Clonal hematopoiesis of indeterminate potential as a prognostic factor: a systematic review and meta-analysis.

ABSTRACT: With advances in sequencing, individuals with clonal hematopoiesis of indeterminate potential (CHIP) are increasingly being identified, making it essential to understand its prognostic implications. We conducted a systematic review of studies comparing the risk of clinical outcomes in individuals with and without CHIP. We searched MEDLINE and EMBASE and included original research reporting an outcome risk measure in individuals with CHIP, adjusted for the effect of age. From the 3305 studies screened, we included 88 studies with 45 to 470 960 participants. Most studies had a low-to-moderate risk of bias in all domains of the Quality in Prognostic Factor Studies tool. Random-effects meta-analyses were performed for outcomes reported in at least 3 studies. CHIP conferred an increased risk of all-cause mortality (hazard ratio [HR], 1.34; 95% confidence interval, 1.19-1.50), cancer mortality (HR, 1.46; 1.13-1.88), composite cardiovascular events (HR, 1.40; 1.19-1.65), coronary heart disease (HR, 1.76; 1.27-2.44), stroke (HR, 1.16; 1.05-1.28), heart failure (HR, 1.27; 1.15-1.41), hematologic malignancy (HR, 4.28; 2.29-7.98), lung cancer (HR, 1.40; 1.27-1.54), renal impairment (HR, 1.25; 1.18-1.33) and severe COVID-19 (odds ratio [OR], 1.46; 1.18-1.80). CHIP was not associated with cardiovascular mortality (HR, 1.09; 0.97-1.22), except in the subgroup analysis restricted to larger clones (HR, 1.31; 1.12-1.54). Isolated DNMT3A mutations did not increase the risk of myeloid malignancy, all-cause mortality, or renal impairment. The reasons for heterogeneity between studies included differences in definitions and measurements of CHIP and the outcomes, and populations studied. In summary, CHIP is associated with diverse clinical outcomes, with clone size, specific gene, and inherent patient characteristics important mediators of risk.

Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3D in vitro tissue models of disease.

Rapid advances in tissue engineering have resulted in more complex and physiologically relevant 3D in vitro tissue models with applications in fundamental biology and therapeutic development. However, the complexity provided by these models is often not leveraged fully due to the reductionist methods used to analyze them. Computational and mathematical models developed in the field of systems biology can address this issue. Yet, traditional systems biology has been mostly applied to simpler in vitro models with little physiological relevance and limited cellular complexity. Therefore, integrating these two inherently interdisciplinary fields can result in new insights and move both disciplines forward. In this review, we provide a systematic overview of how systems biology has been integrated with 3D in vitro tissue models and discuss key application areas where the synergies between both fields have led to important advances with potential translational impact. We then outline key directions for future research and discuss a framework for further integration between fields.

Interim Influenza Vaccine Effectiveness Against Laboratory-Confirmed Influenza - California, October 2023-January 2024.

Surveillance data can provide rapid, within-season influenza vaccine effectiveness (VE) estimates to guide public health recommendations. Mandatory reporting of influenza vaccine administration to California's immunization information registry began January 1, 2023, and mandatory reporting of all influenza laboratory test results, including negative results, was instituted in California on June 15, 2023. These data, collected by the California Department of Public Health during October 1, 2023-January 31, 2024, were used to calculate interim influenza VE against laboratory-confirmed influenza by comparing the odds of vaccination among case-patients (persons who received a positive influenza laboratory test result) and control patients (those who received a negative influenza laboratory test result). VE was calculated as 1 - adjusted odds ratio using mixed-effects logistic regression, with age, race, and ethnicity as fixed effects and specimen collection week and county as random effects. Overall, during October 1, 2023-January 31, 2024, estimated VE was 45% among persons aged ≥6 months, 56% among children and adolescents aged 6 months-17 years, 48% among adults aged 18-49 years, 36% among those aged 50-64 years, and 30% among those aged ≥65 years. Consistent with some previous influenza seasons, influenza vaccination provided moderate protection against laboratory-confirmed influenza among infants, children, adolescents, and adults. All persons aged ≥6 months without a contraindication to vaccination should receive annual influenza vaccination to reduce influenza illness, severe influenza, and strain on health care resources. Influenza vaccination remains the best way to prevent influenza.

Application of improved GalNAc conjugation in development of cost-effective siRNA therapies targeting cardiovascular diseases.

N-Acetylgalactosamine (GalNAc)-conjugated small interfering RNA (siRNA) therapies have received approval for treating both orphan and prevalent diseases. To improve in vivo efficacy and streamline the chemical synthesis process for efficient and cost-effective manufacturing, we conducted this study to identify better designs of GalNAc-siRNA conjugates for therapeutic development. Here, we present data on redesigned GalNAc-based ligands conjugated with siRNAs against angiopoietin-like 3 (ANGPTL3) and lipoprotein (a) (Lp(a)), two target molecules with the potential to address large unmet medical needs in atherosclerotic cardiovascular diseases. By attaching a novel pyran-derived scaffold to serial monovalent GalNAc units before solid-phase oligonucleotide synthesis, we achieved increased GalNAc-siRNA production efficiency with fewer synthesis steps compared to the standard triantennary GalNAc construct L96. The improved GalNAc-siRNA conjugates demonstrated equivalent or superior in vivo efficacy compared to triantennary GalNAc-conjugated siRNAs.

A Spatiotemporal and Machine-Learning Platform Accelerates the Manufacturing of hPSC-derived Esophageal Mucosa.

Human pluripotent stem cell-derived tissue engineering offers great promise in designer cell-based personalized therapeutics. To harness such potential, a broader approach requires a deeper understanding of tissue-level interactions. We previously developed a manufacturing system for the ectoderm-derived skin epithelium for cell replacement therapy. However, it remains challenging to manufacture the endoderm-derived esophageal epithelium, despite both possessing similar stratified structure. Here we employ single cell and spatial technologies to generate a spatiotemporal multi-omics cell atlas for human esophageal development. We illuminate the cellular diversity, dynamics and signal communications for the developing esophageal epithelium and stroma. Using the machine-learning based Manatee, we prioritize the combinations of candidate human developmental signals for in vitro derivation of esophageal basal cells. Functional validation of the Manatee predictions leads to a clinically-compatible system for manufacturing human esophageal mucosa. Our approach creates a versatile platform to accelerate human tissue manufacturing for future cell replacement therapies to treat human genetic defects and wounds.

ChatGPT usage in the Reactome curation process.

Appreciating the rapid advancement and ubiquity of generative AI, particularly ChatGPT, a chatbot using large language models like GPT, we endeavour to explore the potential application of ChatGPT in the data collection and annotation stages within the Reactome curation process. This exploration aimed to create an automated or semi-automated framework to mitigate the extensive manual effort traditionally required for gathering and annotating information pertaining to biological pathways, adopting a Reactome "reaction-centric" approach. In this pilot study, we used ChatGPT/GPT4 to address gaps in the pathway annotation and enrichment in parallel with the conventional manual curation process. This approach facilitated a comparative analysis, where we assessed the outputs generated by ChatGPT against manually extracted information. The primary objective of this comparison was to ascertain the efficiency of integrating ChatGPT or other large language models into the Reactome curation workflow and helping plan our annotation pipeline, ultimately improving our protein-to-pathway association in a reliable and automated or semi-automated way. In the process, we identified some promising capabilities and inherent challenges associated with the utilisation of ChatGPT/GPT4 in general and also specifically in the context of Reactome curation processes. We describe approaches and tools for refining the output given by ChatGPT/GPT4 that aid in generating more accurate and detailed output.

Exploring New Dimensions of Tumor Heterogeneity: The Application of Single Cell Analysis to Organoid-Based 3D In Vitro Models.

Modeling the heterogeneity of the tumor microenvironment (TME) in vitro is essential to investigating fundamental cancer biology and developing novel treatment strategies that holistically address the factors affecting tumor progression and therapeutic response. Thus, the development of new tools for both in vitro modeling, such as patient-derived organoids (PDOs) and complex 3D in vitro models, and single cell omics analysis, such as single-cell RNA-sequencing, represents a new frontier for investigating tumor heterogeneity. Specifically, the integration of PDO-based 3D in vitro models and single cell analysis offers a unique opportunity to explore the intersecting effects of interpatient, microenvironmental, and tumor cell heterogeneity on cell phenotypes in the TME. In this review, the current use of PDOs in complex 3D in vitro models of the TME is discussed and the emerging directions in the development of these models are highlighted. Next, work that has successfully applied single cell analysis to PDO-based models is examined and important experimental considerations are identified for this approach. Finally, open questions are highlighted that may be amenable to exploration using the integration of PDO-based models and single cell analysis. Ultimately, such investigations may facilitate the identification of novel therapeutic targets for cancer that address the significant influence of tumor-TME interactions.

Skin basal cell carcinomas assemble a pro-tumorigenic spatially organized and self-propagating Trem2+ myeloid niche.

Cancer immunotherapies have revolutionized treatment but have shown limited success as single-agent therapies highlighting the need to understand the origin, assembly, and dynamics of heterogeneous tumor immune niches. Here, we use single-cell and imaging-based spatial analysis to elucidate three microenvironmental neighborhoods surrounding the heterogeneous basal cell carcinoma tumor epithelia. Within the highly proliferative neighborhood, we find that TREM2+ skin cancer-associated macrophages (SCAMs) support the proliferation of a distinct tumor epithelial population through an immunosuppression-independent manner via oncostatin-M/JAK-STAT3 signaling. SCAMs represent a unique tumor-specific TREM2+ population defined by VCAM1 surface expression that is not found in normal homeostatic skin or during wound healing. Furthermore, SCAMs actively proliferate and self-propagate through multiple serial tumor passages, indicating long-term potential. The tumor rapidly drives SCAM differentiation, with intratumoral injections sufficient to instruct naive bone marrow-derived monocytes to polarize within days. This work provides mechanistic insights into direct tumor-immune niche dynamics independent of immunosuppression, providing the basis for potential combination tumor therapies.

Long-term safety and maintenance of response with esketamine nasal spray in participants with treatment-resistant depression: interim results of the SUSTAIN-3 study.

Patients with treatment-resistant depression (TRD) have higher rates of relapse and pronounced decreases in daily functioning and health-related quality of life compared to patients with major depressive disorder who are not treatment-resistant, underscoring the need for treatment choices with sustained efficacy and long-term tolerability. Adults with TRD who participated in ≥1 of 6 phase 3 "parent" studies could continue esketamine treatment, combined with an oral antidepressant, by enrolling in phase 3, open-label, long-term extension study, SUSTAIN-3. Based on their status at parent-study end, eligible participants entered a 4-week induction phase followed by an optimization/maintenance phase, or directly entered the optimization/maintenance phase of SUSTAIN-3. Intranasal esketamine dosing was flexible, twice-weekly during induction and individualized to depression severity during optimization/maintenance. At the interim data cutoff (01 December 2020), 1148 participants were enrolled, 458 at induction and 690 at optimization/maintenance. Mean (median) cumulative duration of maintenance esketamine treatment was 31.5 (37.7) months (totaling 2769 cumulative patient-years). Common treatment-emergent adverse events (≥20%) were headache, dizziness, nausea, dissociation, somnolence, and nasopharyngitis. Mean Montgomery-Åsberg Depression Rating Scale (MADRS) total score decreased during induction, and this reduction persisted during optimization/maintenance (mean [SD] change from the baseline to the endpoint of each phase: induction -12.8 [9.73]; optimization/maintenance +1.1 [9.93]), with 35.6% and 46.1% of participants in remission (MADRS total score ≤12) at induction and optimization/maintenance endpoints, respectively. Improvement in depression ratings generally persisted among participants who remained in maintenance treatment, and no new safety signal was identified during long-term treatment (up to 4.5 years) using intermittent-dosed esketamine in conjunction with daily antidepressant.

An Automation Workflow for High-Throughput Manufacturing and Analysis of Scaffold-Supported 3D Tissue Arrays.

Patient-derived organoids have emerged as a useful tool to model tumour heterogeneity. Scaling these complex culture models while enabling stratified analysis of different cellular sub-populations, however, remains a challenge. One strategy to enable higher throughput organoid cultures is the scaffold-supported platform for organoid-based tissues (SPOT). SPOT allows the generation of flat, thin, and dimensionally-defined microtissues in both 96- and 384-well plate footprints that are compatible with longitudinal image-based readouts. SPOT is currently manufactured manually, however, limiting scalability. In this study, an automation approach to engineer tumour-mimetic 3D microtissues in SPOT using a liquid handler is optimized and comparable within- and between-sample variation to standard manual manufacturing is shown. Further, a liquid handler-supported cell extraction protocol to support single-cell-based end-point analysis using high-throughput flow cytometry and multiplexed cytometry by time of flight is developed. As a proof-of-value demonstration, 3D complex tissues containing different proportions of tumour and stromal cells are generated to probe the reciprocal impact of co-culture. It is also demonstrated that primary patient-derived organoids can be incorporated into the pipeline to capture patient-level tumour heterogeneity. It is envisioned that this automated 96/384-SPOT workflow will provide opportunities for future applications in high-throughput screening for novel personalized therapeutic targets.

Sex differences in default mode network connectivity in healthy aging adults.

Women show an increased lifetime risk of Alzheimer's disease (AD) compared with men. Characteristic brain connectivity changes, particularly within the default mode network (DMN), have been associated with both symptomatic and preclinical AD, but the impact of sex on DMN function throughout aging is poorly understood. We investigated sex differences in DMN connectivity over the lifespan in 595 cognitively healthy participants from the Human Connectome Project-Aging cohort. We used the intrinsic connectivity distribution (a robust voxel-based metric of functional connectivity) and a seed connectivity approach to determine sex differences within the DMN and between the DMN and whole brain. Compared with men, women demonstrated higher connectivity with age in posterior DMN nodes and lower connectivity in the medial prefrontal cortex. Differences were most prominent in the decades surrounding menopause. Seed-based analysis revealed higher connectivity in women from the posterior cingulate to angular gyrus, which correlated with neuropsychological measures of declarative memory, and hippocampus. Taken together, we show significant sex differences in DMN subnetworks over the lifespan, including patterns in aging women that resemble changes previously seen in preclinical AD. These findings highlight the importance of considering sex in neuroimaging studies of aging and neurodegeneration.

Utilization of a rapid diagnostic centre during the COVID-19 pandemic reduced diagnostic delays in breast cancer.

BACKGROUND: Access to breast imaging was restricted during the first wave of the COVID-19 pandemic. We assessed the impact of healthcare restrictions on the Gattuso Rapid Diagnostic Centre (GRDC) at the Princess Margaret Cancer Centre. METHODS: A retrospective review of patients seen at the GRDC between March 12 - August 31, 2020 and the corresponding period from 2019 was performed. RESULTS: There was an 18.6% decrease in patients seen at the GRDC (n = 429 in 2020 vs. 527 in 2019). Time from the first abnormal breast image to diagnosis was significantly shorter (17.4 days [IQR 13.0-21.8] in 2020 vs. 25.9 days [21.0-30.8] in 2019; p = 0.020) with no appreciable difference in time from diagnosis to consult or from consult to surgery. CONCLUSION: The GRDC enabled patients with concerning breast symptoms to access breast imaging, which helped to ensure timely treatment during the first wave of the pandemic.

LY6D marks pre-existing resistant basosquamous tumor subpopulations.

Improved response to canonical therapies requires a mechanistic understanding of dynamic tumor heterogeneity by identifying discrete cellular populations with enhanced cellular plasticity. We have previously demonstrated distinct resistance mechanisms in skin basal cell carcinomas, but a comprehensive understanding of the cellular states and markers associated with these populations remains poorly understood. Here we identify a pre-existing resistant cellular population in naive basal cell carcinoma tumors marked by the surface marker LY6D. LY6D+ tumor cells are spatially localized and possess basal cell carcinoma and squamous cell carcinoma-like features. Using computational tools, organoids, and spatial tools, we show that LY6D+ basosquamous cells represent a persister population lying on a central node along the skin lineage-associated spectrum of epithelial states with local environmental and applied therapies determining the kinetics of accumulation. Surprisingly, LY6D+ basosquamous populations exist in many epithelial tumors, such as pancreatic adenocarcinomas, which have poor outcomes. Overall, our results identify the resistant LY6D+ basosquamous population as an important clinical target and suggest strategies for future therapeutic approaches to target them.

An off-the-shelf multi-well scaffold-supported platform for tumour organoid-based tissues.

Complex 3D bioengineered tumour models provide the opportunity to better capture the heterogeneity of patient tumours. Patient-derived organoids are emerging as a useful tool to study tumour heterogeneity and variation in patient responses. Organoid cultures typically require a 3D microenvironment that can be manufactured easily to facilitate screening. Here we set out to create a high-throughput, "off-the-shelf" platform which permits the generation of organoid-containing engineered microtissues for standard phenotypic bioassays and image-based readings. To achieve this, we developed the Scaffold-supported Platform for Organoid-based Tissues (SPOT) platform. SPOT is a 3D gel-embedded in vitro platform that can be produced in a 96- or 384-well plate format and enables the generation of flat, thin, and dimensionally-defined microgels. SPOT has high potential for adoption due to its reproducible manufacturing methodology, compatibility with existing instrumentation, and reduced within-sample and between-sample variation, which can pose challenges to both data analysis and interpretation. Using SPOT, we generate cultures from patient derived pancreatic ductal adenocarcinoma organoids and assess the cellular response to standard-of-care chemotherapeutic compounds, demonstrating our platform's usability for drug screening. We envision 96/384-SPOT will provide a useful tool to assess drug sensitivity of patient-derived organoids and easily integrate into the drug discovery pipeline.

A three-dimensional human adipocyte model of fatty acid-induced obesity.

Obesity prevalence has reached pandemic proportions, leaving individuals at high risk for the development of diseases such as cancer and type 2 diabetes. In obesity, to accommodate excess lipid storage, adipocytes become hypertrophic, which is associated with an increased pro-inflammatory cytokine secretion and dysfunction of metabolic processes such as insulin signaling and lipolysis. Targeting adipocyte dysfunction is an important strategy to prevent the development of obesity-associated disease. However, it is unclear how accurately animal models reflect human biology, and the long-term culture of human hypertrophic adipocytes in anin vitro2D monolayer is challenging due to the buoyant nature of adipocytes. Here we describe the development of a human 3Din vitrodisease model that recapitulates hallmarks of obese adipocyte dysfunction. First, primary human adipose-derived mesenchymal stromal cells are embedded in hydrogel, and infiltrated into a thin cellulose scaffold. The thin microtissue profile allows for efficient assembly and image-based analysis. After adipocyte differentiation, the scaffold is stimulated with oleic or palmitic acid to mimic caloric overload. Using functional assays, we demonstrated that this treatment induced important obese adipocyte characteristics such as a larger lipid droplet size, increased basal lipolysis, insulin resistance and a change in macrophage gene expression through adipocyte-conditioned media. This 3D disease model mimics physiologically relevant hallmarks of obese adipocytes, to enable investigations into the mechanisms by which dysfunctional adipocytes contribute to disease.

A conserved YAP/Notch/REST network controls the neuroendocrine cell fate in the lungs.

The Notch pathway is a conserved cell-cell communication pathway that controls cell fate decisions. Here we sought to determine how Notch pathway activation inhibits the neuroendocrine cell fate in the lungs, an archetypal process for cell fate decisions orchestrated by Notch signaling that has remained poorly understood at the molecular level. Using intratumoral heterogeneity in small-cell lung cancer as a tractable model system, we uncovered a role for the transcriptional regulators REST and YAP as promoters of the neuroendocrine to non-neuroendocrine transition. We further identified the specific neuroendocrine gene programs repressed by REST downstream of Notch in this process. Importantly, we validated the importance of REST and YAP in neuroendocrine to non-neuroendocrine cell fate switches in both developmental and tissue repair processes in the lungs. Altogether, these experiments identify conserved roles for REST and YAP in Notch-driven inhibition of the neuroendocrine cell fate in embryonic lungs, adult lungs, and lung cancer.

Scalable deep learning algorithm to compute percent pulmonary contusion among patients with rib fractures.

BACKGROUND: Pulmonary contusion exists along a spectrum of severity, yet is commonly binarily classified as present or absent. We aimed to develop a deep learning algorithm to automate percent pulmonary contusion computation and exemplify how transfer learning could facilitate large-scale validation. We hypothesized that our deep learning algorithm could automate percent pulmonary contusion computation and that greater percent contusion would be associated with higher odds of adverse inpatient outcomes among patients with rib fractures. METHODS: We evaluated admission-day chest computed tomography scans of adults 18 years or older admitted to our institution with multiple rib fractures and pulmonary contusions (2010-2020). We adapted a pretrained convolutional neural network that segments three-dimensional lung volumes and segmented contused lung parenchyma, pulmonary blood vessels, and computed percent pulmonary contusion. Exploratory analysis evaluated associations between percent pulmonary contusion (quartiles) and odds of mechanical ventilation, mortality, and prolonged hospital length of stay using multivariable logistic regression. Sensitivity analysis included pulmonary blood vessel volumes during percent contusion computation. RESULTS: A total of 332 patients met inclusion criteria (median, 5 rib fractures), among whom 28% underwent mechanical ventilation and 6% died. The study population's median (interquartile range) percent pulmonary contusion was 4% (2%-8%). Compared to the lowest quartile of percent pulmonary contusion, each increasing quartile was associated with higher adjusted odds of undergoing mechanical ventilation (odds ratio [OR], 1.5; 95% confidence interval [95% CI], 1.1-2.1) and prolonged hospitalization (OR, 1.6; 95% CI, 1.1-2.2), but not with mortality (OR, 1.1; 95% CI, 0.6-2.0). Findings were similar on sensitivity analysis. CONCLUSION: We developed a scalable deep learning algorithm to automate percent pulmonary contusion calculating using chest computed tomography scans of adults admitted with rib fractures. Open code sharing and collaborative research are needed to validate our algorithm and exploratory analysis at a large scale. Transfer learning can help harness the full potential of big data and high-performing algorithms to bring precision medicine to the bedside. LEVEL OF EVIDENCE: Prognostic and epidemiological, Level III.

c-FOS drives reversible basal to squamous cell carcinoma transition.

While squamous transdifferentiation within subpopulations of adenocarcinomas represents an important drug resistance problem, its underlying mechanism remains poorly understood. Here, using surface markers of resistant basal cell carcinomas (BCCs) and patient single-cell and bulk transcriptomic data, we uncover the dynamic roadmap of basal to squamous cell carcinoma transition (BST). Experimentally induced BST identifies activator protein 1 (AP-1) family members in regulating tumor plasticity, and we show that c-FOS plays a central role in BST by regulating the accessibility of distinct AP-1 regulatory elements. Remarkably, despite prominent changes in cell morphology and BST marker expression, we show using inducible model systems that c-FOS-mediated BST demonstrates reversibility. Blocking EGFR pathway activation after c-FOS induction partially reverts BST in vitro and prevents BST features in both mouse models and human tumors. Thus, by identifying the molecular basis of BST, our work reveals a therapeutic opportunity targeting plasticity as a mechanism of tumor resistance.

Detection of high-valent iron species in alloyed oxidic cobaltates for catalysing the oxygen evolution reaction.

Iron alloying of oxidic cobaltate catalysts results in catalytic activity for oxygen evolution on par with Ni-Fe oxides in base but at much higher alloying compositions. Zero-field 57Fe Mössbauer spectroscopy and X-ray absorption spectroscopy (XAS) are able to clearly identify Fe4+ in mixed-metal Co-Fe oxides. The highest Fe4+ population is obtained in the 40-60% Fe alloying range, and XAS identifies the ion residing in an octahedral oxide ligand field. The oxygen evolution reaction (OER) activity, as reflected in Tafel analysis of CoFeOx films in 1 M KOH, tracks the absolute concentration of Fe4+. The results reported herein suggest an important role for the formation of the Fe4+ redox state in activating cobaltate OER catalysts at high iron loadings.