STRATEGIC PARTNERING TO EVALUATE CANCER SIGNATURES (SPECS) PROGRAM
Overview
STRATEGIC PARTNERING TO EVALUATE CANCER SIGNATURES (SPECS) PROGRAM
The goals of the SPECS programs are:
- To translate molecular signatures/profiles derived from comprehensive molecular analysis of tumors to improved patient management and ultimately improved patient outcomes.
- To establish strategic partnerships that bring together the interdisciplinary teams needed to evaluate the potential clinical utility of molecular signatures.
- To evaluate molecular signatures previously correlated with important clinical parameters such as recurrence, survival or response to therapy.
- To confirm and refine the molecular signatures.
- To develop robust, reproducible assays for the signatures that can be incorporated into clinical validation trials.
The SPECS programs are evaluating signatures in the following:
- Breast Cancer
- Prostate Cancer
- Lung Cancer
- Lymphoma
- Pediatric and Adult Sarcoma
- Adult and Pediatric Leukemia
These multi-institutional, multi-disciplinary teams include investigators from the Clinical Co-operative Groups, SPOREs, Cancer Centers, NCI intramural, the National Laboratories, community hospitals and individual academic institutions in the US and Europe.
SPECS Projects
Molecular Signatures of Lung Cancer
David P. Carbone, M.D., Ph.D.
Vanderbilt-Ingram Cancer Center
Dr. Carbone's project will refine and evaluate molecular signatures that address critical clinical issues in lung cancer. The project will evaluate diagnostic, prognostic, and predictive signatures developed by members of this team. These studies will combine data generated using several comprehensive technologies including mutation analysis, determination of DNA amplification, gene expression analysis, proteomics profiling and single nucleotide polymorphism (SNP) analysis of a number of genes known to be important in therapeutic response.
Collaborators:
- The project includes investigators from Vanderbilt University, University of Michigan, Dana Farber Cancer Institute, five NCI lung SPOREs at University of Colorado, University of Texas Southwestern Medical Center, MD Anderson, University of Pittsburgh, Duke University, and UCLA, SWOG, the Spanish Lung Cancer Group, and the Institut Catalia d'Oncologia in Barcelona. The project has bioinformatic and statistical support from a number of the participating institutions.
Projects:
- Evaluate serum and plasma protein profiles of patients to develop a diagnostic marker panel.
- Test proteomic and gene expression signatures predictive of outcome and nodal involvement on early stage tumors.
- Combine data for a variety of analytical methods to develop profiles identifying response to standard and targeted therapies.
- Reduce the derived signatures to robust, practical clinical assays.
Molecular Signatures to Improve Diagnosis and Outcome Prediction in Lymphoma
Wing C. Chan, M.D.
University of Nebraska Medical Center
Dr. Chan's program will refine and validate the diagnostic and prognostic profiles that characterize the major subclasses of non-Hodgkin lymphoma (NHL). Dr. Chan's previous Director's Challenge grant provided the support for the extramural components of the Leukemia and Lymphoma Molecular Profiling Project (LLMPP) headed by him and his NCI co-investigator, Dr. Lou Staudt. The LLMPP pooled tissue and other resources from an international collaborating consortium as shown below for previous studies and this effort will continue under the SPECS initiative. Without this consortium, the number of specimens of the less common subtypes of NHL required to carry out these studies would not be sufficient.
Collaborators:
- The project includes investigators from The University of Nebraska, NCI, the British Columbia Cancer Agency, the University of Barcelona, the University of Wurzburg, St. Bartholomew Hospital, Norwegian Radium Hospital and four institution from the Southwest Oncology Group (University of Rochester, University of Arizona, University of Oregon and Cleveland Clinics).
Projects:
- Confirm and refine the lymphoma signatures by analyzing 2400 new lymphoma specimens accrued at the participating sites. These gene expression analyses will be carried out at all of the participating international sites. An important element of this aim is to determine the reproducibility of data generated in different sites.
- Combine genomic data generated by array comparative genomic hybridization (CGH) with gene expression data to provide a more robust prognostic signature.
- Develop a qRT-PCR assay using a subset of the genes so that the diagnostic and prognostic information can be applied to FFPE specimens.
- Determine how changes in therapy will alter the predictive power of previously developed profiles using diffuse large B cell lymphoma as a model. The prognostic profile will be revised to more accurately predict response to the new therapeutic regimen.
Biological Breast Cancer Classification by qRT-PCR
Matthew J. Ellis, M.D., Ph.D.
Washington University
Dr. Ellis' program will refine and validate diagnostic signatures that identify five subtypes of breast tumors. The profiles will be refined by selecting the set of 100 genes that defines all subtypes. qRT-PCR assays will be developed to measure the profile in formalin fixed, paraffin embedded (FFPE) tissues. The ability of the signatures to identify two of the subsets, LumA and Lum B, among ER+, node negative breast cancer patients who will not benefit from chemotherapy will be evaluated. The signature will also be evaluated to determine if it can predict which patients will respond to specific chemotherapies. The predictor will be validated in three CLIA-approved clinical laboratories at University of North Carolina (UNC), University of Utah and Washington University.
Collaborators:
- The project includes investigators from Washington University, University of Utah, UNC, University of British Columbia (UBC) and CALGB.
- Statistical support will be provided by the CALGB Data Center and by statisticians at the individual institutions.
Projects:
- Develop a qRT-PCR assay for the selected 100 genes from the previously developed diagnostic signature.
- Develop the bioinformatics and database support for the project.
- Evaluate the performance of the qRT-PCR assay on specimens from the collaborating institutions including a training set of tissue from UBC.
- Validate the qRT-PCR assay in independent test sets of specimens from UBC and from the NCI Cooperative Breast Cancer Tissue Resource (CBCTR).
- Evaluate the efficacy of chemotherapy in the breast cancer subtypes by analyzing specimens from two clinical trials, CALGB 9344 and SWOG 8814, using the qRT-PCR assay.
- Refine, standardize and then evaluate the assay in the clinical labs at the University of Utah, Washington University and UNC as the final phase of the project.
Evaluation of Predictive Signatures of Prostate Cancer
Dan Mercola, M.D., Ph.D.
University of California, Irvine
Dr. Mercola's program will refine and validate profiles that predict relapse in prostate cancer patients. These profiles will help distinguish indolent disease from disease that will progress. Dr. Mercola has developed anovel algorithm, during work carried out during the Director's Challenge program, that enables the assignment of molecular signatures to different cell types present in the prostate tumor. This algorithm captures important information about tumor-stromal interactions taking place in the diseased gland. Based on this algorithm, ~1,100 genes have been associated with relapse. The profile has been refined to 200 high priority relapse-associated genes. The profile will be refined further, confirmed using independent analytical strategies and validated in an observational clinical validation trial.
Collaborators:
- The project includes investigators form UC Irvine, Sidney Kimmel Cancer Center (SKCC), UC San Diego, the Burnham Institute, the Scripps Research Institute, Northwestern University, the Translational Genomics Institute (TGen), the Sun Health Research Institute and the SKCC/Sharp HealthCare Urology Research Group.
- Individual institutions and a community based specimen procurement network will provide tissue specimens.
Projects:
- Refine and confirm the prognostic signature for prostate cancer.
- Develop a high throughput PCR assay for 200 prioritized genes from the prognostic signature for evaluating the signature in fresh frozen and paraffin embedded specimens.
- Generate tissue micro-arrays (TMAs) containing 1000 prostate specimens for signature validation by immunohistochemistry.
- Carry out an observational clinical validation trial.
Diagnostic and Prognostic Sarcoma Signatures
Timothy J. Triche, M.D., Ph.D.
Children's Hospital Los Angeles
Dr. Triche's program focuses on refining and validating molecular signatures that provide a more accurate diagnosis of the common childhood sarcomas and signatures that more accurately predict clinical behavior of these tumors. The project will build on signatures developed during the Director's Challenge program. Specific hypotheses to test include the accuracy of molecularly defined diagnostic classes versus traditional histopathologic classes of rhabdomyosarcoma, inclusion and exclusion criteria for entry on rhabdomyosarcoma protocols based on myogenic gene expression, distinction of treatment resistant versus metastatic profiles in osteosarcoma, the role of genomic features (particularly fusion gene type and expression level, P53 mutation, and p16 loss) on expression profile and outcome in Ewing's sarcoma, and gene clusters that accurately identify existing and new molecularly defined classes of non-myogenic soft tissue sarcomas. The program will also evaluate the relative accuracy and potential superiority of 'gene' expression analysis at the exon level as opposed to whole-transcript analysis, in order to detect and evaluate the potential role of splice variants and other RNAs as independent predictors of class and outcome. This approach may also allow use of scant amounts of tissue, as often encountered clinically, as well as the possible use of formalin fixed, paraffin embedded tissue, available from all patients. This could facilitate translation of these 'sarcoma signatures' to clinical practice. Following refinement, these signatures will be integrated with standard diagnostic and prognostic criteria to create more accurate predictors for these tumors. The predictors will be prospectively validated in the uniformly treated patient populations available from the Children's Oncology Group (COG), which enrolls virtually all of the childhood sarcoma cases in the North America. This program will also define profiles that predict response to specific therapies and that identify potential new therapeutic targets. The ultimate goal is to incorporate these signatures into the standard of care for sarcoma patients treated on COG clinical therapeutic trials.
Collaborators:
- The project includes investigators from Children's Hospital Los Angeles, Baylor College of Medicine, Children's Memorial Hospital Chicago, Northwestern University, University of Southern California, the Children's Oncology Group (COG) and the National Childhood Cancer Foundation (NCCF).
- Statistical, analytical and bioinformatics expertise is provided by the COG Data and Statistical Center and the individual collaborators. COG provides specimens and data from clinical trials.
Projects:
- Evaluate and validate diagnostic and prognostic signatures in rhabdomyosarcomas and in non-rhabdomyosarcoma soft-tissue sarcomas.
- Evaluate and validate prognostic signatures in osteosarcomas.
- Evaluate and validate prognostic signatures in Ewing's sarcomas.
- Develop signatures that identify diagnostic class and predict response to therapy in all types of sarcoma.
Leukemia Signatures for Risk of Classification & Targeting
Cheryl L. Willman, M.D., Ph.D.
University of New Mexico
Dr. Willman's program will refine and confirm molecular profiles that address three important clinical issues in leukemia using specimens from patients entered on clinical trials. The first goal is to improve risk classification, outcome prediction and therapeutic response in pediatric and adult ALL. The investigators will also refine profiles that differentiate ALL patients who will relapse early vs. those who will relapse late. Dr. Willman has developed profiles that provide additional information and do not simply recapitulate the known genetic alterations in this patient population. The second goal is to refine profiles that more accurately diagnose AML and ALL in infants <1 year of age and that improve outcome prediction. They will also attempt to develop profiles that predict response to different therapeutic regimens. Third, the investigators will refine profiles that improve risk classification, outcome prediction and response to targeted therapies in childhood and adult AML. All three of these goals represent significant clinical problems where patients would benefit from improved molecular diagnostics.
Collaborators:
- The project includes investigators from the Fred Hutchinson Cancer Research Center, New York University and two clinical cooperative groups, COG and SWOG.
- The project is supported by a very strong informatics and statistical team at the University of New Mexico in collaboration with Sandia National Laboratory. Sandia investigators have developed a novel, powerful analytical algorithm, VxInsight. Biostatistical support is also provided by each of the collaborators and the cooperative groups.
Projects:
- Refine the prognostic gene expression signature for high risk pediatric ALL patients. Develop an RT-PCR assay for the ALL prognostic signature and validate the signature in an independent ALL patient population.
- Determine differences in gene expression signatures taken at diagnosis and at relapse in ALL patients who relapse early (<36 months) and late (>36 months).
- Develop a prognostic gene expression signature for high risk adult ALL patients. Develop an RT-PCR assay for the ALL prognostic signature and validate the signature in an independent ALL patient population.
- Refine and validate a prognostic signature for infant (<1 year) leukemia.
- Refine and validate prognostic signatures for adult and pediatric AML.
- Develop signatures that predict response to targeted therapies in adult and pediatric AML.
Section Last Updated: 07/25/07
