Efficacy prediction of first-line immunochemotherapy for patients with advanced gastric cancer:Retrospective,Machine Learning study (Preprint)

Published: Oct. 18, 2024

Immunochemotherapy has brought new hope for the first-line treatment of advanced gastric cancer(GC) patients; however, there is still a lack simple and effective models to predict efficacy immunochemotherapy in this setting. This study aimed develop prognostic chose better one assess benefit patients with GC. To GC patients, we retrospectively collected clinical data at The First Affiliated Hospital Nanjing Medical University between January 2018 October 2023. dataset was split into training (70%) validation (30%) sets. Additionally, temporal cohort November 2023 September 2024 used further model performance over time. Univariate multivariate Cox regression, least absolute shrinkage selection operator (LASSO) experience were select significant features associated response patients. Two survival models, LASSO-Cox Random Survival Forest (RSF) developed using set. Model evaluated on set an area under curve (AUC), continuous concordance index, time-dependent receiver operating characteristic (ROC) curves, calibration plots. decision analysis(DCA) performed evaluate utility, Kaplan-Meier curves compare outcomes. In study, compared RSF predicting progression-free (PFS). ROC showed that significantly outperformed cohorts, higher AUCs 6, 12, 18 months. cohort, demonstrated superior discriminatory ability model. Calibration indicated good discrimination PFS predictions. Continuous C-index values consistently model's robust across different time points. terms application, DCA yielded greater net benefits We calculated risk scores each patient model, categorizing them high-risk (risk score ≥ 55.42) low-risk < groups. analysis revealed group had rate than group, highlighting importance stratification. summary, exhibited accuracy utility PFS, particularly long-term predictions from undergoing established models. By comparing discrimination, calibration, two set, internal found highlights predictive its potential offer valuable insights improving decision-making

Language: Английский

Editorial: Utilizing omics strategies to discover new drug targets for cancers

Frontiers in Pharmacology, Journal Year: 2024, Volume and Issue: 15

Published: Nov. 27, 2024

Utility of Omics Strategies to Discover New Drug Targets for Cancers Cancer is a major public health issue and significant contributor the global disease burden. Since 2010, different kinds cancer have become main cause deaths in China, with incidence, mortality burden all escalating. Data shows that approximately 10 million people die from globally each year, China accounting around 30% this figure (Qi et al., 2023). The incidence rates increase exponentially age, given aging world population, it expected number cancer-related both on scale will continue rise, causing huge costs. Currently, treatment methods include surgery, radiotherapy, chemotherapy, targeted therapy. Surgery usually first-line approach most tumors, suitable patients early stage. Radiotherapy chemotherapy are generally used as complementary options after surgery or who no possibility surgery. Targeted therapy addresses gene mutations has better efficacy, while individual differences emergence drug resistance necessitate discovering new targets development more therapeutic drugs.The pathogenesis involves complex reorganizations various genetic, transcriptional, proteomic, metabolomic processes drive tumor development.Several omics technologies been shown exhibit great potential research, which genomics, epigenetics, transcriptomics, proteomics, metabolomics. Genomics one essential field.Genome sequencing enables researchers identify progression. Meanwhile, epigenetics analysis comprehensive description epigenetic profile patients, referring occurrence, growth, metastasis, immune evasion tumors. Transcriptomics can capture changes between expression patterns cells normal cells, providing perspective molecular occur cancer. Proteomics quantify proteins present tissues insights into functional cancer.Metabolomics detect alterations metabolic cancer, thereby deeper understanding dependences growth.More specifically, genomics examines DNA sequences deciphers genetic information encoded genome. By comparing genomes those healthy scientists pinpoint specific growth. These findings provide clues identifying be develop precise therapies. Epigenetic affect function through chemical modifications nucleotides proteins. There growing evidence play an important role occurrence human cancers; many biomarkers also found Another strategy studying genes cells.By over-or underexpressed prioritize these candidates research. facilitates discovery targets, dysregulated support Restoring protein inhibiting abnormal activity correct cell states mitigate Metabolomics study small molecules pathways, playing Identifying pathways critical survival opens up avenues drugs selectively target pathways.SCLC aggressive neuroendocrine (NE) strong proliferation metastasis potential, resistance, poor prognosis (Megyesfalvi Although immunotherapy greatly improved non-SCLC (NSCLC), advancement SCLC treatments slow, improvement achieved rate therefore still outside field precision medicine. integrating mRNA, phosphorylation data 107 unsupervised clustering based non-negative matrix decomposition (NMF) was applied divide four subtypes: nmf1, nmf2, nmf3, nmf4 (Liu 2024). Firstly, multi-omics revealed nmf1 subtypes were mainly enriched cycle, damage, chromatin organization, regulatory had response score ATR TOP1 inhibition. level NOTCH ligand delta-like 3(DLL3) highest nmf2 subtype. Therefore, subtype likely benefit therapies targeting DLL3. Secondly, phosphorylated proteomic showed RTK signaling significantly upregulated nmf3 Thus, may treat characterized by high MYC enrichment RNA preferentially associated AURKA amplification, further suggesting opportunities AURKA. Multiomics expand our events malignancies contribute effective clinical type.In triple negative breast (TNBC), genomic transcriptomic strategies indicated programmed death ligand-1 (PD-L1) mutational overexpressed about 20% TNBC thus serve (Kudelova 2022). Upon study, anti-PD-L1 antibody atezolizumab became first FDA-approved locally advanced metastatic TNBC. In addition, application facilitated deriving other cancers, such (Neagu 2023), lung (Yan 2024), gastric (Hou, Zhao Zhu hematological (Rosenquist etc. integration approaches accelerated

Language: Английский

A novel semi-quantitative scoring method for CD8+ tumor-infiltrating lymphocytes based on infiltration sites in gastric cancer

American Journal of Cancer Research, Journal Year: 2024, Volume and Issue: 14(12), P. 5965 - 5986

Published: Jan. 1, 2024

No established method currently exists for evaluating tumor-infiltrating lymphocytes (TILs) in gastric cancer (GC), and their clinical significance based on infiltration site GC remains unclear. In this study, we developed a to evaluate TILs according as prognostic marker GC. We retrospectively analyzed 103 patients with advanced who underwent curative resection. located at the invasive margin (TIL

Language: Английский