Biology subjects

Heij, L.

Publications and source records attributed to Heij, L..

4 recordsLinked to original sources

bioRxiv2023

Single-cell immune multi-omics and repertoire analyses in pancreatic ductal adenocarcinoma reveal differential immunosuppressive mechanisms within different tumour microenvironments

Pancreatic ductal adenocarcinoma (PDAC) has an extremely poor prognosis. Understanding the multiple mechanisms by which the tumour evades immune control, and how these mechanisms may be disrupted is critical to developing targeted immunotherapies. Previous studies have shown that higher lymphocyte infiltration is associated with better survival, and here we investigated what mediates these differences. We performed a comprehensive analysis of PDAC-associated immune cells using single cell multi-omics coupled with re-analysis of public PDAC scRNA-seq datasets. We introduce novel single-cell and repertoire analyses that have uncoupled diverse roles and contributions of various immune cell populations within different tumour microenvironments (TMEs). They revealed clear distinctions in the clonal characteristics among different patient groups, provided valuable insights into the mechanisms of immune cell migration and tissue adaptation underlying these disparities. These results point to differential CD4 polarisation of intra-tumoural T cells, differential B cell differentiation, GC reactions, antigen presentation pathways, and distinct cell-cell communication between the myeloid-enriched and adaptive-enriched groups. Overall, we identified two major distinct themes for future immune intervention within PDAC patients between those with higher adaptive versus myeloid immune cell infiltration.

immunology↗

bioRxiv2023

TIGIT drives the immunosuppressive environment by downregulation of metalloproteinases MMP2 and MMP14 in perihilar cholangiocarcinoma.

BackgroundCheckpoint blockade in cholangiocarcinoma (CCA) is promising; however, little is known about the response to treatment in CCA subtypes. In this study, we investigated the spatial immune environment in combination with checkpoint expression in perihilar CCA (pCCA). Materials & MethodsThe levels of checkpoint molecules (PD-1, PD-L1, PD-L2, LAG-3, ICOS, TIGIT, TIM-3, and CTLA-4), macrophages (CD68), and T cells (CD4 and CD8) were assessed by multiplex immunofluorescence (mIF) in 50 patients. We investigated the transcriptomic profile using the NanoString Cancer Progression Panel, and validation was performed by mIF on tissue sections from 24 patients. ResultsThe expression of checkpoint molecules TIGIT, CTLA-4, and LAG-3 alone and in combination with other checkpoint molecules was more abundant in the Central Tumor (CT) and Invasive Margin (IM) than in peritumoral tissue (PT) (CD4 and CD8 TIGIT p<0.0001 for both CD4 and CD8 CTLA-4, p<0.0001 and p < 0.001, respectively, and CD8 LAG-3 p < 0.05). MMP2 and MMP14 were differentially expressed in patients with high TIGIT expression. ConclusionThe immune environment in pCCA is characterized by the expression of multiple checkpoints, demonstrating the complexity of ICI treatment. High TIGIT expression drives an immunosuppressive environment by modulating the extracellular matrix. Future clinical trials in pCCA could consider TIGIT as a therapeutically relevant target for (combination) treatment.

cancer biology↗

bioRxiv2022

PD1+ T-cells correlate with Nerve Fiber Density as a prognostic biomarker in patients with resected perihilar cholangiocarcinoma.

Background & AimsPerihilar cholangiocarcinoma (pCCA) is a rare hepatobiliary malignancy. Nerve fiber invasion (NFI) shows cancer invading the nerve and is considered an aggressive feature. Nerve fiber density (NFD) consists of small nerve fibers without cancer invasion and is divided into high NFD (high numbers of small nerve fibers) or low NFD (low numbers of small nerve fibers). We aim to explore differences in immune cell populations and survival. MethodsWe applied multiplex immunofluorescence (mIF) on 47 pCCA surgically resected patients and investigated the immune cell composition in the tumor microenvironment (TME) of different nerve fiber phenotypes (NFI, high and low NFD). Extensive group comparison was carried out and the association with overall survival (OS) was assessed. ResultsThe NFI ROI was measured with highest CD68+ macrophage levels among 3 ROIs (NFI compared to tumor free p= 0.016 and to tumor p=0.034). Further, for NFI patients the density of co-inhibitory markers CD8+PD1+ and CD68+PD1+ were more abundant in the tumor rather than NFI ROI (p= 0.004 and p= 0.0029 respectively). Comparison between patients with NFD and NFI groups, the signals of co-expression of CD8+PD1+ as well as CD68+PD1+ were significantly higher in the high NFD group (p= 0.027 and p= 0.044, respectively). The OS for high NFD patients was 92 months median OS (95% CI:41-142), for low NFD patients 20 months ((95% CI: 4-36) and for NFI group of patients 19 months (95% CI 7-33). The OS for high NFD patients was significantly better compared to low NFD (p= 0.046) and NFI (p= 0.032). ConclusionsPD1+ T-cells correlate with high NFD as a prognostic biomarker, the biological pathway behind this needs to be investigated. Lay summaryNerve fibers play a dual role in the tumor microenvironment in pCCA. In our previous study, we showed that the presence of high numbers of small nerve fibers is associated with a better overall survival. In addition, we found that in high NFD patients PD1+ T-cells are significantly overexpressed. Therefore, we present high NFD as a promising prognostic biomarker. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC="FIGDIR/small/475344v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@e30fadorg.highwire.dtl.DTLVardef@11a4e4aorg.highwire.dtl.DTLVardef@a04d8org.highwire.dtl.DTLVardef@1c425db_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2020

The Impact of Digital Histopathology Batch Effect on Deep Learning Model Accuracy and Bias

The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital histology. Deep learning (DL) models have been trained on TCGA to predict numerous features directly from histology, including survival, gene expression patterns, and driver mutations. However, we demonstrate that these features vary substantially across tissue submitting sites in TCGA for over 3,000 patients with six cancer subtypes. Additionally, we show that histologic image differences between submitting sites can easily be identified with DL. This site detection remains possible despite commonly used color normalization and augmentation methods, and we quantify the digital image characteristics constituting this histologic batch effect. As an example, we show that patient ethnicity within the TCGA breast cancer cohort can be inferred from histology due to site-level batch effect, which must be accounted for to ensure equitable application of DL. Batch effect also leads to overoptimistic estimates of model performance, and we propose a quadratic programming method to guide validation that abrogates this bias.

bioinformatics↗