Lung adenocarcinoma represents the major histological subtype of lung cancer, which in turn is the leading cause of cancer mortality worldwide (1). The heterogeneous nature of lung adenocarcinoma has long been observed in clinical practice. This has resulted in several revisions of the traditional TNM staging system, with additional architectural grading systems for lung adenocarcinoma, in an attempt to improve clinical risk stratification (2). While stage IIIB and IV diseases are generally considered surgically inoperable, patients with stage I disease are traditionally resected without adjuvant chemotherapy or radiation therapy as short-term and long-term risks for these postop therapies may exceed the benefit if the prognosis for long-term cure is favorable (3). Unfortunately, tumor relapse after surgery still remains the major cause of treatment failure in stage I diseases: The five-year survival rate after potential curative resection is 73% for stage IA and 58% for stage IB diseases (4). Therefore, adjuvant chemotherapy has been suggested for stage IB diseases with a tumor size greater than 4 cm based on retrospective reanalysis of the CALGB 9633 trial (3). A recent retrospective analysis on 25 267 stage IB cases further suggests the use of adjuvant chemotherapy in all stage IB patients (5). Despite these careful clinico-histo-pathological analyses, there are still many unmet needs. For example, a validated prognostic biomarker for high-risk stage I patients is lacking, and T-stage at this time appears to be the only available parameter. In addition, a predictive biomarker for tailored therapeutic regimen selection is warranted. Finally, as a tumor evolves during therapy, monitoring is necessary for therapeutic refinement. Gratefully, advances in genomics may help the field reach these once unachievable goals.
Progress on genetic testing and cancer genomics has been rapid during the past two decades. The identification of epidermal growth factor receptor (EGFR)–activating mutations represented the turning point of lung adenocarcinoma research and opened a new era of biomarker-driven therapy in lung adenocarcinoma (6). The IPASS and Lux-Lung studies further supported the first-line use of EGFR-tyrosine kinase inhibitors (EGFR-TKIs) among the EGFR-mutant lung adenocarcinoma patients (7–8). It is now recognized that EGFR mutation is enriched in Asian lung adenocarcinoma patients and is associated with an increased sensitivity to EGFR-TKIs, with a 70% clinical response rate (9). Nevertheless, drug resistance inevitably occurs usually within one to two years. Mechanisms underlying both the variable responsiveness and the variable response durations are not fully understood, although pretreatment EGFR T790M might serve as an indicator for short EGFR-TKI response duration (10). For lung adenocarcinoma without actionable mutations, predictive and prognostic biomarkers remain unavailable. Even for lung adenocarcinoma with other druggable genetic alterations, such as ALK and ROS-1 fusions, biomarkers that predict poor responsiveness and short response duration are also lacking.
The study of lung adenocarcinoma prognostic gene expression signatures using microarray-based methods represented a new milestone in the field of lung cancer biomarkers (11–13). Nevertheless, microarray is used to assay only protein-coding genes with predesigned probes, which limits the scale of genome exploration and may lead to potential biases. Cross-cohort reproducibility has also been a concern that limits enthusiasm for the clinical translation of biomarker studies.
In this issue of the Journal, Shukla and colleagues report the first RNA-seq-based lung adenocarcinoma prognostic signature (14). They used 255 patients of The Cancer Genome Atlas (TCGA) lung adenocarcinoma data set as the training cohort. Stepwise regression generated an unusually small four-gene signature, including RHOV, CD109, FRRS1, and LINC00941. The signature was validated in two cohorts: One included another 157 patients within the TCGA data set, and the other was the MCTP lung adenocarcinoma cohort comprised of 67 patients. The authors showed the risk stratification ability of the four-gene signature on lung adenocarcinoma patients, including stage I patients with or without EGFR mutations. They also reported the superiority of the four-gene signature to the previously reported microarray-based prognostic signatures.
RNA-seq is advantageous over microarray for its ability to assess the entire transcriptome within one experiment (15). It is therefore reasonable to expect that a prognostic signature derived by this platform would be less biased. However, pitfalls always reside in prognostic biomarkers as therapeutic intervention alters the natural disease history. It is thus expected that the validity or usefulness of a given prognostic signature may diminish with the improvement of therapeutics. EGFR-mutant and ALK-rearranged lung adenocarcinoma may serve as good examples in this regard (9). Therefore, while prognostic biomarkers are needed for patients with stage I disease in terms of postoperative treatment decisions, it is the discovery of a predictive biomarker that will improve precision therapy. While the four-gene prognostic signature undoubtedly represents a step forward in this research field, the retrospective and nonrandomized nature of the study design prohibited the possibility to test its predictive power on specific treatment decisions. In addition, cross-cohort reproducibility remains an issue with the RNA-seq technology. Further verification with expanded sample size will be required to ensure confidence in the translational potential of the four-gene signature. This is especially true with stage I diseases because T-stage remains the only available risk indicator and additional biomarkers are required for risk stratification.
Biomarker-driven therapy is the cornerstone of the current multidisciplinary lung cancer management (9). Druggable target identification at initial diagnosis is the current standard of practice. Unfortunately, a tumor is heterogeneous and diversity increases with tumor evolution during therapy (16). Molecular evolution is phenotypically translated into clinical drug resistance and disease progression. Therefore, timely characterization of unexpected genomic alterations, predictive DNA or expression signatures, and cancer-dependent pathways is a necessity for optimal targeted therapy, chemotherapy, or immunotherapy refinement. These need to be achieved by comprehensive genomics assessments rather than simple genetic testing. Whole transcriptome and whole exome sequencing may serve as potential solutions in this regard. Integrated analysis of the comprehensive genomic information may lead to tailored regimens for precision therapy. It is expected that basic research and clinical translation of precision cancer management will accelerate in the coming years with a transformation of the diversity of lung adenocarcinoma toward precision therapy.
The authors have no conflicts of interest to declare.