scCURE identifies cell types responding to immunotherapy and enables outcome prediction

Revolutionizing breast cancer immunotherapy by integrating AI and nanotechnology approaches: review of current applications and future directions

Breast cancer (BC) is still the most diagnosed cancer for females with an increased focus on immunotherapy as a promising precise treatment. Selecting appropriate patients and monitoring patient treatments are crucial to ensure higher response rates with low adverse events. Various biomarkers were proposed to predict immunotherapy response, including tumor mutation burden, immune cell, and tumor microenvironment expression. However, traditional methods for evaluating immunotherapy are invasive and inaccurate, and their assessments could be biased due to the variability in quantification techniques. Artificial intelligence (AI) has emerged as a powerful technology that addresses these challenges, handling heterogeneous data to identify complex patterns and offering accurate and non-invasive solutions. In this paper, we review emerging AI-based models for immunotherapy prediction in BC using diverse biomarkers. We first discussed the application of AI models for each biomarker, highlighting both direct prediction of immunotherapy response and prognosis, as well as indirect approaches via the identification of immune subtypes or specific predictive biomarkers. Then, we investigated the integration of all biomarkers in multi-modal AI approaches for a precise and personalized prediction of immunotherapy response. We have also addressed the implication of integrating AI in the healthcare ecosystem with other new technologies, including nanodevices, and wearable technologies. We further elucidated the role of AI and healthcare providers with this convergence of personalized medicine and demonstrated its role in enhancing population health management and supporting personalized patient care.

Deep scSTAR: leveraging deep learning for the extraction and enhancement of phenotype-associated features from single-cell RNA sequencing and spatial transcriptomics data

Single-cell sequencing has advanced our understanding of cellular heterogeneity and disease pathology, offering insights into cellular behavior and immune mechanisms. However, extracting meaningful phenotype-related features is challenging due to noise, batch effects, and irrelevant biological signals. To address this, we introduce Deep scSTAR (DscSTAR), a deep learning-based tool designed to enhance phenotype-associated features. DscSTAR identified HSP+ FKBP4+ T cells in CD8+ T cells, which linked to immune dysfunction and resistance to immune checkpoint blockade in non-small cell lung cancer. It has also enhanced spatial transcriptomics analysis of renal cell carcinoma, revealing interactions between cancer cells, CD8+ T cells, and tumor-associated macrophages that may promote immune suppression and affect outcomes. In hepatocellular carcinoma, it highlighted the role of S100A12+ neutrophils and cancer-associated fibroblasts in forming tumor immune barriers and potentially contributing to immunotherapy resistance. These findings demonstrate DscSTAR's capacity to model and extract phenotype-specific information, advancing our understanding of disease mechanisms and therapy resistance.