Abstract Immune infiltration shapes anti-tumor responses and is associated with patient outcomes. Yet, comprehensive studies comparing infiltration patterns and their clinical impact across cancer types are scarce.We profiled the tumor immune microenvironment across 16 solid tumor types from more than 2,700 patients using multiplex immunofluorescence with pathologist-curated image analysis to quantify major lymphoid and myeloid subsets and their spatial organization in situ.Across all cancer types, lung, endometrial, and high-grade serous ovarian cancers were highly infiltrated, whereas prostate and ER-positive breast cancers were comparatively “immune cold”. Cancer-agnostic consensus clustering resolved four reproducible immune archetypes with distinct outcomes, including an immune-hot group enriched for CD4, CD8, and B cells that associated with the best survival (p<0.001). Notably, the tumor type accounted for only a part of the immune contexture, as most cancers spanned multiple immune signature groups. Nevertheless, a machine-learning classifier trained on immune compositions distinguished tumor types with high accuracy (AUC = 0.96), confirming that each cancer maintains a recognizable immune imprint even among pan-cancer archetypes.Analysis of survival associations of individual cell classes revealed a rare CD8+FOXP3+ T-cell phenotype that emerged as a strongest positive prognostic factor across cancers (HR=0.79, 95%CI:[0.70-0.89], p=0.002). Single-cell RNA sequencing data indicated that CD8+FoxP3+ cells exhibit two distinct gene programs, with regulatory and cytotoxic capacities. Spatial mapping suggested context-dependency in the function of CD8+FoxP3+ cells, as their proximity to CD8 T cells was linked to better survival (enrichment at 10um: HR=0.76, 95%CI:[0.67-0.90], p=0.0006), whereas proximity to tumor cells was associated with worse prognosis (enrichment at 10um: HR=1.29, 95%CI:[1.12-1.49], p=0.0005).Together, our findings define pan-cancer immune archetypes that are both shared and disease-specific, explaining why “immune hot” and “cold” states are not synonymous with pure infiltration quantities. We identified CD8+FoxP3+ immune cells as a cell type with strong biomarker potential independent of cancer type. Citation Format: Artur Mezheyeuski, Emma Sandberg, Max Backman, Ali Teymur Kahraman, Amanda Lindberg, Carina Strell, Hans Brunnström, Jutta Huvila, Malin Sund, Fredrik Wärnberg, Bengt Glimelius, Ina Hrynchyk, Siarhei Mauchanski, Salome Khelashvili, Klara Hammarström, Margret Agnarsdottir, Gemma Garcia-Vicién, DAVID G. MOLLEVI, Aine O´Reilly, Sara Corvigno, Hanna Dahlstrand, Johan Botling, Ulrika Segersten, Agnieszka Krzyzanowska, Anders Bjartell, Jacob Elebro, Margareta Heby, Sebastian Lundgren, Charlotta Hedner, David Borg, Jenny Brändstedt, Hanna Sartor, Per-Uno Malmström, Martin Johansson, Anna Portyanko, Björn Nodin, Cecilia Lindskog, Karin Leandersson, Karin Jirström, Tobias Sjöblom, Patrick Micke. Tumor-agnostic analysis identified pan-cancer immune archetypes and CD8+FoxP3+ cells as novel in situ predictors of survival [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 193.

Increased stromal Platelet-derived growth factor receptor beta (PDGFRβ) expression is a hallmark of the desmoplastic tissue reaction in cancer and marks subsets of cancer-associated fibroblasts, pericytes, and smooth muscle cells. However, its functional status in situ has been anticipated from static expression measures, which cannot determine whether high receptor abundance reflects active signaling. We established two second-generation proximity ligation assays (PLAs) to quantify PDGFRβ activation in the in situ environment of human lung cancer by detecting either phosphorylated PDGFRβ or its interaction with the adaptor protein Grb2. The immunofluorescence-based assays were applied to tissue-microarrays including diagnostic samples from over 600 non-small cell lung cancer (NSCLC) patients. In lung cancer tissue, activation scores correlated with PDGFRβ expression but revealed a more nuanced receptor status, indicating variable activation despite similar expression levels. Higher PDGFRβ activation was associated with increased recurrence risk exclusively in squamous cell carcinoma, a finding not captured by conventional immunohistochemistry. This activation was accompanied by a specific stromal profile enriched for LRRC15- and FAP-positive cells, a pattern absent in adenocarcinomas. PDGFRβ activation status provides functional information beyond receptor expression, uncovering clinically relevant, otherwise overlooked, stromal phenotypes. The approach illustrates the diagnostic potential of functional protein assays in the era of precision medicine.

Background Multiplex immunofluorescence imaging enables detailed characterization of the tumor immune microenvironment, but whether immune cell densities add prognostic value beyond established clinical factors in non-small cell lung cancer (NSCLC) remains unclear. Methods Tissue samples from an NSCLC cohort (n = 298) were stained with a multiplex immunofluorescence panel targeting immune cell markers (CD4, CD8, FoxP3, CD20), cancer cells (pan-cytokeratin), and cell nuclei (DAPI). We quantified immune cell densities, nuclear pleomorphism features, and clinical variables, and trained four machine learning models (logistic regression, random forest, support vector machine, and k-nearest neighbors) to predict overall survival. Results Clinical parameters consistently demonstrated the strongest performance in predicting long and short-term survival (logistic regression mean accuracy 0.60 ± 0.01, AUC 0.66 ± 0.01). The addition of immune cell densities revealed a small, statistically significant improvement in survival prediction (accuracy 0.62 ± 0.01, p < 0.01, AUC 0.67 ± 0.01, p = 0.04), while nuclear pleomorphism features did not improve prediction. When combined with clinical parameters, immune cell densities also improved survival stratification in Cox regression analyses numerically (HR = 0.51 vs. 0.55 for clinical parameters alone). Model interpretation analyses showed that stage and performance status have the largest effect on model performance. Selected immune cell densities (tumor CD4-helper and stroma B-cells) have a limited but consistent effect. Conclusion Clinical parameters remain the dominant predictors of outcome in NSCLC, with immune cell densities providing only limited prognostic value for clinical stratification. The openly available code and datasets present a unique resource for method development or focused analysis.

Abstract Background Glucocorticoid prophylaxis is routinely used in oncology. Glucocorticoids potent immunosuppressive and anti-inflammatory properties raise concerns regarding potential impairment of antitumor immune responses. Preclinical and retrospective studies suggest glucocorticoids may impair antitumor immunity, though these findings are subject to significant bias and confounding factors. Understanding the immunomodulatory effects of prophylactic glucocorticoids, isolated from other therapies, is critical to bridging the knowledge gap on their influence on antitumor immunity. Methods We performed a post hoc, retrospective analysis using cryopreserved PBMCs and plasma from patients with treatment-naïve, early stage HER2+ breast cancer, prior to neoadjuvant therapy. Of 192 patients, 129 had no prior glucocorticoid exposure and 63 received 24 mg prophylactic betamethasone before sampling. High-dimensional flow cytometry assessed monocytes, dendritic cells, and T-cell subsets. Plasma proteins were evaluated using the Olink Target 96 Immuno-Oncology proximity extension assay. Results Glucocorticoid exposure was consistently associated with a distinct immune profile. Exposed patients exhibited increased CD163+ monocytes and reduced intermediate, non-classical, and HLA-DRhi classical monocytes, plasmacytoid DCs, and conventional CD1c+ DCs. T-cell alterations included enrichment of CXCR4+ subsets, particularly terminally differentiated CD8+ T-cells, with reductions in naïve, central memory, CD27+ effector memory, and CXCR3+ populations. Inflammatory plasma proteins (IFN-γ, CCL19, CCL2, IL-12, IL-6, Granzyme A/B, CXCL10, CXCL9) were diminished, whereas IL-10 and CXCL13 were elevated. Glucocorticoid-associated immune alterations were heterogeneous across patients, highlighting interindividual variability in observed post-exposure immune states. Conclusions Prophylactic glucocorticoid exposure reshapes the circulating immune landscape, altering monocytes, dendritic cells, naïve and memory T-cell subsets, as well as key inflammatory proteins.

Leaky blood vessels are a hallmark of solid tumors. However, the molecular mechanisms and clinical implications of vascular leakage in human cancer remain unexplored. Here, we identified fibrinopeptide-A (FpA) as a robust in-situ marker of vascular leakage, analyzing diagnostic specimens from two non-small cell lung cancer (NSCLC) cohorts (N = 327 and N = 200). Mechanistically, FpA+ staining localized to discrete stromal niches characterized by increased endothelial VEGF receptor-2 phosphorylation, elevated VEGFA production by tumor cells and loss of the endothelial tyrosine phosphatase PTPRB. Immune profiling revealed reduced density of mature dendritic cells and granzyme B-expressing cytotoxic T cells in tumors with high-leakage. This leakage-associated immunosuppression was linked to the presence of tertiary lymphoid structures (TLS) in lung adenocarcinoma (LUAD). However, compared to low-leakage, TLS in high-leakage tumors were enriched in regulatory T cells and conferred no survival advantage. Patients with high vascular leakage exhibited a reduced overall survival. Moreover, in a separate immunotherapy cohort (N = 64), high-leakage was associated with poor response to anti-PD-1/PD-L1 treatment. This study establishes vascular leakage as an important prognostic factor in NSCLC, and provides mechanistic understanding and a methodological framework to stratify NSCLC patients with regard to responsiveness to immunotherapy.

T-cell activation and clonal expansion are essential for the efficacy of immunotherapy in non-small cell lung cancer (NSCLC) patients. Since the distribution of T-cell clones might provide insights into immunogenic mechanisms, we determined the α/β TCR clonality using RNA-sequencing from frozen tumor tissue of 182 NSCLC patients and paired the results with extensive in situ image and sequence analyses of the immune microenvironment of NSCLC. TCR clonality (Gini index) patterns ranged from high T-cell clone diversity with high evenness (Gini index low) to clonal dominance with low evenness (Gini index high). TCR clonality in cancer tissue was lower than in matched normal lung (p=0.021). High Gini index correlated strongly with distinct mutations (EGFR, P53), tumor mutation burden (p<0.001), and inflamed tumor phenotypes (PRF1, GZMA, GZMB, INFG) with exhaustion signatures (LAG3, TIGIT, IDO1, PD-1, PD-L1). Correspondingly, PD-1+, CD3+, CD8A+, CD163+, and CD138+ immune cells infiltrated cancer tissue with high TCR clonality. In situ sequencing revealed that dominant T-cell clones were more often of CD8-subtype and tended to approximate the tumor cell compartment (p<0.03). In a checkpoint inhibitor-treated NSCLC patient cohort, high TCR clonality was associated with therapy response (p=0.016) and prolonged survival (p=0.003, median survival 13.8 vs 2.9 months). Our robust analysis pipeline revealed diverse TCR repertoires related to genotypes and immune phenotypes. The in situ positioning of expanded T-cell clones indicated functional impact, which was clinically confirmed in NSCLC patients receiving immunotherapy. ### Competing Interest Statement The authors have declared no competing interest.

INTRODUCTION:Tertiary lymphoid structures (TLS) are lymphocyte aggregates resembling secondary lymphoid organs and are pivotal in cancer immunity. The ambiguous morphological definition of TLS makes it challenging to ascertain their clinical impact on patient survival and response to immunotherapy. OBJECTIVES:This study aimed to characterize TLS in hematoxylin-eosin tissue sections from lung cancer patients, assessing their occurrence in relation to the local immune environment, mutational background, and patient outcome. METHODS:Two pathologists evaluated one whole tissue section from resection specimens of 680 NSCLC patients. TLS were spatially quantified within the tumor area or periphery and further categorized based on the presence of germinal centers (mature TLS). Metrics were integrated with immune cell counts, genomic and transcriptomic data, and correlated with clinical parameters. RESULTS:TLS were present in 86% of 536 evaluable cases, predominantly in the tumor periphery, with a median of eight TLS per case. Mature TLS were found in 24% of cases. TLS presence correlated positively with increased plasma cell (CD138+) and lymphocytic cell (CD3+, CD8+, FOXP3+) infiltration. Tumors with higher tumor mutational burden exhibited higher numbers of peripheral TLS. The overall TLS quantity was independently associated with improved patient survival, irrespective of TLS maturation status. This prognostic association held true for peripheral TLS but not for tumor TLS. CONCLUSION:TLS in NSCLC is common and their correlation with a specific immune phenotype suggests biological relevance in the local immune reaction. The prognostic significance of this scoring system on routine hematoxylin-eosin sections has the potential to augment diagnostic algorithms for NSCLC patients.

Background T-cell activation and clonal expansion are essential to effective immunotherapy responses in non-small cell lung cancer (NSCLC). The distribution of T-cell clones may offer insights into immunogenic mechanisms and imply potential prognostic and predictive information.Methods We analyzed α/β T-cell receptor (TCR) clonality using RNA-sequencing of bulk frozen tumor tissue from 182 patients with NSCLC. The data was integrated with molecular and clinical characteristics, extensive in situ imaging, and spatial sequencing of the tumor immune microenvironment. TCR clonality was also determined in an independent cohort of nine patients with immune checkpoint-treated NSCLC.Results TCR clonality (Gini index) patterns ranged from high T-cell clone diversity with high evenness (low Gini index) to clonal dominance with low evenness (high Gini index). Generally, TCR clonality in cancer was lower than in matched normal lung parenchyma distant from the tumor (p=0.021). The TCR clonality distribution between adenocarcinoma and squamous cell carcinoma was similar; however, smokers showed a higher Gini index. While in the operated patient with NSCLC cohort, TCR clonality was not prognostic, in an immune checkpoint inhibitor-treated cohort, high TCR clonality was associated with better therapy response (p=0.016) and prolonged survival (p=0.003, median survival 13.8 vs 2.9 months). On the genomic level, a higher Gini index correlated strongly with a lower frequency of epidermal growth factor receptor (EGFR) and adenomatous polypsis coli (APC) gene mutations, but a higher frequency of P53 mutations, and a higher tumor mutation burden. In-depth characterization of the tumor tissue revealed that high TCR clonality was associated with an activated, inflamed tumor phenotype (PRF1, GZMA, GZMB, INFG) with exhaustion signatures (LAG3, TIGIT, IDO1, PD-1, PD-L1). Correspondingly, PD-1+, CD3+, CD8A+, CD163+, and CD138+immune cells infiltrated cancer tissue with high TCR clonality. In situ sequencing recovered single dominant T-cell clones within the patient tumor tissue, which were predominantly of the CD8 subtype and localized closer to tumor cells.Conclusion Our robust analysis pipeline characterized diverse TCR repertoires linked to distinct genotypes and immunologic tumor phenotypes. The spatial clustering of expanded T-cell clones and their association with immunological activation underscores a functional, clinically relevant immune response, particularly in patients with NSCLC treated with checkpoint inhibitors.

CONTEXT.— The immune microenvironment is involved in fundamental aspects of tumorigenesis, and immune scores are now being developed for clinical diagnostics. OBJECTIVE.— To evaluate how well small diagnostic biopsies and tissue microarrays (TMAs) reflect immune cell infiltration compared to the whole tumor slide, in tissue from patients with non-small cell lung cancer. DESIGN.— A TMA was constructed comprising tissue from surgical resection specimens of 58 patients with non-small cell lung cancer, with available preoperative biopsy material. Whole sections, biopsies, and TMA were stained for the pan-T lymphocyte marker CD3 to determine densities of tumor-infiltrating lymphocytes. Immune cell infiltration was assessed semiquantitatively as well as objectively with a microscopic grid count. For 19 of the cases, RNA sequencing data were available. RESULTS.— The semiquantitative comparison of immune cell infiltration between the whole section and the biopsy displayed fair agreement (intraclass correlation coefficient [ICC], 0.29; P = .01; CI, 0.03-0.51). In contrast, the TMA showed substantial agreement compared with the whole slide (ICC, 0.64; P < .001; CI, 0.39-0.79). The grid-based method did not enhance the agreement between the different tissue materials. The comparison of CD3 RNA sequencing data with CD3 cell annotations confirmed the poor representativity of biopsies as well as the stronger correlation for the TMA cores. CONCLUSIONS.— Although overall lymphocyte infiltration is relatively well represented on TMAs, the representativity in diagnostic lung cancer biopsies is poor. This finding challenges the concept of using biopsies to establish immune scores as prognostic or predictive biomarkers for diagnostic applications.

Abstract Background: Immune checkpoint therapies (ICT) are now widely introduced in clinical oncology and most often guided by PDL1 expression in diagnostic biopsies. However, the predictive value of this testing is only modest, leading to over- and undertreatment. Conceptually, the prevention of binding of ligand PDL1 to PD1 receptor is decisive for the efficacy of ICT. Therefore, directly detecting the spatial PD1-PDL1 interaction would provide more predictive information. Methods: A second-generation in situ proximity ligation assay (isPLA; Navinci Diagnostics) was established to detect PD1-PD-L1 interactions in diagnostic patient samples and linked to an automated analysis pipeline (QuPath). For basic validation, we used pan-cancer tissue microarrays (TMA) including 16 solid tumor types, and a non-small cell lung cancer (NSCLC) TMA (359 ICT-naïve surgically treated patients). In addition, we analyzed diagnostic tissue biopsies from 75 advanced NSCLC patients treated with anti-PD1-PDL1 regiments. Results: The pan-cancer analysis revealed varying levels of PD1-PDL1 interaction among the solid cancer types, with the lowest levels detected in liver cancer and the highest in testicular cancer. A general positive association was observed between the literature-reported objective ICT response rate (ORR) per tumor type and its corresponding PD1-PDL1 interaction status. Conventional immunohistochemical analysis of the ICT-naïve, surgically treated NSCLC cohort revealed variable protein expression level for both, PDL1 and PD1, for 200 cases, but only 108 (54%) of them demonstrated a detectable PD1-PDL1 interaction with our isPLA assay. EGFR mutated cases showed generally lower PD1-PDL1 interaction scores (p=0.01). PD1-PDL1 interaction was not associated with survival (p=0.46). A subsequent analysis of the ICT-NSCLC cohort revealed a significant positive association between PD1-PDL1 interaction status and prolonged overall survival upon ICT (median survival 14.8 vs 32.3 months; p=0.022). The standard tumor proportional score (TPS) of membranous PDL1 expression did not correlate with ICT-specific survival (p=0.40). Conclusions: The second generation isPLA successfully distinguishes a previously difficult-to-define patient subset with positive PD1-PDL1 interaction status. Our data suggest that the PD1-PDL1 interaction is a superior clinical biomarker for patient selection for ICT in NSCLC. The interaction analysis is applicable to sections from minute diagnostics biopsies and accessible by light microscopy either semi-quantitatively by a pathologist or by an automated image analysis program. Thus, the assay holds great potential to be integrated into routine clinical pathological workflow to stratify cancer patients for ICT. Citation Format: Patrick Micke, Amanda Lindberg, Rebecca Artursson, Louise Hellberg, Hui Yu, Max Backman, Johan Botling, Hans Brunnström, Artur Mezheyeuski, Johan Isaksson, Carina Strell. PD1-PDL1 interaction as a superior predictor for response to immune checkpoint therapy in NSCLC patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5139.

INTRODUCTION:Immune checkpoint inhibitors (ICIs) have transformed lung cancer treatment, yet their effectiveness seem restricted to certain patient subsets. Current clinical stratification on the basis of programmed death ligand 1 (PD-L1) expression offers limited predictive value. Given the mechanism of action, directly detecting spatial programmed cell death protein 1 (PD1)-PD-L1 interactions might yield more precise insights into immune responses and treatment outcomes. METHODS:We applied a second-generation in situ proximity ligation assay to detect PD1-PD-L1 interactions in diagnostic tissue samples from 16 different cancer types, a tissue microarray with surgically resected early-stage NSCLC, and finally diagnostic biopsies from 140 patients with advanced NSCLC with and without ICI treatment. RNA sequencing analysis was used to identify potential resistance mechanisms. RESULTS:In the early-stage NSCLC, only approximately half of the cases with detectable PD-L1 and PD1 expression exhibited PD1-PD-L1 interactions, with significantly lower levels in EGFR-mutated tumors. Interaction levels varied across cancer types, aligning with reported ICI response rates. In ICI-treated patients with NSCLC, higher PD1-PD-L1 interactions were linked to complete responses and longer survival, outperforming standard PD-L1 expression assays. Patients who did not respond to ICIs despite high PD1-PD-L1 interactions exhibited additional expression of stromal immune mediators (EOMES, HAVCR1/TIM-1, JAML, FCRL1). CONCLUSION:Our study proposes a diagnostic shift from static biomarker quantification to assessing active immune pathways, providing more precise ICI treatment. This functional concept applies to tiny lung biopsies and can be extended to further immune checkpoints. Accordingly, our results indicate concerted ICI resistance mechanisms, highlighting the need for combination diagnostics and therapies.

Abstract Tumor-related T-cell activation and subsequent clonal expansion are essential for an effective anti-cancer immune response. Thus, quantifying the clonal distribution of T-cells in cancer tissue from patients might provide prognostic and predictive information, particularly in high immunogenic tumors like non-small cell lung cancer (NSCLC). We evaluated TCR clonality in the tumor microenvironment of NSCLC, its relation to the in situ immune phenotype, and its clinical impact in a cohort of 182 resected NSCLC patients. The α and β TCR clones were determined based on RNA sequencing data, and the Gini index was used to weight individual clonal distribution. We used matched FFPE cancer tissue for multiplex immunofluorescence cell analysis and in-situ sequencing of T-cell clones. The analysis revealed a broad spectrum of TCR clonality patterns, including tissue with high TCR diversity and high evenness (low Gini coefficient) and tissue with clonal dominance with low evenness (high Gini coefficient). Highly expended clones were detected in 15/182 patients, whereas 43/182 revealed no clonal enrichment. A low TCR clone evenness (high Gini coefficient) was significantly more frequent in normal lung areas than in the corresponding tumor areas (n=20, p=0.02). In the cancer tissue, a high Gini index further correlated with high tumor mutational burden, indicating neoantigen-induced T cell clone expansions. Correspondingly, high TCR clonality was associated with an inflamed tumor phenotype associated with PD-1+ immune cells, CD3+, CD8A+, CD163+, and CD138+ cells, as well as higher expression of genes connected to immune activation (PRF1, GZMB, GZMH) and T cell exhaustion (PD-L1, LAG3, TIGIT). Cancer tissue with dominant T-cell clones also showed higher numbers of mature tertiary lymphoid structures with germinal centers. In situ sequencing suggested a close spatial relation between specific T cell clones and tumor cells. Finally, NSCLC patients with dominant T-cell clones showed strong responses and longer survival when treated with immune checkpoint inhibitors. This suggests that TCR clonality analysis is a strong candidate for precision diagnostics. Plausibly, the dominant clones found in NSCLC are responsible for a specific but malfunctioning anti-tumor response that can be unleashed by immune checkpoint inhibition. Citation Format: Hui Yu, Anastasia Magoulopoulou, Masafumi Hori, Amanda Lindberg, Max Backman, Hans Brunström, Millaray Marincevic Zuniga, Johanna Mattsson, Akira Saito, Karin Leandersson, Mats Nilsson, Rose-Marie Amini, Patrick Micke, Carina Strell. Large scale, transcriptome-based analysis of TCR clonality reveals functional immunity in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1516.

Abstract The splice variants specific protein expression in tumor cells presents promising targets for cancer therapy. The concept is supported by the recent introduction of the splice variant-specific antibody against FGFR2 IIIb (FGFR2b) in gastric cancer. Indeed, signaling of the two FGFR2 splice variants FGFR2b and FGFR2 IIIc (FGFR2c), whose expression is mutually exclusive on a cellular level, is mediated by different ligands and distinct downstream responses that can contribute to many aspects of tumorigenesis. Our study aimed to characterize the expression FGFR2b and 2c in non-malignant lung and non-small cell lung cancer (NSCLC) tissue and relate splice variant-specific expression to the histological and molecular background of cancer and its clinical impact.FGFR2b/FGFR2c isoforms expression was determined in RNA-seq data from 180 NSCLC patients and 19 tissues from normal lung (patient-matched). Cell-type specific expression and pan-cancer analysis of FGFR2 were explored using a public database, including DBTSS (NSCLC: 26, normal lung epithelium: 1, normal lung fibroblast 1) and CCLE (Total: 1019, NSCLC: 131), GTEx (normal lung tissue: 578) and HPA (scRNAseq for lung).Our results confirm a cell-specific expression in normal cells with dominance of FGFR2b in epithelial cells and FGFR2c in mesenchymal cells. In normal lung tissue, the FGFR2b variant showed high abundance. Pan-cancer analysis revealed FGFR2c dominant cancers (e.g., CNS tumors, kidney, liver) as well as FGFR2b dominant cancer types (e.g., breast, colon, lung cancer). The RNAseq data from our NSCLC cohort showed a chief expression of FGFR2b in 116 cases (64%), FGFR2c in 31 cases (17%), and a mixed FGFR2c/FGFR2b transcriptomics (18%). The ratio of FGFR2b/FGFR2c as a metric for splice variant dominance was associated with squamous histology, higher stage, and KRAS mutations. Differential gene expression analysis of cancer tissue and cell lines revealed an association of the splice variant FGFR2c to epithelial-mesenchymal transition (EMT) and the regulation of genes (ZEP1, ERSP1, CDH1) involved in the TGF-beta signal pathway. Finally, the ratio of FGFR2b/FGFR2c demonstrated a strong association with overall survival in NSCLC patients with the FGFR2c variant as unfavorable predictors (High vs low ratio, p = 0.01). Our study indicates cell-specific splice variant expression in non-malignant and malignant cells. The prognostic unfavorable impact of the FGFR2c variant on NSCLC patient survival might be explained by its association with TGF signaling and EMT. The observed clinical and biological difference between FGFR2b and FGFR2c dominated cancers gives further rationale to exploit splice variants as therapeutical targets. Citation Format: Hui Yu, Masafumi Horie, Amanda Lindberg, Max Backman, Neda Hekmati, Johanna Mattsson, Naoya Miyashita, Hans Brunnström, Fredrik Ponten, Akira Saito, Carina Strell, Patrick Micke. FGFR2 splice variant as a cell fate adjudicator determines clinical outcomes in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 208.

Introduction: Immune cells in the tumor microenvironment are associated with prognosis and response to therapy. We aimed to comprehensively characterize the spatial immune phenotypes in the mutational and clinicopathological background of non-small cell lung cancer (NSCLC). Methods: We established a multiplexed fluorescence imaging pipeline to spatially quantify 13 immune cell subsets in 359 NSCLC cases: CD4 effector cells (CD4-Eff), CD4 regulatory cells (CD4-Treg), CD8 effector cells (CD8-Eff), CD8 regulatory cells (CD8- Treg), B-cells, NK-cells, NKT-cells, M1 macrophages (M1), CD163+myeloid cells (CD163), M2 macrophages (M2), immature dendritic cells (iDCs), mature dendritic cells (mDCs), and plasmacytoid dendritic cells (pDCs). Results: CD4-Eff cells, CD8-Eff cells, and M1 macrophages were the most abundant immune cells invading the tumor cell compartment and indicated a patient group with a favorable prognosis in the cluster analysis. Likewise, single densities of lymphocytic subsets (CD4-Eff, CD4-Treg, CD8-Treg, B-cells), and pDCs, were independently associated with longer survival. However, when these immune cells were located close to CD8-Treg cells, the favorable impact was attenuated. In the multivariate Cox regression model including cell densities and distances, the densities of M1 and CD163 cells and distances between cells (CD8-Treg–B-cells, CD8-Eff–cancer cells, and B-cells–CD4-Treg) demonstrated positive prognostic impact, while short M2–M1 distances were prognostically unfavorable. Conclusion: We present a unique spatial profile of the in situ immune cell landscape in NSCLC as a publicly available data set. Cell densities and cell distances contribute independently to prognostic information on clinical outcomes, suggesting that spatial information is crucial for diagnostic use.

The antigenic repertoire of tumors is critical for successful anti‐cancer immune response and the efficacy of immunotherapy. Cancer–testis antigens (CTAs) are targets of humoral and cellular immune reactions. We aimed to characterize CTA expression in non‐small cell lung cancer (NSCLC) in the context of the immune microenvironment. Of 90 CTAs validated by RNA sequencing, eight CTAs (DPEP3, EZHIP, MAGEA4, MAGEB2, MAGEC2, PAGE1, PRAME, and TKTL1) were selected for immunohistochemical profiling in cancer tissues from 328 NSCLC patients. CTA expression was compared with immune cell densities in the tumor environment and with genomic, transcriptomic, and clinical data. Most NSCLC cases (79%) expressed at least one of the analyzed CTAs, and CTA protein expression correlated generally with RNA expression. CTA profiles were associated with immune profiles: high MAGEA4 expression was related to M2 macrophages (CD163) and regulatory T cells (FOXP3), low MAGEA4 was associated with T cells (CD3), and high EZHIP was associated with plasma cell infiltration (adj. P‐value < 0.05). None of the CTAs correlated with clinical outcomes. The current study provides a comprehensive evaluation of CTAs and suggests that their association with immune cells may indicate in situ immunogenic effects. The findings support the rationale to harness CTAs as targets for immunotherapy.

Background: Cancer immunity is based on the interaction of a multitude of cells in the spatial context of the tumor tissue. Immune cell patterns are connected to patient prognosis and response to therapy. Clinically relevant immune signatures are therefore anticipated to fundamentally improve the accuracy in predicting disease progression.Methods and Findings: Through a multiplex in situ analysis pipeline we evaluated 15 immune cell classes in 352 colorectal cancers and associated immune cell infiltration with clinical outcome. By combining the prognostic information of anti-tumoral CD8+ lymphocytes and tumor supportive CD68+ CD163+ macrophages we generated a signature of immune activation (SIA). The prognostic impact of SIA was independent from conventional parameters and stronger than the state-of-art immune score. The SIA was also associated with patient survival in esophageal adenocarcinoma, bladder cancer, lung adenocarcinoma and melanoma. Single-cell analyses identified the CD68+ CD163+ macrophages as the major producents of complement C1q, and C1q could serve as surrogate marker of this macrophage subset. Consequently, the RNA-based version of SIA (ratio of CD8A to C1QA) was predictive for survival in independent RNAseq data sets from these six cancer types. Finally, the CD8A/C1QA mRNA ratio was also predictive for the response to checkpoint inhibitor therapy.Interpretation: Our findings extend current concepts to procure prognostic information from the tumor immune microenvironment and provide a novel immune activation signature with high clinical potential in common human cancer types.Funding: The Swedish Cancer Society, The Lions Cancer Foundation Uppsala, Sellanders foundation, P.O.Zetterling Foundation,U-CAN supported by Swedish Government (SRA CancerUU), by Uppsala University and Region Uppsala.Declaration of Interest: A.M and T.S are co-inventors on a provisional patent application P42105124SE00 “Novel biomarker” regarding a novel method for the prognosis of survival time of a subject diagnosed with a cancer described herein. KL is a board member of Cantargia AB, a company developing IL1RAP inhibitors. This does not alter the Author’s adherence to all guidelines for publication. No other conflicts of interest were disclosed by the other authors.Ethical Approval: The study was approved by the regional ethical committees.· The colorectal cancer cohort: ethical committee in Uppsala, Sweden (2010/198 and 2015/419).· The melanoma cohort: ethical committee in Uppsala, Sweden (2005/232).· The lung cancer cohort: ethical committee in Uppsala (2012/532).· The gastroesophageal cancer: ethical committee in Lund (2007/445).· The urothelial cancer: ethical committee in Uppsala (2005/143).· The uterine corpus endometrial carcinoma: ethical review board in Helsinki (2016/010).· The ovarian carcinoma: ethical committee in Lund (2007/445)

BACKGROUND Cancer-associated fibroblasts (CAFs) are molecularly heterogeneous mesenchymal cells that interact with malignant cells and immune cells and confer both anti- and pro-tumorigenic functions. Prior in situ profiling studies of human CAFs have largely relied on scoring single markers, thus presenting a very limited view of their molecular complexity. Our objective was to study the complex spatial tumor microenvironment of non-small cell lung cancer (NSCLC) with multiple CAF biomarkers, identify novel CAF subsets and explore their associations with patient outcome. METHODS Multiplex fluorescence immunohistochemistry (mfIHC) was employed to spatially profile the CAF landscape in two population-based NSCLC cohorts (n = 636) using antibodies against four fibroblast markers: Platelet-derived growth factor receptor-alpha (PDGFRA) and -beta (PDGFRB), fibroblast activation protein (FAP), and alpha-smooth muscle actin (αSMA). The CAF subsets were analyzed for their correlations with mutations, immune characteristics, clinical variables as well as overall survival (OS). RESULTS Two CAF subsets, CAF7 (PDGFRA-/PDGFRB+/FAP+/αSMA+) and CAF13 (PDGFRA+/PDGFRB+/FAP-/αSMA+), showed significant but opposite associations with tumor histology, driver mutations (TP53 and EGFR), immune features (PD-L1 and CD163), and prognosis. In patients with early-stage tumors (pTNM IA-IB), CAF7 and CAF13 acted as independent prognostic factors. CONCLUSIONS Multi-marker-defined CAF subsets were identified through high-content spatial profiling. The robust associations of CAFs with driver mutations, immune features, and outcome suggest CAFs as essential factors in NSCLC progression and warrant further studies to explore their potential as biomarkers or therapeutic targets. This study also highlights mfIHC-based CAF profiling as a powerful tool for the discovery of clinically relevant CAF subsets.