Can the plasma PD-1 levels predict the presence and efficiency of tumor-infiltrating lymphocytes in patients with metastatic melanoma?

Background: The immune response in melanoma patients is locally affected by presence of tumor-infiltrating lymphocytes (TILs), generally divided into brisk, nonbrisk, and absent. Several studies have shown that a greater presence of TILs, especially brisk, in primary melanoma is associated with a better prognosis and higher survival rate. Patients and Methods: We investigated by enzyme-linked immunosorbent assay (ELISA) the correlation between PD-1 levels in plasma and the presence/absence of TILs in 28 patients with metastatic melanoma. Results: Low plasma PD-1 levels were correlated with brisk TILs in primary melanoma, whereas intermediate values correlated with the nonbrisk TILs, and high PD-1 levels with absent TILs. Although the low number of samples did not allow us to obtain a statistically significant correlation between the plasma PD-1 levels and the patients' overall survival depending on the absence/presence of TILs, the median survival of patients having brisk type TILs was 5 months higher than that of patients with absent and nonbrisk TILs. Conclusions: This work highlights the ability of measuring the plasma PD-1 levels in order to predict the prognosis of patients with untreated metastatic melanoma without a BRAF mutation at the time of diagnosis

Prognostic Impact of miR-224 and RAS Mutations in Medullary Thyroid Carcinoma

Little is known about the function of microRNA-224 (miR-224) in medullary thyroid cancer (MTC). This study investigated the role of miR-224 expression in MTC and correlated it with mutation status in sporadic MTCs. A consecutive series of 134 MTCs were considered. Patients had a sporadic form in 80% of cases (107/134). In this group, REarranged during transfection (RET) and rat sarcoma (RAS) mutation status were assessed by direct sequencing in the tumor tissues. Quantitative real-time polymerase chain reaction was used to quantify mature hsa-miR-224 in tumor tissue. RAS (10/107 cases, 9%) and RET (39/107 cases, 36%) mutations were mutually exclusive in sporadic cases. miR-224 expression was significantly downregulated in patients with the following: high calcitonin levels at diagnosis (p=0.03, r=−0.3); advanced stage (p=0.001); persistent disease (p=0.001); progressive disease (p=0.002); and disease-related death (p=0.0001). We found a significant positive correlation between miR-224 expression and somatic RAS mutations (p=0.007). Patients whose MTCs had a low miR-224 expression tended to have a shorter overall survival (log-rank test p=0.005). On multivariate analysis, miR-224 represented an independent prognostic marker. Our data indicate that miR-224 is upregulated in RAS-mutated MTCs and in patients with a better prognosis and could represent an independent prognostic marker in MTC patients

Metastatic disease to the pancreas and spleen

Isolated metastases to the pancreas and spleen are a rare occurrence. When they are diagnosed, pancreatic metastases are most often from renal cell carcinoma, lung cancer, and breast cancer. The most common source of splenic metastases is gynecological in origin; the overwhelming majority is ovarian. If extensive staging studies reveal these metastases to be isolated, then curative resection may be warranted. This review will demonstrate that long-term survival may be achieved in patients with isolated metastases and a prolonged disease-free interval

Thyroglobulin measurement in the washout of fine needle aspirates for the diagnosis of suspicious cervical lymph nodes

Ultrasound-guided fine-needle aspiration cytology (FNAC) for suspicious cervical lymph nodes (CLN) is the gold standard technique for the identification of metastases from differentiated thyroid carcinomas. Thyroglobulin protein (Tgp) assay in the washout of needles employed for FNA biopsies (FNAB) has been reported to refine and support FNAC performances, especially in cases of inadequate sampling or cystic lymph nodes. In the present work, we evaluated the usefulness of routine measurement of Tgp in the FNAB washout of suspicious cervical lymph nodes (CLN), and its ability to increase the FNAC accuracy in the diagnosis of metastatic CLN. A case study of 45 CLN with histological diagnosis from 36 patients was analyzed. Histology showed metastases from papillary thyroid carcinomas (PTC) in 31 CLN, from anaplastic thyroid cancer (ATC) in 3 CLN, from medullary thyroid cancer (MTC) in 4 CLN, and metastases from extrathyroidal malignancies in 5 CLN. Two CLN analyzed were found to be non-neoplastic. The overall accuracy of FNAC was 82.9%, and that of Tgp was 91.1%, not statistically different. However, Tgp determination was found essential in 4 cases of metastatic CLN from DTC with inadequate cytology, and in 1 case in which the FNAC provided a false negative result. We demonstrated that FNAC and Tgp assay show similar diagnostic accuracies, and that Tgp measurement may represent the only available information in case of inadequate lymph node sampling or cystic lymph nodes

Yeo, Charles

https://jdc.jefferson.edu/jss_images/1049/thumbnail.jp

Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: Present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review

Background: The role of Artificial intelligence (AI) which is defined as the ability of computers to perform tasks that normally require human intelligence is constantly expanding. Medicine was slow to embrace AI. However, the role of AI in medicine is rapidly expanding and promises to revolutionize patient care in the coming years. In addition, it has the ability to democratize high level medical care and make it accessible to all parts of the world.Main text: Among specialties of medicine, some like radiology were relatively quick to adopt AI whereas others especially pathology (and surgical pathology in particular) are only just beginning to utilize AI. AI promises to play a major role in accurate diagnosis, prognosis and treatment of cancers. In this paper, the general principles of AI are defined first followed by a detailed discussion of its current role in medicine. In the second half of this comprehensive review, the current and future role of AI in surgical pathology is discussed in detail including an account of the practical difficulties involved and the fear of pathologists of being replaced by computer algorithms. A number of recent studies which demonstrate the usefulness of AI in the practice of surgical pathology are highlighted.Conclusion: AI has the potential to transform the practice of surgical pathology by ensuring rapid and accurate results and enabling pathologists to focus on higher level diagnostic and consultative tasks such as integrating molecular, morphologic and clinical information to make accurate diagnosis in difficult cases, determine prognosis objectively and in this way contribute to personalized care

Metastatic disease to the breast: the Washington University experience

<p>Abstract</p> <p>Background</p> <p>Metastases to the breast occur rarely, but may be increasing in incidence as patients live longer with malignant diseases. The aim of this study is to characterize metastatic disease to the breast and to describe the management and prognosis of patients who present with this diagnosis.</p> <p>Methods</p> <p>A retrospective review of our institution's pathology and breast cancer databases was performed in order to identify patients with breast malignancies that were not of primary breast origin. Chart review provided additional information about the patients' primary malignancies and course of illness.</p> <p>Results</p> <p>Between 1991 and 2006, eighteen patients with metastatic disease to the breast of non-hematologic origin were identified and all had charts available for review. Among the 18 patients with disease metastatic to the breast, tissues of origin included 3 ovarian, 6 melanoma, 3 medullary thyroid, 3 pulmonary neuroendocrine, 1 pulmonary small cell, 1 oral squamous cell, and 1 renal cell. Overall mean survival after diagnosis of metastatic disease to the breast was 22.4 months. Treatment of metastases varied and included combinations of observation, surgery, radiation, and chemotherapy. Five patients (27.8%) required a change in management of their breast disease for local control.</p> <p>Conclusion</p> <p>Due to the variable course of patients with metastatic disease, a multi-disciplinary approach is necessary for each patient with disease metastatic to the breast to determine optimal treatment. Based on our review, many patients survive for long periods of time and local treatment of metastases to the breast may be beneficial in these patients to prevent local complications.</p

AI-Enabled Lung Cancer Prognosis

Lung cancer is the primary cause of cancer-related mortality, claiming approximately 1.79 million lives globally in 2020, with an estimated 2.21 million new cases diagnosed within the same period. Among these, Non-Small Cell Lung Cancer (NSCLC) is the predominant subtype, characterized by a notably bleak prognosis and low overall survival rate of approximately 25% over five years across all disease stages. However, survival outcomes vary considerably based on the stage at diagnosis and the therapeutic interventions administered. Recent advancements in artificial intelligence (AI) have revolutionized the landscape of lung cancer prognosis. AI-driven methodologies, including machine learning and deep learning algorithms, have shown promise in enhancing survival prediction accuracy by efficiently analyzing complex multi-omics data and integrating diverse clinical variables. By leveraging AI techniques, clinicians can harness comprehensive prognostic insights to tailor personalized treatment strategies, ultimately improving patient outcomes in NSCLC. Overviewing AI-driven data processing can significantly help bolster the understanding and provide better directions for using such systems.Comment: This is the author's version of a book chapter entitled: "Cancer Research: An Interdisciplinary Approach", Springe