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| ID | Type | Description | Link |
|---|---|---|---|
| 1U54CA274291 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| National Cancer Institute (NCI) | NIH |
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This trial (molecular characterization trial) focuses on rectal cancer, a common cancer that is treated with radiotherapy (RT) as standard of care and represents a setting in which to study the effects of RT on the immune system.
The study aims to test the hypothesis that the radiation therapy will assist in targeting the rectal cancer by mounting a robust immune response against the rectal cancer.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Single cohort | Other | Eligible patients will receive short course radiation therapy (scRT) of 25Gy over 5 days (fractions) for their localized rectal cancer. Research bloods stool and tissue will be collected at three time points: Baseline, end of radiation therapy and at surgery. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Short Course Radiation Therapy (scRT) | Radiation | Eligible patients will receive short course radiation therapy (scRT) of 25Gy over 5 days (fractions) for their localized rectal cancer. Research bloods stool and tissue will be collected at three time points: Baseline, end of radiation therapy and at surgery. |
| Measure | Description | Time Frame |
|---|---|---|
| Number of tissue biopsies obtained from treated patients | To conduct a multi-centric prospective clinical trial of standard short course RT in the neoadjuvant setting of rectal cancer (MCT), with harmonized tissue acquisition and immune characterization across seven international centers, and assess quality of life during MCT and pathological response at surgery. | Baseline |
| Number of tissue biopsies obtained from treated patients | To conduct a multi-centric prospective clinical trial of standard short course RT in the neoadjuvant setting of rectal cancer (MCT), with harmonized tissue acquisition and immune characterization across seven international centers, and assess quality of life during MCT and pathological response at surgery. | Week 1 |
| Number of tissue biopsies obtained from treated patients | To conduct a multi-centric prospective clinical trial of standard short course RT in the neoadjuvant setting of rectal cancer (MCT), with harmonized tissue acquisition and immune characterization across seven international centers, and assess quality of life during MCT and pathological response at surgery. | Week 6 |
| Number of research specimens obtained before RT. | To obtain a unique set of biospecimens of optimal quality for cutting-edge imaging and multi-omics analyses at the single cell level that are spatially integrated, obtained longitudinally before and after RT and at the time of surgery. | Baseline |
| Number of research specimens obtained after RT. | To obtain a unique set of biospecimens of optimal quality for cutting-edge imaging and multi-omics analyses at the single cell level that are spatially integrated, obtained longitudinally before and after RT and at the time of surgery. |
| Measure | Description | Time Frame |
|---|---|---|
| Changes in tumor morphology from pre-treatment and post-treatment MRI will be measured. | All patients will have pre-treatment and post-treatment multi-modality MRI. Both conventional and Deep learning based radiomics (DLR) approaches will be applied to study the changes in the tumor morphology at each time point. Deep learning-based radiomics (DLR) was developed to extract deep information from multiple modalities of magnetic resonance (MR) images. |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Fabiana Gregucci, M.D. | Contact | 646-962-2199 | fgr4002@med.cornell.edu |
| Name | Affiliation | Role |
|---|---|---|
| Silvia Formenti, M.D. | Weill Medical College of Cornell University | Study Chair |
| Encouse Golden, M.D., Ph.D. | Weill Medical College of Cornell University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| The University of Chicago | Recruiting | Chicago | Illinois | 60637 | United States |
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This is a molecular characterization trial (MCT) of 25 consecutively treated rectal cancers with short-course radiotherapy (scRT; 25Gy/5 fractions). Tissue and imaging will be collected at three time-points: 1. Baseline (day -28 to 0) - pelvic MRI and CT, research biopsy, blood (50ml), stool. 2. After 5 RT fractions (day 5-10) - CT, research biopsy, blood (50ml), stool 3. At time of surgery (wk 6) - MRI and CT, surgical tumor and nodal specimens, blood (50ml), stool.
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| Total Mesenteric Excision (TME) | Procedure | Subjects are expected to undergo total mesenteric Excision(TME) even if subjects have achieved complete response by imaging.TME is a specific surgical technique used in the treatment of rectal cancer in which the bowel with the tumor is entirely removed along with surrounding fat and lymph nodes. |
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| Week 1 |
| Number of research specimens obtained at the time of surgery. | To obtain a unique set of biospecimens of optimal quality for cutting-edge imaging and multi-omics analyses at the single cell level that are spatially integrated, obtained longitudinally before and after RT and at the time of surgery. | Week 6 |
| Baseline, Week 1 |
| Changes in tumor morphology from pre-treatment and post-treatment CT will be measured. | All patients will have pre-treatment and post-treatment multi-modality planning CTs. Both conventional and Deep learning based radiomics (DLR) approaches will be applied to study the changes in the tumor morphology at each time point. Deep learning-based radiomics (DLR) was developed to extract deep information from multiple modalities of magnetic resonance (MR) images. | Baseline, Week 1 |
| Changes in tumor texture from pre-treatment and post-treatment MRI will be measured. | All patients will have pre-treatment and post-treatment multi-modality MRI. Tumor texture analysis will be measured using dynamic contrast enhanced (DCE)-MRI. Tumor texture has made the most significant contribution in predicting response for patients receiving radiotherapy Both conventional and Deep learning based radiomics (DLR) approaches will be applied to study the changes in the tumor texture at each time point. | Baseline, Week 1 |
| Changes in tumor texture from pre-treatment and post-treatment CT will be measured. | All patients will have pre-treatment and post-treatment multi-modality planning CTs. Tumor texture analysis will be measured using dynamic contrast enhanced (DCE)-MRI. Tumor texture has made the most significant contribution in predicting response for patients receiving radiotherapy Both conventional and Deep learning based radiomics (DLR) approaches will be applied to study the changes in the tumor texture at each time point. | Baseline, Week 1 |
| Changes in enhancement kinetics from pre-treatment and post-treatment MRI will be measured. | All patients will have pre-treatment and post-treatment multi-modality MRI. Both conventional and Deep learning based radiomics (DLR) approaches will be applied to study the changes in the enhancement kinetics at each time point. Deep learning-based radiomics (DLR) was developed to extract deep information from multiple modalities of magnetic resonance (MR) images. Enhancement kinetics of tumor indicates whether the tumor is benign or malignant. If enhancement kinetics is rapid is indicative of malignancy and if it is delayed, it is indicative of benign tumor. | Baseline, Week 1 |
| Changes in enhancement kinetics from pre-treatment and post-treatment CT will be measured. | All patients will have pre-treatment and post-treatment multi-modality planning CTs. Both conventional and Deep learning based radiomics (DLR) approaches will be applied to study the changes in the enhancement kinetics at each time point. Deep learning-based radiomics (DLR) was developed to extract deep information from multiple modalities of magnetic resonance (MR) images. Enhancement kinetics of tumor indicates whether the tumor is benign or malignant. If enhancement kinetics is rapid is indicative of malignancy and if it is delayed, it is indicative of benign tumor. | Baseline, Week 1 |
| Changes in functional diffusion patterns from pre-treatment and post-treatment MRI will be measured. | All patients will have pre-treatment and post-treatment multi-modality MRI. Functional diffusion patterns are used to measure the alterations in cell density/cell membrane function and microenvironment. Diffusion patterns can be used as an indicator to predict treatment efficacy by measuring the changes in the tumor microevironment. Both conventional and Deep learning based radiomics (DLR) approaches will be applied to study the changes in the function diffusion patterns at each time point. Deep learning-based radiomics (DLR) was developed to extract deep information from multiple modalities of magnetic resonance (MR) images. | Baseline, Week 1 |
| Changes in functional diffusion patterns from pre-treatment and post-treatment CT will be measured. | All patients will have pre-treatment and post-treatment multi-modality planning CTs. Functional diffusion patterns are used to measure the alterations in cell density/cell membrane function and microenvironment. Diffusion patterns can be used as an indicator to predict treatment efficacy by measuring the changes in the tumor microevironment. Both conventional and Deep learning based radiomics (DLR) approaches will be applied to study the changes in the function diffusion patterns at each time point. Deep learning-based radiomics (DLR) was developed to extract deep information from multiple modalities of magnetic resonance (MR) images. | Baseline, Week 1 |
| Changes in Cellular stress (quantification of reactive Oxygen species (ROS)) | ROS is measured using CellRox dye that reacts with ROS and emits fluorescence. | Baseline, Week 1, Week 6 |
| Changes in immunological fitness related to radio-responsiveness and their associated pathological response will be measured by quantifying senescence using vital dye DDAO. | 7-hydroxy-9H-(1,3-dichloro-9,9-dimethylacridin-2-one (DDAO) measures the activity of beta galactosidase. | Baseline, Week 1, Week 6 |
| Changes in immunological fitness related to radio-responsiveness and their associated pathological response will be measured by quantifying aging using p16 protein expression as a marker. | The p16 will be quantified by immunofluorescence technique and by flow cytometry. | Baseline, Week 1, Week 6 |
| Changes in immunological fitness related to radio-responsiveness and their associated pathological response will be measured by quantifying gamma-H2aX (aging). | The markers will be measured using immunofluorescence technique and by flow cytometry. | Baseline, Week 1, Week 6 |
| Comparing levels of cell death related to radio responsiveness will be measured by quantifying cleaved caspase-3 | The markers will be measured using immunofluorescence technique. | Baseline, Week 1, Week 6 |
| Rutgers Cancer Institute of New Jersey | Recruiting | New Brunswick | New Jersey | 08901 | United States |
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| New York Presbyterian Brooklyn Methodist Hospital | Recruiting | Brooklyn | New York | 10065 | United States |
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| Weill Cornell Medical College | Recruiting | New York | New York | 10065 | United States |
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| New York Presbyterian Hospital - Queens | Not yet recruiting | New York | New York | 11355 | United States |
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| ID | Term |
|---|---|
| D012004 | Rectal Neoplasms |
| ID | Term |
|---|---|
| D015179 | Colorectal Neoplasms |
| D007414 | Intestinal Neoplasms |
| D005770 | Gastrointestinal Neoplasms |
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D004066 | Digestive System Diseases |
| D005767 | Gastrointestinal Diseases |
| D007410 | Intestinal Diseases |
| D012002 | Rectal Diseases |
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