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Lung cancer (LC) is the leading cause of cancer‐related mortality. However, the molecular mechanisms associated with the development of metastasis are poorly understood. Understanding the biology of LC metastasis is critical to unveil the molecular mechanisms for designing targeted therapies. We developed two genetically engineered LC mouse models KrasG12D/+; Trp53R172H/+; Ad‐Cre (KPA) and KrasG12D/+; Ad‐Cre (KA). Survival analysis showed significantly (P = 0.0049) shorter survival in KPA tumor‐bearing mice as compared to KA, suggesting the aggressiveness of the model. Our transcriptomic data showed high expression of N‐acetylgalactosaminide alpha‐2, 6‐sialyltransferase 1 (St6galnac‐I) in KPA compared to KA tumors. ST6GalNAc‐I is an O‐glycosyltransferase, which catalyzes the addition of sialic acid to the initiating GalNAc residues forming sialyl Tn (STn) on glycoproteins, such as mucins. Ectopic expression of species‐specific p53 mutants in the syngeneic mouse and human LC cells led to increased cell migration and high expression of ST6GalNAc‐I, STn, and MUC5AC. Immunoprecipitation of MUC5AC in the ectopically expressing p53R175H cells exhibited higher affinity toward STn. In addition, ST6GalNAc‐I knockout (KO) cells also showed decreased migration, possibly due to reduced glycosylation of MUC5AC as observed by low STn on the glycoprotein. Interestingly, ST6GalNAc‐I KO cells injected mice developed less liver metastasis (P = 0.01) compared to controls, while colocalization of MUC5AC and STn was observed in the liver metastatic tissues of control mice. Collectively, our findings support the hypothesis that mutant p53R175H mediates ST6GalNAc‐I expression, leading to the sialyation of MUC5AC, and thus contribute to LC liver metastasis., In the presence of mutant p53R175H, ST6GalNAc‐I mediates MUC5AC sialylation that increases the aggressiveness of lung adenocarcinoma (LUAD). MUC5AC sialylation is required for integrin β4 interaction that induces angiogenesis and liver metastasis. Hence, targeting the ST6GalNAc‐I/MUC5AC could prevent LUAD metastasis.

Background The development of radiation pneumonitis (RP) after Stereotactic Body Radiotherapy (SBRT) is known to be associated with many different factors, although historical analyses of RP have commonly utilized heterogeneous fractionation schemes and methods of reporting. This study aims to correlate dosimetric values and their association with the development of Symptomatic RP according to recent reporting standards as recommended by the American Association of Physicists in Medicine. Methods We performed a single-institution retrospective review for patients who received SBRT to the lung from 2010 to 2017. Inclusion criteria required near-homogeneous tumoricidal (α/β = 10 Gy) biological effective dose (BED10) of 100–105 Gy (e.g., 50/5, 48/4, 60/8), one or two synchronously treated lesions, and at least 6 months of follow up or documented evidence of pneumonitis. Symptomatic RP was determined clinically by treating radiation oncologists, requiring radiographic evidence and the administration of steroids. Dosimetric parameters and patient factors were recorded. Lung volumes subtracted gross tumor volume(s). Wilcoxon Rank Sums tests were used for nonparametric comparison of dosimetric data between patients with and without RP; p-values were Bonferroni adjusted when applicable. Logistic regressions were conducted to predict probabilities of symptomatic RP using univariable models for each radiation dosimetric parameter. Results The final cohort included 103 treated lesions in 93 patients, eight of whom developed symptomatic RP (n = 8; 8.6%). The use of total mean lung dose (MLD) > 6 Gy alone captured five of the eight patients who developed symptomatic RP, while V20 > 10% captured two patients, both of whom demonstrated a MLD > 6 Gy. The remaining three patients who developed symptomatic RP without exceeding either metric were noted to have imaging evidence of moderate interstitial lung disease, inflammation of the lungs from recent concurrent chemoradiation therapy to the contralateral lung, or unique peri-tumoral inflammatory appearance at baseline before treatment. Conclusions This study is the largest dosimetric analysis of symptomatic RP in the literature, of which we are aware, that utilizes near-homogenous tumoricidal BED fractionation schemes. Mean lung dose and V20 are the most consistently reported of the various dosimetric parameters associated with symptomatic RP. MLD should be considered alongside V20 in the treatment planning process. Trial registration Retrospectively registered on IRB 398–17-EP.

Many of the 20-member mucin family are evolutionarily conserved proteins that are often aberrantly expressed and glycosylated in various benign and malignant pathologies including oncogenic signaling leading to tumor invasion, metastasis, and immune evasion. Large size and extensive glycosylation present challenges to study mucin structure using traditional methods, including crystallography. We offer the hypothesis that the functional versatility of mucins may be attributed to the presence of intrinsically disordered regions (IDRs), which provide dynamism and flexibility; further, that these sites offer potential therapeutic targets. Herein, we examined the links between mucin structure and function based on IDRs, post-translational modifications (PTMs), and potential impact on their interactome. Using sequence-based bioinformatics tools, we observed that mucins are predicted to be moderately (20–40%) to highly (>40%) disordered and many conserved mucin domains could be disordered. Phosphorylation sites overlap with IDRs throughout the mucin sequences. Additionally, the majority of predicted O- and N- glycosylation sites in the tandem repeat regions occur within IDRs, and these IDRs contain a large number of functional motifs, i.e. molecular recognition features (MoRFs), which directly influence PPIs. This investigation provides a novel perspective and offers an insight into the complexity and dynamic nature of mucins.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with a 5-year survival rate of

Pancreatic cystic lesions (PCLs) are well-known precursors of pancreatic cancer. Their diagnosis can be challenging as their behavior varies from benign to malignant disease. Precise and timely management of malignant pancreatic cysts might prevent transformation to pancreatic cancer. However, the current consensus guidelines, which rely on standard imaging features to predict cyst malignancy potential, are conflicting and unclear. This has led to an increased interest in radiomics, a high-throughput extraction of comprehensible data from standard-of-care images. Radiomics can be used as a diagnostic and prognostic tool in personalized medicine. It utilizes quantitative image analysis to extract features in conjunction with machine learning and artificial intelligence (AI) methods like support vector machines, random forest, and convolutional neural networks for feature selection and classification. Selected features can then serve as imaging biomarkers to predict high-risk PCLs. Radiomics studies conducted heretofore on PCLs have shown promising results. This cost-effective approach would help us to differentiate benign PCLs from malignant ones and potentially guide clinical decision-making leading to better utilization of healthcare resources. In this review, we discuss the process of radiomics, its myriad applications such as diagnosis, prognosis, and prediction of therapy response. We also discuss the outcomes of studies involving radiomic analysis of PCLs and pancreatic cancer, and challenges associated with this novel field along with possible solutions. Although these studies highlight the potential benefit of radiomics in the prevention and optimal treatment of pancreatic cancer, further studies are warranted before incorporating radiomics into the clinical decision support system.

Background Trefoil factors (TFF1, TFF2, and TFF3) are small secretory molecules that recently have gained significant attention in multiple studies as an integral component of pancreatic cancer (PC) subtype-specific gene signature. Here, we comprehensively investigated the diagnostic potential of all the member of trefoil family, i.e., TFF1, TFF2, and TFF3 in combination with CA19.9 for detection of PC. Methods Trefoil factors (TFFs) gene expression was analyzed in publicly available cancer genome datasets, followed by assessment of their expression in genetically engineered spontaneous mouse model (GEM) of PC (KrasG12D; Pdx1-Cre (KC)) and in human tissue microarray consisting of normal pancreas adjacent to tumor (NAT), precursor lesions (PanIN), and various pathological grades of PC by immunohistochemistry (IHC). Serum TFFs and CA19.9 levels were evaluated via ELISA in comprehensive sample set (n = 362) comprised of independent training and validation sets each containing benign controls (BC), chronic pancreatitis (CP), and various stages of PC. Univariate and multivariate logistic regression and receiver operating characteristic curves (ROC) were used to examine their diagnostic potential both alone and in combination with CA19.9. Findings The publicly available datasets and expression analysis revealed significant increased expression of TFF1, TFF2, and TFF3 in human PanINs and PC tissues. Assessment of KC mouse model also suggested upregulated expression of TFFs in PanIN lesions and early stage of PC. In serum analyses studies, TFF1 and TFF2 were significantly elevated in early stages of PC in comparison to benign and CP control group while significant elevation in TFF3 levels were observed in CP group with no further elevation in its level in early stage PC group. In receiver operating curve (ROC) analyses, combination of TFFs with CA19.9 emerged as promising panel for discriminating early stage of PC (EPC) from BC (AUCTFF1+TFF2+TFF3+CA19.9 = 0.93) as well as CP (AUCTFF1+TFF2+TFF3+CA19.9 = 0.93). Notably, at 90% specificity (desired for blood-based biomarker panel), TFFs combination improved CA19.9 sensitivity by 10% and 25% to differentiate EPC from BC and CP respectively. In an independent blinded validation set, the combination of TFFs and CA19.9 (AUCTFF1+TFF2+TFF3+CA19.9 = 0.82) also improved the overall efficacy of CA19.9 (AUCCA19.9 = 0.66) to differentiate EPC from CP proving unique biomarker capabilities of TFFs to distinguish early stage of this deadly lethal disease. Interpretation In silico, tissue and serum analyses validated significantly increased level of all TFFs in precursor lesions and early stages of PC. The combination of TFFs enhanced sensitivity and specificity of CA19.9 to discriminate early stage of PC from benign control and chronic pancreatitis groups.