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K. Park
Moderator of
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MS07 - Epidemiology and Prevention (ID 24)
- Event: WCLC 2013
- Type: Mini Symposia
- Track: Prevention & Epidemiology
- Presentations: 4
- Moderators:K. Park, N. Van Zandwijk
- Coordinates: 10/28/2013, 14:00 - 15:30, Bayside Auditorium B, Level 1
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MS07.1 - Successful Tobacco Control Approaches in the 21st Century (ID 486)
14:05 - 14:25 | Author(s): M.A. Steliga
- Abstract
- Presentation
Abstract
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MS07.2 - Comorbidity & Competing Causes of Death in Lung Cancer Patients (ID 487)
14:25 - 14:45 | Author(s): M. Janssen-Heijnen
- Abstract
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Abstract
Background Over fifty percent of all newly diagnosed lung cancer patients are aged over 65 years at the time of lung cancer diagnosis, and about 30% are aged over 70. Since lung cancer is a disease that mainly occurs in elderly, and smoking is the most important risk factor [1], many patients have (smoking-related) comorbidity at the time of lung cancer diagnosis. This may complicate the management of lung cancer and may also serve as a competing cause of death. Methods An overview of literature concerning the prevalence and prognostic influence of comorbidity in lung cancer patients as well as competing causes of death. ResultsPrevalence of comorbidity Previous studies have shown that over 70% of patients suffered from at least one serious comorbid condition at the time of lung cancer diagnosis [2, 3]. The prevalence of (especially tobacco-related) comorbidity was higher among lung cancer patients as compared to patients with other major tumour types or the general population [2, 4]. The most frequent concomitant diseases among lung cancer patients were tobacco-related, such as cardiovascular diseases (25-30%), chronic obstructive pulmonary diseases (COPD, 25-30%) and previous malignancies (about 20%) [2, 3]. Prognostic influence of comorbidity Since in most cancer trials significant comorbidity is an exclusion criteria, limited information is available on the prognostic influence of comorbidity (which is important information for everyday clinical practice). Previous studies have shown that comorbidity only had a significant influence on survival in case of a localized lung tumour or in case of severe comorbidity [2, 3, 5-7]. A poorer overall survival in patients with comorbidity might be explained by death due to complications of treatment, death from cancer due to less aggressive treatment, or an increased risk of mortality due to comorbid conditions (competing causes of death). Comorbidity may increase the risk of peroperative and postoperative complications, especially those of the cardiorespiratory system [8]. A previous population-based publication has also shown that up to 75% of elderly SCLC patients receiving chemotherapy developed grade 3-5 toxicity, and two thirds of these patients receiving chemotherapy were unable to complete the treatment [9]. Elderly patients with localized non-small cell lung cancer (NSCLC) underwent less surgery than younger patients, older patients with non-localized NSCLC received less chemotherapy or chemoradiation, and elderly with small cell lung cancer (SCLC) received less chemotherapy and chemoradiation [5, 9, 10]. Competing causes of death Increased mortality due to comorbidity is probably of less importance in case of a lethal disease as non-localized NSCLC or SCLC [2, 10, 11]. Most patients probably die of lung cancer before they become at risk of dying of the comorbid condition. Previous studies have shown that 80-90% of all lung cancer patients died of lung cancer. The most common other causes of death were other tobacco-related conditions as cancers and cardiovascular causes [12-14]. Respiratory failure is the most common immediate cause of death for patients with lung cancer, probably because most of them have lung disease besides cancer and therapy for lung cancer may also add to impairment of lung function [15]. The finding that over 90% of lung cancer patients have contributing causes of death, suggests the possibility that saving a patient from one cause may only allow another disease process to become the immediate cause of death [15]. Conclusions The majority of patients with lung cancer also have serious comorbidity, especially other smoking-related diseases as cardiovascular diseases and COPD. Besides making treatment complex, comorbid conditions may also serve as competing causes of death. References 1. Doll R, Peto R, Wheatley K et al. Mortality in relation to smoking: 40 years' observations on male British doctors. Bmj 1994; 309: 901-911. 2. Piccirillo JF, Tierney RM, Costas I et al. Prognostic importance of comorbidity in a hospital-based cancer registry. Jama 2004; 291: 2441-2447. 3. Janssen-Heijnen ML, Schipper RM, Razenberg PP et al. Prevalence of co-morbidity in lung cancer patients and its relationship with treatment: a population-based study. Lung Cancer 1998; 21: 105-113. 4. Janssen-Heijnen ML, Houterman S, Lemmens VE et al. Prognostic impact of increasing age and co-morbidity in cancer patients: a population-based approach. Crit Rev Oncol Hematol 2005; 55: 231-240. 5. Luchtenborg M, Jakobsen E, Krasnik M et al. The effect of comorbidity on stage-specific survival in resected non-small cell lung cancer patients. Eur J Cancer 2012; 48: 3386-3395. 6. Jorgensen TL, Hallas J, Friis S, Herrstedt J. Comorbidity in elderly cancer patients in relation to overall and cancer-specific mortality. Br J Cancer 2012; 106: 1353-1360. 7. Birim O, Kappetein AP, Bogers AJ. Charlson comorbidity index as a predictor of long-term outcome after surgery for nonsmall cell lung cancer. Eur J Cardiothorac Surg 2005; 28: 759-762. 8. Wang S, Wong ML, Hamilton N et al. Impact of age and comorbidity on non-small-cell lung cancer treatment in older veterans. J Clin Oncol 2012; 30: 1447-1455. 9. Janssen-Heijnen ML, Maas HA, van de Schans SA et al. Chemotherapy in elderly small-cell lung cancer patients: yes we can, but should we do it? Ann Oncol 2011; 22: 821-826. 10. Janssen-Heijnen ML, Smulders S, Lemmens VE et al. Effect of comorbidity on the treatment and prognosis of elderly patients with non-small cell lung cancer. Thorax 2004; 59: 602-607. 11. Phernambucq EC, Spoelstra FO, Verbakel WF et al. Outcomes of concurrent chemoradiotherapy in patients with stage III non-small-cell lung cancer and significant comorbidity. Ann Oncol 2011; 22: 132-138. 12. Janssen-Heijnen ML, Maas HA, Siesling S et al. Treatment and survival of patients with small-cell lung cancer: small steps forward, but not for patients >80. Ann Oncol 2012; 23: 954–960. 13. Pirie K, Peto R, Reeves GK et al. The 21st century hazards of smoking and benefits of stopping: a prospective study of one million women in the UK. Lancet 2013; 381: 133-141. 14. Thun MJ, Carter BD, Feskanich D et al. 50-year trends in smoking-related mortality in the United States. N Engl J Med 2013; 368: 351-364. 15. Nichols L, Saunders R, Knollmann FD. Causes of death of patients with lung cancer. Arch Pathol Lab Med 2012; 136: 1552-1557.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.
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MS07.3 - Genetic Susceptibility (ID 488)
14:45 - 15:05 | Author(s): C. Amos
- Abstract
- Presentation
Abstract not provided
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MS07.4 - Risk Prediction Models (ID 489)
15:05 - 15:25 | Author(s): M.R. Spitz
- Abstract
- Presentation
Abstract
Results from the National Lung Screening Trial (NLST) showing a 20% reduction in lung cancer mortality in the screened arm have heightened awareness of the need for reliable risk prediction tools for estimating the probability of lung cancer. A key issue of uncertainty is which smokers should be targeted for low-dose computed tomography (LDCT) screening. The NLST used 55 - 74 years, ≥30 pack-years of smoking and up to 15 years since quitting as selection criteria. 7 million U.S. adults meet these entry criteria, and an estimated 94 million U.S. adults are current or former smokers. Validated risk prediction models could improve the outcomes of screening efforts. Such models have substantial public health implications and value in clinical decision making as well. Further, risk prediction tools could be incorporated into the design of smaller, more powerful, and “smarter” prevention trials. The first lung cancer risk prediction model was developed by Bach et al. using data from the Carotene and Retinol Efficacy Trial (CARET) of 14,000 heavy smokers and over 4,000 asbestos-exposed men. Variables included in the final model were age, gender, asbestos exposure, smoking history, cigarettes per day, duration of smoking and duration of cessation. Cronin et al. externally validated the Bach model in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study control arm (c statistic of 0.69). Spitz et al. expanded on this model adding epidemiologic and clinical data derived from an ongoing lung cancer case-control study. Their model included environmental tobacco smoke (for never and former smokers), family cancer history, asbestos and dust exposures, prior respiratory disease, history of hay fever, and smoking history variables. These variables have strong biologically plausibility and are relatively easy to ascertain through patient interview. However, the validated area under the curve (AUC) statistics for former and current smoker models were modest (0.63 and 0.58, respectively), although consistent with those from other risk prediction models. The LLP model based on data from the Liverpool Lung Project included age, sex and smoking, as well as family history of lung cancer, exposure to asbestos, prior diagnosis of pneumonia and of a malignancy other than lung cancer. Prior diagnoses of emphysema and lung cancer lost significance in the multivariate model. Young et al. developed a risk model using a 20-single nucleotide polymorphism (SNP) panel including cell-cycle control, oxidant response, apoptosis, and inflammation genes, as well as age, history of COPD, family history of lung cancer, and gender. When numeric scores were assigned to both the SNP and demographic data, and sequentially combined by a simple algorithm in a risk model, the composite score was linearly related to risk with a bimodal distribution. These data have not been well replicated. In 2011,Tammemagi published a carefully constructed risk prediction model based on data from 70,962 control subjects in the Prostate, Lung, Colorectal, Ovarian cancer screening trial (PLCO). Model 1 included age, education, body mass index (BMI), family history of lung cancer, chronic obstructive pulmonary disease (COPD), recent chest x-ray, smoking status (never, former, current), pack-years smoked, and smoking duration. Model 2 also included time in years since ever-smokers permanently quit smoking. In external validation, performed with 44,223 PLCO intervention arm participants, Models 1 and 2 had area under the curves of 0.84 and 0.78, respectively. Tammemagi and colleagues also showed that their risk prediction model for lung cancer incidence was a more sensitive indicator of pre-screening risk of developing lung cancer than were NLST eligibility criteria. Kovalchik et al. subsequently showed that 88% of LDCT-prevented lung cancer deaths occurred among the 60% of NLST participants with highest pre-screening risk, while just 1% occurred among the 20% at lowest risk. This finding reinforces the role for risk-based screening. Maisonneuve et al. incorporated lung nodule characteristics and CT diagnosed emphysema into the Bach model. Presence of nonsolid nodules (RR = 10.1), nodule size > 8 mm (RR = 9.89), and emphysema (RR = 2.36) at baseline CT were all significant predictors of subsequent lung cancers. Incorporation of these variables into the Bach model increased the predictive value of the model (c-index = 0.759). Hoggart et al used prospective data from the European EPIC cohort. Using smoking information alone gave good predictive accuracy: the AUC and 95% CI in ever smokers was 0.843 (0.810-0.875). Adding other risk factors (10 occupational/environmental exposures previously implicated with lung cancer, and SNPs at two loci identified by GWAS of lung cancer) had a negligible effect on the AUC. An extended model was constructed incorporating two markers of DNA repair capacity that have been shown in case-control analyses to be associated with increased lung cancer risk. Addition of the biomarker assays improved the sensitivity of the models over epidemiologic and clinical data alone. These in vitro lymphocyte culture assays, however, are time-consuming and require technical expertise, and are not applicable for widespread population-based implementation. Spitz et al. added 3 SNPS that were most significant in their GWAS data – rs1051730 from 15q25 and two SNPs from the 5p15.33 locus (rs2736100 and rs401681 that were not in strong LD) to the baseline model. The AUC for the baseline epidemiologic/clinical model including 1016 cases and 1111 controls (all ever smokers) was 0.59. There was evidence of a gene dosage effect with an odds ratio over threefold elevated in the highest genetic risk score (GRS) stratum. With addition of the GRS to the model, the AUC showed modest improvement, to 0.61, although this was significantly improved over the baseline model, (P< 0.001). Current lung cancer risk prediction models are hampered by a restricted number of potential predictors, generally low overall predictive performance, and methodological limitations. To date, one can argue that the Tammemagi 2013 model exhibits the highest AUC among all the prediction models. It is important to conduct additional external validations of all models in diverse populations.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.
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E11 - Practical Aspects of Targeted Therapies (ID 11)
- Event: WCLC 2013
- Type: Educational Session
- Track: Medical Oncology
- Presentations: 1
- Moderators:M. Millward, L. Paz-Ares
- Coordinates: 10/30/2013, 14:00 - 15:30, Plenary Hall, Ground Level
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E11.2 - Management Options at First Progression of EGFR Mutant Tumours (ID 423)
14:20 - 14:40 | Author(s): K. Park
- Abstract
- Presentation
Abstract
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MO10 - Molecular Pathology II (ID 127)
- Event: WCLC 2013
- Type: Mini Oral Abstract Session
- Track: Pathology
- Presentations: 1
- Moderators:W.A. Franklin, A. Mahar
- Coordinates: 10/28/2013, 16:15 - 17:45, Bayside 104, Level 1
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MO10.01 - Integrative and comparative genomic analysis of East-Asian lung squamous cell carcinomas (ID 2667)
16:15 - 16:20 | Author(s): K. Park
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- Presentation
Background
Lung squamous cell carcinoma (SqCC) is the second most prevalent type of lung cancer. Currently, no targeted-therapeutics are approved for treatment of this cancer, largely due to a lack of systematic understanding of the molecular pathogenesis of the disease. To identify therapeutic targets and perform comparative analyses of lung SqCC, we probed somatic genome alterations of lung SqCC cases from Korean patients.Methods
We performed whole-exome sequencing of DNA from 104 lung SqCC samples from Korean patients and matched normal DNA. In addition, copy number analysis and transcriptome analysis were conducted for a subset of these samples. Clinical association with cancer-specific somatic alterations was investigated.Results
This cancer cohort is characterized by a very high mutational burden with an average of 261 somatic exonic mutations per tumor and a mutational spectrum showing a signature of cigarette-smoke exposure. Seven genes demonstrated statistical enrichment for mutation (TP53, RB1, PTEN, NFE2L2, KEAP1, MLL2 and PIK3CA). Comparative analysis between Korean and North American lung SqCC demonstrated similar spectrum of alterations in these two populations, in contrast to the differences seen in lung adenocarcinoma. We also uncovered recurrent occurrence of therapeutically actionable FGFR3-TACC3 fusion in lung SqCC.Conclusion
These findings provide new steps towards the identification of genomic target candidates for precision medicine in lung SqCC, a disease with a significant unmet medical need.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.
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P3.06 - Poster Session 3 - Prognostic and Predictive Biomarkers (ID 178)
- Event: WCLC 2013
- Type: Poster Session
- Track: Biology
- Presentations: 2
- Moderators:
- Coordinates: 10/30/2013, 09:30 - 16:30, Exhibit Hall, Ground Level
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P3.06-004 - T790M Mutation in Patients with Acquired Resistance to EGFR Tyrosine Kinase Inhibitors: Is It Associated with Clinically Distinct Features? (ID 352)
09:30 - 09:30 | Author(s): K. Park
- Abstract
Background
The T790M mutation is considered to be the major mechanism of acquired resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs). However, its clinical implication in patients with non-small cell lung cancer (NSCLC) is yet determined.Methods
NSCLC patients with acquired resistance to initial EGFR TKIs such as gefitinib or erlotinib were identified, and post-progression tumor specimens were prospectively collected for T790M mutation analysis. Clinical features including the pattern of disease progression (intrathoracic only versus extrathoracic), treatment outcomes for initial or subsequent TKIs, post-progression survival, and overall survival were compared between patients with and without T790M.Results
Out of 70 cases, 36 (51%) were identified to have the T790M mutation in the rebiopsy specimen. There was no difference in the pattern of disease progression, progression-free survival for initial TKIs (12.8 and 11.3 months), post-progression survival (14.7 and 14.1 months), or overall survival (43.5 and 36.8 months) in patients with and without T790M. In total, 34 patients received afatinib after post-progression biopsy as a subsequent treatment. Among them, six (18%) achieved objective response. The median progression-free survival for afatinib was 3.7 months for the entire group, and 3.2 and 4.6 months for the groups with (n = 21) and without (n = 13) T790M, respectively (p = 0.33). Figure 1Figure 2Conclusion
Although T790M had no prognostic or predictive role in the present study, further research is necessary to identify patients with T790M-mutant tumors who might benefit from new treatment strategies. -
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P3.06-008 - Prognostic and Predictive Value of KRAS Mutations in Advanced Non-Small Cell Lung Cancer (ID 1089)
09:30 - 09:30 | Author(s): K. Park
- Abstract
Background
Clinical implications of KRAS mutations in advanced non-small cell lung cancer remain unclear.Methods
Kras mutation status was identified by direct sequencing test in 844 specimens that were diagnosed of NSCLC at Samsung Medical Center from 2006 to 2011. This study included stage IV NSCLC patients who were treated with systemic chemotherapy. We retrospectively evaluated the prognostic and predictive value of KRAS mutations in patients with advanced NSCLC.Results
Among 484 patients with available results for both KRAS and EGFR mutations, 39 (8%) had KRAS and 182 (38%) EGFR mutations, with two cases having both mutations. The median overall survivals for patients with KRAS mutations, EGFR mutations, or both wild types were 7.7, 38.0, and 15.0 months, respectively (P < 0.001). The KRAS mutation was an independent poor prognostic factor in the multivariate analysis (hazard ratio=2.6, 95% CI: 1.8–3.7). Response rates and progression-free survival (PFS) for the pemetrexed-based regimen in the KRAS mutation group were 14% and 2.1 months, inferior to those (28% and 3.9 months) in the KRAS wild type group.Conclusion
KRAS mutation tended to be associated with inferior treatment outcomes after gemcitabine-based chemotherapy, while there was no difference regarding taxane-based regimen. Although the clinical outcomes to EGFR tyrosine kinase inhibitors (TKIs) seemed to be better in patients with KRAS wild type than those with KRAS mutations, there was no statistical difference in response rates and PFS according to KRAS mutation status when EGFR mutation status was considered. Two patients with both KRAS and EGFR mutations showed partial response to EGFR TKIs. Although G12D mutation appeared more frequently in never smokers, there was no difference in clinical outcomes according to KRAS genotypes. These results suggested KRAS mutations have an independent prognostic value but a limited predictive role for EGFR TKIs or cytotoxic chemotherapy in advanced NSCLC.
