Abstract Background Tumor heterogeneity presents a formidable challenge in understanding the mechanisms driving tumor progression and metastasis. The heterogeneity of hepatocellular carcinoma (HCC) in cellular level is not clear. Methods Integration analysis of single-cell RNA sequencing data and spatial transcriptomics data was performed. Multiple methods were applied to investigate the subtype of HCC tumor cells. The functional characteristics, translation factors, clinical implications and microenvironment associations of different subtypes of tumor cells were analyzed. The interaction of subtype and fibroblasts were analyzed. Results We established a heterogeneity landscape of HCC malignant cells by integrated 52 single-cell RNA sequencing data and 5 spatial transcriptomics data. We identified three subtypes in tumor cells, including ARG1+ metabolism subtype (Metab-subtype), TOP2A+ proliferation phenotype (Prol-phenotype), and S100A6+ pro-metastatic subtype (EMT-subtype). Enrichment analysis found that the three subtypes harbored different features, that is metabolism, proliferating, and epithelial-mesenchymal transition. Trajectory analysis revealed that both Metab-subtype and EMT-subtype originated from the Prol-phenotype. Translation factor analysis found that EMT-subtype showed exclusive activation of SMAD3 and TGF-β signaling pathway. HCC dominated by EMT-subtype cells harbored an unfavorable prognosis and a deserted microenvironment. We uncovered a positive loop between tumor cells and fibroblasts mediated by SPP1-CD44 and CCN2/TGF-β-TGFBR1 interaction pairs. Inhibiting CCN2 disrupted the loop, mitigated the transformation to EMT-subtype, and suppressed metastasis. Conclusion By establishing a heterogeneity landscape of malignant cells, we identified a three-subtype classification in HCC. Among them, S100A6+ tumor cells play a crucial role in metastasis. Targeting the feedback loop between tumor cells and fibroblasts is a promising anti-metastatic strategy.

AIM:Circulating tumor cells (CTC) have shown promise in predicting the outcomes of adjuvant treatments for several malignancies. The clinical significance of CTC in predicting the efficacy of tyrosine kinase inhibitor (TKI) administration in patients with hepatocellular carcinoma (HCC) was unclear. METHODS:A total of 429 patients who underwent liver transplantation for HCC had provided 335 preoperative and 373 postoperative blood samples that could be used for CTC detection (pre-CTC and post-CTC). The association of the pre-CTC and post-CTC findings with the efficacy of TKI administration was assessed. Additionally, CTC surveillance was performed in 27 patients during TKI administration. RESULTS:Patients with detectable post-CTC, instead of pre-CTC, showed a significantly longer time to recurrence when receiving a TKI after liver transplantation for HCC (hazard ratio 0.57; P = 0.042). Whereas patients without detectable post-CTC did not benefit from the TKI administration (P = 0.270). Furthermore, we also found that patients who persistently harbored CTC during TKI administration showed significantly higher early recurrence rates (≤1 year; 40% vs. 5.9%, P < 0.001) and a shorter time to recurrence (HR 7.03; P < 0.001) than those whose CTC status switched from positive to negative. In addition, longitudinal CTC monitoring demonstrated that CTC tended to reflect drug resistance during TKI administration. CONCLUSIONS:The postoperative CTC level could predict the efficacy of TKI treatment for HCC patients after liver transplantation. Dynamic monitoring for CTC during treatment could sensitively reflect the response to the TKI, the development of drug resistance, and foresee tumor recurrence.

e16245 Background: Minimal residual disease (MRD) is considered an essential factor leading to early relapse after radical surgery, which is challenging to be detected by conventional imaging. The majority of MRD studies in HCC are based on mutation detection. Simultaneously, in certain cancers such as lung and colorectal cancers, the use of methylation detection for MRD has been demonstrated to possess significant detection capabilities. In this study, we present a personalized methylation haplotype (MHP) method for MRD detection in HCC patients underwent curative resection. Methods: Tumor and paracancerous tissue samples were obtained from HCC patients undergoing surgical resections. WBC and plasma samples (T0) were collected before surgery, while plasma samples (T1) were collected in one month after surgery, and subsequent follow-up plasma samples were obtained every three months. All samples were tested using the GutSeer Panel. A personalized tumor-informed MRD detection approach, named TORNADO (Tumor-infORmed DNAm hAplotype DetectiOn), was then developed. Individual seed MHPs were pinpointed based on their presence in cancer tissue or plasma samples collected before surgery (T0 plasma), excluding those in para-cancer tissue and blood cells. The relapse risk score was determined by assessing the proportion of these MHPs detectable in follow-up evaluations. Results: We enrolled 39 early-stage HCC patients who have undergone surgical resections. All the patients have completed at least one year of regular clinical follow-up. Among them, 13 out of 39 patients (33.3%) were identified as MRD-positive based on predictions from T1. Of the MRD-positive patients, 6 experienced tumor relapse. In contrast, only 7.7% relapses were observed among the remaining 26 MRD-negative patients. When considering the T1 and T2 MRD cumulative risk score, 53.8% (21/39) of patients were classified as MRD-positive, and none of the MRD-negative patients experienced relapse. Regardless of whether based on T1 plasma alone or cumulative results, recurrence-free survival (RFS) was significantly correlated with MRD status. Kaplan-Meier analysis demonstrated that MRD-positive patients had significantly poorer RFS (p-value < 0.05). Cox regression analysis further revealed that MRD status was a significant variable for predicting recurrence-free survival (HR=16.00, 95% CI 2-130, P=0.009). Conclusions: The MRD status detected byTORNADOmethod emerged as a significant prognostic indicator for disease relapse in early-stage HCC patients undergoing curative resection, underscoring the clinical relevance of MHP-based MRD detection guided by tumor information. These discoveries pave the way for the development of effective postoperative cancer surveillance strategies specifically tailored for early-stage liver cancer.

4125 Background: Hepatocellular carcinoma (HCC), a prevalent malignancy and a leading cause of cancer-related mortality, often exhibits a grim prognosis due to recurrence post-surgical treatment. Minimal residual disease (MRD) is deemed a crucial factor contributing to early relapse following radical surgery. Previous studies have indicated that circulating tumor DNA (ctDNA) may serve as a promising biomarker for evaluating MRD in various cancers. However, its applicability in liver cancer remains unclear. Methods: We prospective included 136 patients during 2019 to 2021 who underwent curative-intention surgery for HCC in Zhongshan hospital. Plasma samples (n=625) were collected prior to surgery, 1-month post-surgery, and during subsequent surveillance. Patient-specific somatic mutations were identified through whole-exome sequencing of tumor tissue. For each patient, 16 patient-specific somatic mutations were selected and employed to assess ctDNA through ultra-deep sequencing of plasma DNA. Results: The median follow-up period was 24 months. At the last follow-up, 45 (33.1%) patients relapsed. The preoperative ctDNA concentration was elevated in patients who experienced tumor relapse after surgical treatment compared to those didn’t relapse (median: 125 vs.8.4 mean tumor molecules/mL, P<0.001). Most of patients (80.9%) demonstrated a significant decrease in ctDNA concentration to negative levels after curative surgery. Those maintaining a positive postoperative ctDNA status faced a higher recurrence risk (HR=11.5, P<0.001). Throughout longitudinal follow-up, positive longitudinal ctDNA (positive in any point) was significantly associated with a higher recurrence risk (HR=69.3, P<0.001). The area under the curve of longitudinal ctDNA for predicting recurrence was 0.956, boasting a sensitivity of 93.3% and specificity of 97.8%. The median interval from the first positive longitudinal ctDNA to clinical recurrence confirmed by imaging test was 4 months. The predictive performance of longitudinal ctDNA for recurrence surpassed that of alpha-fetoprotein (0.956 vs. 0.680, P<0.001). Patients with positive longitudinal ctDNA experienced benefits from adjuvant therapy (HR=0.4, P=0.019). Moreover, a decrease or stability of ctDNA after adjuvant therapy indicated treatment response and delayed clinical relapse (HR=0.3, P=0.00012). Conclusions: Tumor-informed ctDNA proves to be a reliable indicator for MRD detection after surgical treatment for HCC, enabling the prediction and anticipation of recurrence before clinical manifestation. It plays a crucial role in guiding the application of adjuvant therapy and assessing treatment efficacy, thereby facilitating postoperative management and enhancing the precision of medical interventions.

Tumor immune microenvironment is strongly associated with the malignancy behavior of hepatocellular carcinoma (HCC). However, the immune function and regulatory mechanisms of B cells in HCC remain unclear. The expression differences between B cell high- and low-infiltration HCC samples are explored to identify the key regulator. Pre-mRNA processing factor 19 (PRP19) expression is increased in B cell low-infiltrated tissues and negatively correlated with the B cell marker, CD20. Inhibition of PRP19 expression promoted B cell infiltration in tumor tissue and impeded HCC growth. Mechanically, the co-immunoprecipitation (Co-IP) assay revealed that PRP19 interacts with DEAD-box helicase 5 (DDX5), leading to ubiquitination and degradation of the DDX5 protein. The attenuated DDX5 impairs CXCL12 mRNA stability to suppress B cell recruitment and plasma cell differentiation via CXCL12/CXCR4 axis. Moreover, the adoptive transfer of CXCR4+ B cells combined with CXCL12 treatment in mice models effectively inhibits HCC development by reshaping the immune response. The expression of PRP19, DDX5, and infiltrating B cells are recognized as clinical prognosis indicators for HCC patients. Overall, this study provides valuable insights into the clinical benefits of HCC immunotherapy by targeting PRP19 and modulating tumor-infiltrating B cell immune function.

Aim: This study aims to evaluate the feasibility of examining circulating tumor DNA (ctDNA) in urine samples from hepatocellular carcinoma (HCC) patients by droplet digital PCR (ddPCR) and to assess its value in predicting HCC recurrence after surgery. Methods: HCC cases who accepted surgical resection were included. Perioperative urine, tissue and peripheral blood specimens were collected. Four hotspot mutants [TP53 -rs28934571 (c.747G>T), TRET -rs1242535815 (c.1-124C>T), CTNNB1 -rs121913412 (c.121A>G), and CTNNB1 -rs121913407 (c.133T>C)] were chosen for ctDNA analysis, and mutant allele frequency (MAF) was worked out. Sanger sequencing was performed on matched tumor tissues and peripheral blood mononuclear cells (PBMCs). The patients’ clinicopathologic characteristics were retrospectively analyzed. The predictive abilities of urine ctDNA for postoperative recurrence were evaluated using the Kaplan-Meier method. Results: Forty-six patients were enrolled, and 18 patients (39.1%, 18/46) exhibited detectable circulating mutants, with the MAF in the range of 0.07% to 0.91%. The consistency test indicated moderate to substantial concordance between urine and paired tumor tissue mutations. The mutation level dropped dramatically or disappeared after surgery. Positive urine ctDNA before surgery was closely related to greater tumor size and recurrence. Kaplan-Meier curves revealed significantly shorter disease-free survival (DFS) for ctDNA-positive patients. Multivariate analysis identified detectable urine ctDNA as an independent risk factor for tumor recurrence. More than that, receiver operating characteristic (ROC) curves demonstrated that urine ctDNA had the largest area under the curve (AUC) for predicting HCC recurrence. Conclusion: Detecting ctDNA in urine using ddPCR is feasible and holds significant potential for predicting and monitoring HCC recurrence.

Background: Early diagnosis of hepatocellular carcinoma (HCC) can significantly improve patient survival. We aimed to develop a blood-based assay to aid in the diagnosis, detection and prognostic evaluation of HCC. Methods: A three-phase multicentre study was conducted to screen, optimise and validate HCC-specific differentially methylated regions (DMRs) using next-generation sequencing and quantitative methylation-specific PCR (qMSP). Results: Genome-wide methylation profiling was conducted to identify DMRs distinguishing HCC tumours from peritumoural tissues and healthy plasmas. The twenty most effective DMRs were verified and incorporated into a multilocus qMSP assay (HepaAiQ). The HepaAiQ model was trained to separate 293 HCC patients (Barcelona Clinic Liver Cancer (BCLC) stage 0/A, 224) from 266 controls including chronic hepatitis B (CHB) or liver cirrhosis (LC) (CHB/LC, 96), benign hepatic lesions (BHL, 23), and healthy controls (HC, 147). The model achieved an area under the curve (AUC) of 0.944 with a sensitivity of 86.0% in HCC and a specificity of 92.1% in controls. Blind validation of the HepaAiQ model in a cohort of 523 participants resulted in an AUC of 0.940 with a sensitivity of 84.4% in 205 HCC cases (BCLC stage 0/A, 167) and a specificity of 90.3% in 318 controls (CHB/LC, 100; BHL, 102; HC, 116). When evaluated in an independent test set, the HepaAiQ model exhibited a sensitivity of 70.8% in 65 HCC patients at BCLC stage 0/A and a specificity of 89.5% in 124 patients with CHB/LC. Moreover, HepaAiQ model was assessed in paired pre- and postoperative plasma samples from 103 HCC patients and correlated with 2-year patient outcomes. Patients with high postoperative HepaAiQ score showed a higher recurrence risk (Hazard ratio, 3.33, p < .001). Conclusions: HepaAiQ, a noninvasive qMSP assay, was developed to accurately measure HCC-specific DMRs and shows great potential for the diagnosis, detection and prognosis of HCC, benefiting at-risk populations.

Background: To explore biliary tract stone (BTS) as prognostic factors of intrahepatic cholangiocarcinoma (ICC). Methods: Clinical data of 985 ICC patients were classified into no BTS group and BTS group-subgrouped into hepatolithiasis (HL) and non-hepatolithiasis (NHL) group. Propensity score matching was utilized to mitigate baseline characteristics. Preoperative peripheral inflammation parameters (PPIP) were further investigated. Immunostaining of CD3, CD4, CD8, CD68, PD1 and PD-L1 were conducted. Results: Overall survival (OS) of patients without BTS surpassed BTS group (P = 0.040) while no difference of time to recurrence (TTR) was observed (P = 0.146). HL group had shorter OS and TTR than HL-matched group (P < 0.001 and P = 0.017, respectively) and survival time of NHL group differed not with NHL-matched group (P > 0.05). PPIP like neutrophils to lymphocytes ratio (NLR), platelet to lymphocyte ratio (PLR) and systemic immune inflammation (SII) of HL group exceeded no BTS group or NHL group (all P < 0.05). Associations of PPIP and tumorous immunocytes differed vastly among HL group, NHL group and no BTS group. Tumorous CD4+/CD3+ ratio and PD1+/CD3+ ratio of HL group surpassed those in no BTS group (P = 0.036 and P < 0.001, respectively) and NHL group (P = 0.015 and 0.002, respectively). Para-tumorous CD68+ macrophages exceeded that in tumor samples of HL group (P < 0.001). No difference of CD8+/CD3+ lymphocyte ratio and PD-L1 rank were detected. Conclusions: Hepatolithiasis, rather than extra-hepatic biliary stone, is a poor prognostic indicator of ICC. Immunotherapy is promising in treating HL-related ICC.

4169 Background: Five major gastrointestinal (GI) cancers - colorectal (CRC), gastric (GC), liver (LC), esophageal (EC), and pancreatic cancer (PC) - are responsible for hundreds of thousands of mortalities annually worldwide. Unfortunately, there is a lack of cost-effective, blood-based screening method for their early detection. To address this issue, we aimed to develop GutSeer, a noninvasive, targeted methylation sequencing-based test by leveraging methylation and fragmentomic signatures carried by cell-free DNA (cfDNA). Methods: The panel of GutSeer consists of 1656 target regions which were either differentially methylated between healthy and cancer samples, or distinctively methylated in a specific GI cancer. Cancer and healthy participants were recruited and randomly divided into a training and a validation cohort. Their plasma DNA samples were analyzed to generate DNA methylation and fragmentomic features. These multi-dimensional features were integrated to build ensemble stacked machine learning models to differentiate cancer against healthy, and to determine the tissue-of-origin (TOO) of the cancer. Results: A total of 1844 cases (787 healthy, 342 LC, 239 GC, 209 EC, 180 CRC, and 87 PC cases) were recruited for this study. A cancer- vs-healthy model achieved an AUC of 0.94 and 0.95 (sensitivity of 77.7% and 77.1% under the specificity around 96%) using either methylation or fragmentomic features only, respectively. Combining both methylation and fragmentomic features further improved performances, achieving an AUC of 0.96 (sensitivity = 86.2% at a specificity of 96.7%). For individual type of cancer, GutSeer has a sensitivity of 93.3% for CRC, 81.1% for EC, 70.3% for GC, 96.5% for LC, and 86.4% for PC. An independent test using 629 benign cases as controls achieved a specificity of 87.1%. A separate TOO model was built using all features and achieved an overall accuracy of 82% for all cancer cases (66.7% for CRC, 87.0% for GC and EC combined, 89.0% for LC, and 63.2% for PC). Same as the cancer detection model, using multi-dimensional features in TOO prediction yielded higher accuracy than when models using only methylation or fragmentomics features (accuracy = 75.6% or 75.4%, respectively). When compared with whole-genome sequencing (WGS) based approaches, GutSeer showed a comparable performance in cancer detection but a higher accuracy in TOO identification, further confirming its effectiveness for detection of GI cancers. Conclusions: GutSeer, a non-invasive test integrating multi-dimensional features, was demonstrated to detect and localize the 5 main types of GI cancer with high accuracy. Our results further showed that a reasonably sized panel can perform comparably or even better than WGS-based methods in cancer detection and TOO localization, indicating GutSeer may be a low-cost solution for blood-based early screening for GI cancers.

肝细胞癌(简称肝癌)是我国主要肝恶性肿瘤.多数肝癌患者在确诊时为中晚期,不适合接受以外科手术切除为主的根治性治疗.转化治疗的内涵是将初始不可手术切除的肝癌通过系统治疗转化为可手术切除,可显著改善预后.近年来,肝癌靶向药物和以免疫检查点抑制剂为主的免疫治疗药物的大量涌现为转化治疗提供了新的方向.目前基于靶向药物和免疫检查点抑制剂的转化治疗是肝癌临床和基础研究的热点.本文就目前靶向和免疫治疗时代肝癌转化治疗中的相关问题进行探讨和展望.

330 Background: Gastrointestinal (GI) cancers totally account for more than one third of the cancerous deaths, yet there is no cost-effective blood-based assay for the early detection of GI cancers. We sought to develop GutSeer, a noninvasive test based on cell-free DNA (cfDNA) methylation and fragmentation signatures derived from one single targeted DNA methylation sequencing panel, for early detection and localization of five major GI cancers, including colorectal (CC), gastric (GC), liver (LC), esophageal (EC), and pancreatic cancer (PC). Methods: A DNA methylation targeted sequencing panel with 1656 target regions was designed. It was then verified in a large cohort of retrospective cancer and control plasma samples for feature selection and modeling. The participants were randomly divided into a training cohort and a validation cohort in a 1:1 ratio. DNA methylation and fragmentomic features were calculated based on GutSeer sequencing data. An ensemble stacked machine learning approach was built to classify cancer and healthy samples in training cohort and tested in validation cohort. We also constructed a TOO model to predict the tissue of origin of detected cancer samples. Results: To develop GutSeer assay, we have enrolled and tested a total of 1844 retrospective plasma samples (787 healthy, 342 LC, 239 GC, 209 EC, 180 CC, and 87 PC), over half of the cancer samples were diagnosed with early-stage disease (TNM stage I 35.6%; stage II 23.3%; stage III 21.7%; stage IV 12.5%). Cancer- vs-healthy model was built on training cohort and tested in validation cohort, achieving an AUC of 0.94 (sensitivity=77.7%, specificity=96.4%) with methylation features, and 0.95 (sensitivity=77.1%, specificity=95.9%) with fragmentomic features. Combining these features could achieve AUC of 0.963 (sensitivity = 86.2%, specificity = 96.7%). For individual cancer types, the sensitivity was 93.3% (CC), 81.1% (EC), 70.3% (GC), 96.5% (LC) and 86.4% (PC), respectively. For predicted cancer samples, we achieved an 82% top-one (66.7% CC, 87.0% GC/EC, 89.0% LC, 63.2% PC) and 95.2% top-two (86,9% CC, 98.2% GC/EC, 97.6% LC, 89.5% PC) TOO accuracy (ACC, accuracy of predicting the most likely, and the top 2 most likely tissue or organ types where the identified cancer was located, respectively) in validation cohort with TOO model combined all features. Conclusions: Based on a single targeted DNA methylation sequencing assay, GutSeer, which combined cfDNA methylation and fragmentomic signatures, could detect and localize the major five GI cancers with high accuracy but low cost. Although this is a pilot study with limited sample size, GutSeer demonstrated the potential to be further optimized into non-invasive diagnostics for blood-based early screening and diagnosis for GI cancers.

4128 Background: Hepatocellular carcinoma (HCC) is one of the most common cancers in China, and one of the leading causes of cancer-related deaths in the country. With a 5-year survival rate of only 15-20%, early detection is crucial to improve the treatment and survival of HCC patients. Currently, alpha-fetoprotein (AFP) is commonly used as a serum marker for HCC, but it is not a sufficiently specific and can cause false positive readings due to elevated levels caused by other liver conditions. An alternative method is to use circulating free DNA (cfDNA) released by tumor cells as cancer-screening targets, which has been shown to be a more sensitive and specific biomarkers for HCC detection. This study aims to develop a non-invasive screening assay based on cfDNA features to improve the detection of early-stage HCC. Methods: Candidate methylation markers for HCC detection were collected and evaluated using GEO, TCGA and in-house datasets, 1601 of which were incorporated into a targeted sequencing panel named HcSeer. Multiple types of cfDNA features were constructed from the sequencing data, which included methylation-related features such as methylation haplotype blocks (MHBs) and methylated haplotype fraction (MHF), and fragmentomics features such as end motif and CNV. For model building, Cancer and healthy plasma samples were randomly divided into a training and a testing set at a 2:1 ratio. A two-step deep neural network model was built to classify HCC using selected features of both types. Results: We previously enrolled a total of 401 plasma samples (200 healthy, 201 HCC) for model construction and the performance of the HcSeer model have been documented. An independent validation cohort of 421 plasma samples (280 healthy, 141 HCC) was currently collected from different centers. In this independent validation, the HcSeer model achieved an AUC of 0.98 with a sensitivity of 96.5% at a specificity of 96.4%. Importantly, HcSeer maintained a high sensitivity for HCC across all stages: 94.3%, 96%, 100% and 100% for stage I – IV cases, respectively. When compared to AFP, HcSeer achieved a significantly higher sensitivity of 94% than AFP’s 55% in 137 HCC cases having AFP level tested. When AFP level was combined with the HcSeer model, the sensitivity for HCC further increased to 96%. Conclusions: This study demonstrated that the DNA methylation and fragmentomics patterns of cfDNA can accurately distinguish HCC and healthy plasma samples, particularly in the early stages of HCC. The combination of the HcSeer and AFP further improved the accuracy of the prediction. Although this study was limited in sample size, it clearly showed the potential of the HcSeer assay for accurate HCC detection in blood.

Dissecting and understanding the cancer ecosystem, especially that around the tumor margins, which have strong implications for tumor cell infiltration and invasion, are essential for exploring the mechanisms of tumor metastasis and developing effective new treatments. Using a novel tumor border scanning and digitization model enabled by nanoscale resolution-SpaTial Enhanced REsolution Omics-sequencing (Stereo-seq), we identified a 500 µm-wide zone centered around the tumor border in patients with liver cancer, referred to as “the invasive zone”. We detected strong immunosuppression, metabolic reprogramming, and severely damaged hepatocytes in this zone. We also identified a subpopulation of damaged hepatocytes with increased expression of serum amyloid A1 and A2 (referred to collectively as SAAs) located close to the border on the paratumor side. Overexpression of CXCL6 in adjacent malignant cells could induce activation of the JAK-STAT3 pathway in nearby hepatocytes, which subsequently caused SAAs’ overexpression in these hepatocytes. Furthermore, overexpression and secretion of SAAs by hepatocytes in the invasive zone could lead to the recruitment of macrophages and M2 polarization, further promoting local immunosuppression, potentially resulting in tumor progression. Clinical association analysis in additional five independent cohorts of patients with primary and secondary liver cancer (n = 423) showed that patients with overexpression of SAAs in the invasive zone had a worse prognosis. Further in vivo experiments using mouse liver tumor models in situ confirmed that the knockdown of genes encoding SAAs in hepatocytes decreased macrophage accumulation around the tumor border and delayed tumor growth. The identification and characterization of a novel invasive zone in human cancer patients not only add an important layer of understanding regarding the mechanisms of tumor invasion and metastasis, but may also pave the way for developing novel therapeutic strategies for advanced liver cancer and other solid tumors.

背景与目的:三维(3D)可视化技术借助计算机对CT和(或)MRI的检查图像进行3D立体重建,可直观、清晰地将肝脏、胰腺、胆道、血管及肿瘤的形态和空间分布等进行展示,这对于明确肝脏脉管系统的解剖变异、准确计算残余肝体积以及手术规划具有重要的意义.本研究探讨术前肝脏3D可视化评估在中国肝癌分期(CNLC)Ⅱ～Ⅲa期患者外科治疗中的临床价值.方法:回顾性分析2015年—2017年在复旦大学附属中山医院肝肿瘤外科接受手术治疗的CNLC Ⅱ～Ⅲa期肝细胞癌(HCC)患者的临床资料.根据术前接受的评估方式,将患者分为常规影像学评估组和3D可视化评估组.采用Kaplan-Meier法比较两组患者的术后无复发生存期(RFS)和总生存期(OS),并通过单因素和多因素Cox回归分析确定影响患者预后的相关风险因素.结果:共有110例接受手术治疗的CNLC Ⅱ～Ⅲa期HCC患者被纳入研究,其中常规影像学评估组74例,3D可视化评估组36例.两组患者在性别、年龄、乙肝表面抗原、甲胎蛋白、肝功能Child-Pugh分级、肿瘤直径、肿瘤数量、大血管侵犯情况、CNLC分期、预防性介入治疗和辅助靶向治疗方面差异均无统计学意义(均P＞0.05).常规影像学评估组和3D可视化评估组的90 d病死率分别为2.8％(1/36)和4.1％(3/74),差异无统计学意义(P＞0.05).Kaplan-Meier生存分析结果显示,3D可视化评估组的OS率和RFS率均明显优于常规影像学评估组(P=0.024;P=0.014).多因素Cox回归分析结果显示,术前3D可视化评估是OS和RFS的独立保护因素(P=0.015;P=0.010).结论:术前3D可视化评估可显著改善中晚期HCC患者手术治疗的预后,在中晚期HCC外科治疗中具有良好的应用价值,值得进一步探索和推广.

Background Immune checkpoint inhibitor (ICI)-based combination therapy has opened a new avenue for the treatment of multiple malignancies including hepatocellular carcinoma (HCC). However, considering the unsatisfactory efficacy, biomarkers are urgently needed to identify the patients most likely to benefit from ICI-based combination therapy. Methods A total of 194 patients undergoing ICI-based combination therapy for unresectable HCC were retrospectively enrolled and divided into a training cohort (n = 129) and a validation cohort (n = 65) randomly. A novel circulating immune index (CII) defined as the ratio of white blood cell count (×109/L) to lymphocyte proportion (%) was constructed and its prognostic value was determined and validated. Results Patients with CII ≤ 43.1 reported prolonged overall survival (OS) compared to those with CII > 43.1 (median OS: 24.7 vs 15.1 months; 6-, 12-, 18-month OS: 94.2%, 76.7%, 66.1% vs 86.4%, 68.2%, 22.8%, P = 0.019), and CII was identified as an independent prognostic factor for OS (hazard ratio, 2.24; 95% confidence interval, 1.17-4.31; P = 0.015). These results were subsequently verified in the validation cohort. Additionally, patients with low CII levels had improved best radiological tumor response (complete response, partial response, stable disease, progressive disease: 3%, 36%, 50%, 11% vs 0%, 27%, 46%, 27%; P = 0.037) and disease control rate (89% vs 73%; P = 0.031) in the pooled cohort and better pathologic response (pathologic complete response, major pathologic response, partial pathologic response, no pathologic response: 20%, 44%, 28%, 8% vs 0%, 0%, 40%, 60%; P = 0.005) in the neoadjuvant cohort. Detection of lymphocyte subsets revealed that an elevated proportion of CD4+ T cells was related to better OS, while the proportion of CD8+ T cells was not. Conclusions We constructed a novel circulating immune biomarker that was capable of predicting OS and therapeutic efficacy for HCC patients undergoing ICI and lenvatinib combination therapy.

Background:Lenvatinib monotherapy and combination therapy with immune checkpoint inhibitors (ICI) were widely applied for unresectable hepatocellular carcinoma (uHCC). However, many patients failed to benefit from the treatments. A prognostic model was needed to predict the treatment outcomes and guide clinical decisions. Methods:304 patients receiving lenvatinib monotherapy or lenvatinib plus ICI for uHCC were retrospectively included. The risk factors derived from the multivariate analysis were used to construct the predictive model. The C-index and area under the receiver-operating characteristic curve (AUC) were calculated to assess the predictive efficiency. Results:Multivariate analysis revealed that protein induced by vitamin K absence or antagonist-II (PIVKA-II) (HR, 2.05; P=0.001) and metastasis (HR, 2.07; P<0.001) were independent risk factors of overall survival (OS) in the training cohort. Herein, we constructed a prognostic model called PIMET score and stratified patients into the PIMET-low group (without metastasis and PIVKA-II<600 mAU/mL), PIMET-int group (with metastasis or PIVKA-II>600 mAU/mL) and PIMET-high group (with metastasis and PIVKA-II>600 mAU/mL). The C-index of PIMET score for the survival prediction was 0.63 and 0.67 in the training and validation cohort, respectively. In the training cohort, the AUC of 12-, 18-, and 24-month OS was 0.661, 0.682, and 0.744, respectively. The prognostic performances of the model were subsequently validated. The AUC of 12-, 18-, and 24-month OS was 0.724, 0.726, and 0.762 in the validation cohort. Subgroup analyses showed consistent predictive value for patients receiving lenvatinib monotherapy and patients receiving lenvatinib plus ICI. The PIMET score could also distinguish patients with different treatment responses. Notably, the combination of lenvatinib and ICI conferred survival benefits to patients with PIMET-int or PIMET-high, instead of patients with PIMET-low. Conclusion:The PIMET score comprising metastasis and PIVKA-II could serve as a helpful prognostic model for uHCC receiving lenvatinib monotherapy or lenvatinib plus ICI. The PIMET score could guide the treatment decision and facilitate precision medicine for uHCC patients.

Background Minimal residual disease (MRD) is proposed to be responsible for tumor recurrence. The role of circulating tumor DNA (ctDNA) to detect MRD, monitor recurrence, and predict prognosis in liver cancer patients undergoing liver transplantation (LT) remains unrevealed.Methods Serial blood samples were collected to profile ctDNA mutational changes. Baseline ctDNA mutational profiles were compared with those of matched tumor tissues. Correlations between ctDNA status and recurrence rate (RR) and recurrence-free survival (RFS) were analyzed, respectively. Dynamic change of ctDNA was monitored to predict tumor recurrence.Results Baseline mutational profiles of ctDNA were highly concordant with those of tumor tissues (median, 89.85%; range 46.2-100%) in the 74 patients. Before LT, positive ctDNA status was associated with higher RR (31.7% vs 11.5%; p = 0.001) and shorter RFS than negative ctDNA status (17.8 vs 19.4 months; p = 0.019). After LT, the percentage of ctDNA positivity decreased (17.6% vs 47.0%; p < 0.001) and patients with positive ctDNA status had higher RR (46.2% vs 21.3%; p < 0.001) and shorter RFS (17.2 vs 19.2 months; p = 0.010). Serial ctDNA profiling demonstrated patients with decreased or constant negative ctDNA status had lower RR (33.3% vs 50.0%; p = 0.015) and favorable RFS (18.2 vs 15.0 months, p = 0.003) than those with increased or constant positive ctDNA status. Serial ctDNA profiling predicted recurrence months ahead of imaging evidence and serum tumor biomarkers.Conclusions ctDNA could effectively detect MRD and predict tumor recurrence in liver cancer patients undergone LT.

Spontaneous rupture is a fatal complication of hepatocellular carcinoma (HCC). This study compared the prognosis of spontaneously ruptured HCC (srHCC) with that of non-ruptured HCC (nrHCC). A total of 185 srHCC patients and 1085 nrHCC patients treated by hepatectomy between February 2005 and December 2017 at Zhongshan Hospital were retrospectively reviewed and enrolled. The overall survival (OS) and time to recurrence (TTR) were evaluated. A 1:2 propensity score matching (PSM) analysis was performed using the nearest neighbor matching with a caliper of 0.2. Before PSM, patients with srHCC who underwent hepatectomy (n = 185) had a poorer prognosis than those with nrHCC (n = 1085; 5-year OS, 39.1

Circulating cell-free DNA (cfDNA) is a promising biomarker for early cancer detection, and its fragmentomics features have been successfully used to detect cancer signals in blood. However, its ability to predict the tissue of origin (TOO) of cancers remains to be evaluated, which is highly desirable to differentiate the most common types of gastrointestinal (GI) cancers, including colorectal (CC), esophageal (EC), gastric (GC), liver (LC), and pancreatic cancer (PC). Whole-genome sequencing was performed for the cfDNA of 769 cancer patients (149 CCs, 137 ECs, 149 GCs, 272 LCs, and 62 PCs), to calculate the coverage at repetitive genomic regions (RepeatsCov), the depth and the cleavage diversity around transcription start sites (TSSDepth and TSSClvDiv), and the microbiome abundance (MicrobeAb). Together with other classical fragmentomics features, including copy number variation (CNV), end motif diversity (EDM), fragment size ratio (FSR), and promoter fragmentation entropy (PFE), a stacked ensemble machine learning classifier was trained and tested with sample ratio of 1:1 to predict the TOO of the GI cancers. The performance of each single feature was evaluated first, showing that the FSR model had the highest accuracy of 67.1% while the RepeatsCov model had the lowest of 53.9%. The ensemble of all the features resulted in an accuracy of 67.6%. Interestingly, a model combining MicrobeAb, RepeatsCov and FSR achieved the highest accuracy of 69.4% for all cancers (CC: 63.8%, EC&GC: 63.3%, LC: 83.6%, and PC: 43.8%), and an elevated accuracy of 87.8% to predict the top two most likely TOOs. We also trained and tested a previously reported multi-features-based model on our data, and our classifier achieved higher accuracy (69.4% vs. 60.6%). We comprehensively evaluated the classical and our newly developed cfDNA fragmentomics features in predicting the TOO of cancer signals, and showed that by combining features including MicrobeAb, RepeatsCov and FSR, we were able to maximize the accuracy in predicting GI cancers’ TOO. However, results also indicate that features should be carefully selected to avoid multicollinearity or other negative effects.

4103 Background: Hepatocellular carcinoma (HCC) is one of the most common and lethal cancers worldwide, especially in Asian counties. Patients can be treated more effectively if detected earlier, however the current screening strategies with alpha-fetoprotein (AFP) or ultrasound it is largely suboptimal. We aimed to develop non-invasive and cost-effective assay to improve HCC early detection. Methods: HCC-specific DNA methylation markers were screened from tissue and plasma samples through a modified reduce representation bisulfite sequencing assaay,and optimized by a targeted methylation sequencing assay. The most informative markers were then integrated in a multi-locus qPCR assay, HepaQ. Results: Profiling DNA methylation pattern on 61 tissue samples (31 HCC tumor and 30 normal tissues) and 663 plasma samples (276 HCC and 393 control plasma samples) achieved an AUC of 0.99, which corresponds to 91% sensitivity at 94% specificity. The best-performance markers were further screened and analytically verified in additional tissues and plasmas after several rounds of marker selection. A multi-locus qPCR assay, designated as HepaQ, was then developed to incorporate the most effective markers. A cohort of 559 plasma samples including 293 HCC (84% of them at stage 0/A), 60 liver cirrhosis (LC), 36 chronic hepatitis B (CHB) and 170 healthy controls (CTRL), were used to train a classifier for HCC early detection. HepaQ classifier enables to detect 85.3% of HCC under a specificity of 88.3%, 91.7% and 92.4% in LC, CHB and CTRL, respectively. Finally, HepaQ classifier was validated in 374 plasma samples independently collected from multiple clinical centers to confirm its performance of 87.2% sensitivity in HCC and 86.8%,90.5% and 93.4% specificities in LC, CHB and CTRL respectively. Conclusions: We have developed and demonstrated a blood-based ctDNA methylation assay, HepaQ, that can detect early-stage HCC at high sensitivity and specificity. We proposed that HepaQ assay, a cost-effective qPCR assay, has the great potential to benefit the population at-risk for HCC early detection and screening.