Journal Thoracic Oncology

Abstracts Journal of Thoracic Oncology • Volume 12 Issue S1 January 2017
factor for patients with operable NSCLC, and a higher NAR indicates a poorer
prognosis.
Keywords: Non-small-cell lung cancer, Neuron-specific Enolase, albumin,
Prognosis
POSTER SESSION 1 - P1.05: EARLY STAGE NSCLC
TRANSLATIONAL RESEARCH & BIOMARKERS –
MONDAY, DECEMBER 5, 2016
P1.05-025 PROGNOSTIC SIGNIFICANCE OF HEPATITIS B VIRUS TO
STAGE IB NON-SMALL CELL LUNG CANCER PATIENTS IN CHINA
Shun Lu, Ziming Li
Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong
University, Shanghai/China
Background: Hepatitis B virus (HBV) is considered to be a major cause of
hepatocellular carcinoma. However, little is known about the role of chronic
HBV infection in other malignancies. We aimed to determine HBV infection
with other well established prognostic factors and performed multivariate
survival analyses to evaluate its value in Chinese non-small cell lung cancer
(NSCLC) patients. Methods: It is a retrospective evaluation of the impact of
HBV infection status in 366 patients who underwent complete surgical
resection for stage IB NSCLC NSCLC patients in Shanghai Chest Hospital from
1998 to 2008. All the patients were Shanghai Niece and all the stage IB NSCLC
patients didn’t receive adjuvant chemotherapy. The patients’ blood samples
were tested with chemiluminescent immunoassay for the presence of HBV
surface antigen (HBsAg), antibodies against HBV core antigen (anti-HBc), and
antibodies against HBsAg (anti-HBs) before operation. Other variables in the
analysis included age, gender, history of smoking and pathologic type. HBsAg
positive was definite as HBV infection. Results:
for searching potential biomarkers in lung cancer patient. Methods: EBC
sample collected using specific device called R-tube, containing both the
volatile organic compounds (VOCs) and non-volatile organic compounds
(NVOCs), were obtained from patients with lung cancer (n = 20) and control
healthy individuals (n = 5). The EBC samples were applied to high resolution
metabolomics (HRM) based LC-MS for comparison of metabolic differences
among healthy people and lung cancer patients in order to detect potential
biomarkers. The multivariate statistical analysis was performed, including a
false discovery rate (FDR) of q=0.05, to determine the significant metabolites
between the groups. 2-way hierarchical clustering analysis (HCA) was done
for determining the classification of significant features between the control
healthy and lung cancer patients. The significant features were annotated
using Metlin database (metlin.scripps.edu/) and the identified features were
then mapped on the human metabolic pathway of the Kyoto Encyclopedia
of Genes and Genomes (KEGG). This study was approved by Korea University
Guro Hosipital Institutional review board (KUGH14273) Results: Using
metablomics-wide associated study (MWAS), metabolic changes among
healthy group and lung cancer patients were determined. The 2-way HCA
identified the different metabolic profile in lung cancer patients from healthy
control. The identified potential biomarkers are Acetophenone (m/z 103.0542,
[M+H-H 2
O] + ), P-tolualdehyde (m/z 138.0914, [M+NH4] + ), 2,4,6-Trichlorophenol
(m/z 218.9134, [M+Na] + ) and 11(R)-HETE (m/z 343.2233, [M+Na] + ). The
top 5 of affected KEGG pathways are Arachidonic acid metabolism,
Glycerophospholipid metabolism, Bile secretion, Inflammatory mediator
regulation of TRP channels and Tyrosine metabolism. Conclusion: Our result
shows that Acetophenone, P-tolualdehyde, 2,4,6-Trichlorophenol and 11(R)-
HETE are significantly higher in lung cancer patients. Acetophenone and
2,4,6-Trichlorophenol are classified as a Group D human carcinogen approved
by US Environmental Protection Agency (EPA), while 11(R)-HETE is associated
with Arachidonic acid metabolism and P-tolualdehyde is related to xylene
degradation pathway and degradation of aromatic compounds pathway. Our
identified metabolites can be the potential biomarkers in EBC for the early
and non-invasive detection of lung cancer.
Keywords: exhaled breath condensate, metabolomics, Liquid
chromatography (LC-MS/MS), lung cancer
POSTER SESSION 1 - P1.05: EARLY STAGE NSCLC
TRANSLATIONAL RESEARCH & BIOMARKERS –
MONDAY, DECEMBER 5, 2016
51 HBV infection cases (13.93%) were positive in stage IB NSCLC. The 5-year
overall survival of patients without or with chronic HBV infection were 71.25%
and 50.98% (P=0.028). Multivariate analyses revealed that gender, chronic
HBV infection were significant predictive factors for overall survival (P< 0.05).
Conclusion: The chronic HBV infection is a significant independent prognostic
factor in stage IB non-small cell lung cancer.
Keywords: Hepatitis B Virus, non-small cell lung cancer
POSTER SESSION 1 - P1.05: EARLY STAGE NSCLC
TRANSLATIONAL RESEARCH & BIOMARKERS –
MONDAY, DECEMBER 5, 2016
P1.05-026 HIGH RESOLUTION METABOLOMICS ON EXHALED
BREATH CONDENSATE TO DISCOVER LUNG CANCER’S BIOMARKER
Chang Young Park 1 , Adnan Khan 1 , Aryo Pamungkas 1 , Eun Jung Sim 2 , Kyung Ho
Kang 2 , Sung Yong Lee 2 , Youngja Hwang Park 1
1 College of Pharmacy, Korea University, Sejong City/Korea, Republic of, 2 Korea
University Medical Center, Seoul/Korea, Republic of
Background: Early and non-invasive detection of lung cancer is a desirable
prognostic tool for prevention of lung cancer at early stages. Previously,
unusual human breath smell of lung cancer patients detected by trained
dogs played an important role in early detection of lung cancer. Which
suggests that exhaled breath condensate (EBC) is a promising source
P1.05-027 NOVEL PROGNOSTIC GENE EXPRESSION SIGNATURES
FOR SQUAMOUS CELL LUNG CARCINOMA: A STUDY BY THE SPECS
LUNG CONSORTIUM
Raphael Bueno 1 , William Richards 2 , David Beer 3 , Karla Ballman 4 , Ming Tsao 5 ,
Frances Shepard 5 , Daniel Merrick 6 , Adriaan Van Bokhoven 6 , Wilbur Franklin 6 ,
Ramaswamy Govindan 7 , Mark Watson 8 , David R. Gandara 9 , David Harpole 10 ,
Zhengming Chen 11 , Guoan Chen 12 , Lucian Chirieac 2 , Herman Chui 5 , Carlo
Genova 13 , Mary-Beth Joshi 10 , Ashley Kowalewski 6 , Mark Onaitis 10 , Christopher
Rivard 6 , Thomas Sporn 10 , Fred R. Hirsch 6
1 Division of Thoracic Surgery, Brigham and Women’s Hospital/ Harvard Medical
School, Boston/MA/United States of America, 2 Brigham and Women’s Hospital,
Boston/MA/United States of America, 3 University of Michigan, Ann Arbor/MI/
United States of America, 4 Biostatistics and Epidemiology, Weill Cornell University,
New York/NY/United States of America, 5 Princess Margaret Hospital, Toronto/
ON/Canada, 6 Division of Medical Oncology, University of Colorado Anschutz
Medical Campus, Aurora/CO/United States of America, 7 Medical Oncology,
Washington University School of Medicine, St. Louis/MO/United States of
America, 8 Washington University School of Medicine, St. Louis/MO/United States
of America, 9 Uc Cancer Center, University of California Davis, Sacramento/CA/
United States of America, 10 Department of Surgery, Duke University Medical
Center, Durham/NC/United States of America, 11 Weill Cornell University, New York/
NY/United States of America, 12 6304 Cancer Center, U. of Michigan, University of
Michigan, Ann Arbor/MI/United States of America, 13 U.O.S. Tumori Polmonari, IRCCS
San Martino-Ist Istituto Nazionale Per La Ricerca Sul Cancro, Genova/Italy
Background: A multi-institutional squamous lung cancer consortium of
investigators is developing prognostic signatures through the US NCI Lung
SPECS (Strategic Partnership for Evaluation of Cancer Signatures) program.
Six institutions contributed tumor specimens and published/unpublished
expression-based prognostic signatures for validation using standardized
sample cohorts (a primary validation cohort comprising institutional cases,
and additional validation cohorts from two prospective cooperative group
studies) and quality controlled assessment in independent laboratory and
statistical cores. Here, we report on de novo prognostic signatures derived
using the pooled institutional dataset. Methods: Highly quality-controlled
cases of primary SCC from the pooled cohort (N=249) were analyzed to
generate de novo prognostic signatures from among the 147 genes comprising
pre-existing signatures, and from among all profiled genes. Minimax Concave
Penalty (MCP) selection and Ward’s minimum variance clustering yielded
survival analyses with 2 clusters that were evaluated using Cox regression and
bootstrap cross validation (bCV; 500 iterations). Results: Two significantly
S324 Journal of Thoracic Oncology • Volume 12 Issue S1 January 2017