Uncovering molecular features driving lung adenocarcinoma heterogeneity in patients who formerly smoked.

BACKGROUND: An increasing proportion of lung adenocarcinoma (LUAD) occurs in patients even after they have stopped smoking. Here, we aimed to determine whether tobacco smoking induced changes across LUADs from patients who formerly smoked correspond to different biological and clinical factors. METHODS: Random forest models (RFs) were trained utilizing a smoking associated signature developed from differentially expressed genes between LUAD patients who had never smoked (NS) or currently smoked (CS) from TCGA (n = 193) and BCCA (n = 69) cohorts. The RFs were subsequently applied to 299 and 131 formerly smoking patients from TCGA and MSKCC cohorts, respectively. FS were RF-classified as either CS-like or NS-like and associations with patient characteristics, biological features, and clinical outcomes were determined. RESULTS: We elucidated a 123 gene signature that robustly classified NS and CS in both RNA-seq (AUC = 0.85) and microarray (AUC = 0.92) validation test sets. The RF classified 213 patients who had formerly smoked as CS-like and 86 as NS-like from the TCGA cohort. CS-like and NS-like status in formerly smoking patients correlated poorly with patient characteristics but had substantially different biological features including tumor mutational burden, number of mutations, mutagenic signatures and immune cell populations. NS-like formerly smoking patients had 17.5 months and 18.6 months longer overall survival than CS-like patients from the TCGA and MSKCC cohorts, respectively. CONCLUSIONS: Patients who had formerly smoked with LUAD harbor heterogeneous tumor biology. These patients can be divided by smoking induced gene expression to inform prognosis and underlying biological characteristics for treatment selection.

Impact of Value-Driven Healthcare Strategies for Biosimilar Adoption: The Singapore Story.

BACKGROUND: Healthcare sustainability is a global challenge. Various value-driven healthcare strategies have been implemented by Singapore's national health technology assessment (HTA) agency, the Agency for Care Effectiveness (ACE). Considering the high and growing expenditure on biologics, strategies have been implemented to drive the use of biosimilars. As Singapore has reached the 5-year mark since the subsidy listing of the first monoclonal antibody biosimilar infliximab, this review aimed to evaluate the impact of these strategies on the changes in adoption rates, utilisation, spending and cost savings for biosimilars in the public healthcare sector. METHODS: A retrospective cross-sectional study was conducted using aggregated drug utilisation data from all public healthcare institutions. Five monoclonal antibodies with biosimilars, namely infliximab, adalimumab, trastuzumab, rituximab and bevacizumab, were included in this study. The outcomes evaluated were the monthly trends for utilisation volume, proportion attributed to biosimilar use, and drug spending up to December 2022. The simulated cost savings associated with biosimilar adoption were also reported. RESULTS: After subsidy implementation, an upward trend in biosimilar use and proportion attributed to biosimilar adoption was observed, while spending reduced substantially. The adoption rate of most biosimilars reached more than 95% within 1 year of listing. Drugs with more than one approved biosimilar brand at the time of subsidy listing reported substantial price reductions of over 80%. Overall, spending for the five monoclonal antibodies have significantly reduced after biosimilar subsidy listing, with an estimated cumulative cost savings of $136 million over 5 years. CONCLUSION: Value-driven healthcare strategies implemented in Singapore's public healthcare institutions have contributed to high adoption rates of biosimilars and have improved affordable access through lower treatment costs. This in turn has led to significant cost savings to the healthcare system.

iPSC-induced neurons with the V337M MAPT mutation are selectively vulnerable to caspase-mediated cleavage of tau and apoptotic cell death.

BACKGROUND: Tau post-translational modifications (PTMs) result in the gradual build-up of abnormal tau and neuronal degeneration in tauopathies, encompassing variants of frontotemporal lobar degeneration (FTLD) and Alzheimer's disease (AD). Tau proteolytically cleaved by active caspases, including caspase-6, may be neurotoxic and prone to self-aggregation. Also, our recent findings show that caspase-6 truncated tau represents a frequent and understudied aspect of tau pathology in AD in addition to phospho-tau pathology. In AD and Pick's disease, a large percentage of caspase-6 associated cleaved-tau positive neurons lack phospho-tau, suggesting that many vulnerable neurons to tau pathology go undetected when using conventional phospho-tau antibodies and possibly will not respond to phospho-tau based therapies. Therefore, therapeutic strategies against caspase cleaved-tau pathology could be necessary to modulate the extent of tau abnormalities in AD and other tauopathies. METHODS: To understand the timing and progression of caspase activation, tau cleavage, and neuronal death, we created two mAbs targeting caspase-6 tau cleavage sites and probed postmortem brain tissue from an individual with FTLD due to the V337M MAPT mutation. We then assessed tau cleavage and apoptotic stress response in cortical neurons derived from induced pluripotent stem cells (iPSCs) carrying the FTD-related V337M MAPT mutation. Finally, we evaluated the neuroprotective effects of caspase inhibitors in these iPSC-derived neurons. RESULTS: FTLD V337M MAPT postmortem brain showed positivity for both cleaved tau mAbs and active caspase-6. Relative to isogenic wild-type MAPT controls, V337M MAPT neurons cultured for 3 months post-differentiation showed a time-dependent increase in pathogenic tau in the form of caspase-cleaved tau, phospho-tau, and higher levels of tau oligomers. Accumulation of toxic tau species in V337M MAPT neurons was correlated with increased vulnerability to pro-apoptotic stress. Notably, this mutation-associated cell death was pharmacologically rescued by the inhibition of effector caspases. CONCLUSIONS: Our results suggest an upstream, time-dependent accumulation of caspase-6 cleaved tau in V337M MAPT neurons promoting neurotoxicity. These processes can be reversed by caspase inhibition. These results underscore the potential of developing caspase-6 inhibitors as therapeutic agents for FTLD and other tauopathies. Additionally, they highlight the promise of using caspase-cleaved tau as biomarkers for these conditions.

DeepComBat: A statistically motivated, hyperparameter-robust, deep learning approach to harmonization of neuroimaging data.

Neuroimaging data acquired using multiple scanners or protocols are increasingly available. However, such data exhibit technical artifacts across batches which introduce confounding and decrease reproducibility. This is especially true when multi-batch data are analyzed using complex downstream models which are more likely to pick up on and implicitly incorporate batch-related information. Previously proposed image harmonization methods have sought to remove these batch effects; however, batch effects remain detectable in the data after applying these methods. We present DeepComBat, a deep learning harmonization method based on a conditional variational autoencoder and the ComBat method. DeepComBat combines the strengths of statistical and deep learning methods in order to account for the multivariate relationships between features while simultaneously relaxing strong assumptions made by previous deep learning harmonization methods. As a result, DeepComBat can perform multivariate harmonization while preserving data structure and avoiding the introduction of synthetic artifacts. We apply this method to cortical thickness measurements from a cognitive-aging cohort and show DeepComBat qualitatively and quantitatively outperforms existing methods in removing batch effects while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically motivated deep learning harmonization methods.

Predicting which patients with cancer will see a psychiatrist or counsellor from their initial oncology consultation document using natural language processing.

BACKGROUND: Patients with cancer often have unmet psychosocial needs. Early detection of who requires referral to a counsellor or psychiatrist may improve their care. This work used natural language processing to predict which patients will see a counsellor or psychiatrist from a patient's initial oncology consultation document. We believe this is the first use of artificial intelligence to predict psychiatric outcomes from non-psychiatric medical documents. METHODS: This retrospective prognostic study used data from 47,625 patients at BC Cancer. We analyzed initial oncology consultation documents using traditional and neural language models to predict whether patients would see a counsellor or psychiatrist in the 12 months following their initial oncology consultation. RESULTS: Here, we show our best models achieved a balanced accuracy (receiver-operating-characteristic area-under-curve) of 73.1% (0.824) for predicting seeing a psychiatrist, and 71.0% (0.784) for seeing a counsellor. Different words and phrases are important for predicting each outcome. CONCLUSION: These results suggest natural language processing can be used to predict psychosocial needs of patients with cancer from their initial oncology consultation document. Future research could extend this work to predict the psychosocial needs of medical patients in other settings.

Patients with cancer often need support for their mental health. Early detection of who requires referral to a counsellor or psychiatrist may improve their care. This study trained a type of artificial intelligence (AI) called natural language processing to read the consultation report an oncologist writes after they first see a patient to predict which patients will see a counsellor or psychiatrist. The AI predicted this with performance similar to other uses of AI in mental health, and used different words and phrases to predict who would see a psychiatrist compared to seeing a counsellor. We believe this is the first use of AI to predict mental health outcomes from medical documents written by clinicians outside of mental health. This study suggests this type of AI can predict the mental health needs of patients with cancer from this widely-available document.

Machine Learning to Predict the Need for Postmastectomy Radiotherapy after Immediate Breast Reconstruction.

Background: Post mastectomy radiotherapy (PMRT) is an independent predictor of reconstructive complications. PMRT may alter the timing and type of reconstruction recommended. This study aimed to create a machine learning model to predict the probability of requiring PMRT after immediate breast reconstruction (IBR). Methods: In this retrospective study, breast cancer patients who underwent IBR from January 2017 to December 2020 were reviewed and data were collected on 81 preoperative characteristics. Primary outcome was recommendation for PMRT. Four algorithms were compared to maximize performance and clinical utility: logistic regression, elastic net (EN), logistic lasso, and random forest (RF). The cohort was split into a development dataset (75% of cohort for training-validation) and 25% used for the test set. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), precision-recall curves, and calibration plots. Results: In a total of 800 patients, 325 (40.6%) patients were recommended to undergo PMRT. With the training-validation dataset (n = 600), model performance was logistic regression 0.73 AUC [95% confidence interval (CI) 0.65-0.80]; RF 0.77 AUC (95% CI, 0.74-0.81); EN 0.77 AUC (95% CI, 0.73-0.81); logistic lasso 0.76 AUC (95% CI, 0.72-0.80). Without significantly sacrificing performance, 81 predictive factors were reduced to 12 for prediction with the EN method. With the test dataset (n = 200), performance of the EN prediction model was confirmed [0.794 AUC (95% CI, 0.730-0.858)]. Conclusion: A parsimonious accurate machine learning model for predicting PMRT after IBR was developed, tested, and translated into a clinically applicable online calculator for providers and patients.

Aerosol delivery and spatiotemporal tissue distribution of hydroxychloroquine in rat lung.

Inhalation enables the delivery of drugs directly to the lung, increasing the retention for prolonged exposure and maximizing the therapeutic index. However, the differential regional lung exposure kinetics and systemic pharmacokinetics are not fully known, and their estimation is critical for pulmonary drug delivery. The study evaluates the pharmacokinetics of hydroxychloroquine in different regions of the respiratory tract for multiple routes of administration. We also evaluated the influence of different inhaled formulations on systemic and lung pharmacokinetics by identifying suitable nebulizers followed by early characterization of emitted aerosol physicochemical properties. The salt- and freebase-based formulations required different nebulizers and generated aerosol with different physicochemical properties. An administration of hydroxychloroquine by different routes resulted in varied systemic and lung pharmacokinetics, with oral administration resulting in low tissue concentrations in all regions of the respiratory tract. A nose-only inhalation exposure resulted in higher and sustained lung concentrations of hydroxychloroquine with a lung parenchyma-to-blood ratio of 386 after 1440 min post-exposure. The concentrations of hydroxychloroquine in different regions of the respiratory tract (i.e., nasal epithelium, larynx, trachea, bronchi, and lung parenchyma) varied over time, indicating different retention kinetics. The spatiotemporal distribution of hydroxychloroquine in the lung is different due to the heterogeneity of cell types, varying blood perfusion rate, clearance mechanisms, and deposition of inhaled aerosol along the respiratory tract. In addition to highlighting the varied lung physiology, these results demonstrate the ability of the lung to retain increased levels of inhaled lysosomotropic drugs. Such findings are critical for the development of future inhalation-based therapeutics, aiming to optimize target site exposure, enable precision medicine, and ultimately enhance clinical outcomes.

A platform for connecting social media data to domain-specific topics using large language models: an application to student mental health.

Objectives: To design a novel artificial intelligence-based software platform that allows users to analyze text data by identifying various coherent topics and parts of the data related to a specific research theme-of-interest (TOI). Materials and Methods: Our platform uses state-of-the-art unsupervised natural language processing methods, building on top of a large language model, to analyze social media text data. At the center of the platform's functionality is BERTopic, which clusters social media posts, forming collections of words representing distinct topics. A key feature of our platform is its ability to identify whole sentences corresponding to topic words, vastly improving the platform's ability to perform downstream similarity operations with respect to a user-defined TOI. Results: Two case studies on mental health among university students are performed to demonstrate the utility of the platform, focusing on signals within social media (Reddit) data related to depression and their connection to various emergent themes within the data. Discussion and Conclusion: Our platform provides researchers with a readily available and inexpensive tool to parse large quantities of unstructured, noisy data into coherent themes, as well as identifying portions of the data related to the research TOI. While the development process for the platform was focused on mental health themes, we believe it to be generalizable to other domains of research as well.

Public sentiments and the influence of information-seeking preferences on knowledge, attitudes, death conversation, and receptiveness toward palliative care: results from a nationwide survey in Singapore.

Background: Low awareness about palliative care among the global public and healthcare communities has been frequently cited as a persistent barrier to palliative care acceptance. Given that knowledge shapes attitudes and encourages receptiveness, it is critical to examine factors that influence the motivation to increase knowledge. Health information-seeking from individuals and media has been identified as a key factor, as the process of accessing and interpreting information to enhance knowledge has been shown to positively impact health behaviours. Objective: Our study aimed to uncover public sentiments toward palliative care in Singapore. A conceptual framework was additionally developed to investigate the relationship between information-seeking preferences and knowledge, attitudes, receptiveness of palliative care, and comfort in death discussion. Design and Methods: A nationwide survey was conducted in Singapore with 1226 respondents aged 21 years and above. The data were analysed through a series of hierarchical multiple regression to examine the hypothesised role of information-seeking sources as predictors. Results: Our findings revealed that 53% of our participants were aware of palliative care and about 48% were receptive to receiving the care for themselves. It further showed that while information-seeking from individuals and media increases knowledge, attitudes and receptiveness to palliative care, the comfort level in death conversations was found to be positively associated only with individuals, especially healthcare professionals. Conclusion: Our findings highlight the need for public health authorities to recognize people's deep-seated beliefs and superstitions surrounding the concept of mortality. As Asians view death as a taboo topic that is to be avoided at all costs, it is necessary to adopt multipronged communication programs to address those fears. It is only when the larger communicative environment is driven by the media to encourage public discourse, and concurrently supported by timely interventions to trigger crucial conversations on end-of-life issues between individuals, their loved ones, and the healthcare team, can we advance awareness and benefits of palliative care among the public in Singapore.

A nationwide survey to understand public sentiments and the extent that information-seeking preferences can increase knowledge, attitudes, receptiveness of palliative care, and comfort level in death discussion in Singapore Low awareness of palliative care is a barrier that persistently hinders palliative care acceptance among populations in developing and developed countries. As knowledge shapes attitudes and encourages receptiveness, it is vital that researchers uncover factors that influence the motivation to increase knowledge. Health information-seeking is a factor that deserves greater attention in palliative care research because the process of seeking out information on health concerns from other people or the media can greatly increase individuals' knowledge. As such, this nationwide survey involving 1226 participants was carried out in Singapore to understand the public sentiments toward palliative care. It further statistically analyzed if information-seeking (from individuals and the media) will increase knowledge, attitudes, receptiveness toward palliative care, and comfort level in death discussion. Our findings indicated that 53% of our participants were aware of palliative care and about 48% were receptive to receiving the care for themselves. Furthermore, while information-seeking from individuals and media increases knowledge, attitudes, and receptiveness to palliative care, people are only comfortable to engage in death discussion with individuals, especially healthcare professionals. Exposure to media alone is not enough to encourage individuals to want to talk about end-of-life issues including palliative care. As Asians view death as a taboo topic, it is important for public health authorities to recognize people's deep-seated beliefs and superstitions surrounding the concept of mortality. A multipronged communication program is therefore needed to address these fears. It is only when the larger communicative environment driven by the media to encourage public discourse, and concurrently supported by timely interventions to trigger crucial conversations on end-of-life issues between individuals, their loved ones, and the healthcare team, can we advance awareness and benefits of palliative care among the public in Singapore.

Advance Care Planning in Singapore: The genesis and evolution of a national programme.

Singapore is a developed country that is aging rapidly. In 2011, a national programme in Advance Care Planning (ACP) known as Living Matters was launched. Over the years, the programme has grown and evolved. While ACP has become routine in some hospital units, challenges remain in implementing ACP as a standard of care across all levels of the healthcare system. Opportunities abound in improving the quality of the ACP process and in bringing ACP upstream into outpatient clinics, primary care and into the community.

Regulatory T Cell Biomarkers Identify Patients at Risk of Developing Acute Cellular Rejection in the First Year Following Heart Transplantation.

BACKGROUND: Acute cellular rejection (ACR), an alloimmune response involving CD4+ and CD8+ T cells, occurs in up to 20% of patients within the first year following heart transplantation. The balance between a conventional versus regulatory CD4+ T cell alloimmune response is believed to contribute to developing ACR. Therefore, tracking these cells may elucidate whether changes in these cell populations could signal ACR risk. METHODS: We used a CD4+ T cell gene signature (TGS) panel that tracks CD4+ conventional T cells (Tconv) and regulatory T cells (Treg) on longitudinal samples from 94 adult heart transplant recipients. We evaluated combined diagnostic performance of the TGS panel with a previously developed biomarker panel for ACR diagnosis, HEARTBiT, while also investigating TGS' prognostic utility. RESULTS: Compared with nonrejection samples, rejection samples showed decreased Treg- and increased Tconv-gene expression. The TGS panel was able to discriminate between ACR and nonrejection samples and, when combined with HEARTBiT, showed improved specificity compared with either model alone. Furthermore, the increased risk of ACR in the TGS model was associated with lower expression of Treg genes in patients who later developed ACR. Reduced Treg gene expression was positively associated with younger recipient age and higher intrapatient tacrolimus variability. CONCLUSIONS: We demonstrated that expression of genes associated with CD4+ Tconv and Treg could identify patients at risk of ACR. In our post hoc analysis, complementing HEARTBiT with TGS resulted in an improved classification of ACR. Our study suggests that HEARTBiT and TGS may serve as useful tools for further research and test development.

An update on the discovery and development of reversible covalent inhibitors.

Small molecule drugs that covalently bind irreversibly to their target proteins have several advantages over conventional reversible inhibitors. They include increased duration of action, less-frequent drug dosing, reduced pharmacokinetic sensitivity, and the potential to target intractable shallow binding sites. Despite these advantages, the key challenges of irreversible covalent drugs are their potential for off-target toxicities and immunogenicity risks. Incorporating reversibility into covalent drugs would lead to less off-target toxicity by forming reversible adducts with off-target proteins and thus reducing the risk of idiosyncratic toxicities caused by the permanent modification of proteins, which leads to higher levels of potential haptens. Herein, we systematically review electrophilic warheads employed during the development of reversible covalent drugs. We hope the structural insights of electrophilic warheads would provide helpful information to medicinal chemists and aid in designing covalent drugs with better on-target selectivity and improved safety.

DeepComBat: A Statistically Motivated, Hyperparameter-Robust, Deep Learning Approach to Harmonization of Neuroimaging Data.

Neuroimaging data from multiple batches (i.e. acquisition sites, scanner manufacturer, datasets, etc.) are increasingly necessary to gain new insights into the human brain. However, multi-batch data, as well as extracted radiomic features, exhibit pronounced technical artifacts across batches. These batch effects introduce confounding into the data and can obscure biological effects of interest, decreasing the generalizability and reproducibility of findings. This is especially true when multi-batch data is used alongside complex downstream analysis models, such as machine learning methods. Image harmonization methods seeking to remove these batch effects are important for mitigating these issues; however, significant multivariate batch effects remain in the data following harmonization by current state-of-the-art statistical and deep learning methods. We present DeepCombat, a deep learning harmonization method based on a conditional variational autoencoder architecture and the ComBat harmonization model. DeepCombat learns and removes subject-level batch effects by accounting for the multivariate relationships between features. Additionally, DeepComBat relaxes a number of strong assumptions commonly made by previous deep learning harmonization methods and is empirically robust across a wide range of hyperparameter choices. We apply this method to neuroimaging data from a large cognitive-aging cohort and find that DeepCombat outperforms existing methods, as assessed by a battery of machine learning methods, in removing scanner effects from cortical thickness measurements while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically-motivated deep learning harmonization methods.

Microbial dysbiosis and the host airway epithelial response: insights into HIV-associated COPD using multi'omics profiling.

BACKGROUND: People living with HIV (PLWH) are at increased risk of developing Chronic Obstructive Pulmonary Disease (COPD) independent of cigarette smoking. We hypothesized that dysbiosis in PLWH is associated with epigenetic and transcriptomic disruptions in the airway epithelium. METHODS: Airway epithelial brushings were collected from 18 COPD + HIV + , 16 COPD - HIV + , 22 COPD + HIV - and 20 COPD - HIV - subjects. The microbiome, methylome, and transcriptome were profiled using 16S sequencing, Illumina Infinium Methylation EPIC chip, and RNA sequencing, respectively. Multi 'omic integration was performed using Data Integration Analysis for Biomarker discovery using Latent cOmponents. A correlation > 0.7 was used to identify key interactions between the 'omes. RESULTS: The COPD + HIV -, COPD -HIV + , and COPD + HIV + groups had reduced Shannon Diversity (p = 0.004, p = 0.023, and p = 5.5e-06, respectively) compared to individuals with neither COPD nor HIV, with the COPD + HIV + group demonstrating the most reduced diversity. Microbial communities were significantly different between the four groups (p = 0.001). Multi 'omic integration identified correlations between Bacteroidetes Prevotella, genes FUZ, FASTKD3, and ACVR1B, and epigenetic features CpG-FUZ and CpG-PHLDB3. CONCLUSION: PLWH with COPD manifest decreased diversity and altered microbial communities in their airway epithelial microbiome. The reduction in Prevotella in this group was linked with epigenetic and transcriptomic disruptions in host genes including FUZ, FASTKD3, and ACVR1B.

USP9X mediates an acute adaptive response to MAPK suppression in pancreatic cancer but creates multiple actionable therapeutic vulnerabilities.

Pancreatic ductal adenocarcinomas (PDACs) frequently harbor KRAS mutations. Although MEK inhibitors represent a plausible therapeutic option, most PDACs are innately resistant to these agents. Here, we identify a critical adaptive response that mediates resistance. Specifically, we show that MEK inhibitors upregulate the anti-apoptotic protein Mcl-1 by triggering an association with its deubiquitinase, USP9X, resulting in acute Mcl-1 stabilization and protection from apoptosis. Notably, these findings contrast the canonical positive regulation of Mcl-1 by RAS/ERK. We further show that Mcl-1 inhibitors and cyclin-dependent kinase (CDK) inhibitors, which suppress Mcl-1 transcription, prevent this protective response and induce tumor regression when combined with MEK inhibitors. Finally, we identify USP9X as an additional potential therapeutic target. Together, these studies (1) demonstrate that USP9X regulates a critical mechanism of resistance in PDAC, (2) reveal an unexpected mechanism of Mcl-1 regulation in response to RAS pathway suppression, and (3) provide multiple distinct promising therapeutic strategies for this deadly malignancy.

Engaging a Non-catalytic Cysteine Residue Drives Potent and Selective Inhibition of Caspase-6.

Caspases are a family of cysteine-dependent proteases with important cellular functions in inflammation and apoptosis, while also implicated in human diseases. Classical chemical tools to study caspase functions lack selectivity for specific caspase family members due to highly conserved active sites and catalytic machinery. To overcome this limitation, we targeted a non-catalytic cysteine residue (C264) unique to caspase-6 (C6), an enigmatic and understudied caspase isoform. Starting from disulfide ligands identified in a cysteine trapping screen, we used a structure-informed covalent ligand design to produce potent, irreversible inhibitors (3a) and chemoproteomic probes (13-t) of C6 that exhibit unprecedented selectivity over other caspase family members and high proteome selectivity. This approach and the new tools described will enable rigorous interrogation of the role of caspase-6 in developmental biology and in inflammatory and neurodegenerative diseases.