Background Chest x-ray is a relatively accessible, inexpensive, fast imaging modality that might be valuable in the prognostication of patients with COVID-19. We aimed to develop and evaluate an artificial intelligence system using chest x-rays and clinical data to predict disease severity and progression in patients with COVID-19. Methods We did a retrospective study in multiple hospitals in the University of Pennsylvania Health System in Philadelphia, PA, USA, and Brown University affiliated hospitals in Providence, RI, USA. Patients who presented to a hospital in the University of Pennsylvania Health System via the emergency department, with a diagnosis of COVID-19 confirmed by RT-PCR and with an available chest x-ray from their initial presentation or admission, were retrospectively identified and randomly divided into training, validation, and test sets (7:1:2). Using the chest x-rays as input to an EfficientNet deep neural network and clinical data, models were trained to predict the binary outcome of disease severity (ie, critical or non-critical). The deep-learning features extracted from the model and clinical data were used to build time-to-event models to predict the risk of disease progression. The models were externally tested on patients who presented to an independent multicentre institution, Brown University affiliated hospitals, and compared with severity scores provided by radiologists. Findings 1834 patients who presented via the University of Pennsylvania Health System between March 9 and July 20, 2020, were identified and assigned to the model training (n=1285), validation (n=183), or testing (n=366) sets. 475 patients who presented via the Brown University affiliated hospitals between March 1 and July 18, 2020, were identified for external testing of the models. When chest x-rays were added to clinical data for severity prediction, area under the receiver operating characteristic curve (ROC-AUC) increased from 0·821 (95% CI 0·796–0·828) to 0·846 (0·815–0·852; p<0·0001) on internal testing and 0·731 (0·712–0·738) to 0·792 (0·780–0 ·803; p<0·0001) on external testing. When deep-learning features were added to clinical data for progression prediction, the concordance index (C-index) increased from 0·769 (0·755–0·786) to 0·805 (0·800–0·820; p<0·0001) on internal testing and 0·707 (0·695–0·729) to 0·752 (0·739–0·764; p<0·0001) on external testing. The image and clinical data combined model had significantly better prognostic performance than combined severity scores and clinical data on internal testing (C-index 0·805 vs 0·781; p=0·0002) and external testing (C-inde 0·752 vs 0·715; p<0·0001). Interpretation In patients with COVID-19, artificial intelligence based on chest x-rays had better prognostic performance than clinical data or radiologist-derived severity scores. Using artificial intelligence, chest x-rays can augment clinical data i...

Purpose: The h4 integrin has been implicated in functions associated with the genesis and progression of carcinomas based on data obtained from cell lines and mouse models. Data on its expression and relevance to human carcinomas, however, are relatively scant. The aim of this study was to assess its expression and prognostic significance in human breast carcinomas. Experimental Design: We integrated data on h4 expression from multiple gene profiling studies of breast tumors of known clinical outcome with immunohistochemical analysis of 105 breast carcinomas, and we identified genes whose expression correlates with that of h4. Results: The expression of both h4 mRNA and protein is not homogeneous in breast cancer and it associates most significantly with the ''basal-like'' subtype of breast tumors (P = 0.008). No association between h4 and HER2 expression was evident from either gene profiling or immunohistochemical analysis. To gain insight into the relevance of h4 expression to human breast carcinomas, we generated a 65-gene ''h4 signature'' based on integration of four published gene profiling studies that included the top 0.1% of genes that correlated with h4, either positively or negatively. This h4 signature predicted decreased time to tumor recurrence and survival of patients when applied to four data sets including two independent ones. Conclusions: These observations indicate that h4 expression in human breast cancer is restricted and associated with basal-like cancers, and they support the hypothesis that h4 may function in concert with a discrete set of proteins to facilitate the aggressive behavior of a subset of tumors.

The effectiveness of COVID-19 vaccination remains unknown in patients with hematologic malignant disease who have an impaired humoral immunity from both treatment and disease. Phase 3 registration studies of COVID-19 vaccines excluded patients with immunosuppression or immunosuppressive therapies. 1,2 Despite this, professional organizations suggest vaccination, or even its prioritization, for patients with cancer. 3 As the US Centers for Disease Control loosens pandemic-related precautions for vaccinated people, a better understanding of the vaccine response among patients with hematologic malignant disease is critical.

Claudin proteins are a major component of the tight junctions. Dysregulation of claudin protein expression has been described in a number of malignancies. Gene expression profiling has stratified breast cancers into distinct molecular subtypes: luminal, HER2+ and basal-like. Recently, a novel claudin-low molecular subtype has been described. In this study we correlated the expression patterns of claudins with the molecular subtypes of breast cancer. On the basis of immunohistochemical expression 226 grade 3 invasive ductal carcinomas were stratified into 65 luminal (ER+), 65 HER2 positive (HER2+), 86 basal-like, including 14 metaplastic carcinomas (ER−, HER2−, CK5/6 and /or EGFR+), and 10 unclassified. Tissue microarrays were analyzed for expression of claudins 1, 3, 4, 7 and 8 by immunohistochemistry and scored semiquantitatively. High levels of expression were detected in 17% of all cases for claudin 1, 32% claudin 3, 41% claudin 4, 44% claudin 7, and 40% claudin 8. Luminal cancers exhibited increased claudins 7 and 8; basal-like tumors demonstrated increased claudins 1 and 4 expression. Low expression of all five claudins was detected in 30 of 226 cases (13%) and this group was designated “claudin-low”. The majority of the claudin-low subgroup were basal-like cancers (23 of 30, 77%). In contrast, only 1 of 30 (3%) claudin-low tumors were of the luminal phenotype and 6 of 30 cases (20%) were HER2+ (P<0.001). Within the basal-like subgroup, 64% of the metaplastic and 19% of the non-metaplastic tumors were claudin-low. The claudin-low group was strongly associated with disease recurrence (P=0.0093). In conclusion, this study is the first to comprehensively examine the differential expression of claudins 1, 3, 4, 7 and 8 in the molecular subtypes of high grade breast cancer. Claudin-low subtype is a frequent phenomenon in metaplastic and basal-like breast cancer and appears to be a strong predictor of disease recurrence.

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JNK signaling has been implicated in the developmental morphogenesis of epithelial organs. In this study we employed a compound deletion of the murine Jnk1 and Jnk2 genes in the mammary gland to evaluate the requirement for these ubiquitously expressed genes in breast development and tumorigenesis. JNK1/2 was not required for breast epithelial cell proliferation or motility. However, JNK1/2 deficiency caused increased branching morphogenesis and defects in the clearance of lumenal epithelial cells. In the setting of breast cancer development, JNK1/2 deficiency significantly increased tumor formation. Together, these findings established that JNK signaling is required for normal mammary gland development and that it has a suppressive role in mammary tumorigenesis.

. (1999). p53 inhibits alpha 6 beta 4 integrin survival signaling by promoting the caspase 3-dependent cleavage of AKT/PKB. J. Cell Biol. 147, 1063-1072. Cameron, E. E., Bachman, K. E., Myohanen, S., Herman, J. G. and Baylin, S. B.(1999). Synergy of demethylation and histone deacetylase inhibition in the re-expression of genes silenced in cancer. Nat. Genet. 21, 103-107.

Medullary carcinoma of the colon is a unique histologic subtype of microsatellite unstable colorectal carcinoma but little is known regarding its tumor-immunoregulatory microenvironment. The aims of this study were to characterize the immune environment of medullary carcinoma and compare it with other microsatellite unstable and microsatellite stable colorectal carcinomas. An initial gene expression microarray analysis of six cases of medullary carcinoma was used to detect potentially differentially expressed genes. We extended this analysis utilizing genomic data from the Cancer Genome Atlas to compare eight cases of medullary carcinoma with other microsatellite unstable and stable carcinomas. Finally, we evaluated expression of key immune pathway proteins and lymphocyte subsets via immunohistochemistry of a large group of medullary carcinomas (n = 105) and compared these findings with three other groups: poorly differentiated, microsatellite unstable well-differentiated and microsatellite stable well-differentiated carcinomas. Microarray and the Cancer Genome Atlas data analysis identified significant upregulation of several immunoregulatory genes induced by IFNγ including IDO-1, WARS (tRNA(trp)), GBP1, GBP4, GBP5, PDCD1 (PD-1), and CD274 (PD-L1) in medullary carcinoma compared with other microsatellite unstable and microsatellite stable tumors. By immunohistochemistry, IDO-1 was expressed in 64% of medullary carcinomas compared with 19% (9/47) of poorly differentiated carcinomas, 14% (3/22) of microsatellite unstable, and 7% (2/30) of the microsatellite stable well-differentiated carcinomas (Po 0.0001). tRNA(trp) was overexpressed in 81% (84/104) of medullary carcinomas, 19% (9/47) of poorly differentiated, 32% (7/22) of microsatellite unstable, and 3% (1/30) of microsatellite stable well-differentiated carcinomas (Po 0.0001). Medullary carcinoma had higher mean CD8+ and PD-L1+ tumor-infiltrating lymphocytes compared with all other groups (P o0.0001). This study demonstrates overexpression of several immunoregulatory genes in microsatellite unstable colorectal carcinomas and that expression of these genes and proteins is more prevalent in the medullary carcinoma subtype, which may be of use both diagnostically and therapeutically.