Journal: Cancer Medicine

Article Title: CXCL16 Producing Tumor Clones Are Shaping Immunosuppressive Microenvironment in Squamous Cell Carcinoma via CXCR6 Regulatory T Cell

doi: 10.1002/cam4.71060

Figure Lengend Snippet: Digital spatial profiling reveals transcriptional programs and tumor–immune interactions in SCC. (A) Example of a representative SCC sample stained with H&E. Inset shows a higher magnification of a selected ROI. (B) Representative GeoMx spatial transcriptomics images from squamous cell carcinoma (SCC), and pemphigus vulgaris (PV) skin lesions. In SCC samples ( n = 2), regions of interest (ROIs) were selected to capture PanCK + tumor areas in close proximity to immune cell infiltrates. ROIs were segmented into SCC_Tumor and SCC_TME based on PanCK expression. In PV and PSO lesions, epithelial and immune compartments were delineated using CD45/CD31 and PanCK markers. (C) UMAP plot displaying spatial transcriptomic profiles from SCC ( n= 8 tumor, n = 8 TME), and PV ( n = 10 epithelial, n = 9 immune) regions. Each point represents an individual area of interest (AOI), color‐coded by lesion type and tissue compartment. (D) Boxplots illustrating genes associated with SCC progression and previously identified in the Carcinoma 3 cluster, the majority of which were significantly upregulated in SCC_Tumor compared to PV and PSO epithelial regions. (E) Heatmap showing paired Spearman correlation analysis of ligand–receptor gene pairs in SCC. Each row represents a ligand expressed in SCC_Tumor regions, and each column represents its corresponding receptor in SCC_TME regions. Ligand–receptor pairs were pre‐selected based on top‐ranked interactions predicted from Carcinoma 3 in Figure , and only those with Spearman correlation coefficient ≥ 0 are shown. Cell color represents the strength of correlation, and values within each cell indicate the associated p value. The red‐highlighted ligand–receptor pairs were previously identified as key components of the Carcinoma 3—Treg interaction network. (F) Scatter plots showing the expression of selected ligands (CXCL16, TNFSF9) and their corresponding receptors (CXCR6, TNFRSF9) in SCC and PV samples. Ligands were measured in SCC_Tumor and PV epithelial regions, while receptors were measured in SCC_TME and PV immune regions. Among these, CXCL16 and TNFRSF9 showed significant upregulation in SCC samples. p < 0.05 suggested significant differences. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001 and ns, not significant.

Article Snippet: Spatial transcriptomics was performed using the GeoMx DSP platform of the whole‐transcriptome atlas (NanoString Technologies, Seattle, WA, USA).

Techniques: Staining, Expressing

Journal: bioRxiv

Article Title: Integrating 12 Spatial and Single Cell Technologies to Characterise Tumour Neighbourhoods and Cellular Interactions in three Skin Cancer Types

doi: 10.1101/2025.07.25.666708

Figure Lengend Snippet: (a) Simplified cross-section of the human epidermis, highlighting squamous cells, melanocytes and basal cells. Coloured regions represent cSCC (green), which originates from squamous cells, melanoma (orange), which originates from melanocytes, and BCC (blue), which originates from basal cells. Two orange melanocytes are shown in the dermal region as occurs in invasive melanoma; other cells in the lower dermis layer are not depicted. (b) Overview of sample design and technologies used to generate data for this project. ROI - region of interest; FOV - field of view; S - cSCC; B - BCC; M - melanoma; HC - healthy (cancer patient); HNC - healthy (non-cancer patient donor). Technologies included are single cell RNA sequencing for fresh samples, single nuclei sequencing for formalin-fixed samples, Visium, Xenium, CosMX, GeoMX DSP for whole transcriptome, GeoMX DSP for proteins, Polaris, RNAscope, the proximal ligation assay, spatial glycomics and CODEX.

Article Snippet: Each biopsy was measured by up to 12 technologies: Chromium single-cell RNA sequencing (scRNASeq), FLEX single nuclei sequencing (snRNAseq), whole transcriptome Visium spatial transcriptomics, single-cell resolution Xenium spatial transcriptomics, NanoString CosMx Spatial Molecular Imaging (CosMx), NanoString GeoMx Digital Spatial Profiling for proteins (Immune-oncology panel, GeoMx), GeoMx cancer transcriptome atlas (GeoMX CTA), Opal Multiplex Polaris protein assay, RNAScope RNA in situ hybridisation, Proximal Ligation Assay (PLA), MALDI-TOF spatial glycomics, and CODEX spatial proteomics ( ).

Techniques: RNA Sequencing, Sequencing, RNAscope, Ligation

Journal: bioRxiv

Article Title: Integrating 12 Spatial and Single Cell Technologies to Characterise Tumour Neighbourhoods and Cellular Interactions in three Skin Cancer Types

doi: 10.1101/2025.07.25.666708

Figure Lengend Snippet: (a) Dot plot showing the percentage of each Level 2 cell type within patient samples. Dots are coloured by cell type category and dot size indicates their percentage within each sample; all columns sum to 100. Results of differential abundance statistical tests are shown to the right, comparing abundance in cSCC vs melanoma, cSCC vs healthy skin, and melanoma vs healthy skin. Asterisks indicate the sample in which the cell type was found to be more abundant, either healthy skin (pink), cSCC-BCC (blue) or melanoma (yellow). (b) A venn diagram of the top significant upregulated genes across cancerous and non-cancerous KCs and melanocytes. (red) Upregulated in cSCC/BCC KC Cancer cells compared to Malignant Melanocytes from melanoma samples, (green) Upregulated in Malignant Melanocytes from melanoma samples compared to cSCC/BCC KC Cancer cells, (yellow) Upregulated in Malignant melanocytes compared to other melanocytes in melanoma samples, (blue) Upregulated in cancer KCs compared to other KCs in cSCC/BCC sample. (c) Heatmaps showing top 50 differentially expressed genes across Cancer vs Normal KCs (top left), Melanocytes vs Melanoma (bottom). Each column of the heatmap indicates a pseudo-bulked pool. (d) Integrative, multiple platform analysis of differentially expressed genes. From left to right, the Venn diagram shows the overlap between DE genes between cSCC cancer KCs vs normal KCs across scRNAseq and for KCs in cancerous tissues compared to those from the normal tissues from non-cancer donors with spatial datasets of Visium, Xenium and CosMX. e) UMAP plot for scRNAseq data showing the expression of SOX2 in cancer vs non-cancer samples, which matches the location of KC cancer cells in UMAP shown in . f) Tissue gene expression plot of CosMX data showing two of the five shared markers SOX2 and LAMP3. Pathological annotation of the region is shown on the left.

Article Snippet: Each biopsy was measured by up to 12 technologies: Chromium single-cell RNA sequencing (scRNASeq), FLEX single nuclei sequencing (snRNAseq), whole transcriptome Visium spatial transcriptomics, single-cell resolution Xenium spatial transcriptomics, NanoString CosMx Spatial Molecular Imaging (CosMx), NanoString GeoMx Digital Spatial Profiling for proteins (Immune-oncology panel, GeoMx), GeoMx cancer transcriptome atlas (GeoMX CTA), Opal Multiplex Polaris protein assay, RNAScope RNA in situ hybridisation, Proximal Ligation Assay (PLA), MALDI-TOF spatial glycomics, and CODEX spatial proteomics ( ).

Techniques: Expressing, Gene Expression

Journal: bioRxiv

Article Title: Integrating 12 Spatial and Single Cell Technologies to Characterise Tumour Neighbourhoods and Cellular Interactions in three Skin Cancer Types

doi: 10.1101/2025.07.25.666708

Figure Lengend Snippet: (a) Cross-modality comparison of the ten communities identified for each of Visium, CosMx and Xenium. The 4 colored bars represent super-communities (or meta-communities), which group the 10 finer communities based on their dominant cell type composition. Each row shows a community identified from one of the three spatial platforms. The left heatmap shows similarity across communities within and between technologies, measured by pairwise Pearson correlation values between communities based on their cell type composition. This allows similar communities across technology platforms and samples to be grouped to form meta-communities. The right heatmap shows the cellular makeup of each community (i.e. proportion of each cell type per community), providing information to label the groups of communities. The central annotation shows the broad classification of communities into immune, KC, stromal or tumour-related communities, based on the cellular makeup of each. (b) Spatial localisation of cells belonging to communities CosMx_6 (left) and Xenium_2 and Xenium_7 (right). Together with Visium_2, these communities form a meta-community that is enriched for melanocytes. (c) Inter-community communication within melanoma CosMx_6. The chord plot visualises cell-cell communication mediated by Collagen signaling pathways, using the CellChat pathway database. Lines connect communicating cell types; line thickness represents greater communication between cell pairs. (d) Ligand-receptor interactions between pairs of cell types within the melanoma community CosMx_6. Top significant L-R pairs and corresponding cell type pairs are shown. (e) Cell type co-occurrence in CosMx samples between melanocytes and either other melanocytes (brown), Treg cells (blue), fibroblasts (green) or other cells (black). Each line plots the co-occurrence score (y-axis) between melanocytes and the test cell type calculated over increasing spatial distances (x-axis). The samples from left to right are melanoma 23346-105P, 30037-07BR and 6475-07FC. (f-g) Cell type proportions of communities identified in Xenium (f) and CODEX (g) for adjacent sections from the same sample (48974-2B). The melanoma community in both datasets is enriched with melanocytes. (h) Joint pathway analysis using upregulated genes or proteins of the melanocyte communities in Xenium and CODEX data (shown in f and g), and highly expressed glycans of the melanocyte community in MALDI data (shown in Fig S12a). The proteins, genes, metabolites are mapped to KEGG metabolic pathways. The X-axis shows the number of genes/proteins from Xenium and CODEX data found in the pathway, while the Y-axis shows glycans in the same pathway.

Article Snippet: Each biopsy was measured by up to 12 technologies: Chromium single-cell RNA sequencing (scRNASeq), FLEX single nuclei sequencing (snRNAseq), whole transcriptome Visium spatial transcriptomics, single-cell resolution Xenium spatial transcriptomics, NanoString CosMx Spatial Molecular Imaging (CosMx), NanoString GeoMx Digital Spatial Profiling for proteins (Immune-oncology panel, GeoMx), GeoMx cancer transcriptome atlas (GeoMX CTA), Opal Multiplex Polaris protein assay, RNAScope RNA in situ hybridisation, Proximal Ligation Assay (PLA), MALDI-TOF spatial glycomics, and CODEX spatial proteomics ( ).

Techniques: Comparison, Protein-Protein interactions

Journal: bioRxiv

Article Title: Integrating 12 Spatial and Single Cell Technologies to Characterise Tumour Neighbourhoods and Cellular Interactions in three Skin Cancer Types

doi: 10.1101/2025.07.25.666708

Figure Lengend Snippet: (a) Exemplar spatial plots showing the LR score for IL34_CSF1R from patient 48974. The black box indicates a region highlighted below the main image. Here, zoomed-in boxes show the IL34_CSF1R LR score (left) and IL34 (middle) and CSF1R (right) gene expression for the same tissue region. (b) Melanoma high resolution spatial transcriptomics samples from STOmics and Curio-Seeker shows cells expressing IL34 and CSF1R. (c-d) Heatmaps indicating grouped GO terms and associated genes that are enriched in IL34_CSF1R-positive spots in melanoma samples compared to IL34_CSF1R-negative spots. GO term groups were calculated by k-means clustering (k = 3) of GO semantic similarity scores; two such groups are shown here. The full heatmap is shown in Fig S9b . (e) Proximal ligation assay (PLA) for validating CD44 interactions in melanoma (top). A merged image of signal for the ligand and the receptor and a zoom-in window highlighting the interaction on the cell membrane. A positive PLA signal is visible if two interacting proteins are in a proximity less than 20 nm. The bottom panels show signals for positive (E-Cadherin-b-Catenin) and negative (CD31-AQP1) controls.

Article Snippet: Each biopsy was measured by up to 12 technologies: Chromium single-cell RNA sequencing (scRNASeq), FLEX single nuclei sequencing (snRNAseq), whole transcriptome Visium spatial transcriptomics, single-cell resolution Xenium spatial transcriptomics, NanoString CosMx Spatial Molecular Imaging (CosMx), NanoString GeoMx Digital Spatial Profiling for proteins (Immune-oncology panel, GeoMx), GeoMx cancer transcriptome atlas (GeoMX CTA), Opal Multiplex Polaris protein assay, RNAScope RNA in situ hybridisation, Proximal Ligation Assay (PLA), MALDI-TOF spatial glycomics, and CODEX spatial proteomics ( ).

Techniques: Gene Expression, Expressing, Ligation, Membrane

Journal: bioRxiv

Article Title: Integrating 12 Spatial and Single Cell Technologies to Characterise Tumour Neighbourhoods and Cellular Interactions in three Skin Cancer Types

doi: 10.1101/2025.07.25.666708

Figure Lengend Snippet: (a) Heatmap of LR scores for LR pairs enriched per cancer type, with a consistent trend across samples and the two Visium and CosMx platforms. Differentially expressed LR pairs were calculated comparing each cancer type vs the others using a pseudobulked LR scores with 3 pools per sample. Each heatmap row is a distinct CosMx or Visium sample. The two L-R pairs specific for melanoma IL34-CSF1R and FGF2-CD44 were used for experimental validations. (b) Differential interaction analysis based on LR pairs and cell type pairs. The Venn diagram compares differential LR pair results between Melanoma and the combined BCC + cSCC datasets, calculated using edgeR with pseudobulked LR scores. The diagram highlights consistent and unique results between CosMX and Visium, where Up indicates a higher LR score in Melanoma and Down indicates a lower score in BCC + cSCC. Cell-to-cell communication between the LR pairs that are up- and downregulated in melanoma in both CosMx and Visium is shown in the two Network plots flanking the Venn diagram. In both Network plots, the purple arrows show pairs of cell types that have interactions higher in Melanoma and green arrows show interactions between cell type pairs more in the BCC + cSCC than in Melanoma. The number displayed for each arrow shows the integrated p-value across all biological replicates (the thicker arrows indicate more interactions). Interactions between the two cell types can still be significantly upregulated in melanoma even if the set of LR pairs were downregulated. (c) Spatial mapping of cancer type-enriched LR pairs in CosMx data. One of the LR pairs that was significantly different between cancer types across technologies in Panel a, namely IL34-CSF1R (higher in melanoma) is shown. It is visualised in FOVs from melanoma sample (top) and BCC sample (bottom). For each cancer type, the cell type annotation of the FOV is shown (top left) with orange and black boxes indicating the highlighted regions (top right). Magnified boxes (top right) show the presence of the ligand (pink) and receptor (red), with white arrows showing the connections between ligands and receptors of nearby cells. An overview of interactions at tissue level is shown by large coloured arrows, representing cumulative interactions between two cell types in the tissue, with the location of the arrow root as the centroid coordinate of all cells in one cell type (bottom left). (d) Melanoma drug target graph integrating multiple biological and pharmacological knowledge types. Nodes represent genes, drugs, and biological functions. Level 1 connections show melanoma-associated genes and drugs targeting melanoma. Level 2 links display drugs targeting the melanoma-associated genes from Level 1 and a broader gene set targeted by drugs in the network. All genes in the graph are either upregulated or have high ligand-receptor scores. Clusters 1, 2, and 3 are pathways enriched with genes shown in the graph.

Article Snippet: Each biopsy was measured by up to 12 technologies: Chromium single-cell RNA sequencing (scRNASeq), FLEX single nuclei sequencing (snRNAseq), whole transcriptome Visium spatial transcriptomics, single-cell resolution Xenium spatial transcriptomics, NanoString CosMx Spatial Molecular Imaging (CosMx), NanoString GeoMx Digital Spatial Profiling for proteins (Immune-oncology panel, GeoMx), GeoMx cancer transcriptome atlas (GeoMX CTA), Opal Multiplex Polaris protein assay, RNAScope RNA in situ hybridisation, Proximal Ligation Assay (PLA), MALDI-TOF spatial glycomics, and CODEX spatial proteomics ( ).

Techniques:

Journal: bioRxiv

Article Title: Tissue transcriptomics of endomyocardial biopsies reveals widespread molecular perturbations independent of leukocyte-rich foci in human myocarditis

doi: 10.1101/2025.07.11.664335

Figure Lengend Snippet: (A) Proportional Venn diagram showing the overlap of DEGs between Visium (FindAllMarkers) and GeoMx datasets (all compartments). Fourteen DEGs were consistently differentially regulated in myocarditis relative to controls across both platforms. Corresponding fold changes for these overlapping genes are shown in the heatmap below. (B) Proportional Venn diagram comparing DEGs identified only in cardiomyocyte-stained segments (TNNI3⁺CD45⁻) and leukocyte depleted, cardiomyocyte-enriched genes (Visium), revealing ten shared DEGs between both datasets. Fold change values for these overlapping genes are shown in the heatmap. Color intensity in the heatmaps reflects the magnitude of absolute fold change values for each gene. Genes shown were filtered based on adjusted p-value of at least < 0.01 and exhibited consistent directionality of effect across platforms. Heatmap values for upregulated genes with FC higher than 4 were capped to this maximum value to aid visualization (see Supplementary Table 10 for values). (C) Chord plot illustrating inferred ligand– receptor interactions derived from differentially expressed genes in cardiomyocyte-enriched regions from both experimental techniques, focusing on overlapping antigen presentation–related genes, weighted by expression confidence. Arcs represent predicted interactions between ligands and immune receptors. Interactions were inferred using the OmniPath ligand–receptor database, and visualized using network-based filtering of curated, directional signaling interactions. Bolded genes represent overlapped genes present in OmniPath, between the two orthogonal experimental techniques.

Article Snippet: GeoMx Digital Spatial Profiling (DSP) was subsequently performed in the Spatial Cancer Research Immunobiology & Therapeutics (SCRIPT) Laboratory and the Johns Hopkins Experimental and Computational Genomics Core to validate transcriptional findings and enable spatially resolved whole-transcriptome gene expression profiling in cardiac tissue.

Techniques: Staining, Derivative Assay, Immunopeptidomics, Expressing

Journal: bioRxiv

Article Title: Tissue transcriptomics of endomyocardial biopsies reveals widespread molecular perturbations independent of leukocyte-rich foci in human myocarditis

doi: 10.1101/2025.07.11.664335

Figure Lengend Snippet: (A) Representative immunohistochemical (IHC) micrograph of endomyocardial biopsy (EMBx) tissue highlighting cardiomyocytes (TNNI3⁺, yellow), leukocytes (CD45⁺, red), and nuclei (Syto83, green). (B) Representative segmentation overlay into three compartments: cardiomyocytes (TNNI3⁺CD45⁻, yellow), leukocytes (TNNI3⁻CD45⁺, red), and non-myocytes (TNNI3⁻CD45⁻, blue), for IHC-guided transcriptomics (GeoMx DSP). (C) Volcano plot showing all DEGs between controls and myocarditis in all segments, (D) TNNI3 + CD45 - cardiomyocytes, (E) TNNI3 - CD45 + leukocytes, and (F) TNNI3 - CD45 - non-myocytes/stromal cells. DEGs were computed using Q3 normalization followed by linear mixed-effects modeling with a FC threshold > 1.5 and an adjusted p < 0.05. For non-myocyte comparisons (F), unadjusted p -values were used due to lower segment counts and limited detection sensitivity.

Article Snippet: GeoMx Digital Spatial Profiling (DSP) was subsequently performed in the Spatial Cancer Research Immunobiology & Therapeutics (SCRIPT) Laboratory and the Johns Hopkins Experimental and Computational Genomics Core to validate transcriptional findings and enable spatially resolved whole-transcriptome gene expression profiling in cardiac tissue.

Techniques: Immunohistochemical staining

Journal: bioRxiv

Article Title: CHCHD2 mutant mice display mitochondrial protein accumulation and disrupted energy metabolism

doi: 10.1101/2024.08.30.610586

Figure Lengend Snippet: (A) Transcriptomic levels of CHCHD2 and CHCHD10 in DA neurons of SND (dorsal tier of substantia nigra, orange) and SNV (ventral, blue) measured with GeoMx WTA show significant downregulation of CHCHD2 in the SNV of ILB and ePD cases. Data represent mean ± SEM. Brown-Forsythe and Welch ANOVA tests were applied for data covariates with age, sex, and post-mortem delay. *** P < 0.001. NC, normalized counts; Ct, control; ILBD, incidental Lewy body disease; ePD, PD with early Braak stage pathology; lPD, PD with late Braak stage pathology. (B) Moderate positive linear associations were revealed between expression levels of either CHCHD2 or CHCHD10 with SNCA in the SN by Spearman correlation. (C) CHCHD2 localization in control and PD SN DA neurons and (E) orthogonal views of the relative location of CHCHD2 in different staged αSyn aggregations. (D) CHCHD10 localization in control and PD SN DA neurons, and (F) orthogonal views of the relative location of CHCHD10 in different staged αSyn aggregations. Scale bars represent 50 μm in (C, D), and 20 μm in (E, F). (G) Representative images and quantification of TH (green) and either total α-synuclein (Syn1) or phosphorylated synuclein, P-syn (PS129, red) immunoreactivity in SN and VTA at 63X magnification in midbrain sections of WT, HET and HOM mice at 16 months. Syn1 and P-syn immunoreactivities increased in midbrain DA neurons of HOM mice by two-way ANOVA with Tukey’s post hoc test. Data represent mean ± SEM. * P < 0.05. N = 4 sections/mouse, 4 mice/genotype. Scale bar indicates 10 μm. (H) Representative images and quantifications of Syn1 (top) and P-syn (PS129, bottom), immunoreactivity in striatal sections of mice at 16 months. Syn1 and P-Syn areas in dorsal and ventral striatum are indicated with dotted lines. Significant increases in both Syn1 and p-Syn immunoreactivity were observed in HOM mice compared with WT by two-way ANOVA with Tukey’s post hoc test. Data represent mean ± SEM. * P < 0.05, ** P < 0.01. N = 4 sections/mouse, 4 mice/genotype. Scale bar indicates 300 μm.

Article Snippet: Sections were mounted on Fisher Scientific (12-550-15) for GeoMx Human Whole Transcriptome Atlas (WTA, nanostring) according to the manufacturer’s instruction or on Series 2 adhesive microscope slides (Trajan Scientific Medical, AU) for immunofluorescence.

Techniques: Control, Expressing

Journal: Clinical Cancer Research

Article Title: Determinants of Response to Sequential Pembrolizumab with Trastuzumab plus Platinum/5-FU in HER2-Positive Gastric Cancer: A Phase II Chemoimmunotherapy Trial

doi: 10.1158/1078-0432.CCR-24-3528

Figure Lengend Snippet: Sample collection schema and clinical outcomes in a phase II sequential chemoimmunotherapy trial. A, Sample collection schedule in a phase II sequential chemoimmunotherapy trial. B, Waterfall plot with best overall change from baseline among enrolled patients. C , Swimmer plot demonstrating response durability. D , Spider plot demonstrating response kinetics among enrolled patients. E , Patient-level response data by PD-L1 CPS. F , Relationship between the degree of PD-L1 expression and change in tumor volume among PD-L1 + patients (Spearman correlation, P < 0.05).

Article Snippet: To assess the interplay among and spatial orientation of HER2, PD-L1, and immune cell subsets during therapy, we conducted DSP (NanoString GeoMx human whole-transcriptome atlas) of primary tumors from 13 HER2 + patients (total 54 ROIs).

Techniques: Expressing

Journal: Clinical Cancer Research

Article Title: Determinants of Response to Sequential Pembrolizumab with Trastuzumab plus Platinum/5-FU in HER2-Positive Gastric Cancer: A Phase II Chemoimmunotherapy Trial

doi: 10.1158/1078-0432.CCR-24-3528

Figure Lengend Snippet: DSP highlights interplay between HER2, PD-L1, and immune cell subsets during therapy. A, Representative whole slide from patient S69 demonstrating ROI selection for DSP analysis. B, Differences in DSP-derived tumoral CD274 expression among HER2 + and HER2 − cells in patient determined to be PD-L1 + or PD-L1 − by IHC testing (gold standard). C, Changes in CD274 expression in HER2 + tumor cells over time in patient determined to be PD-L1 + or PD-L1 − by IHC testing (gold standard). D, Changes in CD274 expression in CD45 + immune cells over time in patient determined to be PD-L1 + or PD-L1 − by IHC testing (gold standard). E, CD8 TCR gene score differences in pretreatment BL and during therapy in HER2 + GC. F, Relationship between PD-L1 expression level by IHC (22C3, CPS testing) and CD8 TCR signaling gene score (GSVA) with clinical response in HER2 + GC (Spearman correlation). GC, gastric cancer.

Article Snippet: To assess the interplay among and spatial orientation of HER2, PD-L1, and immune cell subsets during therapy, we conducted DSP (NanoString GeoMx human whole-transcriptome atlas) of primary tumors from 13 HER2 + patients (total 54 ROIs).

Techniques: Selection, Derivative Assay, Expressing

Journal: Clinical Cancer Research

Article Title: Determinants of Response to Sequential Pembrolizumab with Trastuzumab plus Platinum/5-FU in HER2-Positive Gastric Cancer: A Phase II Chemoimmunotherapy Trial

doi: 10.1158/1078-0432.CCR-24-3528

Figure Lengend Snippet: Tumor-reactive T-cell subsets are expanded after trastuzumab and pembrolizumab in HER2 + GC. A, DSP-derived changes in major T-cell subsets during therapy stratified by PD-L1 status in HER2 + GC. B, DSP-derived CD8 TCR signaling is correlated with exhausted T-cell phenotype in PD-L1 + patients. C, Bulk TCR sequencing relationship between clonality and exhaustion score in HER2 + GC. D, CD8 T-cell subset proportions obtained from scRNA-seq and shown by timepoint in therapy. E, Comparison of tumor-reactive T-cell module scoring between PD-L1 + and PD-L1 − patients (Mann–Whitney U test). F, Difference in CXCL13 expression, derived from DSP, between clinical responder and nonresponder patients (Mann–Whitney U test). GC, gastric cancer.

Article Snippet: To assess the interplay among and spatial orientation of HER2, PD-L1, and immune cell subsets during therapy, we conducted DSP (NanoString GeoMx human whole-transcriptome atlas) of primary tumors from 13 HER2 + patients (total 54 ROIs).

Techniques: Derivative Assay, Sequencing, Comparison, MANN-WHITNEY, Expressing