Laboratory Investigation最新文献

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.

The glycosaminoglycan hyaluronan (HA) plays an important role in tumor progression. However, its biological and clinical significance in papillary thyroid cancer (PTC) remains unknown. Immunohistochemistry was performed to examine HA expression in tissues from PTC patients. Two PTC cell lines were treated with HA synthesized inhibitor against HA production to assess its function. Serum HA levels from 107 PTC patients, 30 Hashimoto thyroiditis patients, and 45 normal controls (NC) were measured by chemiluminescence immunoassay. HA levels in fine needle aspiration (FNA) washouts obtained from thyroid nodules and lymph nodes (LNs) were measured by chemiluminescence immunoassay. Area under the curve (AUC) was computed to evaluate HA’s clinical value. HA was highly expressed in PTC. Reducing HA production significantly inhibited PTC cell proliferation and invasion. Importantly, serum HA levels in PTC were significantly higher than those in NCs and Hashimoto thyroiditis and allowed distinguishing of thyroid cancers from NCs with high accuracy (AUC = 0.782). Moreover, elevated serum HA levels in PTC correlate with LN metastasis. HA levels in FNA washouts from PTC patients were significantly higher than those in benign controls, with a high AUC value (0.8644) for distinguishing PTC from benign controls. Furthermore, HA levels in FNA washouts from metastatic LN were significantly higher than those in nonmetastatic LN, with a high AUC value (0.8007) for distinguishing metastatic LNs from nonmetastatic LNs. HA levels in serum and FNA washout exhibited a potential significance for PTC diagnosis and an indicator for LN metastasis in patients with PTC.

In our rapidly expanding landscape of artificial intelligence, synthetic data have become a topic of great promise and also some concern. This review aimed to provide pathologists and laboratory professionals with a primer on the role of synthetic data and how it may soon shape the landscape within our field. Using synthetic data presents many advantages but also introduces a milieu of new obstacles and limitations. This review aimed to provide pathologists and laboratory professionals with a primer on the general concept of synthetic data and its potential to transform our field. By leveraging synthetic data, we can help accelerate the development of various machine learning models and enhance our medical education and research/quality study needs. This review explored the methods for generating synthetic data, including rule-based, machine learning model-based and hybrid approaches, as they apply to applications within pathology and laboratory medicine. We also discussed the limitations and challenges associated with such synthetic data, including data quality, malicious use, and ethical bias/concerns and challenges. By understanding the potential benefits (ie, medical education, training artificial intelligence programs, and proficiency testing, etc) and limitations of this new data realm, we can not only harness its power to improve patient outcomes, advance research, and enhance the practice of pathology but also become readily aware of their intrinsic limitations.

Accurate assessment of epidermal growth factor receptor (EGFR) mutation status and subtype is critical for the treatment of non–small cell lung cancer patients. Conventional molecular testing methods for detecting EGFR mutations have limitations. In this study, an artificial intelligence–powered deep learning framework was developed for the weakly supervised prediction of EGFR mutations in non–small cell lung cancer from hematoxylin and eosin–stained histopathology whole-slide images. The study cohort was partitioned into training and validation subsets. Foreground regions containing tumor tissue were extracted from whole-slide images. A convolutional neural network employing a contrastive learning paradigm was implemented to extract patch-level morphologic features. These features were aggregated using a vision transformer-based model to predict EGFR mutation status and classify patient cases. The established prediction model was validated on unseen data sets. In internal validation with a cohort from the University of Science and Technology of China (n = 172), the model achieved patient-level areas under the receiver-operating characteristic curve (AUCs) of 0.927 and 0.907, sensitivities of 81.6% and 83.3%, and specificities of 93.0% and 92.3%, for surgical resection and biopsy specimens, respectively, in EGFR mutation subtype prediction. External validation with cohorts from the Second Affiliated Hospital of Anhui Medical University and the First Affiliated Hospital of Wannan Medical College (n = 193) yielded patient-level AUCs of 0.849 and 0.867, sensitivities of 79.2% and 80.7%, and specificities of 91.7% and 90.7% for surgical and biopsy specimens, respectively. Further validation with the Cancer Genome Atlas data set (n = 81) showed an AUC of 0.861, a sensitivity of 84.6%, and a specificity of 90.5%. Deep learning solutions demonstrate potential advantages for automated, noninvasive, fast, cost-effective, and accurate inference of EGFR alterations from histomorphology. Integration of such artificial intelligence frameworks into routine digital pathology workflows could augment existing molecular testing pipelines.

Epithelioid sarcoma (ES) is a rare aggressive sarcoma that, unlike most soft-tissue sarcomas, shows a tendency toward local recurrence and lymph node metastasis. Novel antitumor agents are needed for ES patients. Forkhead box transcription factor 1 (FOXM1) is a member of the Forkhead transcription factor family and is associated with multiple oncogenic functions; FOXM1 is known to be overexpressed and correlated with pathogenesis in various malignancies. In this study, we immunohistochemically analyzed FOXM1 expression levels and their clinical, clinicopathologic, and prognostic significance in 38 ES specimens. In addition, to investigate potential correlations between FOXM1 downregulation and oncologic characteristics, we treated ES cell lines with thiostrepton, a naturally occurring antibiotic that inhibits both small interfering RNA (siRNA) and FOXM1. In the analyses using ES samples, all 38 specimens were diagnosed as positive for FOXM1 by immunohistochemistry. We separated specimens into high (n = 19) and low (n = 19) FOXM1–protein expression groups by staining index score, and into large (n = 12), small (n = 25), and unknown (n = 1) tumor-size groups using a cutoff of 5 cm maximum diameter. Although there were significantly more samples with high FOXM1 expression in the large tumor group (P = .013), there were no significant differences with respect to age (P = 1.00), gender (P = .51), primary site of origin (P = .74), histologic subtypes (P = 1.00), depth (P = .74), or survival rate (P = .288) between the high and low FOXM1–protein expression groups. In the in vitro experiments using ES cell lines, FOXM1 siRNA and thiostrepton successfully downregulated FOXM1 mRNA and protein expression. Furthermore, downregulation of FOXM1 inhibited cell proliferation, drug resistance against chemotherapeutic agents, migration, and invasion and caused cell cycle arrest in the ES cell lines. Finally, cDNA microarray analysis data showed that FOXM1 regulated cIAP2, which is one of the apoptosis inhibitors activated by the TNFα-mediated NF-κB pathway. In conclusion, the FOXM1 gene may be a promising therapeutic target for ES.

Recent studies have shown that novel antibody–drug conjugates (ADCs) can improve clinical outcomes in patients with HER2-low breast cancers. This study aimed to investigate alteration of HER2 status during breast cancer progression with an emphasis on HER2-low status. Using 386 paired samples of primary and recurrent breast cancers, HER2 discordance rate between primary and matched recurrent samples, the relationships between HER2 discordance and clinicopathological characteristics and clinical outcomes of the patients were analyzed. HER2 discordance rate between primary breast cancer and first recurrence was 25.9% (κ = 0.586) with mostly zero-to-low (10.6%) or low-to-zero (9.3%) conversion. There was no significant difference in the discordant rates according to type or location of the recurrence. Of 70 cases with a second recurrence, HER2 discordance rate between the primary tumor and the second recurrence was 27.1% (κ = 0.554). HER2 discordance was associated with lower HER2 level, lymphovascular invasion, and progesterone receptor positivity of the primary tumor. In further analyses, HER2-zero-to-low conversion was associated with lymph node metastasis and hormone receptor (HR) positivity, whereas HER2-low-to-zero conversion was associated with HR negativity and triple-negative subtype. In survival analyses, HER2 discordance was associated with decreased overall survival of patients in the HR-positive group but not in the HR-negative group. Furthermore, patients with HER2-low-to-zero converted tumors showed worse overall survival compared with those with HER2-low concordant tumors. In conclusion, HER2 status changes during breast cancer progression in significant proportions, mostly between zero and low status. As HER2 instability increases during progression and affects clinical outcome, HER2 status needs to be reevaluated in recurrent settings.

Gastric cancer (GC) is one of the most common clinical malignant tumors worldwide, with high morbidity and mortality. Presently, the overall response rate to immunotherapy is low, and current methods for predicting the prognosis of GC are not optimal. Therefore, novel biomarkers with accuracy, efficiency, stability, performance ratio, and wide clinical application are needed. Based on public data sets, the chemotherapy cohort and immunotherapy cohort from Sun Yat-sen University Cancer Center, a series of bioinformatics analyses, such as differential expression analysis, survival analysis, drug sensitivity prediction, enrichment analysis, tumor immune dysfunction and exclusion analysis, single-sample gene set enrichment analysis, stemness index calculation, and immune cell infiltration analysis, were performed for screening and preliminary exploration. Immunohistochemical staining and in vitro experiments were performed for further verification. Overexpression of COX7A1 promoted the resistance of GC cells to Oxaliplatin. COX7A1 may induce immune escape by regulating the number of fibroblasts and their cellular communication with immune cells. In summary, measuring the expression levels of COX7A1 in the clinic may be useful in predicting the prognosis of GC patients, the degree of chemotherapy resistance, and the efficacy of immunotherapy.

Currently, we cannot provide a conclusive diagnosis for 3% to 5% of people who are confronted with cancer. These patients have cancer of unknown primary (CUP), ie, a metastasized cancer for which the tissue of origin cannot be determined. Studies have shown that the DNA methylation profile is a unique “fingerprint” that can be used to classify tumors. Here we used cell-free reduced representation bisulfite sequencing (cfRRBS), a technique that allows us to identify the methylation profile starting from minimal amounts of highly fragmented DNA, for CUP diagnosis on formalin-fixed paraffin-embedded (FFPE) tissue and liquid biopsies. We collected 80 primary tumor FFPE samples covering 16 tumor entities together with 15 healthy plasma samples to use as a custom cfRRBS reference data set. Entity-specific methylation regions are defined for each entity to build a classifier based on nonnegative least squares deconvolution. This classification framework was tested on 30 FFPE, 19 plasma, and 40 pleural and peritoneal effusion samples of both known metastatic tumors and clinical CUPs for which pathological investigation finally resulted in a cancer diagnosis. Using this framework, 27 of 30 FFPE (all CUPs) and 16 of 19 plasma samples (10/13 CUPs) obtained an accurate diagnosis, with a minimal DNA input of 400 pg. Diagnosis of the 40 pleural and peritoneal effusion samples is possible in 9 of 27 samples with negative/inconclusive cytology (6/13 CUPs), showing that cell-free DNA (cfDNA) methylation profiling could complement routine cytologic analysis. However, a low “cfDNA – high-molecular weight DNA ratio” has a considerable impact on the prediction accuracy. Moreover, the accuracy improves significantly if the predicted tumor percentage is >7%. This proof-of-concept study shows the feasibility of using DNA methylation profiling on FFPE and liquid biopsy samples such as blood, ascites, and pleural effusions in a fast and affordable way. Our novel RRBS-based technique requires minimal DNA input, can be performed in <week, and is highly adaptable to specific diagnostic problems as we only use 5 FFPE references per tumor entity. We believe that cfRRBS methylation profiling could be a valuable addition to the pathologist’s toolbox in the diagnosis of CUPs.