Laboratory Investigation最新文献

This study used artificial intelligence (AI)-based analysis to investigate the immune microenvironment in endometrial cancer (EC). We aimed to evaluate the potential of AI-based immune metrics as prognostic biomarkers. In total, 296 cases with EC were classified into 4 molecular subtypes: polymerase epsilon ultramutated (POLEmut), mismatch repair deficiency (MMRd), p53 abnormal (p53abn), and no specific molecular profile (NSMP). AI-based methods were used to evaluate the following immune metrics: total tumor-infiltrating lymphocytes (TIL), intratumoral TIL, stromal TIL, and tumor cells using Lunit SCOPE IO, as well as CD4+, CD8+, and FOXP3+ T cells using immunohistochemistry (IHC) by QuPath. These 7 immune metrics were used to perform unsupervised clustering. PD-L1 22C3 IHC expression was also evaluated. Clustering analysis demonstrated 3 distinct immune microenvironment groups: immune active, immune desert, and tumor dominant. The immune-active group was highly prevalent in POLEmut, and it was also seen in other molecular subtypes. Although the immune-desert group was more frequent in NSMP and p53mut, it was also detected in MMRd and POLEmut. POLEmut showed the highest levels of CD4+ and CD8+ T cells, total TIL, intratumoral TIL, and stromal TIL with the lowest levels of FOXP3+/CD8+ ratio. In contrast, p53abn in the immune-active group showed higher FOXP3+/CD4+ and FOXP3+/CD8+ ratios. The immune-active group was associated with favorable overall survival and recurrence-free survival. In the NSMP subtype, a significant association was observed between immune active and better recurrence-free survival. The PD-L1 22C3 combined positive score (CPS) showed significant differences among the 3 groups, with the immune-active group having the highest median CPS and frequency of CPS ≥ 1%. The immune microenvironment of EC was variable within molecular subtypes. Within the same immune microenvironment group, significant differences in immune metrics and T cell composition were observed according to molecular subtype. AI-based immune microenvironment groups served as prognostic markers in ECs, with the immune-active group associated with favorable outcomes.

Benign prostatic hyperplasia (BPH) is a common condition marked by the enlargement of the prostate gland, which often leads to significant urinary symptoms and a decreased quality of life. The development of clinically relevant animal models is crucial for understanding the pathophysiology of BPH and improving treatment options. This study aims to establish a patient-derived xenograft (PDX) model using benign prostatic tissues to explore the molecular and cellular mechanisms of BPH. PDXs were generated by implanting fresh BPH (transition zone) and paired normal (peripheral zone) prostate tissue from 8 patients under the renal capsule of immunodeficient male mice. Tissue weight, architecture, cellular proliferation, apoptosis, prostate-specific marker expression, and molecular profiles of PDXs were assessed after 1 week and 1, 2, or 3 months of implantation by immunohistochemistry, enzyme-linked immunosorbent assay, transcriptomics, and proteomics. Responses to finasteride, a standard-of-care therapy, were evaluated. PDXs maintained histologic and molecular characteristics of the parental human tissues. BPH, but not normal PDXs, demonstrated significant increases in weight and cellular proliferation, particularly at 1 month. Molecular profiling revealed specific gene and protein expression patterns correlating with BPH pathophysiology. Specifically, an increased immune and stress response was observed at 1 week, followed by increased expression of proliferation markers and BPH-specific stromal signaling molecules, such as BMP5 and CXCL13, at 1 month. Graft stabilization to preimplant characteristics was apparent between 2 and 3 months. Treatment with finasteride reduced proliferation, increased apoptosis, and induced morphologic changes consistent with therapeutic responses observed in human BPH. Our PDX model recapitulates the morphologic, histologic, and molecular features of human BPH, offering a significant advancement in modeling the complex interactions of cell types in BPH microenvironments. These PDXs respond to therapeutic intervention as expected, providing a valuable tool for preclinical testing of new therapeutics that will improve the well-being of BPH patients.

The high mortality rate of esophageal squamous cell carcinoma (ESCC) is exacerbated by the absence of early diagnostic markers. The pronounced heterogeneity of mutations in ESCC renders copy number alterations (CNAs) more prevalent among patients. The identification of CNA genes within esophageal squamous dysplasia (ESD), a precancerous stage of ESCC, is crucial for advancing early detection efforts. Utilization of liquid biopsies via droplet-based digital PCR (ddPCR) offers a novel strategy for detecting incipient tumor traces. This study undertook a thorough investigation of CNA profiles across ESCC development stages, integrating data from existing databases and prior investigations to pinpoint and confirm CNA markers conducive to early detection of ESCC. Targeted sequencing was employed to select potential early detection genes, followed by the establishment of prediction models for ESCC early detection using ddPCR. Our analysis revealed widespread CNAs during the ESD stage, mirroring the CNA landscape observed in ESCC. A total of 40 CNA genes were identified as highly frequent in both ESCC and ESD lesions, through a comprehensive gene-level CNA analysis encompassing ESD and ESCC tissues, ESCC cell lines, and pan-cancer data sets. Subsequent validation of 5 candidate markers via ddPCR underscored the efficacy of combined predictive models encompassing PIK3CA, SOX2, EGFR, MYC, and CCND1 in early ESCC screening, as evidenced by the area-under-the-curve values exceeding 0.92 (P < .0001) across various detection contexts. The findings highlighted the significant utility of CNA genes in the early screening of ESCC, presenting robust models that could facilitate early detection, broad-scale population screening, and adjunctive diagnosis.

Merkel cell carcinoma (MCC) is an aggressive cutaneous neuroendocrine carcinoma. Checkpoint inhibitor immunotherapy plays an essential role in management of advanced MCC; however, predictors of immunotherapy response remain poorly defined. Syngeneic mouse models suitable for testing novel immunotherapy and combination therapy approaches are likely to soon become available and will require assays for evaluating the tumor microenvironment (TME). Multiplex immunofluorescence (mIF) is a powerful approach to characterize the TME for understanding immunotherapy responses and immune surveillance. In this method article, we provide detailed instructions on assay development for mIF, using as examples 2 new mIF panels for TME investigations of human and murine MCC tumors. Specifically, we demonstrate panels that allow simultaneous visualization of the Merkel cell master transcription factor SOX2 for tumor cell identification, alongside T-cell markers (CD3, CD8, and FOXP3), macrophage markers (F4/80 for mouse and CD163 for human tumors), together with the checkpoint marker PD-L1 for human tumors, and the myeloid-derived suppressor cell marker Arg1 for mouse tumors. We provide detailed protocols for investigators to incorporate these mIF panels into their investigations of human and murine MCC. We also provide fundamental guidance for mIF assay development that will be broadly useful for investigators who consider modifying the panels presented in this study or developing their own mIF panels.

The assessment of chemotherapy response in osteosarcoma (OS), based on the average percentage of viable cells, is limited, as it overlooks the spatial heterogeneity of tumor cell response (foci of resistant cells), immune microenvironment, and bone microarchitecture. Despite the resulting positive classification for response to chemotherapy, some patients experience early metastatic recurrence, demonstrating that our conventional tools for evaluating treatment response are insufficient. We studied the interactions between tumor cells, immune cells (lymphocytes, histiocytes, and osteoclasts), and bone extracellular matrix (ECM) in 18 surgical resection samples of OS using multiplex and conventional immunohistochemistry (IHC: CD8, CD163, CD68, and SATB2), combined with multiscale characterization approaches in territories of good and poor response (GRT/PRT) to treatment. GRT and PRT were defined as subregions with <10% and ≥10% of viable tumor cells, respectively. Local correlations between bone ECM porosity and density of immune cells were assessed in these territories. Immune cell density was then correlated to overall patient survival. Two patterns were identified for histiocytes and osteoclasts. In poor responder patients, CD68 osteoclast density exceeded that of CD163 histiocytes but was not related to bone ECM load. Conversely, in good responder patients, CD163 histiocytes were more numerous than CD68 osteoclasts. For both of them, a significant negative local correlation with bone ECM porosity was found (P < ,01). Moreover, in PRT, multinucleated osteoclasts were rounded and intermingled with tumor cells, whereas in GRT, they were elongated and found in close contact with bone trabeculae. CD8 levels were always low in metastatic patients, and those initially considered good responders rapidly died from their disease. The specific recruitment of histiocytes and osteoclasts within the bone ECM, and the level of CD8 represent new features of OS response to treatment. The associated prognostic signatures should be integrated into the therapeutic stratification algorithm of patients after surgery.

The advent of affordable technology has significantly influenced the practice of digital pathology, leading to its growing adoption within the pathology community. This review article aimed to outline the latest developments in digital pathology, the cutting-edge advancements in artificial intelligence (AI) applications within this field, and the pertinent United States regulatory frameworks. The content is based on a thorough analysis of original research articles and official United States Federal guidelines. Findings from our review indicate that several Food and Drug Administration-approved digital scanners and image management systems are establishing a solid foundation for the seamless integration of advanced technologies into everyday pathology workflows, which may reduce device and operational costs in the future. AI is particularly transforming the way morphologic diagnoses are automated, notably in cancers like prostate and colorectal, within screening initiatives, albeit challenges such as data privacy issues and algorithmic biases remain. The regulatory environment, shaped by standards from the Food and Drug Administration, Centers for Medicare & Medicaid Services/Clinical Laboratory Improvement Amendments, and College of American Pathologists, is evolving to accommodate these innovations while ensuring safety and reliability. Centers for Medicare & Medicaid Services/Clinical Laboratory Improvement Amendments have issued policies to allow pathologists to review and render diagnoses using digital pathology remotely. Moreover, the introduction of new digital pathology Current Procedural Terminology codes designed to complement existing pathology Current Procedural Terminology codes is facilitating reimbursement processes. Overall, these advancements are heralding a new era in pathology that promises enhanced diagnostic precision and efficiency through digital and AI technologies, potentially improving patient care as well as bolstering educational and research activities.

Liver transplantation (LT) is a major treatment for patients with end-stage liver diseases. Steatosis is a significant risk factor for primary graft nonfunction and associated with poor long-term graft outcomes. Traditionally, the evaluation of steatosis is based on frozen section examination to estimate the percentage of hepatocytes containing lipid vesicles. However, this visual evaluation correlates poorly with the true lipid content. This study aimed to address the potential of infrared (IR) microspectroscopy for rapidly estimating lipid content in the context of LT and assessing its impact on survival. Clinical data were collected for >20 months from 58 patients who underwent transplantation. For each liver graft, macrovacuolar steatosis and microvesicular steatosis were evaluated through histologic examination of frozen tissue section. Triglycerides (TG) were further quantified using gas phase chromatography coupled with a flame ionization detector (GC-FID) and estimated by IR microspectroscopy. A linear relationship and significant correlation were observed between the TG measured by GC-FID and those estimated using IR microspectroscopy (R² = 0.86). In some cases, microvesicular steatosis was related to high lipid content despite low levels of macrovacuolar steatosis. Seven patients experienced posttransplantation liver failure, including 5 deceased patients. All patients underwent transplantation with grafts containing significantly high TG levels. A concentration of 250 nmol/mg was identified as the threshold above which the risk of failure after LT significantly increased, affecting 35% of patients. Our study established a strong correlation between LT outcomes and lipid content. IR microspectroscopy proved to be a rapid and reliable approach for assessing the lipid content in clinical settings.

Full-thickness high-grade squamous intraepithelial lesions (HSIL) are precursors of invasive cervical squamous cell carcinoma (SCC). The World Health Organization and Lower Anogenital Squamous Terminology Standardization Project for human papilloma virus (HPV)–associated lesions divide full-thickness HSIL of the cervix into thin HSIL with thickness of 1 to 9 cell layers and the typical full-thickness HSIL of >10 cell layers. Although HPV oncogene transcripts and p16ink4a overexpression, as markers of transforming HPV infection, are detectable in thin HSIL, the biological significance of thin HSIL in cervical carcinogenesis remains poorly understood. To further characterize thin HSIL, we performed a comparative study of chromosomal copy number variations (CNV), an analysis of dysregulated genes present in the segments with CNV, and a generalized genetic complexity calculation for 31 thin HSIL, 31 thick HSIL, 24 microinvasive SCC (pT1a SCC), and 22 highly invasive SCC samples. Thin HSIL share various CNV and specific dysregulated gene pathways with thick HSIL and invasive SCC. Thin HSIL exhibited an average CNV of 11.6% compared with 14.1% for thick HSIL, 15.5% for pT1a SCC, and 26.6% for highly invasive SCC. The CNV included gains at 1q and 3q (40% and 43%, respectively), partial loss of 3p, and loss of chromosomes 11 (18%), 16 (50%), 20 (35%), and 22 (40%). Pathways affected solely in thin HSIL were those enhancing immune evasion and primarily involved the (interleukin) IL6, IL21, and IL23 genes. ILs are transiently upregulated in response to infection and play a crucial role in mounting antitumor T-cell activity. Deregulation reflects an attempt by the HPV to evade the initial immune response of the host. The primary pathways shared by thick HSIL and invasive SCC were interactions between lymphoid and nonlymphoid cells, NOTCH2 signaling, tight junction interactions (primarily of the claudin family), and FGR2 alternative splicing. Our results show that thin HSIL carry similar genetic changes as thick HSIL and SCC, indicating that thin HSIL are true precursor lesions that can progress to thick HSIL and SCC.

DNA mismatch repair gene MutL homolog-1 (MLH1) has divergent effects in many cancers; however, its impact on the metastasis of pancreatic ductal adenocarcinoma (PDAC) remains unclear. In this study, MLH1 stably overexpressed (OE) and knockdowned (KD) sublines were established. Wound healing and transwell assays were used to evaluate cell migration/invasion. In vivo metastasis was investigated in orthotopic implantation models (severe combined immunodeficiency mice). RT-qPCR and western blotting were adopted to show gene/protein expression. MLH1 downstream genes were screened by transcriptome sequencing. Tissue microarray–based immunohistochemistry was applied to determine protein expression in human specimens. In successfully generated sublines, OE cells presented weaker migration/invasion abilities, compared with controls, whereas in KD cells, these abilities were significantly stronger. The metastasis-inhibitory effect of MLH1 was also observed in mice. Mechanistically, G protein–coupled receptor, family C, group 5, member C (GPRC5C) was a key downstream gene of MLH1 in PDAC cells. Subsequently, transient GPRC5C silencing effectively inhibited cell migration/invasion and remarkably reversed the proinvasive effect of MLH1 knockdown in KD cells. In animal models and human PDAC tissues, tumoral GPRC5C expression, negatively associated with MLH1 expressions, was positively correlated with histologic grade, vessel invasion, and poor cancer-specific survival. In conclusion, MLH1 inhibits the metastatic potential of PDAC via downregulation of GPRC5C.