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C.J. Rivard
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MINI 13 - Genetic Alterations and Testing (ID 120)
- Event: WCLC 2015
- Type: Mini Oral
- Track: Biology, Pathology, and Molecular Testing
- Presentations: 2
- Moderators:Y. Koh, R.K. Thomas
- Coordinates: 9/08/2015, 10:45 - 12:15, 205+207
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MINI13.03 - Characterization of MET Gene and MET Protein Expression in Lung Cancer (ID 2155)
10:45 - 12:15 | Author(s): C.J. Rivard
- Abstract
- Presentation
Background:
Activation of the MET signaling pathway can propel the growth of cancer cells in non-small cell lung cancer (NSCLC). Increased MET gene by amplification and/or polysomy can cause MET protein overexpression; less common causes include mutations, translocations, and alternative RNA splicing. Clinical trials using MET as a biomarker for selection of lung cancer patients who might most benefit from targeted therapy have experienced variable outcomes. We aimed to characterize the relationship between MET protein overexpression and MET amplification or mean copy number alterations in patients with NSCLC.
Methods:
The Lung Cancer Mutation Consortium (LCMC) is performing an ongoing study of biomarkers with patients with NSCLC from 16 cancer center sites across the United States. For this analysis, 403 cases had complete data for MET protein expression by immunohistochemistry (IHC, monoclonal antibody SP44, Ventana) and MET gene amplification by fluorescence in-situ hybridization (FISH, MET/CEP7 ratio). Pathologists evaluated MET expression using the H-score, a semi-quantitative assessment of the percentage of tumor cells with no, faint, moderate, and/or strong staining, ranging from 0-300. Spearman's correlation was used to analyze the correlation between MET protein expression (H-scores) and FISH results (MET/CEP7 ratio (N=403) and MET copy number (N=341). Protein overexpression using 5 different cut-offs was compared with amplification defined as MET/CEP7 ≥ 2.2 and high mean copy number defined as ≥ 5 MET gene copies per cell using the Fisher’s exact test. Cox Proportional Hazards models were built to examine the associations of these different definitions of positivity with prognosis, adjusting for stage of disease.
Results:
MET protein expression was significantly correlated with MET copy numbers (r=0.17, p=0.0025), but not MET/CEP7 ratio (r=-0.013, p=0.80). No significant association was observed between protein overexpression using a commonly used definition for MET positivity (“at least moderate staining in ≥ 50% tumor cells”) and MET amplification (p=0.47) or high mean copy number (p=0.09). A definition for MET protein overexpression as “≥ 30% tumor cells with strong staining” was significantly associated with both MET amplification (p=0.03) and high mean copy number (p=0.007), but a definition of “≥ 10% tumor cells with strong staining” was not significantly associated with either. Definitions of protein overexpression based on high H-scores (≥200 or ≥250) were associated with high MET mean copy numbers (p=0.03 and 0.0008, respectively), but not amplification (p=0.46 and 0.12, respectively). All 5 definitions of MET protein overexpression demonstrated a significant association with worse prognosis by survival analyses (p-values ranged from 0.001 to 0.03). High MET copy number (p=0.045) was associated with worse prognosis, but MET amplification was not (p=0.07).
Conclusion:
Evaluation of NSCLC specimens from LCMC sites confirms that MET protein expression is correlated with high MET copy number and protein overexpression is associated with worse prognosis. Definitions of MET protein overexpression as “an H-score ≥250” and “≥30% tumor cells with strong staining” were significantly associated with high mean MET copy number. It may be worth reevaluating the performance of MET as a biomarker by different definitions of positivity to predict response to MET-targeted therapies.
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MINI13.06 - Mutation Prevalence for Oncogenic Drivers in Lung Adenocarcinoma (ID 3279)
10:45 - 12:15 | Author(s): C.J. Rivard
- Abstract
- Presentation
Background:
Identification of mutations which drive pulmonary adenocarcinomas (ADC) has rapidly moved from the research arena to clinical practice. The prevalence of these mutations has been suggested by a multitude of studies but here we describe the prevalence of mutations from a large study of patients with advanced ADC treated in the international phase III study INSPIRE (Lancet Oncology 2015) with all testing performed in one CLIA-certified laboratory under standardized conditions.
Methods:
Mutation testing was performed on 412 adenocarcinoma specimens using SNaPshot® methodology. Mutations were examined in the AKT, EGFR, KRAS, BRAF, NRAS, PIK3CA, TP53, PTEN, CTNNB1, and MEK1 genes. The relative frequencies of genetic alterations were calculated based on the total number of adequate specimens and specific consent for testing.
Results:
Of the 412 adenocarcinoma specimens tested, 372 (90.3%) had evaluable results from mutation testing. A single mutation was detected in 157 (42.2%) specimens, whereas mutations in two genes were detected in an additional 20 (5.4%). The overall prevalence of mutations for each specific gene was as follows: KRAS (34.2%), EGFR (12.2%), TP53 (4.9%), PTEN (2.8%), PIK3CA (2.2%), CTNNB1 (2.2%), NRAS (1.8%), BRAF (1.2%), MEK1 (0.3%), and AKT (0%). Figure 1 Evaluation of smoking status identified a substantially higher percentage of KRAS mutations in ex-light smokers and current smokers (38.2% and 40.5%) combined compared to never smokers (7.6%, p<0.0001) , and a lower proportion of EGFR mutations in ex-light and current smokers (10.9% and 4.9%) combined compared to never smokers (39.7%, p<0.0001). Patients ≥70 years old had a higher proportion of both NRAS (7.1% vs. 0.7%, p=0.009) and TP53 mutations (12.5% vs. 3.3%, p=0.010). In addition, males had a lower incidence of EGFR mutation (8.6% vs. 19.0%, p=0.007) as compared to females. Patients from North America, Europe, and Australia/New Zealand demonstrated lower rates of mutation in CTNNB1 (1.4% vs. 8.6%, p=0.030) and PIK3CA (1.4% vs. 8.3%, p=0.032) compared to patients from Central/South America, South Africa and India. Finally, among specimens with two mutations, combinations involving KRAS were the most prevalent (70%, 14/20) followed by TP53 (50%, 10/20).
Conclusion:
These results demonstrate the wide spectrum of mutations that can be detected in adenocarcinoma specimens, with high prevalence rates in the EGFR and KRAS genes. Most patients had only one identified driver mutation. The study revealed age and geographical associations in some mutations. The clinical relevance of the studied mutations in relation to chemotherapy and the human EGFR antibody, Necitumumab, will be studied.
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MINI 34 - RNA and miRNA (ID 162)
- Event: WCLC 2015
- Type: Mini Oral
- Track: Biology, Pathology, and Molecular Testing
- Presentations: 1
- Moderators:C. Mascaux, L.(. Wang
- Coordinates: 9/09/2015, 18:30 - 20:00, 205+207
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MINI34.05 - Discussant for MINI34.01, MINI34.02, MINI34.03, MINI34.04 (ID 3433)
18:30 - 20:00 | Author(s): C.J. Rivard
- Abstract
- Presentation
Abstract not provided
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ORAL 25 - Biology and Other Issues in SCLC (ID 125)
- Event: WCLC 2015
- Type: Oral Session
- Track: Small Cell Lung Cancer
- Presentations: 1
- Moderators:J. Sage, L. Montuenga
- Coordinates: 9/08/2015, 10:45 - 12:15, 605+607
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ORAL25.06 - Association of Expression of PD-L1 with the Tumor Immune Microenvironment in Small Cell Lung Cancer (ID 859)
10:45 - 12:15 | Author(s): C.J. Rivard
- Abstract
- Presentation
Background:
Small cell lung cancer (SCLC) accounts for 15% of all lung cancers and has been under-studied relative to novel therapies. Therapeutic antibodies to immune checkpoints are showing promising clinical results. Programmed death-ligand 1 (PD-L1), which can be expressed on many cancer and immune cells, plays an important role in blocking the cancer immunity cycle by binding programmed death-ligand 1 receptor (PD-1), which is a negative regulator of T-lymphocyte activation. Since knowledge about PD-L1 expression in SCLC is limited, we aimed to characterize PD-L1 expression in a cohort of 98 SCLC patients.
Methods:
PD-L1 protein expression and mRNA levels were determined by immunohistochemistry (IHC, SP142, Spring Bioscience) and mRNA in situ hybridization (ISH) in primary tumor tissue microarrays obtained from 98 SCLC patients. Membranous staining of PD-L1 protein and mRNA expression on tumor cells and protein expression on tumor-infiltrating immune cells (TIICs) were scored separately using semi-quantitative scores (H-score 0-300 and RNA score 0-4). An H-score ≥ 5 and an RNA score > 2 were defined as the cutoffs for PD-L1 protein and RNA expression positivity. The degree of TIICs was semi-quantitatively scored on hematoxylin and eosin-stained TMA slides as having “0” (no), “1” (mild), “2” (moderate), or “3” (marked) infiltration. The data was analyzed using the Fisher’s exact test, Spearman correlation, two-sample t-test, log-rank test and Kaplan- Meier survival analysis with significance level assumed to be 0.05.
Results:
3.16% of cases (3/95) were positive for PD-L1 protein expression in tumor cells, and 30.21% were positive for PD-L1 in TIICs (29/96, p<0.0001). PD-L1 mRNA expression was positive in 15.46% of the tumor cells (15/97). PD-L1 protein and mRNA expression on tumor cells demonstrated a positive correlation (p<0.0001, r=0.431). PD-L1 mRNA expression on tumor cells positively correlated with PD-L1 protein expression on TIICs (p<0.0001, r=0.354). The degree of TIICs positively correlated with both PD-L1 protein expression in tumor cells (p=0.011, r=0.264) and PD-L1 mRNA expression in tumor cells (p<0.0001, r=0.405). The degree of TIICs positively correlated with PD-L1 protein expression in TIICs (p<0.0001, r=0.625). The only significant association observed between PD-L1 expression with clinical characteristics or prognosis of the 78 SCLC patients with clinical data, was between age of patients and PD-L1 protein (p<0.0001) and mRNA expression (p=0.0006) on tumor cells.
Conclusion:
A subset of SCLCs is characterized by positive PD-L1 protein and/or mRNA expression in tumor cells and TIICs. PD-L1 mRNA expression was more frequently positive than PD-L1 protein expression in the tumor cells. PD-L1 protein expression was expressed more in TIICs than tumor cells. Higher PD-L1 protein and mRNA expression correlated with more infiltration of TIICs. PD-L1 expression represents the immune response in SCLC. The microenvironment may play a major role on the PD-1/PD-L1 pathway of SCLC. SCLC Patients with PD-L1 expression may respond to anti-PD-L1 treatment.
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ORAL 37 - Novel Targets (ID 146)
- Event: WCLC 2015
- Type: Oral Session
- Track: Biology, Pathology, and Molecular Testing
- Presentations: 1
- Moderators:S.S. Ramalingam, E. Thunnissen
- Coordinates: 9/09/2015, 16:45 - 18:15, Mile High Ballroom 4a-4f
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ORAL37.07 - Lung Cancer Mutation Consortium Pathologist Panel Evaluation of MET Protein (ID 2129)
16:45 - 18:15 | Author(s): C.J. Rivard
- Abstract
- Presentation
Background:
MET is a receptor tyrosine kinase with frequently activated signaling in lung cancers. Multiple studies indicate that MET overexpression correlates with poor clinical prognosis. Tumors with MET amplification and overexpression may respond better to MET inhibitors than tumors with low expression. The prevalence of MET overexpression in lung cancer cohorts has varied from 20%-80%, as has the proportion of patient’s testing positive for prospective clinical trials with entry based on MET overexpression. The Lung Cancer Mutation Consortium (LCMC) Pathologist Panel endeavored to standardize evaluation of MET protein expression with “Round Robin” conferences.
Methods:
508 FFPE non-small cell lung cancer specimens were stained by immunohistochemistry for MET protein expression (SP44 antibody, Ventana). Seven pathologists from LCMC sites with specialized training in MET scoring evaluated 78 Aperio-scanned images of MET-stained slides in two successive rounds of 39 different cases per round. The percentage of tumor cells with membranous and/or cytoplasmic staining at different intensities were evaluated with H-scores ranging from 0 to 300. Overall group and individual pathologist’s scores were compared with intraclass correlation coefficients (ICCs). Between rounds, a “Round Robin” teleconference was conducted to review discordant cases and improve consistency of scoring. Steps to improve scoring included: review of a Roche MET training document, sharing pictures of cases with concordant scores (Figure 1), and provision of H&E images for the second round to facilitate identification of tumor areas. Figure 1
Results:
The overall average MET H-score for the 78 cases was 165.3 (H-score range: 42.5-279.7). The average H-score was <125 for 14 specimens, 125-175 for 35 specimens, and >175 for 29 specimens. The overall group ICC comparing the consistency of H-scores from all 7 pathologists improved from 0.50 (95% confidence interval: 0.37-0.64, “fair” correlation) for the first scoring round to 0.74 (95% confidence interval: 0.64-0.83, “good” correlation) for the second round. A comparison of the individual pathologist’s ICCs demonstrated improved individual scoring consistency for all seven pathologists between rounds with an average of 0.64 (“moderate” correlation, range 0.43-0.76) for the first round and 0.82 (“almost perfect” correlation, range 0.75-0.93) for the second round.
Conclusion:
Development of standardized, reproducible strategies for evaluation of complex biomarkers, such as MET, are critical to clinical trial design. The consistency of scoring for MET protein expression and other biomarkers may be improved by continuous training and communication between pathologists with easy access to H&E images and other visual aids.
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P1.06 - Poster Session/ Screening and Early Detection (ID 218)
- Event: WCLC 2015
- Type: Poster
- Track: Screening and Early Detection
- Presentations: 1
- Moderators:
- Coordinates: 9/07/2015, 09:30 - 17:00, Exhibit Hall (Hall B+C)
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P1.06-009 - Volatolomic Signatures to Assess Sensitivity to FGFR Tyrosine Kinase Inhibitors (ID 1711)
09:30 - 17:00 | Author(s): C.J. Rivard
- Abstract
Background:
Targeted therapy is transforming the treatment of lung cancer. Such therapies are critically dependent on companion diagnostics that can predict the response to therapy. An ideal test is one that is quick, inexpensive, and non-invasive. In this regard, artificial intelligence nanosensor-based devices that profile volatolomic signatures (through volatile organic compounds (VOCs) analysis) have shown exciting potential. Numerous studies have shown cancer cells produce characteristic patterns of VOCs as a byproduct of their metabolism. These patterns can be used to diagnose patients with cancer using exhaled-breath samples. Here we asked whether the VOC patterns emanating from cancer cells could also be used to guide targeted therapy. In particular, we investigated whether lung cancer cell lines known to be sensitive to FGFR tyrosine kinase inhibitors (TKIs) can be distinguished from cell lines known to be resistant using an array of cross reactive, highly sensitive chemiresistors composed of gold nanoparticles (GNP) and carbon nanotubes (CNTs) coated with various recognition layers previously shown to be highly effective at profiling VOCs.
Methods:
Fourteen sensitive cell lines having an IC~50~ ≤ 50 nM for Ponatinib and AZD4547 (nonspecific and specific FGFR TKIs, respectively) and 21 resistant cell lines representing small cell and non-small cell lung cancers were cultured in complete media (RPMI 1640, 10% fetal bovine serum, and penicillin/streptomycin) under standard conditions to 50% to 75% confluency. SKC Tenax® TA Adsorbent resin was used to collect the VOCs from the head space of each cell line over a period of 60 to 72 hours. Triplicate measures were collected on each sample along with biological replicates. VOCs were also collected at the same time from control plates containing media only. After thermal desorption, the VOC pattern of each sample was characterized using a chemiresistor array of 36 sensors and 4 features per sensor. A statistical pattern recognition analysis was then conducted using a discriminant function analysis (DFA) algorithm to identify the most informative sensors and features.
Results:
We found that sensitive cell lines could be distinguished from resistant cell lines using only 4 sensors and one feature from each (GNP+dodecanethiol, CNT+PAH, GNP+thiol and CNT+β dextrin). Leave-one-out cross validation indicated a sensitivity of 88% for the FGFR TKI-sensitive cell lines with 100% specificity and 92% accuracy. The area under the receiver-operating characteristic curve was 70% and Wilcoxon p-value of 0.06.
Conclusion:
Profiling the VOCs emanating from lung cancer cells shows excellent diagnostic potential as a means of gauging initial sensitivity to FGFR1 TKIs. Consequently, this study suggests that the electronic nose devices currently being developed to profile exhaled breath for cancer detection could also play an important role in predicting responses to targeted therapies. Although cell lines are useful for identifying the VOC pattern that predicts the cancer cell response to therapy, they do not necessarily reflect the complexity that occurs in vivo due to interactions with the microenvironment. Therefore, future studies are needed to confirm if these results can be extended to project efficacy in patients assigned to FGFR TKI therapy.
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P2.04 - Poster Session/ Biology, Pathology, and Molecular Testing (ID 234)
- Event: WCLC 2015
- Type: Poster
- Track: Biology, Pathology, and Molecular Testing
- Presentations: 1
- Moderators:
- Coordinates: 9/08/2015, 09:30 - 17:00, Exhibit Hall (Hall B+C)
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P2.04-006 - MiRNA Signature to Assess Sensitivity to FGFR Tyrosine Kinase Inhibitors (ID 1717)
09:30 - 17:00 | Author(s): C.J. Rivard
- Abstract
Background:
Increased signaling through the FGF/FGFR signaling pathway has been implicated as a driver in a number of different malignancies including lymphomas, prostate cancer, breast cancer, and lung cancer. This pathway also appears to play a role in conferring de novo and acquired resistance to cancers driven by EGFR mutations. Consequently, drugs that inhibit FGFRs are being investigated as potential therapeutics for cancer. Here we screened a large panel of miRNAs as potential predictors of sensitivity to FGFR tyrosine kinase inhibitors (TKIs).
Methods:
A panel of 377 miRNAs (Megaplex Card A, Life Technologies) was screened for expression level differences between four lung cancer cell lines that are sensitive (IC~50~< 50 nM) and four lines that are resistant (IC~50~ > 100 nM) to ponatinib (non-specific FGFR TKI) and AZD4547 (FGFR-specific TKI). Expression levels were assayed by RT-qPCR and analyzed using the Statistical Analysis of Microarrays (SAM) method. Thirty-nine miRNAs having an estimated false discover rate (FDR) of zero and large median fold differences (> 8) between the sensitive and resistant lines were selected for signature development. RT-qPCR assays were incorporated into a custom microfluidics card (Life Technologies), which was used to profile the original 8 cell lines and 10 additional sensitive lines and 16 additional resistant lines (34 lines total). Logistic regression was then used to identify the best signature panel for distinguishing sensitive cell lines from resistant.
Results:
Univariate analysis indicated three miRNAs (let-7c, miR-338, and miR-218) that differed between the sensitive and resistant lines at p < .05. The best signature panel consisted of let-7c, miR-200a and miR-200b, which gave an area under the receiver operator characteristic (AUROC) curve of 0.90 (95% CI = 0.8 to 1). This performance was nearly as good as using FGFR1 mRNA alone (AUROC = 0.94). The predominant miRNA in our 3-miRNA signature was let-7c, which also exhibited a suggestive additive effect to using FGFR1 as a biomarker (p = 0.09). We also tested whether cell lines with high sensitivity to ponatinib can be made resistant by reducing the high level of let-7c in these lines. We have found that transient transfection of let-7c silencing RNA (Life Technologies) produces a decrease in FGFR1 mRNA levels for some cell lines but not others.
Conclusion:
It appears possible to predict sensitivity to an FGFR1 inhibitor using miRNA expression signatures. More studies, however, are needed to confirm the 3-marker signature developed in this study. Modulating let-7c, the predominant predictor within the signature, appears to modulate FGFR1 levels in a manner consistent with altering ponatinib sensitivity. This effect is most likely indirect as the mRNA of FGFR1 does not contain predicted binding sites for let-7c.
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P2.06 - Poster Session/ Screening and Early Detection (ID 219)
- Event: WCLC 2015
- Type: Poster
- Track: Screening and Early Detection
- Presentations: 1
- Moderators:
- Coordinates: 9/08/2015, 09:30 - 17:00, Exhibit Hall (Hall B+C)
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P2.06-007 - A miRNA Signature Derived From Independently Replicated Biomarkers of Non-Small Cell Lung Cancer (ID 1728)
09:30 - 17:00 | Author(s): C.J. Rivard
- Abstract
Background:
miRNAs have shown exceptional promise as biomarkers of lung cancer; however, no miRNA signatures have yet reached the clinic. Towards developing a signature with a high likelihood of being validated externally for clinical use, we screened a panel of 50 miRNAs shown to be effective biomarkers in at least two previous studies for distinguishing human lung cancer samples from non-cancer samples.
Methods:
Sixty tumor-normal pairs (33 adenocarcinoma, 27 squamous cell carcinoma) were used to identify the best-performing combination of 4 miRNAs for distinguishing tumor samples from normal. The miRNA levels were measured by RT-qPCR using Taqman custom-made microfluidics cards and primer pools purchased from Life Technologies. All possible combinations of 4 miRNAs were tested, and best performance was defined as the highest median area-under the receiver operating curve (AUC) obtained from 1000 bootstrap replicates. A second, independent set of 68 tumor-normal samples (half adenocarcinoma, half squamous) was used as a test set, and bootstrapping was used to determine the 95% confidence interval for the AUC.
Results:
The median AUC for the top-performing panel of 4 miRNAs in our training set was 0.96. Several other miRNA combinations exhibited AUCs > 0.95 as well. In our test set, the top-performing panel (and only panel tested) exhibited an AUC of 0.97 (0.93, 0.99). This panel consisted of miRs 26a, 145, 183 and 486. miRs 145 and183 have previously been shown, when used individually, to be significant lung tumor biomarkers in at least 4 previous studies; miR-486 has been replicated 8 times.Figure 1
Conclusion:
Consistent with previous studies, we’ve identified a panel of 4 miRNAs that shows excellent potential for diagnosing lung tumors. Each of these miRNAs has been replicated as a biomarker of lung cancer in at least two previous studies, suggesting a high likelihood of achieving clinical validation. Several previous studies have also shown that these four miRNAs are potentially useful as biomarkers for diagnosing lung cancer using blood samples, and we are currently pursuing such validation studies.
