SOLICITATION NOTICE
B -- Radiation Inducible Molecular Targets Identification after Single and Fractionated Radiation in Prostate Carcinoma.
- Notice Date
- 3/14/2018
- Notice Type
- Presolicitation
- 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
- 4/10/2018
- 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
- The U.S. Department of Health and Human Services, National Institutes of Health (NIH), National Cancer Institute (NCI), requires scientific services conducting radiation inducible molecular targets identification after single and fractionated radiation in prostate carcinoma. This is a combined synopsis/solicitation for a commercial service, prepared in accordance with the format in FAR 12.6 as supplemented with additional information included in this notice. This announcement constitutes the only solicitation and a separate written solicitation will not be issued. This solicitation, # N02CO82528-95, is issued as a request for quote (RFQ), and includes all applicable provisions and clauses in effect through FAR Federal Acquisition Circular (FAC) 2005-95 / 01-19-2017, simplified procedures for commercial items. The North American Industry Classification System code is 541990 and the business size standard is $15.0 M. Only one award will be made as a result of this solicitation. This will be awarded as a firm fixed price purchase order. 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. DESCRIPTION OF CONTRACTOR 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. TYPE OF ORDER This is a firm fixed price purchase order. NON-SEVERABLE SERVICES The services specified in each contract line item (CLIN) have been determined to be non-severable services - a specific undertaking or entire job with a defined end product of value to the Government. PERIOD OF PERFORMANCE The period of performance shall be April 10, 2018 through April 9, 2019. PLACE OF PERFORMANCE The services shall be performed at the Contractor's facility. REPORT(S)/DELIVERABLES AND DELIVERY SCHEDULE The Contractor shall provide electronic copies of report to the NCI technical POC upon completion of the required services per the table below: DELIVERABLE DESCRIPTION / FORMAT REQUIREMENTS DUE DATE #1 Project Management Plan in Word or PDF 30 days after award
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- Record
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