Immune checkpoint therapy (ICT) provides substantial clinical benefits to cancer patients, but a large proportion of cancers do not respond to ICT. To date, the genomic underpinnings of primary resistance to ICT remain elusive. Here, we performed immunogenomic analysis of data from TCGA and clinical trials of anti-PD-1/PD-L1 therapy, with a particular focus on homozygous deletion of 9p21.3 (9p21 loss), one of the most frequent genomic defects occurring in ~13% of all cancers. We demonstrate that 9p21 loss confers “cold” tumor-immune phenotypes, characterized by reduced abundance of tumor-infiltrating leukocytes (TILs), particularly, T/B/NK cells, altered spatial TILs patterns, diminished immune cell trafficking/activation, decreased rate of PD-L1 positivity, along with activation of immunosuppressive signaling. Notably, patients with 9p21 loss exhibited significantly lower response rates to ICT and worse outcomes, which were corroborated in eight ICT trials of >1,000 patients. Further, 9p21 loss synergizes with PD-L1/TMB for patient stratification. A “response score” was derived by incorporating 9p21 loss, PD-L1 expression and TMB levels in pre-treatment tumors, which outperforms PD-L1, TMB, and their combination in identifying patients with high likelihood of achieving sustained response from otherwise non-responders. Moreover, we describe potential druggable targets in 9p21-loss tumors, which could be exploited to design rational therapeutic interventions.
After lung cancer, breast cancer is the second leading cause of death in women. If breast cancer is detected early, mortality rates in women can be reduced. Because manual breast cancer diagnosis takes a long time, an automated system is required for early cancer detection. This paper proposes a new framework for breast cancer classification from ultrasound images that employs deep learning and the fusion of the best selected features. The proposed framework is divided into five major steps: (i) data augmentation is performed to increase the size of the original dataset for better learning of Convolutional Neural Network (CNN) models; (ii) a pre-trained DarkNet-53 model is considered and the output layer is modified based on the augmented dataset classes; (iii) the modified model is trained using transfer learning and features are extracted from the global average pooling layer; (iv) the best features are selected using two improved optimization algorithms known as reformed differential evaluation (RDE) and reformed gray wolf (RGW); and (v) the best selected features are fused using a new probability-based serial approach and classified using machine learning algorithms. The experiment was conducted on an augmented Breast Ultrasound Images (BUSI) dataset, and the best accuracy was 99.1%. When compared with recent techniques, the proposed framework outperforms them.
Benign Multicystic Peritoneal Mesothelioma (BMPM) is a rare condition that arises from the abdominal peritoneum. Fewer than 200 cases have been reported worldwide. BMPM usually affects premenopausal women and is extremely rare in men. Many factors are suspected to contribute to its development, such as previous surgery, endometriosis, and familial Mediterranean fever. The main management is surgical resection; however, it is estimated that the recurrence rate is up to 50%. Malignant transformation is rare. We report a case series of three male patients who were diagnosed with BMPM and were treated with cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (HIPEC).
Undifferentiated carcinoma of the pancreas with osteoclast-like giant cells (UC-OGC) is a rare and poorly described pancreatic malignancy. It is comprised of mononuclear, pleomorphic, and undifferentiated cells as well as osteoclast-like giant cells (OGC’s). It constitutes less than 1% of pancreatic non-endocrine neoplasia and is twice as likely to occur in females as in males. Its histopathologic properties remain poorly understood. It is suspected that UC-OGC is of epithelial origin that can then transition to mesenchymal elements. As part of this study, we describe a case of a malignant pancreatic neoplasm that was discovered in a 69-year old patient as an incidental finding. We also provide an overview of previously published data to highlight UC-OGC’s clinical and pathologic features.
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