Virtual Library

Start Your Search

Ben-Yuan Jiang



Author of

  • +

    MA16 - Novel Mechanisms for Molecular Profiling (ID 917)

    • Event: WCLC 2018
    • Type: Mini Oral Abstract Session
    • Track: Advanced NSCLC
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/25/2018, 13:30 - 15:00, Room 203 BD
    • +

      MA16.10 - Clinical Utility of Cerebrospinal Fluid Cell-Free DNA for Clarifying Genetic Features of Leptomeningeal Metastases in ALK Rearrangement NSCLC (ID 12142)

      14:35 - 14:40  |  Author(s): Ben-Yuan Jiang

      • Abstract
      • Presentation
      • Slides

      Background

      Leptomeningeal metastases (LM) were associated with a poor prognosis in non small cell lung cancer (NSCLC). LM were much more frequent in EGFR mutant patients, and cerebrospinal fluid (CSF) cell-free DNA (cfDNA) has shown unique genetic profiles of LM in patients harboring EGFR mutations in our previous studies. However, studies in ALK positive NSCLC patients with LM are scarce.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      Lung cancer patients with ALK rearrangement were screened from Sept 2011 to Feb 2018 at our institute. Leptomeningeal metastases were diagnosed by MRI or CSF cytology or next-generation sequencing (NGS) of CSF cfDNAs. Paired plasma were also tested by NGS.

      4c3880bb027f159e801041b1021e88e8 Result

      LM were diagnosed in 22 (7.6%) of 288 ALK rearrangement patients with lung cancer. A total of 11 ALK positive patients with LM were enrolled with CSF cfDNA tested by NGS (one case used CSF precipitates instead of CSF cfDNA). Paired plasma were available in 11 patients. Driver genes were detected in 75.0% CSF samples and 45.5% plasma respectively (P=0.214). Max allele fractions were higher in CSF cfDNA than in plasma (40.8% versus 0%, P=0.021). ALK variant 1 (E13:A20) was detected in 3 cases of CSF and paired plasma, respectively. ALK variant 2 (E20:A20) was identified in 5 cases of CSF and 1 paired plasma. Multiple copy number variants (CNV) were mainly found in CSF cfDNA, including EGFR copy number gains. Resistance mutations including gatekeeper gene ALK G1202R was identified in CSF cfDNA with ALK variant 1 and ALK G1269A was detected in plasma. The detection rate of TP53 was 45.4% versus 27.3% in CSF cfDNA and plasma.

      figures.jpg

      8eea62084ca7e541d918e823422bd82e Conclusion

      CSF cfDNA was more sensitive than plasma to reveal genetic features of ALK-fusion LM, confirming its role as a liquid biopsy medium for LM in driver gene positive NSCLC.

      6f8b794f3246b0c1e1780bb4d4d5dc53

      Only Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login, select "Add to Cart" and proceed to checkout. If you would like to become a member of IASLC, please click here.

      Only Active Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login or select "Add to Cart" and proceed to checkout.

  • +

    P1.01 - Advanced NSCLC (Not CME Accredited Session) (ID 933)

    • Event: WCLC 2018
    • Type: Poster Viewing in the Exhibit Hall
    • Track:
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/24/2018, 16:45 - 18:00, Exhibit Hall
    • +

      P1.01-55 - Unique Genetic Profiles from Cerebrospinal Fluid Could Predict Survival of EGFR-Mutant NSCLC with Leptomeningeal Metastases (ID 12369)

      16:45 - 18:00  |  Author(s): Ben-Yuan Jiang

      • Abstract
      • Slides

      Background

      Leptomeningeal metastases (LM) are more frequent in NSCLC with EGFR mutations;and cerebrospinal fluid (CSF) could reveal the unique genetic profiles of LM in our previous studies, but whether they could predict the overall survival (OS) of LM remains unknown.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      EGFR-mutant NSCLC patients with LM were enrolled,and clinical data and genetic profiles detected by Next-generation sequencing were collected. We further drew nomogram with endpoint of OS after LM, then performed index of concordance (C-index) and survival analysis to evaluate predictive role.

      4c3880bb027f159e801041b1021e88e8 Result

      In total, 61 patients were enrolled and all with genetic profiles from CSF. Patents with high copy number variations (CNVs) or harboring CDK6, TP53 exon5 or FGF19 in CSF demonstrated significant poorer OS than those without (Fig. 1). Cox regression analysis indicated CNVs, CDK6,CDKN2A,TP53,MET and NTRK1 as prognostic factors and further selected for nomogram (Fig. 2). C-index of nomogram was 0.743, indicating the moderate predictive effect. In the calibration curves, we scored the patients based on the model, using bisection and trisection methods to divide into low and high points groups; and low, medium and high points groups (Fig. 3), and significant difference were found in both the survival analyses (NA versus 7.47months, P<0.01) and (NA, 10.33 versus 4.43 months, P<0.01) respectively. Patients who received Osimertinib after LM seemed to have longer OS than those who did not (14.5 months versus 7.7 months) but without significant difference(P=0.10); however interestingly, in those with EGFR T790M negative who took Osimertinib after LM by themselves obtained survival benefit than those who did not(NA versus 7.7 months, P=0.04), and the results needed to be validated.

      figure.jpg

      8eea62084ca7e541d918e823422bd82e Conclusion

      Unique genetic profiles from CSF could well predict OS of LM. High CNVs, CDK6, TP53 exon5 or FGF19 in CSF in CSF may be related to poor prognosis of LM.

      6f8b794f3246b0c1e1780bb4d4d5dc53

      Only Active Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login or select "Add to Cart" and proceed to checkout.

  • +

    P1.11 - Screening and Early Detection (Not CME Accredited Session) (ID 943)

    • Event: WCLC 2018
    • Type: Poster Viewing in the Exhibit Hall
    • Track:
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/24/2018, 16:45 - 18:00, Exhibit Hall
    • +

      P1.11-18 - A Classification-Based Machine Learning Method Reveals Exosomal miRNA Biomarkers for Patients with Pulmonary Ground Glass Nodule (ID 12462)

      16:45 - 18:00  |  Author(s): Ben-Yuan Jiang

      • Abstract

      Background

      Non-invasive detection of lung cancer is of critical importance but has proven challenging due to the rate of false-negative diagnosis with current tests. Plasma exosomes have been implicated as a non-invasive diagnostic source. However, little high throughput screening has been done in the early-stage lung cancer and problems such as bias of enrollment, less rigorous identification exists. This study aimed to reveal the plasma exosome-derived miRNA biomarkers for early-stage lung cancer patients, especially those with ground glass nodule (GGN).

      a9ded1e5ce5d75814730bb4caaf49419 Method

      Pre-operative and paired post-operative plasma samples from patients with solitary pulmonary nodule and healthy volunteers were prospectively collected. Finally 38 malignant nodules, 7 benign nodules and 5 healthy volunteers were enrolled. The malignant nodules included 9 pure GGNs, 11 mixed GGNs and 18 solid nodules. Exosomes were collected from 1mL plasma and were isolated with 3D Medicine EV isolation kit. Exosomal miRNA profiling was performed using miRNA-seq. And an exosomal miRNA diagnostic model for patients with malignant nodules was constructed by using support vector machine (SVM).

      4c3880bb027f159e801041b1021e88e8 Result

      In general, malignant nodules, benign nodules and healthy volunteers were indistinguishable based on overall clustering. Regarding to malignant nodules, pure GGNs and solid nodules could be separated under principal component analysis (PCA), and the mixed GGNs presented a transitional state between the pure GGNs and the solid nodules. Ultimately, a two-dimensional SVM diagnostic model for discriminating malignant and benign nodules was established. The optimal miRNA combination could reach an area under curve (AUC) of 0.96, with sensitivity and specificity of 94.7% and 91.7%, respectively.

      8eea62084ca7e541d918e823422bd82e Conclusion

      This preliminary analysis highlights the potential of exosomal miRNA based liquid biopsy for non-invasive detection of early-stage lung cancer. The SVM model seems could effectively distinguish pulmonary nodules, but needs further verified.

      fig.png

      6f8b794f3246b0c1e1780bb4d4d5dc53

  • +

    P2.01 - Advanced NSCLC (Not CME Accredited Session) (ID 950)

    • Event: WCLC 2018
    • Type: Poster Viewing in the Exhibit Hall
    • Track:
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/25/2018, 16:45 - 18:00, Exhibit Hall
    • +

      P2.01-52 - Identification of Leptomeningeal Metastasis-Specific Exosomal miRNA Signatures in Cerebrospinal Fluids of NSCLC Patients (ID 13074)

      16:45 - 18:00  |  Author(s): Ben-Yuan Jiang

      • Abstract
      • Slides

      Background

      Leptomeningeal metastasis (LM) is a devastating complication with poor prognosis in non-small-cell lung cancer (NSCLC) patients. The confirmed diagnosis of LM usually involves neurological evaluation, MRI imaging, and cytopathology analysis of limited tumor cells from cerebrospinal fluid (CSF). Exosomes are extracellular vesicles in body fluids enriched with microRNAs (miRNAs), which have been implicated to participate in brain metastasis. Here, we aimed to identify LM-specific exosomal miRNA signatures in NSCLC patients to elucidate their potential role in LM mechanism and to predict LM via liquid biopsy.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      Exosomes prepared from CSF and plasma samples of 39 advanced NSCLC patients with (LM+) or without (LM-) LM as well as 12 non-cancer individuals (NC) were underwent small RNA next-generation sequencing. For patients in the LM+ group, paired plasma samples were taken before (PLM+pre) and upon (PLM+post) LM diagnosis. Exosomal miRNA profiles were subjected for differential expression analysis, pathway enrichment analysis, and signature discovery.

      4c3880bb027f159e801041b1021e88e8 Result

      Unsupervised hierarchical clustering of the miRNA expression profiles clearly separated CSF samples into LM+ and LM free groups (LM- and NC). Interestingly, these samples were stratified based on their LM status only, regardless of their intraparenchymal metastasis status. In total, 247 (185 up and 62 down-regulated) miRNAs were identified differentially presented in the LM+ CSF exosome samples compared to the LM- and NC groups. Top altered miRNAs include dramatically up-regulated miR-200 family and down-regulated miR-144/451 cluster. Predicted gene targets of these top-regulated miRNAs were significantly enriched in Ras/MAPK/PI3K-AKT signaling, endocytosis pathways, and so on. Promisingly, a signature of five CSF exosomal miRNAs (let-7e-5p, miR-28-3p, miR-375, miR-200a-3p, and miR-486-5p) was identified for classification of LM+ patients with 100% sensitivity and 100% specificity. Due to the higher background complexity, we only identified one miRNA (miR-24-3p) was significantly up-regulated and one miRNA (miR-92b-5p) was significantly down-regulated in LM+ patients’ plasma-derived exosomes (PLM+pre and PLM+post) compared with the LM free group (PLM- and PNC). However, a combined signature of seven miRNAs (miR-24-3p, miR-223-3p, miR-340-5p, miR-27a-3p, miR-423-5p, miR-2110 and miR-342-5p) from PLM+pre samples was identified for the prediction of future LM with 81% sensitivity and 76% specificity.

      8eea62084ca7e541d918e823422bd82e Conclusion

      NSCLC patients with LM present a remarkably distinct CSF exosomal miRNA signature, which may involve in the progression of LM, and can be used as diagnostic biomarkers for LM. Furthermore, the identification miRNA signature in the pre-LM plasma samples suggests the potential use of liquid biopsy to predict LM for better patient care.

      6f8b794f3246b0c1e1780bb4d4d5dc53

      Only Active Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login or select "Add to Cart" and proceed to checkout.