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Q. Li
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MA 15 - Lung Cancer Biology II (ID 670)
- Event: WCLC 2017
- Type: Mini Oral
- Track: Biology/Pathology
- Presentations: 1
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MA 15.11 - CCNE1, PTGS2, TGFA and WISP2 Predict Benefit from Bevacizumab and Chemotherapy in Patients with Advanced Non-Small Cell Lung Cancer (SAKK19/09) (ID 9592)
16:55 - 17:00 | Author(s): Q. Li
- Abstract
- Presentation
Background:
Bevacizumab (Bev; Avastin[®]) is a monoclonal antibody against the vascular endothelial growth factor. No predictive biomarkers for the use of Bev have been established so far. We aimed identifying genes predictive for progression-free survival (PFS) and overall survival (OS) of patients treated in the trial SAKK19/09 (NCT01116219).
Method:
SAKK19/09 was a non-randomized phase II trial with two sequential cohorts including patients with non-squamous NSCLC and EGFR wild-type. In Cohort 1, 77 patients were treated with cisplatin (C) 75mg/m[2], pemetrexed (Pem) 500mg/m[2] and Bev 7.5mg/kg, followed by Bev+Pem maintenance. Cohort 2 included 52 patients treated with C+Pem followed by Pem maintenance. RNA was isolated from baseline tumor tissue sections and processed for gene expression analysis by Nanostring. Using the Nanostring nCounter® System (Nanostring Technologies) gene expression of 201 genes, including 6 housekeeping genes was measured using a custom-designed codeset. For each gene, a Cox regression was performed with normalized gene expressions, treatment and the interaction for PFS and OS. No adjustment for multiple testing was done.
Result:
We analyzed 99 patient samples (61 in Cohort 1; 38 in Cohort 2) with 201 genes at baseline. We found 7 genes potentially predictive for PFS (AURKB, CCNE1, CDKN2B, MMP2, PTGS2, TGFA, WISP2), 4 of which were also potentially predictive for OS (CCNE1, PTGS2, TGFA and WISP2) (Table 1).Gene Accession HR (95% confidence interval) p-value of interaction Cohort 1 Cohort 2 Potential predictive markers for PFS AURKB NM_004217 1.09 (0.84-1.42) 0.78 (0.61-0.99) 0.0481 CCNE1 NM_001238 1.09 (0.87-1.36) 0.73 (0.53-1.02) 0.0312 CDKN2B NM_004936.3 0.80 (0.67-0.95) 1.10 (0.85-1.43) 0.0375 MMP2 NM_004530.2 0.81 (0.67-0.97) 1.10 (0.91-1.34) 0.0258 PTGS2 (COX-2) NM_000963.1 1.29 (1.06-1.58) 0.90 (0.78-1.04) 0.00352 TGFA NM_003236.2 1.13 (0.94-1.37) 0.74 (0.53-1.03) 0.0452 WISP2 NM_003881.2 0.82 (0.69-0.98) 1.24 (1.02-1.51) 0.0015 Potential predictive markers for OS CCNE1 NM_001238 1.08 (0.86-1.36) 0.71 (0.49-1.02) 0.0324 PTGS2 (COX-2) NM_000963.1 1.35 (1.10-1.65) 0.81 (0.69-0.95) <0.0001 TGFA NM_003236.2 1.17 (0.96-1.43) 0.55 (0.33-0.91) 0.00352 WISP2 NM_003881.2 0.87 (0.73-1.03) 1.14 (0.92-1.42) 0.0314
Conclusion:
We identified several potentially predictive genes for Bev activity in combination with chemotherapy. Several of these (AURKB, CCNE1, CDKN2B, TGFA) have previously been shown to play an important role in cell cycle regulation and cell proliferation supporting the hypothesis that Bev supports chemotherapy activity. Notably, also a gene involved in inflammation (PTGS2) was significantly predictive for outcome. Further work is ongoing to explore changes in gene expression using tumor rebiopsies at progression.
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P2.07 - Immunology and Immunotherapy (ID 708)
- Event: WCLC 2017
- Type: Poster Session with Presenters Present
- Track: Immunology and Immunotherapy
- Presentations: 1
- Moderators:
- Coordinates: 10/17/2017, 09:30 - 16:00, Exhibit Hall (Hall B + C)
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P2.07-012 - Patterns of Response to Nivolumab in Patients with Non-Small Cell Lung Cancer (NSCLC) (ID 8203)
09:30 - 09:30 | Author(s): Q. Li
- Abstract
Background:
Response Evaluation in Solid Tumors (RECIST) criteria were developed to assess response to cytotoxic therapy. Response to immune checkpoint inhibitors depends on tumor and host factors including the presence of immune cells (IC) in the tumor environment. Organs differ in IC content. We hypothesized that nivolumab was more active in tumor lesions in IC rich than IC poor organs.
Method:
We retrospectively analysed serial computed tomography (CT) scans of patients treated with nivolumab applying RECIST 1.1 criteria to assess overall response (ORR) and response in different organ sites. CT examinations were performed on a 3[rd] generation dual-source CT system and read by two experienced radiologists. We classified metastatic sites from NSCLC into three groups: 1) IC rich: lymph nodes, 2) IC intermediate: liver, lungs, 3) IC poor: bone, soft tissue. Standard descriptive statistics were used; time-to-event endpoints were analyzed using Kaplan-Meier methods.
Result:
52 patients with advanced NSCLC were analyzed. Median age was 66 years, 44% were female, 58% had adenocarcinoma, 92% were former or current smokers. Prior to nivolumab treatment start patients had lesions in the lung (42%), liver (25%), lymph node (56%), soft tissue (13%) and bone (23%). In 62% of the patients the primary tumor was still in situ. ORR and disease-control-rate (DCR) were 20% and 45%, respectively. Median overall survival was 11.9 months, median progression-free survival was 2.3 months and median duration of response (DOR) 10.3 months. Response (RR) to nivolumab differed depending on organ site: RR and DCR according to organ sites were 28% and 90% in lymph nodes, classified as IC rich. RR was 8%, 9% and 16% and DCR was 58%, 55% and 81% in liver, lung metastases and primary tumor, respectively, classified as IC intermediate. In IC poor organs RR was 0% in soft tissue metastases and nine out of 12 patients with bone metastases, which included non-measurable non-target lesions only, had progressive lesions at time of overall tumor progression.
Conclusion:
Immunotherapy has differential effects at different organ sites of metastases. Nivolumab treatment appears to be more active in IC rich organs than at IC intermediate and IC poor sites. Our results suggest that the combination of immune checkpoint inhibitors with local treatment strategies to IC intermediate or poor organs should be explored.
