Biology subjects

Chen, E. L. Y.

Publications and source records attributed to Chen, E. L. Y..

4 recordsLinked to original sources

bioRxiv2025

Integrated spatial proteomics of human PDAC uncovers an expanded tumour-immune-stroma spectrum with genomic associations

Distinctively, pancreatic ductal adenocarcinoma (PDAC) consists of sparse tumour lesions intertwined with extensive desmoplastic stroma. The complexity of tumour-microenvironment interactions within this desmoplasia poses a challenge for accurate tumour profiling and patient stratification, and characterizes a profoundly chemoresistant tumour. Here we mapped the spatial relationships between tumour, stroma, and immune cell compartments delineating tumour and microenvironment types that expand the classical to basal spectrum of human PDAC. We used imaging mass cytometry to profile the in situ multi-cellular organization of 81 cell types in resected cases with paired whole genome sequencing. Cell types, functions, and pathway activation were distributed as highly reproducible environments in discrete locations throughout these tumours, which we deep-profiled using laser-capture mass spectrometry. We show that the connections between tumour phenotypes, vascularization, immune response, and stromal biophysical state are reinforced by genomic aberrations, altered by treatment, and associated with patient outcome. Predictive machine-learning models showed that spatial single cell data outperformed genomic or clinical features but integrated multi-omics models provide the best prediction of patient survival with compressed models requiring only 10 non-redundant robust molecular measures associated with the phenotypic spectrum of PDAC. Together, these findings define a phenotypic and molecular framework of PDAC that captures tumour-microenvironment co-dependencies and offers a refined basis for patient stratification and therapeutic targeting.

cancer biology↗

bioRxiv2024

Segmentation error aware clustering for highly multiplexed imaging

Spatial protein expression technologies can map cellular content and organization by simultaneously quantifying the expression of >40 proteins at subcellular resolution within intact tissue sections and cell lines. However, necessary image segmentation to single cells is challenging and error prone, easily confounding the interpretation of cellular phenotypes and cell clusters. To address these limitations, we present STARLING, a novel probabilistic machine learning model designed to quantify cell populations from spatial protein expression data while accounting for segmentation errors. To evaluate performance we developed a comprehensive benchmarking workflow by generating highly multiplexed imaging data of cell line pellet standards with controlled cell content and marker expression and additionally established a novel score to quantify the biological plausibility of discovered cellular phenotypes on patient derived tissue sections. Moreover, we generate spatial expression data of the human tonsil - a densely packed tissue prone to segmentation errors - and demonstrate cellular states captured by STARLING identify known cell types not visible with other methods and enable quantification of intra- and inter- individual heterogeneity. STARLING is available at https://github.com/camlab-bioml/starling.

bioinformatics↗

bioRxiv2022

In vivo CRISPR screens reveal Serpinb9 and Adam2 as regulators of immune therapy response in lung cancer

How the genetic landscape of a tumor governs the tumors response to immunotherapy remains largely elusive. Here, we established a direct in vivo CRISPR/Cas9 gene editing methodology to assess the immune-modulatory capabilities of 573 putative cancer genes associated with altered cytotoxic activity in human cancers. Using KrasG12D- and BrafV600E-driven mouse lung cancer models, we identify Serpinb9 and Adam2 as our top immune suppressive and immune enhancing genes, respectively. Mechanistically, we show that Serpinb9 ablation in KrasG12D- and BrafV600E-mutant lung tumor cells greatly enhances the efficacy of cytotoxic T-cells in vitro and in vivo. ADAM2 is a cancer testis antigen broadly expressed in human cancers such as lung adenocarcinoma (13.9%), renal (74.7%), prostate (72.4%), uterine (28.6%) and invasive breast (9.5%) cancer. In our mouse models, we show that Adam2 expression is induced in KrasG12D- but not BrafV600E-driven murine lung tumors and that its expression is further enhanced by immunotherapy. We show that loss of Adam2 significantly decreases KrasG12D-lung tumor burden but blocks the efficacy of cytotoxic T-cells. Consistently, Adam2 overexpression dramatically increases tumor growth and enhances immunotherapy efficacy. Mechanistically, we find that Adam2s oncogenic function depends on modulating the tumor immune microenvironment by restraining productive type I and type II interferon responses as well as cytokine signaling, reducing the presentation of tumor-associated antigen, and modulating surface expression of several immunoregulatory receptors within Kras-driven lung tumors. Adam2 expression also leads to reduced levels of immune checkpoint inhibitors such as Pd-l1, Lag3, Tigit and Tim3. This reduced exhaustion within the tumor microenvironment may explain why ex vivo expanded and adoptively transferred cytotoxic T-cells show enhanced cytotoxic efficacy against Adam2 overexpressing lung tumors. Together, our study highlights the power of integrating cancer genomic with in vivo CRISPR/Cas9 screens to uncover how cancer-associated genetic alterations control responses to immunotherapies.

cancer biology↗

bioRxiv2019

NOTCH1 signaling establishes the medullary thymic epithelial cell progenitor pool during mouse fetal development

The cortical and medullary thymic epithelial cell (cTEC and mTEC) lineages are essential for inducing T cell lineage commitment, T cell positive selection and the establishment of self-tolerance, but the mechanisms controlling their fetal specification and differentiation are poorly understood. Here, we show that Notch signaling is required to specify and expand the mTEC lineage. Notch1 is expressed by and active in TEC progenitors. Deletion of Notch1 in TECs resulted in depletion of mTEC progenitors and dramatic reductions in mTECs during fetal stages, consistent with defects in mTEC specification and progenitor expansion. Conversely, forced Notch signaling in all TEC resulted in widespread expression of mTEC progenitor markers and profound defects in TEC differentiation. In addition, lineage-tracing analysis indicated that all mTECs have a history of receiving a Notch signal, consistent with Notch signaling occurring in mTEC progenitors. Interestingly, this lineage analysis also showed that cTECs are divided between Notch lineage-positive and lineage-negative populations, identifying a previously unknown complexity in the cTEC lineage.

developmental biology↗