Rectal Cancer
Conditions
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.
Interventions
RADIATIONShort Course Radiation Therapy (scRT)
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.
PROCEDURETotal Mesenteric Excision (TME)
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.
Sponsors
Weill Medical College of Cornell University
National Cancer Institute (NCI)
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
BASIC_SCIENCE
Masking
NONE
Intervention 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.
Eligibility
Sex/Gender
ALL
Age
18 Years to 90 Years
Healthy volunteers
No
Inclusion criteria
* Histologically confirmed diagnosis of adenocarcinoma of the rectum * Age ≥ 18 years * ECOG performance status 0-1 * cT2-T3N0 or cT1-3N1 or cT4 or cN2 * 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 * Total neoadjuvant therapy (short course radiotherapy followed by consolidative chemotherapy) is allowed * 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). * 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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Number of tissue biopsies obtained from treated patients | Baseline | 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. | Baseline | 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. | Week 1 | 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. | Week 6 | 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
| Measure | Time frame | Description |
|---|---|---|
| Changes in tumor morphology from pre-treatment and post-treatment MRI will be measured. | Baseline, Week 1 | 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. | Baseline, Week 1 | 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. | Baseline, Week 1 | 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. | Baseline, Week 1 | 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. | Baseline, Week 1 | 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. | Baseline, Week 1 | 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. | Baseline, Week 1 | 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. | Baseline, Week 1 | 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)) | Baseline, Week 1, Week 6 | 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. | Baseline, Week 1, Week 6 | 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. | Baseline, Week 1, Week 6 | 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). | Baseline, Week 1, Week 6 | 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 | Baseline, Week 1, Week 6 | The markers will be measured using immunofluorescence technique. |
Countries
United States
Contacts
CONTACTFabiana Gregucci, M.D.
STUDY_CHAIRSilvia Formenti, M.D.
Weill Medical College of Cornell University
PRINCIPAL_INVESTIGATOREncouse Golden, M.D., Ph.D.
Weill Medical College of Cornell University
Outcome results
None listed