Journal of Pathology Clinical Research最新文献

Evidence for the tumour-supporting capacities of the tumour stroma has accumulated rapidly in colorectal cancer (CRC). Tumour stroma is composed of heterogeneous cells and components including cancer-associated fibroblasts (CAFs), small vessels, immune cells, and extracellular matrix proteins. The present study examined the characteristics of CAFs and collagen, major components of cancer stroma, by immunohistochemistry and Sirius red staining. The expression status of five independent CAF-related or stromal markers, decorin (DCN), fibroblast activation protein (FAP), podoplanin (PDPN), alpha-smooth muscle actin (ACTA2), and collagen, and their association with clinicopathological features and clinical outcomes were analysed. Patients with DCN-high tumours had a significantly worse 5-year survival rate (57.3% versus 79.0%; p = 0.044). Furthermore, hierarchical clustering analyses for these five markers identified three groups that showed specific characteristics: a solid group (cancer cell-rich, DCN^LowPDPN^Low); a PDPN-dominant group (DCN^MidPDPN^High); and a DCN-dominant group (DCN^HighPDPN^Low), with a significant association with patient survival (p = 0.0085). Cox proportional hazards model identified the PDPN-dominant group (hazard ratio = 0.50, 95% CI = 0.26–0.96, p = 0.037) as a potential favourable factor compared with the DCN-dominant group. Of note, DCN-dominant tumours showed the most advanced pT stage and contained the lowest number of CD8+ and FOXP3+ immune cells. This study has revealed that immunohistochemistry and special staining of five stromal factors with hierarchical clustering analyses could be used for the prognostication of patients with CRC. Cancer stroma-targeting therapies may be candidate treatments for patients with CRC.

Gastric poorly cohesive carcinoma (PCC) manifests with a diffuse pattern and diverse tumor cell morphologies, often indicating a more unfavorable prognosis. Recent consensus has reclassified PCC based on the proportion of signet-ring cells (SRCs) in tumors for research purposes. The two most distinct subtypes, poorly cohesive carcinoma not otherwise specified (PCC-NOS) and signet-ring cell carcinoma (SRCC), are characterized by less than 10% and more than 90% SRCs, respectively. However, research comparing the clinicopathological and transcriptomic differences between these subtypes remains limited. In this study, we conducted a comparative analysis of clinicopathological features in 55 advanced-stage PCCs, consisting of 43 PCC-NOS and 12 SRCC cases. Subsequently, 12 PCC-NOS and 5 SRCC cases were randomly selected for initial cancer-related gene expression profiling and pathway enrichment analysis using the GeoMx digital spatial profiler, followed by validation in a separate validation group comprising 16 PCC-NOS and 6 SRCC cases. These transcriptomic findings were then correlated with tumor morphology and clinicopathological data. PCC-NOS cases exhibited larger tumor size, a higher prevalence of pathological N3 disease, and a worse 1-year progression-free survival rate compared to SRCC cases. Clustering of PCC-NOS and SRCC was successfully achieved using the GeoMx Cancer Transcriptome Atlas. Among all studied genes, only MMP7 showed differential expression, with its overexpression significantly associated with the PCC-NOS subtype, increased perineural invasion, and earlier disease progression. Pathway analysis revealed significantly enriched pathways in PCC-NOS related to vesicle-mediated transport, adaptive immune systems, oncogenic signaling, and extracellular matrix organization, while SRCC displayed significant enrichment in pathways associated with respiratory electron transport and the cell cycle. In conclusion, this study compares and correlates clinicopathological features and transcriptomic data between PCC-NOS and SRCC at advanced stages, employing the latest consensus classification and a novel platform for analysis.

Colorectal cancer remains a leading cause of mortality worldwide. Significant variation in response to treatment and survival is evident among patients with similar stage disease. Molecular profiling has highlighted the heterogeneity of colorectal cancer but has had limited impact in daily clinical practice. Biomarkers with robust prognostic and therapeutic relevance are urgently required. Ideally, biomarkers would be derived from H&E sections used for routine pathological staging, have reliable sensitivity and specificity, and require minimal additional training. The biomarker targets would capture key pathological features with proven additive prognostic and clinical utility, such as the local inflammatory response and tumour microenvironment. The Glasgow Microenvironment Score (GMS), first described in 2014, combines assessment of peritumoural inflammation at the invasive margin with quantification of tumour stromal content. Using H&E sections, the Klintrup–Mäkinen (KM) grade is determined by qualitative morphological assessment of the peritumoural lymphocytic infiltrate at the invasive margin and tumour stroma percentage (TSP) calculated in a semi-quantitative manner as a percentage of stroma within the visible field. The resulting three prognostic categories have direct clinical relevance: GMS 0 denotes a tumour with a dense inflammatory infiltrate/high KM grade at the invasive margin and improved survival; GMS 1 represents weak inflammatory response and low TSP associated with intermediate survival; and GMS 2 tumours are typified by a weak inflammatory response, high TSP, and inferior survival. The prognostic capacity of the GMS has been widely validated while its potential to guide chemotherapy has been demonstrated in a large phase 3 trial cohort. Here, we detail its journey from conception through validation to clinical translation and outline the future for this promising and practical biomarker.

The efficacy of neoadjuvant chemotherapy (NACT) in patients with advanced gastric cancer (GC) varies greatly. Thus, we aimed to verify the predictive value of tumor-infiltrating immune cells (TIICs) on the treatment response to NACT and the prognosis of patients with advanced GC, and to explore the impact of NACT on the tumor immune microenvironment (TIME). Paired tumor tissues (pre- and post-NACT) from patients with advanced GC were collected for this study. TIICs were assessed using immunohistochemistry staining and analyzed using logistic regression to establish an immune microenvironment score for GC (ISGC score) and predict NACT efficacy. Kaplan–Meier curves were used to evaluate the survival outcome of patients. The results showed that TIME was dramatically heterogeneous between NACT response and nonresponse patients. In the validation cohort, the ISGC score demonstrated good predictive performance for treatment response to NACT. Moreover, high ISGC indicated better long-term survival in patients with advanced GC. Furthermore, tumor-infiltrated T cells (CD3⁺ and CD8⁺) and CD11c⁺ macrophages were significantly increased in the response group, while CD163⁺ macrophages and FOXP3⁺ Treg cells were decreased after NACT. However, opposite results were exhibited in the nonresponse group. Finally, we found that the percentage of programmed cell death ligand 1 (PD-L1)-positive tumors was 31% (32/104) pre-NACT and 49% (51/104) post-NACT, and almost all patients with elevated PD-L1 were in the NACT response group. The ISGC model accurately predicted NACT efficacy and classified patients with GC into different survival groups. NACT regulates the TIME in GC, which may provide strategies for personalized immunotherapy.

Even after decades of research, pancreatic ductal adenocarcinoma (PDAC) remains a highly lethal disease and responses to conventional treatments remain mostly poor. Subclassification of PDAC into distinct biological subtypes has been proposed by various groups to further improve patient outcome and reduce unnecessary side effects. Recently, an immunohistochemistry (IHC)-based subtyping method using cytokeratin-81 (KRT81) and hepatocyte nuclear factor 1A (HNF1A) could recapitulate some of the previously established molecular subtyping methods, while providing significant prognostic and, to a limited degree, also predictive information. We refined the KRT81/HNF1A subtyping method to classify PDAC into three distinct biological subtypes. The prognostic value of the IHC-based method was investigated in two primary resected cohorts, which include 269 and 286 patients, respectively. In the second cohort, we also assessed the predictive effect for response to erlotinib + gemcitabine. In both PDAC cohorts, the new HNF1A-positive subtype was associated with the best survival, the KRT81-positive subtype with the worst, and the double-negative with an intermediate survival (p < 0.001 and p < 0.001, respectively) in univariate and multivariate analyses. In the second cohort (CONKO-005), the IHC-based subtype was additionally found to have a potential predictive value for the erlotinib-based treatment effect. The revised IHC-based subtyping using KRT81 and HNF1A has prognostic significance for PDAC patients and may be of value in predicting treatment response to specific therapeutic agents.

The identification of gene fusions has become an integral part of soft tissue and bone tumour diagnosis. We investigated the added value of targeted RNA-based sequencing (targeted RNA-seq, Archer FusionPlex) to our current molecular diagnostic workflow of these tumours, which is based on fluorescence in situ hybridisation (FISH) for the detection of gene fusions using 25 probes. In a series of 131 diagnostic samples targeted RNA-seq identified a gene fusion, BCOR internal tandem duplication or ALK deletion in 47 cases (35.9%). For 74 cases, encompassing 137 FISH analyses, concordance between FISH and targeted RNA-seq was evaluated. A positive or negative FISH result was confirmed by targeted RNA-seq in 27 out of 49 (55.1%) and 81 out of 88 (92.0%) analyses, respectively. While negative concordance was high, targeted RNA-seq identified a canonical gene fusion in seven cases despite a negative FISH result. The 22 discordant FISH-positive analyses showed a lower percentage of rearrangement-positive nuclei (range 15–41%) compared to the concordant FISH-positive analyses (>41% of nuclei in 88.9% of cases). Six FISH analyses (in four cases) were finally considered false positive based on histological and targeted RNA-seq findings. For the EWSR1 FISH probe, we observed a gene-dependent disparity (p = 0.0020), with 8 out of 35 cases showing a discordance between FISH and targeted RNA-seq (22.9%). This study demonstrates an added value of targeted RNA-seq to our current diagnostic workflow of soft tissue and bone tumours in 19 out of 131 cases (14.5%), which we categorised as altered diagnosis (3 cases), added precision (6 cases), or augmented spectrum (10 cases). In the latter subgroup, four novel fusion transcripts were found for which the clinical relevance remains unclear: NAB2::NCOA2, YAP1::NUTM2B, HSPA8::BRAF, and PDE2A::PLAG1. Overall, targeted RNA-seq has proven extremely valuable in the diagnostic workflow of soft tissue and bone tumours.

Pulmonary spindle cell carcinoma (PSCC) is a rare and aggressive non-small cell lung cancer (NSCLC) subtype with a dismal prognosis. The molecular characteristics of PSCC are largely unknown due to its rarity, which limits the diagnosis and treatment of this historically poorly characterized malignancy. We present comprehensive genomic profiling results of baseline tumor samples from 22 patients histologically diagnosed with PSCC, representing the largest cohort to date. Somatic genetic variant detection was compared between paired plasma samples and primary tumors from 13 patients within our cohort. The associations among genomic features, treatment, and prognosis were also analyzed in representative patient cases. TP53 (54.5%), TERT (36.4%), CDKN2A (27.3%), and MET (22.7%) were most frequently mutated. Notably, 81.8% of patients had actionable targets in their baseline tumors, including MET (22.7%), ERBB2 (13.6%), EGFR (9.1%), KRAS (9.1%), ALK (9.1%), and ROS1 (4.5%). The median tumor mutation burden (TMB) for PSCC tumors was 5.5 mutations per megabase (muts/Mb). TMB-high tumors (>10 muts/Mb) exhibited a significantly higher mutation frequency in genes such as KRAS, ARID2, FOXL2, and LRP1B, as well as within the DNA mismatch repair pathway. The detection rates for single nucleotide variants and structural variants were comparable between matched tumor and plasma samples, with 48.6% of genetic variants being mutually identified in both sample types. Additionally, a patient with a high mutation load and positive PD-L1 expression demonstrated a 7-month survival benefit from chemoimmunotherapy. Furthermore, a patient with an ALK-rearranged tumor achieved a remarkable 3-year progression-free survival following crizotinib treatment. Overall, our findings deepen the understanding of the complex genomic landscape of PSCC, revealing actionable targets amenable to tailored treatment of this poorly characterized malignancy.

Colorectal cancer (CRC) is a heterogenous malignancy and research is focused on identifying novel ways to subtype patients. In this study, a novel classification system, tumour microenvironment score (TMS), was devised based on Klintrup–Mäkinen grade (KMG), tumour stroma percentage (TSP), and tumour budding. TMS was performed using a haematoxylin and eosin (H&E)-stained section from retrospective CRC discovery and validation cohorts (n = 1,030, n = 787). TMS0 patients had high KMG, TMS1 were low for KMG, TSP, and budding, TMS2 were high for budding, or TSP and TMS3 were high for TSP and budding. Scores were assessed for association with survival and clinicopathological characteristics. Mutational landscaping and Templated Oligo-Sequencing (TempO-Seq) profiling were performed to establish differences in the underlying biology of TMS. TMS was independently prognostic in both cohorts (p < 0.001, p < 0.001), with TMS3 predictive of the shortest survival times. TMS3 was associated with adverse clinical features including sidedness, local and distant recurrence, higher T stage, higher N stage, and presence of margin involvement. Gene set enrichment analysis of TempO-Seq data showed higher expression of genes associated with hallmarks of cancer pathways including epithelial to mesenchymal transition (p < 0.001), IL2 STAT5 signalling (p = 0.007), and angiogenesis (p = 0.017) in TMS3. Additionally, enrichment of immunosuppressive immune signatures was associated with TMS3 classification. In conclusion, TMS represents a novel and clinically relevant method for subtyping CRC patients from a single H&E-stained tumour section.

The efficacy of pembrolizumab monotherapy versus chemotherapy increased with increasing programmed death ligand 1 (PD-L1) expression, as quantified by combined positive score (CPS; PD-L1 expression on both tumour cells and immune cells) in patients with previously treated metastatic triple-negative breast cancer (mTNBC) in the phase 3 KEYNOTE-119 study. This exploratory analysis was conducted to determine whether the expression of PD-L1 on tumour cells contributes to the predictive value of PD-L1 CPS in mTNBC. PD-L1 expression in tumour samples was assessed using PD-L1 IHC 22C3 pharmDx and quantified using both CPS and tumour proportion score (TPS; PD-L1 expression on tumour cells alone). Calculated immune cell density (CID) was defined as CPS minus TPS. The ability of each scoring method (CPS, TPS, and CID) to predict clinical outcomes with pembrolizumab was evaluated. With pembrolizumab, the area under the receiver operating characteristic curve was 0.69 (95% CI = 0.58–0.80) for CPS, 0.55 (95% CI = 0.46–0.64) for TPS, and 0.67 (95% CI = 0.56–0.77) for CID. After correction for cutoff prevalence, CPS performed as well as, if not better than, CID with respect to predicting objective response rate, progression-free survival, and overall survival. Data from this exploratory analysis suggest that, although PD-L1 expression on immune cells alone is predictive of response to programmed death 1 blockade in mTNBC, adding tumour PD-L1 expression assessment (i.e. CPS, which combines immune cell and tumour cell PD-L1 expression) may improve prediction. PD-L1 CPS thus remains an effective and broadly applicable uniform scoring system for enriching response to programmed death 1 blockade with pembrolizumab in mTNBC as well as other tumour types.

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous and prevalent subtype of aggressive non-Hodgkin lymphoma that poses diagnostic and prognostic challenges, particularly in predicting drug responsiveness. In this study, we used digital pathology and deep learning to predict responses to immunochemotherapy in patients with DLBCL. We retrospectively collected 251 slide images from 216 DLBCL patients treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP), with their immunochemotherapy response labels. The digital pathology images were processed using contrastive learning for feature extraction. A multi-modal prediction model was developed by integrating clinical data and pathology image features. Knowledge distillation was employed to mitigate overfitting on gigapixel histopathology images to create a model that predicts responses based solely on pathology images. Based on the importance derived from the attention mechanism of the model, we extracted histological features that were considered key textures associated with drug responsiveness. The multi-modal prediction model achieved an impressive area under the ROC curve of 0.856, demonstrating significant associations with clinical variables such as Ann Arbor stage, International Prognostic Index, and bulky disease. Survival analyses indicated their effectiveness in predicting relapse-free survival. External validation using TCGA datasets supported the model's ability to predict survival differences. Additionally, pathology-based predictions show promise as independent prognostic indicators. Histopathological analysis identified centroblastic and immunoblastic features to be associated with treatment response, aligning with previous morphological classifications and highlighting the objectivity and reproducibility of artificial intelligence-based diagnosis. This study introduces a novel approach that combines digital pathology and clinical data to predict the response to immunochemotherapy in patients with DLBCL. This model shows great promise as a diagnostic and prognostic tool for clinical management of DLBCL. Further research and genomic data integration hold the potential to enhance its impact on clinical practice, ultimately improving patient outcomes.