Citations

Abstract INTRODUCTION Multi‐omics studies in Alzheimer's disease (AD) revealed many potential disease pathways and therapeutic targets. Despite their promise of precision medicine, these studies lacked Black Americans (BA) and Latin Americans (LA), who are disproportionately affected by AD. METHODS To bridge this gap, Accelerating Medicines Partnership in Alzheimer's Disease (AMP‐AD) expanded brain multi‐omics profiling to multi‐ethnic donors. RESULTS We generated multi‐omics data and curated and harmonized phenotypic data from BA ( n = 306), LA ( n = 326), or BA and LA ( n = 4) brain donors plus non‐Hispanic White ( n = 252) and other ( n = 20) ethnic groups, to establish a foundational dataset enriched for BA and LA participants. This study describes the data available to the research community, including transcriptome from three brain regions, whole genome sequence, and proteome measures. DISCUSSION The inclusion of traditionally underrepresented groups in multi‐omics studies is essential to discovering the full spectrum of precision medicine targets that will be pertinent to all populations affected with AD. Highlights Accelerating Medicines Partnership in Alzheimer's Disease Diversity Initiative led brain tissue profiling in multi‐ethnic populations. Brain multi‐omics data is generated from Black American, Latin American, and non‐Hispanic White donors. RNA, whole genome sequencing and tandem mass tag proteomicsis completed and shared. Multiple brain regions including caudate, temporal and dorsolateral prefrontal cortex were profiled.

Abstract The Blood and Marrow Transplant (BMT) Clinical Trials Network conducted a phase 3 randomized trial comparing gilteritinib with placebo after allogeneic hematopoietic cell transplantation (HCT) for FLT3-ITD+ acute myeloid leukemia (AML). The primary analysis demonstrated no statistically significant difference in relapse-free survival (RFS); however, patients with FLT3-ITD measurable residual disease (MRD) peri-HCT had significantly longer RFS with gilteritinib. This analysis investigates the effect of post-HCT gilteritinib vs placebo on health-related quality of life (HRQOL). HRQOL was measured with Functional Assessment of Cancer Therapy-Bone Marrow Transplant (FACT-BMT), FACT-Leukemia (FACT-Leu), and EuroQOL-5 Dimensions (EQ-5D-5L) at post-HCT randomization; day 29; months 3, 6, 12, 18, 24; and/or end of therapy. HRQOL and clinically meaningful differences were summarized using descriptive statistics and compared using mixed model repeated measures to evaluate longitudinal change from baseline and stratified Cox model to evaluate time to improvement. HRQOL completion rate was acceptable (>70%) across all time points and measures. There were no differences in HRQOL scores at any time point between cohorts. Clinically meaningful and time to improvement in HRQOL were similar in both arms. Despite higher treatment-emergent adverse effects with gilteritinib, response to the question of being “bothered by side effects of treatment” did not differ between groups. Subgroup analysis of MRD-positive and negative patients demonstrated no differences in HRQOL between arms. For patients with FLT3-ITD+ AML undergoing HCT, gilteritinib maintenance was not associated with any difference in HRQOL or patient-reported impact of side effects. This trial was registered at www.ClinicalTrials.gov as #NCT02997202.

Objective: We recently discovered that activation of oxidized soluble guanylate cyclase with ataciguat (ATA) slows valve calcification and dysfunction in mice and humans with moderate aortic valve stenosis (AVS). We examined if ATA would attenuate multiple mechanisms contributing to AVS and end organ dysfunction associated with AVS, and hypothesized: 1) ATA attenuates osteogenic signaling in valve tissue from mice with AVS, thereby attenuating calcification and valve dysfunction, 2) AVS increases rates of sudden cardiac death in our model, and 3) treatment with ATA prevents QT prolongation and deleterious electrical remodeling commonly observed in AVS. Methods: Functional, molecular, and histopathological changes in valve tissue along with QT interval duration were measured in 3 independent groups of mice: non-AVS, AVS, and AVS+ATA (WD for 6 months then WD+ATA for 3 months (AVS+ATA). We gave 5 mg/kg isoproterenol (ISO, i.p.) to ldlr -/- /ApoB100-only mice fed normal chow (non-AVS) or western diet (AVS, fed WD for 9 months) to assess risk for malignant arrhythmias. Retrospective analysis of QT interval was also done on data from our recent Phase 1 clinical trial. Results: AVS+ATA mice had less valve calcification and slower progression of valve dysfunction through attenuation of canonical BMP signaling and reductions in Runx2 and SPP1 compared to AVS mice. QT prolongation was evident in AVS mice, and was significantly attenuated by treatment with ATA (p= 0.001) and coarsely associated with severity of valvular or ventricular function. While all non-AVS mice survived for 24 hours after ISO, 40% of mice with AVS reached a moribund equivalent within 4 hours. Retrospective analyses also showed reductions in QT interval duration in patients treated with 200 mg/day ATA for 14 days (p < 0.05). Conclusion: Collectively, this is the first study reporting increased propensity for malignant arrhythmias in our mouse model of AVS, QT prolongation as a likely underlying mechanism, and restoration of cardiac conduction dynamics with targeted restoration of nitric oxide signaling. Future studies focused on the role of sGC in the evolution and prevention of AVS and consequent deleterious electrical remodeling/malignant arrhythmia risk is warranted.

Purpose of review This review underscores the critical role and challenges associated with the widespread adoption of artificial intelligence in cancer care to enhance disease management, streamline clinical processes, optimize data retrieval of health information, and generate and synthesize evidence. Recent findings Advancements in artificial intelligence models and the development of digital biomarkers and diagnostics are applicable across the cancer continuum from early detection to survivorship care. Additionally, generative artificial intelligence has promised to streamline clinical documentation and patient communications, generate structured data for clinical trial matching, automate cancer registries, and facilitate advanced clinical decision support. Widespread adoption of artificial intelligence has been slow because of concerns about data diversity and data shift, model reliability and algorithm bias, legal oversight, and high information technology and infrastructure costs. Summary Artificial intelligence models have significant potential to transform cancer care. Efforts are underway to deploy artificial intelligence models in the cancer practice, evaluate their clinical impact, and enhance their fairness and explainability. Standardized guidelines for the ethical integration of artificial intelligence models in cancer care pathways and clinical operations are needed. Clear governance and oversight will be necessary to gain trust in artificial intelligence-assisted cancer care by clinicians, scientists, and patients.

AbstractBackgroundThe management of early breast cancer (BC) has witnessed an uprise in the use of neoadjuvant therapy and a remarkable reshaping of the systemic therapy postneoadjuvant treatment in the last few years, with the evolution of many controversial clinical situations that require consensus.MethodsDuring the 14th Breast‐Gynecological and Immuno‐Oncology International Cancer Conference held in Egypt in 2022, a panel of 44 BC experts from 13 countries voted on statements concerning debatable challenges in the neo/adjuvant treatment setting. The recommendations were subsequently updated based on the most recent data emerging. A modified Delphi approach was used to develop this consensus. A consensus was achieved when ≥75% of voters selected an answer.Results and ConclusionsThe consensus recommendations addressed different escalation and de‐escalation strategies in the setting of neoadjuvant therapy for early BC. The recommendations recapitulate the available clinical evidence and expert opinion to individualize patient management and optimize therapy outcomes. Consensus was reached in 63% of the statements (52/83), and the rationale behind each statement was clarified.

Abstract Acute graft-versus-host disease (GVHD) grading systems that use only clinical symptoms at treatment initiation such as the Minnesota risk identify standard and high-risk categories but lack a low-risk category suitable to minimize immunosuppressive strategies. We developed a new grading system that includes a low-risk stratum based on clinical symptoms alone and determined whether the incorporation of biomarkers would improve the model’s prognostic accuracy. We randomly divided 1863 patients in the Mount Sinai Acute GVHD International Consortium (MAGIC) who were treated for GVHD into training and validation cohorts. Patients in the training cohort were divided into 14 groups based on similarity of clinical symptoms and similar nonrelapse mortality (NRM); we used a classification and regression tree (CART) algorithm to create three Manhattan risk groups that produced a significantly higher area under the receiver operating characteristic curve (AUC) for 6-month NRM than the Minnesota risk classification (0.69 vs 0.64, P = .009) in the validation cohort. We integrated serum GVHD biomarker scores with Manhattan risk using patients with available serum samples and again used a CART algorithm to establish 3 MAGIC composite scores that significantly improved prediction of NRM compared to Manhattan risk (AUC, 0.76 vs 0.70, P = .010). Each increase in MAGIC composite score also corresponded to a significant decrease in day 28 treatment response (80% vs 63% vs 30%, P &lt; .001). We conclude that the MAGIC composite score more accurately predicts response to therapy and long-term outcomes than systems based on clinical symptoms alone and may help guide clinical decisions and trial design.

Aim: AI research, development, and implementation are expanding at an exponential pace across healthcare. This paradigm shift in healthcare research has led to increased demands for clinical outcomes, all at the expense of a significant gap in AI literacy within the healthcare field. This has further translated to a lack of tools in creating a framework for literature in the AI in medicine domain. We propose HUMANE (Harmonious Understanding of Machine Learning Analytics Network), a checklist for establishing an international consensus for authors and reviewers involved in research focused on artificial intelligence (AI) or machine learning (ML) in medicine. Methods: This study was conducted using the Delphi method by devising a survey using the Google Forms platform. The survey was developed as a checklist containing 8 sections and 56 questions with a 5-point Likert scale. Results: A total of 33 survey respondents were part of the initial Delphi process with the majority (45%) in the 36–45 years age group. The respondents were located across the USA (61%), UK (24%), and Australia (9%) as the top 3 countries, with a pre-dominant healthcare background (42%) as early-career professionals (3–10 years’ experience) (42%). Feedback showed an overall agreeable consensus (mean ranges 4.1–4.8, out of 5) as cumulative scores throughout all sections. The majority of the consensus was agreeable with the Discussion (Other) section of the checklist (median 4.8 (interquartile range (IQR) 4.8-4.8)), whereas the least agreed section was the Ground Truth (Expert(s) review) section (median 4.1 (IQR 3.9–4.2)) and the Methods (Outcomes) section (median 4.1 (IQR 4.1–4.1)) of the checklist. The final checklist after consensus and revision included a total of 8 sections and 50 questions. Conclusions: The HUMANE international consensus has reflected on further research on the potential of this checklist as an established consensus in improving the reliability and quality of research in this field.

Abstract The significance of biomarkers in second-line treatment for acute graft-versus-host disease (GVHD) has not been well characterized. We analyzed clinical data and serum samples at the initiation of second-line systemic treatment of acute GVHD from 167 patients from 17 centers of the Mount Sinai Acute GVHD International Consortium (MAGIC) between 2016 and 2021. Sixty-two patients received ruxolitinib-based therapy, whereas 102 received other systemic agents. In agreement with prospective trials, ruxolitinib resulted in a higher day 28 (D28) overall response Frate than nonruxolitinib therapies (55% vs 31%, P = .003) and patients who received ruxolitinib had significantly lower nonrelapse mortality (NRM) than those who received nonruxolitinib therapies (point estimates at 2-year: 35% vs 61%, P = .002). Biomarker analyses demonstrated that the benefit from ruxolitinib was observed only in patients with low MAGIC algorithm probabilities (MAPs) at the start of second-line treatment. Among patients with a low MAP, those who received ruxolitinib experienced significantly lower NRM than those who received nonruxolitinib therapies (point estimates at 2-year: 12% vs 41%, P = .016). However, patients with high MAP experienced high NRM regardless of treatment with ruxolitinib or nonruxolitinib therapies (point estimates at 2-year: 67% vs 80%, P = .65). A landmark analysis demonstrated that the relationship between the D28 response and NRM largely depends on the MAP level at the initiation of second-line therapy. In conclusion, MAP measured at second-line systemic treatment for acute GVHD predicts treatment response and NRM. The outcomes of patients with high MAP are poor regardless of treatment choice, and ruxolitinib appears to primarily benefit patients with low MAP.

The landscape of prostate cancer care has rapidly evolved. We have transitioned from the use of conventional imaging, radical surgeries, and single-agent androgen deprivation therapy to an era of advanced imaging, precision diagnostics, genomics, and targeted treatment options. Concurrently, the emergence of large language models (LLMs) has dramatically transformed the paradigm for artificial intelligence (AI). This convergence of advancements in prostate cancer management and AI provides a compelling rationale to comprehensively review the current state of AI applications in prostate cancer care. Here, we review the advancements in AI-driven applications across the continuum of the journey of a patient with prostate cancer from early interception to survivorship care. We subsequently discuss the role of AI in prostate cancer drug discovery, clinical trials, and clinical practice guidelines. In the localized disease setting, deep learning models demonstrated impressive performance in detecting and grading prostate cancer using imaging and pathology data. For biochemically recurrent diseases, machine learning approaches are being tested for improved risk stratification and treatment decisions. In advanced prostate cancer, deep learning can potentially improve prognostication and assist in clinical decision making. Furthermore, LLMs are poised to revolutionize information summarization and extraction, clinical trial design and operations, drug development, evidence synthesis, and clinical practice guidelines. Synergistic integration of multimodal data integration and human-AI integration are emerging as a key strategy to unlock the full potential of AI in prostate cancer care.