Predicting gastric cancer response to anti-HER2 therapy or anti-HER2 combined immunotherapy based on multi-modal data
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ABSTRACT:
SUBMITTER: Chen Z
PROVIDER: S-EPMC11345439 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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| S-EPMC11549022 | biostudies-literature
Project description:BackgroundImmunotherapy plus chemotherapy have been confirmed to be effective in treating advanced or metastatic gastric cancer (GC). Anti- programmed death-1 (PD-1) plus antiangiogenic agents have shown promising activity and tolerant toxicity in subsequent therapy of late-stage gastric cancer. The aim of this study was to assess the efficacy and safety of anti-PD-1 plus anti-angiogenic agents and chemotherapy in advanced or metastatic GC and to explore the potential biomarkers associated with response.MethodsWe retrospectively reviewed thirty human epidermal growth factor receptor 2 (HER2)-negative advanced or metastatic GC patients who received PD-1 plus anti-angiogenic drugs and chemotherapy. Conversion therapy was defined when the patients could undergo resection post combination therapy. Clinical data were retrieved from medical records. We conducted exploratory biomarker analysis of baseline gene mutations and tumor mutation burden (TMB) using the next-generation sequencing (NGS), PD-L1 by immunohistochemistry (IHC), and the tumor immune microenvironment (TIME) by multiplex immunofluorescence.ResultsA total of 30 patients received anti-PD-1plus anti-angiogenic drugs and chemotherapy during the study period. The objective response rate (ORR) was 76.7% [95% confidence interval (CI): 57.7-90.1%] and disease control rate (DCR) was 86.7% (95% CI: 69.3-96.2%). A total of 11 patients (36.7%) achieved conversion therapy and underwent surgery. The R0 resection rate was 90.9%. Of the 11 patients, 9 (81.8%) responded to the treatment, 1 with a pathological complete response (pCR) and 8 with a major pathological response (MPR). No adverse events of grade 3 or higher occurred. Neither PD-L1 expression nor TMB was significantly correlated with treatment response. Analysis of TIME revealed that the fraction of CD8+ T cell in the invasive margin was higher in responders than non-responders before treatment. TAM2 in the tumor center and CD8+ T cell in the invasive margin was significantly increased after combination therapy, which suggested that combination therapy promoted infiltration of CD8+ T cells, thereby exerting an antitumor effect.ConclusionsImmunotherapy plus anti-angiogenic drugs and chemotherapy is a promising treatment strategy for advanced or metastatic GC patients. Tumor infiltration CD8+ T cells may serve as potential predictive biomarker.
| S-EPMC10007938 | biostudies-literature
Project description:PurposeWe aimed to identify the differently expressed genes or related pathways associated with good responses to anti-HER2 therapy and to suggest a model for predicting drug response in neoadjuvant systemic therapy with trastuzumab in HER2-positive breast cancer patients.MethodsThis study was retrospectively analyzed from consecutively collected patient data. We recruited 64 women with breast cancer and categorized them into 3 groups: complete response (CR), partial response (PR), and drug resistance (DR). The final number of patients in the study was 20. RNA from 20 core needle biopsy paraffin-embedded tissues and 4 cultured cell lines (SKBR3 and BT474 breast cancer parent cells and cultured resistant cells) was extracted, reverse transcribed, and subjected to GeneChip array analysis. The obtained data were analyzed using Gene Ontology, Kyoto Gene and Genome Encyclopedia, Database for Annotation, Visualization and Integrated Discovery.ResultsIn total, 6,656 genes differentially expressed between trastuzumab-susceptible and trastuzumab-resistant cell lines were identified. Among these, 3,224 were upregulated and 3,432 were downregulated. Expression changes in 34 genes in several pathways were found to be related to the response to trastuzumab-containing treatment in HER2-type breast cancer, interfering with adhesion to other cells or tissues (focal adhesion) and regulating extracellular matrix interactions and phagosome action. Thus, decreased tumor invasiveness and enhanced drug effects might be the mechanisms explaining the better drug response in the CR group.ConclusionsThis multigene assay-based study provides insights into breast cancer signaling and possible predictions of therapeutic response to targeted therapies such as trastuzumab.
| S-EPMC9950611 | biostudies-literature
Project description:Combination therapies targeting multiple recovery mechanisms have the potential for additive or synergistic effects, but experimental design and analyses of multimodal therapeutic trials are challenging. To address this problem, we developed a data-driven approach to integrate and analyze raw source data from separate pre-clinical studies and evaluated interactions between four treatments following traumatic brain injury. Histologic and behavioral outcomes were measured in 202 rats treated with combinations of an anti-inflammatory agent (minocycline), a neurotrophic agent (LM11A-31), and physical therapy consisting of assisted exercise with or without botulinum toxin-induced limb constraint. Data was curated and analyzed in a linked workflow involving non-linear principal component analysis followed by hypothesis testing with a linear mixed model. Results revealed significant benefits of the neurotrophic agent LM11A-31 on learning and memory outcomes after traumatic brain injury. In addition, modulations of LM11A-31 effects by co-administration of minocycline and by the type of physical therapy applied reached statistical significance. These results suggest a combinatorial effect of drug and physical therapy interventions that was not evident by univariate analysis. The study designs and analytic techniques applied here form a structured, unbiased, internally validated workflow that may be applied to other combinatorial studies, both in animals and humans.
| S-EPMC5311970 | biostudies-literature
Project description:Opioid poisoning mortality is a substantial public health crisis in the United States, with opioids involved in approximately 75% of the nearly 1 million drug related deaths since 1999. Research suggests that the epidemic is driven by both over-prescribing and social and psychological determinants such as economic stability, hopelessness, and isolation. Hindering this research is a lack of measurements of these social and psychological constructs at fine-grained spatial and temporal resolutions. To address this issue, we use a multi-modal data set consisting of natural language from Twitter, psychometric self-reports of depression and well-being, and traditional area-based measures of socio-demographics and health-related risk factors. Unlike previous work using social media data, we do not rely on opioid or substance related keywords to track community poisonings. Instead, we leverage a large, open vocabulary of thousands of words in order to fully characterize communities suffering from opioid poisoning, using a sample of 1.5 billion tweets from 6 million U.S. county mapped Twitter users. Results show that Twitter language predicted opioid poisoning mortality better than factors relating to socio-demographics, access to healthcare, physical pain, and psychological well-being. Additionally, risk factors revealed by the Twitter language analysis included negative emotions, discussions of long work hours, and boredom, whereas protective factors included resilience, travel/leisure, and positive emotions, dovetailing with results from the psychometric self-report data. The results show that natural language from public social media can be used as a surveillance tool for both predicting community opioid poisonings and understanding the dynamic social and psychological nature of the epidemic.
| S-EPMC10238775 | biostudies-literature
Project description:BackgroundGlioma is one of the deadliest human cancers. Although many therapeutic strategies for glioma have been explored, these strategies are seldom used in the clinic. The challenges facing the treatment of glioma not only involve the development of chemotherapeutic drugs and immunotherapeutic agents, but also the lack of a powerful platform that could deliver these two moieties to the targeted sites. Herein, we developed chemoimmunotherapy delivery vehicles based on C6 cell membranes and DC membranes to create hybrid membrane-coated DTX nanosuspensions (DNS-[C6&DC]m).ResultsResults demonstrated successful hybrid membrane fusion and nanosuspension functionalization, and DNS-[C6&DC]m could be used for different modes of anti-glioma therapy. For drug delivery, membrane coating could be applied to target the source cancer cells via a homotypic-targeting mechanism of the C6 cell membrane. For cancer immunotherapy, biomimetic nanosuspension enabled an immune response based on the professional antigen-presenting characteristic of the dendritic cell membrane (DCm), which carry the full array of cancer cell membrane antigens and facilitate the uptake of membrane-bound tumor antigens for efficient presentation and downstream immune n.ConclusionDNS-[C6&DC]m is a multifunctional biomimetic nano-drug delivery system with the potential to treat gliomas through tumor-targeted drug delivery combined with immunotherapy, thereby presenting a promising approach that may be utilized for multiple modes of cancer therapy.
| S-EPMC8606100 | biostudies-literature
Project description:BackgroundTargeted diagnosis and treatment options are dependent on insights drawn from multi-modal analysis of large-scale biomedical datasets. Advances in genomics sequencing, image processing, and medical data management have supported data collection and management within medical institutions. These efforts have produced large-scale datasets and have enabled integrative analyses that provide a more thorough look of the impact of a disease on the underlying system. The integration of large-scale biomedical data commonly involves several complex data transformation steps, such as combining datasets to build feature vectors for learning analysis. Thus, scalable data integration solutions play a key role in the future of targeted medicine. Though large-scale data processing frameworks have shown promising performance for many domains, they fail to support scalable processing of complex datatypes.SolutionTo address these issues and achieve scalable processing of multi-modal biomedical data, we present TraNCE, a framework that automates the difficulties of designing distributed analyses with complex biomedical data types.PerformanceWe outline research and clinical applications for the platform, including data integration support for building feature sets for classification. We show that the system is capable of outperforming the common alternative, based on "flattening" complex data structures, and runs efficiently when alternative approaches are unable to perform at all.
| S-EPMC8434767 | biostudies-literature
Project description:BackgroundTaxane, carboplatin and trastuzumab (TCH) is an effective neoadjuvant regimen for human epidermal growth factor receptor 2 (HER2)-positive breast cancer with high pathologic complete response (pCR) rate. The KATHERINE trial changes the outlook for high-risk HER2-positive breast cancer, which suggests that escalation treatment for patients with residual disease after neoadjuvant anti-HER2 therapy may improve survival. The major objective of this study was to investigate the fewest cycles of neoadjuvant TCH therapy needed to screen out non-pCR patients.MethodsThis retrospective study included patients with HER2-positive breast cancer who received either four or six cycles of TCH preoperatively at Fudan University Shanghai Cancer Center between 2008 and 2019. The pCR status was evaluated, and relevant factors associated with pCR were identified using univariate and multivariable analyses. The pathological results of core needle biopsy (CNB) in the breast tumor after two cycles of neoadjuvant chemotherapy were also collected. Kaplan-Meier curve was used to estimate the event-free survival (EFS).ResultsOf 758 eligible patients, 303 were included and analyzed in the four-cycle group and 455 in the six-cycle group. There was no significant difference between the two groups in terms of the pCR rate (46.5% [95% CI 40.9% - 52.2%] in the four-cycle group and 49.9% [95% CI 45.3% - 54.5%] in the six-cycle group, p = 0.365) or the four-year EFS (90.8% in four-cycle group and 93.8% in six-cycle group; p = 0.264). Multivariable analysis indicated that a negative hormone receptor status and the weekly paclitaxel were independent factors for predicting pCR. After adjusting for factors in the multivariable analysis, there was still no significant difference between four and six cycles of neoadjuvant TCH (OR = 1.252, 95% CI 0.904 - 1.733, p = 0.176). Furthermore, 17.9% patients with invasive carcinoma on CNB after two cycles of TCH ultimately achieved pCR in the breast after the completion of neoadjuvant treatment.ConclusionFour cycles of taxane/carboplatin-based neoadjuvant anti-HER2 therapy may be applied as an optimal treatment duration for screening high-risk HER2-positive breast cancer patients for escalation treatment. Further prospective study is warranted.
| S-EPMC8217668 | biostudies-literature
Project description:This study applied supervised machine learning with multi-modal data to predict remission of major depressive disorder (MDD) after psychotherapy. Genotyped adult patients (n = 894, 65.5% women, age 18-75 years) diagnosed with mild-to-moderate MDD and treated with guided Internet-based Cognitive Behaviour Therapy (ICBT) at the Internet Psychiatry Clinic in Stockholm were included (2008-2016). Predictor types were demographic, clinical, process (e.g., time to complete online questionnaires), and genetic (polygenic risk scores). Outcome was remission status post ICBT (cut-off ≤10 on MADRS-S). Data were split into train (60%) and validation (40%) given ICBT start date. Predictor selection employed human expertise followed by recursive feature elimination. Model derivation was internally validated through cross-validation. The final random forest model was externally validated against a (i) null, (ii) logit, (iii) XGBoost, and (iv) blended meta-ensemble model on the hold-out validation set. Feature selection retained 45 predictors representing all four predictor types. With unseen validation data, the final random forest model proved reasonably accurate at classifying post ICBT remission (Accuracy 0.656 [0.604, 0.705], P vs null model = 0.004; AUC 0.687 [0.631, 0.743]), slightly better vs logit (bootstrap D = 1.730, P = 0.084) but not vs XGBoost (D = 0.463, P = 0.643). Transparency analysis showed model usage of all predictor types at both the group and individual patient level. A new, multi-modal classifier for predicting MDD remission status after ICBT treatment in routine psychiatric care was derived and empirically validated. The multi-modal approach to predicting remission may inform tailored treatment, and deserves further investigation to attain clinical usefulness.
| S-EPMC9437007 | biostudies-literature
Project description:Multi-modal image analysis using deep learning (DL) lays the foundation for neoadjuvant treatment (NAT) response monitoring. However, existing methods prioritize extracting multi-modal features to enhance predictive performance, with limited consideration on real-world clinical applicability, particularly in longitudinal NAT scenarios with multi-modal data. Here, we propose the Multi-modal Response Prediction (MRP) system, designed to mimic real-world physician assessments of NAT responses in breast cancer. To enhance feasibility, MRP integrates cross-modal knowledge mining and temporal information embedding strategy to handle missing modalities and remain less affected by different NAT settings. We validated MRP through multi-center studies and multinational reader studies. MRP exhibited comparable robustness to breast radiologists, outperforming humans in predicting pathological complete response in the Pre-NAT phase (ΔAUROC 14% and 10% on in-house and external datasets, respectively). Furthermore, we assessed MRP's clinical utility impact on treatment decision-making. MRP may have profound implications for enrolment into NAT trials and determining surgery extensiveness.
| S-EPMC11544255 | biostudies-literature