The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.

Cystic fibrosis (CF) is an inherited disorder caused by biallelic mutations of the CF transmembrane conductance regulator ( CFTR ) gene. Converging evidence suggests that CF carriers with only 1 defective CFTR copy are at increased risk for CF-related conditions and pulmonary infections, but the molecular mechanisms underpinning this effect remain unknown. We performed transcriptomic profiling of peripheral blood mononuclear cells (PBMCs) of CF child-parent trios (proband, father, and mother) and healthy control (HC) PBMCs or THP-1 cells incubated with the plasma of these participants. Transcriptomic analyses revealed suppression of cytokine-enriched immune-related genes ( IL-1 β , CXCL8 , CREM ), implicating lipopolysaccharide tolerance in innate immune cells (monocytes) of CF probands and their parents. These data suggest that a homozygous as well as a heterozygous CFTR mutation can modulate the immune/inflammatory system. This conclusion is further supported by the finding of lower numbers of circulating monocytes in CF probands and their parents, compared with HCs, and the abundance of mononuclear phagocyte subsets, which correlated with Pseudomonas aeruginosa infection, lung disease severity, and CF progression in the probands. This study provides insight into demonstrated CFTR -related innate immune dysfunction in individuals with CF and carriers of a CFTR mutation that may serve as a target for personalized therapy.

Cystic fibrosis (CF) is caused by mutations of the gene encoding the CF transmembrane conductance regulator. It remains unclear whether the abnormal immune response in CF involves extrinsic signals released from the external or internal environment. We sought to characterize the peripheral immune signatures in CF and its association with clinical phenotypes. Healthy peripheral blood mononuclear cells (PBMCs) were cultured with plasma from CF probands (CFPs) or healthy control subjects (HCs) followed by nCounter gene and microRNA (miRNA) profiling. A discovery cohort of 12 CFPs and 12 HCs and a validation cohort of 103 CFPs and 31 HCs (our previous microarray data [GSE71799]) were analyzed to characterize the composition of cultured immune cells and establish a miRNA‒mRNA network. Cell compositions and miRNA profiles were associated with clinical characteristics of the cohorts. Significantly differentially expressed genes and abundance of myeloid cells were downregulated in PMBCs after culture with CF plasma (P < 0.05). Top-ranked miRNAs that increased in response to CF plasma (adjusted P < 0.05) included miR-155 and miR-146a, which target many immune-related genes, such as IL-8. Pseudomonas aeruginosa infection was negatively associated with abundance of monocytes and the presence of those regulatory miRNAs. Extrinsic signals in plasma from patients with CF led to monocyte inactivation and miRNA upregulation in PBMCs. An improved understanding of the immune effects of extrinsic factors in CF holds great promise for integrating immunomodulatory cell therapies into current treatment strategies in CF.

Background: The definitive validation evidence of the implications of lymph node metastases regarding the survival of Non-Small Cell Lung Cancer (NSCLC) patients is lacking. We aimed to evaluate the prognostic impact of several lymph node metastases-associated risk factors including Number of Negative Lymph Node (NLN) and risk-stratify NSCLC patients into subsets with different prognosis.

Background: Despite the improved survival observed in PD-1/PD-L1 blockade therapy, a substantial proportion of cancer patients, including those with non-small cell lung cancer (NSCLC), still lack a response. Methods: Transcriptomic profiling was conducted on a discovery cohort comprising 100 whole blood samples, as collected multiple times from 48 healthy controls (including 43 published data) and 31 NSCLC patients that under treatment with a combination of anti-PD-1 Tislelizumab and chemotherapy. Differentially expressed genes (DEGs), simulated immune cell subsets, and germline DNA mutational markers were identified from patients achieved a pathological complete response during the early treatment cycles. The predictive values of mutational markers were further validated in an independent immunotherapy cohort of 1661 subjects, and then confirmed in genetically matched lung cancer cell lines by a co-culturing model. Results: The gene expression of hundreds of DEGs (FDR p < 0.05, fold change < -2 or > 2) distinguished responders from healthy controls, indicating the potential to stratify patients utilizing early on-treatment features from blood. PD-1-mediated cell abundance changes in memory CD4 + and regulatory T cell subset were more significant or exclusively observed in responders. A panel of top-ranked genetic alterations showed significant associations with improved survival (p < 0.05) and heightened responsiveness to anti-PD-1 treatment in patient cohort and co-cultured cell lines. Conclusion: This study discovered and validated peripheral blood-based biomarkers with evident predictive efficacy for early therapy response and patient stratification before treatment for neoadjuvant PD-1 blockade in NSCLC patients.

Breast cancer (BC) subtypes exhibit distinct epigenetic landscapes, with triple-negative breast cancer (TNBC) lacking effective targeted therapies. This study investigates histone biomarkers and therapeutic vulnerabilities across BC subtypes. The immunohistochemical profiling of >20 histone biomarkers, including histone modifications, modifiers, and oncohistone mutations, was conducted on a discovery cohort and a validation cohort of BC tissues, healthy controls, and cell line models. Transcriptomic and cell growth analyses were conducted to evaluate the effects of the small-molecule G9a inhibitor in diverse BC models. Key histone biomarkers, including H3K9me2, H3K36me2, and H3K79me, were differentially expressed across BC subtypes. H3K9me2 emerged as an independent predictor for distinguishing TNBC from other less-aggressive BC subtypes, with elevated expression correlating with higher tumor grade and stage. G9a inhibition impaired cell proliferation and modulated epithelial–mesenchymal transition pathways, with the strongest impact in basal-like TNBC. The disruption of the oncogene and tumor suppressor regulation (e.g., TP53, SATB1) was observed in TNBC. This study highlights G9a’s context-dependent roles in BC, supporting its potential as a therapeutic target. The findings provide a foundation for subtype-specific epigenetic therapies to improve outcomes in aggressive BC subtypes.