Prediction Of Response Research Articles

Abstract B032: Expanding the geographic reach of functional precision oncology through prediction of therapy response with patient-derived tumoroids

Abstract Effective treatment for advanced and metastatic tumors remains a major clinical challenge. Chemotherapy has an objective response rate of 40%, and reliable predictive biomarkers to guide treatment are lacking. Global implementation of Functional Precision Oncology (FPO) offers a promising, personalized approach to predicting tumor response, with potential to improve patient outcomes, optimize resource use, and promote equity across diverse geographic regions. This study aims to establish and validate a functional testing platform using gastrointestinal (GI) patient-derived tumoroids (PDTs) to predict tumor vulnerabilities to standard-of-care (SOC) therapies. In January 2023, the Brazilian National Ethics Committee approved the present prospective, observational clinical study. Biopsies were collected from metastatic lesions of patients with colorectal, pancreatic, and gastric adenocarcinomas, neuroendocrine pancreatic tumors (PNETs), and advanced gastric adenocarcinomas. Isolated cells were embedded in basement extracellular matrix and cultured in media tailored to each tumor type. The PDTs were exposed to SOC drugs, and cell viability was assessed. Drug sensitivity was evaluated by comparing the growth rate (GR) of each PDT to the median GR of PDTs from the same tumor type. PDTs with GR values above the third quartile were classified as resistant, while those b.elow were considered sensitive. Patient tumor responses to SOC therapies were evaluated using the Response Evaluation Criteria in Solid Tumors (RECIST), with outcomes categorized as partial response (PR), stable disease (SD) or progressive disease (PD). Current success rates are 67% (8/12) for colorectal, 69% (11/16) for gastric, and 85% (6/7) for pancreatic adenocarcinomas. PNETs remain challenging, with 26% (4/15) of cultures showing temporary growth and 13% (2/15) establishing permanent cultures. Immunohistochemistry and immunofluorescence confirmed that PDTs retained similar morphology and marker expression as their matched tumors. Fourteen colorectal, fourteen gastric, six pancreatic adenocarcinomas, and two PNETs were treated with SOC drugs. Treatment responses were highly reproducible across biological replicates (Spearman correlation 0.87 to 0.95, p < 10^-6) and demonstrated heterogeneity between PDTs. Treatment response predictions (resistant or sensitive) for six PDTs from adenocarcinomas (three colorectal, one pancreatic, one gastric) and one PNET were correlated with patient clinical outcomes (PR, SD or PD). PDTs demonstrated an 83% concordance in predicting patient therapy response, with 75% accuracy for predicting treatment sensitivity and 100% accuracy for predicting resistance. These findings suggest that FPO approaches are reproducible across different settings and their global implementation could benefit oncology patients. Supported by FAPESP-PIPE (2022/10646-4, 2023/1285-3), and INCT Model3D (CNPq). Citation Format: Luciana Harumi Osaki, Tiago Goss dos Santos, Stefano P. Clerici, Celso A. Mello, Tiago Felismino, Gabriel O. Santos, Rodrigo G. Taboada, Virgilio S. Silva, Samuel Aguiar Jr., Felipe Jose Coimbra, Adriane G. Pelosof, Angelo B Brito, Rubens Chojniack, Paula N.V.P. Barbosa, Denisse Esther Mallaupoma-Camarena, Adrhyann J.S. Portilho, Queila C. Dias, Isadora Rattes, Patricia Gama, Rachel S.P. Riechelmann, Vilma R. Martins. Expanding the geographic reach of functional precision oncology through prediction of therapy response with patient-derived tumoroids [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr B032.

Abstract B044: Deep learning-based integration of tumor omics and functional drug screening for precision treatment selection in high-risk and metastatic breast tumors

Abstract Precision oncology centers on the selection of targeted anticancer agents in tumors harboring actionable molecular alterations. However, most tumors lack actionable alterations, and individual biomarkers are often poor predictors of response. These challenges are particularly acute in metastatic tumors with their vast inter-tumoral heterogeneity and diverse resistance mechanisms. As a result, cytotoxic chemotherapy remains a mainstay of treatment, yet few data-driven tools exist to inform chemotherapy selection. These limitations inform the need for rational treatment selection strategies for drugs lacking validated predictive biomarkers in tumors with or without actionable molecular alterations. To this end, we report a unified computational/experimental approach, combining tumor omic and functional profiling with ScreenDL, a deep learning (DL)-based cancer drug response prediction framework capable of integrating these complementary data modalities to provide treatment recommendations for high-risk/metastatic breast tumors. ScreenDL leverages a phased training schema, incorporating: (1) general-purpose pretraining on large-scale cell line pharmaco-omic screens; and (2) follow-up fine-tuning on a newly generated pharmaco-omic dataset spanning >100 patient-derived xenograft (PDX)-derived organoid (PDxO) models primarily from high-risk, endocrine resistant, treatment-refractory, and/or metastatic breast tumors representing the greatest unmet clinical need. As new patients enter our precision oncology pipeline, tumor omic profiling and functional drug screening in patient-derived organoids (PDOs) enables patient-specific fine-tuning with our ScreenAhead module, integrating a tumor’s transcriptomic and functional characteristics with prior knowledge learned from training samples to generate personalized response predictions. At baseline, ScreenDL outperforms existing DL models in never-before-seen PDxOs, achieving a median Pearson correlation (PCC) between observed and predicted response across drugs of 0.32 compared to 0.20 for the next-best tested model. After incorporating each PDxO’s response to just 12 drugs through PDxO-specific fine-tuning with ScreenAhead, ScreenDL provides high-confidence predictions (PCC > 0.5) for 49 out of 95 drugs (52%), with a median PCC across all 95 drugs of 0.59. These high-confidence drugs included both chemotherapies and targeted agents with diverse biological mechanisms, showcasing the power of personalization with a small pre-screening panel to improve predictions for the broader space of therapies. ScreenDL also outperformed biomarker-only predictors of response to talazoparib and capivasertib, two targeted agents approved for breast tumors harboring mutations in BRCA1/2 or PI3KCA/AKT/PTEN, respectively. In a retrospective preclinical pilot of 11 PDX models, our end-to-end treatment selection strategy achieved a 100% clinical benefit rate and a 65% objective response rate, highlighting the vast clinical potential of our unified computational/experimental treatment selection strategy. Citation Format: Casey Sederman, Chieh-Hsiang Yang, Emilio Cortes-Sanchez, Tony DiSera, Xiaomeng Huang, Yi Qiao, Sandra Scherer, Bryan Welm, Alana Welm, Gabor Marth. Deep learning-based integration of tumor omics and functional drug screening for precision treatment selection in high-risk and metastatic breast tumors [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr B044.

Multimodal deep learning for predicting PD-L1 biomarker and clinical immunotherapy outcomes of esophageal cancer

Although the immune checkpoint inhibitors (ICIs) have demonstrated remarkable anti-tumor efficacy in solid tumors, the proportion of ESCC patients who benefit from ICIs remains limited. Current biomarkers have assisted in identifying potential responders to immunotherapy, yet they all have inherent limitations. In this study, two ESCC cohorts were established from the Third Affiliated Hospital of Soochow University in China. One cohort included 220 patients with PD-L1 expression levels determined by immunohistochemistry, and the other cohort included 75 patients who underwent immunotherapy. For each patient in both cohorts, we curated multimodal data encompassing Hematoxylin and Eosin-stained pathology images, longitudinal computed tomography (CT) scans, and pertinent clinical variables. Next, we introduced a novel multimodal deep learning model that integrated pathological features, radiomic features, and clinical information to predict PD-L1 levels, immunotherapy response, and overall survival. Our model achieved an AUC value of 0.836 for PD-L1 biomarker prediction, and 0.809 for immunotherapy response prediction. Furthermore, our model also achieved an AUC value of 0.8 in predicting overall survival beyond one or three years. Our findings confirmed that the multimodal integration of pathological, radiomic, and clinical features offers a powerful means to predict PD-L1 biomarker levels and immunotherapy response in esophageal cancer.

Advancing sepsis diagnosis and immunotherapy machine learning-driven identification of stable molecular biomarkers and therapeutic targets

Sepsis represents a significant global health challenge, necessitating early detection and effective treatment for improved outcomes. While traditional inflammatory markers facilitate the diagnosis of sepsis, the aspect of immune suppression remains poorly addressed. This study aimed to identify critical immune-related genes (IIRGs) associated with sepsis through genomic analysis and machine learning techniques, thereby enhancing diagnostic and treatment response predictions. Analyses of two extensive datasets were conducted, identifying significant immune genes using the ESTIMATE algorithm, Weighted Gene Correlation Network Analysis (WGCNA), and five machine learning methods. Prediction models were constructed and validated using six machine learning algorithms, achieving high accuracy (AUC > 0.75). Eleven key IIRGs were identified as active in immune pathways, such as the JAK-STAT signaling pathway, and were significantly correlated with immune cell infiltration in sepsis. Additionally, drug sensitivity analysis indicated that IIRGs correlated with responses to anticancer drugs. These results underscore the potential of these genes in enhancing sepsis diagnosis and treatment, highlighting the imperative for further validation across diverse populations.

Cerebellar-Cortical Connectivity and Prediction of Antipsychotic Treatment Response in First-Episode Psychosis

Abstract Background The cerebellum has traditionally been associated with motor functions, but recent evidence highlights its critical role in cognitive and emotional regulation, contributing to the neuropathology of schizophrenia. Our previous data-driven research demonstrated that cerebellar-cortical functional connectivity can predict antipsychotic treatment outcomes in first-episode psychosis (FEP). The present study aimed to investigate specific cerebellar functional systems involved in treatment prediction. Study Design This study included 127 patients with FEP who underwent 12 weeks of antipsychotic monotherapy (either risperidone or aripiprazole). Baseline resting-state functional MRI data were collected from two 3T scanners, and functional connectivity between 10 predefined cerebellar functional systems and the whole brain was analyzed. Psychotic symptom changes were measured using the Brief Psychiatric Rating Scale-Anchored version (BPRS-A). Connectivity patterns were examined in relation to treatment outcomes. Study Results Higher baseline connectivity between the cerebellar auditory system and cortical regions, including the visual cortex, dorsolateral prefrontal cortex, and the hippocampus, predicted worse treatment outcome. In contrast, stronger connectivity between cerebellar cognitive systems (default mode and frontoparietal networks) and the anterior cingulate cortex (ACC) and medial prefrontal cortex was associated with better treatment outcome. These findings were consistently present in data acquired from both scanners and both drugs. Conclusions Our results identify specific cerebellar-cortical circuitries as prognostic biomarkers for predicting psychosis treatment outcomes, and suggest that cerebellar auditory and cognitive systems may be potential targets for future interventions aimed at improving treatment efficacy in FEP.

Exploring a patient-specific in vitro pipeline for stratification and drug response prediction of microglia-based therapeutics

The scarcity of effective biomarkers and therapeutic strategies for predicting disease onset and progression in neurodegenerative diseases such as Alzheimer’s disease (AD) is a major challenge. Conventional drug discovery approaches have been unsuccessful in providing efficient interventions due to their ‘one-size-fits-all’ nature. As an alternative, personalised drug development holds promise to pre-select responders and identifying suitable indicators of drug efficacy. In this exploratory study, we have established a pipeline with the potential to guide patient stratification studies before clinical trials. This pipeline uses 2D and 3D in vitro models of monocyte-derived microglia-like cells (MDMi) from AD and mild cognitive impairment (MCI) patients, and matched healthy control (HC) individuals. By profiling cytokine responses in these models using multidimensional analyses, we have observed that the 3D model offers a more defined separation of profiles between individuals based on disease status. While this pilot study focuses on AD and MCI, future investigations incorporating other neurodegenerative disorders will be necessary to validate the pipeline’s findings and demonstrate its broader applicability.

A Comprehensive Review of Various Machine Learning and Deep Learning Models for Anti-Cancer Drug Response Prediction: Comparative Analysis With Existing State of the Art Methods

A Comprehensive Review of Various Machine Learning and Deep Learning Models for Anti-Cancer Drug Response Prediction: Comparative Analysis With Existing State of the Art Methods

Potential of Radiomics, Dosiomics, and Dose Volume Histograms for Tumor Response Prediction in Hepatocellular Carcinoma following 90Y-SIRT

Abstract Purpose We evaluate the role of radiomics, dosiomics, and dose-volume constraints (DVCs) in predicting the response of hepatocellular carcinoma to selective internal radiation therapy with 90Y with glass microspheres. Methods 99mTc-macroagregated albumin (99mTc-MAA) and 90Y SPECT/CT images of 17 patients were included. Tumor responses at three months were evaluated using modified response evaluation criteria in solid tumors criteria and patients were categorized as responders or non-responders. Dosimetry was conducted using the local deposition method (Dose) and biologically effective dosimetry. A total of 264 DVCs, 321 radiomic features, and 321 dosiomic features were extracted from the tumor, normal perfused liver (NPL), and whole normal liver (WNL). Five different feature selection methods in combination with eight machine learning algorithms were employed. Model performance was evaluated using area under the AUC, accuracy, sensitivity, and specificity. Results No statistically significant differences were observed between neither the dose metrics nor radiomicas or dosiomics features of responders and non-responder groups. 90Y-dosiomics models with any given set of inputs outperformed other models. This was also true for 90Y-radiomics from SPECT and SPECT-clinical features, achieving an AUC, accuracy, sensitivity, and specificity of 1. Among MAA-dosiomic and radiomic models, two models showed AUC ≥ 0.91. While the performance of MAA-dose volume histogram (DVH)-based models were less promising, the 90Y-DVH-based models showed strong performance (AUC ≥ 0.91) when considered independently of clinical features. Conclusion This study demonstrated the potential of 99mTc-MAA and 90Y SPECT-derived radiomics, dosiomics, and dosimetry metrics in establishing predictive models for tumor response.

Effect of Dynamic Flexural Strength on Impact Response Analysis of AlN Substrates for Aerospace Applications

Electronic devices play an extremely important role in the aerospace field. Aluminum nitride (AlN) is a promising ceramic material for high-reliability electronic packaging structures that are subjected to impact loads during service. Quasi-static and dynamic flexural tests were conducted to determine the rate-dependent flexural strengths of AlN ceramics. The impact response of the AlN substrates was investigated using experimental tests and a smeared fixed-crack numerical model. The critical velocity of the impactor and the failure mode of the ceramic plate can be accurately predicted using the Drucker–Prager criterion with the scaled fracture-strength parameter. The radial cracks on the ceramic plate upon impact were well reproduced via the proposed novel numerical technique, showing better accuracy compared to the widely used Johnson–Holmquist II (JH-2) model. The effect of impactor nose shape and deflection angles were further investigated to better illustrate the low-velocity impact response of AlN ceramic substrates. Based on the dynamic flexural-strength testing results, this study achieves the prediction of low-speed impact response for AlN ceramic structures, thereby providing technical support for the impact reliability analysis of aerospace ceramic-packaging devices.

Revolutionizing drug response prediction: An unmet requirement for patients unresponsive to precision medicine

Precision cancer therapies frequently fail due to tumors’ evolving clonal diversity rather than drug efficacy. Even when initial treatment succeeds, resistance often emerges, leading to relapse. Clinicians then find themselves in the same cycle of repeating the process of testing a new drug until therapeutic exhaustion. The cycle escalates with each new treatment until no further options are available. The real-life experience of precision therapy will undeniably lead to an upgrade – from biomarker testing to drug response prediction – accordingly to favor more effective treatment options, more clinical benefit, and more patient coverage to include non-responders. While biomarker tests (or companion diagnostics) advance precision medicine by identifying only a fraction of patients as responders, drug response prediction aims to expand treatment options – particularly for non-responders – by tailoring personalized therapies to optimize outcomes while minimizing side effects. Artificial intelligence-driven approaches (e.g., deep learning and predictive modeling) leverage large datasets to generate these predictions. However, such systems remain experimental, not yet ready for clinical use. Patient-derived gene expression-informed anticancer drug efficacy (PGA) is the ultimate answer to the unmet clinical need for a quick turnaround and cost-efficient drug response prediction technology. With PGA, therapeutic non-responders now are able to benefit from more drug options than ever before. Since the technology is fitted with patient testing, gene activity detection, data mapping, drug matching, and efficacy ranking capabilities, clinicians can be quickly notified of potentially effective drugs, winning the decisive time for decision-making. 

Enhancing the role of MRI in rectal cancer: advances from staging to prognosis prediction.

Rectal cancer (RC) is one of the major health challenges worldwide. Accurate staging, restaging, invasiveness assessment, and treatment efficacy evaluation are crucial for its clinical management. Magnetic resonance imaging (MRI) plays a significant role in these processes. However, standard MRI techniques, including T2-weighted and diffusion-weighted imaging, have uncertainties in identifying early-stage tumors, high-risk nodules, extramural vascular invasion, and treatment efficacy, potentially leading to inappropriate treatment. Recent advances suggest that the integration of traditional MRI methods, including diffusion-weighted imaging, opposed-phase or contrast-enhanced T1-weighted imaging, as well as emerging synthetic MRI, could address these challenges. Additionally, improvements in imaging technology have spurred research into advanced functional MRI techniques such as diffusion kurtosis imaging and amide proton transfer weighted MRI, yielding promising results in RC assessment. Total neoadjuvant therapy has emerged as a new treatment paradigm for locally advanced RC, with neoadjuvant immunotherapy and chemotherapy offering viable alternatives to neoadjuvant chemoradiotherapy. However, the lack of standards for the early prediction of patient survival and tumor response to neoadjuvant therapy highlights a critical unmet need in matching therapies to suitable patients. Furthermore, organ preservation strategies after neoadjuvant therapy provide personalized options based on tumor response and patient preferences, yet traditional MRI assessments show significant variability. Radiomics and artificial intelligence hold promise for revealing complex patterns in MRI images associated with patient prognosis and treatment response. This review provides an overview of current MRI advancements in RC assessment and emphasizes how future research can refine tailored treatment strategies to improve patient outcomes. KEY POINTS: Question The accurate diagnosis of early-stage rectal tumors, high-risk nodules, treatment responses, and the early prediction of patient survival and therapeutic outcomes remain an unmet need. Findings Visual MRI has improved staging, restaging, and invasiveness evaluation. Advanced MRI, radiomics and artificial intelligence provide significant potential for tumor characterization and outcome prediction. Clinical relevance Advances in visual MRI are improving routine imaging protocols and radiomics and artificial intelligence show promise in enhancing treatment decisions through precise tumor characterization and outcome prediction.

Rooting for the little guy: Below‐ground traits predict juvenile grass demography in microsites

Abstract Plant functional traits can be a powerful tool for predicting species demography in response to variable environmental conditions. However, accurate predictions of juvenile plant response require ontogenetically relevant traits that capture the response to microsite variability. This is particularly important when considering drivers of seedling emergence, survival, growth and recruitment of species in the context of population persistence or community assembly. We tested the effect of two different microsites on juvenile demography for eight perennial grass species in a semi‐arid system in Colorado along the western edge of the Great Plains. We used seed and root functional traits across multiple life stages to predict these responses and identify mechanisms driving species' emergence, survival, growth and recruitment. Contrary to our expectations, we found that microsites with increased soil moisture (i.e. furrows) had a negative effect on grass emergence early in the season but no effect on recruitment at the end of the season. This was likely driven by the increased growth and survival of grass juveniles in furrows compared to grass juveniles on the surface (reduced soil moisture). We also found that species with more acquisitive roots—from more rapid root elongation—benefited from the increased soil moisture early in the season, but this benefit disappeared later in the season, speaking to the value of using life‐stage specific traits to predict early life‐stage transitions. Variation in microsites will impact juvenile perennial grass demography differently depending on species' traits across life stages. While species in this system typically experience wet to dry transitions across the growing season, furrows with increased soil moisture reduce the intensity of this dry down and may alter demographic responses depending on grass functional traits across ontogeny. We found grass species that are adapted to take advantage of resource pulses did best in furrows where moisture was greater, but that species adapted to conserve resources under stress had limited capacity to respond to these resource pulses. Read the free Plain Language Summary for this article on the Journal blog.

Fragility assessment of steel jacket offshore platforms using time series prediction with deep learning methods

ABSTRACT Seismic evaluation is important in disaster susceptibility and prediction of structural failure under different seismic conditions. While fragility curves and Incremental Dynamic Analysis (IDA) are particular techniques, traditional methods are typically time-consuming and difficult. In this study, the response of a Steel Jacket Offshore Platform is predicted using time series forecasting models, particularly Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM). The performances of these forecasting models were evaluated by comparing their results with IDA and fragility curves, which are crucial for the evaluation of the platform's seismic vulnerability. The GRU model showed better accuracy and closer agreement with the time series of inter-level drift ratios, IDA curves, and fragility curves. The proposed method can potentially improve the accuracy of seismic response predictions in critical structures and give more credible information on how the structure would react to seismic forces.

Histidine metabolism drives liver cancer progression via immune microenvironment modulation through metabolic reprogramming

BackgroundHistidine metabolism is crucial in role in tumor biology, contributing to tumor progression, immune regulation, and metabolic reprogramming. In hepatocellular carcinoma (HCC), dysregulated histidine metabolism may promote tumor growth and immune evasion, although the specific mechanisms remain poorly understood.MethodsUsing single-cell RNA sequencing, the expression patterns of histidine metabolism–related genes were evaluated across different cell types in HCC samples. In vivo and in vitro experiments were conducted to validate how histidine treatment affects macrophage and T-cell function. Furthermore, the TCGA database was utilized to construct a prognostic model to identify the key gene BUD23 and to examine its correlation with metabolism and immune infiltration.ResultsThe proportion of parenchymal cells exhibiting high histidine metabolism was significantly increased, accompanied by a general reduction in immune and stromal cell infiltration. Notably, macrophages and T cells demonstrated impaired antitumor functions. In the high histidine metabolism group, multiple critical cell communication pathways (e.g., MIF, CLEC, MHC II) were downregulated, macrophages shifted toward immunosuppressive subpopulations, T cells exhibited an exhaustion phenotype, and CD8 + T-cell activation was diminished. Further in vivo and in vitro co-culture experiments confirmed that elevated histidine concentrations promoted M2 polarization in macrophages and weakened T-cell cytotoxicity, accelerating tumor proliferation. According to TCGA analyses, BUD23 was upregulated in the high histidine metabolism group and significantly negatively correlated with patient survival and immune cell infiltration. Silencing BUD23 boosted immune cell activation and cytotoxic effects, effectively reversing the immunosuppressive microenvironment. A multivariable Cox regression–based prognostic model indicated unfavorable outcomes in patients with high histidine metabolism.ConclusionHistidine metabolism drives tumor cell metabolic reprogramming and reshapes the tumor immune microenvironment through intercellular communication, thereby promoting tumor progression. BUD23 shows promise as a biomarker for prognosis and immune response prediction in liver cancer. This study provides new therapeutic targets and theoretical support for liver cancer treatment by targeting histidine metabolism.

Do Parkinson's Disease clinical subtypes really exist?

Parkinson's Disease (PD) is a highly heterogeneous entity in terms of clinical manifestations, progression, and treatment response. This variability has given rise to the hypothesis that different clinical subtypes of the disease exist. To date, several clinical subtypes have been described, mostly based on different clinical features, and sometimes with the support of biomarkers, either fluid, neuroimaging, or neurophysiological. The most homogeneous subtypes detected are a 'benign subtype', characterised by younger age at onset, mild non-motor symptoms, and a slower rate of disease progression, and a 'malignant subtype', which features an older age at onset, a higher burden of non-motor symptoms, and faster disease progression. Despite extensive research, none of the subtypes identified so far seem to be biologically supported, so clinical subtyping does not elucidate PD aetiology and does not allow for the prediction of prognosis or treatment response. This study was aimed to review the literature on this topic and to examine the studies on PD subtyping. We also reviewed the proposed biomarkers for a biological classification of PD, and outlined the role of genetics and pathology within this context. In light of the recent proposal of a biological classification of PD, which might overcome the limits of the clinical diagnosis, PD subtyping should hopefully shepherd researchers towards a biological approach, also aided by recent advances in the field of biomarkers.

Exploring NUP62’s role in cancer progression, tumor immunity, and treatment response: insights from multi-omics analysis

BackgroundNUP62, a key component of the nuclear pore complex, is closely associated with cellular functions and cancer progression. However, its expression patterns, prognostic value, and relationship with tumour immunity and drug sensitivity across multiple cancers have not been systematically studied. This study used multi-omics analyses combined with experimental validation in gastric cancer to investigate the expression, functional characteristics, and clinical relevance of NUP62 in cancer.MethodsData from TCGA, GTEx, and CPTAC databases were used to analyse the expression, mutation characteristics, and clinical associations of NUP62. Tools such as SangerBox, TIMER 2.0, and GSEA were employed to evaluate the relationship between NUP62 and the tumour immune microenvironment, as well as its involvement in signalling pathways. Immunohistochemistry and RT-PCR were used to validate the expression of NUP62 in gastric cancer tissues. PRISM and CTRP databases were utilised to assess the correlation between NUP62 expression and drug sensitivity.ResultsNUP62 was significantly upregulated in multiple cancers and was associated with poor prognosis in cancers such as clear cell renal carcinoma (KIRC), lower-grade glioma (LGG), and adrenocortical carcinoma (ACC), while playing a protective role in others, such as bladder cancer (BLCA) and stomach cancer (STAD). Functional analyses showed that NUP62 is involved in cell cycle regulation, DNA damage repair, and tumour immunity. High NUP62 expression was significantly correlated with increased infiltration of immune cells, such as macrophages and T cells, and a higher response rate to immunotherapy. Drug sensitivity analysis identified NUP62 as a marker of sensitivity to various chemotherapeutic agents. Validation experiments demonstrated that NUP62 mRNA and protein levels were significantly higher in gastric cancer tissues than in adjacent normal tissues.ConclusionsNUP62 plays a critical role in multiple cancers and shows potential as a biomarker for cancer diagnosis, prognosis, and therapeutic response prediction. Its role in tumour immunity and signalling pathways highlights its potential as a target for immunotherapy and precision medicine.

Deciphering population-level response under spatial drug heterogeneity on microhabitat structures.

Bacteria and cancer cells live in a spatially heterogeneous environment, where migration shapes the microhabitat structures critical for colonization and metastasis. The interplay between growth, migration, and microhabitat structure complicates the prediction of population responses to drugs, such as clearance or sustained growth, posing a longstanding challenge. Here, we disentangle growth-migration dynamics and identify that population decline is determined by two decoupled terms: a spatial growth variation term and a microhabitat structure term. Notably, the microhabitat structure term can be interpreted as a dynamic-related centrality measure. For fixed spatial drug arrangements, we show that interpreting these centralities reveals how different network structures, even with identical edge densities, microhabitat numbers, and spatial heterogeneity, can lead to distinct population-level responses. Increasing edge density shifts the population response from growth to clearance, supporting an inversed centrality-connectivity relationship, and mirroring the effects of higher migration rates. Furthermore, we derive a sufficient condition for robust population decline across various spatial growth rate arrangements, regardless of spatial-temporal fluctuations induced by drugs. Additionally, we demonstrate that varying the maximum growth-to-death ratio, determined by drug-bacteria interactions, can lead to distinct population decline profiles and a minimal decline phase emerges. These findings address key challenges in predicting population-level responses and provide insights into divergent clinical outcomes under identical drug dosages. This work may offer a new method of interpreting treatment dynamics and potential approaches for optimizing spatially explicit drug dosing strategies.

Early Circulating Tumor DNA Kinetics as a Dynamic Biomarker of Cancer Treatment Response.

Circulating tumor DNA (ctDNA) assays are promising tools for the prediction of cancer treatment response. Here, we build a framework for the design of ctDNA biomarkers of therapy response that incorporate variations in ctDNA dynamics driven by specific treatment mechanisms. These biomarkers are based on novel proposals for ctDNA sampling protocols, consisting of frequent sampling within a compact time window surrounding therapy initiation-which we hypothesize to hold valuable prognostic information on longer-term treatment response. We develop mathematical models of ctDNA kinetics driven by tumor response to several therapy classes and use them to simulate randomized virtual patient cohorts to test candidate biomarkers. Using this approach, we propose specific biomarkers, on the basis of ctDNA longitudinal features, for targeted therapy and radiation therapy. We evaluate and demonstrate the efficacy of these biomarkers in predicting treatment response within a randomized virtual patient cohort data set. This study highlights a need for tailoring ctDNA sampling protocols and interpretation methodology to specific biologic mechanisms of therapy response, and it provides a novel modeling and simulation framework for doing so. In addition, it highlights the potential of ctDNA assays for making early, rapid predictions of treatment response within the first days or weeks of treatment and generates hypotheses for further clinical testing.

Quantitative Magnetic Resonance Imaging Methods for the Assessment and Prediction of Treatment Response to Transarterial Chemoembolization in Hepatocellular Carcinoma.

Quantitative Magnetic Resonance Imaging Methods for the Assessment and Prediction of Treatment Response to Transarterial Chemoembolization in Hepatocellular Carcinoma.

Extrapolation method of flame nonlinear thermoacoustic response in the time domain based on the kernel embedding of conditional distribution

In this study, we propose a novel method for extrapolating the nonlinear thermoacoustic response of flames in the time domain, based on kernel embedding of conditional distribution (KECD). This approach enables the prediction of full-flame nonlinear responses under high-amplitude oncoming flow disturbances (target domain) using the responses under low-amplitude perturbations (source domain) and a limited dataset of pure-tone flame responses under high-amplitude perturbations. The KECD loss term plays a key role in constraining the neural network to map flame nonlinear responses that are consistent across both the source and target domains. The neural network architecture employed in this work is a Dual-Path model, which integrates two main components: a chronological feature path and a temporal detail feature path, to effectively capture both coarse- and fine-grained temporal features. The proposed method was validated using numerical simulations of a laminar premixed flame. Results demonstrate the method's robust extrapolation performance, achieving an average extrapolation error of approximately 5.1% when the inlet mean flow velocity is 0.6 m/s and 11.1% for a larger inlet flow velocity being 1 m/s.