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Results for “response prediction”

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At least 37 records · Page 2Linked to original sources
PubMed1978

Short-term memory: the "storage" component of human brain responses predicts recall.

An evoked potential component with a poststimulus peak at about 250 milliseconds is related to the storage of information in short-term memory. This storage component was found in an investigation of brain potentials in relation to a number and letter comparison task. In replications of this experiment at three different light intensities spaced 1.0 log unit apart, the component had essentially the same waveform and pattern of scores. The memory storage interpretation was confirmed in a behavioral experiment that probed short-term memory. Recall was predicted by the magnitude of the storage component.

Humans↗
PubMed2025

Biomarker-Based Nomogram to Predict Neoadjuvant Chemotherapy Response in Muscle-Invasive Bladder Cancer.

Background/Objectives: The aim of this study was to identify response prediction and prognostic biomarkers in muscle-invasive bladder cancer (MIBC) patients undergoing neoadjuvant chemotherapy (NAC). Methods: A retrospective multicentre study including 191 patients with MIBC who received NAC previous to radical cystectomy (RC) between 1996 and 2013. Gene expression patterns were analysed in 34 samples from transurethral resection of the bladder (TURB) using Illumina microarrays. The expression levels of 45 selected differentially expressed genes between responders and non-responders to NAC were validated by quantitative PCR in an independent cohort of 157 patients. Regression analysis was used to identify predictors of downstaging and relapse. A nomogram for predicting downstaging and relapse-including clinicopathological and gene expression variables-was developed. Results: The expression levels of 1352 transcripts differed between responders and non-responders to NAC. A nomogram based on the most predictive clinical variables (age, Tis (in situ), gender, history of NMIBC, and lymphadenopathy) and genes selected following the Akaike information criterion (AIC) (CBTB16, CHMP6, DDX54, CASP8, LOR, and PLEC) was then created. In addition, a three-gene expression prognostic model to predict tumour relapse was generated. This model was able to discriminate between two groups of patients with a significantly different probability of tumour relapse (HR: 2.11; CI: 1.16-3.83, p = 0.01). Conclusions: Our nomogram based on gene expression and clinical data is a useful tool to predict downstaging and tumour relapse after NAC in MIBC patients. Further validation is warranted.

bladder cancer↗
PubMed1979

Predicting the response of growth hormone-deficient children to long term treatment with human growth hormone.

A previous study showed that when GH-deficient children below the third percentile in height are treated with 0.168 U human GH (hGH)/kg BW3/4 for 10 days, their height increases by 0.3--1.9 cm during the next 8 weeks. The present study determined whether this acute response would predict the child's long term response to 1 yr of treatment with the same dose of hGH given three times a week. Eighteen GH-deficient children and adolescents, aged 8--16 yr, were measured every 2 weeks over 108 weeks. After a control period of 12 weeks (period 1), the patient received hGH for 10 days. During the remainder of the 12 weeks of period 2 and during the next 12 weeks (period 3), hGH was not given. Patients recieved hGH three times a week during periods 4 and 5 (24 weeks each). Periods 6 and 7 (12 weeks each) were posttreatment control periods. During periods 1, 3, 6, and 7, rate of growth was less than 0.2 cm/month. During period 2, the rate ranged between 0.1--0.8 cm/month. During periods 4 and 5, the growth rate ranged from 0.2--1.0 cm/month. Rate of growth during periods 4 and 5 (y) was related to rate during period 2 (x) by the equation y = 0.027 + 1.17 x. The correlation coefficient between y and x was 0.91 (P less than 0.001). The increment in height which will occur during 48 weeks of treatment can be predicted from the response to 10 days of treatment by this equation. The SE of the prediction averages +/- 1.2 cm/yr.

Adolescent↗
PubMed1978

Factors predicting for response and survival in adults with advanced non-Hodgkin's lymphoma.

Knowledge of the prognostic factors that characterize a disease can assist in planning and analyzing clinical trials. The present study was conducted to determine the characteristics related to response and survival in patients with stage III and IV non-Hodgkin's lymphoma who were treated with combinations of cyclophosphamide, vincristine sulfate, and prednisone. Considering each characteristic individually and using stepwise regression analysis, tumor bulkiness, prior therapy, sex, and pretreatment lymphocyte count were selected as the four most important prognostic variables. Tumor architecture (diffuse or nodular pattern) and cell type, hemoglobin level, and symptoms although not important in predicting response were found to be important in predicting survival. The hemoglobin level had only marginal importance in predicting response. Factors found not be important were age, stage, symptoms, cell type, nodularity, marrow involvement, prior extensive radiotherapy, and bone involvement. A logistic regression equation has been derived that can be used to predict response rate.

Cyclophosphamide↗
PubMed1979

Depressive classification and prediction of response to phenelzine.

A variety of depressive classifications were used to predict response to four weeks' treatment with phenelzine. Better response was found in outpatients rather than inpatients, in atypical depressives, in less severe depressives with a pattern of anxiety and other neurotic symptoms, and in groups characterized as hostile and agitated. The findings, although a little patchy, gave clear support to the concept of a specific clinical group responsive to MAO inhibitors.

Adolescent↗
PubMed2025

Transfer learning with multiomics integration and deep neural networks reveals drug resistance mechanisms in cancer.

Drug resistance remains one of the primary challenges in effective cancer therapy. In this study, we employed a deep neural network (DNN)-based transfer learning (TL) approach to predict drug response and uncover drug resistance mechanisms. We integrated gene expression, somatic mutation, and copy number aberration (CNA) data with drug response profiles using multi-omics integration (MI). We used the Genomics of Drug Sensitivity in Cancer (GDSC) data for training and incorporated drugs with same pathways into the training models. We then evaluated drug response predictions on independent in-vivo PDX Encyclopedia (PDX) and ex-vivo the Cancer Genome Atlas (TCGA) datasets. In addition, we conducted pathway enrichment analyses to elucidate the mechanisms underlying drug resistance for paclitaxel, 5-fluorouracil (5-FU), gemcitabine, and cetuximab. We also applied Fisher's exact test (FET) to assess potential associations between drug resistance and the presence of mutations or CNAs. Our pan-drug models outperformed other methods based on the area under the precision-recall curve (AUCPR). Our pathway enrichment analyses revealed LDHB-mediated pyruvate metabolism and FYN-mediated focal adhesion might have pivotal roles in paclitaxel resistance, while PINK1-mediated mitophagy might be critical in 5-FU resistance. In addition to transcriptional activation, FET suggested that CNAs in LDHB and PINK1 may also be associated with resistance to paclitaxel and 5-FU, respectively. Furthermore, enrichment results for paclitaxel and cetuximab indicated shared resistance mechanisms between the two drugs. Importantly, our findings are consistent with prior experimental studies, providing literature-based validation of our results. Overall, our DNN-based TL approach achieved strong predictive performance across PDX & TCGA datasets and enrichment analyses provided valuable biological insights into drug resistance mechanisms.

Humans↗
PubMed2025

Individualized patient tumor organoids faithfully preserve human brain tumor ecosystems and predict patient response to therapy.

Tumor organoids are important tools for cancer research, but current models have drawbacks that limit their applications for predicting response to therapy. Here, we developed a fast, efficient, and complex culture system (IPTO, individualized patient tumor organoid) that accurately recapitulates the cellular and molecular pathology of human brain tumors. Patient-derived tumor explants were cultured in induced pluripotent stem cell (iPSC)-derived cerebral organoids, thus enabling culture of a wide range of human tumors in the central nervous system (CNS), including adult, pediatric, and metastatic brain cancers. Histopathological, genomic, epigenomic, and single-cell RNA sequencing (scRNA-seq) analyses demonstrated that the IPTO model recapitulates cellular heterogeneity and molecular features of original tumors. Crucially, we showed that the IPTO model predicts patient-specific drug responses, including resistance mechanisms, in a prospective patient cohort. Collectively, the IPTO model represents a major breakthrough in preclinical modeling of human cancers, which provides a path toward personalized cancer therapy.

Humans↗
PubMed2025

Understanding the sources of performance in deep drug response models reveals insights and improvements.

MOTIVATION: Anti-cancer drug response prediction (DRP) using cancer cell lines (CLs) is crucial in stratified medicine and drug discovery. Recently, new deep learning models for DRP have improved performance over their predecessors. However, different models use different input data types and architectures making it hard to find the source of these improvements. Here we consider published DRP models that report state-of-the-art performance predicting continuous response values. These models take chemical structures of drugs and omics profiles of CLs as input. RESULTS: By experimenting with these models and comparing with our simple baselines, we show that no performance comes from drug features, instead, performance is due to the transcriptomics CL profiles. Furthermore, we show that, depending on the testing type, much of the current reported performance is a property of the training target values. We address these limitations by creating BinaryET and BinaryCB that predict binary drug response values, guided by the hypothesis that this reduces the noise in the drug efficacy data. Thus, better aligning them with biochemistry that can be learnt from the input data. BinaryCB leverages a chemical foundation model, while BinaryET is trained from scratch using a transformer-type architecture. We show that these models learn useful chemical drug features, which is the first time this has been demonstrated for multiple testing types to our knowledge. We further show binarizing the drug response values causes the models to learn useful chemical drug features. We also show that BinaryET improves performance over BinaryCB, and the published models that report state-of-the-art performance. AVAILABILITY AND IMPLEMENTATION: Code is available from https://github.com/Nik-BB/Understanding_DRP_models.

Humans↗
PubMed2026

Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer.

Chemotherapy response in colorectal cancer (CRC) exhibits significant heterogeneity, with current clinical predictors failing to capture complex genomic determinants of resistance. We developed a hybrid deep learning framework integrating convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks to analyze whole-genome somatic mutations, evolutionary conservation, chromatin accessibility, and 3D genome architecture in 2,546 TCGA patients. An attention mechanism identified predictive genomic regions. The model achieved an AUC of 0.92 (95% CI: 0.89-0.94) in cross-validation and 0.88 (95% CI: 0.85-0.91) in independent validation, outperforming clinical models (&#x394;AUC = +0.18, p < 0.001). Key predictors included non-coding variants in TP53, KRAS, and PIK3CA regulatory regions. Triple-positive patients (mutations in all 3 regions) had significantly worse progression-free survival (HR = 4.7, p < 0.001). Our framework enables accurate chemotherapy response prediction and reveals novel non-coding resistance mechanisms, advancing precision oncology in CRC.

Humans↗
PubMed1976

Pharmacokinetic considerations in the design of optimal chemotherapeutic regimens for the treatment of breast carcinoma: a comceptual approach.

Pharmacological data are currently available for a number of antineoplastic agents which have shown clinical activity in advanced breast carcincoma. Preclinical data reveal a relationship between therapeutic response and certain pharmacokinetic parameters such as time of effective cytotoxic exposure (Teff) and the product of concentration with time (Cxt). We have attempted to apply human pharmacologic data to get estimates of these parameters for 6 active agents in breast cancer, to relate them to response rates, and to suggest a method for estimating the role of individual drugs in a multidrug combination. The response rates for 6 single agents were obtained from literature review and related to estimates of Teff and Cxt. The Cxt-response relations for single drugs were linear for cyclophosphamide, 5-fluorouracil, and thiotepa; exponential for vincristine; and relatively flat for methotrexate and cytoxine arabinoside. Most Teff values for the active single agents clustered about 15 hr/dose. From the graphs of response rate vs Cxt, the individual contribution of each agent in a combination study was estimated to arrive at a predicted response rate. The predicted response rates for the combination studies correlated with the actual response rates determined in the clinical study, for 6 of 6 nonrandomized studies and for 12 of 14 randomized studies analyzed. In 2 studies, deviations from the predicted response rate were attributed to differences in study design or analysis. There was no correlation between Teff and predicted response rate. Analyses of pharmacokinetic data may be useful to simulate combination chemotherapy studies to predict the effectiveness of clinical trials in breast cancer. Since the pharmacologic data were not obtained for any of the agents in the actual clinical trials done, we can only speculate on the usefulness of this method. We would encourage the prospective collection of this data in future clinical trails.

Antibiotics, Antineoplastic↗
PubMed1978

Prediction of response to chlordiazepoxide and placebo in anxious outpatients: an attempt at replication.

The present study seeks to determine the extent to which a set of non-specific factors can stably predict response to either chlordiazepoxide (CDZ) or placebo (PBO) and the extent to which such prediction is specific to or distinctive for each treatment agent. For this purpose data were assembled for 447 primarily anxious neurotic outpatients treated with either CDZ or PBO in 4 week double blind drug trials performed over the past ten years and divided into two comparable subsamples for each treatment agent. A series of analyses revealed modest replicability but considerable drug specificity for a Global improvement measure. Replicability was considerable higher for a patient measure of Symptom Change, but its specificity to treatment agent was considerably less.

Adult↗
PubMedDate not supplied

Predicting selection response for growth of channel catfish.

Estimates of heritability, phenotypic and genetic correlations were obtained for body length and weight at 5 months and body length and weight at 15 months in channel catfish. The estimates were obtained from a half-sib analysis by mating 20 males to two females each, producing 20 half-sib and 40 full-sib progeny families. Mortality of fish during the study reduced the number of families to 17 half-sib and 34 full-sib. Comparisons of selection methods were made for body length at 5 months and body weight at 15 months. The results of the study show: 1) heritability estimates for body length and weight at 5 months was 0.12 and 0.61, respectively, and body length and weight at 15 months was 0.67 and 0.75, respectively; 2) from the magnitude of the estimates of heritability, it can be inferred that large amounts of additive genetic variance exist in three of the traits (length at 5 months and length and weight at 15 months) and that selection for these traits should be successful; 3) estimates of phenotypic correlation among the four traits ranged from 0.90 to 0.30, and genotypic correlation among the four traits ranged from 1.47 to 0.71; 4) the magnitude and positive nature of the genetic correlations indicated that simultaneous selection for the four traits in channel catfish would be effective; and 5) comparisons of predicted response under individual, indirect and index selection, show that selection on an index based on all four traits gives maximum response in length at 5 months and weight at 15 months.

Animals↗
PubMed2025

ProgModule: A novel computational framework to identify mutation driver modules for predicting cancer prognosis and immunotherapy response.

BACKGROUND: Cancer originates from dysregulated cell proliferation driven by driver gene mutations. Despite numerous algorithms developed to identify genomic mutational signatures, they often suffer from high computational complexity and limited clinical applicability. METHODS: Here, we presented ProgModule, an advanced computational framework designed to identify mutation driver modules for cancer prognosis and immunotherapy response prediction. In ProgModule, we introduced the Prognosis-Related Mutually Exclusive Mutation (PRMEM) score, which optimizes the balance between exclusive mutation coverage and the incorporation of mutation combination mechanisms critical for cancer prognosis. RESULTS: Applying to BLCA and HNSC cohorts, ProgModule successfully identified driver modules that stratify patients into distinct prognostic subgroups, and the combination of these modules could serve as an effective prognostic biomarker. Extending our method to diverse cancers, ProgModule presented robust prognostic performance and stability across model parameters, including stopping criteria and network topology. Moreover, our analysis suggested that driver modules can predict immunotherapeutic benefit more effectively than existing signatures. Further analyses based on published CRISPR data indicated that genes within these modules may serve as potential therapeutic targets. CONCLUSIONS: Altogether, ProgModule emerges as a powerful tool for identifying mutation driver modules as prognostic and immunotherapy response biomarkers, and genes within these modules may be used as potential therapeutic targets for cancer, offering new insights into precision oncology.

Humans↗
PubMed2026

scRNA-seq and bulk RNA-seq reveal the characteristics of macrophage copper metabolism and establish a risk signature in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent malignancy with an urgent need for improved prognostic stratification and treatment-response prediction. This study aimed to explore a macrophage copper metabolism-associated prognostic model and to investigate the relationship between this risk model and the tumor immune microenvironment. METHODS: The FindClusters function was used to analyze cell clusters, and CellChat and CellPhoneDB/LIANA were employed for cell-cell communication analysis. Copper metabolism-related genes were sourced from the MSigDB database. A prognostic risk model was established using least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox regression analysis, and a nomogram was constructed by integrating the prognostic model with clinicopathological factors. Additional analyses were performed to map the seven model genes in single-cell data, assess model uncertainty and robustness, evaluate macrophage/copper/cuproptosis-related transcriptional programs, and examine the correlations between risk score, immune infiltration and predicted drug sensitivity. RESULTS: Using single-cell RNA sequencing (scRNA-seq) data, we identified four macrophage subpopulations. Macrophages with high SPP1 expression showed close interaction with T cell populations and were associated with copper ion metabolism. By incorporating 141 copper metabolism-related genes and using The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort, we constructed a seven-gene risk prediction model. Additional single-cell mapping showed that the model genes were detectable in the HCC single-cell dataset and showed a macrophage-associated expression pattern. The model showed moderate prognostic discrimination in TCGA-LIHC, whereas its external performance was heterogeneous and remained evaluable across external cohorts, with performance varying among datasets. Immune and mechanism-related analyses suggested that the risk signature was associated with macrophage-related infiltration, copper metabolism and cuproptosis-related transcriptional programs. Drug sensitivity analysis nominated Daporinad as a computationally predicted candidate compound, supporting Daporinad as a pharmacogenomic candidate for follow-up investigation. CONCLUSIONS: By integrating scRNA-seq and bulk RNA sequencing (RNA-seq) data, we constructed a macrophage copper metabolism-associated prognostic signature for HCC. The risk score was associated with survival, immune microenvironment features and predicted drug response, providing a transcriptomic framework for risk stratification and therapeutic hypothesis generation.

Hepatocellular carcinoma (HCC)↗