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K. Fong
Moderator of
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MS 21 - Immunotherapy Predictive Biomarkers (ID 39)
- Event: WCLC 2015
- Type: Mini Symposium
- Track: Biology, Pathology, and Molecular Testing
- Presentations: 4
- Moderators:K. Fong, P. Dennis
- Coordinates: 9/09/2015, 14:15 - 15:45, Four Seasons Ballroom F1+F2
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MS21.01 - Overview of Immunotherapy (ID 1941)
14:15 - 15:45 | Author(s): J.R. Brahmer
- Abstract
- Presentation
Abstract not provided
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MS21.02 - PD1/PDL1 Biomarker Strategies (ID 1942)
14:15 - 15:45 | Author(s): E. Brambilla
- Abstract
- Presentation
Abstract:
Introduction: Cancer cells express antigens that potentially differentiate them from normal cells. These are known to be numerous in lung cancer and characterized by a high mutational rate (7-11 mutations / MegaBase), especially in relation with smoking derived genetic instability, P53 mutations, and/or the presence of targetable mutations in adenocarcinoma. These tumor antigens should confer immunogenicity to lung cancer transformed cells. However, immune-editing occurs in most lung cancer along a three phases sequence: 1) Elimination, where transformed cells are destroyed by the immune system; 2) Equilibrium, equivalent to a functional state of dormancy in which tumor cells growth is controlled by adaptive immunity, a state characterized by typically dense lymphocytic infiltration rich in CD8 cytotoxic cells (E. Brambilla et al. JCO, under review); 3) Escape from immune surveillance. PD-L1 in NSCLC is expressed on the membrane of tumor cells, and/or on immune infiltrating cells dendritic cells (DC), other antigen presenting cells (APC) and T lymphocytes. PD-1, the PDL1 receptor, is expressed on tumor infiltrating lymphocytes (TILS), mainly CD4 T cells, T regulatory (T-reg) and B, NK, monocytes and DC. Upon PD-L1 binding, PD-1 inhibits kinases involved in T cell activation. There are two mechanisms of expression of immune checkpoints on tumor cells and their immune stromal counterparts: oncogenic signaling, and response to inflammatory signals, both of which occur potentially in lung cancer. Tumor cells express multiple ligands and receptors and antitumor immune response can be enhanced by multi-level blockade of immune checkpoints. PD-1/PD-L1 engagement leads to HSP-2 phosphatase activity which dephosphorylates Pi3K and thus downregulate AKT. The necessary patient selection for immunotherapy has stressed the search for predictive biomarker of PD-1/PD-L1 pathway inhibition. The cutoff for positivity on tumor cells[1–3]: The cutoff for positivity in and out of trials on tumor cells has never been assessed nor optimized or standardized. The percentage of PD-L1 membrane staining considered as the cutoff for positivity was from ≥1%, ≥5%, ≥10%, ≥50% and the intensity was or not defined and taking into account (any intensity, 1+, 2+, 3+, or a scale from 1 to 3+/H Score , or 2+3+only). At least, most if not all reports considered only membrane staining on tumor cells, although cytoplasmic staining was also considered with AQUA techniques. Stromal expression of PD-L1 on immune infiltrate (T cells, macrophages, DC) is also needed for scoring. Whereas DC and macrophages display a clear cytoplasmic membrane stain, this is not appreciated on lymphocytes. We have set up a study to assess a cutoff of positivity for prognosis analysis (1500 randomized early stage operable NSCLC patients with or without adjuvant cisplatin therapy after surgery) using E1L3N Cell Signaling antibody commercially available. We found that 20% of lung tumors cell expressed PD-L1 (≥20% intensity 2+3+), and 29% the immune stromal cells (T, macrophages, DC ) ≥10% intensity 2+3+. PD-L1 positivity in both tumor and immune cells were seen in only 9% of NSCLC, 20,7% were both negative . We double-check the scoring cells with Ming Tsao. The best concordance was for intensity 2+ /3+ (83%) although the intensity 1 was not reproducible ( 40%) . There was no prognostic relevance of PD-L1 (tumor cells or stroma) in the control arm and pooled analysis, whatever cutoff by 10% increment or linear scoring was used. There was no statistical correlation between PDL1 expression (Tumor or Immune cells ) with clinicopathological criteria or histology . Only immune PD-L1 expression was correlated with a highly intense immune infiltrations (TILs ) ( P = 002 ). Not surprisingly, previous published evaluations of prognostic value were discordant likely because immune checkpoints modulators play both positive and negative roles in the immune inhibitory pathways with some redundancy, and patients series and assays were not comparable .The two meta-analyses with their numerous biases ( different antibodies, cutoffs, patient series composition in early and advanced stage, ethnicities and contribution of oncogene driven cancers, time of use of the initial resection sample or contemporary biopsy…) rendered their interpretation extremely problematic . Global result was favoring a poor prognosis of “PD-L1 positivity” on tumor cells. PD-L1 expression as a predictive biomarker in cancer immunotherapy[1,4–7]: In the majority of phase I trials with four antibodies targeting the co-inhibitory receptor PD-1 or its primary ligand PD-L1 (Table 1), response rates appear higher in patients with increased tumor PD-L1 membrane expression by immunohistochemistry (IHC). However, different antibody assays, lack of standardization, different cutoff point to determine PD-L1 positivity, the usual various pharmaceutic companies to recommend their companion test, and the small number of specimens available for testing, in addition with the variability of the intervals between biopsy and test, has surely hampered the conclusion and prevent consensus to be reached[7,8]. The most pertinent threshold was provided by Garon et al, with ≥50% of tumor cells PD-L1 positive to allow the highest response rate of 45% in pembrolizumab treated patients in the validation group[1]. In most trial series, biopsies or resected specimen were used restropectively although considerable difference between these samples occurs due to tumor heterogeneity. The reliability of small biopsy samples is questionned[9]. Indeed lung tumor heterogeneity is exemplary , and PD-L1 is typically heterogeneous in its distribution in the tumor bulk as is PD-L1 positive immune cells . Multiple issues are yet addressed before PD-L1 is considered as a robust and definitive molecular predictor of efficacy. Various clones are currently being evaluated in and out of clinical trials (Ventana SP263, SP6242, Dako 28-8 and 22C3, Cell Signaling E1L3N). As for prognostic evaluations, thresholds of ≥1%, ≥5%, ≥10%, ≥50% or continuous H score have been used. In addition in a few trials, PD-L1 expression in TILs was predictive more than PD-L1 on tumor cells but the cutoff was not disclosed. IASLC pathology panel is leading a large multicentric reproducibility study ( Fred Hirsch )with lung pathologists of the IASLC Pathology Committee to address these questions. Alternative regulations of PD-1/PD-L1 pathway The ability of cancer cells to evade immunosurveillance results from the production of immunosuppressive chemokines by the tumor cells, loss of MHC antigen expression, a higher number of T-reg cells in the tumor microenvironment and inhibitory pathways referred to as immune checkpoints, which result in a link of inhibitory ligands to their receptors (CTLA~4~-PD-1, PD-L1/PD-L2-PD-1) are unfrequently upregulated in lung cancer. Moreover immune-editing was associated with the illegitimate expression of tumor germ cell (testis /placenta) antigens[10], normally absent in normal tissue but testis and placenta, inducing a state of immune escape when aberrantly expressed in lung cancer correlating with highly and metastatic aggressive behavior. While patients with PD-L1 overexpression based on different assays, cutoff, tumor material, have more robust response to PD-L1 (67-100% ORR), PD-L1 negative NSCLC ranges from 0 to 15%, suggesting that PD-L1 IHC is not a clear and exclusive predictive biomarker. This is not surprising due to multiple regulations at the two clinically relevant immunologic synapses: the tumor-T cell interface, and the APC-T cell interface, both playing role in tumor control. In all cohorts, PD-L1 in tumor cells was observed with or without immune infiltration. TILs intense infiltration occurred in 10% of NSCLC across histology and was a statistically significant good prognosis factor although the oncogene driven adenocarcinomas lack immune infiltrate. EGFR pathway upregulates PD-L1 as well as PTEN loss[11–14]. In addition the 2 synapses are functionally affected by HLA loss (>50% of NSCLC), EGFR signaling, PTEN loss, the density of CD8 in infiltrate available for cytotoxicity and even more CD8 +/PD1+ exhausted cytotoxic T cells among TILs . The best predictive biomarker might not be simply binary . Biopsies may underevaluate the pertinent tumor-stroma interface , PD-L1 biologically relevant ( more than 1-10% of tumor cell ! ) has already taken place and destroyed the potentially reactive CD8 T cells. Indeed secondary biomarkers may drive the tumor in association or independently of PD-1/PD-L1 pathway. Table 1: Prevalence of PD-L1 in NSCLC:
References: 1. Garon EB, et al. Pembrolizumab for the treatment of NSCLC. N Engl J Med. 2015;372(21):2018-2028. 2. Sorensen S, et al. PD-L1 expression and survival among advances NSCLC patients treated with chemotherapy. Ann Oncol. (25 (Supplement 4)). 3. Soria J-C, et al. Clinical activity, safety and biomarkers of PD-L1 blockade in NSCLC: Additional analyses from a clinical study of the engineered antibody MPDL3280A (anti-PDL1). 4. Patel SP, Kurzrock R. PD-L1 Expression as a Predictive Biomarker in Cancer Immunotherapy. Mol Cancer Ther. 2015;14(4):847-856. 5. Taube JM, et al. Association of PD-1, PD-1 ligands, and other features of the tumor immune microenvironment with response to anti-PD-1 therapy. CCR. 2014;20(19):5064-5074. 6. Herbst RS, et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature. 2014;515(7528):563-567. 7. Soria J-C, et al. Immune checkpoint modulation for non-small cell lung cancer. CCR. 2015;21(10):2256-2262. 8. Brahmer JR, et al. Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. NEJM. 2012;366(26):2455-2465. 9. Kitazono S, et al. Reliability of Small Biopsy Samples Compared With Resected Specimens for the Determination of PD-L1 Expression in NSCLC. Clin Lung Cancer. 2015. 10. Rousseaux S, et al. Ectopic activation of germline and placental genes identifies aggressive metastasis-prone lung cancers. Sci Transl Med. 2013;5(186):186ra66. 11. Akbay EA, et al. Activation of the PD-1 pathway contributes to immune escape in EGFR-driven lung tumors. Cancer Discov. 2013;3(12):1355-1363. 12. D’Incecco A, Andreozzi M, Ludovini V, et al. PD-1 and PD-L1 expression in molecularly selected NSCLC patients. Br J Cancer. 2015;112(1):95-102. 13. Chen N, et al. Upregulation of PD-L1 by EGFR Activation Mediates the Immune Escape in EGFR-Driven NSCLC: Implication for Optional Immune Targeted Therapy for NSCLC Patients with EGFR Mutation. J Thorac Oncol. 2015 14. Lin C, et al. Programmed Death-Ligand 1 Expression Predicts TKI Response and Better Prognosis in a Cohort of Patients With EGFR Mutation-Positive Lung Adenocarcinoma. Clin Lung Cancer. 2015.Percent tumor samples expressing PD-L1 Tumor surface expression cutoff for positivity PD-L1 detection antibody Reference 49% 5% 28-8 Grosso et al. JCO, 2013 52% NR R&D B7-H1 Gatalica et al. Cancer Epidemiology biomarkers prevention, 2014 95% >10% 5H1 Dong et al. Nature Medicine, 2002 50% 11% MIH1 Konishi et al. CCR, 2004 21% (squamous only) >1% vs >5% vs H-score 5H1 Marti et al. JCO, 2014 60% 5% DAKO IHC Gettinger et al. JCO, 2014 50% 1% NR Sun et al. JCO, 2014 25% ≥50% NR Garon et al. NEJM, 2015
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MS21.03 - Assessment of Immune Cells in Tumor Biopsies as a Biomarker (ID 1943)
14:15 - 15:45 | Author(s): I.I. Wistuba, E. Parra, J. Rodrigiuez-Canales
- Abstract
- Presentation
Abstract:
Multiple genetic and epigenetic changes in several cancer types cause resistance to immune attack of tumors by inducing specific T cells tolerance and by expressing ligands that engage inhibitory receptors and block T cells activation, all resulting on T-cells anergy or exhaustion within the tumor microenvironment (1). In this process, programmed death 1 (PD-1) protein, a T-cell co-inhibitory receptor, and one of its ligands, PD-L1 (B7-H1 or CD274), play a pivotal role in the ability of tumor cells to evade the host’s immune system. Antibody-mediated blockade PD-1/PD-L1 induced durable tumor regression and prolonged disease stabilization in non-small cell carcinoma (NSCLC) (2). Although these studies have reported correlations between PD-L1 immunohistochemical (IHC) expression levels on NSCLC tumor cells and clinical responses to PD-1 and PD-L1 inhibitors, there are patients with negative PD-L1 expression tumors who have showed similar responses than patients with positive expression. Recently, it has been shown that across multiple cancer types, including NSCLC, responses to anti-PD-L1 therapy were observed in patients with tumors expressing high levels of PD-L1, especially when PD-L1 was expressed by tumor-associated infiltrating cells (TAICs). Altogether, these findings suggest that there are other factors in the tumor microenvironment, including tumor infiltrating lymphocytes (TILs) and tumor-associated macrophages (TAMs) that may drive responses to anti-PD-1/PD-L1 therapies, and be involved in lung cancer pathogenesis and progression. A number of studies have characterized the PD-L1 protein expression by immunohistochemistry (IHC) or immunofluorescence (IF) in all NSCLC stages using formalin-fixed and paraffin-embedded (FFPE) tumor tissues, and correlated those findings with patient’s outcome, and in a limited number of cases with response to immunotherapy (3, 4). Those studies differ on the type of specimens (whole histology sections vs. tissue microarrays [TMAs]), the protein expression analysis (IHC vs. IF), and the quantification assessment (image analysis vs. microscope observation). Only few studies have attempted to correlate the expression of PD-L1 and TAICs, particularly TILs, using a limited number of IHC markers (e.g., CD8, CD45) (5). Up to date, there is no published study in which a comprehensive panel of immune markers, including PD-L1, has been performed attempting to develop a clinical relevant immuno-score system in surgically resected NSCLCs and explore their role as predictive markers of response to immunotherapy. We will present data on the characterization of TAICs in lung cancer tumor specimens using a large panel of markers (PD-L1, PD-1, CD3, CD4, CD8, CD45RO, CD57, Granzyme B, FOXP3, OX-40, and CD68) examined by both uniplex IHC and multiple immunofluorescence (IF) methodologies, and quantitated using image analysis systems (Aperio, Vectra and MultiOmyx). In surgically resected NSCLC tumor tissues the analysis was performed at both peri-tumoral and intra-tumoral compartments, and those data provided interesting data on the spatial distribution of TAICs and the expression of immune checkpoints in lung tumors. Our approach allowed us to devise an immuno-score system for lung cancer tissue specimens using both surgically resected and small diagnostic biopsies (core needle biopsies, CNBs) that correlated with clinical, pathological and molecular features of tumors. References: 1. Mellman I, Coukos G, Dranoff G: Cancer immunotherapy comes of age. Nature 2011, 480:480-9. 2. Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, McDermott DF, Powderly JD, Carvajal RD, Sosman JA, Atkins MB, Leming PD, Spigel DR, Antonia SJ, Horn L, Drake CG, Pardoll DM, Chen L, Sharfman WH, Anders RA, Taube JM, McMiller TL, Xu H, Korman AJ, Jure-Kunkel M, Agrawal S, McDonald D, Kollia GD, Gupta A, Wigginton JM, Sznol M: Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. The New England journal of medicine 2012, 366:2443-54. 3. Herbst RS, Soria JC, Kowanetz M, Fine GD, Hamid O, Gordon MS, Sosman JA, McDermott DF, Powderly JD, Gettinger SN, Kohrt HE, Horn L, Lawrence DP, Rost S, Leabman M, Xiao Y, Mokatrin A, Koeppen H, Hegde PS, Mellman I, Chen DS, Hodi FS: Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 2014, 515:563-7. 4. Taube JM, Klein A, Brahmer JR, Xu H, Pan X, Kim JH, Chen L, Pardoll DM, Topalian SL, Anders RA: Association of PD-1, PD-1 Ligands, and Other Features of the Tumor Immune Microenvironment with Response to Anti-PD-1 Therapy. Clinical cancer research : an official journal of the American Association for Cancer Research 2014, 20:5064-74. 5. Schalper KA, Brown J, Carvajal-Hausdorf D, McLaughlin J, Velcheti V, Syrigos KN, Herbst RS, Rimm DL. Objective measurement and clinical significance of TILs in non-small cell lung cancer. J Natl Cancer Inst. 2015 Feb 3;107(3).
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MS21.04 - Search for Genetic/Molecular Predictors of Immune Checkpoint Therapy - Role of KRAS, LKB1, Other Genetic Markers as Predictors for Immunotherapy (ID 1944)
14:15 - 15:45 | Author(s): S.N. Gettinger
- Abstract
- Presentation
Abstract not provided
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ORAL 39 - Potential Biomarkers for CT Screening (ID 149)
- Event: WCLC 2015
- Type: Oral Session
- Track: Screening and Early Detection
- Presentations: 8
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- Abstract
Background:
Currently, a blood test for lung cancer does not exist. Low-dose spiral computed tomography (CT) has been proposed as an early detection screening tool. However, despite its high sensitivity, the specificity of CT in lung cancer detection is poor. In addition, the necessity for repeated CT scans to determine growth rates over time can expose patients to potentially harmful radiation. Therefore, a minimally-invasive biomarker-based test that could further characterize CT-positive patients based on risk of malignancy would greatly enhance its clinical efficacy.
Methods:
From 2009 through 2013, six hospitals enrolled 1148 patients with lung cancer, 889 blood donors as healthy participants and 36 patients with other lung diseases. The lung cancer associated biomarker panels were identified from the pretreated serum samples and subsequently analyzed in three randomly determined subgroups, the discovery cohort (40 patients with lung cancer, and 45 healthy participants), test cohort (204 patients with lung cancer, and 120 healthy participants), and validation cohort (904 patients with lung cancer, 724 healthy participants, and 36 patients with other lung diseases). Finally the panel of biomarkers were used to predict 12 prospective patients with a suspicious pulmonary nodule by CT images.
Results:
The discovery cohort demonstrated that 4 serum biomarkers (C-reactive protein, prolactin, hepatocyte growth factor, and NY-ESO-1 autoantibody) were significantly higher in patients with lung cancer compared to healthy controls. The 4-biomarker panel was independently investigated in the test cohort and validation cohort. The test characteristics were area under the curve (AUC) of 0.835 (95% CI 0.79-0.873, sensitivity 70.1%, specificity 88.3%) in the test cohort, and 0.818 (95% CI 0.798-0.836, sensitivity 70.0%, specificity 79.6%) in the expanded validation cohort. The 4 biomarkers had no discriminatory power for detecting other benign lung diseases. The performance of the panels in patients with stage I-II lung cancer was AUC of 0.774 (95% CI 0.746-0.801) in the combined test and validation cohorts. Remarkably, analysis model generated by the biomarkers correctly predicted 7 out of 9 prospective patients having lung cancer, and 2 out of 3 patients as benign, which were further verified by the pathologist.
Conclusion:
This study identified four diagnostic biomarkers in serum samples with the potential to distinguish patients with lung cancer from healthy controls. This panel of serum proteins is valuable in suggesting the diagnosis of patients with an indeterminate pulmonary lesion, and potentially in predicting individuals at high risk for lung cancer. Further research is necessary to understand whether these have clinical implications for early detection of lung cancer.
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ORAL39.02 - Early Detection of Lung Cancer by a FISH-Based Sputum Test (ID 711)
16:45 - 18:15 | Author(s): W.R. Burfeind Jr., G. Soo Hoo, R. Batra, Y. Schwarz, N. Peled, T. Kaplan, G. Fink
- Abstract
- Presentation
Background:
Early detection represents an important opportunity for decreasing lung cancer mortality. Lung cancer screening with low-dose CT scanning is plagued by a high false-positive rate and non-invasive adjuncts that improve diagnostic accuracy or serve as a pre-screen may be helpful. This study evaluated the performance of a sputum based lung cancer detection (LCD) test that utilizes fluorescence in-situ hybridization (FISH) to detect chromosomal alterations at the 3p22.1 and 10q22.3 loci caused by a cancerous process.
Methods:
At 5 international centers, between March 2012 and July 2014, induced sputum samples were collected from 173 subjects with 8-30 mm solitary pulmonary nodules, where imaging and other subject characteristics mandated biopsy. At least 50 lower respiratory tract cells were required for analysis. The LCD Test, performed at one of 3 reference labs, enabled a combined analysis of sputum cytology and Target-FISH analysis on the same cell using an FDA approved imaging analysis system (BioView Duet™). The LCD test was considered positive if at least 7.5% of the target cells had an abnormal FISH pattern. The results of the LCD were then compared to the clinical pathology. Subjects with an initial non-surgical negative biopsy result were followed for up to 2 years to determine their final diagnosis.
Results:
There were 116 subjects who met the inclusion criteria, had a pathologic diagnosis of lung cancer if the nodule was malignant, and produced adequate sputum for analysis. Seventy-two subjects were diagnosed with lung cancer from the initial biopsy, 7 had definitive negative surgical biopsies, and 37 subjects were classified as indeterminate due to non-surgical negative biopsies. Initial positive concordance was 86.1% (62/72) and initial negative concordance was 71.4% (5/7). From the initial 37 indeterminate negative subjects, additional clinical analyses during the follow up period enabled a definitive classification for 23 subjects: 11 were diagnosed with lung cancer and 12 were reclassified as definitive negative. From this group the LCD test had a positive concordance of 81.8% (9/11) and a negative concordance of 91.7% (11/12). Overall, sensitivity was 85.5% (71/83), specificity was 84.2% (16/19), positive predictive value was 95.9%, and negative predictive value was 57.1%. Fourteen indeterminate negative subjects are still being clinically monitored. The test performance for nodules of 8-20mm was as good as the results for 21-30mm nodules.
Conclusion:
In a cohort of patients with a high risk of lung cancer, the LCD test had a high positive predictive value. A positive LCD test could potentially lead to an earlier intervention in a nodule that might otherwise have been monitored for growth. An adequate cancer resection might then be accomplished by segmental resection rather than lobectomy in smaller lesions. The LCD test may be useful as a decision support tool at critical points in the management of solitary pulmonary nodules detected by screening CT scans in subjects at high risk for lung cancer.
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ORAL39.03 - Clinical Utility of a Blood Based Circulating Tumour DNA Signature for the Diagnosis of Lung Cancer (ID 2457)
16:45 - 18:15 | Author(s): E. Lim, M. Freidin, D. Freidina, M. Leung, A. Rice, A.M. Fernandez, A. Nicholson
- Abstract
- Presentation
Background:
Lung cancer is conventionally diagnosed by confirmatory tissue biopsy, an invasive procedure that involves waiting time, costs and complications. The development push for a blood based liquid biopsy is a less invasive, more readily acceptable means to expedite the diagnosis and management of cancer. Circulating tumour DNA is promising in this regard as cancer specific genetic mutations are not usually found in the circulation of healthy individuals. The aim of our study is to report the performance of a three gene signature in for the diagnosis of cancer.
Methods:
Pre-operative blood samples were obtained from patients undergoing surgery for known or suspected lung cancer and 1ml aliquots of plasma were extracted from 9ml of EDTA preserved blood. DNA was extracted from the plasma using the QIAamp DNA blood mini kit. High resolution melt analysis was undertaken to identify mutations in hotspots of the TP53, KRAS and EGFR genes in the ctDNA from plasma as well as matching FFPE tissue. A positive test result was defined as a mutation identified in the plasma ctDNA and compared against the reference clinical histopathology report of the resected lung abnormality. Clinical test performance was quantified and reported conventionally using sensitivity and specificity.
Results:
Pre-operative blood was analysed in a blinded manner from 223 patients undergoing surgery at our institution, and the pathology reports were issued blinded to the blood test results. In total, 116 (52%) had primary lung cancer, 64 (29%) had secondary cancer, 6 (3%) had primary thoracic (not lung) cancer and 35 (16%) did not have any evidence of cancer. Of the 186 patients with confirmed cancer, a mutation was identified in the FFPE sections of the primary tumour of 113 (61%) and in the plasma ctDNA in 127 (68%) with substantial agreement of 85% and a kappa statistic of 0.70 (P<0.001). The clinical test performance for the blood based diagnostic signature was a sensitivity of 68% (95% CI 61-75), specificity of 91% (77 to 98), positive predictive value 98% (93-100) and a negative predictive value of 35% (25 to 46) when compared to conventional clinical histopathology reporting of the resected tissue.
Conclusion:
There is substantial agreement between the detection of ctDNA and FFPE tumour tissue mutations. We postulate higher mutation levels detected in the plasma is due to heterogeneity of tumour and FFPE sections in comparison to a global (plasma based ctDNA) estimate of mutation burden. Our results suggest blood based ctDNA analysis of cancer mutations is a specific, non-invasive test for the diagnosis of cancer. A positive test strongly rules in the diagnosis but a negative test does not have sufficient discriminatory ability to exclude the diagnosis of cancer.
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ORAL39.04 - Discussant for ORAL39.01, ORAL39.02, ORAL39.03 (ID 3437)
16:45 - 18:15 | Author(s): A. Vachani
- Abstract
- Presentation
Abstract not provided
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- Abstract
Background:
Current clinical diagnostic methods lack the specificity in detecting lung cancer patients. The issue is critical for stage I & II patients as there are no available biomarkers to indicate which high-risk patients should undergo adjuvant therapy. There is considerable evidence that microRNA plays a very important role in lung carcinogenesis. We postulated that the expression pattern of multiple microRNAs (miRNAs) could aid clinicians in detecting cancer patients thus reducing the mortality of lung cancer.
Methods:
Differential expressed miRNAs were analyzed by miRNA microarrays in 15 paired non-small-cell lung cancer (NSCLC) tumors and distant normal tissues. The identified miRNAs were further validated by qRT-PCR using snap-frozen lung tissue samples collected from independent 22 patients with NSCLC. Classification analyses of miRNA expression data were performed by the Bayesian Compound Covariate predictor (BCCP). The expression levels of miR-141-5p, miR-301a-3p and miR-1244 were also analyzed by qRT-PCR in serum samples collected from 60 patients with lung cancer and 50 healthy controls.
Results:
A total of 41 miRNAs was identified with significantly elevated levels in patients with lung cancer by profiling microRNA array, of which 12 miRNAs were further validated in the independent sample cohort. Multiplexing analysis with the panel of 12 miRNAs generated the highest discriminatory power in separating NSCLC from normal tissues with an AUC of 0.915 (95% CI = 0.894-1.000; P <0.001). Leave-one-out cross-validation revealed the 85% sensitivity and 95% specificity at a cutoff score of 0.5. In addition, serum miR-1244 was significantly upregulated in an independent trial and could distinguish NSCLC from controls with 77.6% sensitivity and 78.7% specificity.
Conclusion:
Our 12-miRNA classifier might have potential clinical utility in discriminating NSCLC from healthy population.
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ORAL39.06 - Whole Blood microRNA Expression May Not Be Useful for Screening Non-Small Cell Lung Cancer (ID 2547)
16:45 - 18:15 | Author(s): R. Mallick, S.K. Patnaik, E. Kannisto, A. Vachani, S. Yendamuri
- Abstract
- Presentation
Background:
Five studies have shown that microRNA levels in whole blood can be used to diagnose lung cancer. We conducted a large bi-institutional study to validate this finding.
Methods:
PAXgene[TM] Blood miRNA System (Qiagen®) was used for peripheral venous blood collection and total RNA isolation for 85 pathologic stage IA-IIIB non-small cell lung cancer cases and 76 clinically-relevant controls who either had a high risk of developing lung cancer because of smoking and age >50 y, or had a benign pulmonary nodule. Cases and controls were accrued at two institutions in the United States, Roswell Park Cancer Institute, Buffalo and University of Pennsylvania, Philadelphia. MiRCURY™ microarrays (Exiqon®) with locked nucleic acid hybridization probes were used to quantify microRNAs in RNA isolates. Quantification was also performed using Taqman™ microRNA reverse transcription (RT)-PCR assays (ABI®) for five microRNAs whose lung cancer-diagnostic biomarker utility had been suggested by the five published studies.
Results:
Cases (n=85) and controls (n=76) were similar for age, gender, race, and blood hemoglobin and leukocyte but not platelet levels (Table 1). Of the 1936 human mature microRNAs detectable with the microarray platform, 586 (30%) were identified as expressed and reliably quantified among the study's subjects. However, none of the microRNAs was differentially expressed between cases and controls (P >0.05 in test using empirical Bayes-moderated t statistics and false discovery rate <5%). In classification analysis using the whole blood microRNA profiles with leave-one-out internal cross-validation, accuracy was 48% and 50% with the support vector machines and top-scoring pair methods, respectively. With RT-PCR assays, cases and controls did not differ for any of the five microRNAs whose biomarker potential had been suggested by previous studies.Table 1. Characteristics of study groups; *Fisher's exact test for categorical variables, and t test for others; #blood values for 84 cases and 30 controls.
Cases Controls P* 85 76 Mean age, y (range, SD) 64 (41-83, 8) 61 (45-83, 9) 0.07 %male 49 51 0.87 %white 90 93 0.57 RPCI 42 32 0.43 U. Pennsylvania 43 44 Adenocarcinoma 43 Squamous cell 33 Other non-small cell 9 High-risk control 58 Nodule control 18 Leukocytes (x1000/µl; mean, SD)# 8.2 (2.6) 7.8 (2.1) 0.37 Platelets (x1000/µl; mean, SD)# 291.8 (114.3) 238.2 (50.2) 0.01 Hemoglobin (g/dl; mean, SD)# 13.4 (1.8) 13.9 (1.4) 0.15
Conclusion:
This study suggests that whole blood microRNA expression profiles may not be useful for developing biomarkers for use in non-invasive blood-based assays for generic screening of non-small lung cancer. Further studies are required to examine if whole blood microRNA diagnostic biomarkers may exist for use with specific types of lung cancer or non-cancer control conditions.
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ORAL39.07 - A Bronchial Genomic Classifier Measured in Airway Epithelial Cells Improves Diagnostic Sensitivity of Bronchoscopy for Lung Cancer (ID 2215)
16:45 - 18:15 | Author(s): A. Vachani, D. Whitney, A.C. Gower, K. Porta-Smith, J.S. Ferguson, J. Brody, G. Silvestri, M. Lenburg, A. Spira
- Abstract
- Presentation
Background:
Bronchoscopy is often used for the diagnosis of lung cancer however its sensitivity is imperfect, especially for small and peripheral lesions. Adjunctive methods to improve the sensitivity of cancer detection would reduce the need for more invasive follow-up procedures when bronchoscopy is non-diagnostic. It has previously been shown that gene expression of cytologically-normal bronchial airway epithelial cells is altered in smokers with lung cancer. In this study we evaluated the performance of a bronchial genomic classifier to predict malignancy in an independent cohort of suspect lung cancer patients.
Methods:
A bronchial genomic classifier consisting of the expression of 23 genes measured in the airway epithelium was evaluated in a previously published, independent cohort (n=163) of current and former undergoing bronchoscopy for suspect lung cancer. In cases where bronchoscopy was non-diagnostic for malignancy, the performance of the classifier was evaluated using ROC-AUC, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
Results:
In the test set, bronchoscopy led to a diagnosis in 40 of 78 patients with cancer (sensitivity=51%, 95% CI 40-63%). The combination of the classifier with bronchoscopy improved the sensitivity to 96% (95% CI 89-99%; p <0.001); see Table. The prediction accuracy of the classifier was similar in lesions <3cm, as well as across cancer stage and histology. Among the 123 patients with a non-diagnostic bronchoscopy, 38 were ultimately diagnosed with lung cancer (prevalence of 31%). In this group of patients, the classifier had an AUC of 0.81 (95% CI, 0.73-0.88), accurately identifying 35 of the 38 lung cancer patients (sensitivity=92%; 95% CI, 78-98%), and 45 of 85 patients with benign lesions (specificity=53%; 95% CI, 42-63%). Of the 48 patients with a negative classifier result, 45 were diagnosed with benign lesions (NPV=94%, 95% CI 83-99%).Table. Performance of bronchoscopy, classifier, and the combined procedures in the test set
a) The performance of the classifier was evaluated for patients in whom bronchoscopy did not result in a finding of lung cancer (n=123).Category Bronchoscopy Classifier[a] Combined Total, N 163 123 163 Lung Cancer, N 78 38 78 Benign Lesion, N 85 85 85 Sens. (95% CI) 51% (40-62%) 92% (78-98%) 96% (89-99%) Spec. (95% CI) 100% (95-100%) 53% (42-63%) 53% (42-63%) NPV (95% CI) 69% (60-77%) 94% (83-99%) 94% (83-98%) PPV (95% CI) 100% (90-100%) 47% (36-58%) 65% (56-73%)
Conclusion:
A gene expression classifier measured in bronchial epithelial cells is able to accurately identify those at low risk for lung cancer in patients who have undergone bronchoscopy with non-diagnostic results. Due to the high sensitivity and NPV of the classifier, it could potentially inform clinical decisions regarding the need for further invasive testing for lung cancer in patients whose bronchoscopy is non diagnostic.
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ORAL39.08 - Discussant for ORAL39.05, ORAL39.06, ORAL39.07 (ID 3438)
16:45 - 18:15 | Author(s): J.M. Siegfried
- Abstract
- Presentation
Abstract not provided
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ORAL 22 - Moving Beyond a Smoking Related-Cancer to the Young, Never-smokers and Inherited Disease (ID 117)
- Event: WCLC 2015
- Type: Oral Session
- Track: Biology, Pathology, and Molecular Testing
- Presentations: 1
- Moderators:K. Suda, H.A. Wakelee
- Coordinates: 9/08/2015, 10:45 - 12:15, Mile High Ballroom 1a-1f
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ORAL22.03 - Inter-, and Intratumoural Genomic Heterogeneity of Primary Pulmonary Adenocarcinoma in Never Smokers (ID 3231)
10:45 - 12:15 | Author(s): K. Fong
- Abstract
- Presentation
Background:
Lung cancer in never smokers may be enriched with oncogenic drivers. To explore patterns genomic changes among and within NS-LC, we performed multi-region whole genome sequencing (WGS) of primary pulmonary adenocarcinoma (LUAC).
Methods:
An observational study was performed on 8 cases of never-smoking LUAC resected with curative intent. Post-diagnostic residual fresh tumor was procured with informed consent, with constitutional samples from normal lung or blood. Selection criteria included: histologically confirmed LUAC; never-smoker [< 100 cigarettes in a lifetime]; no prior malignancy, cytotoxic therapy or thoracic radiotherapy. Tissue samples were procured by an anatomical pathologist (Table 1). Quality criteria were >40% tumor cellularity and <20% necrosis as assessed visually by 2 anatomical pathologists (Table 1). DNA was extracted using Qiagen AllPrep DNA/RNA Mini Kit and Blood Maxi Kit. WGS was performed on paired end libraries using Illumina's HiSeq 2000 platform (Table 1). Single nucleotide variants (SNVs) called by MuTect, Varscan, Strelka and SomaticSniper were considered ‘high priority’ if their predicted functional significance was ‘moderate’ or ‘high’ according to SNPEff. Genotyping was performed using Illumina’s HumanOmni2.5-8 array for copy number calling using the Genome Alteration Print tool.
Results:
14 tumour samples and 8 constitutional samples were sequenced (table 1). Figure 1 Common CNVs and SNVs were observed among and within cases (figure 1). Figure 2 In case 1, 3 of 6 (50%) genes harboring high priority variants were altered in all 4 regions. Similarly, for cases 2 and 3, 8/10 (80%) and 4/8 (50%) genes were altered by high priority variants in all regions.
Conclusion:
Patterns of SNVs and CNVs in LUAC demonstrate areas of common genomic changes and significant inter-, and intratumoral heterogeneity. These findings have significant implications for our understanding of lung cancer biology, also diagnostic testing of lung cancer and clinical trial design.
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