Biomarker-Driven Staging—Are We There Yet?

Siddhartha Devarakonda, Ramaswamy Govindan
2019 JAMA Network Open  
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
more » ... uring 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 al 2 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 study 7 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 +
doi:10.1001/jamanetworkopen.2019.17052 pmid:31808918 fatcat:tj5ugiwzzjcihma5o3hcyhyay4