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I. Laird-Offringa
Moderator of
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MS 13 - The Other "-omics" (ID 31)
- Event: WCLC 2015
- Type: Mini Symposium
- Track: Biology, Pathology, and Molecular Testing
- Presentations: 4
- Moderators:I. Laird-Offringa, J. Herman
- Coordinates: 9/08/2015, 14:15 - 15:45, Mile High Ballroom 2a-3b
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MS13.01 - Epigenomics (ID 1904)
14:20 - 14:40 | Author(s): J. Herman
- Abstract
Abstract:
DNA methylation changes in lung cancer: Defining functional events and use of cancer specific changes for early detection. Epigenetic alterations in lung cancer represent early changes which are associated with tumor initiation and progression. Alterations in DNA methylation include the global loss of DNA methylation in non-promoter region and selective CpG island promoter region methylation leading to gene silencing. Previous studies have focused on individual loci identified through candidate gene approaches. However, recent improvements in technology allow the assessment of genome wide patterns of DNA methylation. The comprehensive genome wide analysis of molecular changes in cancer completed by The Cancer Genome Atlas (TCGA) includes determination of DNA methylation using the Illumina Infinium 450K array. Initial analyses have primarily focused upon defining methylation subtypes. However, this data can be used to determine novel cancer specific events which are associated with transcriptional silencing to identify candidate driver epigenetic alterations. New promoter region DNA methylation changes leading to transcriptional silencing are found in multiple signaling pathways critical for lung cancer development. In addition, a search for common tumor specific DNA methylation provides new markers for early detection strategies. These novel biomarkers can be combined with novel methods developed with extremely sensitive assays for the detection of hypermethylated DNA sequences. By combining these more sensitive methods of detection with highly prevalent methylation changes in lung cancer, utrasensitive detection of tumor specific changes in DNA methylation in blood and sputum samples is possible. This molecular detection can complement CT screening to address the important issue of early detection of lung cancer.
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MS13.02 - Proteomics and Phosphoproteomics (ID 1905)
14:40 - 15:00 | Author(s): E. Haura
- Abstract
Abstract:
I will discuss opportunities and future directions in profiling lung cancer using mass spectrometry based proteomic technologies. This includes a proposal to perform deep integrated proteo-genomics studies on cancer subtypes to produce more complete views of the tumor architecture, allow contextual understanding of major drug targets, and discover new lung cancer subtypes. Alterations in the genomes of cancers ultimately get integrated and produce a cancer proteome that can be analyzed using modern state of the art mass spectrometry proteomic tools. For example, signaling pathways and networks involved in cancers are built using a ‘parts list’ of the cancer genome, such as through integrating mutated genes, genes altered through differential expression (i.e. copy number gain or loss), and through regulation by micro-RNA molecules. DNA sequencing-based atlases exist for major tumors allowing ‘part lists’ for cancers; however, these atlases lack integration with expressed proteomes and signaling architectures. By taking into account all these alterations in the cancer genome, cancer proteomics can annotate and prioritize proteins and pathways important for cancer growth and survival. Furthermore, microenvironmental influences, known to be important in drug response, are lacking from these DNA based studies. Proteomics can inform about active pathways driving cancers and lead to novel combination therapy approaches for targeting complex oncogenic networks. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is increasingly used to study cancer proteomes. This includes examining the ‘expressed proteome’ through shotgun proteomics, global signaling by annotating key post-translational events (phosphorylation, acetylation, ubiquination) events in cancers or assembling protein-protein interaction data that yield network views of cancer. This allows unbiased and global views of signaling events in cancer thus offering complementary views of cancer biology that are not considered by sequencing of genes or gene expression. By integrating DNA-RNA-proteome-network type data, the co-existing driver processes instilled by the genome that either surround or act in parallel to drug targets can be mapped directly onto cancer molecular machines that drive cancer progression and response to therapy. Discovery proteomics has become a widely used tool in our laboratory. This approach provides an unbiased view of the components in a sample, supporting the testing of multiple hypotheses and generating new leads. I will discuss examples integrating complementary mass spectrometry approaches to build molecular snapshots of cancer proteomes, including phosphoproteomics in tumors related to drug resistance (1, 2), drug affinity selection of proteins and identification of drug targets using mass spectrometry (3-6), and protein-protein interaction mapping(7-9). Literature Cited: 1. Yoshida T, Zhang G, Smith MA, Lopez AS, Bai Y, Li J, Fang B, Koomen JM, Rawal B, Fisher KJ, Chen YA, Kitano M, Morita Y, Yamaguchi H, Shibata K, Okabe T, Okamoto I, Nakagawa K, Haura EB. Tyrosine phosphoproteomics identified both co-drivers and co-targeting strategies for T790M-related EGFR-TKI resistance in non-small cell lung cancer. Clin Cancer Res. 2014. doi: 10.1158/1078-0432.CCR-13-1559. PubMed PMID: 24919575. 2. Bai Y, Kim JY, Watters JM, Fang B, Kinose F, Song L, Koomen JM, Teer JK, Fisher K, Chen YA, Rix U, Haura EB. Adaptive Responses to Dasatinib-Treated Lung Squamous Cell Cancer Cells Harboring DDR2 Mutations. Cancer Res. 2014;74(24):7217-28. doi: 10.1158/0008-5472.CAN-14-0505. PubMed PMID: 25348954. 3. Remsing Rix LL, Kuenzi BM, Luo Y, Remily-Wood E, Kinose F, Wright G, Li J, Koomen JM, Haura EB, Lawrence HR, Rix U. GSK3 alpha and beta are new functionally relevant targets of tivantinib in lung cancer cells. ACS Chem Biol. 2014;9(2):353-8. doi: 10.1021/cb400660a. PubMed PMID: 24215125; PubMed Central PMCID: PMC3944088. 4. Gridling M, Ficarro SB, Breitwieser FP, Song L, Parapatics K, Colinge J, Haura EB, Marto JA, Superti-Furga G, Bennett KL, Rix U. Identification of kinase inhibitor targets in the lung cancer microenvironment by chemical and phosphoproteomics. Mol Cancer Ther. 2014;13(11):2751-62. doi: 10.1158/1535-7163.MCT-14-0152. PubMed PMID: 25189542; PubMed Central PMCID: PMC4221415. 5. Li J, Rix U, Fang B, Bai Y, Edwards A, Colinge J, Bennett KL, Gao J, Song L, Eschrich S, Superti-Furga G, Koomen J, Haura EB. A chemical and phosphoproteomic characterization of dasatinib action in lung cancer. Nat Chem Biol. 2010;6(4):291-9. doi: 10.1038/nchembio.332. PubMed PMID: 20190765; PubMed Central PMCID: PMC2842457. 6. Chamrad I, Rix U, Stukalov A, Gridling M, Parapatics K, Muller AC, Altiok S, Colinge J, Superti-Furga G, Haura EB, Bennett KL. A miniaturized chemical proteomic approach for target profiling of clinical kinase inhibitors in tumor biopsies. J Proteome Res. 2013;12(9):4005-17. doi: 10.1021/pr400309p. PubMed PMID: 23901793; PubMed Central PMCID: PMC4127982. 7. Smith MA, Hall R, Fisher K, Haake SM, Khalil F, Schabath MB, Vuaroqueaux V, Fiebig HH, Altiok S, Chen YA, Haura EB. Annotation of human cancers with EGFR signaling-associated protein complexes using proximity ligation assays. Sci Signal. 2015;8(359):ra4. doi: 10.1126/scisignal.2005906. PubMed PMID: 25587191. 8. Li J, Bennett K, Stukalov A, Fang B, Zhang G, Yoshida T, Okamoto I, Kim JY, Song L, Bai Y, Qian X, Rawal B, Schell M, Grebien F, Winter G, Rix U, Eschrich S, Colinge J, Koomen J, Superti-Furga G, Haura EB. Perturbation of the mutated EGFR interactome identifies vulnerabilities and resistance mechanisms. Mol Syst Biol. 2013;9:705. doi: 10.1038/msb.2013.61. PubMed PMID: 24189400; PubMed Central PMCID: PMC4039310. 9. Haura EB, Muller A, Breitwieser FP, Li J, Grebien F, Colinge J, Bennett KL. Using iTRAQ combined with tandem affinity purification to enhance low-abundance proteins associated with somatically mutated EGFR core complexes in lung cancer. Journal of Proteome Research. 2011;10(1):182-90. Epub 2010/10/16. doi: 10.1021/pr100863f. PubMed PMID: 20945942; PubMed Central PMCID: PMC3017669.
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MS13.03 - Genomics - Beyond the Driver Oncogene (Role of Tumor Suppressors, TP53, LKB1, PTEN, Etc.) (ID 1906)
15:00 - 15:20 | Author(s): K. Wong
- Abstract
- Presentation
Abstract not provided
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MS13.04 - Integrating "omics" for a Unified View of Lung Cancer (ID 1907)
15:20 - 15:40 | Author(s): J. Spicer
- Abstract
- Presentation
Abstract:
Study of genomics, epigenomics and proteomics may contribute to an understanding aetiology, prevention, early diagnosis, classification, treatment selection, and novel trial design in lung cancer. The clinical material available for analysis ranges from tumour biopsy to pleural fluid, bronchoalveolar lavage, saliva and even urine. The available techniques are in many cases sensitive (including PCR and mass spectrometry (MS)), and specificity can be optimised especially with reference to normal material such as germline DNA. The omic landscape of lung cancer has been extensively characterised (The Cancer Genome Atlas Research Network, 2014). This provides insight into disease biology via SNP/exome/whole genome sequencing, CpG DNA methylation, mRNA sequencing and protein expression profiling. Epigenomics is a key component since promoter hypermethylation occurs an early event in lung tumourigenesis (Belinsky, S. et al. 2015), targeting tumour suppressor genes. Indeed epigenomics and genomics are intimately linked, with CpG methylation leading to base substitution through 5-methylcytosine deamination, and enhancing the effect of exogenous carcinogens. Although the contribution of smoking to lung cancer aetiology has long been recognised, genomics is now providing insight into somatic mutagenesis as the mechanism of this causal interaction, as well as into tumourigenesis in non-smokers. However, this wealth of genetic and epigenetic information requires further analysis to establish which of these events really drive the phenotype, and which can be biologically validated as targets for therapy. Both genetic and epigenetic targets for therapy of lung cancer have been identified, in the form of both activated oncogenes and loss of tumour suppressor gene function. In some cases tumour genotype proves valuable as a predictive biomarker for patient selection. Several current biomarker-directed trials (such as Lung-MAP and MATRIX) are seeking to identify further successful genotype/therapy pairings. Despite impressive response rates in genomically stratified populations, regulators seem still to require validation of omics-driven treatment selection in a strategy-testing design, randomising to standard of care or personalised therapy. A further therapeutic application of genomics is characterisation of resistance mechanisms, an understanding of which has already led directly to next generation drugs in several drug classes including inhibitors of EGFR and ALK. It is genetic events that are at the origin of the hallmarks of cancer, but proteins, as the effectors of cellular processes, are key to a full understanding of the cancer phenotype. Some have argued that proteomic markers, as a surrogate for the genetic drivers, may be inferior to genomics. Certainly proteomic biomarkers are the less dynamic because their half life is measured in weeks, compared with a few hours for nucleic acids. Beyond the small number of actionable mutations already described in non-small cell lung cancer, the diagnostic, prognostic, and predictive potential of a large number of omic markers has been studied, and in most cases problems with reproducibility have limited their clinical impact. Indeed the utility of multi-gene predictive markers described to date, most likely to be of clinical value in therapy, is limited. An eight-peak MALDI-MS proteomic profile has been developed as a predictive tool (Taguchi, F. et al. 2007). Long suspected, the contribution of tumour heterogeneity to an analysis of tumour omics is now proven to be potentially problematic (Bedard, P. et al. 2013). The study of circulating genomic (eg circulating free DNA, cfDNA) and proteomic tumour markers provides an opportunity for integration of this heterogeneity. Nevertheless, further questions remain. For example, do primary and metastatic sites release similar amounts of DNA and protein into the circulation? However, potential advantages of these liquid biopsies are obvious, as they can be repeated over time without risk or inconvenience to the patient. Still to be fully clarified is the clinical utility of this approach. Possible applications include early discontinuation of toxic failing therapy, evaluation of an emerging resistance mechanism and selection of next therapy, and prognostication (for example, selection for adjuvant therapy). Earlier liquid biopsy methods required initial analysis of potential biomarkers in a tumour, to identify what to look for, followed by detection of this marker in blood samples. This approach requires personalisation for each patient. Newer techniques allow direct analysis, for example next generation sequencing of cfDNA. It is also possible to study the methylation status of cfDNA, so these liquid biopsies may in addition be relevant to the study of tumour epigenomics. cfDNA may be superior to circulating tumour cells (CTCs) as a biomarker since in some patients cfDNA but not CTC is detectable (Bettegowda, C. et al. 2014) The most prominent recent therapeutic advance in lung cancer is the validation of immunotherapy in the context of checkpoint inhibition. While this approach appears to target the tumour only indirectly, via host immunity, there is already good evidence that the genomic context of the target tumour is critically significant (Gubin, M. et al. 2015) The optimum strategy for selection of patients for clinical omic testing remains to be finalised. Should this be for all patients, from the time of diagnosis, or only after completion of standard care? And what material is ideal for testing (archival or contemporaneous biopsy, for example)? Guidelines on what, when and how to test are available (Lindeman, N. et al. 2013), but this advice quickly becomes out of date given the pace of change in the field. Further practical concerns include access to technology, turnaround time for testing, interpretation of molecular pathology results and bioinformatics, and clinical relevance. Fundamental questions arise about which changes are actionable, and the importance of any findings in the germline sequence (incidental or deleterious). Finally, quality control and regulation of omic technologies is demanding and not necessarily well served by existing approaches and infrastructure (Evans, B. et al. 2015), and these aspects must be developed alongside the emergence of these novel technologies. Progress in development of these techniques has been rapid, but maximum utility to patients is still to be developed. Omics have made major contributions to the understanding of lung cancer biology, and to the identification of a growing spectrum of therapeutic targets, but more work remains to be done. References Bedard, P et al. (2013). Tumour heterogeneity in the clinic. Nature 501; 355-364 Belinsky S, et al. (2015). Gene promoter methylation in plasma and sputum increases with lung cancer risk. Clin Cancer Res 11; 6505-11 Bettegowda, C et al. (2014). Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci Transl Med 6, 224ra24 Evans, B et al. (2015). The FDA and genomic tests - getting regulation right. New Engl J Med 372; 2258-2264 Gubin, M et al. (2015). PD-1 blockade in tumors with mismatch-repair deficiency. New Engl J Med 372; 2509-2520 Lindeman, N et al. (2013). Molecular testing guideline for selection of lung cancer patients for EGFR and ALK tyrosine kinase inhibitors. J Thoracic Oncol 8; 823-59 Pastor, M et al. (2013). Proteomic biomarkers in lung cancer. Clin Transl Oncol 15; 671-682 Taguchi F et al. (2007). Mass spectrometry to classify non-small-cell lung cancer patients for clinical outcome after treatment with epidermal growth factor receptor tyrosine kinase inhibitors: a multicohort cross-institutional study. J Natl Cancer Inst 99; 838-46 The Cancer Genome Atlas Research Network (2014). Comprehensive molecular profiling of lung adenocarcinoma. Nature 511; 543-550
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P3.04 - Poster Session/ Biology, Pathology, and Molecular Testing (ID 235)
- Event: WCLC 2015
- Type: Poster
- Track: Biology, Pathology, and Molecular Testing
- Presentations: 1
- Moderators:
- Coordinates: 9/09/2015, 09:30 - 17:00, Exhibit Hall (Hall B+C)
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P3.04-096 - A Feasibility Pilot Study Testing Six DNA Methylation Markers to Improve Detection of Malignant Pleural Effusions in Lung Cancer (ID 1382)
09:30 - 09:30 | Author(s): I. Laird-Offringa
- Abstract
Background:
Patients presenting with pleural effusion and suspected primary lung cancer raise suspicion for pleural metastasis. Accurate diagnosis is critical; metastatic pleural effusion indicates stage IV disease with significantly different treatment and prognosis. Currently diagnosis is obtained by cytology, however the mean sensitivity of cytology is only 60%. Lung cancer-specific DNA methylation markers may improve sensitivity. Here we determine whether six previously identified DNA methylation markers can detect malignancy in pleural effusions of lung cancer patients.
Methods:
Pleural effusions were collected from one small cell lung cancer (SCLC), 5 non-small cell lung cancer (NSCLC) and 3 patients with benign conditions not suspicious for cancer, presenting to USC Keck Hospital and Los Angeles County Hospital (June 2013 to March 2015). The 6 lung cancer patients underwent drainage and pleural fluid cytology. Samples were centrifuged (3000g, 10 minutes) to remove cellular material. DNA was extracted from 1 ml of pleural fluid and bisulfite converted. MethyLight was used to quantitate the methylation levels of the markers. The preliminary specificity and sensitivity were calculated.
Results:
Percent of methylated reference (PMR, a measure of methylation levels compared to enzymatically fully methylated DNA) is shown in Table 1. Markers LuCa-1 and LuCa-2 had 100% sensitivity and 100% specificity (Table 2). Two patients (5 and 6) had negative cytology but positive malignancy based on markers. Pathologic confirmation of malignant involvement of the pleura was obtained in both cases, one with a different cytology specimen and the other by thoracoscopic exploration. Figure 1Table 2 DNA Methylation Marker Sensitivity & Specificity
Marker Sensitivity Specificity LuCa-1 100% 100% LuCa-2 100% 100% LuCa-3 100% 67% LuCa-4 100% 33% LuCa-5 100% 33% LuCa-6 100% 33%
Conclusion:
This pilot study indicates that DNA methylation markers can detect lung cancer in pleural effusions, potentially with sensitivity and specificity that exceed routine cytology. Further analysis of these 6 markers, used separately or in combination as a multiplexed panel to detect pleural malignancy is warranted.
