In recent years, new therapy options for the treatment of advanced lung cancer have been realized through better understanding of its molecular underpinnings. In adenocarcinoma of the lung (ADC), identification of somatic gene mutations, gene amplifications, or gene fusions of oncogenes such as receptor tyrosine kinases has facilitated development of targeted agents with small molecule kinase inhibitors or antibody therapies. Mutations in the epidermal growth factor receptor (EGFR) or rearrangements creating fusion proteins of the EML4 and ALK tyrosine kinase now enable treatments with either EGFR or ALK tyrosine kinase inhibitors and improved patient outcomes 1-3. However, little inroads in targeted therapy have been made for squamous cell lung cancer (SCC), despite initial enthusiasm about targets including EGFR, fibroblast growth factor receptors (FGFR), discoidin domain receptors (DDR2), and phosphatidylinositide 3-kinases (PI3K) 4. In contrast, use of immune checkpoint antibody therapy has demonstrated durable tumor regressions in both ADC and SCC histologies with prolonged survival. This has led to approval of multiple antibodies targeting the PD-1/PD-L1 interaction for patients with advanced disease and now provides an alternative therapy beyond conventional cytotoxic chemotherapy for patients with advanced SCC 5-8.
Genomic technologies have provided important insights into the molecular underpinnings of SCC. The Cancer Genome Atlas (TCGA) identified recurrent mutations in genes associated with cell cycle and apoptosis (TP53, CDKN2A, RB1), antioxidant gene expression (NFE2L2, KEAP1), phosphatidylinositide 3-kinase signaling (PIK3CA, PTEN), and epigenetic signaling (MLL2) 9. This study also found high level changes in chromosome gain and loss associated with severe genomic instability. Subclasses of SCC have also been defined using transcriptomic data 9-11. In addition to tumor autonomous features, patterns of infiltrating immune cell types have been associated with tumor progression and patient prognosis 12,13. Based on these results, newer studies such as NCI Molecular Analysis for Therapy Choice (MATCH) are attempting to capitalize on improved molecular knowledge of SCC and are employing precision medicine to approach targets including PI3K, CDK4/6, FGFR, MET, and PD-L1.
To provide additional insights into SCC, Stewart et al. reported an integrated analysis incorporating expression proteomics analysis of protein abundance alongside DNA copy number analyses, somatic mutations, and mRNA expression via RNA sequencing in 108 surgically resected SCC (from Moffitt's Total Cancer Care® Protocol) with accompanying clinical outcome data, evaluation of tumor pathology, and other clinically relevant data 14. The incorporation of mass spectrometry-based proteomic data was a critical new addition as protein abundance can correlate poorly with corresponding mRNA abundance 15-18. The study by Stewart et al. leveraged prior deep genomic and transcriptomic studies of SCC allowing a focused examination of the SCC proteome and its relationship to previously observed genomic or transcriptomic subgroups 9,11.
In their study, Stewart et al. determined that the SCC tumors could be grouped into 3 subtypes based on their protein expression patterns, named Inflamed, Redox, and Mixed. The Inflamed subtype accounted for 40% of the cohort, and tumors in this subtype had higher levels of proteins associated with immune cells, especially neutrophils or myeloid cells, and an active inflammatory response. Based on RNA data, they discovered that the Inflamed subtype also had a high proportion of other immune cells, including memory B-cells and monocytes, and was associated with higher levels of PD-1 than the other two subtypes.
The Redox comprised 47% of the cohort. These tumors were characterized by higher levels of proteins that are associated with oxidation-reduction cellular signaling pathways. The Redox subtype also had a higher number of genetic and chromosomal alterations that are known to be involved in SCC development. Using these data as guides, they identified new vulnerabilities that could be possible future therapeutic targets.
The final subtype, Mixed, represented only 13% of the tumors and only displayed an increased level of four proteins. The authors did not find any significant chromosomal alterations in this subtype but did learn that the mixed group had more mutations in the APC gene and had a greater infiltration of stromal cells than the other subtype.
The analysis showed that the three subtypes did not correspond to better or worse patient outcomes. However, tertiary lymph node structures, more commonly found in the Inflamed subtype, were associated with better outcomes. "These findings are in line with the general lack of agreement of prognostic signatures in SCC but now strongly suggest that an active immune response, indicated by tertiary lymph node structures, is associated with better outcomes. We hope to better understand this in future studies and determine how to exploit this knowledge for new therapy," said Eric Haura, M.D., senior author, director of the Lung Cancer Center of Excellence, and Associate Center Director of Clinical Science at Moffitt.
The team at Moffitt hopes that their results will lead to an improved understanding of SCC and highlight potential therapeutic targets for each subtype. Ongoing studies are examining metabolic targets for treatment of the Redox group. "Our results show SCC can be thought of as a disease with three subtypes, the bulk (87%) of which are associated with either immune infiltration (Inflamed) or oxidation-reduction (Redox) biology. This line of thinking is compelling, because it indicates that the majority of patients could benefit from therapies directed against immune cell types (Inflamed) or metabolic modulation of tumor intrinsic pathways (Redox)," Haura added.
To refer a patient to Moffitt, complete our online form or contact a physician liaison for assistance or support. As part of our efforts to shorten referral times as much as possible, online referrals are typically responded to within 24 - 48 hours.
1 Shaw, A. T. et al. Crizotinib versus chemotherapy in advanced ALK-positive lung cancer. N Engl J Med 368, 2385-2394, doi:10.1056/NEJMoa1214886 (2013).
2 Eberhard, D. A. et al. Mutations in the epidermal growth factor receptor and in KRAS are predictive and prognostic indicators in patients with non-small-cell lung cancer treated with chemotherapy alone and in combination with erlotinib. J Clin Oncol 23, 5900-5909, doi:10.1200/JCO.2005.02.857 (2005).
3 Kris, M. G. et al. Using multiplexed assays of oncogenic drivers in lung cancers to select targeted drugs. JAMA 311, 1998-2006, doi:10.1001/jama.2014.3741 (2014).
4 Gandara, D. R., Hammerman, P. S., Sos, M. L., Lara, P. N., Jr. & Hirsch, F. R. Squamous cell lung cancer: from tumor genomics to cancer therapeutics. Clin Cancer Res 21, 2236-2243, doi:10.1158/1078-0432.CCR-14-3039 (2015).
5 Horn, L. et al. Nivolumab Versus Docetaxel in Previously Treated Patients With Advanced Non-Small-Cell Lung Cancer: Two-Year Outcomes From Two Randomized, Open-Label, Phase III Trials (CheckMate 017 and CheckMate 057). J Clin Oncol 35, 3924-3933, doi:10.1200/JCO.2017.74.3062 (2017).
6 Borghaei, H. et al. Nivolumab versus Docetaxel in Advanced Nonsquamous Non-Small-Cell Lung Cancer. N Engl J Med 373, 1627-1639, doi:10.1056/NEJMoa1507643 (2015).
7 Brahmer, J. et al. Nivolumab versus Docetaxel in Advanced Squamous-Cell Non-Small-Cell Lung Cancer. N Engl J Med 373, 123-135, doi:10.1056/NEJMoa1504627 (2015).
8 Reck, M. et al. Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer. N Engl J Med 375, 1823-1833, doi:10.1056/NEJMoa1606774 (2016).
9 Cancer Genome Atlas Research, N. Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519-525, doi:10.1038/nature11404 (2012).
10 Chen, F. et al. Multiplatform-based molecular subtypes of non-small-cell lung cancer. Oncogene 36, 1384-1393, doi:10.1038/onc.2016.303 (2017).
11 Wilkerson, M. D. et al. Lung squamous cell carcinoma mRNA expression subtypes are reproducible, clinically important, and correspond to normal cell types. Clin Cancer Res 16, 4864-4875, doi:10.1158/1078-0432.CCR-10-0199 (2010).
12 Gentles, A. J. et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat Med 21, 938-945, doi:10.1038/nm.3909 (2015).
13 Al-Shibli, K. I. et al. Prognostic effect of epithelial and stromal lymphocyte infiltration in non-small cell lung cancer. Clin Cancer Res 14, 5220-5227, doi:10.1158/1078-0432.CCR-08-0133 (2008).
14 Stewart, P. A. et al. Proteogenomic landscape of squamous cell lung cancer. Nat Commun 10, 3578, doi:10.1038/s41467-019-11452-x (2019).
15 Mertins, P. et al. Proteogenomics connects somatic mutations to signalling in breast cancer. Nature 534, 55-62, doi:10.1038/nature18003 (2016).
16 Wilhelm, M. et al. Mass-spectrometry-based draft of the human proteome. Nature 509, 582-587, doi:10.1038/nature13319 (2014).
17 Zhang, B. et al. Proteogenomic characterization of human colon and rectal cancer. Nature 513, 382-387, doi:10.1038/nature13438 (2014).
18 Zhang, H. et al. Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer. Cell 166, 755-765, doi:10.1016/j.cell.2016.05.069 (2016).