Modern Pathology最新文献

Acute promyelocytic leukemia (APL) with variant RARA translocation is linked to over 15 partner genes. Recent publications encompassing 6 cases have expanded the spectrum of RARA partners to torque teno mini virus (TTMV). This entity is likely underrecognized due to the lack of clinician and pathologist familiarity, inability to detect the fusion using routine testing modalities, and informatic challenges in its recognition within next-generation sequencing (NGS) data. We describe a clinicopathologic approach and provide the necessary tools to screen and diagnose APL with TTMV::RARA using existing clinical DNA- or RNA-based NGS assays, which led to the identification of 4 cases, all without other known cytogenetic/molecular drivers. One was identified prospectively and 3 retrospectively, including 2 from custom automated screening of multiple data sets (50,257 cases of hematopoietic malignancy, including 4809 acute myeloid leukemia/myeloid sarcoma/APL cases). Two cases presented as myeloid sarcoma, including 1 with multiple relapses after acute myeloid leukemia-type chemotherapy and hematopoietic stem cell transplant. Two cases presented as leukemia, had a poor response to induction chemotherapy, but achieved remission upon reinduction (including all-trans retinoic acid in 1 case) and subsequent hematopoietic stem cell transplant. Neoplastic cells demonstrated features of APL including frequent azurophilic granules and dim/absent CD34 and HLA-DR expression. RARA rearrangement was not detected by karyotype or fluorescent in situ hybridization. Custom analysis of NGS fusion panel data identified TTMV::RARA rearrangements and, in the prospectively identified case, facilitated monitoring in sequential bone marrow samples. APL with TTMV::RARA is a rare leukemia with a high rate of treatment failure in described cases. The diagnosis should be considered in leukemias with features of APL that lack detectable RARA fusions and other drivers, and may be confirmed by appropriate NGS tests with custom informatics. Incorporation of all-trans retinoic acid may have a role in treatment but requires accurate recognition of the fusion for appropriate classification as APL.

Undifferentiated small round cell sarcomas (USRS) of bone and soft tissue are a group of tumors with heterogenic genomic alterations sharing similar morphology. In the present study, we performed a comparative large-scale proteomic analysis of USRS (n = 42) with diverse genomic translocations including classic Ewing sarcomas with EWSR1::FLI1 fusions (n = 24) or EWSR1::ERG fusions (n = 4), sarcomas with an EWSR1 rearrangement (n = 2), CIC::DUX4 fusion (n = 8), as well as tumors classified as USRS with no genetic data available (n = 4). Proteins extracted from formalin-fixed, paraffin-embedded pretherapeutic biopsies were analyzed qualitatively and quantitatively using shotgun mass spectrometry (MS). More than 8000 protein groups could be quantified using data-independent acquisition. Unsupervised hierarchical cluster analysis based on proteomic data allowed stratification of the 42 cases into distinct groups reflecting the different molecular genotypes. Protein signatures that significantly correlated with the respective genomic translocations were identified and used to generate a heatmap of all 42 sarcomas with assignment of cases with unknown molecular genetic data to either the EWSR1- or CIC-rearranged groups. MS-based prediction of sarcoma subtypes was molecularly confirmed in 2 cases where next-generation sequencing was technically feasible. MS also detected proteins routinely used in the immunohistochemical approach for the differential diagnosis of USRS. BCL11B highly expressed in Ewing sarcomas, and BACH2 as well as ETS-1 highly expressed in CIC::DUX4-associated sarcomas, were among proteins identified by the present proteomic study, and were chosen for immunohistochemical confirmation of MS data in our study cohort. Differential expressions of these 3 markers in the 2 genetic groups were further validated in an independent cohort of n = 34 USRS. Finally, our proteomic results point toward diverging signaling pathways in the different USRS subgroups.

Image-based deep learning models are used to extract new information from standard hematoxylin and eosin pathology slides; however, biological interpretation of the features detected by artificial intelligence (AI) remains a challenge. High-grade serous carcinoma of the ovary (HGSC) is characterized by aggressive behavior and chemotherapy resistance, but also exhibits striking variability in outcome. Our understanding of this disease is limited, partly due to considerable tumor heterogeneity. We previously trained an AI model to identify HGSC tumor regions that are highly associated with outcome status but are indistinguishable by conventional morphologic methods. Here, we applied spatially resolved transcriptomics to further profile the AI-identified tumor regions in 16 patients (8 per outcome group) and identify molecular features related to disease outcome in patients who underwent primary debulking surgery and platinum-based chemotherapy. We examined formalin-fixed paraffin-embedded tissue from (1) regions identified by the AI model as highly associated with short or extended chemotherapy response, and (2) background tumor regions (not identified by the AI model as highly associated with outcome status) from the same tumors. We show that the transcriptomic profiles of AI-identified regions are more distinct than background regions from the same tumors, are superior in predicting outcome, and differ in several pathways including those associated with chemoresistance in HGSC. Further, we find that poor outcome and good outcome regions are enriched by different tumor subpopulations, suggesting distinctive interaction patterns. In summary, our work presents proof of concept that AI-guided spatial transcriptomic analysis improves recognition of biologic features relevant to patient outcomes.

Invasive lobular carcinoma (ILC) is the second most frequent type of breast cancer (BC) and its peculiar morphology is mainly driven by inactivation of CDH1, the gene coding for E-cadherin cell adhesion protein. ILC-specific therapeutic and disease-monitoring approaches are gaining momentum in the clinic, increasing the importance of accurate ILC diagnosis. Several essential and desirable morphologic diagnostic criteria are currently defined by the World Health Organization, the routine use of immunohistochemistry (IHC) for E-cadherin is not recommended. Disagreement in the diagnosis of ILC has been repeatedly reported, but interpathologist agreement increases with the use of E-cadherin IHC. In this study, we aimed to harmonize the pathological diagnosis of ILC by comparing 5 commonly used E-cadherin antibody clones (NCH-38, EP700Y, Clone 36, NCL-L-E-cad [Clone 36B5], and ECH-6). We determined their biochemical specificity for the E-cadherin protein and IHC staining performance according to type and location of mutation on the CDH1 gene. Western blot analysis on mouse cell lines with conditional E-cadherin expression revealed a reduced specificity of EP700Y and NCL-L-E-cad for E-cadherin, with cross-reactivity of Clone 36 to P-cadherin. The use of IHC improved interpathologist agreement for ILC, lobular carcinoma in situ, and atypical lobular hyperplasia. The E-cadherin IHC staining pattern was associated with variant allele frequency and likelihood of nonsense-mediated RNA decay but not with the type or position of CDH1 mutations. Based on these results, we recommend the indication for E-cadherin staining, choice of antibodies, and their interpretation to standardize ILC diagnosis in current pathology practice.

Homozygous deletion of the chromosomal region 9p21.3 is common in urothelial carcinoma (UC) and leads to loss of several genes, including CDKN2A and MTAP, resulting in loss of MTAP protein expression. Here, we aimed to explore the diagnostic potential of MTAP immunohistochemistry (IHC) as a surrogate marker for homozygous 9p21.3 deletion (9p21 homozygous deletion [HD]) in UC. MTAP status was determined by IHC on 27 UC tissue specimens with known 9p21.3 status as defined by fluorescence in situ hybridization in matched cytological specimens, by IHC and fluorescence in situ hybridization on a tissue microarray (TMA) containing 359 UC at different stages, and by IHC on 729 consecutive UC from routine practice. Moreover, we analyzed a longitudinal series of matched specimens from 38 patients with MTAP-negative recurrent UC. MTAP loss by IHC was found in all 17 patients with 9p21 HD and in 2/8 cases without 9p21 HD. In the TMA, MTAP loss was more common in metastases (53%) than in muscle-invasive (33%) and non–muscle-invasive UC (29%) (P = .03). In the consecutive series, 164/729 (22%) cases showed loss of MTAP expression. In 41 of these 164 cases (25%), loss of MTAP expression was heterogenous. We also discovered loss of MTAP expression in flat urothelium adjacent to MTAP-negative low-grade UC, suggesting true flat low-grade neoplasia that could not be diagnosed by morphology alone. Longitudinal analysis of recurrences showed persistent negative MTAP status over time in 37/38 (97%) patients. MTAP IHC can serve as a surrogate marker for 9p21 HD in UC and as a diagnostic tool to differentiate reactive urothelium from urothelial neoplasia. It also provides a unique opportunity to study clinicopathological associations and the heterogeneity of 9p21 HD across the whole spectrum of UC manifestations.

Juxtaglomerular cell tumor (JGCT) is a rare neoplasm, part of the family of mesenchymal tumors of the kidney. Although the pathophysiological and clinical correlates of JGCT are well known, as these tumors are an important cause of early-onset arterial hypertension refractory to medical treatment, their molecular background is unknown, with only few small studies investigating their karyotype. Herein we describe a multi-institutional cohort of JGCTs diagnosed by experienced genitourinary pathologists, evaluating clinical presentation and outcome, morphologic diversity, and, importantly, the molecular features. Ten JGCTs were collected from 9 institutions, studied by immunohistochemistry, and submitted to whole exome sequencing. Our findings highlight the morphologic heterogeneity of JGCT, which can mimic several kidney tumor entities. Three cases showed concerning histologic features, but the patient course was unremarkable, which suggests that morphologic evaluation alone cannot reliably predict the clinical behavior. Gain-of-function variants in RAS GTPases were detected in JGCTs, with no evidence of additional recurrent genomic alterations. In conclusion, we present the largest series of JGCT characterized by whole exome sequencing, highlighting the putative role of the MAPK–RAS pathway.

Lymph node metastasis (LNM) detection can be automated using artificial intelligence (AI)-based diagnostic tools. Only limited studies have addressed this task for colorectal cancer (CRC). This study aimed to develop of a clinical-grade digital pathology tool for LNM detection in CRC using the original fast-track framework.

The training cohort included 432 slides from one department. A segmentation algorithm detecting 8 relevant tissue classes was trained. The test cohorts consisted of materials from 5 pathology departments digitized by 4 different scanning systems.

A high-quality, large training data set was generated within 7 days and a minimal amount of annotation work using fast-track principles. The AI tool showed very high accuracy for LNM detection in all cohorts, with sensitivity, negative predictive value, and specificity ranges of 0.980 to 1.000, 0.997 to 1.000, and 0.913 to 0.990, correspondingly. Only 5 of 14,460 analyzed test slides with tumor cells over all cohorts were classified as false negative (3/5 representing clusters of tumor cells in lymphatic vessels).

A clinical-grade tool was trained in a short time using fast-track development principles and validated using the largest international, multi-institutional, multiscanner cohort of cases to date, showing very high precision for LNM detection in CRC. We are releasing a part of the test data sets to facilitate academic research.

GLI1(12q13.3) amplification is identified in a subset of mesenchymal neoplasms with a distinct nested round cell/epithelioid phenotype. MDM2 and CDK4 genes are situated along the oncogenic 12q13-15 segment, amplification of which defines well-differentiated liposarcoma (WDLPS)/dedifferentiated liposarcoma (DDLPS). The 12q amplicon can occasionally include GLI1, a gene in close proximity to CDK4. We hereby describe the first cohort of GLI1/MDM2/CDK4 coamplified WD/DDLPS. The departmental database was queried retrospectively for all cases of WD/DDLPS having undergone next-generation (MSK-IMPACT) sequencing with confirmed MDM2, CDK4, and GLI1 coamplification. Clinicopathologic data was obtained from a review of the medical chart and available histologic material. Four hundred eighty-six WD/DDLPS cases underwent DNA sequencing, 92 (19%) of which harbored amplification of the GLI1 locus in addition to that of MDM2 and CDK4. These included primary tumors (n = 60), local recurrences (n = 29), and metastases (n = 3). Primary tumors were most frequently retroperitoneal (47/60, 78%), mediastinal (4/60, 7%), and paratesticular (3/60, 5%). Average age was 63 years, with a male:female ratio of 3:2. The cohort was comprised of DDLPS (86/92 [93%], 6 of which were WDLPS with early dedifferentiation) and WDLPS without any longitudinal evidence of dedifferentiation (6/92, 7%). One-fifth (13/86, 17%) of DDLPS cases showed no evidence of a well-differentiated component in any of the primary, recurrent, or metastatic specimens. Dedifferentiated areas mostly showed high-grade undifferentiated pleomorphic sarcoma-like (26/86,30%) and high-grade myxofibrosarcoma-like (13/86,16%) morphologies. A disproportionately increased incidence of meningothelial whorls with/without osseous metaplasia was observed as the predominant pattern in 16/86 (19%) cases, and GLI1-altered morphology as described was identified in a total of 10/86 (12%) tumors. JUN (1p32.1), also implicated in the pathogenesis of WD/DDLPS, was coamplified with all 3 of MDM2, CDK4, and GLI1 in 7/91 (8%) cases. Additional loci along chromosomal arms 1p and 6q, including TNFAIP3, LATS1, and ESR1, were also amplified in a subset of cases. In this large-scale cohort of GLI1 coamplified WD/DDLPS, we elucidate uniquely recurrent features including meningothelial whorl-like and GLI-altered morphology in dedifferentiated areas. Assessment of tumor location (retroperitoneal or mediastinal), identification of a well-differentiated liposarcoma component, and coamplification of other spatially discrete genomic segments (1p and 6q) might aid in distinction from tumors with true driver GLI1 alterations.

Demand for anal cancer screening is expected to rise following the recent publication of the Anal Cancer–HSIL Outcomes Research trial, which showed that treatment of high-grade squamous intraepithelial lesions significantly reduces the rate of progression to anal cancer. While screening for human papillomavirus–associated squamous lesions in the cervix is well established and effective, this is less true for other sites in the lower anogenital tract. Current anal cancer screening and prevention rely on high-resolution anoscopy with biopsies. This procedure has a steep learning curve for providers and may cause patient discomfort. Scattering-based light-sheet microscopy (sLSM) is a novel imaging modality with the potential to mitigate these challenges through real-time, microscopic visualization of disease-susceptible tissue. Here, we report a proof-of-principle study that establishes feasibility of dysplasia detection using an sLSM device. We imaged 110 anal biopsy specimens collected prospectively at our institution’s dysplasia clinic (including 30 nondysplastic, 40 low-grade squamous intraepithelial lesion, and 40 high-grade squamous intraepithelial lesion specimens) and found that these optical images are highly interpretable and accurately recapitulate histopathologic features traditionally used for the diagnosis of human papillomavirus–associated squamous dysplasia. A reader study to assess diagnostic accuracy suggests that sLSM images are noninferior to hematoxylin and eosin images for the detection of anal dysplasia (sLSM accuracy = 0.87; hematoxylin and eosin accuracy = 0.80; P = .066). Given these results, we believe that sLSM technology holds great potential to enhance the efficacy of anal cancer screening by allowing accurate sampling of diagnostic tissue at the time of anoscopy. While the current imaging study was performed on ex vivo biopsy specimens, we are currently developing a handheld device for in vivo imaging that will provide immediate microscopic guidance to high-resolution anoscopy providers.

Lung adenocarcinoma (LUAD) is the most common primary lung cancer and accounts for 40% of all lung cancer cases. The current gold standard for lung cancer analysis is based on the pathologists’ interpretation of hematoxylin and eosin (H&E)-stained tissue slices viewed under a brightfield microscope or a digital slide scanner. Computational pathology using deep learning has been proposed to detect lung cancer on histology images. However, the histological staining workflow to acquire the H&E-stained images and the subsequent cancer diagnosis procedures are labor-intensive and time-consuming with tedious sample preparation steps and repetitive manual interpretation, respectively. In this work, we propose a weakly supervised learning method for LUAD classification on label-free tissue slices with virtual histological staining. The autofluorescence images of label-free tissue with histopathological information can be converted into virtual H&E-stained images by a weakly supervised deep generative model. For the downstream LUAD classification task, we trained the attention-based multiple-instance learning model with different settings on the open-source LUAD H&E-stained whole-slide images (WSIs) dataset from the Cancer Genome Atlas (TCGA). The model was validated on the 150 H&E-stained WSIs collected from patients in Queen Mary Hospital and Prince of Wales Hospital with an average area under the curve (AUC) of 0.961. The model also achieved an average AUC of 0.973 on 58 virtual H&E-stained WSIs, comparable to the results on 58 standard H&E-stained WSIs with an average AUC of 0.977. The attention heatmaps of virtual H&E-stained WSIs and ground-truth H&E-stained WSIs can indicate tumor regions of LUAD tissue slices. In conclusion, the proposed diagnostic workflow on virtual H&E-stained WSIs of label-free tissue is a rapid, cost effective, and interpretable approach to assist clinicians in postoperative pathological examinations. The method could serve as a blueprint for other label-free imaging modalities and disease contexts.