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CDK4/6 inhibition in combination with endocrine therapy is the standard of care for estrogen receptor (ER+) breast cancer, and although cytostasis is frequently observed, new treatment strategies that enhance efficacy are required. Here, we perform two independent genome-wide CRISPR screens to identify genetic determinants of CDK4/6 and endocrine therapy sensitivity. Genes involved in oxidative stress and ferroptosis modulate sensitivity, with GPX4 as the top sensitiser in both screens. Depletion or inhibition of GPX4 increases sensitivity to palbociclib and giredestrant, and their combination, in ER+ breast cancer models, with GPX4 null xenografts being highly sensitive to palbociclib. GPX4 perturbation additionally sensitises triple negative breast cancer (TNBC) models to palbociclib. Palbociclib and giredestrant induced oxidative stress and disordered lipid metabolism, leading to a ferroptosis-sensitive state. Lipid peroxidation is promoted by a peroxisome AGPAT3-dependent pathway in ER+ breast cancer models, rather than the classical ACSL4 pathway. Our data demonstrate that CDK4/6 and ER inhibition creates vulnerability to ferroptosis induction, that could be exploited through combination with GPX4 inhibitors, to enhance sensitivity to the current therapies in breast cancer. While CDK4/6 inhibitors (CDK4/6i) have improved outcomes for breast cancer patients, estrogen receptor (ER + ) breast cancers often develop resistance, and triple negative breast cancer (TNBC) show poor sensitivity. Here, the authors identify a vulnerability of CDK4/6i treated ER+ and TNBC on ferroptosis and identify the combination of CDK4/6i and GPX4 inhibition as synergistic.

Background Norway has a long history of using telemedicine, especially for geographical reasons. Despite the availability of promising telemedicine applications and the implementation of national initiatives and policies, the sustainability and scaling-up of telemedicine in the health system is still far from accomplished. The main objective of this study was to explore and identify the multi-level (micro, meso and macro) factors affecting telemedicine utilization in Norway. Methods We used a mixed methods approach. Data from a national registry were collected to analyze the use of outpatient visits and telemedicine contacts in Norway from 2009 to 2015. Interviews with key stakeholders at national, regional and local level helped complete and contextualize the data analysis and explore the main issues affecting the use of telemedicine by health authorities and hospitals. Relevant national documents were also used to support, contradict, contextualize or clarify information and data. Results Telemedicine use in Norway from 2009 to 2015 remained very low, not exceeding 0.5% of total outpatient activity at regional level and 0.1% at national level. All four regions used telemedicine. Of the 29 hospitals, 24 used it at least once over the 7-year period. Telemedicine was not used regularly everywhere, with some hospitals using it sporadically. Telemedicine was mostly used in selected specialties, including rehabilitation, neurosurgery, skin and venereal diseases. Three major themes affecting implementation and utilization of telemedicine in Norway emerged: (i) governance and strategy; (ii) organizational and professional dimensions; (iii) economic and financial dimensions. For each theme, a number of factors and challenges faced at different health care levels were identified. Conclusions This study allowed shedding light on multi-level and interdependent factors affecting utilization of telemedicine in Norway. The identification of the main implementation and utilization challenges might support decision makers and practitioners in the successful scaling-up of telemedicine. This work provides a knowledge base useful to other countries which intend to implement telemedicine or other digital health services into their healthcare systems.

Background Cardinality matching (CM), a novel matching technique, finds the largest matched sample meeting prespecified balance criteria thereby overcoming limitations of propensity score matching (PSM) associated with limited covariate overlap, which are especially pronounced in studies with small sample sizes. The current study proposes a framework for large-scale CM (LS-CM); and compares large-scale PSM (LS-PSM) and LS-CM in terms of post-match sample size, covariate balance and residual confounding at progressively smaller sample sizes. Methods Evaluation of LS-PSM and LS-CM within a comparative cohort study of new users of angiotensin-converting enzyme inhibitor (ACEI) and thiazide or thiazide-like diuretic monotherapy identified from a U.S. insurance claims database. Candidate covariates included patient demographics, and all observed prior conditions, drug exposures and procedures. Propensity scores were calculated using LASSO regression, and candidate covariates with non-zero beta coefficients in the propensity model were defined as matching covariates for use in LS-CM. One-to-one matching was performed using progressively tighter parameter settings. Covariate balance was assessed using standardized mean differences. Hazard ratios for negative control outcomes perceived as unassociated with treatment (i.e., true hazard ratio of 1) were estimated using unconditional Cox models. Residual confounding was assessed using the expected systematic error of the empirical null distribution of negative control effect estimates compared to the ground truth. To simulate diverse research conditions, analyses were repeated within 10 %, 1 and 0.5 % subsample groups with increasingly limited covariate overlap. Results A total of 172,117 patients (ACEI: 129,078; thiazide: 43,039) met the study criteria. As compared to LS-PSM, LS-CM was associated with increased sample retention. Although LS-PSM achieved balance across all matching covariates within the full study population, substantial matching covariate imbalance was observed within the 1 and 0.5 % subsample groups. Meanwhile, LS-CM achieved matching covariate balance across all analyses. LS-PSM was associated with better candidate covariate balance within the full study population. Otherwise, both matching techniques achieved comparable candidate covariate balance and expected systematic error. Conclusions LS-CM found the largest matched sample meeting prespecified balance criteria while achieving comparable candidate covariate balance and residual confounding. We recommend LS-CM as an alternative to LS-PSM in studies with small sample sizes or limited covariate overlap.

Objectives The Charlson comorbidity index (CCI), the most ubiquitous comorbid risk score, predicts one-year mortality among hospitalized patients and provides a single aggregate measure of patient comorbidity. The Quan adaptation of the CCI revised the CCI coding algorithm for applications to administrative claims data using the International Classification of Diseases (ICD). The purpose of the current study is to adapt and validate a coding algorithm for the CCI using the SNOMED CT standardized vocabulary, one of the most commonly used vocabularies for data collection in healthcare databases in the U.S. Methods The SNOMED CT coding algorithm for the CCI was adapted through the direct translation of the Quan coding algorithms followed by manual curation by clinical experts. The performance of the SNOMED CT and Quan coding algorithms were compared in the context of a retrospective cohort study of inpatient visits occurring during the calendar years of 2013 and 2018 contained in two U.S. administrative claims databases. Differences in the CCI or frequency of individual comorbid conditions were assessed using standardized mean differences (SMD). Performance in predicting one-year mortality among hospitalized patients was measured based on the c-statistic of logistic regression models. Results For each database and calendar year combination, no significant differences in the CCI or frequency of individual comorbid conditions were observed between vocabularies (SMD ≤ 0.10). Specifically, the difference in CCI measured using the SNOMED CT vs. Quan coding algorithms was highest in MDCD in 2013 (3.75 vs. 3.6; SMD = 0.03) and lowest in DOD in 2018 (3.93 vs. 3.86; SMD = 0.02). Similarly, as indicated by the c-statistic, there was no evidence of a difference in the performance between coding algorithms in predicting one-year mortality (SNOMED CT vs. Quan coding algorithms, range: 0.725–0.789 vs. 0.723–0.787, respectively). A total of 700 of 5,348 (13.1%) ICD code mappings were inconsistent between coding algorithms. The most common cause of discrepant codes was multiple ICD codes mapping to a SNOMED CT code ( n  = 560) of which 213 were deemed clinically relevant thereby leading to information gain. Conclusion The current study repurposed an important tool for conducting observational research to use the SNOMED CT standardized vocabulary.

Background Glioblastoma (GBM), the most common primary malignant brain tumor in adults, remains uniformly fatal due to the lack of effective targeted therapies. The epidermal growth factor receptor (EGFR) is the most frequently altered receptor tyrosine kinase oncogene in GBM with most alterations impacting the receptor ectodomain function, including gene amplification, mutation, rearrangement, and splicing site changes, which occur in approximately 50% of GBM tumors. Depatuxizumab mafodotin (Depatux-M; ABT-414), an antibody-drug conjugate composed of an EGFR-specific antibody (ABT-806) that recognizes the EGFR ectodomain linked to the cytotoxic agent monomethyl auristatin F, initially showed clinical promise. However, it failed to improve survival in phase III trials, highlighting an urgent need to understand mechanisms of resistance. Methods We generated in vivo ABT-414 resistant GBM models using patient-derived xenografts (PDXs) and performed genomics and transcriptomic profiling, including whole exome sequencing, bulk RNA sequencing, and single-cell RNA sequencing. Results ABT-414-resistant tumors exhibited transcriptional reprogramming characterized by upregulation of synaptic and developmental gene networks and downregulation of biosynthetic processes, indicative of a plastic, therapy-adaptive state. Whole-exome sequencing revealed novel mutations exclusive to resistant tumors, including a recurrent TEK (TIE2) S466I point mutation present in all ABT-414 resistant GBM12 PDX tumors. Functional validation demonstrated that ectopic expression of TEK S466I and TEK WT in PDX models reduced EGFR levels, suggesting a novel feedback mechanism linking TEK signaling to EGFR downregulation and contributes to resistance. Conclusion Our findings demonstrate that resistance to ABT-414 arises through both adaptive transcriptional remodeling and newly acquired genetic alterations. TEK-mediated suppression of EGFR represents a previously unrecognized mechanism of resistance, with potential implications for overcoming antibody-drug conjugate failure in GBM.

Objective:To study the longitudinal expression of interferon (IFN)-inducible genes in systemic lupus erythematosus (SLE) and determine their suitability as disease biomarkers.Methods:RNA was isolated from the peripheral blood of 94 patients with SLE and 11 controls and reverse transcribed into cDNA. The expression levels of five IFN-responsive genes (LY6E, OAS1, IFIT1, ISG15 and MX1) were determined by quantitative PCR, normalised to GAPDH and summed to generate a global IFN score. Patients were followed longitudinally for a period of 3–12 months, and the association between disease activity, as measured by the SLE disease activity index (SLEDAI-2K), and other clinical and laboratory variables was examined.Results:The expression of all five IFN-responsive genes was significantly higher in patients with SLE than in controls. The expression of LY6E, OAS1, IFIT1 and the global IFN score was associated with high disease activity. The global IFN score was also associated with active renal disease, a decreased C3, and the presence of anti-dsDNA or anti-RNA binding protein antibodies at a single point in time. However, there was a poor correlation between changes in this score and changes in disease activity, C3 or anti-dsDNA antibody levels in patients followed longitudinally. In most patients the levels of IFN-induced gene expression remained relatively stable over 3–12 months despite marked changes in disease activity. Nevertheless, in patients with low/moderate disease activity, those with high IFN scores had a more recent history of sustained high disease activity.Conclusion:The findings indicate that IFN-induced gene expression has limited clinical utility as a biomarker of acute changes in disease activity.

Objectives To determine the frequency, accrual, attribution and outcome of neuropsychiatric (NP) events and impact on quality of life over 3 years in a large inception cohort of patients with systemic lupus erythematosus (SLE). Methods The study was conducted by the Systemic Lupus International Collaborating Clinics. Patients were enrolled within 15 months of SLE diagnosis. NP events were identified using the American College of Rheumatology case definitions, and decision rules were derived to determine the proportion of NP disease attributable to SLE. The outcome of NP events was recorded and patient-perceived impact determined by the SF-36. Results 1206 patients (89.6% female) with a mean (±SD) age of 34.5±13.2 years were included in the study. The mean disease duration at enrolment was 5.4±4.2 months. Over a mean follow-up of 1.9±1.2 years, 486/1206 (40.3%) patients had ≥1 NP events, which were attributed to SLE in 13.0–23.6% of patients using two a priori decision rules. The frequency of individual NP events varied from 47.1% (headache) to 0% (myasthenia gravis). The outcome was significantly better for those NP events attributed to SLE, especially if they occurred within 1.5 years of the diagnosis of SLE. Patients with NP events, regardless of attribution, had significantly lower summary scores for both mental and physical health over the study. Conclusions NP events in patients with SLE are of variable frequency, most commonly present early in the disease course and adversely impact patients' quality of life over time. Events attributed to non-SLE causes are more common than those due to SLE, although the latter have a more favourable outcome.

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults, with few effective treatments. EGFR alterations, including expression of the truncated variant EGFRvIII, are among the most frequent genomic changes in these tumors. EGFRvIII is known to preferentially signal through STAT5 for oncogenic activation in GBM, yet targeting EGFRvIII has yielded limited clinical success to date. In this study, we employed patient-derived xenograft (PDX) models expressing EGFRvIII to determine the key points of therapeutic vulnerability within the EGFRvIII-STAT5 signaling axis in GBM. Our findings reveal that exogenous expression of paralogs STAT5A and STAT5B augments cell proliferation and that inhibition of STAT5 phosphorylation in vivo improves overall survival in combination with temozolomide (TMZ). STAT5 phosphorylation is independent of JAK1 and JAK2 signaling, instead requiring Src family kinase (SFK) activity. Saracatinib, an SFK inhibitor, attenuates phosphorylation of STAT5 and preferentially sensitizes EGFRvIII+ GBM cells to undergo apoptotic cell death relative to wild-type EGFR. Constitutively active STAT5A or STAT5B mitigates saracatinib sensitivity in EGFRvIII+ cells. In vivo, saracatinib treatment decreased survival in mice bearing EGFR WT tumors compared to the control, yet in EGFRvIII+ tumors, treatment with saracatinib in combination with TMZ preferentially improves survival.