Heterogeneity In Treatment Response Research Articles

Which intervention works for whom: Identifying pre-treatment characteristics that predict who will benefit from a specific alcohol text message intervention from a randomized trial

IntroductionEffective interventions show heterogeneity in treatment response. Addressing this heterogeneity involves identifying which intervention works best for whom. One method to address this heterogeneity identifies treatment-subgroup interactions to determine which of two interventions has greater effect for certain individuals based on their profile of pre-treatment characteristics. This secondary analysis of a randomized clinical trial (RCT) to address binge drinking examined whether two of the RCT's interventions, GOAL and COMBO, which produced similar reductions in drinking outcomes, might have involved treatment-subgroup interactions. Identifying treatment-subgroup interactions can inform efficient patient-treatment matching that optimizes individual outcomes. MethodsThese secondary analyses included young adults (n = 344; 68.6 % female, ages 18–25) randomized to GOAL or COMBO 12-week alcohol text message interventions and who completed 3-month follow-up (end of intervention). GOAL provided weekly support for drinking limit goals. COMBO included all GOAL features, in addition to pre-event feedback on drinking plans and post-event feedback on alcohol consumption. QUINT, a tree-based algorithm, aimed to identify treatment-subgroup interactions using 21 pre-treatment (baseline) characteristics (e.g., demographics, perceived risk of binge-drinking related harm, perceived number of peers drinking to intoxication) that predicted the primary outcome of binge drinking at follow-up. ResultsThe algorithm used five pre-treatment characteristics (sex, race, perceived risk of binge drinking-related harm, perceived number of peers drinking to intoxication, and any cannabis use in the past 3 months) to identify 7 treatment-subgroup interactions. COMBO had greater effectiveness than GOAL, for example, for females who reported lower risk of binge-drinking related harm and no cannabis use in the past 3 months, whereas GOAL had greater effectiveness for females who reported higher risk of binge-drinking related harm and more peers who drank to intoxication. In comparison, GOAL had greater effectiveness than COMBO among White males, whereas males of other racial backgrounds benefitted more from COMBO than GOAL. ConclusionsThe identified treatment-subgroup interactions involving GOAL and COMBO indicated which intervention had greater effectiveness for which subgroups of individuals based on pre-treatment characteristics. These findings can help efficiently match individuals to effective interventions, bringing the field closer to personalized, precision care.Clinical trials registration number: NCT02918565.

Effects of Sacubitril/Valsartan on all-cause hospitalizations in heart failure: participant-level pooled analysis of PARADIGM-HF and PARAGON-HF

Abstract Background Sacubitril/valsartan is indicated to reduce the risk of cardiovascular (CV) death and heart failure (HF) hospitalizations in patients with chronic HF. However, many of these patients are older and have multiple comorbidities that increase the risk of hospitalizations other than HF, yet the effects of sacubitril/valsartan on hospitalizations of any cause have not been well described. Methods PARADIGM-HF and PARAGON-HF were phase-3, global multicenter randomized clinical trials that evaluated sacubitril/valsartan versus enalapril (in PARADIGM-HF) or valsartan (in PARAGON-HF) across the spectrum of left ventricular ejection fraction (LVEF; ≤40% in PARADIGM-HF and ≥45% in PARAGON-HF). We pooled individual participant-level data from these 2 trials to examine the effects of sacubitril/valsartan on time-to-first investigator-reported all-cause and cause-specific hospitalization using Cox proportional hazards models, stratified by geographic region and trial. We additionally examined heterogeneity in treatment response by LVEF. Results Over 2.8 years median follow-up, among 13,194 participants in the pooled PARADIGM-HF/PARAGON-HF, sacubitril/valsartan significantly reduced the risk of all-cause hospitalizations compared with renin-angiotensin system inhibitor (HR 0.92; 95% CI 0.88-0.97; P=0.002); Figure 1 Panel A. The absolute risk reduction (ARR) was 2.1 per 100 patient-years corresponding to a number-needed-to-treat (NNT) of 48 patient-years of treatment exposure to prevent 1 all-cause hospitalization. Reductions in overall hospitalizations among patients with identifiable causes (N=5,783) seemed to be primarily driven by lower rates of cardiac and pulmonary hospitalizations associated with sacubitril/valsartan. Patients treated with sacubitril/valsartan did not have a higher rate of composite non-cardiac hospitalizations (Figure 1 Panel B). Similarly, sacubitril/valsartan reduced the risk of the composite of all-cause hospitalization or all-cause mortality (HR 0.92; 95% CI 0.87-0.96; P<0.001), with an ARR of 2.5 per 100 patients-years and an NNT of 40 patient-years. For all-cause hospitalization, we observed significant heterogeneity by LVEF as a continuous measure (Pinteraction=0.027); treatment effects were most apparent in those with an LVEF below 60% (HR 0.91 95% CI 0.86-0.96; Figure 2). Conclusions In a pooled analysis of >13,000 patients with chronic HF, sacubitril/valsartan reduced hospitalization for any reason with benefits most apparent among those with an LVEF below normal.

Macrophage activation syndrome in Sepsis: from pathogenesis to clinical management.

Sepsis represents a significant global health and hygiene challenge. Excessive activation of macrophages in sepsis can result in certain patients displaying characteristics akin to those observed in Macrophage Activation Syndrome (MAS). MAS represents a grave immune system disorder characterized by persistent and severe inflammation within the body. In the context of sepsis, MAS presents atypically, leading some researchers to refer to it as Macrophage Activation-Like Syndrome (MALS). However, there are currently no effective treatment measures for this situation. The purpose of this article is to explore potential treatment methods for sepsis-associated MALS. The objective of this review is to synthesize the specific pathophysiological mechanisms and treatment strategies of MAS to investigate potential therapeutic approaches for sepsis-associated MALS. We searched major databases (including PubMed, Web of Science, and Google Scholar etc.) for literature encompassing macrophage activation syndrome and sepsis up to Mar 2024 and combined with studies found in the reference lists of the included studies. We have synthesized the underlying pathophysiological mechanism of MALS in sepsis, and then summarized the diagnostic criteria and the effects of various treatment modalities utilized in patients with MAS or MALS. In both scenarios, heterogeneous treatment responses resulting from identical treatment approaches were observed. The determination of whether the patient is genuinely experiencing MALS significantly impacts the ultimate outcomes of therapeutic efficacy. In order to tackle this concern, additional clinical trials and research endeavors are imperative.

B-110 Descriptive Analysis of Multiple Myeloma Participants in the NIH All of Us Research Program Cohort: Insights into Demographics Disparities

Abstract Background Multiple Myeloma (MM) is a complex hematological malignancy characterized by significant heterogeneity in participant characteristics and treatment responses. The All of Us Research Program seeks to enroll 1 million diverse participants who share multiple sources of data, providing unique opportunities for research. A comprehensive descriptive analysis of MM participants within Electronic Health Records (EHR) databases can provide valuable insights into the disease's epidemiology, participant demographics, comorbidities, and treatment outcomes. Objective Using AI/ML predictive analytics, this study conducted a detailed descriptive analysis of MM participants’ EHR data in the All of Us Research Program cohort, which is enrolling 1 million diverse participants who share multiple sources of data. Methods We extracted data for participants diagnosed with MM from an extensive EHR database. The analysis included participant demographics (age, gender, race, ethnicity), diagnostic laboratory tests (SPEP, UPEP, CBC, renal function), comorbidities (renal impairment, bone disease, anemia, cardiovascular diseases), treatment details (chemotherapy, stem cell transplantation), and genetic markers (cytogenetic abnormalities). Results The analysis revealed diverse demographic patterns and a wide range of comorbidities associated with MM (Table 1). Conclusions The analysis demonstrated marked disparities in MM prevalence among African Americans and “More than one race,” revealing social and medical disparities. Implications: By understanding the diverse profiles of MM participants, stakeholders can more effectively address disparities in healthcare outcomes (1). Reference: 1. Zeng, C., Schlueter, D. J., Tran, T. C., Babbar, A., Cassini, T., Bastarache, L. A., & Denny, J. C. (2024). Comparison of phenomic profiles in the All of Us Research Program against the US general population and the UK Biobank. Journal of the American Medical Informatics Association. Retrieved from, https://doi.org/10.1093/jamia/ocad260

Parenting Profiles in Military Families: Intervention-Related Transitions and Relationships to Child Adjustment

Parenting programs aim to improve parenting quality, which may, in turn, support various aspects of child development, including behavior and mental health. However, parenting interventions show considerable heterogeneity in response patterns across different families, demonstrating that they are not one-size-fits-all programs. This variability points to a need for greater understanding of which families benefit most from these interventions and how to improve response among those who do not. Following this literature gap, this study employed a person-centered approach to identify different parenting profiles associated with heterogeneity in treatment responses to a family-based prevention interventions adapted for military families. This study used data from a randomized controlled trial of the ADAPT intervention for 336 US military families in which at least one parent had deployed to war. Latent profile analyses revealed three unobserved parenting profiles among mothers and fathers, reflecting High positive, Moderate positive, and Coercive parenting styles. Latent transition analysis (LTA) suggested that the ADAPT program led to improvements in parenting, particularly among mothers who began the program with moderate or typical levels of parenting skills, and that these positive changes in parenting may help to decrease child externalizing problems. For fathers, the ADAPT program was most effective in preventing declines in positive parenting among those with initially typical levels of positive parenting. Overall, study findings demonstrate considerable heterogeneity in parenting behavior among a sample of military families that is associated with variability in parent responses to the evidence-based parenting program.

Cisplatin-Induced Metabolic Responses Measured with Raman Spectroscopy in Cancer Cells, Spheroids, and Canine-Derived Organoids.

Ex vivo assessment of drug response with conventional cell viability assays remains the standard practice for guiding initial therapeutic choices. However, such ensemble approaches fail to capture heterogeneities in treatment response and cannot identify early markers of response. Here, we leverage Raman spectroscopy (RS) as an accurate, low-cost, extraction-free, and label-free approach to track metabolic changes in cancer cells, spheroids, and organoids in response to cisplatin treatment. We identified 12 statistically significant metabolites in cells and 19 metabolites in spheroids and organoids as a function of depth. We show that the cisplatin treatment of 4T1 cells and spheroids results in a shift in metabolite levels; metabolites including nucleic acids such as DNA, 783 cm-1 with p = 0.00021 for cells; p = 0.02173 for spheroids, major amino acids such as threonine, 1338 cm-1 with p = 0.00045 for cells; p = 0.01022 for spheroids, proteins such as amide III, 1248 cm-1 with p = 0.00606 for cells; p = 0.00511 for spheroids serve as early predictors of response. Our RS findings were also applicable to canine-derived organoids, showing spatial variations in metabolic changes as a function of organoid depth in response to cisplatin. Further, the metabolic pathways such as tricarboxylic acid (TCA)/citric acid cycle and glyoxylate and dicarboxylate metabolism that drive drug response showed significant differences based on organoid depth, replicating the heterogeneous treatment response seen in solid tumors where there is a difference from the periphery to the tumor core. Our study showcases the versatility of RS as a predictive tool for treatment response applicable from cells to organotypic cultures, that has the potential to decrease animal burden and readout time for preclinical drug efficacy.

Enhancing Predictive Accuracy for Recurrence-Free Survival in Head and Neck Tumor: A Comparative Study of Weighted Fusion Radiomic Analysis.

Despite advancements in oncology, predicting recurrence-free survival (RFS) in head and neck (H&N) cancer remains challenging due to the heterogeneity of tumor biology and treatment responses. This study aims to address the research gap in the prognostic efficacy of traditional clinical predictors versus advanced radiomics features and to explore the potential of weighted fusion techniques for enhancing RFS prediction. We utilized clinical data, radiomic features from CT and PET scans, and various weighted fusion algorithms to stratify patients into low- and high-risk groups for RFS. The predictive performance of each model was evaluated using Kaplan-Meier survival analysis, and the significance of differences in RFS rates was assessed using confidence interval (CI) tests. The weighted fusion model with a 90% emphasis on PET features significantly outperformed individual modalities, yielding the highest C-index. Additionally, the incorporation of contextual information by varying peritumoral radii did not substantially improve prediction accuracy. While the clinical model and the radiomics model, individually, did not achieve statistical significance in survival differentiation, the combined feature set showed improved performance. The integration of radiomic features with clinical data through weighted fusion algorithms enhances the predictive accuracy of RFS outcomes in head and neck cancer. Our findings suggest that the utilization of multi-modal data helps in developing more reliable predictive models and underscore the potential of PET imaging in refining prognostic assessments. This study propels the discussion forward, indicating a pivotal step toward the adoption of precision medicine in cancer care.

Schizophrenia is associated with altered DNA methylation variance.

Varying combinations of genetic and environmental risk factors are thought to underpin phenotypic heterogeneity between individuals in psychiatric conditions such as schizophrenia. While epigenome-wide association studies in schizophrenia have identified extensive alteration of mean DNA methylation levels, less is known about the location and impact of DNA methylation variance, which could contribute to phenotypic and treatment response heterogeneity. To explore this question, we conducted the largest meta-analysis of blood DNA methylation variance in schizophrenia to date, leveraging three cohorts comprising 1036 individuals with schizophrenia and 954 non-psychiatric controls. Surprisingly, only a small proportion (0.1%) of the 213 variably methylated positions (VMPs) associated with schizophrenia (Benjamini-Hochberg FDR < 0.05) were shared with differentially methylated positions (DMPs; sites with mean changes between cases and controls). These blood-derived VMPs were found to be overrepresented in genes previously associated with schizophrenia and amongst brain-enriched genes, with evidence of concordant changes at VMPs in the cerebellum, hippocampus, prefrontal cortex, or striatum. Epigenetic covariance was also observed with respect to clinically significant metrics including age of onset, cognitive deficits, and symptom severity. We also uncovered a significant VMP in individuals with first-episode psychosis (n = 644) from additional cohorts and a non-psychiatric comparison group (n = 633). Collectively, these findings suggest schizophrenia is associated with significant changes in DNA methylation variance, which may contribute to individual-to-individual heterogeneity.

Common protein networks for various drug regimens of major depression are associated with complement and immunity.

Major depressive disorder (MDD) can present a variety of clinical presentations and has high inter-individual heterogeneity. Multiple studies have suggested various subtype models related to symptoms, etiology, sex, and treatment response. Employing different regimens is common when treating MDD, and identifying effective therapeutics requires time. Frequent treatment attempts and failures can lead to a diagnosis of treatment resistance, and the heterogeneity of treatment responses among individuals makes it difficult to understand and interpret the biological mechanisms underlying MDD. This study explored the differentially expressed proteins and commonly altered protein networks across drug treatments by comparing the serum proteomes of patients with MDD treated with drug regimens (T-MDD, n = 20) and untreated patients (NT-MDD, n = 20). Differentially expressed proteins were profiled in non-drug-treated and drug-treated patients with depression using liquid chromatography-mass spectrometry. The common protein networks affected by different medications were studied. Of the proteins profiled, 12 were significantly differentially expressed between the T-MDD and NT-MDD groups. Commonly altered proteins and networks of various drug treatments for depression were related to the complement system and immunity. Our results provide information on common biological changes across different pharmacological treatments employed for depression and provide an alternative perspective for improving our understanding of the biological mechanisms of drug response in MDD with great heterogeneity in the background of the disease.

Constrained Pseudo-time Ordering for Clinical Transcriptomics Data.

Time series RNASeq studies can enable understanding of the dynamics of disease progression and treatment response in patients. They also provide information on biomarkers, activated and repressed pathways, and more. While useful, data from multiple patients is challenging to integrate due to the heterogeneity in treatment response among patients, and the small number of timepoints that are usually profiled. Due to the heterogeneity among patients, relying on the sampled time points to integrate data across individuals is challenging and does not lead to correct reconstruction of the response patterns. To address these challenges, we developed a new constrained based pseudotime ordering method for analyzing transcriptomics data in clinical and response studies. Our method allows the assignment of samples to their correct placement on the response curve while respecting the individual patient order. We use polynomials to represent gene expression over the duration of the study and an EM algorithm to determine parameters and locations. Application to three treatment response datasets shows that our method improves on prior methods and leads to accurate orderings that provide new biological insight on the disease and response. Code for the method is available at https://github.com/Sanofi-Public/ RDCS-bulkRNASeq-pseudo ordering.

Assessing the Role of Patient Generation Techniques in Virtual Clinical Trial Outcomes.

Virtual clinical trials (VCTs) are growing in popularity as a tool for quantitatively predicting heterogeneous treatment responses across a population. In the context of a VCT, a plausible patient is an instance of a mathematical model with parameter (or attribute) values chosen to reflect features of the disease and response to treatment for that particular patient. A number of techniques have been introduced to determine the set of model parametrizations to include in a virtual patient cohort. These methodologies generally start with a prior distribution for each model parameter and utilize some criteria to determine whether a parameter set sampled from the priors should be included or excluded from the plausible population. No standard technique exists, however, for generating these prior distributions and choosing the inclusion/exclusion criteria. In this work, we rigorously quantify the impact that VCT design choices have on VCT predictions. Rather than use real data and a complex mathematical model, a spatial model of radiotherapy is used to generate simulated patient data and the mathematical model used to describe the patient data is a two-parameter ordinary differential equations model. This controlled setup allows us to isolate the impact of both the prior distribution and the inclusion/exclusion criteria on both the heterogeneity of plausible populations and on predicted treatment response. We find that the prior distribution, rather than the inclusion/exclusion criteria, has a larger impact on the heterogeneity of the plausible population. Yet, the percent of treatment responders in the plausible population was more sensitive to the inclusion/exclusion criteria utilized. This foundational understanding of the role of virtual clinical trial design should help inform the development of future VCTs that use more complex models and real data.

Tapinarof Cream 1% Once Daily is Efficacious for the Treatment of Atopic Dermatitis in Patients with Skin of Color Down to 2 Years of Age in Two Pivotal Phase 3 Trials

Introduction: Patients with atopic dermatitis (AD) and skin of color can have heterogeneous presentations and treatment responses. In the pivotal phase 3 ADORING 1 and 2 trials, tapinarof cream 1% (VTAMA®, Dermavant Sciences, Inc.) once daily (QD) was significantly efficacious and well tolerated versus vehicle in adults and children down to 2 years of age with AD. Objective: Here, we report analyses of efficacy by skin color in ADORING 1 and 2, based on patients’ self-identified race and investigator-assessed Fitzpatrick skin type. Methods: In ADORING 1 and 2, patients with a Validated Investigator Global Assessment for Atopic DermatitisTM (vIGA-ADTM) score of ≥3, an Eczema Area and Severity Index score of ≥6, and body surface area involvement of 5–35% were randomized 2:1 to tapinarof cream or vehicle QD for 8 weeks. The primary efficacy endpoint was a vIGA-ADTM score of 0 (clear) or 1 (almost clear) and ≥2-grade improvement from baseline at Week 8. Secondary endpoints included proportion of patients with a ≥75% improvement in Eczema Area and Severity Index (EASI75). Results: Of the 407 and 406 randomized patients, 8.8–15.3% were Asian, 26.5–35.0% were Black, 44.8–56.8% were White, and 2.7–5.2% were Other groups (American Indian or Alaska Native, Native Hawaiian or Pacific Islander, or multiple races) across trials. Patients with Fitzpatrick skin types IV, V, and VI represented 23.8–25.1%, 20.6–22.2%, and 7.6–8.9%, respectively, of patients (&gt;50% in both trials). Across trials, vIGA-ADTM responses (ranges) for tapinarof versus vehicle were: Asian, 39.5–48.9% vs 3.7–18.5%; Black, 43.1–47.0% vs 17.5–24.1%; White, 49.4–52.1% vs 12.2–14.5%; and Other, 26.0–44.8% vs 0.0–40.2%. EASI75 responses for tapinarof versus vehicle were: Asian, 47.6–76.6% vs 17.7–20.2%; Black, 48.9–55.3% vs 25.7–30.0%; White, 61.4–67.8% vs 19.6–20.7%; and Other, 38.3–63.3% vs 0.0–40.6%. Similarly, high and consistent vIGA-ADTM and EASI75 responses were reported with tapinarof versus vehicle in patients with Fitzpatrick skin types I–III and IV–VI. Conclusion: Tapinarof cream 1% QD was consistently efficacious among all racial groups and Fitzpatrick skin types in adults and children down to 2 years of age with AD, including patients with skin of color, who were highly represented in these trials.

Cancer marker TNFRSF1A: From single‑cell heterogeneity of renal cell carcinoma to functional validation.

During the progression of renal cell carcinoma (RCC), tumor growth, metastasis and treatment response heterogeneity are regulated by both the tumor itself and the tumor microenvironment (TME). The aim of the present study was to investigate the role of the TME in RCC and construct a crosstalk network for clear cell RCC (ccRCC). An additional aim was to evaluate whether TNF receptor superfamily member 1A (TNFRSF1A) is a potential therapeutic target for ccRCC. Single-cell data analysis of RCC was performed using the GSE152938 dataset, focusing on key cellular components and their involvement in the ccRCC TME. Additionally, cell-cell communication was analyzed to elucidate the complex network of the ccRCC microenvironment. Analyses of data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium databases were performed to further mine the key TNF receptor genes, with a particular focus on the prediction and assessment of the cancer-associated features of TNFRSF1A. In addition, following the silencing of TNFRSF1A using small interfering RNA in the 786-O ccRCC cell line, a number of in vitro experiments were conducted to further investigate the cancer-promoting characteristics of TNFRSF1A. These included 5-ethynyl-2'-deoxyuridine incorporation, Cell Counting Kit-8, colony formation, Transwell, cell cycle and apoptosis assays. The TNF signaling pathway was found to have a critical role in the development of ccRCC. Based on the specific crosstalk identified between TNF and TNFRSF1A, the communication of this signaling pathway within the TME was elucidated. The results of the cellular phenotype experiments indicated that TNFRSF1A promotes the proliferation, migration and invasion of ccRCC cells. Consequently, it is proposed that targeting TNFRSF1A may disrupt tumor progression and serve as a therapeutic strategy. In conclusion, by understanding the TME and identifying significant crosstalk within the TNF signaling pathway, the potential of TNFRSF1A as a therapeutic target is highlighted. This may facilitate an advance in precision medicine and improve the prognosis for patients with RCC.

Development of a differential treatment selection model for depression on consolidated and transformed clinical trial datasets

Major depressive disorder (MDD) is the leading cause of disability worldwide, yet treatment selection still proceeds via “trial and error”. Given the varied presentation of MDD and heterogeneity of treatment response, the use of machine learning to understand complex, non-linear relationships in data may be key for treatment personalization. Well-organized, structured data from clinical trials with standardized outcome measures is useful for training machine learning models; however, combining data across trials poses numerous challenges. There is also persistent concern that machine learning models can propagate harmful biases. We have created a methodology for organizing and preprocessing depression clinical trial data such that transformed variables harmonized across disparate datasets can be used as input for feature selection. Using Bayesian optimization, we identified an optimal multi-layer dense neural network that used data from 21 clinical and sociodemographic features as input in order to perform differential treatment benefit prediction. With this combined dataset of 5032 individuals and 6 drugs, we created a differential treatment benefit prediction model. Our model generalized well to the held-out test set and produced similar accuracy metrics in the test and validation set with an AUC of 0.7 when predicting binary remission. To address the potential for bias propagation, we used a bias testing performance metric to evaluate the model for harmful biases related to ethnicity, age, or sex. We present a full pipeline from data preprocessing to model validation that was employed to create the first differential treatment benefit prediction model for MDD containing 6 treatment options.

Risk score constructed with neutrophil extracellular traps-related genes predicts prognosis and immune microenvironment in multiple myeloma.

Multiple myeloma (MM) exhibits considerable heterogeneity in treatment responses and survival rates, even when standardized care is administered. Ongoing efforts are focused on developing prognostic models to predict these outcomes more accurately. Recently, neutrophil extracellular traps (NETs) have emerged as a potential factor in MM progression, sparking investigation into their role in prognostication. In this study, a multi-gene risk scoring model was constructed using the intersection of NTEs and differentially expressed genes (DEGs), applying the least absolute shrinkage and selection operator (LASSO) Cox regression model. A nomogram was established, and the prognostic model's effectiveness was determined via Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). The ESTIMATE algorithm and immune-related single-sample gene set enrichment analysis (ssGSEA) were employed to evaluate the level of immune infiltration. The sensitivity of chemotherapy drugs was assessed using the Genomics of Drug Sensitivity in Cancer (GDSC) database. Ultimately, the presence of the detected genes was confirmed through quantitative real-time polymerase chain reaction (qRT-PCR) analysis in MM cell specimens. 64 NETs-DEGs were yielded, and through univariate Cox regression and LASSO regression analysis, we constructed a risk score composed of six genes: CTSG, HSPE1, LDHA, MPO, PINK1, and VCAM1. MM patients in three independent datasets were classified into high- and low-risk groups according to the risk score. The overall survival (OS) of patients in the high-risk group was significantly reduced compared to the low-risk group. Furthermore, the risk score was an independent predictive factor for OS. In addition, interactions between the risk score, immune score, and immune cell infiltration were investigated. Further analysis indicated that patients in the high-risk group were more sensitive to a variety of chemotherapy and targeted drugs, including bortezomib. Moreover, the six genes provided insights into the progression of plasma cell disorders. This study offers novel insights into the roles of NETs in prognostic prediction, immune status, and drug sensitivity in MM, serving as a valuable supplement and enhancement to existing grading systems.

IPSC-derived hindbrain organoids to evaluate escitalopram oxalate treatment responses targeting neuropsychiatric symptoms in Alzheimer’s disease

Alzheimer’s disease (AD) is the most common cause of dementia, and the gradual deterioration of brain function eventually leads to death. Almost all AD patients suffer from neuropsychiatric symptoms (NPS), the emergence of which correlates with dysfunctional serotonergic systems. Our aim is to generate hindbrain organoids containing serotonergic neurons using human induced Pluripotent Stem Cells (iPSCs). Work presented here is laying the groundwork for the application of hindbrain organoids to evaluate individual differences in disease progression, NPS development, and pharmacological treatment response. Human peripheral blood mononuclear cells (PBMCs) from healthy volunteers (n = 3), an AD patient without NPS (n = 1), and AD patients with NPS (n = 2) were reprogrammed into iPSCs and subsequently differentiated into hindbrain organoids. The presence of serotonergic neurons was confirmed by quantitative reverse transcription PCR, flow cytometry, immunocytochemistry, and detection of released serotonin (5-HT). We successfully reprogrammed PBMCs into 6 iPSC lines, and subsequently generated hindbrain organoids from 6 individuals to study inter-patient variability using a precision medicine approach. To assess patient-specific treatment effects, organoids were treated with different concentrations of escitalopram oxalate, commonly prescribed for NPS. Changes in 5-HT levels before and after treatment with escitalopram were dose-dependent and variable across patients. Organoids from different people responded differently to the application of escitalopram in vitro. We propose that this 3D platform might be effectively used for drug screening purposes to predict patients with NPS most likely to respond to treatment in vivo and to understand the heterogeneity of treatment responses.

Clinical characteristics and independent prognostic factors of patients with mantle cell lymphoma over last two decades with a focus on the interaction between tumor stage and age.

e19065 Background: Mantle Cell Lymphoma (MCL) poses challenges in clinical management due to its rarity and aggressiveness. Understanding its epidemiology and outcomes is crucial. This study examines demographic and clinicopathologic profiles of 746 MCL patients in the USA from 2000-2015, focusing on the interplay between age and tumor stage. Methods: Using SEER data, this retrospective study analyzes demographic and clinical variables to assess mortality risks. Univariate and multivariate Cox regression models evaluate overall mortality (OM) and MCL-specific mortality (CSM), identifying independent prognostic factors (HR &gt;1). The study explores age-tumor stage interaction. Results: Multivariate Cox proportional hazard regression analysis revealed age as pivotal, with those ≥80 facing higher risks for overall mortality (HR=3.93) and MCL-related mortality (HR=4.02) . Advanced Ann Arbor staging (II, III, IV) predicts heightened risks for both mortality types. Widowed status significantly influences outcomes ,with widowed individuals facing notably higher risks of overall mortality (HR=1.85) and MCL-related mortality (HR=1.81).Stage II and age groups 60-79 and ≥80 show substantial mortality risks, with similar trends in Stage IV combined with these age groups. Intriguingly, Stage III lacks significant age interactions, suggesting heterogeneous clinical profiles and treatment responses compared to patients in Stages II and IV. Therefore, the influence of age on mortality outcomes may be obscured by the complexities inherent in Stage III disease presentation and management. Conclusions: This study underscores age, staging, and marital status as key factors influencing MCL mortality. Widowed status emerges as notable, emphasizing social support's importance. Integrating these insights into clinical practice can enhance risk stratification and guide tailored interventions for improved outcomes. Our findings, particularly the lack of significant interactions between Stage III and age groups, highlight the intricate interplay between age and disease stage in MCL patients. This observation prompts us to advocate for further research aimed at elucidating the underlying mechanisms driving these complex interactions. Understanding the biological and clinical factors that influence the relationship between age and disease stage could provide valuable insights into MCL pathogenesis and progression. Further research in this area is warranted to inform personalized therapeutic approaches and ultimately enhance patient care and outcomes.

Relationship of adaptive subtyping and tumour heterogeneity of treatment response to neoadjuvant therapy in hormone receptor–positive HER2-negative early breast cancer: PENELOPE-B.

566 Background: The concept of intrinsic subtyping has been an important step for understanding of breast cancer (BC) as a heterogenous disease. However, these subtypes are not adapted to therapy-induced molecular plasticity. We have evaluated a high-risk luminal BC clinical trial cohort to identify new additional adaptive BC subtypes based on molecular alterations induced during neoadjuvant chemotherapy (NACT). Methods: A total of 1250 ER+/HER2- BC patients (pts) with residual disease after NACT and increased risk (CPS-EG score of ≥3 or 2 with ypN+) were randomized into the PENELOPE-B (NCT01864746) trial to receive palbociclib or placebo. Biomarker analysis was performed in 1411 pre- or postNACT tumor samples including 540 paired samples using the HTG EdgeSeq Oncology Biomarker Panel targeting 2549 genes (HTG Molecular Diagnostics Inc.), with assessment of the absolute assignment of breast cancer intrinsic molecular subtype (AIMS). For a subcohort of 29 pts, we evaluated triplicate samples before and after NACT as well as at the time of metastatic disease. Results: In paired biopsies, a total of 335 genes were significantly different between the pre- and the postNACT cohort. With hierarchical unsupervised clustering of these 335 genes, we characterized five different tumor subtypes with highly significant iDFS survival differences (p&lt;0.0001, n=539). We identified two large groups of tumors with excellent prognosis (adaptive cluster AC1 and AC2, together n=284, 6 events), as well as two groups with a poor prognosis; AC-3 (n=163, 82 events) and AC-4 (n=78, 29 events). The worst prognosis was observed in a small group of tumors with a pre-NACT Basal/HER2E AIMS subtype (n=14, 11 events). AC1 and AC3 are enriched for tumors with an AIMS subtype change from LumB to LumA during NACT, with improved iDFS for AC1. AC2 and AC4 are enriched for tumors with a constant LumA AIMS subtype before and after NACT, with improved iDFS for AC2. The low-risk adaptive subtypes AC1 and AC2 cover a total of 52.7% of pts with a combined event rate of 2.1%. In contrast, the best pretherapeutic conventional AIMS-subtype, preNACT LumA, covers 51.7% of pts, but still has an event rate of 15.4%. This suggests that adaptive subtypes are superior to classical AIMS subtypes for prediction of survival after NACT in luminal BC. Conclusions: Classical approach of intrinsic subtyping relies on constant gene expression, limiting its scope. Adaptive subtyping can complement the classical approach, focusing on those genes that are changed during therapy. Comparison of paired samples before and after therapy is superior to classical subtyping of baseline samples. This approach identified large subgroups of pts with excellent prognosis after NACT despite clinical high risk. This can be an important step to focus on high-risk pts for new neoadjuvant and post-neoadjuvant therapy concepts.

Clinical implications of synovial tissue phenotypes in rheumatoid arthritis.

Autoimmune forms of inflammatory arthritis, such as Rheumatoid Arthritis (RA), are clinically heterogeneous in presentation and disease course. Treatment-related outcomes vary despite patient exposure to similar treatment strategies. It is likely that variation seen in synovial pathogenesis influences outcomes and is heterogeneous outcomes influenced by patient factors, including environmental exposures, microbiota, behaviors, timely access to therapy, and synovial cell variation. Patients' unique complex factors manifest as specific synovial phenotypes characterized by clusters of synovial cell types and states. Precision medicine aims to use such clinical and biological data to identify the right treatment for the right patient at the right time, enabling patients to achieve sustained remission. Identifying synovial targets susceptible to a given treatment, enabling the choice of effective therapy for a given patient, will realize the goals of precision medicine. Over the last 7 years, improved acquisition and processing of synovial tissue obtained by ultra-sound guided biopsy has enabled researchers to define synovial pathotypes using histologic features and predominant cell types associated with clinical manifestations. Technical advances have enabled single-cell simultaneous sequencing of proteins and gene expression that, through increasingly sophisticated bioinformatics methods, have taken transcriptional and proteomic data to identify diverse and novel cell types and states that cluster in the RA synovium to further define patient subgroups. Synovial pathotypes and endotypes are now integrated into clinical studies and trials to explain clinical heterogeneity in disease course and treatment response. Rapidly evolving clinical-translational research has linked an expanded understanding of RA synovial pathogenesis with clinically meaningful subgroups and treatment outcomes and the clinical heterogeneity in RA.

What every clinician should know about inflammation in COPD.

Inflammation drives COPD pathogenesis and exacerbations. Although the conceptual framework and major players in the inflammatory milieu of COPD have been long established, the nuances of cellular interactions and the etiological differences that create heterogeneity in inflammatory profiles and treatment response continue to be revealed. This wealth of data and understanding is not only a boon to the researcher but also provides guidance to the clinician, moving the field closer to precision medicine. It is through this lens that this review seeks to describe the inflammatory processes at play in COPD, relating inflammation to pathological and functional changes, identifying patient-specific and disease-related factors that may influence clinical observations, and providing current insights on existing and emerging anti-inflammatory treatments and treatment targets, including biological therapies and phosphodiesterase (PDE) inhibitors.