Citations

Abstract Invasive lobular breast carcinoma (ILC) is the most common special histological subtype of breast cancer, which accounts for 10-15% of all cases. To study the phenotype characteristics, metastatic growth kinetic and immune microenvironment of ILC, we developed an orthotopically transplantable cell line model from the spontaneous mammary fat pad tumor of CDH1-PTEN dual knockout C57BL/6 mouse with Cre-loxP system, designated CPT6. CPT6 recapitulates single-file growth pattern of human ILC, with pleomorphic features and a high mitotic index. RNA sequencing together with whole exome sequencing reveals a luminal A subtype with targetable driver mutations such as Kras G12C. As a novel orthotopically transplantable ILC model in immune competent mice, CPT6 shows robust in vivo growth and metastatic rate, and has moderate immunogenicity which appears to be T-cell independent. We also profiled the immune microenvironment of CPT6, revealing a myeloid-rich environment with dominant M2-macrophage population, which is concordant with human ILC. In summary, this model recapitulates human ILC phenotype and represents a valuable preclinical platform for evaluating immunotherapy and other therapeutic strategies for invasive lobular breast carcinoma.

Key Points In individuals undergoing bariatric surgery, the magnitude of decline in GFR postbariatric surgery is directly associated with presurgery GFR. Reductions in GFR postsurgery were only weakly correlated with weight loss. In obese individuals, GFR-estimating equations performed best when deindexed and when applied in people with reduced kidney function. Background Important questions remain about how bariatric ( i.e ., weight loss) surgery affects measured GFR (mGFR) and eGFR as well as what factors influence change in mGFR postsurgery. Methods Data were pooled from all seven available studies (dates: 2004–2018) measuring GFR prebariatric and postbariatric surgery using gold standard methods. Change in postsurgery mGFR, factors that could influence change in mGFR, and effects on five GFR-estimating equations were analyzed using standard statistical methods. Results The cohort included 105 individuals from the United States and Europe. Sixty-eight percent were female, 97% were White, the mean age was 50 years (range, 24–70), and the mean body mass index was 46±8 kg/m 2 . The mean presurgery mGFR of 107 ml/min (range, 31–215) fell to 92 ml/min (−14%; 95% confidence interval, −21 to −10) postsurgery, with a strong linear relationship existing between presurgery mGFR and % change in GFR ( r =−0.51; −0.64 to −0.35). Individuals with presurgery mGFR ≥90, 60 to <90, and<60 ml/min had −25 (−32 to −19), 1 (−11 to 13), and 7 (0 to 14) ml/min mean postoperative changes in mGFR. Change in weight was significantly correlated with change in mGFR ( r =0.22; 0.03 to 0.40). After adjusting for sex, changes in mGFR postsurgery were associated with higher presurgical age (−0.6 ml/min per year; −1.1 to −0.2), mGFR (−0.5 ml/min per 1 ml/min; −0.6 to −0.4), and change in systolic BP (−0.3 ml/min per 1 mm Hg; −0.6 to 0.0). All GFR-estimating equations significantly underestimated postsurgical GFR reductions in people with preserved kidney function and had improved bias, precision, and accuracy when deindexed and applied to individuals with mGFR <90 ml/min. Conclusions In individuals with obesity undergoing bariatric surgery, the magnitude of postsurgical decline in mGFR was directly associated with presurgery GFR and weakly correlated with weight loss. In addition, GFR-estimating equations' performance improved when deindexed and used in people with reduced kidney function, with the combined creatinine/cystatin C equations having the best overall performance.

Hypertension is among the most important risk factors for cardiovascular disease, chronic kidney disease, and dementia. The artificial intelligence (AI) field is advancing quickly, and there has been little discussion on how AI could be leveraged for improving the diagnosis and management of hypertension. AI technologies, including machine learning tools, could alter the way we diagnose and manage hypertension, with potential impacts for improving individual and population health. The development of successful AI tools in public health and health care systems requires diverse types of expertise with collaborative relationships between clinicians, engineers, and data scientists. Unbiased data sources, management, and analyses remain a foundational challenge. From a diagnostic standpoint, machine learning tools may improve the measurement of blood pressure and be useful in the prediction of incident hypertension. To advance the management of hypertension, machine learning tools may be useful to find personalized treatments for patients using analytics to predict response to antihypertension medications and the risk for hypertension-related complications. However, there are real-world implementation challenges to using AI tools in hypertension. Herein, we summarize key findings from a diverse group of stakeholders who participated in a workshop held by the National Heart, Lung, and Blood Institute in March 2023. Workshop participants presented information on communication gaps between clinical medicine, data science, and engineering in health care; novel approaches to estimating BP, hypertension risk, and BP control; and real-world implementation challenges and issues.

Cardiovascular disease surveillance involves quantifying the evolving population‐level burden of cardiovascular outcomes and risk factors as a data‐driven initial step followed by the implementation of interventional strategies designed to alleviate this burden in the target population. Despite widespread acknowledgement of its potential value, a national surveillance system dedicated specifically to cardiovascular disease does not currently exist in the United States. Routinely collected health care data such as from electronic health records (EHRs) are a possible means of achieving national surveillance. Accordingly, this article elaborates on some key strengths and limitations of using EHR data for establishing a national cardiovascular disease surveillance system. Key strengths discussed include the: (1) ubiquity of EHRs and consequent ability to create a more “national” surveillance system, (2) existence of a common data infrastructure underlying the health care enterprise with respect to data domains and the nomenclature by which these data are expressed, (3) longitudinal length and detail that define EHR data when individuals repeatedly patronize a health care organization, and (4) breadth of outcomes capable of being surveilled with EHRs. Key limitations discussed include the: (1) incomplete ascertainment of health information related to health care–seeking behavior and the disconnect of health care data generated at separate health care organizations, (2) suspect data quality resulting from the default information‐gathering processes within the clinical enterprise, (3) questionable ability to surveil patients through EHRs in the absence of documented interactions, and (4) the challenge in interpreting temporal trends in health metrics, which can be obscured by changing clinical and administrative processes.

Background and objectives It is unclear whether the presence of albuminuria modifies the effects of intensive systolic BP control on risk of eGFR decline, cardiovascular events, or mortality. Design, setting, participants, & measurements The Systolic Blood Pressure Intervention Trial randomized nondiabetic adults ≥50 years of age at high cardiovascular risk to a systolic BP target of <120 or <140 mm Hg, measured by automated office BP. We compared the absolute risk differences and hazard ratios of ≥40% eGFR decline, the Systolic Blood Pressure Intervention Trial primary cardiovascular composite outcome, and all-cause death in those with or without baseline albuminuria (urine albumin-creatinine ratio ≥30 mg/g). Results Over a median follow-up of 3.1 years, 69 of 1723 (4%) participants with baseline albuminuria developed ≥40% eGFR decline compared with 61 of 7162 (1%) participants without albuminuria. Incidence rates of ≥40% eGFR decline were higher in participants with albuminuria (intensive, 1.74 per 100 person-years; standard, 1.17 per 100 person-years) than in participants without albuminuria (intensive, 0.48 per 100 person-years; standard, 0.11 per 100 person-years). Although effects of intensive BP lowering on ≥40% eGFR decline varied by albuminuria on the relative scale (hazard ratio, 1.48; 95% confidence interval, 0.91 to 2.39 for albumin-creatinine ratio ≥30 mg/g; hazard ratio, 4.55; 95% confidence interval, 2.37 to 8.75 for albumin-creatinine ratio <30 mg/g; P value for interaction <0.001), the absolute increase in ≥40% eGFR decline did not differ by baseline albuminuria (incidence difference, 0.38 events per 100 person-years for albumin-creatinine ratio ≥30 mg/g; incidence difference, 0.58 events per 100 person-years for albumin-creatinine ratio <30 mg/g; P value for interaction =0.60). Albuminuria did not significantly modify the beneficial effects of intensive systolic BP lowering on cardiovascular events or mortality evaluated on relative or absolute scales. Conclusions Albuminuria did not modify the absolute benefits and risks of intensive systolic BP lowering.

Significance Statement Dominantly inherited polycystic kidney and liver phenotypes occur when epithelial cells in these organs have reduction of polycystin-1 functional dosage. In a cohort of genetically unresolved polycystic liver and kidney disease, the authors identified heterozygous loss of function mutations in ALG9 . ALG9 encodes an endoplasmic reticulum enzyme that builds N -glycans, and the authors show that Alg9 inactivation results in impaired polycystin-1 maturation. Using a novel ‘genotype-first’ approach to ascertain individuals based strictly on their ALG9 genotype from a large cohort of exome-sequenced individuals, the authors find that 7/8 (88%) of ALG9 mutation carriers over age 50 have multiple kidney cysts. Together, these findings identify ALG9 as a novel human polycystic kidney and liver disease gene and support the utility of a genotype-driven approach to candidate disease gene validation and genotype-phenotype correlation. Background Mutations in PKD1 or PKD2 cause typical autosomal dominant polycystic kidney disease (ADPKD), the most common monogenic kidney disease. Dominantly inherited polycystic kidney and liver diseases on the ADPKD spectrum are also caused by mutations in at least six other genes required for protein biogenesis in the endoplasmic reticulum, the loss of which results in defective production of the PKD1 gene product, the membrane protein polycystin-1 (PC1). Methods We used whole-exome sequencing in a cohort of 122 patients with genetically unresolved clinical diagnosis of ADPKD or polycystic liver disease to identify a candidate gene, ALG9 , and in vitro cell-based assays of PC1 protein maturation to functionally validate it. For further validation, we identified carriers of ALG9 loss-of-function mutations and noncarrier matched controls in a large exome-sequenced population-based cohort and evaluated the occurrence of polycystic phenotypes in both groups. Results Two patients in the clinically defined cohort had rare loss-of-function variants in ALG9 , which encodes a protein required for addition of specific mannose molecules to the assembling N-glycan precursors in the endoplasmic reticulum lumen. In vitro assays showed that inactivation of Alg9 results in impaired maturation and defective glycosylation of PC1. Seven of the eight (88%) cases selected from the population-based cohort based on ALG9 mutation carrier state who had abdominal imaging after age 50; seven (88%) had at least four kidney cysts, compared with none in matched controls without ALG9 mutations. Conclusions ALG9 is a novel disease gene in the genetically heterogeneous ADPKD spectrum. This study supports the utility of phenotype characterization in genetically-defined cohorts to validate novel disease genes, and provide much-needed genotype-phenotype correlations.