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J. Herman
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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Author of
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MINI 12 - Biomarkers and Lung Nodule Management (ID 109)
- Event: WCLC 2015
- Type: Mini Oral
- Track: Screening and Early Detection
- Presentations: 1
- Moderators:J.M. Siegfried, H.I. Pass
- Coordinates: 9/07/2015, 16:45 - 18:15, 401-404
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MINI12.13 - Early Detection of Lung Cancer Using DNA Methylation in Plasma and Sputum (ID 1691)
17:55 - 18:00 | Author(s): J. Herman
- Abstract
Background:
Lung cancer is the worldwide leading cause of cancer-related mortality. Almost 85% of lung cancer cases are diagnosed at late stages with a five-year-survival probability at the time of diagnosis of 16.8%. The National Lung Screening Trial (NLST) showed a 20% reduction in lung cancer mortality using low-dose computed tomography (CT) screening, but there was also a 96.4% false positive rate. Lung cancer screening might be improved through cancer specific biomarkers detected in body fluids such as plasma or sputum. Previous studies using DNA methylation failed to achieve adequate sensitivity because of use of infrequently methylated genes and detection techniques unable to detect the small amounts of DNA yielded from blood and sputum. We sought to improve the diagnostic accuracy using gene promoter methylation in blood and sputum through the use of Methylation On Beads (MOB) and a highly lung-cancer specific panel of genes for detection of lung cancer.
Methods:
We conducted a prospective case-control study obtaining cases and controls from the Lung Cancer Spore. Cases had pathological confirmation of Non-Small Cell Lung Cancer (NSCLC) lesion stage IA or IB. Controls were defined as patients with pathological confirmation of non-cancerous lesion in the surgical specimens. Plasma, sputum and CT scans were obtained pre-operatively. We quantified methylation levels and the amplification cycle threshold from sputum and plasma samples by using MOB and quantitative methylation specific real-time PCR lung cancer-related genes previously identified from The Cancer Genome Atlas (TCGA). This panel of genes include: CDO1, TAC1, HOXA7, HOXA9, SOX17 and ZFP42.
Results:
A total of 210 subjects fulfilled inclusion criteria, including 150 patients with NSCLC and 60 patients with non-cancerous lesions. All six genes were methylated in significantly more people with cancer than without cancer in both plasma and sputum (p<0.001) with the exception of HOXA9 in sputum, which was methylated in more than 90% of people with cancer and more than 90% of people without cancer. After adjusting by age and pack·year, the methylated genes that were significantly associated with risk of lung cancer stage IA & IB from blood samples were: CDO1 (p=0.009), TAC1 (<0.001), HOXA9 (p=0.005), SOX17 (<0.001) & ZFP42 (p=0.003) and from sputum samples were: CDO1 (p=0.066), TAC1 (p=0.007), ZFP42 (p=0.009). Sensitivity and specificity for lung cancer diagnosis using the 3 best genes in plasma was 91% and 68% respectively and for sputum 91% and 88%. Area under the curve for 3 best genes in plasma was 0.78 95% confidence interval (CI) (0.69-0.87) (p<0.001) and for the best 3 genes in sputum 0.88 95% CI (0.77-0.99) (p<0.001).
Conclusion:
This study shows that its is possible to obtain high diagnostic accuracy for Lung Cancer in early stages using a panel of methylated promoter genes in Plasma and Sputum, by using Methylation-on-beads. These epigenetic biomarkers could potentially be used to identify patients with high risk of lung cancer development. reducing unnecessary tests and increasing the chance to diagnose lung cancer at earlier stages
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MS 13 - The Other "-omics" (ID 31)
- Event: WCLC 2015
- Type: Mini Symposium
- Track: Biology, Pathology, and Molecular Testing
- Presentations: 1
- 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.
