, including overrepresented T cells and underrepresented neutrophils. Most strikingly, a rare subset of CD4 and CD8 double positive T (DPT) cells showed an up to 140-fold enrichment in the CTC clusters versus its frequency in WBCs. DPT cells shared part of the profiles with CD4+ T cells and others with CD8+ T cells but exhibited unique features of T cell exhaustion and immune suppression with higher expression of TIM-3 and PD-1. Single-cell RNA sequencing and genetic perturbation studies further pinpointed the integrin VLA4 (4{beta}1) in DPT cells and its ligand VCAM1 in tumor cells as essential mediators of heterotypic WBC-CTC clusters. Neoadjuvant administration of anti-4 (VLA4) neutralizing antibodies markedly blocked CTC-DPT cell clustering and inhibited metastasis for extended survival in preclinical mouse models in vivo. These findings uncover a pivotal role of rare DPT cells with immune suppressive features in fostering cancer dissemination through direct interactive clustering with CTCs. It lays a foundation for developing innovative biomarkers and therapeutic strategies to prevent and target cancer metastasis, ultimately benefiting cancer care. Brief summaryOur findings uncover a fostering role of immune-suppressive T cells in contact with circulating tumor cells and identify therapeutic approaches to eliminate devastating cancer metastasis. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=191 SRC="FIGDIR/small/646421v1_ufig1.gif" ALT="Figure 1"> View larger version (67K): org.highwire.dtl.DTLVardef@13b7862org.highwire.dtl.DTLVardef@15c97b2org.highwire.dtl.DTLVardef@1eb4fd2org.highwire.dtl.DTLVardef@1c588e1_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Physics-driven structural docking and protein language models accelerate antibody screening and design for broad-spectrum antiviral therapy

Therapeutic antibodies have become one of the most influential therapeutics in modern medicine to fight against infectious pathogens, cancer, and many other diseases. However, experimental screening for highly efficacious targeting antibodies is labor-intensive and of high cost, which is exacerbated by evolving antigen targets under selective pressure such as fast-mutating viral variants. As a proof-of-concept, we developed a machine learning-assisted antibody generation pipeline AbGen that greatly accelerates the screening and re-design of immunoglobulins G (IgGs) against a broad spectrum of SARS-CoV-2 coronavirus variant strains. Our AbGen centers around a novel antibody language model (AbLM) that is pretrained on 12 million generic protein domain sequences and fine-tuned on 4,000+ paired VH-VL sequences, with IgG-specific CDR-masking and VH-VL cross-attention. AbLM provides a latent space of IgG sequence embeddings for AbGen, including (a) landscapes of IgGs activities in neutralizing the wild-type virus are analyzed through structure prediction for IgG and IgG-antigen (viral protein spikes receptor binding domain, RBD) interactions; and (b) landscapes of IgGs susceptibility in neutralizing variant viruses are predicted through Gaussian process regression, despite that as few as 14 clinical antibodies responses to variants of concern are available. The AbGen pipeline was applied to over 1300 IgG sequences we collected from RBD-binding B cells of convalescent patients. With experimental validations, AbGen efficiently prioritized IgG candidates against a broad spectrum of viral variants (wildtype, Delta, and Omicron), preventing the infection of host cells in vitro and hACE2 transgenic mice in vivo. Compared to other existing protein language models that require 10-100 times more model parameters, AbLM improved the precision from around 50% to 75% to predict IgGs with low variant susceptibility. Furthermore, AbGen enables structure-based computational protein redesign for selected IgG clones with single amino acid substitutions at the RBD-binding interface that doubled the IgG blockade efficacy for one of the severe, therapy-resistant strains - Delta (B.1.617). Our work expedites applications of artificial intelligence in antibody screen and re- design combining data-driven protein language models and Kriging for antibody sequence analysis and activity prediction, in synergy with physics-driven protein docking and design for antibody-antigen interface analyses and functional optimization.

immunology↗