SARS-CoV-2, the virus responsible for COVID-19, is causing a devastating worldwide pandemic, and there is a pressing need to understand the development, specificity, and neutralizing potency of humoral immune responses during acute infection. We report a cross-sectional study of antibody responses to the receptor-binding domain (RBD) of the spike protein and virus neutralization activity in a cohort of 44 hospitalized COVID-19 patients. RBD-specific IgG responses are detectable in all patients 6 days after PCR confirmation. Isotype switching to IgG occurs rapidly, primarily to IgG1 and IgG3. Using a clinical SARS-CoV-2 isolate, neutralizing antibody titers are detectable in all patients by 6 days after PCR confirmation and correlate with RBD-specific binding IgG titers. The RBD-specific binding data were further validated in a clinical setting with 231 PCR-confirmed COVID-19 patient samples. These findings have implications for understanding protective immunity against SARS-CoV-2, therapeutic use of immune plasma, and development of much-needed vaccines.

Allele-specific copy number analysis of tumors (ASCAT) assesses copy number variations (CNV) while accounting for aberrant cell fraction and tumor ploidy. We evaluated if ASCAT-assessed CNV are associated with survival outcomes in 56 patients with WHO grade IV gliomas. Tumor data analyzed by Affymetrix OncoScan FFPE Assay yielded the log ratio (R) and B-allele frequency (BAF). Input into ASCAT quantified CNV using the segmentation function to measure copy number inflection points throughout the genome. Quantified CNV was reported as log R and BAF segment counts. Results were confirmed on The Cancer Genome Atlas (TCGA) glioblastoma dataset. 25 (44.6%) patients had MGMT hyper-methylated tumors, 6 (10.7%) were IDH1 mutated. Median follow-up was 36.4 months. Higher log R segment counts were associate with longer progression-free survival (PFS) [hazard ratio (HR) 0.32, p < 0.001], and overall survival (OS) [HR 0.45, p = 0.01], and was an independent predictor of PFS and OS on multivariable analysis. Higher BAF segment counts were linked to longer PFS (HR 0.49, p = 0.022) and OS (HR 0.49, p = 0.052). In the TCGA confirmation cohort, longer 12-month OS was seen in patients with higher BAF segment counts (62.3% vs. 51.9%, p = 0.0129) and higher log R (63.6% vs. 55.2%, p = 0.0696). Genomic CNV may be a novel prognostic biomarker for WHO grade IV glioma patient outcomes.

Pathology text mining is a challenging task given the reporting variability and constant new findings in cancer sub-type definitions. However, successful text mining of a large pathology database can play a critical role to advance 'big data' cancer research like similarity-based treatment selection, case identification, prognostication, surveillance, clinical trial screening, risk stratification, and many others. While there is a growing interest in developing language models for more specific clinical domains, no pathology-specific language space exist to support the rapid data-mining development in pathology space. In literature, a few approaches fine-tuned general transformer models on specialized corpora while maintaining the original tokenizer, but in fields requiring specialized terminology, these models often fail to perform adequately. We propose PathologyBERT - a pre-trained masked language model which was trained on 347,173 histopathology specimen reports and publicly released in the Huggingface1 repository2. Our comprehensive experiments demonstrate that pre-training of transformer model on pathology corpora yields performance improvements on Natural Language Understanding (NLU) and Breast Cancer Diagnose Classification when compared to nonspecific language models.

Background: Oncotype DX Recurrence Score (RS) has been widely used to predict chemotherapy benefits in patients with estrogen receptor-positive breast cancer. Studies showed that the features used in Magee equations correlate with RS. We aimed to examine whether deep learning (DL)-based histology image analyses can enhance such correlations. Methods: We retrieved 382 cases with RS diagnosed between 2011 and 2015 from the Emory University and the Ohio State University. All patients received surgery. DL models were developed to detect nuclei of tumor cells and tumor-infiltrating lymphocytes (TILs) and segment tumor cell nuclei in hematoxylin and eosin (H&E) stained histopathology whole slide images (WSIs). Based on the DL-based analysis, we derived image features from WSIs, such as tumor cell number, TIL number variance, and nuclear grades. The entire patient cohorts were divided into one training set (125 cases) and two validation sets (82 and 175 cases) based on the data sources and WSI resolutions. The training set was used to train the linear regression models to predict RS. For prediction performance comparison, we used independent variables from Magee features alone or the combination of WSI-derived image and Magee features. Results: The Pearson’s correlation coefficients between the actual RS and predicted RS by DL-based analysis were 0.7058 (p-value = 1.32 × 10–13) and 0.5041 (p-value = 1.15 × 10–12) for the validation sets 1 and 2, respectively. The adjusted R2 values using Magee features alone are 0.3442 and 0.2167 in the two validation sets, respectively. In contrast, the adjusted R2 values were enhanced to 0.4431 and 0.2182 when WSI-derived imaging features were jointly used with Magee features. Conclusion: Our results suggest that DL-based digital pathological features can enhance Magee feature correlation with RS.

Pseudoangiomatous stromal hyperplasia (PASH) is a benign mesenchymal proliferative lesion of the breast. In 2005, only 109 cases had been reported since its initial description in 1986 by Vuitch et al. Our 24 cases represent one of the largest series to be reported from a single institution. We retrospectively reviewed data from 2004 to 2010 of patients diagnosed with PASH by surgical excision or image-guided biopsy. All pathological specimens were reviewed by a single pathologist. The samples were stained for estrogen and progesterone receptors (ER and PR), CD34, and the lymphatic marker D2-40. All but one of 24 (96%) patients presented with breast masses either on imaging or clinically. Fourteen of the 24 patients (58%) were diagnosed on surgical excision, 10 (42%) diagnosed with core needle biopsy, and five (20%) were diagnosed using both techniques. The tumors ranged in size from 0.3 cm to 7.0 cm. All women except two were premenopausal or perimenopausal at diagnosis. Nineteen samples were available for hormonal receptor staining and of these 18 of 19 (95%) were ER or PR positive. PASH was diagnosed in two men, a transgender male on hormones and the other with gynecomastia. The patients' ages ranged from 18 to 86 years old. In addition to PASH other benign histopathological findings include stromal fibrosis and atypical ductal or lobular hyperplasia. Imaging revealed no distinguishing feature for PASH with benign histology. One patient had synchronous ductal carcinoma in-situ (DCIS). Patients were treated with local excision or observation. This study suggests that PASH is primarily a diagnosis of premenopausal and perimenopausal women. Our series supports a hormonal basis for its development due to the positive staining for hormonal receptors. Management is conservative surgery for larger masses with careful observation being an option in patients not at high risk for breast cancer.