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Invited Commentary
December 6, 2019

Biomarker-Driven Staging—Are We There Yet?

Author Affiliations
  • 1Section of Medical Oncology, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
  • 2Siteman Cancer Center, St Louis, Missouri
JAMA Netw Open. 2019;2(12):e1917052. doi:10.1001/jamanetworkopen.2019.17052

The goal of grouping cancers into stages is to aid clinicians in treatment planning, to inform prognosis, to establish a consistent framework for interpreting results from studies, and to transfer health care information among clinicians with minimal ambiguity.1 Staging in cancers has historically been and continues to be predominantly driven by the anatomical extent of the disease identified at diagnosis. However, as our knowledge of cancer biology has continued to evolve during the past few decades, it is now clear that other factors play an important role in determining prognosis and identifying treatment options for patients. This makes a compelling argument for considering factors other than the mere anatomical extent of the disease when assigning a stage at diagnosis. For example, cancers arising from the same anatomical site and assigned to the same stage by the current staging system may vastly vary in terms of their behavior and associated outcomes depending on histology. For this reason, tumor histology determines stage assignment in some cancers, such as thyroid cancer. Even in cancers belonging to the same histological subtype, prognosis can vary by pathological grade or tumor biology. These factors are being increasingly taken into account when staging malignant neoplasms, such as sarcomas (ie, grade) and head and neck cancers (ie, human papillomavirus status). While such staging systems are not currently implemented in lung cancer, studies, such as those reported by Haro et al2 and other groups,3 have shown that the integration of molecular information that is reflective of underlying tumor biology has the potential to improve the prognostic ability of the current TNM staging system.

Lung cancer is a molecularly heterogeneous disease. Striking differences at the genomic and transcriptional level have been observed among different histological subtypes of lung cancer and also among tumors belonging to the same histological subtype.4 Nevertheless, all lung cancers are staged similarly by the current TNM system, regardless of histology. This approach disregards information that could play an important role in determining outcomes and management. For example, lung adenocarcinomas are typically characterized by alterations that activate the receptor tyrosine kinase/RAS/RAF signaling pathway. However, a spectrum of alterations can lead to activation of this pathway in different tumors, and the presence of some of these alterations can predict response to targeted therapies that have been shown to meaningfully prolong survival in patients with metastatic disease (such as alterations in the EGFR and ALK genes). The role of these therapies in managing patients with early-stage disease or locally advanced disease, where anatomical staging plays an important role in driving treatment choice, is still under investigation. However, a role for these drugs among patients without metastatic disease is continuing to gradually emerge and could potentially inform perioperative therapy in the near future. The presence of some genomic alterations can also predict a lack of treatment response and forebode poor outcomes with certain treatment modalities. An increasing number of studies have observed poor responses to radiation in tumors that are coaltered for KRAS and TP53 and poor responses to immunotherapy in tumors that are coaltered for KRAS and STK11.5,6 Similarly, genomic profiling of 908 tumor samples from the Lung Adjuvant Cisplatin Evaluation–Bio study7 suggested a possible role for high tumor mutation burden in predicting a lack of response to adjuvant chemotherapy. Furthermore, with the increasing role of immunotherapies in lung cancer management, determining the level at which cancer cells express immune checkpoint markers, such as programmed death-ligand 1, will play an important role clinically because they can predict responses to immunotherapy. The level of programmed death-ligand 1 expression can predict benefit from immunotherapy, both in the metastatic setting and in the setting of consolidation therapy after concurrent chemoradiation for locally advanced disease. As more and more novel therapies become available for use (eg, inhibitors targeting KRAS, MET, and others) and with many studies examining the role of targeted therapies and immunotherapies in the management of early-stage and locally advanced cancers, it is plausible that tumor genotyping and immune profiling will play an increasingly important role in guiding management and prognostication.

While these data make a strong case for incorporating biological variables in stage assignment, this approach is not without pitfalls, and implementing such a system is likely to be extremely challenging. Any attempt to incorporate biological variables in staging will necessitate comprehensive molecular profiling of the tumor and correlating myriad features in the tumor genome with clinical information. Molecular profiling, often limited gene panel testing, is now largely confined to stage IV adenocarcinoma. Very little information is collected regarding tumor genomic alterations in early-stage and locally advanced non–small cell lung cancers and metastatic squamous cell carcinoma of the lung. Integration of molecular profiles in an analyzable form (ie, through curated files) with clinical information through electronic health records for detailed analyses has not become a common practice in the United States. Furthermore, molecular assays that examine a single aspect of tumor biology, such as mutations on a limited gene panel, gene expression, or methylation status of a gene, are likely to provide a unidimensional and incomplete molecular portrait of the tumor at best while ignoring other crucial aspects of tumor biology, which can limit the prognostic ability of the test. For example, multiregional tumor sequencing studies have shown clonal heterogeneity, not only at the level of mutations but also at the level of copy number changes, to be associated with prognosis in resected non–small cell lung cancers.8 Given that determining the clonal heterogeneity of a tumor is not possible without multiple biopsies, such testing would only be feasible in the postoperative setting for research. In addition, some tests are better able to provide clinically useful information when performed at specific points or multiple points, such as the quantification of circulating tumor cells or circulating tumor DNA, and may warrant serial testing. The fact that results from these assays can vary depending on several tumor-related and technical factors is likely to create its own set of challenges. Finally, the cost associated with these tests is high and will present a significant barrier to their widespread adoption, especially in resource-limited settings. It is also easy to imagine a scenario in which adopting such approaches could create conflicting stage assignments when molecular profiling is performed at different time points, particularly if the findings vary widely in their prognostic implications (eg, very good prognosis on preoperative test results contradicted by results indicating poor prognosis at a later point).

To date, most prognostic biomarkers in lung cancer have only been described in small prospective or retrospective studies, and their ability to consistently guide treatment decision-making is unknown. As our knowledge of tumor biology and immunology continues to evolve, our understanding of biomarkers and their prognostic abilities is likely to change as well. This makes validation of biomarkers in the clinical setting extremely challenging and creates a need for constant revision of staging systems and/or the development of complex and dynamic staging systems that are not easy to apply. Constant revision of staging systems will invariably make it extremely difficult to compare findings among clinical trials. Outcomes in patients with cancer are also influenced by several social, environmental, and demographic factors.

Despite these challenges, there is little question that the advent of next-generation sequencing, targeted therapies, and immunotherapies has substantially altered the treatment landscape of lung cancer. As the cost and turnaround time associated with sequencing technologies continue to decrease and sampling becomes less invasive (eg, peripheral blood collection replacing tissue biopsy), it is not difficult to envision a scenario in which multiomic tumor genotyping is necessary for guiding staging and treatment in the near future. For now, it is best to focus on refining stage IV adenocarcinoma by incorporating known and common molecular alterations that are validated in large data sets. More efforts should be made to incorporate genomic information in electronic health records using curated files that would readily lend to large-scale analyses of outcomes. As we extend molecular profiling to early-stage and locally advanced non–small cell lung cancer and other cancers, it is not difficult to imagine an integrated staging system that incorporates critical gene alterations to guide therapy, determine outcomes, and enable the exchange of information without ambiguity.

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Article Information

Published: December 6, 2019. doi:10.1001/jamanetworkopen.2019.17052

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2019 Devarakonda S et al. JAMA Network Open.

Corresponding Author: Ramaswamy Govindan, MD, Section of Medical Oncology, Division of Oncology, Washington University School of Medicine, 660 S Euclid Box 8056, St Louis, MO 63110 (rgovindan@wustl.edu).

Conflict of Interest Disclosures: Dr Devarakonda reported serving as a principal investigator for industry-sponsored clinical trials outside the submitted work. Dr Govindan reported receiving personal fees from AbbVie, Eli Lilly and Co, Inivata Limited, Pfizer, AstraZeneca, Genentech, Roche Holding, Merck Sereno, Janssen Pharmaceuticals, Bristol-Myers Squibb, Nektar Therapeutics, Merck and Co, Celgene, Partner Therapeutics, GlaxoSmithKline, Jounce Therapeutics, and Achilles Therapeutics outside the submitted work.

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