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 that greatly accelerates the screening and re-design of immunoglobulins G (IgGs) against a broad spectrum of SARS-CoV-2 coronavirus variant strains. These viruses infect human host cells via the viral spike protein binding to the host cell receptor angiotensin-converting enzyme 2 (ACE2). Using over 1300 IgG sequences derived from convalescent patient B cells that bind with spike's receptor binding domain (RBD), we first established protein structural docking models in assessing the RBD-IgG-ACE2 interaction interfaces and predicting the virus-neutralizing activity of each IgG with a confidence score. Additionally, employing Gaussian process regression (also known as Kriging) in a latent space of an antibody language model, we predicted the landscape of IgGs' activity profiles against individual coronaviral variants of concern. With functional analyses and experimental validations, we efficiently prioritized IgG candidates for neutralizing a broad spectrum of viral variants (wildtype, Delta, and Omicron) to prevent the infection of host cells in vitro and hACE2 transgenic mice in vivo. Furthermore, the computational analyses enabled rational redesigns of selective IgG clones with single amino acid substitutions at the RBD-binding interface to improve 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 screening and re-design even in low-data regimes combining protein language models and Kriging for antibody sequence analysis, activity prediction, and efficacy improvement, in synergy with physics-driven protein docking models for antibody-antigen interface structure analyses and functional optimization.

Dynamic Glycoprotein Hyposialylation Promotes Chemotherapy Evasion and Metastatic Seeding of Quiescent Circulating Tumor Cell Clusters in Breast Cancer.

Most circulating tumor cells (CTC) are detected as single cells, whereas a small proportion of CTCs in multicellular clusters with stemness properties possess 20- to 100-times higher metastatic propensity than the single cells. Here we report that CTC dynamics in both singles and clusters in response to therapies predict overall survival for breast cancer. Chemotherapy-evasive CTC clusters are relatively quiescent with a specific loss of ST6GAL1-catalyzed α2,6-sialylation in glycoproteins. Dynamic hyposialylation in CTCs or deficiency of ST6GAL1 promotes cluster formation for metastatic seeding and enables cellular quiescence to evade paclitaxel treatment in breast cancer. Glycoproteomic analysis reveals newly identified protein substrates of ST6GAL1, such as adhesion or stemness markers PODXL, ICAM1, ECE1, ALCAM1, CD97, and CD44, contributing to CTC clustering (aggregation) and metastatic seeding. As a proof of concept, neutralizing antibodies against one newly identified contributor, PODXL, inhibit CTC cluster formation and lung metastasis associated with paclitaxel treatment for triple-negative breast cancer. SIGNIFICANCE: This study discovers that dynamic loss of terminal sialylation in glycoproteins of CTC clusters contributes to the fate of cellular dormancy, advantageous evasion to chemotherapy, and enhanced metastatic seeding. It identifies PODXL as a glycoprotein substrate of ST6GAL1 and a candidate target to counter chemoevasion-associated metastasis of quiescent tumor cells. This article is featured in Selected Articles from This Issue, p. 1949.

Machine learning-assisted elucidation of CD81-CD44 interactions in promoting cancer stemness and extracellular vesicle integrity.

Tumor-initiating cells with reprogramming plasticity or stem-progenitor cell properties (stemness) are thought to be essential for cancer development and metastatic regeneration in many cancers; however, elucidation of the underlying molecular network and pathways remains demanding. Combining machine learning and experimental investigation, here we report CD81, a tetraspanin transmembrane protein known to be enriched in extracellular vesicles (EVs), as a newly identified driver of breast cancer stemness and metastasis. Using protein structure modeling and interface prediction-guided mutagenesis, we demonstrate that membrane CD81 interacts with CD44 through their extracellular regions in promoting tumor cell cluster formation and lung metastasis of triple negative breast cancer (TNBC) in human and mouse models. In-depth global and phosphoproteomic analyses of tumor cells deficient with CD81 or CD44 unveils endocytosis-related pathway alterations, leading to further identification of a quality-keeping role of CD44 and CD81 in EV secretion as well as in EV-associated stemness-promoting function. CD81 is coexpressed along with CD44 in human circulating tumor cells (CTCs) and enriched in clustered CTCs that promote cancer stemness and metastasis, supporting the clinical significance of CD81 in association with patient outcomes. Our study highlights machine learning as a powerful tool in facilitating the molecular understanding of new molecular targets in regulating stemness and metastasis of TNBC.

ICAM1 initiates CTC cluster formation and trans-endothelial migration in lung metastasis of breast cancer.

Circulating tumor cell (CTC) clusters mediate metastasis at a higher efficiency and are associated with lower overall survival in breast cancer compared to single cells. Combining single-cell RNA sequencing and protein analyses, here we report the profiles of primary tumor cells and lung metastases of triple-negative breast cancer (TNBC). ICAM1 expression increases by 200-fold in the lung metastases of three TNBC patient-derived xenografts (PDXs). Depletion of ICAM1 abrogates lung colonization of TNBC cells by inhibiting homotypic tumor cell-tumor cell cluster formation. Machine learning-based algorithms and mutagenesis analyses identify ICAM1 regions responsible for homophilic ICAM1-ICAM1 interactions, thereby directing homotypic tumor cell clustering, as well as heterotypic tumor-endothelial adhesion for trans-endothelial migration. Moreover, ICAM1 promotes metastasis by activating cellular pathways related to cell cycle and stemness. Finally, blocking ICAM1 interactions significantly inhibits CTC cluster formation, tumor cell transendothelial migration, and lung metastasis. Therefore, ICAM1 can serve as a novel therapeutic target for metastasis initiation of TNBC.

ITGA2 promotes expression of ACLY and CCND1 in enhancing breast cancer stemness and metastasis.

Cancer metastasis is largely incurable and accounts for 90% of breast cancer deaths, especially for the aggressive basal-like or triple negative breast cancer (TNBC). Combining patient database analyses and functional studies, we examined the association of integrin family members with clinical outcomes as well as their connection with previously identified microRNA regulators of metastasis, such as miR-206 that inhibits stemness and metastasis of TNBC. Here we report that the integrin receptor CD49b-encoding ITGA2, a direct target of miR-206, promotes breast cancer stemness and metastasis. ITGA2 knockdown suppressed self-renewal related mammosphere formation and pluripotency marker expression, inhibited cell cycling, compromised migration and invasion, and therefore decreased lung metastasis of breast cancer. ITGA2 overexpression reversed miR-206-caused cell cycle arrest in G1. RNA sequencing analyses revealed that ITGA2 knockdown inhibits genes related to cell cycle regulation and lipid metabolism, including CCND1 and ACLY as representative targets, respectively. Knockdown of CCND1 or ACLY inhibits mammosphere formation of breast cancer cells. Overexpression of CCND1 rescues the phenotype of ITGA2 knockdown-induced cell cycle arrest. ACLY-encoded ATP citrate lyase is essential to maintain cellular acetyl-CoA levels. CCND1 knockdown further mimics ITGA2 knockdown in abolishing lung colonization of breast cancer cells. We identified that the low levels of miR-206 as well as high expression levels of ITGA2, ACLY and CCND1 are associated with an unfavorable relapse-free survival of the patients with estrogen receptor-negative or high grade breast cancer, especially basal-like or TNBC, possibly serving as potential biomarkers of cancer stemness and therapeutic targets of breast cancer metastasis.

EGFR inhibition blocks cancer stem cell clustering and lung metastasis of triple negative breast cancer.

Triple-negative breast cancer (TNBC) is one of the most aggressive and metastatic breast cancer subtypes lacking targeted therapy. Our recent work demonstrated that circulating tumor cell (CTC) clusters and polyclonal metastasis of TNBC are driven by aggregation of CD44+ cancer stem cells (CSC) and associated with an unfavorable prognosis, such as low overall survival. However, there is no existing therapeutic that can specifically block CTC or CSC cluster formation. Methods: Using patient-derived xenograft (PDX) models, we established an ex vivo tumor cell clustering assay for a pilot screening of blockade antibodies. After identifying EGFR as a target candidate, we modulated the gene expression and inhibited its kinase activity to determine its functional importance in tumor cell clustering and therapeutic inhibition of lung metastasis. We also examined the molecular regulation network of EGFR and a potential connection to CSC marker CD44 and microRNAs, which regulate CTC clustering. Results: We report here that EGFR inhibition successfully blocks circulating CSC (cCSC) clustering and lung metastasis of TNBC. EGFR enhances CD44-mediated tumor cell aggregation and CD44 stabilizes EGFR. Importantly, blocking EGFR by a novel anti-EGFR monoclonal antibody (clone LA1) effectively blocked cell aggregation in vitro and reduced lung metastasis in vivo. Furthermore, our data demonstrated that the tumor suppressor microRNA-30c serves as another negative regulator of cCSC clustering and lung metastasis by targeting CD44 as well as its downstream effector EGFR. Conclusion: Our studies identify a novel anti-EGFR therapeutic strategy to inhibit cCSC aggregation and therefore abolish cCSC cluster-mediated metastasis of TNBC.

New Advances and Challenges of Targeting Cancer Stem Cells.

The second International Cancer Stem Cell Conference in Cleveland, Ohio, on September 20-23, 2016, convened 330 attendees from academic, industrial, and clinical organizations. It featured a debate on the concepts and challenges of the cancer stem cells (CSC) as well as CSC-centered scientific sessions on clinical trials, genetics and epigenetics, tumor microenvironment, immune suppression, metastasis, therapeutic resistance, and emerging novel concepts. The conference hosted 35 renowned speakers, 100 posters, 20 short talks, and a preconference workshop. The reported advances of CSC research and therapies fostered new collaborations across national and international borders, and inspired the next generation's young scientists. Cancer Res; 77(19); 5222-7. ©2017 AACR.