Expression of HMB45, MelanA and SOX10 is rare in non-small cell lung cancer

Melan-A expression in non-melanocytic carcinoma: A potential diagnostic pitfall

BACKGROUND

Melan-A/MART-1 is a melanocytic differentiation marker recognized as an antigen on melanoma cells. It is a useful diagnostic marker for pathologists in the diagnosis of melanocytic tumors. However, we recently found that Melan-A can be expressed in some non-melanocytic carcinomas that are rarely reported in the literature.

METHODS

We analyzed the expression of Melan-A in 87 non-melanocytic carcinoma tissue samples by immunohistochemistry. Marker positivity was defined as ≥10% positive tumor cells.

RESULTS

In 87 non-melanocytic carcinoma tissue samples, Melan-A was positive in six (6.89%) cases, of which four (66.7%) were male and two (33.3%) were female, with a mean age of 60 years (range 21-82 years). Five (83.3%) of the Melan-A-positive cases had distant metastases. Compared with Melan-A negative cases, Melan-A positive non-melanocytic carcinomas were significantly associated with poor prognosis (P=0.0023).

CONCLUSIONS

Melan-A expression is relatively rare in non-melanocytic carcinoma cases. This report highlights a potential diagnostic pitfall in the diagnosis of melanoma, urges pathologists to exercise caution in cases of Melan-A positivity, and illustrates the need for an immunohistochemical marker panel to avoid misdiagnosis.

Metastatic uveal melanoma: The final frontier

Treatment of primary intraocular uveal melanoma has developed considerably, its driver genes are largely unraveled, and the ways to assess its risk for metastases are very precise, being based on an international staging system and genetic data. Unfortunately, the risk of distant metastases, which emerge in approximately one half of all patients, is unaltered. Metastases are the leading single cause of death after uveal melanoma is diagnosed, yet no consensus exists regarding surveillance, staging, and treatment of disseminated disease, and survival has not improved until recently. The final frontier in conquering uveal melanoma lies in solving these issues to cure metastatic disease. Most studies on metastatic uveal melanoma are small, uncontrolled, retrospective, and do not report staging. Meta-analyses confirm a median overall survival of 10-13 months, and a cure rate that approaches nil, although survival exceeding 5 years is possible, estimated 2% either with first-line treatment or with best supportive care. Hepatic ultrasonography and magnetic resonance imaging as surveillance methods have a sensitivity of 95-100% and 83-100%, respectively, to detect metastases without radiation hazard according to prevailing evidence, but computed tomography is necessary for staging. No blood-based tests additional to liver function tests are generally accepted. Three validated staging systems predict, each in defined situations, overall survival after metastasis. Their essential components include measures of tumor burden, liver function, and performance status or metastasis free interval. Age and gender may additionally influence survival. Exceptional mutational events in metastases may make them susceptible to checkpoint inhibitors. In a large meta-analysis, surgical treatment was associated with 6 months longer median overall survival as compared to conventional chemotherapy and, recently, tebentafusp as first-line treatment at the first interim analysis of a randomized phase III trial likewise provided a 6 months longer median overall survival compared to investigator's choice, mostly pembrolizumab; these treatments currently apply to selected patients. Promoting dormancy of micrometastases, harmonizing surveillance protocols, promoting staging, identifying predictive factors, initiating controlled clinical trials, and standardizing reporting will be critical steppingstones in reaching the final frontier of curing metastatic uveal melanoma.

Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines

There are many types of cancers. Although they share some hallmarks, such as proliferation and metastasis, they are still very different from many perspectives. They grow on different organ or tissues. Does each cancer have a unique gene expression pattern that makes it different from other cancer types? After the Cancer Genome Atlas (TCGA) project, there are more and more pan-cancer studies. Researchers want to get robust gene expression signature from pan-cancer patients. But there is large variance in cancer patients due to heterogeneity. To get robust results, the sample size will be too large to recruit. In this study, we tried another approach to get robust pan-cancer biomarkers by using the cell line data to reduce the variance. We applied several advanced computational methods to analyze the Cancer Cell Line Encyclopedia (CCLE) gene expression profiles which included 988 cell lines from 20 cancer types. Two feature selection methods, including Boruta, and max-relevance and min-redundancy methods, were applied to the cell line gene expression data one by one, generating a feature list. Such list was fed into incremental feature selection method, incorporating one classification algorithm, to extract biomarkers, construct optimal classifiers and decision rules. The optimal classifiers provided good performance, which can be useful tools to identify cell lines from different cancer types, whereas the biomarkers (e.g. NCKAP1, TNFRSF12A, LAMB2, FKBP9, PFN2, TOM1L1) and rules identified in this work may provide a meaningful and precise reference for differentiating multiple types of cancer and contribute to the personalized treatment of tumors.

Cytokeratin 7, GATA3, and SOX-10 is a Comprehensive Panel in Diagnosing Triple Negative Breast Cancer Brain Metastases

Following lung cancer, breast cancer is the second most common metastatic tumor to the brain, of which triple-negative breast cancer (TNBC) and human epidermal growth factor receptor 2+ (HER2+) breast cancer are the most common subtypes. TNBC does not have standard immunoprofiles and can be difficult to distinguish from other metastases. A tissue microarray was created from 47 patients with breast cancer metastases to the brain and 12 paired breast primaries. Of 47 breast cancer metastases, 24 were HER2+, 14 were TNBC, and 9 were luminal. Forty-five were cytokeratin 7 (CK7) positive, 36 were GATA-binding protein 3 (GATA3) positive, 7 were Sry-related HMg-Box gene 10 (SOX-10) positive, 20 were mammaglobin positive, and 19 were gross cystic disease fluid protein 15 positive. At least one of the CK7, GATA3, or SOX-10 was positive in all TNBC metastases. A panel of CK7, GATA3, and SOX-10 is complementary in the diagnosis of breast cancer brain metastasis. SOX-10 appears to be a specific but not particularly sensitive marker in this context.

Deep Learning for the Classification of Small-Cell and Non-Small-Cell Lung Cancer

Reliable entity subtyping is paramount for therapy stratification in lung cancer. Morphological evaluation remains the basis for entity subtyping and directs the application of additional methods such as immunohistochemistry (IHC). The decision of whether to perform IHC for subtyping is subjective, and access to IHC is not available worldwide. Thus, the application of additional methods to support morphological entity subtyping is desirable. Therefore, the ability of convolutional neuronal networks (CNNs) to classify the most common lung cancer subtypes, pulmonary adenocarcinoma (ADC), pulmonary squamous cell carcinoma (SqCC), and small-cell lung cancer (SCLC), was evaluated. A cohort of 80 ADC, 80 SqCC, 80 SCLC, and 30 skeletal muscle specimens was assembled; slides were scanned; tumor areas were annotated; image patches were extracted; and cases were randomly assigned to a training, validation or test set. Multiple CNN architectures (VGG16, InceptionV3, and InceptionResNetV2) were trained and optimized to classify the four entities. A quality control (QC) metric was established. An optimized InceptionV3 CNN architecture yielded the highest classification accuracy and was used for the classification of the test set. Image patch and patient-based CNN classification results were 95% and 100% in the test set after the application of strict QC. Misclassified cases mainly included ADC and SqCC. The QC metric identified cases that needed further IHC for definite entity subtyping. The study highlights the potential and limitations of CNN image classification models for tumor differentiation.

An Algorithmic Immunohistochemical Approach to Define Tumor Type and Assign Site of Origin

Immunohistochemistry represents an indispensable complement to an epidemiology and morphology-driven approach to tumor diagnosis and site of origin assignment. This review reflects the state of my current practice, based on 15-years' experience in Pathology and a deep-dive into the literature, always striving to be better equipped to answer the age old questions, "What is it, and where is it from?" The tables and figures in this manuscript are the ones I "pull up on the computer" when I am teaching at the microscope and turn to myself when I am (frequently) stuck. This field is so exciting because I firmly believe that, through the application of next-generation immunohistochemistry, we can provide better answers than ever before. Specific topics covered in this review include (1) broad tumor classification and associated screening markers; (2) the role of cancer epidemiology in determining pretest probability; (3) broad-spectrum epithelial markers; (4) noncanonical expression of broad tumor class screening markers; (5) a morphologic pattern-based approach to poorly to undifferentiated malignant neoplasms; (6) a morphologic and immunohistochemical approach to define 4 main carcinoma types; (7) CK7/CK20 coordinate expression; (8) added value of semiquantitative immunohistochemical stain assessment; algorithmic immunohistochemical approaches to (9) "garden variety" adenocarcinomas presenting in the liver, (10) large polygonal cell adenocarcinomas, (11) the distinction of primary surface ovarian epithelial tumors with mucinous features from metastasis, (12) tumors presenting at alternative anatomic sites, (13) squamous cell carcinoma versus urothelial carcinoma, and neuroendocrine neoplasms, including (14) the distinction of pheochromocytoma/paraganglioma from well-differentiated neuroendocrine tumor, site of origin assignment in (15) well-differentiated neuroendocrine tumor and (16) poorly differentiated neuroendocrine carcinoma, and (17) the distinction of well-differentiated neuroendocrine tumor G3 from poorly differentiated neuroendocrine carcinoma; it concludes with (18) a discussion of diagnostic considerations in the broad-spectrum keratin/CD45/S-100-"triple-negative" neoplasm.

Multifocal perivascular epithelioid cell tumor of the uterus: report of one case and literature review

BACKGROUND

The tumor with perivascular epithelioid cells (PEComa) is a rare mesenchymal tumor originating from a perivascular epithelioid cell line. The uterus is the most common location of PEComa, but multifocal lesions are rare. This study aimed to analyze the clinicopathologic and histochemical characteristics of uterine PEComa.

METHODS

1 case of uterine PEComa was detected by H&E staining and immunohistochemical SP method.

RESULTS

The 41-year-old female patient was admitted to the hospital due to gynecologic ultrasonography which showed a substantial heterogeneous mass in the pelvic cavity. According to the microscopic features and immunohistochemical markers, the tumor was diagnosed as multifocal tumors with perivascular epithelioid cells, specifically located on the serous surface of the uterus and below the right appendix. The immunophenotype of the patient was positive for Vimentin, HMB45, TFE-3 and WT-1, but negative for SMA, S-100, CD10, CK, EMA, CD117, CD31 and Melan-A.

CONCLUSION

PEComa is a rare mesenchymal tumor with benign manifestations. Pathological diagnosis should be combined with morphology and immunophenotype. The characteristic immunomarkers are HMB45, Melan-A and SMA. The understanding of clinical manifestations and pathologic features can improve the diagnosis and prevention of this type of tumor in female patients.

Role of Immunocytochemistry in the Cytological Diagnosis of Pulmonary Tumors

Pulmonary cytology is a challenging diagnostic tool, and it is usually evaluated considering medical history and radiological findings in order to reach an accurate diagnosis. Since the majority of lung cancer patients have an advanced stage at diagnosis, a cytological specimen is frequently the only material available for diagnosis and further prognostic/predictive marker determination. Several types of specimens can be obtained from the respiratory system (including sputum, bronchoalveolar lavage, bronchial brushing, fine needle aspiration, and pleural fluid) with different technical preclinical management protocols and different diagnostic yields. Immunocytochemistry (ICC) has a pivotal role in the determination of diagnostic, prognostic, and predictive markers. Therefore, limited cytology samples are to be used with a cell-sparing approach, to allow both diagnostic ICC evaluation as well as predictive marker assessment by ICC or specific molecular assays. In this review, we describe the most common ICC markers used for the diagnosis and prognostic/predictive characterization of thoracic tumors in different cytological specimens.