search
Back to results

The Radiation Oncology-Biology Integration Network (ROBIN) Molecular Characterization Trial (MCT) of Standard Short Course Radiotherapy for Rectal Cancer (ROBIN)

Primary Purpose

Rectal Cancer

Status
Recruiting
Phase
Not Applicable
Locations
United States
Study Type
Interventional
Intervention
Short Course Radiation Therapy (scRT)
Total Mesenteric Excision (TME)
Sponsored by
Weill Medical College of Cornell University
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional basic science trial for Rectal Cancer

Eligibility Criteria

18 Years - 90 Years (Adult, Older Adult)All SexesDoes not accept healthy volunteers

Inclusion Criteria: Histologically confirmed diagnosis of adenocarcinoma of the rectum Age ≥ 18 years ECOG performance status 0-1 cT2-T3N0 or cT1-3N1 Rectal cancer amenable to total mesorectal excision No evidence of distant metastases No prior pelvic radiation therapy No prior chemotherapy or surgery for rectal cancer No infections requiring systemic antibiotic treatment Hgb >8.0 gm/dL, PLT > 150,000/mm3, total bilirubin ≤ 1.5x upper limit of normal, AST ≤ upper limit of normal, ALT ≤ 3x upper limit of normal Patients must read, agree to, and sign a statement of informed consent prior to participation in this study. Patients who do not read or understand English or eligible but must have the consent form read to them in its entirety by an official translator. Informed consent for non-literate or non-English speaking patients may not be obtained by using a relative or a member of the patient's clinical team as a translator. Female participants or reproductive potential, defined as not surgically sterilized and between menarche and 1 year post menopause, must have a negative serum pregnancy test within 4 weeks prior to initiation of study treatment. Women with childbearing potential who are negative for pregnancy (urine or blood) and who agree to use effective contraceptive methods. A woman of childbearing potential is defined by one who is biologically capable of becoming pregnant. Reliable contraception should be used from trial screening and must be continued throughout the study. Exclusion Criteria: Recurrent rectal cancer Primary unresectable rectal cancer is defined as a primary rectal tumor which, on the basis of either physical exam or pelvic MRI, is deemed to be adherent or fixed to adjacent pelvic structures (en bloc resection will not be achieved with negative margins). cT4 will be excluded. ≥4 regional lymph nodes each ≥10 mm on pelvic MRI Patients who have received prior pelvic radiotherapy Patients with prior allogenic stem cell or solid organ transplantation. Patients receiving treatment with systemic immunosuppressive medication (including, but not limited to, corticosteroids, cyclophosphamide, azathioprine, methotrexate, thalidomide, and antitumor necrosis factor-α agents) administered at >10 mg/day prednisone or equivalent within 2 weeks prior to initiation of study treatment. Patients with any other concurrent medical or psychiatric condition or disease which, in the investigator's judgment would make them inappropriate candidates for entry into this study Patients receiving other anticancer or experimental therapy. No other experimental therapies (including chemotherapy, radiation, hormonal treatment, antibody therapy, immunotherapy, gene therapy, vaccine therapy, angiogenesis inhibitors, matrix metalloprotease inhibitors, thalidomide, anti-VEGF/Flk-1 monoclonal antibody, or other experimental drugs) of any kind are permitted while the patient is receiving study treatment. Women who are pregnant or breastfeeding. Women of childbearing potential who are unwilling or unable to use an acceptable method of birth control to avoid pregnancy for the entire study period and for up to four weeks after the study.

Sites / Locations

  • Cedars-Sinai Medical Center
  • The University of Chicago
  • Rutgers Cancer Institute of New Jersey
  • New York Presbyterian Brooklyn Methodist HospitalRecruiting
  • Weill Cornell Medical CollegeRecruiting
  • New York Presbyterian Hospital - Queens

Arms of the Study

Arm 1

Arm Type

Other

Arm Label

Single cohort

Arm Description

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.

Outcomes

Primary Outcome Measures

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.
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.
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.
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.
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.
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.

Secondary Outcome Measures

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.
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.
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.
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.
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.
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.
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.
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.
Changes in Cellular stress (quantification of reactive Oxygen species (ROS))
ROS is measured using CellRox dye that reacts with ROS and emits fluorescence.
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.
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.
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.
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.

Full Information

First Posted
June 2, 2023
Last Updated
July 3, 2023
Sponsor
Weill Medical College of Cornell University
Collaborators
National Cancer Institute (NCI)
search

1. Study Identification

Unique Protocol Identification Number
NCT05943210
Brief Title
The Radiation Oncology-Biology Integration Network (ROBIN) Molecular Characterization Trial (MCT) of Standard Short Course Radiotherapy for Rectal Cancer
Acronym
ROBIN
Official Title
The Radiation Oncology-Biology Integration Network (ROBIN) Molecular Characterization Trial (MCT) of Standard Short Course Radiotherapy for Rectal Cancer.
Study Type
Interventional

2. Study Status

Record Verification Date
July 2023
Overall Recruitment Status
Recruiting
Study Start Date
May 22, 2023 (Actual)
Primary Completion Date
October 31, 2025 (Anticipated)
Study Completion Date
October 31, 2027 (Anticipated)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Sponsor
Name of the Sponsor
Weill Medical College of Cornell University
Collaborators
National Cancer Institute (NCI)

4. Oversight

Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Product Manufactured in and Exported from the U.S.
No
Data Monitoring Committee
Yes

5. Study Description

Brief Summary
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.
Detailed Description
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.

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Rectal Cancer

7. Study Design

Primary Purpose
Basic Science
Study Phase
Not Applicable
Interventional Study Model
Single Group Assignment
Model Description
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.
Masking
None (Open Label)
Allocation
N/A
Enrollment
25 (Anticipated)

8. Arms, Groups, and Interventions

Arm Title
Single cohort
Arm Type
Other
Arm Description
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.
Intervention Type
Radiation
Intervention Name(s)
Short Course Radiation Therapy (scRT)
Intervention Description
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.
Intervention Type
Procedure
Intervention Name(s)
Total Mesenteric Excision (TME)
Intervention Description
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.
Primary Outcome Measure Information:
Title
Number of tissue biopsies obtained from treated patients
Description
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.
Time Frame
Baseline
Title
Number of tissue biopsies obtained from treated patients
Description
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.
Time Frame
Week 1
Title
Number of tissue biopsies obtained from treated patients
Description
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.
Time Frame
Week 6
Title
Number of research specimens obtained before RT.
Description
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.
Time Frame
Baseline
Title
Number of research specimens obtained after RT.
Description
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.
Time Frame
Week 1
Title
Number of research specimens obtained at the time of surgery.
Description
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.
Time Frame
Week 6
Secondary Outcome Measure Information:
Title
Changes in tumor morphology from pre-treatment and post-treatment MRI will be measured.
Description
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.
Time Frame
Baseline, Week 1
Title
Changes in tumor morphology from pre-treatment and post-treatment CT will be measured.
Description
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.
Time Frame
Baseline, Week 1
Title
Changes in tumor texture from pre-treatment and post-treatment MRI will be measured.
Description
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.
Time Frame
Baseline, Week 1
Title
Changes in tumor texture from pre-treatment and post-treatment CT will be measured.
Description
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.
Time Frame
Baseline, Week 1
Title
Changes in enhancement kinetics from pre-treatment and post-treatment MRI will be measured.
Description
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.
Time Frame
Baseline, Week 1
Title
Changes in enhancement kinetics from pre-treatment and post-treatment CT will be measured.
Description
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.
Time Frame
Baseline, Week 1
Title
Changes in functional diffusion patterns from pre-treatment and post-treatment MRI will be measured.
Description
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.
Time Frame
Baseline, Week 1
Title
Changes in functional diffusion patterns from pre-treatment and post-treatment CT will be measured.
Description
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.
Time Frame
Baseline, Week 1
Title
Changes in Cellular stress (quantification of reactive Oxygen species (ROS))
Description
ROS is measured using CellRox dye that reacts with ROS and emits fluorescence.
Time Frame
Baseline, Week 1, Week 6
Title
Changes in immunological fitness related to radio-responsiveness and their associated pathological response will be measured by quantifying senescence using vital dye DDAO.
Description
7-hydroxy-9H-(1,3-dichloro-9,9-dimethylacridin-2-one (DDAO) measures the activity of beta galactosidase.
Time Frame
Baseline, Week 1, Week 6
Title
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.
Description
The p16 will be quantified by immunofluorescence technique and by flow cytometry.
Time Frame
Baseline, Week 1, Week 6
Title
Changes in immunological fitness related to radio-responsiveness and their associated pathological response will be measured by quantifying gamma-H2aX (aging).
Description
The markers will be measured using immunofluorescence technique and by flow cytometry.
Time Frame
Baseline, Week 1, Week 6
Title
Comparing levels of cell death related to radio responsiveness will be measured by quantifying cleaved caspase-3
Description
The markers will be measured using immunofluorescence technique.
Time Frame
Baseline, Week 1, Week 6

10. Eligibility

Sex
All
Minimum Age & Unit of Time
18 Years
Maximum Age & Unit of Time
90 Years
Accepts Healthy Volunteers
No
Eligibility Criteria
Inclusion Criteria: Histologically confirmed diagnosis of adenocarcinoma of the rectum Age ≥ 18 years ECOG performance status 0-1 cT2-T3N0 or cT1-3N1 Rectal cancer amenable to total mesorectal excision No evidence of distant metastases No prior pelvic radiation therapy No prior chemotherapy or surgery for rectal cancer No infections requiring systemic antibiotic treatment Hgb >8.0 gm/dL, PLT > 150,000/mm3, total bilirubin ≤ 1.5x upper limit of normal, AST ≤ upper limit of normal, ALT ≤ 3x upper limit of normal Patients must read, agree to, and sign a statement of informed consent prior to participation in this study. Patients who do not read or understand English or eligible but must have the consent form read to them in its entirety by an official translator. Informed consent for non-literate or non-English speaking patients may not be obtained by using a relative or a member of the patient's clinical team as a translator. Female participants or reproductive potential, defined as not surgically sterilized and between menarche and 1 year post menopause, must have a negative serum pregnancy test within 4 weeks prior to initiation of study treatment. Women with childbearing potential who are negative for pregnancy (urine or blood) and who agree to use effective contraceptive methods. A woman of childbearing potential is defined by one who is biologically capable of becoming pregnant. Reliable contraception should be used from trial screening and must be continued throughout the study. Exclusion Criteria: Recurrent rectal cancer Primary unresectable rectal cancer is defined as a primary rectal tumor which, on the basis of either physical exam or pelvic MRI, is deemed to be adherent or fixed to adjacent pelvic structures (en bloc resection will not be achieved with negative margins). cT4 will be excluded. ≥4 regional lymph nodes each ≥10 mm on pelvic MRI Patients who have received prior pelvic radiotherapy Patients with prior allogenic stem cell or solid organ transplantation. Patients receiving treatment with systemic immunosuppressive medication (including, but not limited to, corticosteroids, cyclophosphamide, azathioprine, methotrexate, thalidomide, and antitumor necrosis factor-α agents) administered at >10 mg/day prednisone or equivalent within 2 weeks prior to initiation of study treatment. Patients with any other concurrent medical or psychiatric condition or disease which, in the investigator's judgment would make them inappropriate candidates for entry into this study Patients receiving other anticancer or experimental therapy. No other experimental therapies (including chemotherapy, radiation, hormonal treatment, antibody therapy, immunotherapy, gene therapy, vaccine therapy, angiogenesis inhibitors, matrix metalloprotease inhibitors, thalidomide, anti-VEGF/Flk-1 monoclonal antibody, or other experimental drugs) of any kind are permitted while the patient is receiving study treatment. Women who are pregnant or breastfeeding. Women of childbearing potential who are unwilling or unable to use an acceptable method of birth control to avoid pregnancy for the entire study period and for up to four weeks after the study.
Central Contact Person:
First Name & Middle Initial & Last Name or Official Title & Degree
Pragya Yadav, Ph.D.
Phone
646-962-2199
Email
pry2003@med.cornell.edu
First Name & Middle Initial & Last Name or Official Title & Degree
Sharanya Chandrasekhar, M.S.
Phone
646-962-3110
Email
shc2043@med.cornell.edu
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Silvia Formenti, M.D.
Organizational Affiliation
Weill Medical College of Cornell University
Official's Role
Study Chair
First Name & Middle Initial & Last Name & Degree
Encouse Golden, M.D., Ph.D.
Organizational Affiliation
Weill Medical College of Cornell University
Official's Role
Principal Investigator
Facility Information:
Facility Name
Cedars-Sinai Medical Center
City
Los Angeles
State/Province
California
ZIP/Postal Code
90048
Country
United States
Individual Site Status
Not yet recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Sharanya Chandrasekhar
Phone
646-962-3110
Email
shc2043@med.cornell.edu
Facility Name
The University of Chicago
City
Chicago
State/Province
Illinois
ZIP/Postal Code
60637
Country
United States
Individual Site Status
Not yet recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Sharanya Chandrasekhar
Phone
646-962-3110
Email
shc2043@med.cornell.edu
Facility Name
Rutgers Cancer Institute of New Jersey
City
New Brunswick
State/Province
New Jersey
ZIP/Postal Code
08901
Country
United States
Individual Site Status
Not yet recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Sharanya Chandrasekhar
Phone
646-962-3110
Email
shc2043@med.cornell.edu
Facility Name
New York Presbyterian Brooklyn Methodist Hospital
City
Brooklyn
State/Province
New York
ZIP/Postal Code
10065
Country
United States
Individual Site Status
Recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Izael Nino
Email
izn4001@med.cornell.edu
First Name & Middle Initial & Last Name & Degree
Mary Palmer
Email
map9505@med.cornell.edu
First Name & Middle Initial & Last Name & Degree
Hani Ashamalla, M.D.
Facility Name
Weill Cornell Medical College
City
New York
State/Province
New York
ZIP/Postal Code
10065
Country
United States
Individual Site Status
Recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Pragya Yadav, Ph.D.
Phone
646-962-2199
Email
pry2003@med.cornell.edu
First Name & Middle Initial & Last Name & Degree
Sharanya Chandrasekhar, M.S.
Phone
646-962-3110
Email
shc2043@med.cornell.edu
First Name & Middle Initial & Last Name & Degree
Encouse Golden, M.D., Ph.D.
Facility Name
New York Presbyterian Hospital - Queens
City
New York
State/Province
New York
ZIP/Postal Code
11355
Country
United States
Individual Site Status
Not yet recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Hina Ali, M.D.
Phone
718-670-1541
Email
hia4002@med.cornell.edu
First Name & Middle Initial & Last Name & Degree
Pragya Yadav, Ph.D
Phone
6469622196
Email
pry2003@med.cornell.edu
First Name & Middle Initial & Last Name & Degree
Andrew Brandmaier, M.D.

12. IPD Sharing Statement

Plan to Share IPD
No

Learn more about this trial

The Radiation Oncology-Biology Integration Network (ROBIN) Molecular Characterization Trial (MCT) of Standard Short Course Radiotherapy for Rectal Cancer

We'll reach out to this number within 24 hrs