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Supplementary Table S1

Supplementary Figure S1-7

Non-small cell lung cancer (NSCLC) is the leading cause of cancer deaths worldwide. Only a fraction of NSCLC harbor actionable driver mutations and there is an urgent need for patient-derived model systems that will enable the development of new targeted therapies. NSCLC and other cancers display profound proteome remodeling compared to normal tissue that is not predicted by DNA or RNA analyses. Here, we generate 137 NSCLC patient-derived xenografts (PDXs) that recapitulate the histology and molecular features of primary NSCLC. Proteome analysis of the PDX models reveals 3 adenocarcinoma and 2 squamous cell carcinoma proteotypes that are associated with different patient outcomes, protein-phosphotyrosine profiles, signatures of activated pathways and candidate targets, and in adenocarcinoma, stromal immune features. These findings portend proteome-based NSCLC classification and treatment and support the PDX resource as a viable model for the development of new targeted therapies.

Purpose: Lung squamous cell carcinoma (LUSC) is a major subtype of non–small cell lung cancer characterized by multiple genetic alterations, particularly PI3K pathway alterations which have been identified in over 50% of LUSC cases. Despite being an attractive target, single-agent PI3K inhibitors have demonstrated modest response in LUSC. Thus, novel combination therapies targeting LUSC are needed. Experimental Design: PI3K inhibitors alone and in combination with CDK4/6 inhibitors were evaluated in previously established LUSC patient-derived xenografts (PDX) using an in vivo screening method. Screening results were validated with in vivo expansion to 5 to 8 mice per arm. Pharmacodynamics studies were performed to confirm targeted inhibition of compounds. Results: Consistent with results from The Cancer Genome Atlas analysis of LUSC, genomic profiling of our large cohort of LUSC PDX models identified PI3K pathway alterations in over 50% of the models. In vivo screening using PI3K inhibitors in 12 of these models identified PIK3CA mutation as a predictive biomarker of response ( Conclusions: PIK3CA mutations predict response to PI3K inhibitors in LUSC. Combined PI3K and CDK4/6 inhibition enhances response to either single agents alone. Our findings provide a rationale for clinical testing of combined PI3K and CDK4/6 inhibitors in PIK3CA-mutant LUSC.

Lung squamous cell carcinoma (LUSC) is a major subtype of non-small cell lung cancer characterized by multiple genetic alterations, particularly PI3K pathway alterations which have been identified in over 50% of LUSC cases. Despite being an attractive target, single-agent PI3K inhibitors have demonstrated modest response in LUSC. Thus, novel combination therapies targeting LUSC are needed.PI3K inhibitors alone and in combination with CDK4/6 inhibitors were evaluated in previously established LUSC patient-derived xenografts (PDX) using anConsistent with results from The Cancer Genome Atlas analysis of LUSC, genomic profiling of our large cohort of LUSC PDX models identified PI3K pathway alterations in over 50% of the models.

miRNAs are a class of regulatory molecules involved in a wide range of cellular functions, including growth, development and apoptosis. Given their widespread roles in biological processes, understanding their patterns of expression in normal and diseased states will provide insights into the consequences of aberrant expression. As such, global miRNA expression profiling of human malignancies is gaining popularity in both basic and clinically driven research. However, to date, the majority of such analyses have used microarrays and quantitative real-time PCR. With the introduction of digital count technologies, such as next-generation sequencing (NGS) and the NanoString nCounter System, we have at our disposal many more options. To make effective use of these different platforms, the strengths and pitfalls of several miRNA profiling technologies were assessed, including a microarray platform, NGS technologies and the NanoString nCounter System. Overall, NGS had the greatest detection sensitivity, largest dynamic range of detection and highest accuracy in differential expression analysis when compared with gold-standard quantitative real-time PCR. Its technical reproducibility was high, with intrasample correlations of at least 0.95 in all cases. Furthermore, miRNA analysis of formalin-fixed, paraffin-embedded (FFPE) tissue was also evaluated. Expression profiles between paired frozen and FFPE samples were similar, with Spearman's ρ>0.93. These results show the superior sensitivity, accuracy and robustness of NGS for the comprehensive profiling of miRNAs in both frozen and FFPE tissues.

Genome-wide studies of lung cancer began with RNA expression microarrays, followed by DNA copy number microarrays. More recently, microRNA profiling and high-throughput sequencing studies have entered the arena. Cancer genomics is a quickly moving field. Here, we summarize the pertinent findings of lung cancer genomics studies to date, with an emphasis on diagnostic, prognostic, and predictive findings that have been validated or confirmed by multiple studies.

microRNAs (miRNA) are endogenous, small non-coding nucleotides that negatively regulate gene expression post-transcriptionally. Through interactions with Argonaute (Ago) proteins, they form the RNA-induced silencing complex (RISC) and can recognize and bind to the 3’UTR of mRNAs in a sequence-specific manner, leading to translational inhibition or mRNA degradation. Over 30% of human protein-coding genes are predicted to be conserved targets of miRNAs. Consequently, changes in their expression are likely to be associated with the development and progression of diseases, including cancer. An increasing number of studies are utilizing high-throughput sequencing over microarrays for the expression profiling of miRNAs. Processing of the raw sequencing data usually involves filtering based on quality measures, trimming for adapters and mapping the reads to a (genome or miRNA) reference. Then, normalization of the data is crucial before any downstream analysis can be performed. Normalization is the process of removing sources of variation, which are of non-biological origins (stemming from sample handling, library preparation, imaging and so on), and can affect the measured expression levels. An effective normalization method should minimize technical and experimental bias without introducing noise; the differences that remain should be truly biological effects. Several normalization methods for miRNA-seq data have been proposed, including linear scaling, non-linear scaling, quantile normalization and variance stabilization normalization. These methods differ in terms of complexity and the assumptions made. However, no standard technique has been recommended. Read counts from each experiment are usually simply adjusted for differences in sequencing depth (library size) to reads-per-million (RPM). Unfortunately, the performance and appropriateness of any of the normalization methods cannot be assessed using real data because the true values are not known. To this end, we have used a 12x12 Latin Square design to spike in 12 different oligonucleotides with known nominal concentrations, into a complex mixture of human miRNAs. These spike-in pools were subjected to all the preparatory steps of small RNA library construction for sequencing on the Illumina HiSeq2000. Preliminary results show that the spike-in sequences can be recovered successfully from the data. Using this data set, the relative merits of different normalization procedures are being assessed based on measures of bias, variance and improved sensitivity and specificity for the detection of differentially expressed miRNAs. The goal is to identify an optimal normalization method for miRNA-seq data, which would reduce variance without increasing bias. Citation Format: Shirley Tam, Richard de Borja, Ming-Sound Tsao, John D. McPherson. Normalization of miRNA-sequencing data. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5276. doi:10.1158/1538-7445.AM2013-5276