Journal of Pathology Clinical Research最新文献

A limited number of patients with lung squamous cell carcinoma (SCC) benefit clinically from molecular targeted drugs because of a lack of targetable driver alterations. We aimed to understand the prevalence and clinical significance of lysine-specific demethylase 5D (KDM5D) copy number loss in SCC and explore its potential as a predictive biomarker for ataxia-telangiectasia and Rad3-related (ATR) inhibitor treatment. We evaluated KDM5D copy number loss in 173 surgically resected SCCs from male patients using fluorescence in situ hybridization. KDM5D copy number loss was detected in 75 of the 173 patients (43%). Genome-wide expression profiles of the transcription start sites (TSSs) were obtained from 17 SCCs, for which the cap analysis of gene expression assay was performed, revealing that upregulated genes in tumors with the KDM5D copy number loss are associated with ‘cell cycle’, whereas downregulated genes in tumors with KDM5D copy number loss were associated with ‘immune response’. Clinicopathologically, SCCs with KDM5D copy number loss were associated with late pathological stage (p = 0.0085) and high stromal content (p = 0.0254). Multiplexed fluorescent immunohistochemistry showed that the number of tumor-infiltrating CD8⁺/T-bet⁺ T cells was lower in SCCs with KDM5D copy number loss than in wild-type tumors. In conclusion, approximately 40% of the male patients with SCC exhibited KDM5D copy number loss. Tumors in patients who show this distinct phenotype can be ‘cold tumors’, which are characterized by the paucity of tumor T-cell infiltration and usually do not respond to immunotherapy. Thus, they may be candidates for trials with ATR inhibitors.

In recent years, technological advances in tissue preparation, high-throughput volumetric microscopy, and computational infrastructure have enabled rapid developments in nondestructive 3D pathology, in which high-resolution histologic datasets are obtained from thick tissue specimens, such as whole biopsies, without the need for physical sectioning onto glass slides. While 3D pathology generates massive datasets that are attractive for automated computational analysis, there is also a desire to use 3D pathology to improve the visual assessment of tissue histology. In this perspective, we discuss and provide examples of potential advantages of 3D pathology for the visual assessment of clinical specimens and the challenges of dealing with large 3D datasets (of individual or multiple specimens) that pathologists have not been trained to interpret. We discuss the need for artificial intelligence triaging algorithms and explainable analysis methods to assist pathologists or other domain experts in the interpretation of these novel, often complex, large datasets.

Early-stage estrogen receptor positive and human epidermal growth factor receptor negative (ER+/HER2−) luminal breast cancer (BC) is quite heterogeneous and accounts for about 70% of all BCs. Ki67 is a proliferation marker that has a significant prognostic value in luminal BC despite the challenges in its assessment. There is increasing evidence that spatial colocalization, which measures the evenness of different types of cells, is clinically important in several types of cancer. However, reproducible quantification of intra-tumor spatial heterogeneity remains largely unexplored. We propose an automated pipeline for prognostication of luminal BC based on the analysis of spatial distribution of Ki67 expression in tumor cells using a large well-characterized cohort (n = 2,081). The proposed Ki67 colocalization (Ki67CL) score can stratify ER+/HER2− BC patients with high significance in terms of BC-specific survival (p < 0.00001) and distant metastasis-free survival (p = 0.0048). Ki67CL score is shown to be highly significant compared with the standard Ki67 index. In addition, we show that the proposed Ki67CL score can help identify luminal BC patients who can potentially benefit from adjuvant chemotherapy.

Liver is one of the most common sites for metastases, which can occur on account of primary tumors from multiple sites of origin. Identifying the primary site of origin (PSO) of a metastasis can help in guiding therapeutic options for liver metastases. In this pilot study, we hypothesized that computer extracted handcrafted (HC) histomorphometric features can be utilized to identify the PSO of liver metastases. Cellular features, including tumor nuclei morphological and graph features as well as cytoplasm texture features, were extracted by computer algorithms from 175 slides (114 patients). The study comprised three experiments: (1) comparing and (2) fusing a machine learning (ML) model trained with HC pathomic features and deep learning (DL)-based classifiers to predict site of origin; (3) identifying the section of the primary tumor from which metastases were derived. For experiment 1, we divided the cohort into training sets composed of primary and matched liver metastases [60 patients, 121 whole slide images (WSIs)], and a hold-out validation set (54 patients, 54 WSIs) composed solely of liver metastases of known site of origin. Using the extracted HC features of the training set, a combination of supervised machine classifiers and unsupervised clustering was applied to identify the PSO. A random forest classifier achieved areas under the curve (AUCs) of 0.83, 0.64, 0.82, and 0.64 in classifying the metastatic tumor from colon, esophagus, breast, and pancreas on the validation set. The top features related to nuclear and peri-nuclear shape and textural attributes. We also trained a DL network to serve as a direct comparison to our method. The DL model achieved AUCs for colon: 0.94, esophagus: 0.66, breast: 0.79, and pancreas: 0.67 in identifying PSO. A decision fusion-based strategy was deployed to fuse the trained ML and DL classifiers and achieved slightly better results than ML or DL classifier alone (colon: 0.93, esophagus: 0.68, breast: 0.81, and pancreas: 0.69). For the third experiment, WSI-level attention maps were also generated using a trained DL network to generate a composite feature similarity heat map between paired primaries and their associated metastases. Our experiments revealed that epithelium-rich and moderately differentiated tumor regions of primary tumors were quantitatively similar to paired metastatic tumors. Our findings suggest that a combination of HC and DL features could potentially help identify the PSO for liver metastases while at the same time also potentially identify the spatial sites of origin for the metastases within primary tumors.

Gastric-type endocervical adenocarcinoma (GEA) is the second most common subtype of endocervical adenocarcinoma and has a poor prognosis. Anti-programmed death-1 and anti-programmed death-ligand 1 (PD-L1) inhibitors have emerged as a major treatment option for GEA; however, data on the expression of other immune checkpoints in GEA are limited. We analyzed the expression of T-cell immunoglobulin and mucin-domain containing-3 (TIM-3) and B7 homolog 3 protein (B7-H3) in 58 GEA and investigated their prognostic significance as well as association with PD-L1 expression and other known prognostic factors. Applying the tumor proportion score (TPS) with a cutoff of 1%, B7-H3 and TIM-3 were present in 48.3% and 17.2% of cases, respectively. Applying the combined positive score (CPS) with a cutoff of 1, TIM-3 expression was present in 70.7% of cases. Moreover, the expression of three checkpoints (B7-H3, TIM-3, and PD-L1) was incompletely overlapping. Patients with B7-H3 positive tumors (by TPS) or TIM-3 positive tumors (by TPS) had significantly worse recurrence-free survival (RFS) and overall survival (OS) (log-rank). Using CPS, patients with TIM-3 positive tumors showed significantly worse RFS (log-rank). Similarly, B7-H3 positivity (by TPS) and TIM-3 positivity (by TPS) were associated with worse RFS and OS in univariate analysis. TIM-3 positivity (by CPS) was associated with worse RFS in univariate analysis and the final Cox multivariate analysis. In conclusion, our results show that (1) B7-H3 and TIM-3 are frequently expressed in GEA and their expression overlaps incompletely with PD-L1; and (2) both B7-H3 and TIM-3 are independent negative prognostic markers in GEA.

Multiplex immunofluorescence (mIF) imaging can provide comprehensive quantitative and spatial information for multiple immune markers for tumour immunoprofiling. However, application at scale to clinical trial samples sourced from multiple institutions is challenging due to pre-analytical heterogeneity. This study reports an analytical approach to the largest multi-parameter immunoprofiling study of clinical trial samples to date. We analysed 12,592 tissue microarray (TMA) spots from 3,545 colorectal cancers sourced from more than 240 institutions in two clinical trials (QUASAR 2 and SCOT) stained for CD4, CD8, CD20, CD68, FoxP3, pan-cytokeratin, and DAPI by mIF. TMA slides were multi-spectrally imaged and analysed by cell-based and pixel-based marker analysis. We developed an adaptive thresholding method to account for inter- and intra-slide intensity variation in TMA analysis. Applying this method effectively ameliorated inter- and intra-slide intensity variation improving the image analysis results compared with methods using a single global threshold. Correlation of CD8 data derived by our mIF analysis approach with single-plex chromogenic immunohistochemistry CD8 data derived from subsequent sections indicates the validity of our method (Spearman's rank correlation coefficients ρ between 0.63 and 0.66, p ≪ 0.01) as compared with the current gold standard analysis approach. Evaluation of correlation between cell-based and pixel-based analysis results confirms equivalency (ρ > 0.8, p ≪ 0.01, except for CD20 in the epithelial region) of both analytical approaches. These data suggest that our adaptive thresholding approach can enable analysis of mIF-stained clinical trial TMA datasets by digital pathology at scale for precision immunoprofiling.

Studies describing the clinical presentation and prognosis of patients with silent PIT1 (pituitary specific transcription factor)-lineage pituitary neuroendocrine tumors (PitNETs) are rare. We identified patients with positive PIT1 tumor staining but without evidence of hormone hypersecretion at a tertiary center. Clusters were obtained according to cell morphology and immunostaining from each patient's digitally segmented whole slide image. We compared the clinical presentations, radiological features, and prognoses of the different clusters. We identified 146 patients (68 male, 42.9 ± 14.1 years old) with silent PIT1-lineage PitNETs. Morphology clustering suggested that tumors with large nuclei and apparent eccentricity were associated with a higher proportion of aggressiveness and a higher hazard of recurrence [hazard ratio (HR): 2.64, (95% CI, 1.06–6.55), p = 0.037]. Immunohistochemical clustering suggested that tumors with thyroid stimulating hormone (TSH) staining or all negative PIT1-lineage hormones were associated with a higher proportion of aggressiveness and a higher risk of recurrence [HR: 12.4, (95% CI, 1.60–93.5), p = 0.015]. We obtained three-tier risk profiles by combining morphological and immunohistochemical clustering. Patients with the high-risk profile presented the highest recurrence rate compared with those in the medium-risk and low-risk profiles [HR: 3.54, (95% CI, 1.40–8.93), p = 0.002]. In conclusion, digital image analysis based on cell morphology and immunohistochemical staining allows objective stratification of patients with silent PIT1-lineage tumors. Typical morphological characteristics of high-risk tumors are large tumor nuclei and high eccentricity, and typical immunostaining characteristics are TSH staining or negative staining for all PIT1-lineage hormones.

Recently, telomerase reverse transcriptase (TERT) gene rearrangements have been identified in neuroblastoma (NB), the typical pathological type of neuroblastic tumours (NTs); however, the prevalence of TERT rearrangements in other types of NT remains unknown. This study aimed to develop a practical method for detecting TERT defects and to evaluate the clinical relevance of TERT rearrangements as a biomarker for NT prognosis. A TERT break-apart probe for fluorescence in situ hybridisation (FISH) was designed, optimised, and applied to assess the genomic status of TERT in Chinese children with NTs at the Beijing Children's Hospital from 2016 to 2019. Clinical, histological, and genetic characteristics of TERT-rearranged NTs were further addressed. Genomic TERT rearrangements could be effectively detected by FISH and were mutually exclusive with MYCN amplification. TERT rearrangements were identified in 6.0% (38/633) of NTs overall, but 12.4% (31/250) in high-risk patients. TERT rearrangements identified a subtype of aggressive NTs with the characteristics of Stage 3/4, high-risk category, over 18 months old, and presenting all histological subtypes of NB and ganglioneuroblastoma nodular. Moreover, TERT rearrangements were significantly associated with elevated TERT expression levels and decreased survival chances. Multivariable analysis confirmed that it was an independent prognostic marker for NTs. FISH is an easily applicable method for evaluating TERT defects, which define a subgroup of NTs with unfavourable prognosis. TERT rearrangements would contribute to characterising NT molecular signatures in clinical practice.