Background: In order to correctly decode phenotypic information from RNA-sequencing (RNA-seq) data, careful selection of the RNA-seq quantification measure is critical for inter-sample comparisons and for downstream analyses, such as differential gene expression between two or more conditions. Several methods have been proposed and continue to be used. However, a consensus has not been reached regarding the best gene expression quantification method for RNA-seq data analysis. Methods: In the present study, we used replicate samples from each of 20 patient-derived xenograft (PDX) models spanning 15 tumor types, for a total of 61 human tumor xenograft samples available through the NCI patient-derived model repository (PDMR). We compared the reproducibility across replicate samples based on TPM (transcripts per million), FPKM (fragments per kilobase of transcript per million fragments mapped), and normalized counts using coefficient of variation, intraclass correlation coefficient, and cluster analysis. Results: Our results revealed that hierarchical clustering on normalized count data tended to group replicate samples from the same PDX model together more accurately than TPM and FPKM data. Furthermore, normalized count data were observed to have the lowest median coefficient of variation (CV), and highest intraclass correlation (ICC) values across all replicate samples from the same model and for the same gene across all PDX models compared to TPM and FPKM data. Conclusion: We provided compelling evidence for a preferred quantification measure to conduct downstream analyses of PDX RNA-seq data. To our knowledge, this is the first comparative study of RNA-seq data quantification measures conducted on PDX models, which are known to be inherently more variable than cell line models. Our findings are consistent with what others have shown for human tumors and cell lines and add further support to the thesis that normalized counts are the best choice for the analysis of RNA-seq data across samples.

Background: The pathogenesis of nasopharyngeal carcinoma (NPC) is a complicated process involving genetic predisposition, Epstein-Bar Virus infection, and genetic alterations. Although some oncogenes and tumor suppressor genes have been previously reported in NPC, a complete understanding of the pathogenesis of NPC in the context of global gene expression, transcriptional pathways and biomarker assessment remains to be elucidated. Methods: Total RNA from 32 pathologically-confirmed cases of poorly-differentiated NPC was divided into pools inclusive of four consecutive specimens and each pool (T1 to T8) was co-hybridized with pooled RNA from 24 normal non-cancerous nasopharyngeal tissues (NP) to a human 8K cDNA array platform. The reliability of microarray data was validated for selected genes by semi-quantitative RT-PCR and immunohistochemistry. Results: Stringent statistical filtering parameters identified 435 genes to be up-regulated and 257 genes to be down-regulated in NPC compared to NP. Seven up-regulated genes including CYC1, MIF, LAMB3, TUBB2, UBE2C and TRAP1 had been previously proposed as candidate common cancer biomarkers based on a previous extensive comparison among various cancers and normal tissues which did not, however, include NPC or NP. In addition, nine known oncogenes and tumor suppressor genes, MIF, BIRC5, PTTG1, ATM, FOXO1A, TGFBR2, PRKAR1A, KLF5 and PDCD4 were identified through the microarray literature-based annotation search engine MILANO, suggesting these genes may be specifically involved in the promotion of the malignant conversion of nasopharyngeal epithelium. Finally, we found that these differentially expressed genes were involved in apoptosis, MAPK, VEGF and B cell receptor signaling pathways and other functions associated with cell growth, signal transduction and immune system activation. Conclusion: This study identified potential candidate biomarkers, oncogenes/tumor suppressor genes involved in several pathways relevant to the oncogenesis of NPC. This information may facilitate the determination of diagnostic and therapeutic targets for NPC as well as provide insights about the molecular pathogenesis of NPC.

Purpose: Abnormalities in the constitutive and IFN-γ–inducible HLA class I surface antigen expression of tumor cells is often associated with an impaired expression of components of the antigen processing machinery (APM). Hence, we analyzed whether there exists a link between the IFN-γ signaling pathway, constitutive HLA class I APM component expression, and IFN-γ resistance.Experimental Design: The basal and IFN-γ–inducible expression profiles of HLA class I APM and IFN-γ signal transduction cascade components were assessed in melanoma cells by real-time PCR (RT-PCR), Western blot analysis and/or flow cytometry, the integrity of the Janus activated kinase (JAK) 2 locus by comparative genomic hybridization. JAK2 was transiently overexpressed in JAK2− cells. The effect of IFN-γ on the cell growth was assessed by XTT [2,3-bis(2-methoxy-4-nitro-S-sulfophenynl)-H-tetrazolium-5-carboxanilide inner salt] assay.Results: The analysis of 8 melanoma cell lines linked the IFN-γ unresponsiveness of Colo 857 cells determined by lack of inducibility of HLA class I surface expression on IFN-γ treatment to a deletion of JAK2 on chromosome 9, whereas other IFN-γ signaling pathway components were not affected. In addition, the constitutive HLA class I APM component expression levels were significantly reduced in JAK2− cells. Furthermore, JAK2-deficient cells were also resistant to the antiproliferative effect of IFN-γ. Transfection of wild-type JAK2 into JAK2− Colo 857 not only increased the basal APM expression but also restored their IFN-γ sensitivity.Conclusions: Impaired JAK2 expression in melanoma cells leads to reduced basal expression of MHC class I APM components and impairs their IFN-γ inducibility, suggesting that malfunctional IFN-γ signaling might cause HLA class I abnormalities. Clin Cancer Res; 17(9); 2668–78. ©2011 AACR.

Supported by the Office of International Affairs, National Cancer Institute (NCI), the "US-Japan Workshop on Immunological Biomarkers in Oncology" was held in March 2009. The workshop was related to a task force launched by the International Society for the Biological Therapy of Cancer (iSBTc) and the United States Food and Drug Administration (FDA) to identify strategies for biomarker discovery and validation in the field of biotherapy. The effort will culminate on October 28th 2009 in the "iSBTc-FDA-NCI Workshop on Prognostic and Predictive Immunologic Biomarkers in Cancer", which will be held in Washington DC in association with the Annual Meeting. The purposes of the US-Japan workshop were a) to discuss novel approaches to enhance the discovery of predictive and/or prognostic markers in cancer immunotherapy; b) to define the state of the science in biomarker discovery and validation. The participation of Japanese and US scientists provided the opportunity to identify shared or discordant themes across the distinct immune genetic background and the diverse prevalence of disease between the two Nations. Converging concepts were identified: enhanced knowledge of interferon-related pathways was found to be central to the understanding of immune-mediated tissue-specific destruction (TSD) of which tumor rejection is a representative facet. Although the expression of interferon-stimulated genes (ISGs) likely mediates the inflammatory process leading to tumor rejection, it is insufficient by itself and the associated mechanisms need to be identified. It is likely that adaptive immune responses play a broader role in tumor rejection than those strictly related to their antigen-specificity; likely, their primary role is to trigger an acute and tissue-specific inflammatory response at the tumor site that leads to rejection upon recruitment of additional innate and adaptive immune mechanisms. Other candidate systemic and/or tissue-specific biomarkers were recognized that might be added to the list of known entities applicable in immunotherapy trials. The need for a systematic approach to biomarker discovery that takes advantage of powerful high-throughput technologies was recognized; it was clear from the current state of the science that immunotherapy is still in a discovery phase and only a few of the current biomarkers warrant extensive validation. It was, finally, clear that, while current technologies have almost limitless potential, inadequate study design, limited standardization and cross-validation among laboratories and suboptimal comparability of data remain major road blocks. The institution of an interactive consortium for high throughput molecular monitoring of clinical trials with voluntary participation might provide cost-effective solutions.

The traditional oncology drug development paradigm of single arm phase II studies followed by a randomized phase III study has limitations for modern oncology drug development. Interpretation of single arm phase II study results is difficult when a new drug is used in combination with other agents or when progression-free survival is used as the endpoint rather than tumor shrinkage. Randomized phase II studies are more informative for these objectives but increase both the number of patients and time required to determine the value of a new experimental agent. In this article, we compare different phase II study strategies to determine the most efficient drug development path in terms of number of patients and length of time to conclusion of drug efficacy on overall survival. (Clin Cancer Res 2009;15(19):5950–5)

Background: The weight that gene copy number plays in transcription remains controversial; although in specific cases gene expression correlates with copy number, the relationship cannot be inferred at the global level. We hypothesized that genes steadily expressed by 15 melanoma cell lines (CMs) and their parental tissues (TMs) should be critical for oncogenesis and their expression most frequently influenced by their respective copy number. Results: Functional interpretation of 3,030 transcripts concordantly expressed (Pearson's correlation coefficient p-value < 0.05) by CMs and TMs confirmed an enrichment of functions crucial to oncogenesis. Among them, 968 were expressed according to the transcriptional efficiency predicted by copy number analysis (Pearson's correlation coefficient p-value < 0.05). We named these genes, "genomic delegates" as they represent at the transcriptional level the genetic footprint of individual cancers. We then tested whether the genes could categorize 112 melanoma metastases. Two divergent phenotypes were observed: one with prevalent expression of cancer testis antigens, enhanced cyclin activity, WNT signaling, and a Th17 immune phenotype (Class A). This phenotype expressed, therefore, transcripts previously associated to more aggressive cancer. The second class (B) prevalently expressed genes associated with melanoma signaling including MITF, melanoma differentiation antigens, and displayed a Th1 immune phenotype associated with better prognosis and likelihood to respond to immunotherapy. An intermediate third class (C) was further identified. The three phenotypes were confirmed by unsupervised principal component analysis. Conclusions: This study suggests that clinically relevant phenotypes of melanoma can be retraced to stable oncogenic properties of cancer cells linked to their genetic back bone, and offers a roadmap for uncovering novel targets for tailored anti-cancer therapy.