Accurately evaluating minimal residual disease (MRD) could facilitate early intervention and personalized adjuvant therapies. Here, using ultradeep targeted next-generation sequencing (NGS), we evaluate the clinical utility of circulating tumor DNA (ctDNA) for dynamic recurrence risk and adjuvant chemotherapy (ACT) benefit prediction in resected non-small cell lung cancer (NSCLC). Both postsurgical and post-ACT ctDNA positivity are significantly associated with worse recurrence-free survival. In stage II-III patients, the postsurgical ctDNA positive group benefit from ACT, while ctDNA negative patients have a low risk of relapse regardless of whether or not ACT is administered. During disease surveillance, ctDNA positivity precedes radiological recurrence by a median of 88 days. Using joint modeling of longitudinal ctDNA analysis and time-to-recurrence, we accurately predict patients’ postsurgical 12-month and 15-month recurrence status. Our findings reveal longitudinal ctDNA analysis as a promising tool to detect MRD in NSCLC, and we show pioneering work of using postsurgical ctDNA status to guide ACT and applying joint modeling to dynamically predict recurrence risk, although the results need to be further confirmed in future studies.

Neoadjuvant immunotherapy has significantly changed the therapeutic approach for treating patients with surgically resectable non‐small cell lung cancer (NSCLC). Here, peripheral blood inflammation‐based biomarkers as well as previously less focused eosinophil fraction, modified Glasgow prognostic score (mGPS), and prognostic nutritional index (PNI) were systematically included to comprehensively analyze their potential in predicting neoadjuvant immunotherapy efficacy and prognosis. We enrolled 189 patients (94 in training and 95 in validation cohorts) with stage I–III B surgically resectable NSCLC treated with neoadjuvant immunotherapy from the National Cancer Center of China. Baseline and post‐treatment eosinophils fraction, neutrophil‐to‐lymphocyte ratio (NLR), platelet‐to‐lymphocyte ratio (PLR), systemic immune‐inflammation index (SII), monocyte‐to‐lymphocyte ratio (MLR), PNI, mGPS, and their changes were calculated and analyzed for correlation with neoadjuvant immunotherapy efficacy and prognosis. In patients in the major pathological response (MPR) group, the post‐treatment eosinophil fraction was significantly high, and NLR, PLR, SII, and MLR were significantly lower compared to the non‐MPR group in both the training and validation cohorts. The receiver operating characteristic curve showed that post‐treatment, eosinophil fraction and SII and their changing were two of the most important factors. Univariate and multivariate logistic regression analyses showed that post‐treatment eosinophil fraction, SII, mGPS, and ΔSII could independently predict MPR in patients treated with neoadjuvant immunotherapy. Survival analysis showed a significant correlation between high post‐treatment NLR, PLR, SII, mGPS, and their changes in ΔNLR and ΔSII elevation with poor overall survival and event‐free survival of patients. Our results suggest that inflammatory biomarkers could predict the patient's response to neoadjuvant immunotherapy and prognosis.

The past decade has witnessed an exponential increase in research on exosomes. For many years considered to be extracellular debris, exosomes are now considered important mediators in intercellular communication. The capability of exosomes to transfer proteins, DNA, mRNA, as well as non-coding RNAs has made them an attractive focus of research into the pathogenesis of different diseases, including cancer. Increasing evidence suggests that tumor cells release a large sum of exosomes, which may not only influence proximal tumor cells and stromal cells in local microenvironment, but also can exert systemic effects when participating in blood circulation. In this study, we review the current understanding on this topic. The literature outlines two broad facets of exosomes in cancer: 1) promotion of tumor growth, tumorigenesis, tumor angiogenesis, tumor immune escape, drug resistance, and metastasis and 2) their role as promising biomarkers for cancer diagnosis and even as potential treatment targets for cancer patients.

Background: Lung adenocarcinomas (LUAD) is the most common histological subtype of lung cancers. Tumor immune microenvironment (TIME) is involved in tumorigeneses, progressions, and metastases. This study is aimed to develop a robust immune‐related signature of LUAD.Methods: A total of 1774 LUAD cases sourced from public databases were included in this study. Immune scores were calculated through ESTIMATE algorithm and weighted gene co‐expression network analysis (WGCNA) was applied to identify immune‐related genes. Stability selections and Lasso COX regressions were implemented to construct prognostic signatures. Validations and comparisons with other immune‐related signatures were conducted in independent Gene Expression Omnibus (GEO) cohorts. Abundant infiltrated immune cells and pathway enrichment analyses were carried out, respectively, through ImmuCellAI and gene set enrichment analysis (GSEA).Results: In Cancer Genome Atlas (TCGA) LUAD cohorts, immune scores of higher levels were significantly associated with better prognoses (P < .05). Yellow (n = 270) and Blue (n = 764) colored genes were selected as immune‐related genes, and after univariate Cox regression analysis (P < .005), a total of 133 genes were screened out for subsequent model constructions. A four‐gene signature (ARNTL2, ECT2, PPIA, and TUBA4A) named IPSLUAD was developed through stability selection and Lasso COX regression. It was suggested by multivariate and subgroup analyses that IPSLUAD was an independent prognostic factor. It was suggested by Kaplan‐Meier survival analysis that eight out of nine patients in high‐risk groups had significantly worse prognoses in validation data sets (P < .05). IPSLUAD outperformed other signatures in two independent cohorts.Conclusions: A robust immune‐related prognostic signature with great performances in multiple LUAD cohorts was developed in this study.

Multimodal epigenetic characterization of cell-free DNA (cfDNA) could improve the performance of blood-based early cancer detection. However, integrative profiling of cfDNA methylome and fragmentome has been technologically challenging. Here, we adapt an enzyme-mediated methylation sequencing method for comprehensive analysis of genome-wide cfDNA methylation, fragmentation, and copy number alteration (CNA) characteristics for enhanced cancer detection. We apply this method to plasma samples of 497 healthy controls and 780 patients of seven cancer types and develop an ensemble classifier by incorporating methylation, fragmentation, and CNA features. In the test cohort, our approach achieves an area under the curve value of 0.966 for overall cancer detection. Detection sensitivity for early-stage patients achieves 73% at 99% specificity. Finally, we demonstrate the feasibility to accurately localize the origin of cancer signals with combined methylation and fragmentation profiling of tissue-specific accessible chromatin regions. Overall, this proof-of-concept study provides a technical platform to utilize multimodal cfDNA features for improved cancer detection.

Neoadjuvant immunochemotherapy (nICT) has dramatically changed the treatment landscape of operable esophageal squamous cell carcinoma (ESCC), but factors influencing tumor response to nICT are not well understood. Here, using single-cell RNA sequencing paired with T cell receptor sequencing, we profile tissues from ESCC patients accepting nICT treatment and characterize the tumor microenvironment context. CXCL13⁺CD8⁺ Tex cells, a subset of exhausted CD8⁺ T cells, are revealed to highly infiltrate in pre-treatment tumors and show prominent progenitor exhaustion phenotype in post-treatment samples from responders. We validate CXCL13⁺CD8⁺ Tex cells as a predictor of improved response to nICT and reveal CXCL13 to potentiate anti-PD-1 efficacy in vivo. Post-treatment tumors from non-responders are enriched for CXCL13⁺CD8⁺ Tex cells with notably remarkable exhaustion phenotype and TNFRSF4⁺CD4⁺ Tregs with activated immunosuppressive function and a significant clone expansion. Several critical markers for therapeutic resistance are also identified, including LRRC15⁺ fibroblasts and SPP1⁺ macrophages, which may recruit Tregs to form an immunosuppressive landscape. Overall, our findings unravel immune features of distinct therapeutic response to nICT treatment, providing a rationale for optimizing individualized neoadjuvant strategy in ESCC.