Gynecologic cancers are among the leading causes of cancer death in women worldwide. These patients typically are socioeconomically disadvantaged, with poor access to screening and vaccination. Consequently, they often present with locoregionally advanced disease, for which pelvic radiotherapy (RT) with concurrent cisplatin (i.e., chemoradiotherapy) is the standard of care. This treatment is limited, however, by high rates of treatment failure. Intensifying treatment through the delivery of chemotherapy doublets, either concurrently or as adjuvant therapy following chemoradiotherapy, is a promising strategy to improve outcomes. However, the delivery of intensive chemotherapy is complicated by high rates of gastrointestinal and hematologic toxicity. Strategies to reduce toxicity while increasing efficacy of chemoradiotherapy are needed. Standard pelvic RT techniques encompass large volumes of normal tissue including bowel, bone marrow, bone, bladder, and rectum, leading to preventable radiation-induced toxicity. Image-guided radiation therapy (IGRT) can improve target localization and dosimetry, optimizing target dose while minimizing dose to surrounding normal tissues. However, IGRT can be highly resource intensive, and comparative effectiveness trials have been lacking. For this reason, there is considerable controversy as to the utility of IG-IMRT in this disease. Our research group has been at the forefront of developing novel, cost-effective IGRT approaches with wide potential to facilitate better delivery of concurrent and/or adjuvant chemotherapy. Previously we have found that radiation-induced injury to hematopoietically active bone marrow is a critical determinant of tolerance to intensive chemotherapy. Using machine learning methods, we recently developed a multi-atlas-based IGRT method that can predict canonical distributions of active bone marrow, which can obviate the need for positron emission tomography (PET) in settings where this technology is unavailable or unaffordable. The proposed new research will study the ability of multi-atlas-based IGRT to reduce hematologic toxicity and improve chemotherapy delivery compared to standard treatment, using data from 450 patients enrolled to a randomized phase III trial (NRG-GY006). Furthermore, we will use serial whole body PET/CT to study the impact of radiation dose and chemotherapy intensity on the compensatory hematopoietic response, and have developed novel whole body radiomics biomarkers to quantify the inflammatory state, which we hypothesize can influence patients' outcomes and tolerance to chemotherapy. The new research extends our work associated with a current R01 grant (1R01CA197059-01) to conduct correlative science associated with the GY006 trial. The overarching goal of this research line is to augment the therapeutic ratio of chemoradiotherapy for pelvic cancers using advanced image-guided radiation techniques. If successful, this research would significantly alter the approach to the treatment of many pelvic malignancies for which chemoradiotherapy is standard.

Public Health Relevance

In this study, we will test the ability of a novel method called multi-atlas-based image guided radiation therapy (IGRT) to reduce acute hematologic toxicity and improve chemotherapy delivery compared to conventional RT, which could obviate the need for expensive functional imaging in socioeconomically disadvantaged and resource constrained populations, such as patients with gynecologic cancers. In addition, we will use serial positron emission tomography to study effects of chemotherapy and radiation on the subacute compensatory hematopoietic response, and will seek to develop and validate novel whole body radiomics models of the inflammatory state as predictive biomarkers for gynecologic cancers. We are in an optimal situation to conduct impactful and innovative research in the context of an ongoing phase III cooperative group randomized registration trial (NRG GY006), affording us the opportunity to conduct rigorous correlative science on a large sample with high data quality, quality assurance, and carefully controlled treatment effects.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Special Emphasis Panel (ZRG1)
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Vikram, Bhadrasain
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University of California, San Diego
Schools of Medicine
La Jolla
United States
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