Citations

(1) Background: The association between proton pump inhibitor (PPI) use and hepatocellular carcinoma (HCC) has been controversial, especially in the general population. We aimed to determine the impact of PPI on HCC risk in participants without liver cirrhosis or chronic hepatitis virus infection. (2) Methods: We assessed 406,057 participants from the Korean National Health Insurance Service database who underwent health screening from 2003 to 2006. We evaluated exposure to PPI before the index date using a standardized daily defined dose (DDD) system. The association of proton pump inhibitor use with the risk of HCC was evaluated using multivariable-adjusted Cox proportional hazards regression. (3) Results: Compared with non-users, PPI use was not associated with the HCC risk in low (<30 DDDs; aHR, 1.07; 95% CI, 0.91–1.27), intermediate (30 ≤ PPI < 60 DDDs; aHR, 0.96; 95% CI, 0.73–1.26), and high (≥60 DDDs; aHR, 0.86; 95% CI, 0.63–1.17) PPI groups in the final adjustment model. In addition, risks of cirrhosis-associated HCC and non-cirrhosis-associated HCC were not significantly associated with PPI use. The results remained consistent after excluding events that occurred within 1, 2, and 3 years to exclude pre-existing conditions that may be associated with the development of HCC. We also found no PPI-associated increase in HCC risk among the selected population, such as those with obesity, older age, and chronic liver diseases. (4) Conclusions: PPI use may not be associated with HCC risk regardless of the amount. We call for future studies conducted in other regions to generalize our findings.

Alzheimer’s disease (AD) is characterized by the deposition of extracellular amyloid plaques and intracellular accumulation of neurofibrillary tangles (NFT). Amyloid beta (Aβ) and tau imaging are widely used for diagnosing and monitoring AD in clinical settings. We evaluated the pathology of a recently developed 6 × Tg − AD (6 × Tg) mouse model by crossbreeding 5 × FAD mice with mice expressing mutant (P301L) tau protein using micro-positron emission tomography (PET) image analysis. PET studies were performed in these 6 × Tg mice using [18F]Flutemetamol, which is an amyloid PET radiotracer; [18F]THK5351 and [18F]MK6240, which are tau PET radiotracers; moreover, [18F]DPA714, which is a translocator protein (TSPO) radiotracer, and comparisons were made with age-matched mice of their respective parental strains. We compared group differences in standardized uptake value ratio (SUVR), kinetic parameters, biodistribution, and histopathology. [18F]Flutemetamol images showed prominent cortical uptake and matched well with 6E10 staining images from 2-month-old 6 × Tg mice. [18F]Flutemetamol images showed a significant correlation with [18F]DPA714 in the cortex and hippocampus. [18F]THK5351 images revealed prominent hippocampal uptake and matched well with AT8 immunostaining images in 4-month-old 6 × Tg mice. Moreover, [18F]THK5351 images were confirmed using [18F]MK6240, which revealed significant correlations in the cortex and hippocampus. Uptake of [18F]THK5351 or [18F]MK6240 was highly correlated with [18F]Flutemetamol in 4-month-old 6 × Tg mice. In conclusion, PET imaging revealed significant age-related uptake of Aβ, tau, and TSPO in 6 × Tg mice, which was highly correlated with age-dependent pathology.

NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Non–Small Cell Lung Cancer (NSCLC) provide recommended management for patients with NSCLC, including diagnosis, primary treatment, surveillance for relapse, and subsequent treatment. Patients with metastatic lung cancer who are eligible for targeted therapies or immunotherapies are now surviving longer. This selection from the NCCN Guidelines for NSCLC focuses on targeted therapies for patients with metastatic NSCLC and actionable mutations.

AbstractIdentifying biomarkers associated with disease progression and drug resistance are important for personalized care. We investigated the expression of 121 curated genes, related to immunomodulatory drugs (IMiDs) and proteasome inhibitors (PIs) responsiveness. We analyzed 28 human multiple myeloma (MM) cell lines with known drug sensitivities and 130 primary MM patient samples collected at different disease stages, including newly diagnosed (ND), on therapy (OT), and relapsed and refractory (RR, collected within 12 months before the patients’ death) timepoints. Our findings led to the identification of a subset of genes linked to clinical drug resistance, poor survival, and disease progression following combination treatment containing IMIDs and/or PIs. Finally, we built a seven‐gene model (MM‐IMiD and PI sensitivity‐7 genes [IP‐7]) using digital gene expression profiling data that significantly separates ND patients from IMiD‐ and PI‐refractory RR patients. Using this model, we retrospectively analyzed RNA sequcencing (RNAseq) data from the Mulltiple Myeloma Research Foundation (MMRF) CoMMpass (n = 578) and Mayo Clinic MM patient registry (n = 487) to divide patients into probabilities of responder and nonresponder, which subsequently correlated with overall survival, disease stage, and number of prior treatments. Our findings suggest that this model may be useful in predicting acquired resistance to treatments containing IMiDs and/or PIs.

Background: The morphologic distinction between thymic carcinomas and thymomas, specifically types B3, A, and occasionally micronodular thymomas with lymphoid stroma (MNTLS) can be challenging, as has also been shown in interobserver reproducibility studies. Since thymic carcinomas have a worse prognosis than thymomas, the diagnosis is important for patient management and treatment. This study aimed to identify a panel of immunohistochemical (IHC) markers that aid in the distinction between thymomas and thymic carcinomas in routine practice. Materials and Method: Thymic carcinomas, type A and B3 thymomas, and MNTLS were identified in an institutional database of thymic epithelial tumors (TET) (1963–2021). IHC was performed using antibodies against TdT, Glut-1, CD5, CD117, BAP1, and mTAP. Percent tumor cell staining was recorded (Glut-1, CD5, CD117); loss of expression (BAP1, mTAP) was considered if essentially all tumor cells were negative; TdT was recorded as thymocytes present or absent (including rare thymocytes). Results: 81 specimens included 44 thymomas (25 type A, 11 type B3, 8 MNTLS) and 37 thymic carcinomas (including 24 squamous cell carcinomas). Using BAP1, mTAP, CD117 (cut-off, 10%), and TdT, 88.9% of thymic carcinomas (95.7% of squamous cell carcinomas) and 77.8% of thymomas could be predicted. Glut-1 expression was not found to be useful in that distinction. All tumors that expressed CD5 in ≥50% of tumor cells also expressed CD117 in ≥10% of tumor cells. In four carcinomas with homozygous deletion of CDKN2A, mTAP expression was lost in two squamous cell carcinomas and in a subset of tumor cells of an adenocarcinoma and was preserved in a lymphoepithelial carcinoma. Conclusion: A panel of immunostains including BAP1, mTAP, CD117 (using a cut-off of 10% tumor cell expression), and TdT can be useful in the distinction between thymomas and thymic carcinomas, with only a minority of cases being inconclusive.

Abstract Background The use of menopausal hormone therapy (MHT) may interact with genetic variants to influence colorectal cancer (CRC) risk. Methods We conducted a genome-wide, gene-environment interaction between single nucleotide polymorphisms and the use of any MHT, estrogen only, and combined estrogen-progestogen therapy with CRC risk, among 28 486 postmenopausal women (11 519 CRC patients and 16 967 participants without CRC) from 38 studies, using logistic regression, 2-step method, and 2– or 3–degree-of-freedom joint test. A set-based score test was applied for rare genetic variants. Results The use of any MHT, estrogen only and estrogen-progestogen were associated with a reduced CRC risk (odds ratio [OR] = 0.71, 95% confidence interval [CI] = 0.64 to 0.78; OR = 0.65, 95% CI = 0.53 to 0.79; and OR = 0.73, 95% CI = 0.59 to 0.90, respectively). The 2-step method identified a statistically significant interaction between a GRIN2B variant rs117868593 and MHT use, whereby MHT-associated CRC risk was statistically significantly reduced in women with the GG genotype (OR = 0.68, 95% CI = 0.64 to 0.72) but not within strata of GC or CC genotypes. A statistically significant interaction between a DCBLD1 intronic variant at 6q22.1 (rs10782186) and MHT use was identified by the 2–degree-of-freedom joint test. The MHT-associated CRC risk was reduced with increasing number of rs10782186-C alleles, showing odds ratios of 0.78 (95% CI = 0.70 to 0.87) for TT, 0.68 (95% CI = 0.63 to 0.73) for TC, and 0.66 (95% CI = 0.60 to 0.74) for CC genotypes. In addition, 5 genes in rare variant analysis showed suggestive interactions with MHT (2-sided P &lt; 1.2 × 10−4). Conclusion Genetic variants that modify the association between MHT and CRC risk were identified, offering new insights into pathways of CRC carcinogenesis and potential mechanisms involved.

Abstract Introduction Late‐onset Alzheimer's disease (LOAD) is a complex neurodegenerative disease characterized by multiple progressive stages, glucose metabolic dysregulation, Alzheimer's disease (AD) pathology, and inexorable cognitive decline. Discovery of metabolic profiles unique to sex, apolipoprotein E ( APOE ) genotype, and stage of disease progression could provide critical insights for personalized LOAD medicine. Methods Sex‐ and APOE ‐specific metabolic networks were constructed based on changes in 127 metabolites of 656 serum samples from the Alzheimer's Disease Neuroimaging Initiative cohort. Results Application of an advanced analytical platform identified metabolic drivers and signatures clustered with sex and/or APOE ɛ4, establishing patient‐specific biomarkers predictive of disease state that significantly associated with cognitive function. Presence of the APOE ɛ4 shifts metabolic signatures to a phosphatidylcholine‐focused profile overriding sex‐specific differences in serum metabolites of AD patients. Discussion These findings provide an initial but critical step in developing a diagnostic platform for personalized medicine by integrating metabolomic profiling and cognitive assessments to identify targeted precision therapeutics for AD patient subgroups through computational network modeling.

Background Current mortality data for pulmonary arterial hypertension (PAH) in the United States are based on registries that enrolled patients prior to 2010. We sought to determine mortality in PAH in the modern era using the PHAR (Pulmonary Hypertension Association Registry). Methods and Results We identified all adult patients with PAH enrolled in the PHAR between September 2015 and September 2020 (N=935). We used Kaplan‐Meier survival analysis and Cox proportional hazards models to assess mortality at 1, 2, and 3 years. Patients were stratified based on disease severity by 3 validated risk scores. In treatment‐naïve patients, we compared survival based on initial treatment strategy. The median age was 56 years (44–68 years), and 76% were women. Of the 935 patients, 483 (52%) were ≤6 months from PAH diagnosis. There were 121 deaths (12.9%) during a median follow‐up time of 489 days (281–812 days). The 1‐, 2‐, and 3‐year mortality was 8% (95% CI, 6%–10%), 16% (95% CI, 13%–19%), and 21% (95% CI, 17%–25%), respectively. When stratified into low‐, intermediate‐, and high‐risk PAH, the mortality at 1, 2, and 3 years was 1%, 4% to 6%, and 7% to 11% for low risk; 7% to 8%, 11% to 16%, and 18% to 20% for intermediate risk; and 12% to 19%, 22% to 38%, and 28% to 55% for high risk, respectively. In treatment‐naïve patients, initial combination therapy was associated with better 1‐year survival (adjusted hazard ratio, 0.43 [95% CI, 0.19–0.95]; P =0.037). Conclusions Mortality in the intermediate‐ and high‐risk patients with PAH remains unacceptably high in the PHAR, suggesting the importance for early diagnosis, aggressive use of available therapies, and the need for better therapeutics.

Abstract Background: Currently known associations between common genetic variants and colorectal cancer explain less than half of its heritability of 25%. As alcohol consumption has a J-shape association with colorectal cancer risk, nondrinking and heavy drinking are both risk factors for colorectal cancer. Methods: Individual-level data was pooled from the Colon Cancer Family Registry, Colorectal Transdisciplinary Study, and Genetics and Epidemiology of Colorectal Cancer Consortium to compare nondrinkers (≤1 g/day) and heavy drinkers (&gt;28 g/day) with light-to-moderate drinkers (1–28 g/day) in GxE analyses. To improve power, we implemented joint 2df and 3df tests and a novel two-step method that modifies the weighted hypothesis testing framework. We prioritized putative causal variants by predicting allelic effects using support vector machine models. Results: For nondrinking as compared with light-to-moderate drinking, the hybrid two-step approach identified 13 significant SNPs with pairwise r2 &gt; 0.9 in the 10q24.2/COX15 region. When stratified by alcohol intake, the A allele of lead SNP rs2300985 has a dose–response increase in risk of colorectal cancer as compared with the G allele in light-to-moderate drinkers [OR for GA genotype = 1.11; 95% confidence interval (CI), 1.06–1.17; OR for AA genotype = 1.22; 95% CI, 1.14–1.31], but not in nondrinkers or heavy drinkers. Among the correlated candidate SNPs in the 10q24.2/COX15 region, rs1318920 was predicted to disrupt an HNF4 transcription factor binding motif. Conclusions: Our study suggests that the association with colorectal cancer in 10q24.2/COX15 observed in genome-wide association study is strongest in nondrinkers. We also identified rs1318920 as the putative causal regulatory variant for the region. Impact: The study identifies multifaceted evidence of a possible functional effect for rs1318920.