Abstract
Recent positive clinical results in cancer immunotherapy point to the potential of immune-based strategies to provide effective treatment of a variety of cancers. In some patients, the responses to cancer immunotherapy are durable, dramatically extending survival. Extensive research efforts are being made to identify and validate biomarkers that can help identify subsets of cancer patients that will benefit most from these novel immunotherapies. In addition to the clear advantage of such predictive biomarkers, immune biomarkers are playing an important role in the development, clinical evaluation and monitoring of cancer immunotherapies. This Cancer Immunotherapy Resource Document, prepared by the Society for Immunotherapy of Cancer (SITC, formerly the International Society for Biological Therapy of Cancer, iSBTc), provides key references and online resources relevant to the discovery, evaluation and clinical application of immune biomarkers. These key resources were identified by experts in the field who are actively pursuing research in biomarker identification and validation. This organized collection of the most useful references, online resources and tools serves as a compass to guide discovery of biomarkers essential to advancing novel cancer immunotherapies.
Introduction
Immunotherapy has emerged as an important treatment strategy for patients with cancer. With several recent approvals by the U.S. Food and Drug Administration (FDA), cancer immunotherapy has become the latest addition to the toolbox of effective cancer treatments that includes chemotherapy, signal transduction inhibitors, anti-angiogenic agents, radiotherapy, and surgery.
Successful development and testing, regulatory approval and clinical application of cancer immunotherapies require the identification and validation of biomarkers of efficacy. The importance of reliable biomarkers to guide immune-based and personalized cancer therapies is clear. Biomarkers can aid in early disease diagnosis, help clinicians identify patients most likely to benefit from these expensive treatments, and facilitate drug discovery, development and biological/clinical evaluation of cancer immunotherapies.
For over twenty-five years the Society for Immunotherapy of Cancer (SITC; formerly the International Society for Biological Therapy of Cancer, iSBTc) has advanced the science, development and application of biological therapy/immunotherapy of cancer. The society has long recognized the importance of biomarkers for cancer immunotherapy, which has been the focus of a number of SITC/iSBTc symposia and workshops [1-5], and has published recommendations [6] and summaries [7-10].
To support the efforts of investigators involved in research to identify and validate biomarkers for cancer immunotherapy, the authors and members of the SITC Biomarkers Taskforce have identified key biomarker references and online resources and organized these into this SITC/iSBTc Cancer Immunotherapy Biomarkers Resource Document. This document provides an overview of suggested publications and resources for studies on biomarkers for cancer immunotherapy. This resource document is divided into two sections: Part I: Immunotherapy Biomarker References; and Part II: High Throughput and New Technologies for Biomarker Discovery: Arrays, Platforms, Tools for The Bench and Online Resources. While many important references and resources in the field are included in this document, it does not intend to represent an exhaustive list of all relevant publications, products or resources in the growing, and important field of immune biomarkers. A comprehensive list of online tools for bioinformatics and molecular biology research is available from the Bioinformatics Links Directory [11,12].
A draft of the present document was originally provided to attendees of the SITC/iSBTc Symposium on Immuno-Oncology Biomarkers, 2010 and Beyond: Perspectives from the iSBTc Biomarker Task Force [1], which was held September 30, 2010 at the National Institutes of Health in conjunction with the society's 25th Annual Meeting. Following the symposium, the draft document was posted on the society's website for open comment. The comments were reviewed by the authors and incorporated into this manuscript. The references and online resources are organized as outlined in Table 1.
Table 1.
Structure of the Biomarker References and Online Resources Provided
| Part I. Immunotherapy Biomarker References |
| 1. Guideline and Meeting Reports |
| 2. Clinical Trials |
| A. Recent Immunotherapy Trials in Cancer Patients - Positive Randomized Phase III Studies B. Emerging Biomarkers for Immunotherapy of Cancer - Clinical Trials C. Correlation between Immune Response and Clinical Outcome in Cancer Patients; Positive Studies |
| 3. Gene Expression Profiling in Cancer Immunotherapy |
| 4. Useful Reviews |
| 5. Bioinformatics for Biomarkers Discovery |
| 6. New Technologies for Biomarkers Discovery |
| 7. Standardization and Harmonization of Sample Collection and Use of Immunological Assays |
| A. Processing, Storage and Shipping of Blood Samples and Serum for Immunological Studies - Technical Considerations B. Cellular Immunotherapy: Characterization of Cellular Products C. Assay Standardization and Harmonization D. Assays for Determination of Antitumor Immune-Response in Clinical Trials |
| 8. Reporting Biomarkers Data in Publications |
| Part II. High Throughput and New Technologies for Biomarker Discovery: Arrays, Platforms, Tools for The Bench and Online Resources |
| 1. Genomic Biomarkers Discovery |
| A. Single Nucleotide Polymorphisms (SNPs) Arrays B. Comparative Genomic Hybridization (CGH) Arrays C. Mitochondrial Genome Arrays |
| 2. Epigenomic Biomarkers Discovery |
| A. Methylation Arrays B. microRNA (miRNA) Arrays C. Chromatin Immunoprecipitation (ChIP) Arrays (ChIP on chip) |
| 3. Transcriptomic Biomarkers Discovery |
| A. Expression Arrays B. Quantitative Assays |
| 4. Proteomic Biomarkers Discovery |
| A. Protein and Phosphoprotein Assays B. Multicolor Cytometric Systems |
| 5. Next Generation Sequencing: Whole Genomic and Transcriptomic Sequencing Systems |
| 6. Software and Tools for Data Analysis |
| A. Microarrays/Sequencing Data Analysis B. Next Generation Sequencing Data Analysis C. Multicolor Cytometric Data Analysis |
| 7. Software and Tools for Function and Pathway Analysis |
| 8. siRNA Libraries |
| 9. Public Databases |
| 10. Tools for the Bench and Other Useful Websites |
| A. Primer Design Software B. Transcription Factors Binding Sites Prediction Software C. Design of Antisense Oligonucleotides, Nucleic Acid Probes, siRNA Software D. miRNA Prediction E. Alternative Splicing Analysis F. Linkage Disequilibrium Analysis G. Analysis Support, Laboratory Optimization and Other Useful Websites H. Nanotechnology I. Clinical Trials Registries |
Part I. Immunotherapy Biomarker References
1. Guideline and Meeting Reports
Recommendations from the iSBTc/FDA/NCI Workshop on Immunotherapy Biomarkers
Butterfield LH, Disis ML, Fox BA, et al.: A systematic approach to biomarker discovery; Preamble to "The iSBTc-FDA Taskforce on Immunotherapy Biomarkers". J Transl Med 6:81, 2008
Butterfield LH, Palucka AK, Britten CM et al:: Recommendations from the iSBTc-SITC/FDA/NCI Workshop on Immunotherapy Biomarkers, Clin Cancer Res 17:3064-3076, 2010
2009 Report of the US - Japan Workshop on Immunological Biomarkers in Oncology
Tahara H, Sato M, Thurin M, et al.: Emerging concepts in biomarker discovery; The US-Japan workshop on immunological molecular markers in oncology. J Transl Med 7:45, 2009
2010 Guidelines of the NCI-Investigational Drug Steering Committee (IDSC) Biomarker Task Force
Dancey JE, Dobbin KK, Groshen S, et al.: Guidelines for the development and incorporation of biomarker studies in early clinical trials of novel agents. Clin Cancer Res 16:1745-1755, 2010
2010 Report of the Translational Research Cancer Centers Consortium (TrC3) Immunotherapy Network Annual Meeting
Lesinski GB, Carson WE, Repasky EA, et al.: Meeting Report: The 13th Annual Meeting of the Translational Research Cancer Centers Consortium (TrC3); Immune Suppression and the Tumor Microenvironment, Columbus, Ohio; March 1-2, 2010. J Immunother, 2010
2008 Report on EU-USA Workshop on System Biology in Cancer Research
Aebersold R, Auffray C, Baney E, et al.: Report on EU-USA workshop: how systems biology can advance cancer research (27 October 2008). Mol Oncol 3:9-17, 2009
2007 AACR-FDA-NCI Cancer Biomarkers Collaborative Consensus Report
Khleif SN, Doroshow JH, Hait WN: AACR-FDA-NCI Cancer Biomarkers Collaborative Consensus Report: Advancing the Use of Biomarkers in Cancer Drug Development. Clin Cancer Res 16:3299-3318, 2010
2005 Report on Workshop on Cancer Biometrics Held at NIH Masur Auditorium
(This manuscript also includes a brief description of high throughput technologies)
Lotze MT, Wang E, Marincola FM, et al.: Workshop on cancer biometrics: identifying biomarkers and surrogates of cancer in patients: a meeting held at the Masur Auditorium, National Institutes of Health. J Immunother 28:79-119, 2005
2002 Report on Workshop on Immunologic Monitoring of Cancer Vaccine Trials
Keilholz U, Weber J, Finke JH, et al.: Immunologic monitoring of cancer vaccine therapy: results of a workshop sponsored by the Society for Biological Therapy. J Immunother 25:97-138, 2002
2. Clinical Trials
A. Recent Immunotherapy Trials in Cancer Patients - Positive Randomized Phase III Studies
Examples
gp100:209-217 (210M) Peptide Followed by High-Dose IL-2 - Metastatic Melanoma
Schwartzentruber DJ, Lawson D, Richards J, et al: gp100 peptide vaccine and interleukin-2 in patients with advanced melanoma. N Eng J Med 364:2119-27, 2011
Anti-GD2 Antibody with GM-CSF, Interleukin-2, and Isotretinoin - High-Risk Neuroblastoma
Yu AL, Gilman AL, Ozkaynak MF, eta al.: Anti-GD2 antibody with GM-CSF, interleukin-2, and isotretinoin for neuroblastoma. N Engl J Med. 363:1324-34, 2010
Ipilimumab and Dacarbazine in Metastaic Melanoma Patients - Untreated Metastatic Melanoma
Robert C, Thomas L, Bondarenko I, et al.: Ipilimumab plus dacarbazine for previously untreated metastatic melanoma. N Engl J Med 364:2517-26, 2011
Ipilimumab in Metastatic Melanoma Patients - Previously Treated Metastatic Melanoma
Hodi FS, O'Day SJ, McDermott DF, et al.: Improved survival with ipilimumab in patients with metastatic melanoma. N Engl J Med 363: 711-23, 2010
Sipuleucel-T Immunotherapy - Metastatic Prostate Cancer
Kantoff PW, Higano CS, Shore ND, et al.: Sipuleucel-T immunotherapy for castration-resistant prostate cancer. N Engl J Med 363:411-422, 2010
Interferon α Adjuvant Treatment - High Risk Melanoma
Eggermont AMM, Suciu S, Santinami M, et al.: Adjuvant therapy with pegylated interferon alfa-2b versus observation alone in resected stage III melanoma: final results of EORTC 18991, a randomised phase III trial. Lancet 372:117-126, 2008
Idiotype Vaccine Therapy - Complete Remission Follicular Lymphoma
Schuster SJ, Neelapu SS, Gause BL, et al: Vaccination with patient-specific tumor-derived antigen in first remission improves disease-free survival in follicular lymphoma. J Clin Oncol 20: 2787-2794, 2011
B. Emerging Biomarkers for Immunotherapy of Cancer - Clinical Trials
Examples
Parameter: HLA Cw*06 Allele
Setting: Interferon α Adjuvant Treatment - High-Risk Melanoma
Gogas H, Kirkwood JM, Falk CS, et al.: Correlation of molecular human leukocyte antigen typing and outcome in high-risk melanoma patients receiving adjuvant interferon. Cancer 116:4326-33, 2010
Parameter: CCR5 and CXCR3 Polymorphisms and Gene Expression
Setting: Adoptive Therapy - Metastatic Melanoma
Bedognetti D, Uccellini L, Wang, et al.: Evaluation of CXCR3 and CCR5 polymorphisms and gene-expression as predictive biomarkers of clinical response to adoptive therapy in melanoma patients. J Immunother 33:860, 2010 (Meeting Abstract)
Parameter: Differentiated Effector Phenotype, ''B and T Lymphocyte Attenuator'' (BTLA) Expression in CD8+ TIL
Setting: Adoptive Therapy - Metastatic Melanoma
Laszlo G. Radvanyi, Chantale Bernatchez, Minying Zhang, et al. Adoptive T cell therapy for metastatic melanoma: The MD Anderson experience. J Immunother 33:863, 2010 (Meeting Abstract)
Parameter: VEGF Serum Level
Setting: IL-2 Therapy - Metastatic Melanoma
Sabatino M, Kim-Schulze S, Panelli MC, et al.: Serum vascular endothelial growth factor and fibronectin predict clinical response to high-dose interleukin-2 therapy. J Clin Oncol 27:2645-2652, 2009
Parameter: Treg Numbers
Setting: hTERT Pulsed DCs Therapy - Metastatic Solid Tumors
Aloysius MM, Mc Kechnie AJ, Robins RA, et al.: Generation in vivo of peptide-specific cytotoxic T cells and presence of regulatory T cells during vaccination with hTERT (class I and II) peptide-pulsed DCs. J Transl Med 7, 2009
Parameter: CD54 Expression by APC
Setting: Sipuleucel-T Cellular Immunotherapy - Metastatic Prostate Cancer
Higano CS, Schellhammer PF, Small EJ, et al.: Integrated data from 2 randomized, double-blind, placebo-controlled, phase 3 trials of active cellular immunotherapy with sipuleucel-T in advanced prostate cancer. Cancer 115:3670-3679, 2009
Parameter: Delta32 CCR5 Polymorphism
Setting: Immunotherapy (IL2/IFNα ± Chemotherapy/Vaccination) - Metastatic Melanoma
Ugurel S, Schrama D, Keller G, et al.: Impact of the CCR5 gene polymorphism on the survival of metastatic melanoma patients receiving immunotherapy. Cancer Immunol Immunother 57:685-691, 2008
Parameter: Telomere Length
Setting: Adoptive Therapy - Metastatic Melanoma
Dudley ME, Yang JC, Sherry R, et al.: Adoptive cell therapy for patients with metastatic melanoma: evaluation of intensive myeloablative chemoradiation preparative regimens. J Clin Oncol 26:5233-5239, 2008
Parameter: CTLA-4 Polymorphisms
Setting: Anti-CTLA-4 Therapy - Metastatic Melanoma
Breunis WB, Tarazona-Santos E, Chen R, et al.: Influence of cytotoxic T lymphocyte-associated antigen 4 (CTLA4) common polymorphisms on outcome in treatment of melanoma patients with CTLA-4 blockade. J Immunother 31:586-9, 2008
Parameter: CCL5, CCL11, IFN-γ, ICOS, CD20
Setting: MAGEA3-Based Vaccination - Metastatic Melanoma
Louahed J, Gruselle O, Gaulis S, et al.: Expression of defined genes identified by pretreatment tumor profiling: Association with clinical responses to the GSK MAGE- A3 immunotherapeutic in metastatic melanoma patients (EORTC 16032-18031). J Clin Oncol 26:9045, 2008 (Meeting Abstract)
Parameter: Fc Gamma Receptor Polymorphisms
Setting: Immuno (Trastuzumab) Chemotherapy - Metastatic Breast Cancer
Musolino A, Naldi N, Bortesi B, et al.: Immunoglobulin G fragment C receptor polymorphisms and clinical efficacy of trastuzumab-based therapy in patients with HER-2/neu-positive metastatic breast cancer. J Clin Oncol 26:1789-1796, 2008
Parameter: Phosphorylated - STAT-1/STAT-3 Ratio
Setting: Neoadjuvant IFN-α Therapy - Stage IIIB Melanoma
Wang WJ, Edington HD, Rao UNM, et al.: Modulation of signal transducers and activators of transcription 1 and 3 signaling in melanoma by high-dose IFN alpha 2b. Clin Cancer Res 13:1523-1531, 2007
Parameter: IL 1β, IL-1α, IL-6, TNF-α, CCL3, CCL4
Setting: Adjuvant IFN-α Therapy - High Risk Melanoma
Yurkovetsky ZR, Kirkwood JM, Edington HD, et al Multiplex analysis of serum cytokines in melanoma patients treated with interferon-alpha 2b. Clin Cancer Res 13:2422-2428, 2007
Parameter: Autoantibodies and Clinical Manifestations of Autoimmunity
Setting: Adjuvant IFN-α Therapy - Melanoma
Gogas H, Ioannovich J, Dafni U, et al.: Prognostic significance of autoimmunity during treatment of melanoma with interferon. N Engl J Med: 354:709-18, 2006
Parameter: IFN-γ Polymorphism
Setting: Immuno (IL-2) Chemotherapy - Metastatic Melanoma
Liu DX, O'Day SJ, Yang DY, et al.: Impact of gene polymorphisms on clinical outcome for stage IV melanoma patients treated with biochemotherapy: An exploratory study. Clin Cancer Res 11:1237-1246, 2005
Parameter: IL-6, PPARG
Setting: BCG - Bladder Cancer
Leibovici D, Grossman HB, Dinney CP, et al.: Polymorphisms in inflammation genes and bladder cancer: From initiation to recurrence, progression, and survival. J Clin Oncol 23:5746-5756, 2005
C. Correlation between Immune Response and Clinical Outcome in Cancer Patients; Positive Studies
Examples
Parameter: Melan-A-tetramer+ Immunity
Setting: High Dose Poly-Epitope Vaccine - Metastatic Melanoma
Dangoor A, Lorigan P, Keilholz U, et al.: Clinical and immunological responses in metastatic melanoma patients vaccinated with a high-dose poly-epitope vaccine. Cancer Immunol Immunother 59:863-73, 2010
Parameter: High Interferon-gamma-associated Proliferative CD4+ T-cell Response and Broad Response of CD8+ Interferon-gamma T Cells
Setting: HPV-16 Vaccine Therapy - Vulvar Intraepithelial Neoplasia
Welters MJ, Kenter GG, de Vos van Steenwijk PJ, et al.: Success or failure of vaccination for HPV16-positive vulvar lesions correlates with kinetics and phenotype of induced T-cell responses. Proc Natl Acad Sci U S A. 107:11895-9, 2010
Parameter: High Interferon-gamma-associated Proliferative CD4+ T-cell Response and Broad Response of CD8+ Interferon-gamma T Cells
Setting: HPV-16 Vaccine Therap - Vulvar Intraepithelial Neoplasia
Kenter GG, Welters MJ, Valentijn AR, et al.: Vaccination against HPV-16 oncoproteins for vulvar intraepithelial neoplasia. N Engl J Med 361:1838-47, 2009
Parameter: Tumor Specific Immune Response (ELISPOT)
Setting: Tumor Multi-Epitope Vaccine - Metastatic Melanoma
Kirkwood JM, Lee S, Moschos SJ, et al.: Immunogenicity and antitumor effects of vaccination with peptide vaccine+/-granulocyte-monocyte colony-stimulating factor and/or IFN-alpha2b in advanced metastatic melanoma: Eastern Cooperative Oncology Group Phase II Trial E1696. Clin Canc Res 15:1443-1451, 2009
Parameter: NY-ESO-1-specific B and T Cell Responses
Setting: Anti CTLA-4 Immunotherapy - Metastatic Melanoma
Yuan J, Gnjatic S, Li H, Powel S, et al.: CTLA-4 blockade enhances polyfunctional NY-ESO-1 specific T cell responses in metastatic melanoma patients with clinical benefit. Proc Natl Acad Sci U S A. 105:20410-5, 2008
Parameter: Epitope Spreading
Setting: Adenovirus MART-1 Dendritic Cell Vaccination - Metastatic Melanoma
Butterfield LH, Comin-Anduix B, Vujanovic L, et al.: Adenovirus MART-1-engineered autologous dendritic cell vaccine for metastatic melanoma. J immunother 31:294-309, 2008
Parameter: Epitope Spreading
Setting: Dendritic Cell Based Vaccination - Stage II-IV Melanoma
Ribas A, Glaspy JA, Lee Y, et al.: Role of dendritic cell phenotype, determinant spreading, and negative costimulatory blockade in dendritic cell-based melanoma immunotherapy. J Immunother 27:354-367, 2004
Parameter: Epitope Spreading
Setting: Dendritic Cell Based Vaccination - Stage III-IV Melanoma
Butterfield LH, Ribas A, Dissette VB, et al. Determinant spreading associated with clinical response in dendritic cell-based immunotherapy for malignant melanoma. Clin Canc Res 9:998-1008, 2003
Parameter: Epitope Spreading
Setting: Peptide-Pulsed Dendritic Cells Vaccination - Metastatic Renal Cell Carcinoma
Wierecky J, Müller MR, Wirths S, et al.: Immunologic and clinical responses after vaccinations with peptide-pulsed dendritic cells in metastatic renal cancer patients. Cancer Res 66:5910-8, 2006
Parameter: Epitope Spreading
Setting: Her2 Specific Vaccination - Stage III-IV Breast Cancer
Salazar LG, Goodell V, O'Meara M, et al: Persistent immunity and survival after immunization with a HER2/neu (HER2) vaccine. J Clin Oncol 27(15S):2010 (Meeting Abstract)
Parameter: Overall Immunity to MelAgs
Setting: Dendritic Cell Based Vaccination - Metastatic Melanoma
Banchereau J, Palucka AK, Dhodapkar M, et al. Immune and clinical responses in patients with metastatic melanoma to CD34(+) progenitor-derived dendritic cell vaccine. Cancer Res 6451-8. 2001
Parameter: T-cell Response (ELISPOT)
Setting: 12-Peptide Vaccine - Stage IIB-IV - Melanoma
Slingluff CL, Jr., Petroni GR, Chianese-Bullock KA, et al.: Immunologic and clinical outcomes of a randomized phase II trial of two multipeptide vaccines for melanoma in the adjuvant setting. Clin Canc Res 13:6386-6395, 2007
Parameter: Tumor specific IFN-γ ELISPOT Response
Setting: Adjuvant Autologous Tumor Lysate Vaccine - Colon Cancer
Barth RJ Jr, Fisher DA, Wallace PK, et al.: A randomized trial of ex vivo CD40L activation of a dendritic cell vaccine in colorectal cancer patients: tumor-specific immune responses are associated with improved survival. Clin Cancer Res 16:5548-56, 2010
Parameter: Delayed Type IV Hypersensitivity
Setting: Dendritic Cell Vaccine - Stage III-IV Melanoma Patients
López MN, Pereda C, Segal G, et al: Prolonged survival of dendritic cell-vaccinated melanoma patients correlates with tumor-specific delayed type IV hypersensitivity response and reduction of tumor growth factor beta-expressing T cells. J Clin Oncol 27:945-52, 2009
Parameter: Delayed Type IV Hypersensitivity
Setting: Adjuvant Autologous Tumor Cell Vaccination - Stage III-IV Melanoma Patients
Baars A, Claessen AM, van den Eertwegh AJ et al.: Skin tests predict survival after autologous tumor cell vaccination in metastatic melanoma: experience in 81 patients. Ann Oncol 11:965-70, 2000
Parameter: Delayed Type IV Hypersensitivity
Setting: Allogeneic GM-CSF - Stage I-III Pancreatic Cancer
Jaffee EM, Hruban RH, Biedrzycki B, et al.: Novel allogeneic granulocyte-macrophage colony-stimulating factor-secreting tumor vaccine for pancreatic cancer: a phase I trial of safety and immune activation. J Clin Oncol 19: 45-56, 2009
Parameter: Delayed Type IV Hypersensitivity
Setting: Dendritic Cell Vaccination - Stage IV Melanoma
de Vries IJ, Bernsen MR, Lesterhuis WJ, et al: Immunomonitoring tumor-specific T cells in delayed-type hypersensitivity skin biopsies after dendritic cell vaccination correlates with clinical outcome. J Clin Oncol 23:5779-87, 2005
3. Gene Expression Profiling in Cancer Immunotherapy
Examples
Gajewski TF, Fuertes M, Spaapen R et al.: Molecular profiling to identify relevant immune resistance mechanisms in the tumor microenvironment. Curr Opin Immunol 23:286-92, 2011
Bedognetti D, Wang E, Sertoli MR, Marincola FM: Gene-expression profiling in vaccine therapy and immunotherapy for cancer. Expert Rev Vaccines 6:555-65, 2010
Worschech A, Chen NH, Yu YA, et al.: Systemic treatment of xenografts with vaccinia virus GLV-1h68 reveals the immunologic facet of oncolytic therapy. BMC Genomics 10, 2009
Harlin H, Meng Y, Peterson AC, et al.: Chemokine expression in melanoma metastases associated with CD8(+) T-cell recruitment. Cancer Res 69:3077-3085, 2009
Wang E, Miller LD, Ohnmacht GA, et al.: Prospective molecular profiling of melanoma metastases suggests classifiers of immune responsiveness. Cancer Res 62:3581-3586, 2002
Panelli MC, Stashower ME, Slade HB, et al.: Sequential gene profiling of basal cell carcinomas treated with imiquimod in a placebo-controlled study defines the requirements for tissue rejection. Genome Biol 8:R8, 2007
Sullivan RJ, Hoshida Y, Brunet J, et al.: A single center experience with high-dose (HD) IL-2 treatment for patients with advanced melanoma and pilot investigation of a novel gene expression signature as a predictor of response. J Clin Oncol 27:9003, 2009 (Meeting Abstract)
Gajewski TF, Zha Y, Thurner B, et al.: Association of gene expression profile in metastatic melanoma and survival to a dendritic cell-based vaccine. J Clin Oncol 27:9002, 2009 (Meeting Abstract)
Louahed J, Gruselle O, Gaulis S, et al.: Expression of defined genes identified by pretreatment tumor profiling: Association with clinical responses to the GSK MAGE- A3 immunotherapeutic in metastatic melanoma patients (EORTC 16032-18031). J Clin Oncol 26:9045, 2008 (Meeting Abstract)
Vansteenkiste JF, Zielinski, M, Dahabreh IJ, et al.: Association of gene expression signature and clinical efficacy of MAGE-A3 antigen-specific cancer immunotherapeutic (ASCI) as adjuvant therapy in resected stage IB/II non-small cell lung cancer (NSCLC). J Clin Oncol 26:7501, 2008 (Meeting Abstract)
Mandruzzato S, Callegaro A, Turcatel G, et al.: A gene expression signature associated with survival in metastatic melanoma. J Transl Med. 4:50, 2006
Panelli MC, Wang E, Phan G, et al.: Gene-expression profiling of the response of peripheral blood mononuclear cells and melanoma metastases to systemic IL-2 administration. Genome Biol 3, 2002
4. Useful Reviews
Marincola FM: The trouble with translational medicine. J Intern Med. 2011 May
16. [Epub ahead of print]
Kirkwood JM, Butterfield LH, Tarhini AA, Zarour H, Kalinski P, Ferrone S: Immunotherapy of Cancer in 2011 CA: A Cancer Journal for Clinicians [in press]
Ascierto ML, De Giorgi V, Liu Q et al.: An immunologic portrait of cancer. Journal of Translational Medicine, (2011, in press).
Gajewski TF, Fuertes M, Spaapen R, et al.: Molecular profiling to identify relevant immune resistance mechanisms in the tumor microenvironment. Curr Opin Immunol 23:286-92, 2011
Disis ML: Immunologic biomarkers as correlates of clinical response to cancer immunotherapy. Cancer Immunol Immunother. 60:433-42, 2011
Sznol M: Molecular markers of response to treatment for melanoma Cancer J 17:127-33, 2011
Bedognetti D, Wang E, Sertoli MR, Marincola FM. Gene-expression profiling in vaccine therapy and immunotherapy for cancer. Expert Rev Vaccines 6:555-65, 2010
Slota M, Lim JB, Dang Y, Disis ML: ELISpot for measuring human immune responses to vaccines. Expert Rev Vaccines 10:299-306, 2011
Gogas H, Eggermont AM, Hauschild et al: Biomarkers in melanoma. Ann Oncol. 20 Suppl 6:vi8-13, 2009
Simon R: Translational research in oncology: key bottlenecks and new paradigms. Expert Rev Mol Med 12:e32, 2010
Kirkwood JM, Tarhini AA, Panelli MC, Moschos SJ, Zarour HM, Butterfield LH,
Gogas HJ: Next generation of immunotherapy for melanoma. J Clin Oncol 26:3445-55, 2008
Freidlin B, McShane LM, Korn EL: Randomized clinical trials with biomarkers:
design issues. J Natl Cancer Inst 3;102:152-60, 2011
5. Bioinformatics for Biomarkers Discovery
Zhao Y, Simon R: Development and validation of predictive indices for a continuous outcome using gene expression profiles. Cancer Inform 9:105-114, 2010
Chaussabel D: Data management: it starts at the bench. Nat Immunol. 10:1225-7, 2009
Chaussabel D, Quinn C, Shen J, et al.: A modular analysis framework for blood genomics studies: Application to systemic lupus erythematosus. Immunity 29:150-164, 2008
Bredel M, Scholtens D, Harsh GR, et al.: Model of a cooperative genetic landscape in gliomas. Neuro-Oncology 11:606, 2009
Simon R: The use of genomics in clinical trial design. Clin Cancer Res 14:5984-5993, 2008
Simon R: Development and validation of biomarker classifiers for treatment selection. J Stat Plan Infer 138:308-320, 2008
Nacu S, Critchley-Thorne R, Lee P, et al.: Gene expression network analysis and applications to immunology. Bioinformatics 23:850-858, 2007
Dupuy A, Simon RM: Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Natl Cancer Inst 99:147-57, 2007
6. New Technologies for Biomarkers Discovery
Linnarsson S: Recent advances in DNA sequencing methods - general principles of sample preparation. Exp Cell Res 316:1339-1343, 2010
Metzker ML: Sequencing technologies - the next generation. Nat Rev Genet 11:31-46, 2010
Maecker HT, Nolan GP, Fathman CG: New technologies for autoimmune disease monitoring. Curr Opin Endocrinol Diabetes Obes 17:322-328, 2010
Ornatsky O, Bandura D, Baranov V, et al.: Highly multiparametric analysis by mass cytometry. J Immunol Method 361:1-20, 2010
Newell EW, Klein LO, Yu W, Davis MM.: Simultaneous detection of many T-cell specificities using combinatorial tetramer staining. Nat Methods 6:497-9, 2009
Hadrup SR, Bakker AH, Shu CJ, et al.: Parallel detection of antigen-specific T-cell responses by multidimensional encoding of MHC multimers. Nat Methods 6:520-6, 2009
Lugli E, Roederer M, Cossarizza A: Data analysis in flow cytometry: the future just started. Cytometry A 77:705-713, 2010
Ryan BM, Robles AI, Harris CC: Genetic variation in microRNA networks: the implications for cancer research. Nat Rev Cancer 10:389-402, 2010
Collas P: The current state of chromatin immunoprecipitation. Mol Biotechnol 45:87-100, 2010
Espina V, Wulfkuhle J, Liotta LA: Application of laser microdissection and reverse-phase protein microarrays to the molecular profiling of cancer signal pathway networks in the tissue microenvironment. Clin Lab Med 29:1-13, 2009
Luchini A, Longo C, Espina V, et al.: Nanoparticle technology: Addressing the fundamental roadblocks to protein biomarker discovery. J Mater Chem 19:5071-5077, 2009
Park PJ: ChIP-seq: advantages and challenges of a maturing technology. Nat Rev Genet 10:669-680, 2009
Hanash SM, Pitteri SJ, Faca VM: Mining the plasma proteome for cancer biomarkers. Nature 452:571-579, 2008
Wang E, Marincola FM: Bottom up: a modular view of immunology. Immunity 2:9-11, 2008
Gulmann C, Sheehan KM, Kay EW, et al.: Array-based proteomics: mapping of protein circuitries for diagnostics, prognostics, and therapy guidance in cancer. J Pathol 208:595-606, 2006
DeAngelis JT, Farrington WJ, Tollefsbol TO: An overview of epigenetic assays. Mol Biotechnol 38:179-183, 2008
Gilad Y, Borevitz J: Using DNA microarrays to study natural variation. Curr Opin Genet Dev 16:553-558, 2006
Speer R, Wulfkuhle JD, Liotta LA, et al.: Reverse-phase protein microarrays for tissue-based analysis. Curr Opin Mol Ther 7:240-245, 2005
Perfetto SP, Chattopadhyay PK, Roederer M: Seventeen-colour flow cytometry: unravelling the immune system. Nat Rev Immunol 4:648-655, 2004
Hanash S: Disease proteomics. Nature 422:226-232, 2003
7. Standardization and Harmonization of Sample Collection and Use of Immunological Assays
A. Processing, Storage and Shipping of Blood Samples and Serum for Immunological Studies - Technical Considerations
Examples
Disis ML, dela Rosa C, Goodell V, et al.: Maximizing the retention of antigen specific lymphocyte function after cryopreservation. J Immunol Methods 308:13-18, 2006
Bull M, Lee D, Stucky J, et al.: Defining blood processing parameters for optimal detection of cryopreserved antigen-specific responses for HIV vaccine trials. J Immunol Methods 322:57-69, 2007
Kierstead LS, Dubey S, Meyer B, et al.: Enhanced rates and magnitude of immune responses detected against an HIV vaccine: effect of using an optimized process for isolating PBMC. AIDS Res Human Retroviruses 23:86-92, 2007
Ruitenberg JJ, Mulder CB, Maino VC, et al.: VACUTAINER CPT and Ficoll density gradient separation perform equivalently in maintaining the quality and function of PBMC from HIV seropositive blood samples. BMC Immunol 7:11, 2006
Tree TI, Roep BO, Peakman M.: Enhancing the sensitivity of assays to detect T cell reactivity: the effect of cell separation and cryopreservation media. Ann N Y Acad Sci 1037:26-32, 2004
Smith JG, Joseph HR, Green T, et al.: Establishing acceptance criteria for cell-mediated immunity assays using frozen peripheral blood mononuclear cells stored under optimal and suboptimal conditions. Clin Vaccine Immunol 14:527-37, 2007
McKenna KC, Beatty KM, Vicetti Miguel R, Bilonick RA: Delayed processing of blood increases the frequency of activated CD11b+ CD15+ granulocytes which inhibit T cell function. J Immunol Method 341:68-75, 2009
B. Cellular Immunotherapy: Characterization of Cellular Products
Examples
Jin P, Han TH, Ren J, Saunders S, et al.: Molecular signatures of maturing dendritic cells: implications for testing the quality of dendritic cell therapies. J Transl Med 8:4, 2010
Sheikh NA, Jones LA: CD54 is a surrogate marker of antigen presenting cell activation. Immunol Immunother 57:1381-1390, 2008
Higano CS, Schellhammer PF, Small EJ, et al.: Integrated data from 2 randomized, double-blind, placebo-controlled, phase 3 trials of active cellular immunotherapy with Sipuleucel-T in advanced prostate cancer. Cancer 115:3670-3679, 2009
Butterfield LH, Gooding W, Whiteside TL: Development of a potency assay for human dendritic cells: IL-12p70 production. J Immunother 31:89-100, 2008
Ayache S, Panelli M, Marincola FM, et al.: Effects of storage time and exogenous protease inhibitors on plasma protein levels. American Journal of Clinical Pathology 126:174-184, 2006
C. Assay Standardization and Harmonization
Examples
Clinical Laboratory Improvement Amendments (CLIA) Guideline: http://www.cms.hhs.gov/CLIA/:
International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) - common platform of Europe, Japan and United States authorities: http://www.ich.org/products/guidelines.html
Attig S, Price L, Janetzki S, Kalos M, Pride M, McNeil L, Clay T, Yuan J, Odunsi K, Hoos A, Romero P, Britten CM, Assay Working Group CC. A critical assessment for the value of markers to gate-out undesired events in HLA-peptide multimer staining protocols. J Transl Med, 9:108, 2011
Maecker HT, Hassler J, Payne JK, et al.: Precision and linearity targets for validation of an IFNgamma ELISPOT, cytokine flow cytometry, and tetramer assay using CMV peptides. BMC Immunol 9:9, 2008
Nomura L, Maino VC, Maecker HT: Standardization and optimization of multiparameter intracellular cytokine staining. Cytometry A 73:984-991, 2008
Afonso G, Scotto M, Renand A, et al.: Critical parameters in blood processing for T-cell assays: validation on ELISpot and tetramer platforms. J Immunol Methods 359:28-36, 2010
Britten CM, Gouttefangeas C, Welters MJ, et al.: The CIMT-monitoring panel: a two-step approach to harmonize the enumeration of antigen-specific CD8+ T lymphocytes by structural and functional assays. Cancer Immunol Immunother 57:289-302, 2008
Britten CM, Janetzki S, Ben Porat L, et al.: Harmonization guidelines for HLA-peptide multimer assays derived from results of a large scale international proficiency panel of the Cancer Vaccine Consortium. Cancer Immunol Immunother 2009:1701-1713, 2009
Britten CM, Janetzki S, van der Burg SH, et al.: Toward the harmonization of immune monitoring in clinical trials: quo vadis? Cancer Immunol Immunother 57:285-288, 2008
Mander A, Gouttefangeas C, Ottensmeier C, et al.: Serum is not required for ex vivo IFN-gamma ELISPOT: a collaborative study of different protocols from the European CIMT Immunoguiding Program. Cancer Immunol Immunother 59:619-627
Fahey JL, Aziz N, Spritzler J, et al.: Need for an external proficiency testing program for cytokines, chemokines, and plasma markers of immune activation. Clin Diagn Lab Immunol 7:540-548, 2000
Denny TN, Gelman R, Bergeron M, et al.: A North American multilaboratory study of CD4 counts using flow cytometric panLeukogating (PLG): a NIAID-DAIDS Immunology Quality Assessment Program Study. Cytometry B Clin Cytom 74 Suppl 1: S52-S64, 2008
Boaz MJ, Hayes P, Tarragona T, et al.: Concordant proficiency in measurement of T-cell immunity in human immunodeficiency virus vaccine clinical trials by peripheral blood mononuclear cell and enzyme-linked immunospot assays in laboratories from three continents. Clin Vaccine Immunol 16:147-155, 2009
Smith SG, Joosten SA, Verscheure V, et al.: Identification of major factors influencing ELISpot-based monitoring of cellular responses to antigens from Mycobacterium tuberculosis. PLoS One 4:e7972, 2009
Hanekom WA, Dockrell HM, Ottenhoff TH, et al.: Immunological outcomes of new tuberculosis vaccine trials: WHO panel recommendations. PLoS Med 5:e145, 2008
Schloot NC, Meierhoff G, Karlsson Faresjö M, et al.: Comparison of cytokine ELISpot assay formats for the detection of islet antigen autoreactive T cells. Report of the third immunology of diabetes society T-cell workshop. J Autoimmun 21:365-76, 2003
Moodie Z, Price L, Gouttefangeas C, et al.: Response definition criteria for ELISPOT assays revisited. Cancer Immunol Immunother 59:1489-501, 2010
D. Assays for Determination of Antitumor Immune-Response in Clinical Trials
Examples
ELISPOT
Quast S, Zhang W, Shive C, et al.: IL-2 absorption affects IFN-gamma and IL-5, but not IL-4 producing memory T cells in double color cytokine ELISPOT assays. Cell Immunol 237:28-36, 2005
Granzyme B ELISPOT
Shafer-Weaver K, Rosenberg S, Strobl S, et al.: Application of the granzyme B ELISPOT assay for monitoring cancer vaccine trials. J Immunother 29:328-335, 2006
Modified ELISPOT
Malyguine A, Strobl SL, Shafer-Weaver KA, et al.: A modified human ELISPOT assay to detect specific responses to primary tumor cell targets. J Trans Med 2:9, 2004
FACS-based Cytotoxicity Assay
Kim GG, Donnenberg VS, Donnenberg AD, et al.: A novel multiparametric flow cytometry-based cytotoxicity assay simultaneously immunophenotypes effector cells: comparisons to a 4 h 51Cr-release assay. J Immunol Methods 325:51-66, 2007
Devevre E, Romero P, Mahnke YD: LiveCount Assay: concomitant measurement of cytolytic activity and phenotypic characterisation of CD8(+) T-cells by flow cytometry. J Immunol Methods 311:31-46, 2006
Zaritskaya L, Shafer-Weaver KA, Gregory MK, et al.: Application of a flow cytometric cytotoxicity assay for monitoring cancer vaccine trials. J Immunother 32:186-194, 2009
Maecker HT, Dunn HS, Suni MA, et al.: Use of overlapping peptide mixtures as antigens for cytokine flow cytometry. J Immunol Methods 255:27-40, 2001
FACS/ELISPOT/TETRAMERS
Whiteside TL, Zhao Y, Tsukishiro T, Elder EM, Gooding W, Baar J: Enzyme-linked immunospot, cytokine flow cytometry, and tetramers in the detection of T-cell responses to a dendritic cell-based multipeptide vaccine in patients with melanoma. Clin Cancer Res, 2003
8. Reporting Biomarkers Data in Publications
Examples
REMARK Recommendations
(accepted by most major journal included those published by the American Society of Clinical Oncology and the American Association for Cancer Research)
McShane LM, Altman DG, Sauerbrei W, et al.: Reporting recommendations for tumor marker prognostic studies (REMARK). J Natl Canc Inst 97:1180-1184, 2005
MIBBI Project - Guidelines Promotion
Taylor CF, Field D, Sansone SA, et al.: Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project. Nat Biotechn 26:889-896, 2008
MIAME - Microarray Experiment
Brazma A, Hingamp P, Quackenbush J, et al.: Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 29:365-371, 2001
MIATA - T Cell Assays
Janetzki S, Britten CM, Kalos M, et al.: "MIATA"-minimal information about T cell assays. Immunity 31:527-528, 2009
Britten CM, Janetzki S, van der Burg SH, et al: Minimal information about T cell assays: the process of reaching the community of T cell immunologists in cancer and beyond. Cancer Immunol Immunother 60:15-22, 2011
MIFlowCyt - Flow Cytometry
Lee JA, Spidlen J, Boyce K, et al.: MIFlowCyt: the minimum information about a Flow Cytometry Experiment. Cytometry A 73:926-930, 2008
Data File for Flow Cytometry
Spidlen J, Moore W, Parks D, et al.: Data file standard for flow cytometry, version FCS 3.1. Cytometry A 77:97-100, 2010
Part II. High Throughput and New Technologies for Biomarker Discovery: Arrays, Platforms, Tools for The Bench and Online Resources
1. Genomic Biomarkers Discovery
A. Single Nucleotide Polymorphisms (SNPs) Arrays
Examples
Name: Affymetrix Genome-Wide Human SNP Array 6.0
Comment:1.8 million genetic markers, including more than 906,600 single nucleotide polymorphisms (SNPs) and more than 946,000 probes for the detection of copy number variation
Name: Affymetrix DMET Plus Premier Pack
Comment:Drug metabolism studies. Coverage of a wide range of genetic variations, including common and rare SNPs, insertions, deletions, tri-alleles, and copy number. 1,936 drug metabolism markers in 225 genes
Name: Illumina Omni Microarray
Comment:Next generation genome-wide association studies. The Omni family of microarrays will soon allow researchers to assay up to 5 million markers per sample, including comprehensive coverage of both common and rare variants identified by 1000 Genomes Project. As novel SNP sets are released into the public database, researchers using Omni products will have exclusive access to supplemental arrays that build up to the full 5 million variants
Website:http://www.illumina.com/landing/click/gwas.ilmn?scid=2011110PPC1&gclid=CJij6ffe26kCFQPc4AodcmxEZQ
Name: Fluidigm Dynamic Array for SNP Genotyping
Comment:The Fluidigm Dynamic Arrays allow you to use your existing TaqMan® SNP Genotyping assays in a flexible and cost effective fashion. Each dynamic array allows you to setup up to 9,216 individual TaqMan reactions in a single experiment
Website:http://www.expressionanalysis.com/services/category/fluidigm_dna_focused_set_snp
Name: TaqMan® Pre-Designed SNP Genotyping Assays
Comment:Includes over 4.5 million SNP assays, including 3.5 million HapMap, and ~70,000 coding SNP assays. This collection now includes ~160,000 validated assays with associated minor allele frequency data available
B. Comparative Genomic Hybridization (CGH) Arrays
Examples
Name:Agilent Human Genome CGH Microarrays
Comment: High-resolution tool for genome-wide DNA copy number variation profiling. Comprehensive probe coverage is enhanced with emphasis on known genes, promoters, miRNAs, pseudoautosomal, and telomeric regions
Name: Nimblegen Cytogenetics (CGX) Arrays
Comment: Genome-wide analysis of DNA copy number changes with a subset of probes focused in disease-associated regions
Website: http://www.nimblegen.com/products/cgh/human.html#cyto
C. Mitochondrial Genome Arrays
Example
Name: Affymetrix GeneChip® Human Mitochondrial Resequencing Array 2.0
Comment:Detection of germ line and heteroplasmic mutations by delivering the complete mitochondrial genome with minimal PCR in only 48 hours
Website:http://www.affymetrix.com/support/technical/byproduct.affx?product=humitoreseq
2. Epigenomic Biomarkers Discovery
A. Methylation Arrays
Examples
Name: Agilent Human DNA Methylation Microarrays
Comment:The array is specifically designed for analysis of methylated DNA derived from affinity-based isolation methods such as methylated DNA immunoprecipitation (MDIP)
Name: Nimblegen 2.1 M Whole-Genome Tiling Set
Comment:Whole-genome formats are available in two versions: a 10-array set at 100 bp probe interval or a 4-array set at > 200 bp probe interval
Website:http://www.nimblegen.com/products/methylation/whole_genome.html
Name: Illumina 450 K Infinium Methylation BeadChip Kit
Comment:This assay allow to interrogate > 450,000 methylation sites per sample at single-nucleotide resolution
Website:http://www.illumina.com/products/methylation_450_beadchip_kits.ilmn
B. microRNA (miRNA) Arrays
Examples
Name: Illumina MicroRNA Universal Array Matrix
Comment:miRNA analysis
Website:http://www.illumina.com/products/microrna_universal_array_matrix.ilmn
Name: Affymetrix GeneChip miRNA Array
Comment:miRNA analysis
Name: Exiqon's miRCURY LNA microRNA Array
Comment:miRNA analysis
C. Chromatin Immunoprecipitation (ChIP) Arrays (ChIP on chip)
[ChIP-on-chip (chromatin immunoprecipitation-on-chip), also known as Location Analysis (LA), is a high throughput (genome-wide) identification and analysis of DNA fragments that are bound by specific proteins such as histones, transcriptional factors and polymerases]
Examples
Name: Nimblegen 2.1 M ChIP-Chip Array
Comment:ChIP-chip analysis
Website:http://www.nimblegen.com/products/chip/index.html
Name: Agilent SurePrint G3 Human Promoter Microarray
Comment:ChIP-chip analysis
Name: Affymetrix GeneChip Tiling Arrays
Comment:ChIP-chip analysis
3. Transcriptomic Biomarkers Discovery
A. Expression Arrays
Examples
Name: Affymetrix Gene Chip Human Exon 1.0 ST Array
Comment:Whole genome microarray. With approximately four probes per exon and roughly 40 probes per gene, the GeneChip Human Exon 1.0 ST Array enables two complementary levels of analysis--gene expression and alternative splicing
Name: Agilent SurePrint G3 Human GE 8 × 60 K Kit
Comment:Whole genome microarray. The SurePrint G3 Human GE 8 × 60 K Microarrays and Human GE 4 × 44 K v2 Microarrays are based on updated transcriptome databases for mRNA targets, while the SurePrint G3 arrays also include probes for lincRNAs (long intergenic non-coding RNAs). With the combination of mRNA and lincRNAs, it is possible to perform two experiments on a single microarray, confidently predicting lincRNA function
Name: Illumina HumanHT-12 v4 Expression BeadChip Kits
Comment:The HumanHT-12 v4 Expression BeadChip provides high throughput processing of 12 samples per BeadChip. Each array on the HumanHT-12 v4 Expression BeadChip targets more than 31,000 annotated genes with more than 47,000 probes
Website:http://www.illumina.com/products/humanht_12_expression_beadchip_kits_v4.ilmn
B. Quantitative Assays
Examples
Name: QuantiGene® Plex 2.0 Reagent System (Luminex Assay)
Comment:Quantitatively measure 3-36 RNA targets simultaneously
Website:http://www.panomics.com/index.php?id=product_6
Name: Procarta Transcription Factor Profiling Kits (Luminex Assay)
Comment:Quantitate the DNA binding activity of up to 44 different transcription factors (TFs) in a single well using nuclear extracts or whole cell lysates
Website:http://www.panomics.com/index.php?id=product_17
Name: TaqMan® Probe-Based Gene Expression Analysis
Comment:Quantitatively measure 1-384 RNA targets simultaneously
Name: Fluidigm Dynamic Array for Single Cell Gene Expression
Comment:The Fluidigm Dynamic Array enables you to test up to 96 individual cells against 96 genes in a single experiment. The dynamic array assembles the cDNA material from individual cells and reagents to create individual qPCR reactions
Website:http://www.fluidigm.com/docs/Application_Note_Dynamic_Array_for_Single-Cell_GE_Analysis.pdf
Name: Nanostring nCounter Analysis System for digital gene expression analysis
Comment: The nCounter Analysis System utilizes a novel digital technology that is based on direct multiplexed measurement of gene expression and offers high levels of precision and sensitivity (< 1 copy per cell). The technology uses molecular "barcodes" and single molecule imaging to detect and count hundreds of unique transcripts in a single reaction.
4. Proteomic Biomarkers Discovery
A. Protein and Phosphoprotein Assays
Examples
Name: Nodality
Comment:Single-cells network profiling (SCNP): advanced multiparametric quantitative flow cytometry to measure compound effects on multiple signaling cascades in a cell-type-specific manner
Website:http://www.nodalityinc.com/
Name: NanoPro Immunoassay
Comment:Characterization of proteins in extremely small and precious samples. Unlike traditional protein analysis techniques which require thousands to millions of cells, these assays require as few as 25 cells per assay
Website:http://www.cellbiosciences.com/nanopro.html
Name: Invitrogen Immunoassays
Comment:Wide range of immunoassays using antibody pairs for single-analyte (ELISA kits) as well as multi-analyte (Lumibex assays) analysis or accurate quantitation of intracellular or extracellular proteins
Name: Procarta Cytokine Profiling Assays (Luminex Assay)
Comment:Quantitatively measure 3-30 cytokine proteins simultaneously from a variety of matrices including cell culture supernatants, serum or plasma
Website:http://www.panomics.com/index.php?id=products_luminexAssays
Name: SearchLight Multiplex Immunoassay Kits
Comment:As many as 16 proteins (4 × 4 array in each well) can be measured per well simultaneously with each 50 μl sample
Website:http://www.aushon.com/Products-and-Services.php
Name: MSD Multiplex Cytokines and Chemokines
Comment:96-well; multiplex cytokine/chemokine kits for up to ten (10) analytes per well.
384-well: Order multiplex cytokine/chemokine kits for up to four (4) analytes per well
Website:http://www.mesoscale.com
Name: Zeptomark
Comment:Protein profiling for analysis of cell signaling pathways
Website:http://www.zeptosens.com/en/
Name: Signaling Protein Luminex Assays
Comment:Multiplex assays for measuring signal transduction, post-translational modification & cleaved proteins in multiple research areas, using Luminexbead-based technology
Name: VeraCode Carboxyl Bead Sets and BeadXpress Reader
Comment:Multiplexed protein arrays (up to 48 immunoassays in a single reaction in a standard 96-well microplate: detection of protein as low as 10 pg/ml)
Website:http://www.illumina.com/products/veracode_carboxyl_bead_sets.ilmn
Name: Procarta SH2 Domain Plex Profiling Kits (Luminex Assay)
Comment:Profile phosphotyrosine protein interactions against 30 SH2 binding in a single well using treated and untreated cell lysates. Protein-Protein interaction
Website:http://www.panomics.com/downloads/13_4_SH2UM_2_V1.pdf
Name: Proto-Array
Comment:Advanced, high-content, functional protein microarray enables to scan thousands of proteins (> 9000) in as little as one day. Highly sensitive results for protein-protein interaction, kinase substrate identification, and serum profiling studies
Website:http://www.invitrogen.com/protoarray
Name: Metal Nanoparticle Probes and Dynamic Light Scattering
Comment:This assay is based on the use of gold nanoparticle probes combined with dynamic light scattering (DLS) technique, named nanoDLSAY, a highly sensitive, fast and convenient one-step homogeneous immunoassay for monitoring and detecting biotargets, including cancer biomarkers
Website:http://tt.research.ucf.edu/LinkClick.aspx?fileticket=6p5A2nGSYzA%3d&tabid=119
B. Multicolor Cytometric Systems
Examples
Name: BD LSRFortessa™ Cell Analyzer
Comment:Up to 4 lasers to detect up to 18 colors simultaneously
Website:http://www.bdbiosciences.com/instruments/lsr/index.jsp
Name: The MACSQuant® Analyzer
Comment: Three lasers, up to 8 colors, compact benchtop flow cytometer that is small in size; MACSQuant® Analyzer allows researchers to perform sensitive rare cell analysis using magnetic separation
Website:http://www.miltenyibiotec.com/downloads/6760/6764/18602/MQ_brochure.pdf
Name: CyAn ADP Analyzer
Comment:Up to 11 standard parameters and 9 colors
Website:http://www.coulterflow.com/bciflow/instrumentsus.php
Name: Partec CyFlow® ML
Comment:3-laser configuration to detect up to 16 colors
Website:http://www.partec.com/cms/front_content.php?idcat=13
Name: Accuri C6
Comment:Standard 2-laser configuration, 4- color system
5. Next Generation Sequencing: Whole Genomic and Transcriptomic Sequencing Systems
Next generation sequencing, possible applications: Whole Genome and Transcriptome Sequencing-Based analysis, Gene Regulation Analysis, SNP Discovery and Structural Variation Analysis, Cytogenetic Analysis, DNA-Protein Interaction Analysis (ChIP-Seq), Sequencing-Based Methylation Analysis, Small RNA Discovery and Analysis
Examples
Name: Illumina - several systems, examples: HiSeq 2000 (Sequencing); HiScanSQ (Sequencing + Arrays) and MiSeq
Comment:Next generation sequencing
Website:http://www.illumina.com/systems.ilmn
Name: Roche - several systems, examples: Genome Sequencer FLX System and GS Junior System (454 Sequencing)
Comment:Next generation sequencing
Website:http://www.454.com/
Name: Applied Biosystems - several systems, examples: Solid 4 System and Solid PI System (Solid System)
Comment:Next generation sequencing
Name: Ion Torrent PGM
Comment:Next generation sequencing: semiconductor technology allows runs in about 2 hours.
Website:http://www.iontorrent.com/products-ion-pgm/
Name:PacBio RS
Comment:Third generation sequencing: Long readlengths, single molecule sequencing
6. Software and Tools for Data Analysis
A. Microarrays/Sequencing Data Analysis
Examples:
Name: BRB - ArrayTools
Comment:Microarray/array CGH analysis. BRB-Array Tools was developed by Dr. Richard Simon and BRB-ArrayTools Development Team, NCI, NIH. The program can be used for non-commercial purposes free-of-charge
Website:http://linus.nci.nih.gov/pilot/index.htm
License needed:No
Name: Partek
Comment:Next generation sequencing technologies, including gene expression and DGE, RNA-seq and alternative splicing, copy number and association, ChIP-chip, ChIP-seq, microRNA and SNP association study
Website:http://www.partek.com/
License needed:Yes
Name: Nexus Copy Number
Comment:aCGH and SNP copy number analysis
Website:http://www.biodiscovery.com/index/nexus
License needed:Yes
Name: Nexus Expression
Comment:Microarray gene expression analysis
Website:http://www.biodiscovery.com/index/nexus-expression
License needed:Yes
Name: mAdb (aka Mad Bee)
Comment:Microarray gene expression analysis
Website:http://madb.nci.nih.gov/
License needed:No
Name: Bioconductor
Comment:Tools for the analysis and comprehension of high-throughput genomic data. Bioconductor uses the R statistical programming language
Website:http://www.bioconductor.org/
License needed:No
Name: MATLAB 7.11
Comment:MATLAB is a high-level technical computing language and interactive environment for algorithm development, data visualization, data analysis, and numeric computation
Website:http://www.mathworks.com/products/matlab/description1.html
License needed:Yes
Name: GeneSifter® Analysis
Comment:Software microarray and next generation sequencing analysis
Website:http://www.geospiza.com/Contact/genesiftertrial_ng.shtml
License needed:Yes
Name: ADaCGH
Comment:Web tool for the analysis of aCGH data sets
Website:http://adacgh.bioinfo.cnio.es/
License needed:No
Name: ArrayStar
Comment:Gene expression analysis software package that includes visualization tools to help analyze microarray data, including Venn diagrams, a scatter plot, heat maps and line graphs for clustering, and a gene ontology tree
Website:http://www.dnastar.com/t-products-arraystar.aspx
License needed:Yes
Name: GeneSpring GX
Comment:Statistical tools for fast visualization and analysis of expression and genomic structural variation data
License needed:Yes
Name: ANAIS
Comment:User-friendly web-based tool for the processing of NimbleGen expression data
Website:http://anais.versailles.inra.fr/
License needed:No
Name: JMP
Comment:Software for Microarray, SNP and Proteomics Expression Analysis
Website:http://www.jmp.com/software/webinars/jmp_microarray.shtml
License needed:Yes
Name: SAM: Significance Analysis of Microarrays
Comment:Supervised learning software for genomic expression data mining
Website:http://www-stat.stanford.edu/~tibs/SAM/
License needed:No
Name: GenePublisher
Comment:Gene expression analysis
Website:http://www.cbs.dtu.dk/services/GenePublisher/
License needed:No
B. Next Generation Sequencing Data Analysis
Blog
Name: SEQanswers
Comment:SEQanswers is a blog founded to be an information resource and user-driven community focused on all aspects of next-generation genomics. A reasonably thorough table of next-gen-seq software available in the commercial and public domain is provided
Website:http://seqanswers.com/forums/showthread.php?t=43
Note:
Examples
Name: Cufflinks
Comment:Cufflinks assembles transcripts, estimates their abundances, and tests for differential expression and regulation in RNA-Seq samples. It accepts aligned RNA-Seq reads and assembles the alignments into a parsimonious set of transcripts, free of charge
Website:http://cufflinks.cbcb.umd.edu/
License needed:No
Name: Bowtie
Comment:Bowtie is an ultrafast, memory-efficient short read aligner. It aligns short DNA sequences (reads) to the human genome at a rate of over 25 million 35-bp reads per hour. Bowtie indexes the genome with a Burrows-Wheeler index to keep its memory footprint small: typically about 2.2 GB for the human genome (2.9 GB for paired-end).
Website:http://bowtie-bio.sourceforge.net/index.shtml
License needed:No
Name: Tophat
Comment:TopHat is a program that aligns RNA-Seq reads to a genome in order to identify exon-exon splice junctions. It is built on the ultrafast short read mapping program Bowtie. TopHat runs on Linux and OS X.
Website:http://tophat.cbcb.umd.edu/
License needed:No
Name: Oases
Comment:Oases is a de novo transcriptome assembler designed to produce transcripts from short read sequencing technologies, such as Illumina, SOLiD, or 454 in the absence of any genomic assembly
Website:http://www.ebi.ac.uk/~zerbino/oases/
License needed:No
Name: Genomatix Genome Analyzer (GGA) and Genomatix Software Suite (GSS)
Comment:GGA comprehensive second-level analysis of Next Generation Sequencing (NGS) data from ChIP-Seq, RNA-Seq or genotyping experiments. GSS conducts a scientific analysis of genomic data in gene regulation, networks, pathways and genome annotation visualization.
Website:http://www.genomatix.de/en/index.html
License needed:yes
Name: ALLPATHS-LG
Comment:genome assembly algorithms recommended by the Broad Institute
License needed:no
C. Multicolor Cytometric Data-Analysis
Examples
Name: FlowJo
Comment:FlowJo is designed around the structure of flow data and the researcher's experiments. Through FlowJo's patent-pending Groups structure, for example, gates can be applied to many samples as easily as one
Website:http://www.treestar.com
License needed:Yes
Name: WinList/FCOM
Comment:Load FCS files from instruments and do FACS data analysis with a full set of region tools and gates. The algorithms allow for rapid generation of registers with frequencies/numbers of all possible suphenotype combinations. Can be used to input data for cluster analysis and heat maps, allowing rapid visualization of numerous complex data sets.
Website:http://www.vsh.com/
License needed:Yes
Name: FCSPress
Comment:FCSPress is an easy-to-use Macintosh program that produces presentation quality graphics and generates statistics from flow-cytometric data
Website:http://www.fcspress.com/
License needed:Yes
Name: FCS EXpress
Comment:FCS Express is designed to bring the power sophisticated analysis protocols to users in an intuitive, easy to grasp manner
Website:http://www.denovosoftware.com/
License needed:Yes
Name: GemStone
Comment:GemStone is software for analysis of high-dimensional, flow cytometry data. Based on patented Probability State Modeling technology, GemStone eliminates some problems that have faced flow cytometry. Subjective gating and associated errors are eliminated. Population overlaps in multidimensional data are accounted for. Multiple samples may be combined into one coherent analysis
Website:http://www.vsh.com/
License needed:Yes
Name: SPICE
Comment:SPICE is a data mining software application that analyzes large FLOWJO data sets from polychromatic flow cytometry and organizes the normalized data graphically. SPICE enables users to discover potential correlations in their experimental data within complex data sets.
Website:http://exon.niaid.nih.gov/spice/
License needed:Yes
7. Software and Tools for Function and Pathway Analysis
Examples
Name: Ingenuity Pathway Analysis (IPA)
Comment:User friendly software that allows analysis of biological and chemical systems
Website:http://www.ingenuity.com/
License needed:Yes
Name: GeneGo MetaCore
Comment:User friendly software that allows analysis of biological and chemical systems
Website:http://www.genego.com/about.php
License needed:Yes
Name: Ariadne Pathway Studio
Comment:Analysis of biological system with an interactive software interface and the computational approach to generating database content from the literature
Website:http://www.ariadnegenomics.com/
License needed:Yes
Name: DAVID
Comment:Analysis of biological system
Website:http://david.abcc.ncifcrf.gov/content.jsp?file=functional_annotation.html#intro
License needed:No
Name: KEGG Pathway
Comment:Collection of manually drawn pathway maps representing knowledge on the molecular interaction and reaction networks, free of charge
Website:http://www.genome.jp/kegg/pathway.html
License needed:No
Name: BioCarta pathway
Comment:BioCarta pathway provides displays of gene interactions within pathways for human cellular processes, such as apoptosis and signal transduction
Website:http://cgap.nci.nih.gov/Pathways/BioCarta_Pathways
License needed:No
Name: Interactive Genomics Viewer
Comment:The Integrative Genomics Viewer (IGV) is a high-performance visualization tool for interactive exploration of large, integrated datasets. It supports a wide variety of data types including sequence alignments, microarrays, and genomic annotations
Website:http://www.broadinstitute.org/igv/home
License needed:No
Name: GSEA
Comment:Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g., phenotypes)
Website:http://www.broadinstitute.org/gsea/
License needed:No
Name: GOMiner
Comment:A tool for biological interpretation of 'omic' data - including data from gene expression microarrays. Omic experiments often generate lists of dozens or hundreds of genes that differ in expression between samples
Website:http://discover.nci.nih.gov/gominer/index.jsp
License needed:No
Name: MatchMiner
Comment:Set of tools that enables the user to translate between disparate ids for the same gene. It uses data from the UCSC, LocusLink, Unigene, OMIM, Affymetrix and Jackson data sources to determine how different ids relate. Supported id types include, gene symbols and names, IMAGE and FISH clones, GenBank accession numbers and UniGene cluster ids
Website:http://discover.nci.nih.gov/matchminer/index.jsp
License needed:No
Name: PANTHER Classification System
Comment:Panther is a resource that classifies genes by their functions, using published scientific experimental evidence and evolutionary relationships to predict function even in the absence of direct experimental evidence. Proteins are also classified
Website:http://www.pantherdb.org/
License needed:Yes
8. siRNA Libraries
Examples
Name: Ambion siRNA Libraries
Comment:siRNA Libraries
Website:http://www.ambion.com/techlib/tn/116/11.html
Name: Thermo Scientific siRNA Libraries
Comment:siRNA Libraries
Website:http://www.dharmacon.com/catalog/catalogitemtemplate.aspx?id=1435&imageid=1882
Name: siRNA Libraries
Comment:siRNA Libraries
9. Public Databases
Name: NCBI Databases
Comment:Gene/Protein/SNP/Nucleotide and several other databases
Website:http://www.ncbi.nlm.nih.gov/gquery/
Name: Gene Expression Omnibus (GEO)
Comment:Public functional genomics data repository supporting MIAME-compliant (Minimum information about a microarray experiment) data submissions. Array- and sequence-based data are accepted. Tools are provided to help users query and download experiments and curated gene expression profiles
Website:http://www.ncbi.nlm.nih.gov/geo/
Name: Catalogue Of Somatic Mutations In Cancer (COSMIC)
Comment:COSMIC is designed to store and display somatic mutation information and related details and contains information related to human cancers
Website:http://www.sanger.ac.uk/genetics/CGP/cosmic/
Name: The Cancer Genome Atlas (TCGA)
Comment:The Cancer Genome Atlas (TCGA) Data Portal provides a platform for researchers to search, download, and analyze data sets generated by TCGA. Launched in 2006 as a partnership between the National Cancer Institute and the National Human Genome Research Institute, both NIH components, The Cancer Genome Atlas (TCGA) has developed a comprehensive strategy for comparing the genome of cancer cells to the genome of normal cells from the same patient
Website:http://cancergenome.nih.gov/
Name: The Human Protein Atlas
Comment:The Human Protein Atlas portal is a publicly available database with millions of high-resolution images showing the spatial distribution of proteins in 46 different normal human tissues and 20 different cancer types, as well as 47 different human cell lines. The data is released together with application-specific validation performed for each antibody. The database was developed in a gene-centric manner with the inclusion of all human genes predicted from genome efforts
Website:http://www.proteinatlas.org
Name:DIP (Database of Interacting Proteins)
Comment:The DIP database catalogs experimentally determined interactions between proteins. It combines information from a variety of sources to create a single, consistent set of protein-protein interactions
Website:http://dip.doe-mbi.ucla.edu/dip/Main.cgi
Name:MINT (Molecular INTeraction Database)
Comment:MINT focuses on experimentally verified protein-protein interactions mined from the scientific literature by expert curators. The curated data can be analyzed in the context of the high throughput data and viewed graphically with the 'MINT Viewer'
Website:http://mint.bio.uniroma2.it/mint/Welcome.do
Name: Human Protein Reference Database
Comment:The Human Protein Reference Database represents a centralized platform to visually depict and integrate information pertaining to domain architecture, post-translational modifications, interaction networks and disease association for each protein in the human proteome
Website:http://www.hprd.org
Name: Cancer Genome Anatomy Project (CGAP)
Comment:The NCI's Cancer Genome Anatomy Project sought to determine the gene expression profiles of normal, precancer, and cancer cells, leading eventually to improved detection, diagnosis, and treatment for the patient. Resources generated by the CGAP initiative are available to the broad cancer community
Website:http://cgap.nci.nih.gov/cgap.html
Name: caBIG
Comment:caBIG stands for the cancer Biomedical Informatics Grid. caBIG is an information network enabling members of the cancer community - researchers, physicians, and patients - to share data and knowledge
Website:https://cabig.nci.nih.gov/
Name: mirbase
Comment:Searchable database of published miRNA sequences and annotation
Website:http://www.mirbase.org
Name: deepBase
Comment:deepBase is a database for annotating and discovering small and long ncRNAs (microRNAs, siRNAs and piRNAs) from high-throughput deep sequencing data
Website:http://deepbase.sysu.edu.cn/miRDeep.php
Name: Gene Ontology Annotation (UniProtKB-GOA) Database
Comment:The UniProtKB-GOA project aims to provide high-quality Gene Ontology (GO) annotations to proteins in the UniProt Knowledgebase (UniProtKB) and International Protein Index (IPI) and is a central dataset for other major multi-species databases; such as Ensembl and NCBI
Website:http://www.ebi.ac.uk/GOA/
Name: Protfun
Comment:The ProtFun 2.2 server produces ab initio predictions of protein function from sequence
Website:http://www.cbs.dtu.dk/services/ProtFun/
Name: TRANSFAC 7.0 Public 2005
Comment:Data on transcription factors, their experimentally-proven binding sites, and regulated genes. Its broad compilation of binding sites allows the derivation of positional weight matrices
Website:http://www.gene-regulation.com/pub/databases.html
Name: OptiTope
Comment:OptiTope aims at assisting immunologists in designing epitope-based vaccines. It is an easy-to-use tool to determine a provably optimal set of epitopes with respect to overall immunogenicity in a specific individual or a target population, free of charge
Website:http://www.epitoolkit.org/optitope
Name: Cancer Central Clinical Database (C3D)
Comment:Cancer Central Clinical Database (C3D) is a clinical trials data management system. C3D collects clinical trial data using standard case report forms (CRFs) based on common data elements (CDEs)
Website:https://cabig.nci.nih.gov/tools/c3d
Name: UCSC Genome Browser
Comment:Provides a large database of publicly available sequence and annotation data along with an integrated tool set for examining and comparing the genomes of organisms, aligning sequence to genomes, and displaying and sharing users' own annotation data
Website:http://genome.ucsc.edu/
Name: Next Bio
Comment:Exhaustive collection of public microarray data. NextBio's platform combines powerful tools with unique correlated content. With NextBio you can search tens of thousands of studies containing billions of data points spanning different experimental platforms, organisms and data types
Website:http://www.nextbio.com/b/corp/faq.nb
Name: SYFPEITHI
Comment:SYFPEITHI is a database comprising more than 7,000 peptide sequences known to bind class I and class II MHC molecules. The entries are compiled from published reports only
Website:http://www.syfpeithi.de/
Name: Melanoma Molecular Map Project
Comment:MMMP Databases, putting together the pieces of the melanoma puzzle. Seven interconnected databanks for the interactive collection, update and consultation of the translational and clinical information on melanoma biology and treatment.
Website:http://www.mmmp.org
Name: The Targeted Therapy Database (TTD)
Comment:Systematic collection of the scientific knowledge regarding the development of targeted therapy for melanoma
Website:http://www.mmmp.org
10. Tools for the Bench and Other Useful Websites
A. Primer Design Software
Examples
Name: Primer3
Comment:Primer3 is a free online tool to design and analyze primers for PCR and real time PCR experiments. Primer3 can also select single primers for sequencing reactions and can design oligonucelotide hybridization probes
Website:http://frodo.wi.mit.edu/primer3
Name: Oligo Analyzer 3.0
Comment:Software developed by IDT (Integrated DNA Technologies) that analyzes physical properties of a specific oligo sequence. The results show a complementary sequence, oligo length, GC content, melting temperature, extinction coefficient, molecular weight, μg/OD, and nmoles/OD.
Website:http://www.idtdna.com/analyzer/applications/oligoanalyzer
Name: MethPrimer
Comment:MethPrimer is a program for designing bisulfite-conversion-based methylation PCR Primers. It can design primers for Methylation-Specific PCR (MSP), Bisulfite-Sequencing PCR (BSP) and Bisulfite-Restriction PCR
B. Transcription Factors Binding Sites Prediction Software
Examples
Name: TFSEARCH
Comment:TFSEARCH predicts transcription factors binding sites from a given sequence. It does simple correlation calculation with binding site profile matrices
Website:http://www.cbrc.jp/research/db/TFSEARCH.html
C. Design of Antisense Oligonucleotides, Nucleic Acid Probes, siRNA Software
Name: Sfold
Comment:Sfold predicts RNA duplex thermodynamics for rational siRNA design. It supports target accessibility prediction and rational design of antisense oligonucleotides (ASO) and nucleic acid probes. It can design an ASO for a gene of interest based on the mRNA sequence
D. miRNA Prediction
Examples
Name: TargetScan
Comment:Prediction of biological targets of miRNAs
Website:http://www.targetscan.org/
License needed:No
Name:http://microRNA.org
Comment:A resource for predicted microRNA targets and expression, free of charge
Website:http://www.microrna.org/microrna/home.do
License needed:No
Name: DIANA LAb
Comment:miRNA computational predictive models. Experimental supported databases
Website:http://diana.cslab.ece.ntua.gr/?sec=home
License needed:No
Name: RNA22
Comment:This software first finds putative microRNA binding sites in the sequence of interest, then identifies the targeting microRNA
Website:http://cbcsrv.watson.ibm.com/rna22.html
Name: PicTar
Comment:PicTar provides combinatorial microRNA target predictions
Website:http://pictar.mdc-berlin.de
Name: miRANDA
Comment:miRanda is an algorithm for finding genomic targets for microRNAs
E. Alternative Splicing Analysis
Example
Name: SpliceCenter
Comment:The SpliceCenter applications are user-friendly tools that provide information on the target location of probesets, primers, or siRNAs within the known splice variants of a gene
Website:http://www.tigerteamconsulting.com/SpliceCenter/
License needed:No
F. Linkage Disequilibrium Analysis
Examples
Name: PhAT
Comment:It analyzes SNP data, showing pairwise Linkage Disequilibrium of various types (r2, r, D' and abs(D)) and producing graphical matrices of obtained results
G. Analysis Support, Laboratory Optimization and Other Useful Websites
Examples
Name: Immuneering
Comment: Computer Model. Development of computer models that aim to predict the response to biological therapies in cancer patients
Website:http://www.immuneering.com/
Name: Cellumen CellCiphr Patient Sample Profiling
Comment:To use the cellular systems biology approach to improve patient stratification for clinical trial enrollments. Cellumen is collaborating with the Mayo Clinic and Foundation to create panels of cellular biomarkers using multiplexed fluorescence to apply to patient cells and tissues, starting with breast cancer
Website:http://www.cellumen.com/solutions/patient.php
Name: Biotracker
Comment: Lab Management. Biotracker is a specialty Lab Information Management Solution (LIMS) for enhancing productivity and effectiveness in life sciences research laboratories
Website:http://www.ocimumbio.com/lims2/products/
Name: Gene's Logic Expression Array Analysis
Comment:Array analysis support; team dedicated to statistical and bioinformatic analyses of microarray data, ranging from basic quality control and differential gene expression analysis
Website:http://www.genelogic.com/services/bioinformatic-analysis
Name: Microsoft Word Add-In for the GenePattern Reproducible Research Document (GRRD)
Comment:To facilitate publishing reproducible results, GenePattern automatically captures the history of any computational work being done, allowing scientists to easily generate pipelines to reproduce computational methods
Website:http://www.broadinstitute.org/cancer/software/genepattern/grrd/AddIn.html
License needed:No
Name: IUBio Archive
Comment:Archive of biology data and software, established in 1989 to promote public access to freely available information, primarily in the field of molecular biology and bioinformatics
Website:http://iubio.bio.indiana.edu/
Name: Labome
Comment:Tools for searching antibodies, siRNA/shRNA, ELISA, cDNA clones, proteins/peptides, microRNA, and biochemicals from all suppliers
Website:http://www.labome.com/
License needed:No
H. Nanotechnology
Examples
Name: Inno.CNT
Comment:The Inno.CNT website gives an updated and wide overview of the research status of carbon nanotubes as one of the most promising nanomaterial to open up completely new dimensions in biomedical applications
Website:http://www.inno-cnt.de/en/uebercnt.php
Name: ObservatoryNano
Comment:The ObservatoryNano provides wide-ranging analysis focusing on nanoscience and nanotechnology developments
Website:http://www.observatory-nano.eu/project/
Name: Nanofutures
Comment:Nanofutures is an integration platform aimed at becoming a long-lasting nanotechnology hub, connecting the most relevant nanotechnology stakeholders
Website:http://www.nanofutures.eu/
Name: Nanotechnology Medicine
Comment:Nanotechnology Medicine brings you informative nanomedicine articles, educational nanomedicine videos, and lively nanomedicine dialogue and chatter.
Website: http://nanotechnologymedicine.org/
I. Clinical Trials Registries
Example
Name: Clinical Trial Registries
Comment:The five clinical trials registries approved by the International Committee of Medical Journal Editors (ICMHE)
Website:http://ClinicalTrials.gov, http://www.actr.org.au, http://www.ISRCTN.org, http://www.umin.ac.jp/ctr/index/htm, http://www.trialregister.nl
Conclusion
Immune biomarkers are playing an increasingly important role in the successful development, clinical evaluation, and immune monitoring of cancer immunotherapies. The references, products and online resources in this Cancer Immunotherapy Biomarkers Resource Document were identified by the authors and the SITC/iSBTc Taskforce on Immunotherapy Biomarkers to support the discovery, evaluation and application of biomarkers for cancer immunotherapy. These selected references and links serve as a compass to point investigators to useful resources in this ever growing, and important field of cancer immunotherapy biomarkers. Emerging issues surrounding cancer immunotherapy biomarker discovery and clinical application will continue to be addressed in upcoming SITC Annual Meetings and Associated Programs [13].
Competing interests
DB, JB, EW, MLD, CMB, LGD, ST, TFG, LHB and FFM declare that they have no competing interests. BAF is co-founder of UbiVac, LLC and serves on the scientific advisory boards of Micromet, Inc. and MannKind Corporation.
Authors' contributions
DB and JB prepared the manuscript. EW, MLD, CMB, LGD, ST, BAF, TFG, FMM and LHB provided substantive editing and critical review. All authors read and approved the final manuscript.
Contributor Information
Davide Bedognetti, Email: Davide.Bedognetti@nih.gov.
James M Balwit, Email: jbalwit@sitcancer.org.
Ena Wang, Email: EWang@cc.nih.gov.
Mary L Disis, Email: ndisis@u.washington.edu.
Cedrik M Britten, Email: britten@uni-mainz.de.
Lucia G Delogu, Email: lgdelogu@uniss.it.
Sara Tomei, Email: Sara.Tomei@nih.gov.
Bernard A Fox, Email: foxb@foxlab.org.
Thomas F Gajewski, Email: tgajewsk@medicine.bsd.uchicago.edu.
Francesco M Marincola, Email: FMarincola@mail.cc.nih.gov.
Lisa H Butterfield, Email: butterfieldl@upmc.edu.
Acknowledgements
The authors wish to acknowledge the members of the SITC/iSBTc Taskforce on Immunotherapy Biomarkers, Working Group 2 who reviewed these resources as part of the planning for the Symposium on Immuno-Oncology Biomarkers, 2010 and Beyond. This working group includes: Francesco Marincola, Peter P. Lee, Damien Chaussabel, George Coukos, Bernard Fox, John Kirkwood, Leif Håkansson, Michele Maio, Antoni Ribas, Dolores Schendel, Barbara Selinger, Senthamil Selvan, Wenru Song, Howard Streicher, Magdalena Thurin, Giorgio Trinchieri, Ena Wang, Xifeng Wu, Ben Zeskind, Yingdong Zhao. The authors also wish to acknowledge all colleagues who reviewed and commented on previous versions of this document, including: Pietro Blandini, Enrico Lugli, Stefania Santamaria, Tim Greten, Sylvia Janetzki, Janet Siebert, Wenji Zhang and Gabriele Zoppoli.
Davide Bedognetti is a participant in the NIH Graduate Partnership Program and a graduate student at University of Genoa. Davide Bedognetti's fellowship is supported by the Conquer Cancer Foundation of the American Society of Clinical Oncology (2011 Young Investigator Award).
References
- Symposium on Immuno-Oncology Biomarkers, 2010 and Beyond: Perspectives from the iSBTc Biomarker Task Force. http://www.sitcancer.org/meetings/am10/biomarkers10/ [DOI] [PMC free article] [PubMed]
- SITC/iSBTc-FDA-NCI Workshop on Prognostic and Predictive Immunologic Biomarkers in Cancer. http://www.sitcancer.org/meetings/am09/workshop09/
- Immune Monitoring Workshop II. http://www.sitcancer.org/meetings/am04/workshop.php
- Workshop on Cancer Biometrics: Identifying Biomarkers and Surrogates of Tumors in Patients. http://www.sitcancer.org/meetings/am03/biometrics.php [DOI] [PubMed]
- Immune Monitoring Workshop. http://www.sitcancer.org/meetings/bethesda/immune_monitoring_workshop.php
- Butterfield LH, Palucka AK, Britten CM, Dhodapkar MV, Hakansson L, Janetzki S, Kawakami Y, Kleen TO, Lee PP, Maccalli C, Maecker HT, Maino VC, Maio M, Malyguine A, Masucci G, Pawelec G, Potter DM, Rivoltini L, Salazar LG, Schendel DJ, Slingluff CL Jr, Song W, Stroncek DF, Tahara H, Thurin M, Trinchieri G, van Der Burg SH, Whiteside TL, Wigginton JM, Marincola F, Khleif S, Fox BA, Disis ML. Recommendations from the iSBTc-SITC/FDA/NCI Workshop on Immunotherapy Biomarkers. Clin Cancer Res. 2011;17:3064–3076. doi: 10.1158/1078-0432.CCR-10-2234. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Butterfield LH, Disis ML, Khleif SN, Balwit JM, Marincola FM. Immuno-Oncology Biomarkers 2010 and Beyond: Perspectives from the iSBTc/SITC Biomarker Task Force. J Transl Med. 2010;8(1):130. doi: 10.1186/1479-5876-8-130. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Butterfield LH, Disis ML, Fox BA, Lee PP, Khleif SN, Thurin M, Trinchieri G, Wang E, Wigginton J, Chaussabel D, Coukos G, Dhodapkar M, Håkansson L, Janetzki S, Kleen TO, Kirkwood JM, Maccalli C, Maecker H, Maio M, Malyguine A, Masucci G, Palucka AK, Potter DM, Ribas A, Rivoltini L, Schendel D, Seliger B, Selvan S, Slingluff CL Jr, Stroncek DF, Streicher H, Wu X, Zeskind B, Zhao Y, Zocca MB, Zwierzina H, Marincola FM. A systematic approach to biomarker discovery; Preamble to "the iSBTc-FDA taskforce on immunotherapy biomarkers". J Transl Med. 2008;6:81. doi: 10.1186/1479-5876-6-81. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lotze MT, Wang E, Marincola FM, Hanna N, Bugelski PJ, Burns CA, Coukos G, Damle N, Godfrey TE, Howell WM, Panelli MC, Perricone MA, Petricoin EF, Sauter G, Scheibenbogen C, Shivers SC, Taylor DL, Weinstein JN, Whiteside TL. Workshop on cancer biometrics: identifying biomarkers and surrogates of cancer in patients. J Immunother. 2005;28:79–119. doi: 10.1097/01.cji.0000154251.20125.2e. [DOI] [PubMed] [Google Scholar]
- Keilholz U, Weber J, Finke J, Gabrilovich D, Kast WM, Disis N, Kirkwood JM, Scheibenbogen C, Schlom J, Maino VC, Lyerly HK, Lee PP, Storkus W, Marincola F, Worobec A, Atkins MB. Immunologic monitoring of cancer vaccine therapy: results of a Workshop sponsored by the Society of Biological Therapy. J Immunother. 2002;25:97–138. doi: 10.1097/00002371-200203000-00001. [DOI] [PubMed] [Google Scholar]
- Brazas MD, Yamada JT, Ouellette BFF. Providing web servers and training in Bioinformatics: 2010 update on the Bioinformatics Links Directory. Nucleic Acids Res. 2010;38(Web Server):W3–W6. doi: 10.1093/nar/gkq553. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Bioinformatics Links Directory. http://bioinformatics.ca/links_directory/journals/nar/2010
- SITC 2011 Annual Meeting and Associated Programs. http://www.sitcancer.org/meetings/