Pancreatic Cancer Research

GrantP01CA

High Resolution Dissection of Response Mechanisms to Chemoradiation in Gastrointestinal Cancers

Activity
P01
Institute
CA
Fiscal years
2026–2026
Amount counted
$1,652,508

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Abstract public domain (NIH RePORTER)

1 OVERALL PROJECT SUMMARY/ABSTRACT 2 3 Esophagogastric adenocarcinomas (EGC) and pancreatic adenocarcinomas (PAAD) are among the most 4 recalcitrant gastrointestinal malignancies. In non-metastatic EGC and PAAD neoadjuvant chemoradiation 5 containing approaches followed by surgery (NACRT) are a global standard. Intrinsic and adaptive radioresistant 6 tumor biology represents a major challenge as local control rates after NACRT are modest and the pathologic 7 complete tumor response (pCR) rate is only 10-25% in these tumor types. Molecularly uninformed strategies 8 such as increases in radiation dose and/or chemotherapy intensification have not improved outcomes. In fact, 9 insights into the biologic and immunologic evolution of EGC and PAAD remains a major scientific blind spot. An 10 improvement in biologic understanding and tumor response to standard chemoradiotherapy is needed to lay the 11 foundation to advance our therapeutic approaches and design biomarker enriched novel clinical trials to make 12 more than incremental advances. This is in direct alignment with the mission of the National Cancer Institute. 13 Our proposed P01 uses samples from two recently completed investigator-initiated phase II neoadjuvant 14 chemoradiation trials in EGC (NCT04656041) and pancreatic cancer (NCT03563248) to dissect chemoradiation 15 resistance at unprecedented resolution. Based on our preliminary data several thematic hypotheses run through 16 our interconnected projects and cores. Patient samples from the paired clinical trials feed each project, and 17 observations are subsequently mechanistically interrogated in a suite of preclinical model systems. The overall 18 goal of this proposed P01 is to identify targetable mechanisms of radiation resistance that can be reverse 19 translated back to trial concepts by the end of the P01 funding. Our team has a substantial track record in 20 exploring chemoradiation resistance and will take the following approaches via three complementary projects. 21 In project 1 evaluate tumor cell intrinsic molecular response to chemoradiation by evaluating the repeat RNA 22 expression in response to chemoradiation and relationship between RNA expression and cell state in mouse 23 xenografts and human tumors.. Project 2 will evaluate cancer cell extrinsic features NACRT resistance 24 applying single-cell resolution spatial proteo-transcriptomic approaches to patient tumor specimens and 25 genetically engineered syngeneic orthotopic transplant models. In project 3 we focus on novel plasma clinical 26 biomarkers ORf1p and ctDNA to define early clinical resistance during NACRT in EGC and explore biologic 27 relationships with tissue-defined genomic and transcriptomic evolution. Our projects and are supported by an 28 Administrative Core and two shared resource cores: Core 1, is a biospecimen repository and data acquisition 29 center; Core 2 is focused on advanced dosimetry, computational modeling, and biostatistics.

Public health relevance public domain (NIH RePORTER)

OVERALL PROJECT NARRATIVE This P01 aims to improve the lives of people with esophagogastric (EGC) and pancreatic (PAAD) cancers by using deeply annotated patient samples from two recently completed investigator-initiated phase II neoadjuvant chemoradiation trials. Consistent with the NCI-stated public health goal of increasing cure rates for localized cancers, we will mechanistically define the biologic and immunologic evolution of EGC and PAAD during chemoradiation to nominate new approaches combat chemoradiation resistance. We have designed our projects and cores to work independently but aligned by a common goal to deeply understand biology, identify clinical biomarkers, facilitate data collection, develop novel analysis models, and train the next generation of investigators to perpetuate and iterate on our expected findings.