Citations

Objective: To evaluate glycemic outcomes in the Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) randomized clinical trial (RCT) participants during an observational extension phase. Research Design and Methods: WISDM RCT was a 26-week RCT comparing continuous glucose monitoring (CGM) with blood glucose monitoring (BGM) in 203 adults aged ≥60 years with type 1 diabetes. Of the 198 participants who completed the RCT, 100 (98%) CGM group participants continued CGM (CGM-CGM cohort) and 94 (98%) BGM group participants initiated CGM (BGM-CGM cohort) for an additional 26 weeks. Results: CGM was used a median of >90% of the time at 52 weeks in both cohorts. In the CGM-CGM cohort, median time <70 mg/dL decreased from 5.0% at baseline to 2.6% at 26 weeks and remained stable with a median of 2.8% at 52 weeks ( P < 0.001 baseline to 52 weeks). Participants spent more time in range 70–180 mg/dL (TIR) (mean 56% vs. 64%; P < 0.001) and had lower hemoglobin A1c (HbA1c) (mean 7.6% [59 mmol/mol] vs. 7.4% [57 mmol/mol]; P = 0.01) from baseline to 52 weeks. In BGM-CGM, from 26 to 52 weeks median time <70 mg/dL decreased from 3.9% to 1.9% ( P < 0.001), TIR increased from 56% to 60% ( P = 0.006) and HbA1c decreased from 7.5% (58 mmol/mol) to 7.3% (57 mmol/mol) ( P = 0.025). In BGM-CGM, a severe hypoglycemic event was reported for nine participants while using BGM during the RCT and for two participants during the extension phase with CGM ( P = 0.02). Conclusions: CGM use reduced hypoglycemia without increasing hyperglycemia in older adults with type 1 diabetes. These data provide further evidence for fully integrating CGM into clinical practice. Clinicaltrials.gov (NCT03240432)

Abstract Background: Data supporting high tumor mutational burden (TMB-H) as a lone biomarker for an immune responsive tumor microenvironment (TME) or of response to immune checkpoint inhibition (ICI) in metastatic breast cancer (MBC) is weak; yet the tumor agnostic approval in TMB-H advanced tumors makes ICI an option in the clinic. PDL-1 positivity (PDL-1+) is the only consistent biomarker for ICI benefit in metastatic triple-negative BC (mTNBC). We sought to further evaluate concurrent predictors of an immune responsive or non-responsive TME within TMB-H MBC. Methods: Tumor samples (N=5621) obtained from patients with MBC were analyzed by next-generation sequencing (NGS) of DNA (592-gene panel or whole exome sequencing) and RNA (whole transcriptome sequencing) at Caris Life Sciences (Phoenix, AZ). Friends of Cancer Research TMB Harmonization Project (Merino et al., 2020) recommendations, with the TMB-H threshold set to ≥ 10 muts/Mb, were used. PDL-1+ (≥1% immune cells stained) assessed by IHC (Ventana SP142). Deficient-mismatch repair (dMMR) and high microsatellite instability (MSI-H) were determined by IHC and NGS, respectively. Gene expression profiling assessed 21 immune-related signatures and TMEs using MCP-counter (Betch et al., 2016) and quanTIseq (Finotello et al., 2019). Immune cell types were classified as immune-responsive (CD8+ and CD4+ T cells, B cells, myeloid dendritic cells, M1 macrophages, NK cells, and neutrophils) or non-responsive (Tregs, M2 macrophages, and monocytes). P-values adjusted by the Benjamini-Hochberg procedure. Exploratory p-values reported for sub-analyses of TMB-H responsive vs nonresponsive TME and PDL1+ vs PDL1- subgroups. Results: TMB-H was identified in 461/5621 (8.2%) MBC samples. TMB-H tumors exhibit significant enrichment of dMMR/MSI-H (7 vs 0%, p&lt;0.0001) and IHC-PDL1+ (36 vs 28%, p&lt;0.05) compared to TMB-L. TMB-H was not associated with increased abundance or proportion of immune responsive cell types or immune response gene signatures (e.g. antigen presentation), yet positive trends were observed. To identify predictive biomarkers of immune responsive (hot) or non-responsive (cold) TME profiles within TMB-H MBC, we generated a composite TME score representing the overall proportion of hot vs cold immune cell types. APOBEC mutagenesis was common in TMB-H tumors (72%) and was more frequently associated with an immune cold TME (77 vs 66% in hot TME subgroup, p=0.068). Within TMB-H tumors, cold TMEs were enriched with mutations (muts) in PIK3CA (61 vs 47%), CHEK2 (5 vs 0%), CBFB (4 vs 0%), amplifications (amp) of CCND1 (13 vs 4%), FGF19 (16 vs 5%), FGF4 (11 vs 4%), and MMLT6 (6 vs 0%), and expression of IHC-AR (75 vs 58%) compared to hot TMEs (all p&lt;0.05). Hot TMEs had increased frequency of MSI-H (12 vs 5%) and KMT2D muts (12 vs 3%) compared to cold TMEs (all p&lt;0.05). Amongst TMB-H and TMB-L tumors, PDL1+ was significantly associated with increased proportion of immune responsive cell types (ie CD8+) compared to PDL1- tumors (0.14 vs -0.08 TME score, p&lt;0.0001). Comparison of TMB-H tumors by PDL1 status revealed enrichment of TP53 (67 vs 53%) and B2M (8 vs 0%) muts in PDL1+ tumors, while IHC-ER positivity (68 vs 54%) and CDH1 muts (32 vs 16%) were increased in PDL1- tumors (all p&lt;0.05). Conclusions: High TMB alone does not strongly correlate with immune infiltrate or immune response gene signatures in MBC. Within TMB-H MBC, concurrent mutations in MSI-H and KMT2D are associated with an immune responsive TME while mutations in PIK3CA, CHEK2, CBFB, amplifications of CCND1, FGF19, FGF4, and MMLT6, and expression of IHC-AR are associated with an immune non-responsive TME. Co-occurring biomarkers within TMB-H breast cancer warrant evaluation in prospective cohorts for response or resistance to ICI to help develop composite biomarkers in breast cancer. Citation Format: Sarah Sammons, Andrew Elliott, Jeremy Force, Saranya Chumsri, Carey Anders, Antoinette R Tan, Daniel Magee, Jia Zeng, W. Michael Korn, Mustafa Kahsraw, Evanthia T Roussos Torres. Concurrent predictors of an immune responsive tumor microenvironment within tumor mutational burden-high breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-08-08.

Abstract Alcohol use disorder (AUD) is a chronic relapsing brain disorder characterized by an impaired ability to stop or control alcohol consumption despite adverse social, occupational, or health consequences. AUD affects nearly one-third of adults at some point during their lives, with an associated cost of approximately $249 billion annually in the U.S. alone. The effects of alcohol consumption are expected to increase significantly during the COVID-19 pandemic, with alcohol sales increased by approximately 54%, potentially exacerbating health concerns and risk-taking behaviors. Unfortunately, existing pharmacological and behavioral therapies for AUD have historically been associated with poor success rates, with approximately 40% of individuals relapsing within three years of treatment. Pre-clinical studies have shown that chronic alcohol consumption leads to significant changes in synaptic function within the dorsal medial striatum (DMS), one of the brain regions associated with AUD and responsible for mediating goal-directed behavior. Specifically, chronic alcohol consumption has been associated with hyperactivity of dopamine receptor 1 (D1) medium spiny neurons (MSN) and hypoactivity of dopamine receptor 2 (D1) MSNs within the DMS. Optogenetic, chemogenetic, and transgenic approaches have demonstrated that reducing the D1/D2 MSN signaling imbalance decreases alcohol self-administration in rodent models of AUD. Here, we present an electrical stimulation approach that uses ultra-low (≤ 1Hz) frequency (ULF) spike-timing dependent plasticity (STDP) in mouse models of AUD to reduce DMS D1/D2 MSN signaling imbalances by stimulating D1-MSN afferents into the GPi and ACC glutamatergic projections to the DMS in a time-locked stimulation sequence. Our data suggest that GPi/ACC ULF-STDP selectively decreases DMS D1-MSN hyperactivity leading to reduced alcohol consumption without evoking undesired affective behaviors using electrical stimulation rather than approaches requiring genetic modification. This work represents a step towards fulfilling the unmet need for a reliable method of treating severe AUD through cell-type specific control with clinically available neuromodulation tools.

Abstract Type 2 diabetes has reached endemic proportions and is a substantial burden for the affected patients and the society. Along with lifestyle factors, a number of genetic loci predisposing to type 2 diabetes have been identified, including SORCS1 that encodes the transmembrane receptor SorCS1. The ectodomain of SorCS1 (sol-SorCS1) is shed from plasma membranes but the biological function of this fragment is unknown. Here we show that sol-SorCS1 acts as a high-affinity binding partner for the insulin receptor to stabilize the receptor and increase insulin affinity, protein kinase B activation, and glucose uptake in myocytes. Sol-SorCS1 is liberated from adipocytes, and in diabetic patients the plasma concentration positively correlates with body mass index, but inversely with plasma glucose. In mouse models of insulin resistance, exogenous sol-SorCS1 restored insulin sensitivity. We conclude that sol-SorCS1 increases peripheral insulin sensitivity and propose sol-SorCS1 as a novel insulin sensitizing adipokine and potential antidiabetic agent.

Abstract Patients with metastatic breast cancer (MBC) typically have short survival times and their successful treatment represents one of most challenging aspects of patient care. This poor prognostic behavior is in part due to molecular features including increased tumor cell clonal heterogeneity, multiple drug resistance mechanisms, and alterations of the tumor microenvironment. The AURORA US Metastasis Project was established with the goal to identify molecular features specifically associated with metastasis. We therefore collected and molecularly characterized specimens from 55 metastatic breast cancer (BC) patients representing 51 primary cancers and 102 metastases. The 153 unique tumors were assayed using RNAseq, tumor/germline DNA exomes and low pass whole genome sequencing, and global DNA methylation microarrays. We found intrinsic molecular subtype differences between primary tumors and their matched metastases to be rare in triple negative breast cancer (TNBC)/Basal-like subtype tumors. Conversely, tumor subtype changes were relatively frequent in estrogen receptor positive (ER+) cancers where ~30% of Luminal A cases switched to Luminal B or HER2-enriched (HER2E) subtypes. Clonal evolution studies identified changes in expression subtype coincident with DNA clonality shifts, especially involving HER2 amplification and/or the HER2E expression subtype. We further found evidence for ER-mediated downregulation of genes involved in cell-cell adhesion in metastases. Microenvironment differences varied according to tumor subtype where ER+/Luminal metastases had lower fibroblast and endothelial cell content, while TNBC/Basal-like metastases showed a dramatic decrease in B cells and T cells. In 17% of metastatic tumors, we identified DNA hypermethylation and/or focal DNA deletions near HLA-A that were associated with its’ significantly reduced expression, and with lower immune cell infiltrates. We also identified low immune cell features in brain and liver metastases when compared to other metastatic sites, even within the same patient. These findings have direct implications for the treatment of metastatic breast cancer patients with immune- and HER2-targeting therapies and suggest potential novel therapeutic avenues for the improvement of outcomes for some patients with MBC.

The NCCN Guidelines for Hepatobiliary Cancers focus on the screening, diagnosis, staging, treatment, and management of hepatocellular carcinoma (HCC), gallbladder cancer, and cancer of the bile ducts (intrahepatic and extrahepatic cholangiocarcinoma). Due to the multiple modalities that can be used to treat the disease and the complications that can arise from comorbid liver dysfunction, a multidisciplinary evaluation is essential for determining an optimal treatment strategy. A multidisciplinary team should include hepatologists, diagnostic radiologists, interventional radiologists, surgeons, medical oncologists, and pathologists with hepatobiliary cancer expertise. In addition to surgery, transplant, and intra-arterial therapies, there have been great advances in the systemic treatment of HCC. Until recently, sorafenib was the only systemic therapy option for patients with advanced HCC. In 2020, the combination of atezolizumab and bevacizumab became the first regimen to show superior survival to sorafenib, gaining it FDA approval as a new frontline standard regimen for unresectable or metastatic HCC. This article discusses the NCCN Guidelines recommendations for HCC.

AimThe Cockcroft‐Gault (CG) creatinine‐based equation is still used to estimate glomerular filtration rate (eGFR) for drug dosage adjustment. Incorrect eGFR may lead to hazardous over‐ or underdosing.MethodsIn a cross‐sectional analysis, CG was validated against measured GFR (mGFR) in 14 804 participants and compared with the Modification‐of‐Diet‐in‐Renal‐Diseases (MDRD), Chronic‐Kidney‐Disease‐Epidemiology (CKD‐EPI), Lund‐Malmö‐Revised (LMR) and European‐Kidney‐Function‐Consortium (EKFC) equations. Validation focused on bias, imprecision and accuracy (percentage of estimates within ±30% of mGFR, P30), overall and stratified for mGFR, age and body mass index at mGFR <60 mL/min, as well as classification in mGFR stages.ResultsThe CG equation performed worse than the other equations, overall and in mGFR, age and BMI subgroups in terms of bias (systematic overestimation), imprecision and accuracy except for patients ≥65 years where bias and P30 were similar to MDRD and CKD‐EPI, but worse than LMR and EKFC. In subjects with mGFR <60 mL/min and at BMI 18.5‐25 kg/m2, all equations performed similarly, and for BMI < 18.5 kg/m2 CG and LMR had the best results though all equations had poor P30‐accuracy. At BMI ≥ 25 kg/m2 the bias of the CG increased with increasing BMI (+17.2 mL/min at BMI ≥ 40 kg/m2). The four more recent equations also classified mGFR stages better than CG.ConclusionsThe CG equation showed poor ability to estimate GFR overall and in analyses stratified for mGFR, age and BMI. CG was inferior to correctly classify the patients in the mGFR staging compared to more recent creatinine‐based equations.

Background: Several factors are associated with an increased risk of anterior cruciate ligament (ACL) reconstruction revision. However, the ability to accurately translate these factors into a quantifiable risk of revision at a patient-specific level has remained elusive. We sought to determine if machine learning analysis of the Norwegian Knee Ligament Register (NKLR) can identify the most important risk factors associated with subsequent revision of primary ACL reconstruction and develop a clinically meaningful calculator for predicting revision of primary ACL reconstruction. Methods: Machine learning analysis was performed on the NKLR data set. The primary outcome was the probability of revision ACL reconstruction within 1, 2, and/or 5 years. Data were split randomly into training sets (75%) and test sets (25%). Four machine learning models were tested: Cox Lasso, survival random forest, generalized additive model, and gradient boosted regression. Concordance and calibration were calculated for all 4 models. Results: The data set included 24,935 patients, and 4.9% underwent a revision surgical procedure during a mean follow-up (and standard deviation) of 8.1 ± 4.1 years. All 4 models were well-calibrated, with moderate concordance (0.67 to 0.69). The Cox Lasso model required only 5 variables for outcome prediction. The other models either used more variables without an appreciable improvement in accuracy or had slightly lower accuracy overall. An in-clinic calculator was developed that can estimate the risk of ACL revision (Revision Risk Calculator). This calculator can quantify risk at a patient-specific level, with a plausible range from near 0% for low-risk patients to 20% for high-risk patients at 5 years. Conclusions: Machine learning analysis of a national knee ligament registry can predict the risk of ACL reconstruction revision with moderate accuracy. This algorithm supports the creation of an in-clinic calculator for point-of-care risk stratification based on the input of only 5 variables. Similar analysis using a larger or more comprehensive data set may improve the accuracy of risk prediction, and future studies incorporating patients who have experienced failure of ACL reconstruction but have not undergone subsequent revision may better predict the true risk of ACL reconstruction failure. Level of Evidence: Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.