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  • American Society of Clinical Oncology (ASCO)  (5)
  • Zhou, Jian  (5)
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  • American Society of Clinical Oncology (ASCO)  (5)
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  • 1
    Online Resource
    Online Resource
    Detection and localization of gastrointestinal cancers based on multi-dimentional signatures from a single cfDNA targeted sequencing assay.
    American Society of Clinical Oncology (ASCO) ; 2023
    In:  Journal of Clinical Oncology Vol. 41, No. 16_suppl ( 2023-06-01), p. 4169-4169
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 41, No. 16_suppl ( 2023-06-01), p. 4169-4169
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2023.41.16_suppl.4169
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2023
    detail.hit.zdb_id: 2005181-5
    Location Call Number Limitation Availability
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  • 2
    Online Resource
    Online Resource
    GaEsSeer: Early detection of gastric and esophageal cancer by integrating methylation and fragmentomics signatures in cfDNA.
    American Society of Clinical Oncology (ASCO) ; 2023
    In:  Journal of Clinical Oncology Vol. 41, No. 16_suppl ( 2023-06-01), p. 4069-4069
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 41, No. 16_suppl ( 2023-06-01), p. 4069-4069
    Abstract: 4069 Background: Esophageal and gastric cancer (EC and GC) are two common cancer types that severely impact patients’ health. The 5-year survival rate for EC and GC is as low as 19% and 31%, respectively. However, early detection will significantly increase the survival rate: stage-1 EC has a 5-year survival rate of 51%, while for stage-1 GC it’s 69%. Invasive screening methods, such as endoscopy and biopsy, caused low compliance. Computational tomography and carcinoembryonic antigen were limited by low sensitivity. To address this problem, we developed GaEsSeer, a non-invasive targeted-sequencing-based assay that utilizes multiple methylation and fragmentomics features of cell-free DNA (cfDNA) to accurately detect EC and GC signals in blood. Methods: cfDNA was tested using the GaEsSeer panel, which was developed using in-house genome-wide sequencing data on EC and GC samples, and public datasets from databases and literature. Methylation features, which was quantified as methylation haplotypes or methylation encoding score, and fragmentomics features including copy number and end motif ratio were taken for modeling. Separate sub-models were trained utilizing each type of feature, which were eventually combined via logistic regression to establish the final predicting model. Results: A total of 1770 participants were recruited from multiple centers. This included 787 healthy individuals, 448 cancers (209 EC, 239 GC; stage I:156, -II:120, -III:78, and -IV:58), 174 benign esophageal diseases, and 361 benign gastric diseases. For cancer detection, the methylation-only model had an AUC of 0.909 and 0.897 in training (618 total) and test sets (617 total), respectively; while the AUC of the fragmentomics-based model was 0.885 and 0.911, respectively. The combinatorial model further improved performances, which achieves an AUC of 0.940 and 0.931 in the training and test cohorts, respectively. While the specificity remained at 96.7%, GaEsSeer detected 81.1% EC and 70.3% GC cases in the test cohort. It had a sensitivity of 74.2% and 48.9% for stage-I EC and GC, respectively. GaEsSeer also has high specificities of 87.9% and 89.8% for benign esophageal and gastric diseases, respectively. Additionally, the performance of GaEsSeer was compared with known serum cancer markers such as CEA, CA19-9, and CA72-4; and the results show that it had significantly higher sensitivity than any of these serum markers (54.8% vs 6.4% when against CEA; 53.5% vs 7.1% when against CA19-9; 50% and 16.7% when against CA72-4). Conclusions: In this pilot study, we developed the blood-based GaEsSeer assay and a model for EC and GC detection with high accuracy by stacking multiple methylation- and fragmentomics-based submodules together. Further optimization and validation of GaEsSeer using larger prospective cohorts are needed to validate its potentials for clinical application.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2023.41.16_suppl.4069
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2023
    detail.hit.zdb_id: 2005181-5
    Location Call Number Limitation Availability
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Early tumor marker decrease to predict the efficacy of combination therapy with lenvatinib plus anti-PD-1 antibodies in unresectable hepatocellular carcinoma (uHCC).
    American Society of Clinical Oncology (ASCO) ; 2021
    In:  Journal of Clinical Oncology Vol. 39, No. 3_suppl ( 2021-01-20), p. 304-304
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 39, No. 3_suppl ( 2021-01-20), p. 304-304
    Abstract: 304 Background: Combination therapy with anti-angiogenic agents plus anti-PD-1 antibodies has shown high anti-tumor activity in uHCC. However, predicting the efficacy of this combination therapy remains a challenge. Methods: This study included consecutive patients with uHCC who received lenvatinib (8 mg/d regardless of body weight) and an anti-PD-1 antibody as first-line systemic therapy between Sep 2018 and July 2020, and had at least one imaging evaluation. Tumor response was assessed every 2 months (± 2 week) by the investigators using modified RECIST criteria. Patients were evaluated as having a radiological response (complete or partial response) or non-radiological response (stable disease or progressive disease) at the best overall response evaluation. Serum tumor markers for HCC, including alpha-fetoprotein (AFP) and protein induced by vitamin K absence or antagonist-II (PIVKA-II), were evaluated at baseline and 2-3 weeks after therapy was initiated and their association with radiological response were assessed. Patients with baseline AFP or PIVKA-II between the upper limit of normal and the maximum measuring range of the kit were evaluable for AFP decrease or PIVKA-II decrease. Results: A total of 76 patients were eligible for this study. Baseline AFP ≥400 ng/mL or PIVKA-II ≥400 mAU/mL was not associated with radiological response (P = 0.167 and P = 0.916, respectively). At 2-3 weeks after the initiation of therapy, 51 patients were evaluable for AFP decrease; 78.4% experienced a 〉 20% AFP decrease and 51.0% experienced a 〉 50% AFP decrease. Patients with a 〉 50% AFP decrease had a higher rate of radiological response than those with AFP increase or a ≤50% AFP decrease (73.1% vs 32.0%, P = 0.003). In 57 patients evaluable for PIVKA-II decrease, 50.9% and 35.1% experienced a 〉 20% and 〉 50% PIVKA-II decrease, respectively. Patients with a 〉 50% PIVKA-II decrease had a higher rate of radiological response than those with PIVKA-II increase or a ≤50% PIVKA-II decrease (85.0% vs 29.7%, P 〈 0.001). Both AFP decrease 〉 50% and PIVKA-II decrease 〉 50% predicted radiological response, with an area under receiver operating characteristic curve of 0.706 (95% CI, 0.560-0.852, P = 0.012) and 0.752 (95% CI, 0.621-0.883, P = 0.001), respectively. Furthermore, patients with a 〉 20% AFP or PIVKA-II increase from baseline had a lower rate of radiological response (0% vs 57.4%, P = 0.043; and 21.7% vs 69.7%, P 〈 0.001, respectively). Conclusions: A tumor marker decrease was seen in most patients as early as 2-3 weeks after the initiation of the combination therapy. Early on-treatment AFP or PIVKA-II decrease may serve as a predictor for objective response for patients with uHCC receiving combination anti-angiogenic and immune therapy.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2021.39.3_suppl.304
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2021
    detail.hit.zdb_id: 2005181-5
    Location Call Number Limitation Availability
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    Online Resource
  • 4
    Online Resource
    Online Resource
    Development of DNA methylation markers for predicting the prognosis of pancreatic cancer.
    American Society of Clinical Oncology (ASCO) ; 2021
    In:  Journal of Clinical Oncology Vol. 39, No. 15_suppl ( 2021-05-20), p. e16216-e16216
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 39, No. 15_suppl ( 2021-05-20), p. e16216-e16216
    Abstract: e16216 Background: Pancreatic cancer is an extremely malignant tumor that is associated with low survival rates. Currently, TNM staging, serum CA19–9, CA125 and CEA are used to assess the risk level and estimate prognosis. However, it is still a challenge to predict survival of pancreatic cancer patients (pts) with due to the impact of wide variability of outcomes and genetic heterogeneity. In our study, we aimed to develop a model based on multiple prognostic-related methylation markers and clinical parameters to predict the overall survival (OS) of pancreatic cancer pts. Methods: A total of 50 pts with early-stage resectable pancreatic cancer were included in this study. Preoperative blood, tumor and tumor-distant normal tissue samples were obtained from the pts. Methylation levels from all samples were profiled using targeted bisulfite sequencing using bespoke pancreatic cancer methylation panel covering 80,672 CpG sites, spanning 1.05 mega bases of the human genome. To improve the linkage of methylation sites, we further analyzed the methylation profile as methylation blocks. Results: A total of 1162 tumor-specific methylation blocks, including 737 hypermethylated and 425 hypomethylated blocks were found to be differentially methylated in tumor tissues as compared to tumor-distant normal tissues (P 〈 0.05). Genes in hypermethylated blocks were significantly enriched in neuroactive ligand−receptor interaction and ca+ signaling pathways, whereas those in hypomethylated blocks were focused in E. coli infection and leukocyte transendothelial migration pathways. All these involved pathways are pancreatic cancer related. Moreover, 7 differentially methylated blocks were identified to significantly associate with OS, including 5 hyper- and 2 hypomethylated blocks. Therefore, we constructed a model based on prognostic-related blocks to predict OS of pancreatic cancer pts. A risk score was derived for each patient based on the model. Pts in the high-risk (HR) group (median risk score as cutoff) showed significantly poorer OS than those in the low-risk (LR) group in survival analysis (p = 0.0016, HR = 0.30). When clinical parameters were also considered, the risk score was found to be the only independent prognostic parameter (p 〈 0.001) by Cox regression analysis. The prognostic effect of the risk score remained significant in patient groups separated by CA125 levels (p 〈 0.01). In pre-operative blood samples, risk score was also significantly associated with OS (p = 0.031). Pts in the LR group showed significantly longer OS than those in the HR group. Conclusions: Our data revealed distinct methylation patterns between tumor tissues and tumor-distant normal tissues, suggesting tumor-specific methylation patterns could potentially be developed as diagnostic biomarkers. Importantly, our study also identified 7 blocks-based risk score that could potentially be used as prognostic markers for pancreatic cancer.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2021.39.15_suppl.e16216
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2021
    detail.hit.zdb_id: 2005181-5
    Location Call Number Limitation Availability
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    Online Resource
  • 5
    Online Resource
    Online Resource
    Early detection and localization of major gastrointestinal cancers by combining DNA methylation and fragmentomic signatures generated from a single cfDNA targeted sequencing assay.
    American Society of Clinical Oncology (ASCO) ; 2023
    In:  Journal of Clinical Oncology Vol. 41, No. 4_suppl ( 2023-02-01), p. 330-330
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 41, No. 4_suppl ( 2023-02-01), p. 330-330
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2023.41.4_suppl.330
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2023
    detail.hit.zdb_id: 2005181-5
    Location Call Number Limitation Availability
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    Online Resource
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