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Everolimus (EVE) combined with letrozole is an approved treatment for hormone receptor‐positive/human epidermal growth factor receptor 2‐negative (HR+/HER2−) advanced breast cancer (ABC). However, predictive biomarkers for EVE efficacy remain undefined. In the phase 2 MIRACLE trial, we performed digital spatial profiling (DSP) on pretreatment tumor samples from 21 patients receiving EVE plus letrozole. Patients were divided into resistant and sensitive groups based on their best response to EVE. A total of 119 regions across three compartments—tumor, leukocytes, and stroma—were profiled for immune and transcriptomic markers. Responders had significantly higher fibroblast infiltration in PANCK+ (p = 0.011) and CD45−/PANCK− (p = 0.043) regions, whereas non‐responders exhibited increased neutrophils in CD45+ (p = 0.0061) and PANCK+ (p = 0.03) regions. Prolactin‐induced protein (PIP) mRNA expression was significantly elevated in non‐responders in both PANCK+ (p < 0.0001) and CD45−/PANCK− (p = 0.0006) regions. PIP mRNA expression was found to be associated with EVE resistance and unfavorable progression‐free survival (PFS). PIP mRNA expression and specific immune‐stromal features are associated with resistance to EVE. These findings suggest the potential of PIP as a spatially resolved predictive biomarker for patient stratification in HR+/HER2− ABC. In the phase 2 MIRACLE trial, we performed digital spatial profiling (DSP) on pretreatment tumor samples from 21 patients receiving EVE plus letrozole. Patients were divided into resistant and sensitive groups based on their best response to EVE. Responders had significantly higher fibroblast infiltration in PANCK+ and CD45−/PANCK− regions, whereas non‐responders exhibited increased neutrophils in CD45+ and PANCK+ regions. Prolactin‐induced protein (PIP) mRNA expression was found to be associated with EVE resistance and unfavorable progression‐free survival (PFS).

Gliosarcoma is a rare subtype of IDH‐wildtype glioblastoma defined by mixed malignant glial and high‐grade sarcomatous histological elements. Gliosarcoma is clinically managed similarly to glioblastoma and has a poor outcome. The sarcoma‐like regions of gliosarcoma are thought to represent extreme mesenchymal metaplasia of neoplastic glial cells. Factors contributing to this phenomenon are not completely understood. Here we report a single‐institution series of 37 gliosarcomas including next‐generation sequencing data on 25 cases and digital spatial whole‐transcriptome analysis on four cases to characterize differential gene expression between glial and mesenchymal components. Gliosarcoma demographic and genetic features were compared to a cohort of 75 primary adult hemispheric IDH‐wildtype non‐sarcomatous glioblastomas. Patient age, tumor location, sex, and overall survival in gliosarcoma were similar to glioblastoma. Gliosarcomas showed a significantly lower rate of EGFR amplification and a higher rate of NF1 mutation compared to glioblastomas in next‐generation sequencing analysis. Digital spatial whole‐transcriptome analysis showed a distinct transcriptomic profile in sarcomatous regions with over‐expression of genes involved in extracellular matrix development and remodeling. Selected differentially expressed transcripts were examined further by immunohistochemistry. The glial elements of gliosarcomas showed higher immunoreactivity for Chitinase‐3‐like protein 1 (CHI3L1) than glioblastomas, and low to absent immunoreactivity within the sarcomatous elements. Lymphoid Enhancer‐Binding Factor 1 (LEF1) immunoreactivity was identified within sarcomatous regions of gliosarcoma without detectable nuclear β‐catenin, suggesting a role for β‐catenin independent wingless (WNT) effector signaling in sarcomatous transformation. This study adds to the growing literature demonstrating differences in the genetic underpinning of gliosarcoma and glioblastoma, establishes feasibility of spatial transcriptomic approaches in gliosarcoma, and builds on digital spatial profiling‐based results as a discovery platform to identify pathways and immunohistochemical markers for further study.

Fibrosing interstitial lung diseases (ILDs) encompass a diverse range of scarring disorders that lead to progressive lung failure. Previous gene expression profiling studies focused on idiopathic pulmonary fibrosis (IPF) and bulk tissue samples. We employed digital spatial profiling to gain new insights into the spatial resolution of gene expression across distinct lung microenvironments (LMEs) in IPF, chronic hypersensitivity pneumonitis (CHP) and non-specific interstitial pneumonia (NSIP). We identified differentially expressed genes between LMEs within each condition, and across histologically similar regions between conditions. Uninvolved regions in IPF and CHP were distinct from normal controls, and displayed potential therapeutic targets. Hallmark LMEs of each condition retained distinct gene signatures, but these could not be reproduced in matched lung tissue samples. Based on these profiles and unsupervised clustering, we grouped previously unclassified ILD cases into NSIP or CHP. Overall, our work uniquely dissects gene expression profiles between LMEs within and across different types of fibrosing ILDs.

Classical Hodgkin lymphoma (cHL) is a paradigmatic example of a malignancy in which the tumor microenvironment (TME) plays a dominant role in disease biology. Malignant Hodgkin and Reed–Sternberg (HRS) cells typically constitute only a small minority of the tumor mass (approximately 1–5%). HRS cells are embedded within a complex, highly structured immune and stromal milieu that drives survival, immune evasion, and therapy response. Over the past decade, transcriptomic approaches, particularly single-cell RNA sequencing, have reshaped our understanding of cellular heterogeneity within cHL. However, these approaches lack spatial context, a limitation that is especially relevant in cHL, where cell–cell interactions and physical proximity are central to immune evasion and tumor support. Recent advances in spatial transcriptomics now enable genome-scale, spatially resolved interrogation of gene expression in situ. In this review, we summarize spatially resolved studies of the cHL microenvironment, discuss what they reveal about HRS-centered cellular niches and immune evasion, and highlight how these findings may inform risk stratification, biomarker discovery and microenvironment-directed therapies.

The formation of intratumoral spatial niches has been reported for many human malignancies. However, the translational potential of such spatial niches is understudied. Herein, we utilize digital spatial profiling (DSP) to explore the prognostic relevance of spatially defined protein expression in high-risk prostate cancer. A total of 49 patient samples were analyzed for the expression of 46 proteins in 463 regions of interest (ROIs) from the tumor center ( n  = 198) and the tumor periphery ( n  = 265) resulting in 21,298 primary data points (mean per patient n = 9.4). Expression data from either the tumor center or the tumor periphery were not found to be prognostic. Protein expression of tumor center and periphery was then integrated into single datapoints by calculating the log 2 -transformed relative expression between the two niches for each protein and patient. Unsupervised hierarchical clustering of these data yielded two distinct patient subgroups. These clusters did not show a statistically significant correlation with known prognostic parameters yet significantly correlated with progression-free survival ( p  = 0.014, log-rank, HR 0.43; 95% CI, 0.22–0.86). Our results thus reveal that spatial protein expression contains prognostic information, however, only when expression data from both spatial niches are taken into account. In conclusion, our proof-of-concept study shows that DSP can be exploited for the development of novel prognostic biomarkers that rely on spatially resolved protein expression.

Ischemic stroke remains a leading cause of death and long-term disability, yet effective treatments that promote recovery beyond the acute phase are lacking. Neuregulin-1 (NRG-1) has shown potent neuroprotective and anti-inflammatory properties in preclinical stroke models, with evidence of enhanced neuronal regeneration when administered after injury. To investigate the spatial mechanisms underlying its neuroregenerative therapeutic effects, we examined brain proteomic responses to post-ischemic NRG-1 treatment in mice using NanoString Digital Spatial Profiling (DSP). Adult C57BL/6 mice were subjected to photothrombotic middle cerebral artery occlusion (MCAO) and treated with NRG-1β (5 μg/kg/day) or vehicle at 24- and 48-h post-stroke. Brains were collected at 3 days post-ischemia for spatial proteomic analysis of 68 neural proteins across the ischemic core, peri-infarct tissue, and peri-infarct normal tissue (PiNT). While NRG-1 did not significantly alter overall neuronal death, it markedly reshaped the neuroregenerative milieu, upregulating myelin basic protein (MBP) and synaptophysin and attenuating inflammatory mediators (SPP1, P2RX7, and CD39). NRG-1 also enhanced expression of autophagy and mitophagy markers (ULK1, LC3B, ATG5, PINK1, and Park7), suggesting restoration of cellular clearance and mitochondrial quality control. Pathway and network analyses revealed activation of neuroregeneration, autophagy, and lysosomal biogenesis pathways, while suppressing neuroinflammatory signaling. These findings demonstrate that delayed NRG-1 therapy, even when initiated 24 h after stroke, induces early molecular programs that prime an anti-inflammatory and neuroregenerative environment. The results support further development of NRG-1 as a clinically translatable, multimodal therapy for extending the post-stroke treatment window and promoting functional recovery.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal human malignancies. Tissue microarrays (TMA) are an established method of high throughput biomarker interrogation in tissues but may not capture histological features of cancer with potential biological relevance. Topographic TMAs (T-TMAs) representing pathophysiological hallmarks of cancer were constructed from representative, retrospective PDAC diagnostic material, including 72 individual core tissue samples. The T-TMA was interrogated with tissue hybridization-based experiments to confirm the accuracy of the topographic sampling, expression of pro-tumourigenic and immune mediators of cancer, totalling more than 750 individual biomarker analyses. A custom designed Next Generation Sequencing (NGS) panel and a spatial distribution-specific transcriptomic evaluation were also employed. The morphological choice of the pathophysiological hallmarks of cancer was confirmed by protein-specific expression. Quantitative analysis identified topography-specific patterns of expression in the IDO/TGF-β axis; with a heterogeneous relationship of inflammation and desmoplasia across hallmark areas and a general but variable protein and gene expression of c-MET. NGS results highlighted underlying genetic heterogeneity within samples, which may have a confounding influence on the expression of a particular biomarker. T-TMAs, integrated with quantitative biomarker digital scoring, are useful tools to identify hallmark specific expression of biomarkers in pancreatic cancer.

The advent of “omics” technologies for high-depth tumor profiling has provided new information regarding cancer heterogeneity. However, a bulk omics profile can only partially reproduce tumor complexity, and it does not meet the preferences of pathologists used to perform an in situ assessment of marker expression, for instance, with immunohistochemistry. The NanoString GeoMx® Digital Spatial Profiler (DSP) is a platform for morphology-guided multiplex profiling of tissue slides, which allows the digital quantification of target analytes in different neoplastic settings. To illustrate the feasibility and opportunities offered by DSP from a pathologist’s perspective, we applied DSP in three different representative neoplastic settings: breast carcinoma, thyroid anaplastic carcinoma, and biphasic mesothelioma. Because of the perfect overlap between the hematoxylin–eosin–stained slide and the GeoMx areas of interest, in breast carcinoma, two different antibodies allowed the distinction of the tumor cells from the surrounding tumor microenvironment. In biphasic mesothelioma, we could distinguish the epithelioid from the sarcomatoid neoplastic component, and in the thyroid, we easily separated the anaplastic areas from the well-differentiated carcinoma. DSP is a promising tool that combines traditional histological evaluation, allowing spatial assessment of a tumor and its surroundings, and innovative in situ digital profiling. Pathologists should not miss the opportunity to combine morphological and genomic analyses and be at the forefront of investigating the progression of dysplasia/neoplasia, low-grade or high-grade, epithelial/mesenchymal, and, more in general, overcoming the concept of in situ vs. bulk genomic methods.

Glioblastoma is the most common primary malignant brain tumor in adults with an overall survival of only 14.6 months. Hypoxia is known to play a role in tumor aggressiveness but the influence of hypoxia on the immune microenvironment is not fully understood. The aim of this study was to investigate the expression of immune‐related proteins in normoxic and hypoxic tumor areas by digital spatial profiling. Tissue samples from 10 glioblastomas were stained with a panel of 40 antibodies conjugated to photo‐cleavable oligonucleotides. The free oligo‐tags from normoxic and hypoxic areas were hybridized to barcodes for digital counting. Differential expression patterns were validated by Ivy Glioblastoma Atlas Project (GAP) data and an independent patient cohort. We found that CD44, Beta‐catenin and B7‐H3 were upregulated in hypoxia, whereas VISTA, CD56, KI‐67, CD68 and CD11c were downregulated. PD‐L1 and PD‐1 were not affected by hypoxia. Focusing on the checkpoint‐related markers CD44, B7‐H3 and VISTA, our findings for CD44 and VISTA could be confirmed with Ivy GAP RNA sequencing data. Immunohistochemical staining and digital quantification of CD44, B7‐H3 and VISTA in an independent cohort confirmed our findings for all three markers. Additional stainings revealed fewer T cells and high but equal amounts of tumor‐associated microglia and macrophages in both hypoxic and normoxic regions. In conclusion, we found that CD44 and B7‐H3 were upregulated in areas with hypoxia whereas VISTA was downregulated together with the presence of fewer T cells. This heterogeneous expression should be taken into consideration when developing novel therapeutic strategies.

Background Tertiary lymphoid structures (TLSs) play key roles in tumour adaptive immunity. However, the prognostic value and molecular properties of TLSs in oesophageal squamous cell carcinoma (ESCC) patients have not been studied. Methods The prognostic values of the presence and maturation status of tumour‐associated TLSs were determined in 394 and 256 ESCC patients from Sun Yat‐sen University Cancer Center (Centre A) and the Cancer Hospital of Shantou University Medical College (Centre B), respectively. A deep‐learning (DL) TLS classifier was established with haematoxylin and eosin (H&E)‐stained slides using an inception‐resnet‐v2 neural network. Digital spatial profiling was performed to determine the cellular and molecular properties of TLSs in ESCC tissues. Results TLSs were observed in 73.1% of ESCCs from Centre A via pathological examination of H&E‐stained primary tumour slides, among which 42.9% were TLS‐mature and 30.2% were TLS‐immature tumours. The established DL TLS classifier yielded favourable sensitivities and specificities for patient TLS identification and maturation evaluation, with which 55.1%, 39.5% and 5.5% of ESCCs from Centre B were identified as TLS‐mature, TLS‐immature and TLS‐negative tumours. Multivariate analyses proved that the presence of mature TLSs was an independent prognostic factor in both the Centre A and Centre B cohorts (p < .05). Increased proportions of proliferative B, plasma and CD4+ T helper (Th) cells and increased B memory and Th17 signatures were observed in mature TLSs compared to immature ones. Intratumoural CD8+ T infiltration was increased in TLS‐mature ESCC tissues compared to mature TLS‐absent tissues. The combination of mature TLS presence and high CD8+ T infiltration was associated with the best survival in ESCC patients. Conclusions Mature TLSs improve the prognosis of ESCC patients who underwent complete resection. The use of the DL TLS classifier would facilitate precise and efficient evaluation of TLS maturation status and offer a novel probability of ESCC treatment individualization. Presence of mature (tertiary lymphoid structures) TLSs was a promising prognostic factor for oesophageal squamous cell carcinoma patients who underwent complete resection. A deep‐learning TLS classifier model for TLS identification and maturation evaluation was established to facilitate routine TLS evaluation and stratify prognosis in slide diagnosis. Mature TLSs were characterized by increased proliferative B, plasma and CD4+ Th cells, and B memory and Th17 signatures