Liquid biopsy is moving beyond mutation-centric assays to multimodal frameworks that integrate cell-free DNA (cfDNA) signals with additional analytes such as circulating tumor cells (CTCs) and extracellular vesicles (EVs). In this review, we summarize emerging technologies across analytes for early cancer detection, emphasizing sequencing and error-suppression strategies and the growing evidence for multi-cancer early detection (MCED), tissue-of-origin (TOO) inference, diagnostic triage, and longitudinal surveillance. At low tumor fractions, fragmentomic and methylation features preserve tissue and chromatin context; when combined with radiomics using deep learning, they support blood-first, high-specificity risk stratification, increase positive predictive value (PPV), reduce unnecessary procedures, and enhance early prediction of treatment response and relapse. Building on these findings, we propose a pathway-aware workflow: initial blood-based risk scoring, followed by organ-directed imaging, and targeted secondary testing when indicated. We further recommend that model reports include not only discrimination metrics but also calibration, decision-curve analysis, PPV/negative predictive value (NPV) at fixed specificity, and TOO accuracy, alongside multi-site external validation and blinded dataset splits to improve generalizability. Overall, liquid biopsy is transitioning from signal discovery to deployable multimodal decision systems; standardized pre-analytical and analytical workflows, robust error suppression, and prospective real-world evaluations will be pivotal for clinical implementation.

Background: Neoadjuvant immunotherapy has reshaped the treatment paradigm for esophageal squamous cell carcinoma (ESCC), yet mechanisms of resistance in non-responders remain poorly understood. Specifically, the spatial orchestration of the tumor microenvironment that limits effective antitumor immunity is not fully elucidated. Methods: Integrating spatial transcriptomic analysis with large-scale tissue pathology, we mapped the spatial architecture of the immune landscape in immunotherapy-resistant ESCC to identify cellular and molecular determinants of treatment failure. Results: We revealed a distinct immune exclusion phenotype in non-responders, characterized by peritumoral CD8 ⁺ T cell enrichment coupled with intratumoral depletion. At the invasive front, we identified COL11A1 ⁺ cancer-associated fibroblasts (CAFs) and SPP1 ⁺ tumor-associated macrophages as spatially correlated markers of immune exclusion, demarcating regions characterized by limited T cell infiltration. Mechanistically, a distinct subpopulation of LAMC2 ⁺ tumor cells localized at the tumor boundary acts as the master orchestrator of this barrier. These LAMC2 ⁺ cells exhibit aberrant lactate metabolism and elevated stemness, driving CAF activation via Semaphorin 3C secretion. Strikingly, this ‘LAMC2 ⁺ boundary tumor cell-immune-privileged niche’ axis exhibits pan-cancer correlations with immunotherapy resistance, positioning LAMC2 as a robust predictive biomarker. Conclusion: In this study, we identify the tumor invasive margin as a critical determinant of immunotherapy resistance in ESCC. By defining the specific spatial markers and molecular architecture of an immune-privileged niche associated with the immune-exclusion barrier, our findings demonstrate the value of spatially resolved microenvironmental analysis. Moreover, we propose that therapeutic targeting of this boundary niche represents a promising strategy to overcome resistance in ESCC.