SOURCES SOUGHT
B -- Radiation Inducible Molecular Targets Identification after Single and Fractionated Radiation in Prostate Carcinoma.
- Notice Date
- 2/9/2018
- Notice Type
- Sources Sought
- NAICS
- 541990
— All Other Professional, Scientific, and Technical Services
- Contracting Office
- Department of Health and Human Services, National Institutes of Health, National Cancer Institute, Office of Acquisitions, 9609 Medical Center Drive, Room 1E128, Rockville, Maryland, 20852, United States
- ZIP Code
- 20852
- Solicitation Number
- N02CO82528-95
- Archive Date
- 3/3/2018
- Point of Contact
- Ricky J. Watson, Phone: 2402766594
- E-Mail Address
-
ricky.watson@nih.gov
(ricky.watson@nih.gov)
- Small Business Set-Aside
- N/A
- Description
- This Small Business Sources Sought Notice (SBSS) is for information and planning purposes only and shall not be construed as a solicitation or as an obligation on the part of the National Cancer Institute (NCI). The purpose of this Sources Sought Notice is to identify qualified small business concerns including 8(a), HUBZone or Service-Disabled Veteran-owned business concerns that are interested in and capable of performing the work described herein. The NCI does not intend to award a contract based on responses received nor otherwise pay for the preparation of any information submitted. Your responses to the information requested will assist the Government in determining the appropriate acquisition method, including whether a set-aside is possible. An organization that is not considered a small business under the applicable NAICS code should not submit a response to this notice. This requirement is assigned North American Industry Classification System (NAICS) code 541990 with a size standard of $15.0 million is being considered. NCI may issue a request for quotation (RFQ) as a result of this Sources Sought Notice. THERE IS NO SOLICITATION AVAILABLE AT THIS TIME. However, should such a requirement materialize, no basis for claims against NCI shall arise as a result of a response to this Sources Sought Notice or the NCI's use of such information as either part of our evaluation process or in developing specifications for any subsequent requirement. OBJECTIVE: The objective of this requirement is to investigate common and differential changes in gene (mRNA, miRNA and lncRNA) profiles associated with fractionated and single dose radiation for the discovery of markers and druggable pathways in multiple human prostate cancer models. REQUIREMENTS: TASK 1 Analyze in vivo experimental cell population data to identify signals for radiation injury classification. This task seeks to understand differences in the cellular response to a bolus dose of radiation vs a fractionated dose by examining data from cells irradiated in vivo. In this task, the Contractor will use the sets of mRNA and lncRNA data to identify relevant signals for classification between three radiation-injury responses: 0Gy, 10Gy, and 10x1Gy doses. For both mRNA and lncRNA, the Contractor will look at the in vivo experimental data to understand signals important to identifying radiation injury in mice. This analysis will involve the development of an optimal model to distinguish between 0Gy, 10Gy and 10x1Gy doses, as well as 10Gy and 10x1Gy doses without the 0Gy control group. This work will involve data cleaning, feature selection of relevant signals by using wrapper and filter algorithms, and classification model development using several methods. These methods could include elastic net regression, random decision forests, support vector machines, and fused support vector machines. TASK 2 Merge in vivo, in vitro, long term in vitro, and in vitro 3d model datasets to identify signals across all three experimental conditions relevant to radiation injury classification. This task seeks to understand differences in the cellular response to a bolus dose of radiation vs a fractionated dose by examining data from all datasets available. In this analysis, the Contractor will merge the in vivo, in vitro, long term in vitro and in vitro 3d model data to create one comprehensive dataset, for both the mRNA and lncRNA datasets. The Contractor will look at the combined experimental data to understand signals important to identifying radiation injury in mice, then will run a qualitative analysis of the identified signals to determine if the signals are relevant to study radiation injury in humans. This analysis will involve the development of an optimal model to distinguish between 0Gy, 10Gy and 10x1Gy doses, as well as 10Gy and 10x1Gy doses without the 0Gy control group. This work will involve data cleaning, feature selection of relevant signals by using wrapper and filter algorithms, and classification model development using several methods. Performance of these models will be compared to the in vivo model results from Option 1 to determine if additional experimental data adds information to the classification of radiation injury. By using the performance in a similar classifying scheme between radiation doses as a point of comparison, the Contractor will highlight the common signals that are potentially found using different experimental methods to support future research into these signals as biomarkers of disease. TASK 3 Analyze in vitro and in vitro 3d model experimental cell population data to identify signals for radiation injury classification. This task seeks to characterize similarities and differences between the response to radiation of cells cultured in conventional vs. 3-D in vitro cell culture methods. Similar to Option 1, the Contractor will use the sets of mRNA and lncRNA data to identify relevant signals for classification between three radiation-injury responses: 0Gy, 10Gy, and 10x1Gy doses. For both mRNA and lncRNA, the Contractor will examine the in vitro and in vitro 3d model experimental data to understand signals important to identifying radiation injury in mice. This analysis will involve the development of an optimal model to distinguish between 0Gy, 10Gy and 10x1Gy doses, as well as 10Gy and 10x1Gy doses without the 0Gy control group. This work will involve data cleaning, feature selection of relevant signals by using wrapper and filter algorithms, and classification model development using several methods. These methods could include elastic net regression, random decision forests, support vector machines, and fused support vector machines. TASK 4 Analyze in vitro and long term in vitro experimental cell population data to identify signals for radiation injury classification. This task seeks to understand similarities and differences in the response to radiation between cells cultured for a short term and long term in vitro. Similar to Option 1, we will use the sets of mRNA and lncRNA data to identify relevant signals for classification between three radiation-injury responses: 0Gy, 10Gy, and 10x1Gy doses. For both mRNA and lncRNA, we will examine the in vitro and long term in vitro data to understand signals important to identifying radiation injury in mice. This analysis will involve the development of an optimal model to distinguish between 0Gy, 10Gy and 10x1Gy doses, as well as 10Gy and 10x1Gy doses without the 0Gy control group. This work will involve data cleaning, feature selection of relevant signals by using wrapper and filter algorithms, and classification model development using several methods. These methods could include elastic net regression, random decision forests, support vector machines, and fused support vector machines. DELIVERY POINT: All information furnished must be in writing and must contain sufficient detail to allow the NCI to determine if it can meet the unique specifications described herein. All questions must be in writing and should be emailed to Ricky Watson, Contract Specialist at ricky.watson@nih.gov. A determination by the Government not to compete this requirement based upon responses to this notice is solely within the discretion of the Government. Information received will be considered solely for the purpose of determining whether to conduct a competitive procurement. In order to receive an award, contractors must have valid registration and certification in the System for Awards Management (SAM) at www.sam.gov. No collect calls will be accepted. Please reference number SBSS- N02CO82528-95 on all correspondence. Disclaimer and Important Notes: This notice does not obligate the Government to award a contract or otherwise pay for the information provided in response. The Government reserves the right to use information provided by respondents for any purpose deemed necessary and legally appropriate. Any organization responding to this notice should ensure that its response is complete and sufficiently detailed to allow the Government to determine the organization's qualifications to perform the work. Respondents are advised that the Government is under no obligation to acknowledge receipt of the information received or provide feedback to respondents with respect to any information submitted. After a review of the responses received, an RFQ may be published. However, responses to this notice will not be considered adequate responses to a solicitation(s).
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