Dr. McGinnis is a cancer immunologist studying cellular and molecular mechanisms of tumor-microenvironment interactions during metastasis. Dr. McGinnis’ approach is fundamentally interdisciplinary, functioning at the interface of cancer immunology, metastasis biology, and genomics. His long-term goal is to improve our understanding of how tumors co-opt distant organ sites during metastasis and translate these basic biological insights into anti-metastatic therapies.

Dr. McGinnis completed his B.A. in Molecular Biology & Biochemistry at Wesleyan University in 2014. In 2016, he joined Dr. Zev Gartner’s lab at UC San Francisco as a Ph.D. student, where he developed computational and molecular tools for single-cell genomics analysis including MULTI-seq and DoubletFinder. In 2021, Dr. McGinnis started a postdoctoral fellowship at Stanford University in the lab of Dr. Ansu Satpathy, where he profiled the temporal patterns of lung metastatic niche co-option in breast cancer and developed a single-cell chemical transcriptomics platform for interrogating microenvironmental responses to drug perturbations in the lung metastatic niche. Dr. McGinnis’ research has been recognized with honors including the NCI K99/R00 Pathway to Independence Award and the METAVivor Early Career Investigator Award.

The McGinnis Laboratory

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Decades of cancer biology have led to the discovery of ‘cancer genes’ (e.g., oncogenes and tumor suppressors) and, in turn, rationally-designed chemotherapies. In contrast, efforts to find ‘metastasis genes’ have largely failed, leading to a complete lack of clinically-approved therapies for limiting metastatic progression. This failure calls for new conceptual and experimental approaches to develop anti-metastatic therapies by focusing on the metastatic microenvironment.

Diverse interactions between primary tumor ‘seeds’ and immune/stromal cells in the microenvironmental ‘soil’ are necessary for metastatic progression and could be targeted by anti-metastatic therapies. However, two barriers exist that make achieving this goal a challenge. First, how pro-metastatic niche remodeling mechanisms manifest over time and differ between tumor types and metastatic sites remains poorly understood. Second, traditional drug screening platforms are ill-suited to discover anti-metastatic therapies because they leverage cell systems and read-outs that fail to model perturbation responses in the metastatic niche.

The McGinnis Lab works to address these two barriers by using longitudinal and perturbational single-cell genomics to discover how the metastatic niche is co-opted during disease progression and target pro-metastatic immune/stromal remodeling mechanisms to develop anti-metastatic therapies.

We previously performed a longitudinal single-cell genomics analysis of the lung immune microenvironment before, during, and after breast cancer metastasis, revealing previously unreported immunological changes such as elevated myeloid TLR-NFkB inflammation in the pre-metastatic niche, increased NK cytotoxicity in metastasis-bearing lungs, and cell-type-specific differential regulation of CCL6 signaling. However, learning the design principles of pro-metastatic niche co-option will require zooming out from this single tissue (lung), cell lineage (immune), and disease context (aggressive breast cancer). Thus, we build organism-level temporal maps of metastatic progression (primary tumor, metastatic site, peripheral immune system, and lymphoid organs) spanning diverse tumor models (aggressive and dormant) and metastatic sites (lung, liver, bone, and brain) using multiplexed single-cell genomics with tumor/immune lineage-tracing approaches. We are interested in the following questions:

(1) What changes in the immune/stromal niche precede the formation of (dormant) micrometastases and macrometastatic outgrowth?
(2) How do metastasis-associated niche remodeling signatures compare between different metastatic sites and tumor models?
(3) Across primary tumors with overlapping tissue tropism (e.g., lung tropism for breast cancer, osteosarcoma, and melanoma), are different ‘trajectories’ of niche remodeling associated with metastatic progression? Or is pro-metastatic remodeling agnostic of primary tumor source?

Traditional high-throughput screening (HTS) platforms use simple read-outs (e.g., cell proliferation) and contrived in vitro systems (e.g., tumor cell lines) that fail to adequately capture how complex biological systems respond to chemical perturbation. To address this limitation, we couple our MULTI-seq sample multiplexing technology with ex vivo tissue slice culture systems to perform single-cell genomics-coupled HTS experiments, focusing on the lung metastatic niche. By measuring transcriptome-wide single-cell perturbation responses in systems that preserve the cellular composition of the in vivo lung niche, we aim to address the following questions:

(1) What chemical compounds optimally target metastasis-associated immune and stromal signaling pathways without inducing undesirable off-target effects?
(2) How do standard-of-care chemotherapy regimens influence the metastatic niche and potentially promote disease progression?
(3) Can we use machine learning approaches to leverage our database of genomic drug ‘fingerprints’ to predict the effects of untested drugs and drug combinations in the metastatic microenvironment?

Schiffner’s research focuses on developing advanced vaccines that induce broadly neutralizing antibodies against highly diverse pathogens, combining AI/machine-learning-assisted computational protein design with deep analysis of antibody responses in vivo to iteratively optimize next-generation immunogens.

Schiffner earned an undergraduate degree in Molecular Life Sciences from the University of Hamburg, Germany, and a Master’s degree in Molecular Biology and Pathology of Viruses from Imperial College London. He completed a Doctor of Philosophy at the University of Oxford, where he investigated strategies to redirect antibody responses against HIV-1. At The Scripps Research Institute in California, Schiffner completed postdoctoral training in computational immunogen design and germline-targeting HIV vaccine development.

In 2020, Schiffner was awarded the Sofja Kovalevskaja Award by the Alexander von Humboldt Foundation, one of Germany’s most prestigious early-career investigator awards, and was recruited as junior faculty to the Institute for Drug Discovery, Leipzig University Medical School, where he began applying artificial intelligence to vaccine design.

He returned to Scripps Research in 2023 as an institute investigator, where he established his independent research group and secured major awards from ARPA-H and the NIH to advance AI-driven immunogen design against HIV, alphaviruses, and influenza. 

The Schiffner Laboratory

Despite decades of effort, traditional vaccine development has not yielded broadly protective vaccines against highly variable pathogens such as HIV-1, influenza viruses, hepatitis C virus, and alphaviruses. The key to protection against such diverse viruses is the induction of broadly neutralizing antibodies (bnAbs), rare antibodies that recognize conserved sites of vulnerability shared across viral strains.

The Schiffner Laboratory designs next-generation vaccine immunogens that selectively engage and mature the rare B cells that give rise to bnAbs, with the goal of producing vaccines that protect against entire families of antigenically diverse viruses.

To accomplish this, the lab integrates emerging deep-learning technologies with traditional physics-based computational design methods. By coupling these computational approaches with high-throughput experimental pipelines, the lab generates the data needed to train new neural networks for predicting protein–protein interactions and improving immunogen design.

Building on strategies the team has helped develop, including germline targeting, epitope-scaffold design, glycan masking, and pre-fusion stabilization, the Schiffner Lab applies these tools to the design of vaccine candidates against HIV-1, alphaviruses, influenza, and other emerging viral threats. The lab works in close collaboration with experts in structural biology, and clinical testing to rapidly translate computationally designed immunogens into validated vaccine candidates.

A central focus of the Schiffner Lab is the development of computational tools that accelerate and improve the design of vaccine immunogens. While modern deep-learning methods have transformed protein design, no current algorithm can reliably predict protein–protein interaction affinities — a key requirement for designing immunogens that selectively engage rare antibody precursors. The lab is generating high-throughput affinity datasets using yeast-display technologies and using these data to train new neural networks, including message-passing neural networks (MPNNs) and fine-tuned protein language models, that combine sequence and structural information. By integrating these emerging methods with established physics-based design platforms such as Rosetta, the lab aims to substantially increase the success rate of computational immunogen design.

The Schiffner Lab continues a long-standing program in HIV-1 vaccine development built around germline targeting, the strategy of designing immunogens that selectively activate the unmutated B-cell precursors of broadly neutralizing antibodies. Schiffner led the development of 10E8-GT12 24mer, a germline-targeting epitope-scaffold immunogen that activates precursors of the HIV-1 bnAb 10E8 in preclinical models and engages corresponding precursors in human blood (Schiffner et al., Nat Immunol, 2024; Ray et al., Nat Immunol, 2024). Building on these advances, the lab is developing additional epitope scaffolds and heterologous boosting immunogens needed to shepherd antibody affinity maturation toward broad neutralization. Recent advances in the lab’s computational pipeline have achieved >50% success rates in de novo epitope-scaffold design, enabling the rapid generation of multiple diverse immunogens for the same antibody lineage.

As part of the ARPA-H–funded “Protect against Emergent Alphaviruses through Computation” (PEAC) consortium, a multi-institutional collaboration combining 14 principal investigators from seven institutions, the Schiffner Lab strives to design AI/ML-based vaccines that broadly protect against diverse alphaviruses. Unlike HIV-1 bnAbs, which are highly mutated, broadly neutralizing antibodies against alphaviruses carry relatively few somatic mutations, opening the possibility of priming and boosting bnAbs with a single vaccine formulation. The lab is developing combined prime-boost cocktails and heterologous prime-boost nanoparticle platforms tailored to lightly mutated bnAb lineages.

Through an NIH U01 award, the Schiffner Lab is applying its AI-enabled immunogen design pipeline to influenza A virus, with the goal of developing immunogens that engage broadly neutralizing antibody precursors against conserved epitopes on hemagglutinin. The lab is also extending its work on pre-fusion stabilization, glycan masking, and germline targeting to additional pathogens including hepatitis C virus and emerging coronaviruses.