Citations

Abstract Pancreatic ductal adenocarcinoma (PDAC) is predicted to be the second cause of cancer death in the US by 2040. Five-year overall survival is only ~12% despite decades of research. The mainstay of treatment remains chemotherapy in the absence of highly recurrent, therapeutically actionable, genetic targets. For the minority of patients (~15-20%) who are candidates for curative-intent surgical resection, six months of intensive neoadjuvant chemotherapy with/without chemoradiation has become standard-of-care. However, despite evidence of radiographic, metabolic, and biochemical responses, significant viable residual disease on pathologic analysis is almost universally observed with approximately 40-60% risk of disease recurrence. Thus, there is a critical need to better understand why PDAC remains recalcitrant to current therapeutic modalities. Pathologic evaluation of PDAC reveals a complex microenvironment consisting of neoplastic cells dynamically interacting with both “normal” cellular (e.g. mesenchymal, immune) and non-cellular (e.g. collagen) stromal elements. While biologically important, experimental models and small-scale efforts to dissect the PDAC ecosystem are limited by focus on individual tumor compartments and generation of conflicting results. Consequently, there is a need for a more comprehensive understanding of the structure and dynamics of the PDAC TME through the theoretical lens of evolutionary ecology. Current therapies are not focused on the PDAC ecosystem. We hypothesize that cell-cell interactions of PDAC cells with its local niche are important for conferring treatment resistance. Thus, better understanding of said interactions could yield novel ecologically-informed therapies. Consequently, we developed a digital pathology library of approximately 1000 PDAC resection cases (whole-slide H&E images) performed at Mayo Clinic between 2013-2021. Approximately 400 cases are treatment naive while about 600 were treated with neoadjuvant therapy. While some qualitative features of pathologic analysis have prognostic value (e.g., pathologic treatment response, presence of perineural or lymphovascular invasion, extent of desmoplasia), we aim to identify prognostic spatial relationships using tools from landscape ecology. We utilized the HoVer-Net model for nuclei segmentation and classification and spatial statistics methods and landscape fragmentation analysis to understand the spatial organization within the PDAC tumor ecology. With these methodologies, we can assess the spatial context of cell interactions, habitat/niche/microenvironment formation, ecological relationships between cell populations, and the different tissue components. The different spatial statistics/metrics are used to quantify levels of immune cell infiltration, mixing, as well as the complexity and diversity of microenvironment phenotypes leading to high levels of tumor heterogeneity. Such measurements can help predict the likelihood of a PDAC tumor adaptive potential that could lead to fast evolutionary responses to therapy, progression, and poor prognosis. Citation Format: Merih D. Toruner, Luis Cisneros, Zafar Siddiqui, Chady Meroueh, Ritambhara Singh, Carlo Maley, Martin E. Fernandez-Zapico, Ryan M. Carr. Dissecting the pancreatic cancer microenvironment using landscape ecology [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr B026.

Adaptive therapy, an ecologically inspired approach to cancer treatment, aims to overcome resistance and reduce toxicity by leveraging competitive interactions between drug-sensitive and drug-resistant subclones, prioritizing patient survival and quality of life instead of killing the maximum number of cancer cells. In preparation for a clinical trial, we used endocrine-resistant MCF7 breast cancer to stimulate second-line therapy and tested adaptive therapy using capecitabine, gemcitabine, or their combination in a mouse xenograft model. Dose modulation adaptive therapy with capecitabine alone increased survival time relative to MTD but not statistically significantly (HR = 0.22, 95% CI = 0.043–1.1, p = 0.065). However, when we alternated the drugs in both dose modulation (HR = 0.11, 95% CI = 0.024–0.55, p = 0.007) and intermittent adaptive therapies, the survival time was significantly increased compared to high-dose combination therapy (HR = 0.07, 95% CI = 0.013–0.42, p = 0.003). Overall, the survival time increased with reduced dose for both single drugs (p < 0.01) and combined drugs (p < 0.001), resulting in tumors with fewer proliferation cells (p = 0.0026) and more apoptotic cells (p = 0.045) compared to high-dose therapy. Adaptive therapy favors slower-growing tumors and shows promise in two-drug alternating regimens instead of being combined.