Heterogeneity In Treatment Response Research Articles

What Every Clinician Should Know About Inflammation in COPD

Inflammation drives chronic obstructive pulmonary disease (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 etiologic 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 pathologic 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 biologic therapies and phosphodiesterase inhibitors.

Sex differences in the prognostic role of achieving target doses of heart failure medications: Data from the Swedish Heart Failure Registry.

Guidelines recommend target doses (TD) of heart failure (HF) with reduced ejection fraction (HFrEF) medications regardless of sex. Differences in pharmacokinetics and pharmacodynamics may explain heterogeneity in treatment response, adverse reactions, and tolerability issues across sexes. The aim of this study was to explore sex-based differences in the association between TD achievement and mortality/morbidity in HFrEF. Patients with HFrEF and HF duration ≥6 months registered in the Swedish HF Registry between May 2000 and December 2020 (follow-up until December 2021) were analysed. Treatments of interest were renin-angiotensin system inhibitors (RASI) or angiotensin receptor-neprilysin inhibitors (ARNI), and beta-blockers. Multivariable Cox regression models were performed to explore the risk of cardiovascular mortality or hospitalization for HF across dose categories in females versus males. A total of 17 912 patients were analysed (median age 77.0 years, interquartile range [IQR] 70.0-83.0), 29% were female. Over a median follow-up of 1.33 years (IQR 0.29-3.22), for RASI/ARNI there was no significant difference in outcome for females achieving 50-99% versus 100% of TD (hazard ratio 0.92, 95% confidence interval 0.83-1.03), whereas males showed a gradual lowering in risk together with the achievement of higher % of TD (p-interaction = 0.030). For beta-blockers the achievement of TD was associated with the lowest risk of outcome regardless of sex. Our findings suggest that females and males might differently benefit from the same dose of RASI/ARNI, and do represent a general call for randomized controlled trials to consider sex-specific up-titration schemes when testing HFrEF treatments in need of up-titration.

Abstract PO1-15-02: Comprehensive characterization of genetic interactions in breast cancer reveals therapeutic vulnerabilities

Abstract Background: Genome-informed and genome-targeted precision treatment for breast cancer have achieved remarkable progress in improving clinical outcomes for patients with specific genetic alterations. However, treatment efficacy is compromised by the current practice of basing treatment decision-making solely on single driver alterations, without considering the role of genetic interactions. Consequently, it is of great necessity to conduct systematic investigations to determine the clinical relevance of genetic interactions. Methods: We established a large-scale multi-omics cohort (N=873) and a real-world clinical sequencing cohort (N=4,421) representing the Asian breast cancer population. Detailed treatment records were collected. We then investigated the prognostic and predictive effects of genetic interactions based on multivariate Cox proportional hazards model and logistic regression model. To validate our findings, we utilized patient-derived organoids and tumor fragments to confirm the associations observed between genetic interactions and drug response. Results: Through integrated analysis of genomics, transcriptomics, proteomics, and metabolomics, we constructed a network comprising 54 co-occurring events and 38 mutually exclusive events, elucidating their association with dysregulated biological processes. External validations were performed in TCGA-BRCA, MSK-IMPACT, METABRIC, and AACR-GENIE datasets, respectively. Furthermore, we systematically revealed the prognostic effects of genetic interactions across distinct clinical subtypes. In triple-negative breast cancer, we found that the co-occurrence of PIK3CAmut-FOXA1mut was associated with unfavorable distant metastasis-free survival while TP53mut-MYBamp and TP53mut-CCNE1amp were associated with decreased overall survival. Additionally, we characterized the genetic interactions that impact the clinical outcomes of patients undergoing specific treatments in the neoadjuvant, adjuvant, and advanced settings. Notably, we identified associations such as TP53mut-AURKAamp with tamoxifen resistance, ERBB2amp-PAK1amp with resistance to trastuzumab-pertuzumab combinations, germline BRCA1mut-MYCamp with sensitivity to PARP1 inhibitors, and TP53mut-MYBamp with immunotherapy resistance. Conclusion: Overall, the consideration of genetic interactions may enhance our understanding of the heterogeneity in treatment response and complement ongoing efforts in precision oncology. Our study suggests that decision-making regarding genome-informed and genome-targeted treatment should extend beyond the scope of single driver alterations. Citation Format: Caijin Lin, Xi Jin, Ding Ma, Chao Chen, Xin Hu, Yi-Zhou Jiang, Zhi-Ming Shao. Comprehensive characterization of genetic interactions in breast cancer reveals therapeutic vulnerabilities [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-15-02.

Abstract PO1-15-07: Subtyping-directed precision treatment refines traditional one-size-fits-all therapy in HR+/HER2- breast cancer: a sub-study of the MULAN umbrella trial

Abstract Background: The standard approach of using one-size-fits-all endocrine therapy for hormone receptor-positive and HER2-negative (HR+/HER2-) breast cancers has faced significant challenges due to variations in treatment response among individuals. Consequently, there is still an urgent need to understand the molecular biology of HR+/HER2- breast cancer and explore precision treatment strategy. Methods: We established a multiomics cohort (n = 351), multicenter real-world clinical cohorts (n = 643), a prospective clinical cohort (MULAN trial), and a drug-testing platform containing patient-derived organoids (n = 126) and patient-derived tumor fragments (n = 49) of HR+/HER2- breast cancers. Integrating "bench" and “bedside” data, we conducted comprehensive preclinical and clinical translational research on precision strategies in HR+/HER2- breast cancer. Results: We implemented a comprehensive classification system for HR+/HER2- breast cancer, comprising four distinct subtypes. We further demonstrated the efficacy and mechanisms of subtyping-directed precision treatment strategies through clinical cohort studies, omics analysis and functional assays: endocrine therapy alone for the canonical luminal subtype; the addition of CDK4/6 inhibitor and PARP inhibitor for the proliferative subtype; the immunotherapy for the immunogenic subtype; and tyrosine kinase inhibitors for the receptor tyrosine kinase-driven subtype. Using clinically applicable subtyping methods, we validated that matched precision treatment strategies outperformed unmatched approaches in a real-world cohort, almost doubling the median progression-free survival time for patients with refractory advanced HR+/HER2- breast cancer (9.83 months [95% CI, 5.74-13.93] vs 4.77 months [95% CI, 3.35-6.18]; hazard ratio 0.64 [95% CI, 0.41-0.99]). Importantly, the first-stage analysis of the MULAN phase II umbrella clinical trial (NCT04355858) verified the higher objective response rate (88.9%, 95%CI: 51.7%-99.7%) of the subtyping-directed precision treatment. Conclusion: Our study emphasizes the superiority of subtyping-directed precision treatment strategies for HR+/HER2- breast cancer, refines traditional “one-size-fits-all” therapy, and facilitates the translation of precision treatment strategies from bench to bedside. Overall study design Part 1 Subtype Molecular Characteristics: Molecular features of the four subtypes of HR+/HER2- breast cancer. Part 2 Multidimensional Efficacy Validation: Integrating data from a multiomics cohort, real-world study, drug testing platform and prospective clinical research to validate the subtyping-directed precision treatment strategy in HR+/HER2- breast cancer. Part 3 Subtyping-directed Precision Treatment Strategy: Integrating clinical cohort studies, omics analysis and functional assays revealed the heterogeneity of treatment response in HR+/HER2- breast cancer, and proposed a subtyping-directed precision treatment strategy. CIN, chromosomal instability; RTK, receptor tyrosine kinase; CNA, copy-number alteration; ER, estrogen receptor; HR, hormone receptor; HER2, human epidermal growth factor receptor 2. Citation Format: Xiu-Zhi Zhu, Xi Jin, Hu-Yun-Long Zhang, Xiyu Liu, Yi-Fan Zhou, Yi-Yu Chen, Tong Fu, Ming-Liang Jin, Ya-Xin Zhao, Yi-Fan Xie, Ruo-Xi Wang, Zhong-Hua Wang, Lei Fan, Yi-Zhou Jiang, Zhi-Ming Shao. Subtyping-directed precision treatment refines traditional one-size-fits-all therapy in HR+/HER2- breast cancer: a sub-study of the MULAN umbrella trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-15-07.

Heterogeneity in response to Elexacaftor/Tezacaftor/Ivacaftor in people with cystic fibrosis

BackgroundHighly effective modulators of the CFTR channel have been demonstrated to dramatically impact disease progression and outcome. However, real-world data indicates that the magnitude of the clinical benefit is not equal among all patients receiving the treatment. We aimed to assess the variability in treatment response (as defined by the 6-month change in sweat chloride concentration, forced expiratory volume in one second [ppFEV1], body mass index [BMI], and CF Questionnaire-Revised [CFQ-R] respiratory domain score) and identify potential predictors in a group of patients receiving Elexacaftor-Tezacaftor-Ivacaftor (ETI) triple combination therapy. MethodsThis was a single-center, prospective cohort study enrolling adults with CF at a major center in Italy. We used linear regression models to identify a set of potential predictors (including CFTR genotype, sex, age, and baseline clinical characteristics) and estimate the variability in treatment response. ResultsThe study included 211 patients (median age: 29 years, range: 12–58). Median changes (10–90th percentile) from baseline were: - 56 mEq/L (–76; –27) for sweat chloride concentration, +14.5 points (2.5; 32.0) for ppFEV1, +0.33 standard deviation scores (–0.13; 1.05) for BMI and +17 points (0; 39) for the CFQ-R respiratory domain score. The selected predictors explained 23 % of the variability in sweat chloride concentration changes, 18 % of the variability in ppFEV1 changes, 39 % of the variability in BMI changes, and 65 % of the variability in CFQ-R changes. ConclusionsThis study highlights a high level of heterogeneity in treatment response to ETI, which can only be partially explained by the baseline characteristics of the disease.

Gustave Roussy Immune Score (GRIm-Score) as a Prognostic and Predictive Score in Metastatic Colorectal Cancer.

Metastatic colorectal cancer (mCRC) presents significant clinical challenges due to its heterogeneous nature and variable treatment responses. The Gustave Roussy Immune Score (GRIm-Score) has emerged as a potential biomarker for prognostication and prediction in mCRC, although its precise role remains under investigation. We conducted a retrospective study that included 173 patients diagnosed with mCRC. The patients were treated in the first line with5-fluorouracil (5-FU)-based chemotherapy (CHT) and a molecular agent based on their eligibility. We assessed the overall survival (OS) time, progression-free survival (PFS) time, and the overall response rate (ORR),utilizing the GRIm-score measured at baseline (referred to as GRImT0) and the variance between GRImT0 and the GRIm score measured three months after treatment initiation (referred to as GRIm∆). We also performed a subgroup analysis based on the type of treatment received. Our analysis revealed that the GRIm-Score holds promise as a prognostic marker in mCRC, with high scores correlating with poorer survival outcomes. However, in the subgroup analysis, this prognostic value remained relevant only for patients treated with CHT and anti-EGFR (epidermal growth factor receptor) agents, such as cetuximab and panitumumab. GRIm-Score exhibited no predictive value irrespective of the treatment received. The GRIm-Score shows potential as a prognostic mCRC, although we believe that this potential is limited. Integration of the GRIm-Score into clinical practice should be done with caution and is not recommended at this time. However, further research is needed to fully elucidate its clinical utility and optimize its incorporation into routine clinical care.

Identification of two distinct head and neck squamous cell carcinoma subtypes based on fatty acid metabolism-related signatures: Implications for immunotherapy and chemotherapy.

The dysregulation of lipid metabolism is a critical factor in the initiation and progression of tumors. In this investigation, we aim to characterize the molecular subtypes of head and neck squamous cell carcinoma (HNSCC) based on their association with fatty acid metabolism and develop a prognostic risk model. The transcriptomic and clinical data about HNSCC were obtained from public databases. Clustering analysis was conducted on fatty acid metabolism genes (FAMG) associated with prognosis, utilizing the non-negative matrix factorization algorithm. The immune infiltration, response to immune therapy, and drug sensitivity between molecular subtypes were evaluated. Differential expression genes were identified between subtypes, and a prognostic model was constructed using Cox regression analyses. A nomogram for HNSCC was constructed and evaluated. Thirty FAMGs have been found to exhibit differential expression in HNSCC, out of which three are associated with HNSCC prognosis. By performing clustering analysis on these 3 genes, 2 distinct molecular subtypes of HNSCC were identified that exhibit significant heterogeneity in prognosis, immune landscape, and treatment response. Using a set of 7778 genes that displayed differential expression between the 2 molecular subtypes, a prognostic risk model for HNSCC was constructed comprising 11 genes. This model has the ability to stratify HNSCC patients into high-risk and low-risk groups, which exhibit significant differences in prognosis, immune infiltration, and immune therapy response. Moreover, our data suggest that this risk model is negatively correlated with B cells and most T cells, but positively correlated with macrophages, mast cells, and dendritic cells. Ultimately, we constructed a nomogram incorporating both the risk signature and radiotherapy, which has demonstrated exceptional performance in predicting prognosis for HNSCC patients. A molecular classification system and prognostic risk models were developed for HNSCC based on FAMGs. This study revealed the potential involvement of FAMGs in modulating tumor immune microenvironment and response to treatment.

Translation velocity determines the efficacy of engineered suppressor tRNAs on pathogenic nonsense mutations

Nonsense mutations – the underlying cause of approximately 11% of all genetic diseases – prematurely terminate protein synthesis by mutating a sense codon to a premature stop or termination codon (PTC). An emerging therapeutic strategy to suppress nonsense defects is to engineer sense-codon decoding tRNAs to readthrough and restore translation at PTCs. However, the readthrough efficiency of the engineered suppressor tRNAs (sup-tRNAs) largely varies in a tissue- and sequence context-dependent manner and has not yet yielded optimal clinical efficacy for many nonsense mutations. Here, we systematically analyze the suppression efficacy at various pathogenic nonsense mutations. We discover that the translation velocity of the sequence upstream of PTCs modulates the sup-tRNA readthrough efficacy. The PTCs most refractory to suppression are embedded in a sequence context translated with an abrupt reversal of the translation speed leading to ribosomal collisions. Moreover, modeling translation velocity using Ribo-seq data can accurately predict the suppression efficacy at PTCs. These results reveal previously unknown molecular signatures contributing to genotype-phenotype relationships and treatment-response heterogeneity, and provide the framework for the development of personalized tRNA-based gene therapies.

An Extracellular Matrix Overlay Model for Bioluminescence Microscopy to Measure Single-Cell Heterogeneous Responses to Antiandrogens in Prostate Cancer Cells.

Prostate cancer (PCa) displays diverse intra-tumoral traits, impacting its progression and treatment outcomes. This study aimed to refine PCa cell culture conditions for dynamic monitoring of androgen receptor (AR) activity at the single-cell level. We introduced an extracellular matrix-Matrigel (ECM-M) culture model, enhancing cellular tracking during bioluminescence single-cell imaging while improving cell viability. ECM-M notably tripled the traceability of poorly adherent PCa cells, facilitating robust single-cell tracking, without impeding substrate permeability or AR response. This model effectively monitored AR modulation by antiandrogens across various PCa cell lines. Single-cell imaging unveiled heterogeneous antiandrogen responses within populations, correlating non-responsive cell proportions with drug IC50 values. Integrating ECM-M culture with the PSEBC-TSTA biosensor enabled precise characterization of ARi responsiveness within diverse cell populations. Our ECM-M model stands as a promising tool to assess heterogeneous single-cell treatment responses in cancer, offering insights to link drug responses to intracellular signaling dynamics. This approach enhances our comprehension of the nuanced and dynamic nature of PCa treatment responses.

Frequency of Complete Remission With R-CHOP Therapy in Patients With Diffuse Large B Cell Lymphoma.

Background Diffuse large B-cell lymphoma (DLBCL) exhibits notable heterogeneity in clinical presentations and treatment responses, posing challenges in predicting outcomes and tailoring therapeutic strategies for affected patients. Despite advancements in molecular subtyping and prognostic assessment, uncertainties persist regarding the optimal management of DLBCL, highlighting the need for localized investigations to better understand treatment responses and outcomes within specific patient populations. Objective To assess the frequency of complete remission (CR) in diffuse large B-cell lymphoma (DLBCL) patients undergoing first-line rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone (R-CHOP) therapy within a specific adult population. Material and methods This descriptive study was conducted within the Department of Oncology Hayatabad Medical Complex, Peshawar, Pakistan from August 8, 2022, to April 8, 2023. The study included newly diagnosed DLBCL patients aged 20-70 years, excluding those who had received prior treatment. There were 55 (57.9%) males and 40 (42.1%) females. Data on demographic characteristics, disease duration, and CR outcomes were collected using a predefined data collection form. Results The majority of patients (80, 84.2%) achieved CR following R-CHOP therapy. In terms of age distribution, 43 (45.3%) patients were aged ≤45 years, while the remaining belonged to the >45 years age group. The duration of the disease was ≤ 3 months in 60 (63.2%) cases, whereas it exceeded three months in 35 (36.8%) cases. With regards to BMI classification, nine (9.5%) patients had a BMI < 18.5 kg/m2, 49 (51.6%) fell within the range of 18.5-24.9 kg/m2, and the remaining 37 (38.9%) patients had a BMI between 25-30 kg/m2. Conclusion Diffuse large B-cell lymphoma (DLBCL) remains a heterogeneous disease entity with variable clinical outcomes. While R-CHOP therapy demonstrates promising efficacy in achieving CR, concerns regarding late adverse effects persist. Addressing these challenges requires continued research efforts to validate novel prognostic markers and develop alternative treatment approaches, ultimately improving patient outcomes and reducing the global burden of DLBCL.

Prediction uncertainty estimates elucidate the limitation of current NSCLC subtype classification in representing mutational heterogeneity

The heterogeneous pathogenesis and treatment response of non-small cell lung cancer (NSCLC) has led clinical treatment decisions to be guided by NSCLC subtypes, with lung adenocarcinoma and lung squamous cell carcinoma being the most common subtypes. While histology-based subtyping remains challenging, NSCLC subtypes were found to be distinct at the transcriptomic level. However, unlike genomic alterations, gene expression is generally not assessed in clinical routine. Since subtyping of NSCLC has remained elusive using mutational data, we aimed at developing a neural network model that simultaneously learns from adenocarcinoma and squamous cell carcinoma samples of other tissue types and is regularized using a neural network model trained from gene expression data. While substructures of the expression-based manifold were captured in the mutation-based manifold, NSCLC classification accuracy did not significantly improve. However, performance was increased when rejecting inconclusive samples using an ensemble-based approach capturing prediction uncertainty. Importantly, SHAP analysis of misclassified samples identified co-occurring mutations indicative of both NSCLC subtypes, questioning the current NSCLC subtype classification to adequately represent inherent mutational heterogeneity. Since our model captures mutational patterns linked to clinical heterogeneity, we anticipate it to be suited as foundational model of genomic data for clinically relevant prognostic or predictive downstream tasks.

BEHAV3D: a 3D live imaging platform for comprehensive analysis of engineered T cell behavior and tumor response.

Modeling immuno-oncology by using patient-derived material and immune cell co-cultures can advance our understanding of immune cell tumor targeting in a patient-specific manner, offering leads to improve cellular immunotherapy. However, fully exploiting these living cultures requires analysis of the dynamic cellular features modeled, for which protocols are currently limited. Here, we describe the application of BEHAV3D, a platform that implements multi-color live 3D imaging and computational tools for: (i) analyzing tumor death dynamics at both single-organoid or cell and population levels, (ii) classifying T cell behavior and (iii) producing data-informed 3D images and videos for visual inspection and further insight into obtained results. Together, this enables a refined assessment of how solid and liquid tumors respond to cellular immunotherapy, critically capturing both inter- and intratumoral heterogeneity in treatment response. In addition, BEHAV3D uncovers T cell behavior involved in tumor targeting, offering insight into their mode of action. Our pipeline thereby has strong implications for comparing, prioritizing and improving immunotherapy products by highlighting the behavioral differences between individual tumor donors, distinct T cell therapy concepts or subpopulations. The protocol describes critical wet lab steps, including co-culture preparations and fast 3D imaging with live cell dyes, a segmentation-based image processing tool to track individual organoids, tumor and immune cells and an analytical pipeline for behavioral profiling. This 1-week protocol, accessible to users with basic cell culture, imaging and programming expertise, can easily be adapted to any type of co-culture to visualize and exploit cell behavior, having far-reaching implications for the immuno-oncology field and beyond.

Abstract P492: Polygenic Risk for Obesity and Race Modify the Effect of a Low Fat Dietary Clinical Trial on Weight Loss

Introduction: Research populations harbor innate and acquired characteristics that can introduce substantial treatment response heterogeneity. Weight loss interventions show this variability, likely due to behavioral and physiological characteristics. Genetic risk for obesity is a potential but largely unexplored factor in weight loss trials. We examined whether genetic risk for obesity modified the effect of a low-fat diet on weight change in a randomized controlled trial. Methods: A data from the Women’s Health Initiative Dietary Modification Trial, including 10,353 postmenopausal women aged 50-79 with genotype data [63% European American (EAs), 37% African American (AAs)] were randomized into control and low-fat diet groups. The outcome was visit-specific weight difference (kg) from baseline. The Polygenic Risk Score of body mass index (PRS BMI ) was computed using the PRS-CS method and an independent trans-ancestry genome-wide association study of BMI (N = ~ 2M; 79 % EAs and 4 % AAs). We operationalized PRS BMI as a binary variable, distinguishing individuals in the 95 th percentile from the remaining individuals. Association was tested using generalized estimated equations with a 3-way interaction term (PRS х randomization х time), adjusting for 10 principal components, age, baseline BMI, and follow-up status. Analyses were performed separately by race/ethnicity, because body weight is influenced by ancestral diversity, which affects both linkage disequilibrium, PRS effect sizes, and cultural factors (e.g., racism). Results: Baseline, we had 6,473 EAs (mean age: 65.4 ± 6.6 years, mean BMI: 29.5 ± 6.5 kg/m 2 ) and 3,880 AAs (mean age: 60.9 ± 6.7years, mean BMI: 32.1 ± 6.8 kg/m 2 ) and mean follow-up was 6.0 ± 1.8 years. PRS BMI explained 12% of the variance in the baseline BMI measure in EAs and 8% in AAs. During the first year of intensive dietary intervention (18 sessions/yr), the low-fat diet group experienced 2.1 kg and 1.1 kg weight loss in European and AAs respectively, with no differences in PRS BMI . During years 1-7 with reduced intervention intensity (4 sessions/yr), the rate of weight change in both race/ethnic groups were modified by PRS BMI , although in different directions. EAs with high PRS (95 th percentile) in the intervention group rebounded towards initial weight (0.35 kg/year, p&lt;0.001) nearly twice as fast as those women with PRS &lt; 95 th percentile (0.19 kg/year, p&lt;0.001). Conversely, high PRS African American women in the intervention group maintained their weight loss without rebound (-0.10 kg/year, p=0.48), distinctly different from those with PRS &lt; 95 th percentile, who gained 0.25 kg annually (p&lt;0.001). Conclusions: Impact of long-term low-fat dietary intervention on weight change was modified by genetic risk and race/ethnicity. Genetic predisposition and other contextual factors may be considered in the design and analysis of relevant clinical trials.

The Role of Pharmacogenomics in Rare Diseases.

Rare diseases have become an increasingly important public health priority due to their collective prevalence and often life-threatening nature. Incentive programs, such as the Orphan Drug Act have been introduced to increase the development of rare disease therapeutics. While the approval of these therapeutics requires supportive data from stringent pre-market studies, these data lack the ability to describe the causes of treatment response heterogeneity, leading to medications often being more harmful or less effective than predicted. If a Goal Line were to be used to describe the multifactorial continuum of phenotypic variations occurring in response to a medication, the 'Goal Posts', or the two defining points of this continuum, would be (1) Super-Response, or an extraordinary therapeutic effect; and (2) Serious Harm. Investigation of the pharmacogenomics behind these two extreme phenotypes can potentially lead to the development of new therapeutics, help inform rational use criteria in drug policy, and improve the understanding of underlying disease pathophysiology. In the context of rare diseases where cohort sizes are smaller than ideal, 'small data' and 'big data' approaches to data collection and analysis should be combined to produce the most robust results. This paper presents the importance of studying drug response in parallel to other research initiatives in rare diseases, as well as the need for international collaboration in the area of rare disease pharmacogenomics.

Integrated analysis of a competing endogenous RNA network reveals a ferroptosis-related 6-lncRNA prognostic signature in clear cell renal cell carcinoma.

Establishing a robust signature for prognostic prediction and precision treatment is necessary due to the heterogeneous prognosis and treatment response of clear cell renal cell carcinoma (ccRCC). This study set out to elucidate the biological functions and prognostic role of ferroptosis-related long non-coding RNAs (lncRNAs) based on a synthetic analysis of competing endogenous RNA networks in ccRCC. Ferroptosis-related genes were obtained from the FerrDb database. The expression data and matched clinical information of lncRNAs, miRNAs and mRNAs from The Cancer Genome Atlas (TCGA) database were obtained to identify differentially expressed RNAs. The lncRNA-miRNA-mRNA ceRNA network was established utilizing the common miRNAs that were predicted in the RNAHybrid, StarBase and TargetScan databases. Then, using progressive univariate Cox regression, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis of gene expression data and clinical information, a ferroptosis-related lncRNA prognosis signature was constructed based on the lncRNAs in ceRNA. Finally, the influence of independent lncRNAs on ccRCC was explored. A total of 35 ferroptosis-related mRNAs, 356 lncRNAs and 132 miRNAs were sorted out after differential expression analysis in the TCGA-KIRC. Subsequently, overlapping lncRNA-miRNA and miRNA-mRNA interactions among the RNAHybrid, StarBase and TargetScan databases were constructed and identified; then a ceRNA network with 77 axes related to ferroptosis was established utilizing mutual miRNAs in 2 interaction networks as nodes. Next, a 6-ferroptosis-lncRNA signature including PVT1, CYTOR, MIAT, SNHG17, LINC00265, and LINC00894 was identified in the training set. Kaplan-Meier analysis, PCA, t-SNE analysis, risk score curve, and receiver operating characteristic (ROC) curve were performed to confirm the validity of the signature in the training set and verified in the validation set. Finally, single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) analysis showed that the signature was related to immune cell infiltration. Our research underlines the role of the 6-ferroptosis-lncRNA signature as a predictor of prognosis and a therapeutic alternative for ccRCC.

Designing patient-oriented combination therapies for acute myeloid leukemia based on efficacy/toxicity integration and bipartite network modeling

Acute myeloid leukemia (AML), a heterogeneous and aggressive blood cancer, does not respond well to single-drug therapy. A combination of drugs is required to effectively treat this disease. Computational models are critical for combination therapy discovery due to the tens of thousands of two-drug combinations, even with approved drugs. While predicting synergistic drugs is the focus of current methods, few consider drug efficacy and potential toxicity, which are crucial for treatment success. To find effective new drug candidates, we constructed a bipartite network using patient-derived tumor samples and drugs. The network is based on drug-response screening and summarizes all treatment response heterogeneity as drug response weights. This bipartite network is then projected onto the drug part, resulting in the drug similarity network. Distinct drug clusters were identified using community detection methods, each targeting different biological processes and pathways as revealed by enrichment and pathway analysis of the drugs’ protein targets. Four drugs with the highest efficacy and lowest toxicity from each cluster were selected and tested for drug sensitivity using cell viability assays on various samples. Results show that ruxolitinib-ulixertinib and sapanisertib-LY3009120 are the most effective combinations with the least toxicity and the best synergistic effect on blast cells. These findings lay the foundation for personalized and successful AML therapies, ultimately leading to the development of drug combinations that can be used alongside standard first-line AML treatment.

Assessing the potential for precision medicine in body weight reduction with regard to type 2 diabetes mellitus therapies: A meta-regression analysis of 120 randomized controlled trials.

To assess the potential for precision medicine in type 2 diabetes by quantifying the variability of body weight as response to pharmacological treatment and to identify predictors which could explain this variability. We used randomized clinical trials (RCTs) comparing glucose-lowering drugs (including but not limited to sodium-glucose cotransporter-2 inhibitors, glucagon-like peptide-1 receptor agonists and thiazolidinediones) to placebo from four recent systematic reviews. RCTs reporting on body weight after treatment to allow for calculation of its logarithmic standard deviation (log[SD], i.e., treatment response heterogeneity) in verum (i.e., treatment) and placebo groups were included. Meta-regression analyses were performed with respect to variability of body weight after treatment and potential predictors. A total of 120 RCTs with a total of 43 663 participants were analysed. A slightly larger treatment response heterogeneity was shown in the verum groups, with a median log(SD) of 2.83 compared to 2.79 from placebo. After full adjustment in the meta-regression model, the difference in body weight log(SD) was -0.026 (95% confidence interval -0.044; 0.008), with greater variability in the placebo groups. Scatterplots did not show any slope divergence (i.e., interaction) between clinical predictors and the respective treatment (verum or placebo). We found no major treatment response heterogeneity in RCTs of glucose-lowering drugs for body weight reduction in type 2 diabetes. The precision medicine approach may thus be of limited value in this setting.

Predicting the outcome of psilocybin treatment for depression from baseline fMRI functional connectivity

BackgroundPsilocybin is a serotonergic psychedelic drug under assessment as a potential therapy for treatment-resistant and major depression. Heterogeneous treatment responses raise interest in predicting the outcome from baseline data. MethodsA machine learning pipeline was implemented to investigate baseline resting-state functional connectivity measured with functional magnetic resonance imaging (fMRI) as a predictor of symptom severity in psilocybin monotherapy for treatment-resistant depression (16 patients administered two 5 mg capsules followed by 25 mg, separated by one week). Generalizability was tested in a sample of 22 patients who participated in a psilocybin vs. escitalopram trial for moderate-to-severe major depression (two separate doses of 25 mg of psilocybin 3 weeks apart plus 6 weeks of daily placebo vs. two separate doses of 1 mg of psilocybin 3 weeks apart plus 6 weeks of daily oral escitalopram). The analysis was repeated using both samples combined. ResultsFunctional connectivity of visual, default mode and executive networks predicted early symptom improvement, while the salience network predicted responders up to 24 weeks after treatment (accuracy≈0.9). Generalization performance was borderline significant. Consistent results were obtained from the combined sample analysis. Fronto-occipital and fronto-temporal coupling predicted early and late symptom reduction, respectively. LimitationsThe number of participants and differences between the two datasets limit the generalizability of the findings, while the lack of a placebo arm limits their specificity. ConclusionsBaseline neurophysiological measurements can predict the outcome of psilocybin treatment for depression. Future research based on larger datasets should strive to assess the generalizability of these predictions.

Animal Models for Understanding the Mechanisms of Beta Cell Death during Type 2 Diabetes Pathogenesis.

Type 2 diabetes (T2D) has become a worldwide epidemic, primarily driven by obesity from overnutrition and sedentariness. Recent results reveal there is heterogeneity in both pathology and treatment responses in T2D patients. Therefore, a variety of T2D animal models are necessary to obtain a mechanistic understanding of distinct disease processes. T2D results from insufficient insulin, either due to beta cell loss or inborn deficiency. Although decreases in beta cell mass can occur through loss of identity or cell death, in this review, we will highlight the T2D animal models that display beta cell death, including the Zucker Diabetic Fatty Rat, sand rat, db/db mouse, and a novel diabetic zebrafish model, the Zebrafish Muscle Insulin-Resistant (zMIR) fish. Procuring a mechanistic understanding of different T2D progression trajectories under a variety of contexts is paramount for developing and testing more individualized treatments.

Who Benefited Most from the Internet-Based Conversational Engagement RCT (I-CONECT)? Application of the Personalized Medicine Approach to a Behavioral Intervention Study

BackgroundMany Alzheimer’s Disease (AD) clinical trials have failed to demonstrate treatment efficacy on cognition. It is conceivable that a complex disease like AD may not have the same treatment effect due to many heterogeneities of disease processes and individual traits.ObjectivesWe employed an individual-level treatment response (ITR) approach to determine the characteristics of treatment responders and estimated time saved in cognitive decline using the Internet-based Conversational Engagement Clinical Trial (I-CONECT) behavioral intervention study as a model.Design and SettingI-CONECT is a multi-site, single-blind, randomized controlled trial aimed to improve cognitive functions through frequent conversational interactions via internet/webcam. The experimental group engaged in video chats with study staff 4 times/week for 6 months; the control group received weekly 10-minute check-in phone calls.ParticipantsOut of 186 randomized participants, current study used 139 participants with complete information on both baseline and 6-month follow-up (73 with mild cognitive impairment (MCI), 66 with normal cognition; 64 in the experimental group, and 75 in the control group).MeasurementsITR scores were generated for the Montreal Cognitive Assessment (MoCA) (global cognition, primary outcome) and Category Fluency Animals (CFA) (semantic fluency, secondary outcome) that showed significant efficacy in the trial. ITR scores were generated through 300 iterations of 3-fold cross-validated random forest models. The average treatment difference (ATD) curve and the area between the curves (ABC) were estimated to measure the heterogeneity of treatment responses. Responder traits were identified using SHapley Additive exPlanations (SHAP) and decision tree models. The time saved in cognitive decline was explored to gauge clinical meaningfulness.ResultsABC statistics showed substantial heterogeneity in treatment response with MoCA but modest heterogeneity in treatment response with CFA. Age, cognitive status, time spent with family and friends, education, and personality were important characteristics that influenced treatment responses. Intervention group participants in the upper 30% of ITR scores demonstrated potential delays of 3 months in semantic fluency (CFA) and 6 months in global cognition (MoCA), assuming a 5-fold faster natural cognitive decline compared to the control group during the post-treatment period.ConclusionsITR-based analyses are valuable in profiling treatment responders for features that can inform future trial design and clinical practice. Reliably measuring time saved in cognitive decline is an area of ongoing research to gain insight into the clinical meaningfulness of treatment.