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Glenwood Goss
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MA 20 - Recent Advances in Pulmonology/Endoscopy (ID 685)
- Event: WCLC 2017
- Type: Mini Oral
- Track: Pulmonology/Endoscopy
- Presentations: 1
- Moderators:C. Lee, S. Sasada
- Coordinates: 10/18/2017, 14:30 - 16:15, F205 + F206 (Annex Hall)
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MA 20.11 - Chronic Obstructive Pulmonary Disease Prevalence in a Lung Cancer Screening Population (ID 9588)
15:40 - 15:45 | Author(s): Glenwood Goss
- Abstract
- Presentation
Background:
Chronic obstructive pulmonary disease (COPD) and lung cancer are associated through tobacco use. COPD is underdiagnosed in both the primary care and lung cancer populations. Diagnosis of COPD should lead to improved care and quality of life. Screening programs could provide an opportunity to capture undiagnosed COPD. We analyzed the Pan-Canadian Early Detection of Lung Cancer Study (PanCan Study) to evaluate the prevalence of COPD in a screening population.
Method:
The PanCan Study was a single arm lung cancer screening trial which recruited individuals to low dose CT scan, autofluorescence bronchoscopy, and biomarker screening. Eligible individuals were 50-75 years of age, had smoked within 15 years, and had a minimum six-year risk of lung cancer ≥ 2% based on a risk prediction model derived from PLCO study data, which included COPD as a risk factor. Consenting subjects completed a questionnaire including background medical conditions, high-risk work exposures, and smoking history. Baseline spirometry was performed, and COPD was defined by GOLD criteria. For individuals not receiving post-bronchodilator spirometry, COPD was defined as ‘probable’ if GOLD criteria were met pre-bronchodilator and there was no prior diagnosis of asthma. Individuals with definite or probable COPD were defined as having COPD.
Result:
Of 2537 individuals recruited, 2514 had available spirometry data. Mean age was 62.3 years, 55.3% were male, median pack-years smoked was 50, 62.3% were active smokers, 45.1% had symptoms of dyspnea, 52.4% cough, and 37.5% wheeze. 35.2% had worked in a high-risk occupation. Overall, 1136 (45.2%) met spirometry criteria for COPD. Of 1987 individuals without a prior history of COPD, 41.9% met spirometry criteria for COPD, of which 53.7% had moderate to severe disease. Of 527 individuals (21%) reporting a diagnosis of COPD at baseline, 57.5% met spirometry criteria for COPD, 32.2% did not, and 10.3% had a prior diagnosis of asthma. In a multivariate model for risk of COPD, age (odds ratio (OR)~per year~ 1.06), dyspnea (OR 1.42), being a current smoker (OR 1.43), and pack-years (log transformed OR 1.42) were significant (all p < 0.001) as were high-risk occupation (OR 1.24, p=0.013) and wheeze (OR 1.24, p = 0.024).
Conclusion:
A diagnosis of COPD by spirometry is common in a lung cancer screening trial population. Individuals with a pre-existing self-reported diagnosis of COPD often fail to meet spirometry criteria for their diagnosis. Testing a lung cancer screening population for COPD could significantly improve COPD diagnosis and treatment.
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MS 12 - Sustainable Care System in Each Region (ID 534)
- Event: WCLC 2017
- Type: Mini Symposium
- Track: Regional Aspects/Health Policy/Public Health
- Presentations: 1
- Moderators:Yasushi Goto, M.E. Hand
- Coordinates: 10/17/2017, 11:00 - 12:30, Room 501
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MS 12.01 - Sustainable Care System in North America (ID 7698)
11:00 - 11:15 | Presenting Author(s): Glenwood Goss
- Abstract
- Presentation
Abstract not provided
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OA 14 - New Paradigms in Clinical Trials (ID 681)
- Event: WCLC 2017
- Type: Oral
- Track: Clinical Design, Statistics and Clinical Trials
- Presentations: 1
- Moderators:Alex Adjei, Eun Kyung Cho
- Coordinates: 10/18/2017, 11:00 - 12:30, Room 311 + 312
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OA 14.02 - Rethinking Progression-Free Survival (PFS) as a Clinical Trials Surrogate for Overall Survival (OS) (ID 10276)
11:10 - 11:20 | Author(s): Glenwood Goss
- Abstract
- Presentation
Background:
►►OS assessment requires high follow-up times and patient numbers and is impacted by crossover (CO). OS hazard ratios (HRs) are generally inferior to PS HRs due to impact of post-progression survival (PPS) and CO. Some authors propose that absolute OS gains (ΔOS) should be similar to those in PFS (ΔPFS). Hence, ΔPFS might be a useful OS surrogate (Clin Cancer Res 2013;19:2646; Ann Oncol 2016;27:373).
Method:
To assess this further, we reviewed Journal of Clinical Oncology and New England Journal of Medicine 01/01/2012-06/12/2017 for randomized drug trials in incurable solid tumors. We extracted data for PFS and OS medians and HRs, calculated ΔPFS and ΔOS (experimental medians minus control medians), and did paired comparisons between 2-6 different arms in each study (245 comparisons across 201 trials).
Result:
Mean ΔOS across studies (1.03 months) was similar to mean ΔPFS (1.06 months) (n=201 evaluable, p=0.88). ΔOS correlated with ΔPFS (r=0.50, p<0.0001). With CO in <20% of patients or unstated %CO (n=144), mean ΔOS and ΔPFS were 0.93 and 0.92 months, respectively. With CO in >20% of patients (n=57), mean ΔOS and ΔPFS were 1.29 and 1.41 months, while with CO>50% (n=20), they were 1.4 and 1.9 months. OS HRs (mean=0.92) were inferior to PFS HRs (mean=0.82, n=196, p<0.0001), although OS and PFS HRs correlated with each other (r=0.64, p<0.0001). With CO<20% or unstated (n=135), mean OS and PFS HRs were 0.93 and 0.83, while with CO>20% (n=61), they were 0.90 and 0.80, and with CO>50% (n=20), they were 0.94 and 0.71.
Conclusion:
OS HRs were inferior to PFS HRs, probably due to PPS, competing causes of death and CO. The better mean gains and HRs in high vs low CO trials may be due to more frequently allowing CO in trials with more effective therapies. This increases risk of false-negative OS results with effective therapies if CO is permitted, but it is potentially unethical to withhold CO of effective therapies. With PFS, clinically insignificant gains may be statistically significant. Since ΔOS and ΔPFS are similar, an alternate approach would be a primary study outcome requiring PFS HR to be statistically significant and ΔPFS 95% CIs in a range considered clinically relevant for OS gains. To better understand the limitations of this approach, we are analyzing examples with minimal OS gains despite ΔPFS>2 months and examples of ΔOS>2 months but no gain in PFS, and have formulated a potential biological/statistical explanation for the latter.
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OA 15 - Diagnostic Radiology, Staging and Screening for Lung Cancer II (ID 684)
- Event: WCLC 2017
- Type: Oral
- Track: Radiology/Staging/Screening
- Presentations: 1
- Moderators:Y. Satoh, Jin Mo Goo
- Coordinates: 10/18/2017, 14:30 - 16:15, Room 303 + 304
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OA 15.01 - Lung Cancer Screening: Participant Selection by Risk Model – the Pan-Canadian Study (ID 8466)
14:30 - 14:40 | Author(s): Glenwood Goss
- Abstract
- Presentation
Background:
Retrospective studies indicate that selecting individuals for low dose computed tomography (LDCT) lung cancer screening based on a highly predictive risk model is superior to applying National Lung Screening Trial (NLST)-like criteria, which use only categorized age, pack-year and smoking quit-time information. The Pan-Canadian Early Detection of Lung Cancer Study (PanCan Study) was designed to prospectively evaluate whether individuals at high risk for lung cancer could be identified for screening using a risk prediction model. This paper describes the study design and results.
Method:
2537 individuals were recruited through 8 centers across Canada based on a ≥2% of lung cancer risk estimated by the PanCan model, a precursor to the validated PLCOm2012 model. Individuals were screened at baseline and 1 and 4 years post-baseline.
Result:
At a median 5.5 years of follow-up, 164 individuals (6.5%) were diagnosed with 172 lung cancers. This was a significantly greater percentage of persons diagnosed with lung cancers than was observed in the NLST(4.0%)(p<0·001). Compared to 57% observed in the NLST, 77% of lung cancers in the PanCan Study were early stage (I or II) (p<0.001) and to 25% in a comparable population, age 50-75 during 2007-2009 in Ontario, Canada’s largest province, (p<0·001).
Conclusion:
Enrolling high-risk individuals into a LDCT screening study or program using a highly predictive risk model, is efficient in identifying individuals who will be diagnosed with lung cancer and is compatible with a strong stage shift – identifying a high proportion at early, potentially curable stage. Funding This study was funded by the Terry Fox Research Institute and Canadian Partnership Against Cancer. ClinicalTrials.gov number, NCT00751660
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P2.01 - Advanced NSCLC (ID 618)
- Event: WCLC 2017
- Type: Poster Session with Presenters Present
- Track: Advanced NSCLC
- Presentations: 1
- Moderators:
- Coordinates: 10/17/2017, 09:00 - 16:00, Exhibit Hall (Hall B + C)
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P2.01-032 - A Randomized Phase Ii Trial of Selumetinib + Platinum-pemetrexed (Pem-c) in Kras Wildtype (Wt)/Unknown NSCLC: CCTG Ind219 (ID 9083)
09:00 - 09:00 | Author(s): Glenwood Goss
- Abstract
Background:
Selumetinib (SEL), an oral inhibitor of MEK 1 and 2, could be particularly effective in tumours with an activated Ras/Raf/MEK/ERK pathway, but has not been fully studied in KRAS WT nor in the first-line setting. The scheduling of SEL with chemotherapy might impact efficacy and/or toxicity.
Method:
IND219 is an open-label three-arm study of PEM-C±SEL. Arm A: PEM-C+SEL days 2-19; Arm B: PEM-C+SEL days 1-21; Arm C: PEM-C alone. Primary objective was response rate (ORR); secondary objectives were tolerability and progression-free survival (PFS). Pts were stratified by KRAS WT versus unknown and cisplatin versus carboplatin. Before the planned interim analysis (60 pts), pts were allocated 1:1:1 to arm A, B or C, with a plan to continue either Arm A or B plus Arm C a 3:1 ratio to ensure that the final analysis includes Arm A or B and Arm C in a 2:1 ratio. The trial would stop if neither Arm A or B had > 4 responses; if both did, the arm would be selected based on response and toxicity data. Correlative studies included genomic testing.
Result:
Arm A/B/C enrolled 20/21/21 pts. PEM-C exposure was lower with SEL (median cycles 5 versus 6 for Arm C). Seven pts on Arm A (35%; 95% CI 15-59% median duration 3.8m), 13 on Arm B (62%; 95% CI 38-82%; median duration 6.3m), and 5 on Arm C (24%; 95% CI 8-47%; median duration 11.6m) had PR, meeting the criteria to continue. PFS was 7.5m (95% CI 4.0 to 9.0 m) for Arm A, 6.7m (95% CI 4.1 to 8.2 m) on Arm B, and 4.0m on Arm C (95% CI 1.4 to 6.8 m). HR for PFS of Arm A over Arm C was 0.76 (95% CI 0.38 to 1.51, 2-sided p=0.42); HR for PFS of Arm B over Arm C was 0.75 (95% CI 0.37 to 1.54, p=0.43). After adjusting for age, performance status, gender and KRAS, PFS comparisons remained NS. Toxicity was most commonly grade 1-2, but more frequent with SEL especially mucositis, diarrhea, anorexia, dehydration, edema and rash. A high rate of venous thromboembolism (VTE) was seen in all arms, highest in Arm A (Arm A 45 % versus 14 % [p=0.11])
Conclusion:
SEL+PEM-C is associated with higher, but less durable ORR. In this small study, PFS is numerically prolonged adding SEL to PEM-C with expected additive toxicity. Further exploration of these intriguing results is ongoing.
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P3.01 - Advanced NSCLC (ID 621)
- Event: WCLC 2017
- Type: Poster Session with Presenters Present
- Track: Advanced NSCLC
- Presentations: 1
- Moderators:
- Coordinates: 10/18/2017, 09:30 - 16:00, Exhibit Hall (Hall B + C)
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P3.01-043 - Impact of ErbB Mutations on Clinical Outcomes in Afatinib- or Erlotinib-Treated Patients with SCC of the Lung (ID 9457)
09:30 - 09:30 | Author(s): Glenwood Goss
- Abstract
Background:
In LUX-Lung 8 (LL8), second-line afatinib (an irreversible ErbB family blocker) significantly improved OS (median 7.9 versus 6.8 months; HR [95% CI]: 0.81 [0.69‒0.95]; p=0.0077), and PFS (2.6 versus 1.9 months; 0.81 [0.69‒0.96]; p=0.0103) versus erlotinib in lung SCC (N=795). Comprehensive genetic analysis in LL8 patients assessed whether afatinib efficacy varied according to genetic aberrations in cancer-related genes, including ErbB family mutations.
Method:
Tumor genetic analysis (TGA) was performed using Foundation Medicine FoundationOne™ next-generation sequencing (NGS). The cohort was enriched for patients with PFS >2 months, reflecting a range of responsiveness to EGFR-TKIs. EGFR expression was assessed by immunohistochemistry (IHC) in a largely separate cohort. Cox regression analysis correlated PFS/OS with genetic mutations (individual/grouped) and EGFR expression.
Result:
Of 440 patients selected for TGA (PFS >2 months: n=320; ≤2 months: n=120), samples from 245 were eligible (afatinib: n=132; erlotinib: n=113). In the selected TGA population, PFS/OS outcomes were improved in the afatinib versus erlotinib arm. Baseline characteristics were similar in TGA and IHC cohorts and LL8 overall. In the TGA subset, 53 patients (21.6%) had ≥1 ErbB family mutation (EGFR: 6.5%; HER2: 4.9%; HER3: 6.1%; HER4: 5.7%). Beyond the benefit seen for afatinib in the overall population, in afatinib-treated patients, PFS/OS were longer when ErbB mutations were present (PFS: 4.9 versus 3.0 months; OS: 10.6 versus 8.1 months). Conversely, survival outcomes in erlotinib-treated patients were similar with/without ErbB mutations. Presence of HER2 mutations predicted favorable PFS/OS with afatinib versus erlotinib. The Table shows outcomes in patients with/without ErbB family mutations, and by EGFR expression levels (afatinib: n=157; erlotinib: n=188).
Conclusion:
These data are provocative and suggest that NGS may enable identification of lung SCC patients who would derive additional clinical benefit from afatinib. Differential outcomes with respect to ErbB mutations for afatinib and erlotinib are hypothesized to reflect afatinib’s broader mechanism of action.Subgroup n Afatinib vs erlotinib PFS OS HR (95% CI) p~interaction~ HR (95% CI) p~interaction~ ErbB mutation-positive ErbB mutation-negative 53 192 0.56 (0.29–1.08) 0.70 (0.50–0.97) 0.718 0.62 (0.35‒1.12) 0.76 (0.56‒1.03) 0.683 EGFR mutation-positive EGFR mutation-negative 16 229 0.64 (0.17–2.44) 0.67 (0.50–0.91) 0.981 1.01 (0.32–3.16) 0.72 (0.54–0.95) 0.529 HER2 mutation-positive HER2 mutation-negative 12 233 0.06 (0.01–0.59) 0.72 (0.54–0.97) 0.006 0.06 (0.01–0.57) 0.76 (0.58–1.00) 0.004 HER3 mutation-positive HER3 mutation-negative 15 230 0.52 (0.16–1.72) 0.69 (0.51–0.94) 0.692 0.84 (0.27–2.59) 0.73 (0.56–0.97) 0.998 HER4 mutation-positive HER4 mutation-negative 14 231 0.21 (0.02–1.94) 0.67 (0.50–0.91) 0.909 0.22 (0.05–1.04) 0.75 (0.56–0.99) 0.272 EGFR IHC positive EGFR IHC negative 292 53 0.74 (0.56–0.97) 0.76 (0.41–1.40) 0.985 0.82 (0.63–1.06) 0.75 (0.41–1.40) 0.882 EGFR amplification present EGFR amplification absent 17 228 0.72 (0.18–2.90) 0.68 (0.50–0.92) 0.994 0.50 (0.15–1.65) 0.76 (0.58–1.00) 0.413 HER2 amplification present HER2 amplification absent 9 236 0.94 (0.20–4.38) 0.68 (0.50–0.91) 0.861 1.10 (0.27–4.48) 0.72 (0.54–0.94) 0.388
