Hao Tang

Lewis Cantley and colleagues report an integrated metabolic and transcriptomic study of non–small cell lung cancer (NSCLC) cell lines. They show that the activity of the serine/glycine biosynthetic pathway in NSCLC is highly heterogeneous and is regulated by NRF2 and that elevated expression of genes in this pathway confers poor prognosis in human NSCLC. Tumors have high energetic and anabolic needs for rapid cell growth and proliferation1, and the serine biosynthetic pathway was recently identified as an important source of metabolic intermediates for these processes2,3. We integrated metabolic tracing and transcriptional profiling of a large panel of non–small cell lung cancer (NSCLC) cell lines to characterize the activity and regulation of the serine/glycine biosynthetic pathway in NSCLC. Here we show that the activity of this pathway is highly heterogeneous and is regulated by NRF2, a transcription factor frequently deregulated in NSCLC. We found that NRF2 controls the expression of the key serine/glycine biosynthesis enzyme genes PHGDH, PSAT1 and SHMT2 via ATF4 to support glutathione and nucleotide production. Moreover, we show that expression of these genes confers poor prognosis in human NSCLC. Thus, a substantial fraction of human NSCLCs activates an NRF2-dependent transcriptional program that regulates serine and glycine metabolism and is linked to clinical aggressiveness.

PURPOSE: Prospectively identifying who will benefit from adjuvant chemotherapy (ACT) would improve clinical decisions for non-small cell lung cancer (NSCLC) patients. In this study, we aim to develop and validate a functional gene set that predicts the clinical benefits of ACT in NSCLC. EXPERIMENTAL DESIGN: An 18-hub-gene prognosis signature was developed through a systems biology approach, and its prognostic value was evaluated in six independent cohorts. The 18-hub-gene set was then integrated with genome-wide functional (RNAi) data and genetic aberration data to derive a 12-gene predictive signature for ACT benefits in NSCLC. RESULTS: Using a cohort of 442 stage I to III NSCLC patients who underwent surgical resection, we identified an 18-hub-gene set that robustly predicted the prognosis of patients with adenocarcinoma in all validation datasets across four microarray platforms. The hub genes, identified through a purely data-driven approach, have significant biological implications in tumor pathogenesis, including NKX2-1, Aurora Kinase A, PRC1, CDKN3, MBIP, and RRM2. The 12-gene predictive signature was successfully validated in two independent datasets (n = 90 and 176). The predicted benefit group showed significant improvement in survival after ACT (UT Lung SPORE data: HR = 0.34, P = 0.017; JBR.10 clinical trial data: HR = 0.36, P = 0.038), whereas the predicted nonbenefit group showed no survival benefit for 2 datasets (HR = 0.80, P = 0.70; HR = 0.91, P = 0.82). CONCLUSIONS: This is the first study to integrate genetic aberration, genome-wide RNAi data, and mRNA expression data to identify a functional gene set that predicts which resectable patients with non-small cell lung cancer will have a survival benefit with ACT.

We used CDK4/hTERT-immortalized normal human bronchial epithelial cells (HBEC) from several individuals to study lung cancer pathogenesis by introducing combinations of common lung cancer oncogenic changes (p53, KRAS, and MYC) and followed the stepwise transformation of HBECs to full malignancy. This model showed that: (i) the combination of five genetic alterations (CDK4, hTERT, sh-p53, KRAS(V12), and c-MYC) is sufficient for full tumorigenic conversion of HBECs; (ii) genetically identical clones of transformed HBECs exhibit pronounced differences in tumor growth, histology, and differentiation; (iii) HBECs from different individuals vary in their sensitivity to transformation by these oncogenic manipulations; (iv) high levels of KRAS(V12) are required for full malignant transformation of HBECs, however, prior loss of p53 function is required to prevent oncogene-induced senescence; (v) overexpression of c-MYC greatly enhances malignancy but only in the context of sh-p53+KRAS(V12); (vi) growth of parental HBECs in serum-containing medium induces differentiation, whereas growth of oncogenically manipulated HBECs in serum increases in vivo tumorigenicity, decreases tumor latency, produces more undifferentiated tumors, and induces epithelial-to-mesenchymal transition (EMT); (vii) oncogenic transformation of HBECs leads to increased sensitivity to standard chemotherapy doublets; (viii) an mRNA signature derived by comparing tumorigenic versus nontumorigenic clones was predictive of outcome in patients with lung cancer. Collectively, our findings show that this HBEC model system can be used to study the effect of oncogenic mutations, their expression levels, and serum-derived environmental effects in malignant transformation, while also providing clinically translatable applications such as development of prognostic signatures and drug response phenotypes.

PURPOSE: Most non-small cell lung cancers (NSCLC) are now diagnosed from small specimens, and classification using standard pathology methods can be difficult. This is of clinical relevance as many therapy regimens and clinical trials are histology dependent. The purpose of this study was to develop an mRNA expression signature as an adjunct test for routine histopathologic classification of NSCLCs. EXPERIMENTAL DESIGN: A microarray dataset of resected adenocarcinomas (ADC) and squamous cell carcinomas (SCC) was used as the learning set for an ADC-SCC signature. The Cancer Genome Atlas (TCGA) lung RNAseq dataset was used for validation. Another microarray dataset of ADCs and matched nonmalignant lung was used as the learning set for a tumor versus nonmalignant signature. The classifiers were selected as the most differentially expressed genes and sample classification was determined by a nearest distance approach. RESULTS: We developed a 62-gene expression signature that contained many genes used in immunostains for NSCLC typing. It includes 42 genes that distinguish ADC from SCC and 20 genes differentiating nonmalignant lung from lung cancer. Testing of the TCGA and other public datasets resulted in high prediction accuracies (93%-95%). In addition, a prediction score was derived that correlates both with histologic grading and prognosis. We developed a practical version of the Classifier using the HTG EdgeSeq nuclease protection-based technology in combination with next-generation sequencing that can be applied to formalin-fixed paraffin-embedded (FFPE) tissues and small biopsies. CONCLUSIONS: Our RNA classifier provides an objective, quantitative method to aid in the pathologic diagnosis of lung cancer. Clin Cancer Res; 22(19); 4880-9. ©2016 AACR.