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Egloff, A. M.

Publications and source records attributed to Egloff, A. M..

4 recordsLinked to original sources

High-avidity TCR signaling induces a distinct KLR-positive exhaustion state in human tumor-infiltrating CD8 T cells associated with immunotherapy response

T cells adopt diverse states in human disease, with tumor-specific CD8 T cells serving as the quintessential example. These cells are critical for immunotherapy response, yet acquire hypofunctional states of exhaustion that impair treatment efficacy. While significant heterogeneity is observed among exhausted CD8 T cells (TEX), the mechanisms directing their unique phenotypes remain unresolved, limiting our efforts to augment TEX cell function. Here, we profile TEX cells in human head and neck squamous cell carcinoma (HNSCC) tumors and define a major mechanism regulating their cell state. With single-cell RNA and T cell receptor (TCR) sequencing of 106,667 tumor-infiltrating CD8 T cells, we define and validate three fundamental TEX subsets, each harboring dominant clonotypes: a progenitor subset (TPEX) and two intra-tumoral populations marked by intermediate (TEX-int) or high expression of inhibitory receptors, including killer cell lectin-like receptors, such as KLRC1/NKG2A (TEX-KLR). Using an in vitro model, we show that TCR avidity-dependent NFAT signaling governs an axis between the TEX-int and TEX-KLR states across human cancers, with high-avidity signaling driving the TEX-KLR state. Additionally, we uncover that interleukin-12 signaling selectively antagonizes high-avidity TEX-KLR-specific genes, despite broadly potentiating activation and cytotoxicity. Finally, we find that TEX-int and TEX-KLR cells are associated with anti-PD-1 response in HNSCC, with both of these cell types enriched near viable cancer cells following treatment. Together, these findings reveal that TCR avidity orchestrates tumor-infiltrating TEX cell states through NFAT signaling, a tunable axis that may be targeted to improve TEX cell function.

immunology↗

Cancer vaccine attenuates carcinogen induced head and neck cancer with impaired early T cell response

Effective T cell immunotherapy requires understanding antigen-specific T cell development during tumorigenesis and immune surveillance. Here, we aimed to examine the dynamics of antigen-specific T cells from tumor initiation through progression in a tobacco carcinogen mimetic, 4-nitroquinoline-1-oxide (4NQO)-induced head and neck carcinogenesis model utilizing genetically engineered K5CreERT/+/ROSAOVA-GFP/p53fl/fl (KOG) mice. Our findings showed that early ovalbumin (OVA) expression via direct lingual tamoxifen (T) did not impact cancer development and survival, by comparing mice with tongue epithelium expressing OVA (KOG/T/OVA+) to those without OVA (KOG/T/OVA-) controlled by doxycycline. This equivalent tumor growth cannot be attributed to the loss of OVA expression. Intriguingly, although OVA-specific T cells were initially generated in tumor-draining lymph nodes (TDLN), they became undetectable 3 weeks after tamoxifen injection. Moreover, therapeutic anti-PD-1 was unable to restore OVA-specific T cells in TDLN and did not yield anti-tumor activity. Remarkably, OVA synthetic long peptide (SLP) vaccine induced OVA-specific T cells in KOG/T/OVA+ mice, and the combination of SLP vaccine and anti-PD-1 significantly reduced tongue tumor burden and prolonged survival. This study highlights the role of impaired endogenous antigen-specific T cell responses in immune resistance in head and neck cancer and the potential of cancer vaccines to improve outcomes.

cancer biology↗

Ultrasensitive detection of circulating LINE-1 ORF1p as a specific multi-cancer biomarker

Improved biomarkers are needed for early cancer detection, risk stratification, treatment selection, and monitoring treatment response. While proteins can be useful blood-based biomarkers, many have limited sensitivity or specificity for these applications. Long INterspersed Element-1 (LINE-1, L1) open reading frame 1 protein (ORF1p) is a transposable element protein overexpressed in carcinomas and high-risk precursors during carcinogenesis with negligible detectable expression in corresponding normal tissues, suggesting ORF1p could be a highly specific cancer biomarker. To explore the potential of ORF1p as a blood-based biomarker, we engineered ultrasensitive digital immunoassays that detect mid-attomolar (10-17 M) ORF1p concentrations in patient plasma samples across multiple cancers with high specificity. Plasma ORF1p shows promise for early detection of ovarian cancer, improves diagnostic performance in a multi-analyte panel, and provides early therapeutic response monitoring in gastric and esophageal cancers. Together, these observations nominate ORF1p as a multi-cancer biomarker with potential utility for disease detection and monitoring. Statement of SignificanceLINE-1 ORF1p transposon protein is pervasively expressed in many cancers and a highly specific biomarker of multiple common, lethal carcinomas and their high-risk precursors in tissue and blood. Ultrasensitive ORF1p assays from as little as 25 L plasma are novel, rapid, cost-effective tools in cancer detection and monitoring.

cancer biology↗

SPACE-GM: geometric deep learning of disease-associated microenvironments from multiplex spatial protein profiles.

Multiplexed immunofluorescence imaging enables high-dimensional molecular profiling at subcellular resolution. However, learning disease-relevant cellular environments from these rich imaging data is an open challenge. We developed SPAtial CEllular Graphical Modeling (SPACE-GM), a geometric deep learning framework that flexibly models tumor microenvironments (TMEs) as cellular graphs. We applied SPACE-GM to 658 head-and-neck and colorectal human cancer samples assayed with 40-plex immunofluorescence imaging to identify spatial motifs associated with cancer recurrence and patient survival after immunotherapy. SPACE-GM is substantially more accurate in predicting patient outcomes than previous approaches for modeling spatial data using neighborhood cell-type compositions. Computational interpretation of the disease-relevant microenvironments identified by SPACE-GM generates insights into the effect of spatial dispersion of tumor cells and granulocytes on patient prognosis.

bioinformatics↗