Journal Thoracic Oncology

Abstracts Journal of Thoracic Oncology • Volume 12 Issue S1 January 2017
to targeted therapy. Other simple and innovative tools for monitoring of
treatment response are also being explored. For example, recent data showed
that breath analysis using nanoarray technology may represent a quick and
patient-friendly monitoring tool for earlier recognition of treatment failure
and thus potentially serve as a surrogate marker for response to lung cancer
therapy. Monitoring of treatment-related toxicity Subjective toxicity -which
cannot be assessed by current toxicity scales- is frequently under-reported
in clinical trials, with important negative implications on drug safety
evaluations and patient care in general. Recent data highlight the need to
improve the current system of toxicity assessment in the trial setting, mainly
via implementation of patient-reported outcomes (PROs). Self-reported
(and, ideally, real-time) monitoring of toxicity is increasingly investigated
in the setting of routine oncology practice as well. As of yet it remains to be
firmly established whether this system may significantly contribute to earlier
identification and better management of adverse events, improved patientphysician
communication and higher quality of life. Suggested reading
1.Nishino M, Hatabu H, Johnson BE, McLoud TC. State of the art: response
assessment in lung cancer in the era of genomic medicine. Radiology 2014;
271: 6-27. 2.Bennett CW, Berchem G, Kim YJ, El-Khoury V. Cell-free DNA and
next-generation sequencing in the service of personalized medicine for lung
cancer. Oncotarget 2016; doi: 10.18632/oncotarget.11717. 3.Nishino M, Giobbie-
Hurder A, Gargano M, Suda M, Ramaiya NH, Hodi FS. Developing a common
language for tumor response to immunotherapy: immune-related response
criteria using unidimensional measurements. Clin Cancer Res 2013; 19: 3936-
43. 4.Di Maio M, Basch E, Bryce J, Perrone F. Patient-reported outcomes in the
evaluation of toxicity of anticancer treatments. Nat Rev Clin Oncol 2016; 13:
319-25.
Keywords: clinical trials, Monitoring, treatment outcome, oncology practice
SESSION MTE21: NEXT GENERATION SEQUENCING (TI-
CKETED SESSION)
WEDNESDAY, DECEMBER 7, 2016 - 07:30-08:30
MTE21.01 NEXT GENERATION SEQUENCING
Ignacio Wistuba 1 , Xuefei Li 2
1 Translational Molecular Pathology, The University of Texas MD Anderson
Cancer Center, Houston/United States of America, 2 Medical Oncology, Shanghai
Pulmonary Hospital, Shanghai/China
Non small cell lung cancer(NSCLC) with sensitive epidermal growth factor
receptor (EGFR) mutations invariably develop resistance to EGFR tyrosine
kinase inhibitors (TKIs). 20%-30% of NSCLC patients haboring sensitive
mutations have no good initial clinical response to EGFR-TKIs, which is defined
as having intrinsic resistance to EGFR-TKIs; while the rest of patients with
activating mutations who are initially responsive to EGFR-TKIs eventually
develop acquired resistance after 10–12 months of consistent clinical beneft,
followed by disease progression. The drug resistance is a really tough and
urgent clinical problem. Part of resistant mechanisms have been reported,
including BIM deletion polymorphism, combined with other bypass signal
pathway activation, epithelial-mesenchymal transition (EMT) for primary
resistance; T790M, cMET amplification, SCLC transformation for acquired
resistance. However, partial resistant mechanisms still unknown. In contrast
to acquired resistance to EGFR-TKIs, intrinsic resistance is more complicated.
Next-generation sequencing (NGS) is a promising tool for analysis of tumor
mutations. We aimed to investigate the intrinsic resistant mechanisms to
EGFR-TKIs by NGS, further to optimize treatment strategies and improve
clinical outcome in EGFR activating mutant patients having intrinsic
resistance to EGFR-TKIs. At present, the study is underway, and the results
will be presented at the 2016 WCLC.
Keywords: next-generation sequencing, NSCLC, EGFR-TKIs, drug resistance
SESSION MTE22: PERSPECTIVES IN LUNG CANCER
IMAGING (TICKETED SESSION)
WEDNESDAY, DECEMBER 7, 2016 - 07:30-08:30
MTE22.01 PERSPECTIVES IN LUNG CANCER IMAGING
Thomas Henzler
Institute of Clinical Radiology and Nuclear Medicine, University Medical Center
Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim/Germany
Lung cancer is still the leading cause of cancer-related death in both men
and women with 80% to 85% of cases being non-small-cell lung cancer
(NSCLC). 1 Over the past years, the IASLC Staging and Prognostic Factors
Committee has collected a new database of 94,708 cases of lung cancer as
the backbone for the upcoming 8 th edition of the TNM classification for lung
cancer due to be published late 2016 2,3 . The 8 th edition will significantly
impact lung cancer staging with CT and/or PET-CT due to the subclassification
of T1 and T2 into a,b and c categories, the reclassification of tumors more than
5 cm but not more than 7 cm in greatest dimension as T3, the reclassification
of tumors more than 7 cm in greatest dimension as T4, the grouping of the
involvement of the main bronchus as a T2 descriptor, regardless of distance
from the carina, but without invasion of the carina, the grouping of partial
and total atelectasis or pneumonitis as a T2 descriptor, the reclassification of
diaphragm invasion as T4 and the elimination of mediastinal pleura invasion
as a T descriptor 2,3 . Moreover, the upcoming 8 th edition will also lead to a novel
classification of distant metastasis, in which single extrathoracic metastasis
will be classified as M1b whereas multiple extrathoracic metastasis are
classified as M1c. The changes made within the proposal of the 8 th edition of
the TNM will be discussed within the presentation using clinical examples.
Beside the accurate staging of patients with lung cancer early detection
using CT screening with novel low radiation dose CT technologies will also
be discussed. Within this context, a special focus will be given on novel
methods that may improve a more accurate characterization of detected
lung nodules using deep machine learning and Radiomics. Radiomics refers to
the comprehensive quantification of lung nodule and tumour phenotypes by
applying a large number of quantitative image features that are standardized
collected with specific software algorithms. Radiomics features have the
capability to further enhance imaging data regarding prognostic tumour
signatures, detection of tumour heterogeneity as well as the detection of
underlying gene expression patterns which is of special interest in patients
with metastatic disease. The third part of the presentation will focus on
novel techniques in lung cancer imaging. The past fifteen years have brought
significant breakthroughs in the understanding of the molecular biology of
lung cancer. Signalling pathways and genetic driver mutations that are vital
for tumour growth have been identified and can be effectively targeted by
novel pharmacologic agents, resulting in significantly improved survival of
patients with lung cancer 4 . Parallel to the progress in lung cancer treatment,
imaging techniques aiming at improving diagnosis, staging, response
evaluation, and detection of tumour recurrence have also considerably
advanced in recent years 5 . However, standard morphologic computed
tomography (CT) and magnetic resonance imaging (MRI) as well as fluor-18-
fluorodeoxyglucose ( 18 F-FDG) positron emission tomography CT (PET-CT)
are still the currently most frequently utilized imaging modalities in clinical
practice and most clinical trials 6,7 . Novel state-of-the-art functional imaging
techniques such as dual-energy CT (DECT), dynamic contrast enhanced CT
(DCE-CT), diffusion weighted MRI (DW-MRI), perfusion MRI, and PET-CT with
more specific tracers that visualize angiogenesis, tumour oxygenation or
tumour cell proliferation have not yet been broadly implemented, neither in
clinical practice nor in phase I–III clinical trials. In this context, Nishino et al. 4
published an article on personalized tumour response assessment in the era
of molecular treatment in oncology. The authors showed that the concept
of personalized medicine with regard to cancer treatment has been well
applied in therapeutic decision-making and patient management in clinical
oncology. With regard to imaging techniques, however, it was criticized
that the developments in tumour response assessment that should parallel
the advances in cancer treatment are not sufficient to produce state-ofthe-art
functional information that directly reflect treatment targets.
Functional information on tumour response is highly required because
there is growing evidence that the current objective criteria for treatment
response assessment may not reliably indicate treatment failure and do
not adequately capture disease biology. Molecular-targeted therapies and
novel immunotherapies induce effects that differ from those induced by
classic cytotoxic treatment including intratumorale haemorrhage, changes
in vascularity, and tumour cavitation. Thus, conventional approaches for
therapy response assessment such as RECIST or WHO criteria that exclusively
focus on the change in tumour size are of decreasing value for drug response
assessment in clinical trials 8,9 . In summary, the aim of of this presentation
is to provide an overview on the changes made within the upcoming 8 th of
the TNM classification as well as to provide an overview on state-of-the-art
imaging techniques for lung cancer screening, staging, response evaluation
as well as surveillance in patients with lung cancer. The various techniques
will be discussed regarding their pros and cons to further provide functional
information that best reflects specific targeted therapies including antiangiogenetic
treatment, immunotherapies and stereotactic body radiation
therapy. Literature: 1. Rami-Porta R, Crowley JJ, Goldstraw P. The revised TNM
staging system for lung cancer. Ann Thorac Cardiovasc Surg 2009;15:4-9. 2.
Asamura H, Chansky K, Crowley J, et al. The International Association for the
Study of Lung Cancer Lung Cancer Staging Project: Proposals for the Revision
of the N Descriptors in the Forthcoming 8th Edition of the TNM Classification
for Lung Cancer. Journal of thoracic oncology : official publication of the
International Association for the Study of Lung Cancer 2015;10:1675-84. 3.
Rami-Porta R, Bolejack V, Crowley J, et al. The IASLC Lung Cancer Staging
Project: Proposals for the Revisions of the T Descriptors in the Forthcoming
Eighth Edition of the TNM Classification for Lung Cancer. Journal of thoracic
oncology : official publication of the International Association for the Study
S88 Journal of Thoracic Oncology • Volume 12 Issue S1 January 2017