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S. Devarakonda

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    ED01 - Biology of Lung Cancer (ID 263)

    • Event: WCLC 2016
    • Type: Education Session
    • Track: Biology/Pathology
    • Presentations: 1
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      ED01.03 - Insights from TCGA (ID 6423)

      11:00 - 12:30  |  Author(s): S. Devarakonda

      • Abstract
      • Presentation
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      Advances in sequencing technologies have made it possible to characterize and catalogue genomic alterations in several cancers in an unbiased manner. Multiple individual groups and large-scale consortia such as The Cancer Genomic Atlas (TCGA), have sequenced close to a thousand lung cancer samples to date. [1-8]Apart from furthering our understanding of the frequently altered pathways in common histological subtypes of lung cancer, data from these studies have also highlighted the molecular heterogeneity underlying this disease. Investigators from TCGA initially reported genomic, transcriptomic, methylation and copy-number alterations in 230 adenocarcinoma (LUAD) and 178 squamous cell carcinoma (SQCC) samples.[1][,][2]An updated analysis, that included a total of 660 LUAD and 484 SQCC samples, was subsequently published in early 2016.[9] While the majority of lung cancer patients have a history of cigarette smoking, nearly 10% of patients are lifelong never-smokers.[3]Lung cancers that arise in smokers exhibit some of the highest mutational burdens across all human cancers (8-10 mutations/Mb). The vast majority of these mutations are C>A transversions. On the contrary, tumors from never smokers demonstrate a much lower mutational burden (0.8-1 mutations/Mb) and are enriched for C>T transitions. [1][,][2] Single nucleotide variations (SNVs) and copy number alterations (CNAs) While both LUAD and SQCC show frequent inactivation of the tumor suppressors TP53 and CDKN2A, these alterations are considerably more common in SQCCs. CDKN2A harbors the loci for two isoforms, p14ARF and p16INK4A, and is inactivated in SQCC through homozygous deletion (29%), methylation (21%), inactivating mutations (18%), or exon 1b skipping (4%). [1][,][2]These findings indicate a strong selective pressure for the loss of these tumor suppressors in NSCLC. The pattern of oncogenic alterations varies considerably between LUAD and SQCC. While LUADs typically showed activating RTK/RAS/RAF pathway mutations, these mutations are highly infrequent in SQCCs - which predominantly showed alterations in oxidative stress response (NFE2L2, KEAP1 and CUL3) and squamous differentiation pathways (SOX2, TP63, NOTCH1, etc.) in 44% of samples. [1,2]KRAS is the most commonly mutated oncogene in LUAD, followed by EGFR, BRAF, PIK3CA, and MET. The majority of EGFR mutations in LUAD are targetable (L858R or exon 19 deletion) with tyrosine kinase inhibitors (TKIs).[1]In contrast, such alterations are absent in SQCC. Two SQCC samples however demonstrated L861Q mutations in EGFR, which are potentially targetable with TKIs. [1][,][2]Although SQCC and LUAD shared several CNAs at the chromosomal arm level, amplification of 3q was frequent in SQCC. This region harbors important oncogenes such as SOX2, PIK3CA, and TP63. LUADs frequently showed amplifications in genes such as NKX2-1, TERT, MDM2, KRAS, and EGFR.[1][,][2]Oncogenic activation of kinases such as ALK, ROS1, and RET through rearrangement has been well described in LUAD, and these fusions are targetable with TKIs. These fusions were seen in 1-2% (ALK : 3/230, ROS1: 4/230, and RET: 2/230 samples) of LUADs. [1][,][2] Transcriptome analysis Deregulated splicing can be a consequence of mutations that alter splice-sites within a gene or splicing factors. Mutations in the proto-oncogene MET that lead to exon 14 skipping, and abnormal splicing of proto-oncogenes such as CTNNB1 as a result of U2AF1 mutation have been described in LUAD. [1] Transcriptome analyses have also enabled a reclassification of LUADs and SQCCs into three and four distinct subtypes, respectively. LUAD samples can be categorized as terminal respiratory unit (enriched for EGFR mutations and fusions; favorable prognosis), proximal-inflammatory (NF1 and TP53 co-mutation), or proximal-proliferative (KRAS and STK11 alterations) subtypes. Similarly, SQCCs can be classified as classical, basal, secretory, or primitive. Alterations in genes that participate in the oxidative stress response pathway, hypermethylation, and chromosomal instability are characteristic of the classical subtype (associated with heavy smoking and poor prognosis). [1][,][2] Key pathogenic alterations TCGA analysis revealed alterations in well known oncogenic drivers involving RAS signaling pathway in 62% of LUAD.. These samples with readily identifiable oncogenic driver alterations were collectively labeled ‘oncogene-positive’. Additional analyses of the ‘oncogene-negative’ sample cohort showed enrichment for RIT1, and NF1 mutations. Given the role of RIT1 and NF1 in RTK/RAS/RAF signaling, samples with these mutations were reclassified as oncogene positive, increasing the overall percentage of oncogene positive samples in LUAD to 76%. Nearly 69% of SQCC samples showed alterations in genes regulating PI3K/AKT, or RTK/RAS signaling. [1][,][2] The inability to readily identify an oncogenic driver in nearly a third of sequenced lung cancer samples highlights the need for greater powering of subsequent studies to identify novel low frequency genomic alterations. For instance, previously uncharacterized alterations in the RTK/RAS/RAF pathway were observed in RASA1, SOS1 in the updated TCGA analysis which analyzed a much larger cohort of samples.[9] Overall, despite showing a few similarities between LUAD and SQCC, investigators of TCGA reported prominent differences between the genomic landscapes of these subtypes. These subtypes have more of their alterations in common with other cancers than with one another. SQCCs more closely resembled head and neck squamous cell and bladder cancer, while LUAD resembled glioblastoma multiforme and colorectal cancer in this regard. [9] Immunotherapies The vast majority of lung cancers do not harbor alterations that are targetable by TKIs. [1][,][2 ]Immune checkpoint inhibitors are approved for use in patients with metastatic NSCLC. There is a clear need to develop optimal predictive biomarkers to identify those who are likely to respond to immune checkpoint inhibitors. Mutational burden has been correlated with better response to checkpoint inhibitors. Furthermore, using exome and transcriptome sequencing and sophisticated bioinformatics, it is now possible to identify mutated and expressed genes that could potentially serve as a trigger for immune response (so called neoantigens) once immune checkpoints like programmed death-1 or programmed death ligand-1 are inhibited.. Swanton and colleagues performed a neoantigen and clonality analysis on TCGA samples to examine characteristics such as neoantigen burden and intratumor heterogeneity (ITH), and their impact on survival. In LUAD, a higher neoantigen burden was significantly associated with longer survival. Although not statistically significant, there was a trend towards longer survival in molecularly homogeneous tumors (<1% ITH) as opposed to heterogeneous tumors. The updated TCGA analysis showed that 47% of LUAD and 53% of SQCC samples exhibited at least five predicted neoantigens. Efforts are ongoing to develop personalized vaccine therapy using predicted neoantigens in lung cancer and other malignancies. Outcomes for patients with advanced lung cancer are likely to improve in the near future with further advances in genome sequencing, molecularly targeted therapies and immunotherapies . [12] References 1. Network CGAR. Comprehensive molecular profiling of lung adenocarcinoma. Nature 2014;511:543-50. 2. Network CGAR. Comprehensive genomic characterization of squamous cell lung cancers. Nature 2012;489:519-25. 3. Govindan R, Ding L, Griffith M, et al. Genomic landscape of non-small cell lung cancer in smokers and never-smokers. Cell 2012;150:1121-34. 4. Imielinski M, Berger AH, Hammerman PS, et al. Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell 2012;150:1107-20. 5. George J, Lim JS, Jang SJ, et al. Comprehensive genomic profiles of small cell lung cancer. Nature 2015;524:47-53. 6. Rudin CM, Durinck S, Stawiski EW, et al. Comprehensive genomic analysis identifies SOX2 as a frequently amplified gene in small-cell lung cancer. Nat Genet 2012;44:1111-6. 7. Peifer M, Fernández-Cuesta L, Sos ML, et al. Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer. Nat Genet 2012;44:1104-10. 8. Seo JS, Ju YS, Lee WC, et al. The transcriptional landscape and mutational profile of lung adenocarcinoma. Genome Res 2012;22:2109-19. 9. Campbell JD, Alexandrov A, Kim J, et al. Distinct patterns of somatic genome alterations in lung adenocarcinomas and squamous cell carcinomas. Nat Genet 2016;48:607-16. 10. Katayama R, Shaw AT, Khan TM, et al. Mechanisms of acquired crizotinib resistance in ALK-rearranged lung Cancers. Sci Transl Med 2012;4:120ra17. 11. Choi YL, Soda M, Yamashita Y, et al. EML4-ALK mutations in lung cancer that confer resistance to ALK inhibitors. N Engl J Med 2010;363:1734-9. 12. McGranahan N, Furness AJ, Rosenthal R, et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 2016;351:1463-9.

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