Virtual Library

 x 
My Library Virtual Library FAQ

Start Your Search

C.L. Fhied



Author of

  • +

    MINI 29 - Meta Analyses and Trial Conduct (ID 156)

    • Event: WCLC 2015
    • Type: Mini Oral
    • Track: Treatment of Advanced Diseases - NSCLC
    • Presentations: 1
    • +

      MINI29.03 - Prognostic Value of Biomarkers Associated with Glucose Metabolism and Systemic Inflammation in Advanced On-Small Cell Lung Cancer (NSCLC) (ID 3061)

      18:30 - 20:00  |  Author(s): C.L. Fhied

      • Abstract
      • Presentation
      • Slides

      Background:
      Alterations in glucose metabolism and appetite stimulating hormones have been correlated with inflammation but there is little information on frequency and prognosis in newly diagnosed stage IV non-small cell lung cancer (NSCLC) This study objective was to identify associations of circulating biomarkers of glucose metabolism and inflammation with prognosis in pre-treatment sera from stage IV NSCLC patients selected for platinum doublet based chemotherapy.

      Methods:
      Pretreatment serum from 118 Pts with frontline stage IV NSCLC were evaluated with the Bio-Plex Pro Human Diabetes Assay panel (adiponectin, adipsin, c-peptide, ghrelin, gastrin inhibitory peptide (GIP), glucagon-like peptide-1 (GLP-1), glucagon, IL-6, insulin, leptin, Plasminogen activator inhibitor-1, resistin, TNFα, vistatin) and HSTCMAG-28SK | MILLIPLEX MAP Human High Sensitivity T Cell Panel - Immunology Multiplex Assay (Fractalkin, GM-CSF, IFNγ, IL-1 β, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p70), IL-13, IL-17A, IL-21, IL-23, ITAC, macrophage inflammatory protein (MIP)-1α, MIP-1β, MIP-3α, TNFα) on a FlexMAP 3D system (Luminex Corp.). Pts were treated with standard platinum doublets based chemotherapy. Associations of biomarkers with progression free and overall survival (PFS,OS) outcomes were assessed using multivariate Cox PH analyses.

      Results:
      Most patients had metabolic levels below the prognostic threshold. However, high levels of insulin, GIP, glucagon, visfatin, ghrelin, GLP-1 were significantly associated (p<0.05) with shorter PFS. Low levels of adipisin (deficiency of which is associated with obesity) was associated with shorter PFS (p=.0185). High levels of pro-inflammatory markers: ITAC, GM-CSF, Fratalkine, INF-ϒ, IL-12p70, IL-13, IL17A, IL-4, IL-23, IL8.4, MIP-α, MIP-1 were also associated with poor PFS (p<0.05) (See Table I for more details on select biomarkers) High levels of these endocrine markers (except insulin and GIP) were associated with shorter OS as were ITAC, GMCSF, IL12p70, IL-13, IL4, IL23, IL5 (p<0.05). Table I. Biomarker correlation with progression free survival

      Marker Cutoff-pg/mL N < N > Median PFS < Median PFS> Logrank p
      Insulin 1004.9 82 36 6.08 4.04 0.026161
      Glucagon 361.2 110 8 5.46 1.71 0.010219
      Visfatin 8298.3 109 9 5.65 1.45 8.77E-06
      Ghrelin 2897.2 104 14 6.02 2.12 0.009423
      GLP.1 268.8 109 9 5.65 1.97 0.000618
      ITAC 104.7 99 19 5.82 2.96 0.012529
      Fractalkine 271.7 97 21 6.08 3.16 0.0067
      IL.12.p70. 17.0 109 9 5.65 3.16 0.010631
      IL.13 14.9 105 13 5.82 2.76 0.001533
      IL.17A 49.4 102 16 5.82 3.65 0.004862
      IL.4 66.1 104 14 6.02 2.96 0.000917
      IL.8.4 3.0 25 93 12.8 4.8 0.008985


      Conclusion:
      Imbalances in the glucose metabolism pathway and increased levels of pro-inflammatory circulating markers were uncommon but consistently associated with a poor prognosis in stage IV NSCLC patients early in their treatment cycle. Alterations in these systems have been associated with cancer cachexia and may be targets for intervention in improving prognosis for select patients with NSCLC.

      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.

  • +

    P2.06 - Poster Session/ Screening and Early Detection (ID 219)

    • Event: WCLC 2015
    • Type: Poster
    • Track: Screening and Early Detection
    • Presentations: 1
    • +

      P2.06-012 - A Model Incorporating Clinical, Radiographic, and Biomarker Characteristics Predicts Malignancy in Indeterminate Pulmonary Nodules (ID 2890)

      09:30 - 17:00  |  Author(s): C.L. Fhied

      • Abstract
      • Slides

      Background:
      The high false-positive rate associated with low-dose computed tomography (CT) lung cancer screening results in unnecessary testing, cost, and patient anxiety. We hypothesized that an algorithm incorporating clinical, radiographic, and serum biomarker data would be capable of differentiating benign from malignant pulmonary nodules.

      Methods:
      An institutional biorepository was used to identify 84 patients with ≤ 2 cm indeterminate pulmonary nodules identified on CT scan, including 50 patients with biopsy-proven, node-negative, non-small cell lung cancer (NSCLC) and 34 patients with benign, non-calcified, solitary pulmonary nodules. Clinical and radiographic data were collected from patient charts and imaging studies. Serum specimens were evaluated in a blinded manner for 55 biomarkers using multiplex immunoassays. Random forest analyses were used to generate a multivariate cross-validation prediction model incorporating clinical, radiographic, and serum biomarker data.

      Results:
      A total of 84 patients were identified with a median nodule size of 5 mm for benign nodules and 15 mm for NSCLC. Median smoking histories were 21 and 28 pack-years and patient age was 62 and 70 years, respectively. An algorithm incorporating serum biomarker profile (IGFBP-4, IGFBP-5, IL-10, IL-1ra, IL-6, SDF-1alpha, IGF-2), age, sex, BMI, COPD, smoking history, hemoptysis, previous cancer, nodule size, nodule location, spiculation, nodule type, and nodule count provided the optimal performance with a sensitivity 92%, specificity 65%, NPV 85%, and PPV 79%. This model performed with an overall accuracy of 81% with a cross-validated AUC=0.904.

      Conclusion:
      An algorithm incorporating clinical, radiographic, and serum biomarker characteristics may help differentiate benign from malignant pulmonary nodules. This model is currently being externally validated in a second-site patient cohort.

      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.