Commentary: “Hot” gene signatures for checkpoint response prediction: A very cool idea

Abstract

Central MessageTranscriptomic signatures can reduce lung cancer tumor microenvironment heterogeneity and potentially improve the sensitivity and specificity of immunotherapy response prediction.See Article page 1598. Transcriptomic signatures can reduce lung cancer tumor microenvironment heterogeneity and potentially improve the sensitivity and specificity of immunotherapy response prediction. See Article page 1598. Lung cancers are pathologically and genomically heterogeneous, and this heterogeneity governs how an individual patient may recur after an R0 resection or respond to systemic therapy. Moreover, lung cancers are influenced by the soil in which they form and progress, now termed the tumor microenvironment (TME), and this microenvironment promotes immune evasion by checkpoint inhibitory molecules including CTLA-4 and PD-L1. Antibody-mediated inactivation of these inhibitory molecules has produced durable responses in a subset of patients with advanced tumors, and checkpoint inhibitors represent the newest first- and second-line lung cancer therapies. But prediction of response using such “biomarkers” as PD-L1 tumor staining or tumor mutation burden remains an inexact science. Why? Perhaps we should reconsider novel prediction, and the first step should be to consolidate immune heterogeneity of lung cancer into more immunologically relevant “bins” that have “hot” or immunologically alterable TME or “cold” for which other modalities should be considered, a strategy used by Jang and colleagues.2Jang H. Lee H.-S. Ramos D. Park I.K. Kang C.H. Burt V.M. et al.Transcriptome-based molecular subtyping of non–small cell lung cancer may predict response to immune checkpoint inhibitors.J Thorac Cardiovasc Surg. 2020; 159: 1598-1610.e3Abstract Full Text Full Text PDF PubMed Scopus (16) Google Scholar The take-home message in this landmark report is that transcriptomic analysis of lung cancer histology bolstered by first-class computational biology can possibly discover signatures for these “bins,” both in adenocarcinoma and squamous cell carcinoma, and potentially improve selection of candidates for checkpoint inhibition. They defined clusters of patients who had all the characteristics for responding to immunotherapy: the correct white cell phenotype in the TME, elevated checkpoint markers, and theoretical in silico functional prediction of immune activation. But the proof of a signature must come from a population where you actually know what happened! These investigators applied a signature from an immuno-treated population with known endpoints and reported that the “response” signature had high sensitivity and specificity for recognition of the “hot bins” of patients with lung cancer who should respond. These findings are more than “proof of principle,” and whether the “Barcelona” signature has use in prospective studies is secondary to the meaning of this paper. We need to apply and advance the ideas of this paper into as many prospective neoadjuvant immunotherapy or late-stage immunotherapy trials. We already recognize that transcriptomics of fresh tissue using single-cell RNAseq may not only be able to define these types of signatures on an individual basis but also be able to validate in short-term spheroid cultures whether these signatures are specific for a given therapy to be used. Combining such in vitro predictive assays with digital spatial profiling multiplex immunofluorescent platforms will allow us to then investigate the TME as different regions of immune cell/tumor interactions on a single slide. Specificity of individual treatment for a response is the holy grail of immunotherapy, considering the complexity and number of checkpoint molecules, and even after this is “standardized,” we will need to further dial up sensitivity and specificity with neoantigen analyses. There is much more to come, thoracic surgeons. This is a great paper to read slowly, understand the thinking behind it, and recognize it as a foundational piece of information when conversing with our medical colleagues. Transcriptome-based molecular subtyping of non–small cell lung cancer may predict response to immune checkpoint inhibitorsThe Journal of Thoracic and Cardiovascular SurgeryVol. 159Issue 4PreviewWe set out to investigate whether transcriptome-based molecular subtypes in lung adenocarcinoma and lung squamous cell carcinoma are predictive of the response to programmed cell death 1 blockade. Full-Text PDF Open Archive