Pancreatic ductal adenocarcinoma (PDAC) remains the most lethal and poorly understood human malignancy. The poor prognosis for patients with PDAC may depend on to the limited efficacy of currently approved classic chemotherapeutic treatments, and the ineffectiveness of molecularly targeted- and immune checkpoint-inhibitors. The largest effort of integrated genomic analysis in understanding the molecular pathology of PDAC has recently confirmed the transforming growth factor β (TGFβ) as the most recurrently mutated signal transduction pathway. Moreover, this transcriptional network analysis indicated that programs enriched with a macrophage specific signature have the most significant association with poor survival in this disease. Having demonstrated that TGFβ signaling inhibition is an active strategy in combination with classic chemotherapy in unselected advanced PDAC patient populations, the identification of biomarkers that could predict which patients would benefit more from these combination strategies remains of unique importance for the development of more personalized treatments. In this study, we explored the mechanisms responsible for the exceptional response to the inhibition of TGFβ signaling pathway of pancreatic tumors characterized by high levels of the chemokine Ccl3. We demonstrate that tumor-derived Ccl3 acts as a poor prognostic factor in PC by inducing tumor infiltration of immunosuppressive tumor associated macrophages (TAMs). Using in vitro and in vivo assays, we show that TGFβ acts as a mediator of Ccl3 in sustaining macrophage recruitment and their M2-polarization. We also provide evidence that targeting of TGFβ signaling potentiates the response of high-Ccl3 tumors to gemcitabine. Our findings suggest CCL3 as a valid biomarker to predict which patients would benefit more from these combination strategies, allowing the development of more personalized treatments for PDAC therapy.
CCL3/MIP-1a predicts an exceptional response to TGFb receptor inhibitor by sustaining a M2 TAMs-enriched microenvironment in pancreatic cancer
BERTOLINI, MONICA
2023
Abstract
Pancreatic ductal adenocarcinoma (PDAC) remains the most lethal and poorly understood human malignancy. The poor prognosis for patients with PDAC may depend on to the limited efficacy of currently approved classic chemotherapeutic treatments, and the ineffectiveness of molecularly targeted- and immune checkpoint-inhibitors. The largest effort of integrated genomic analysis in understanding the molecular pathology of PDAC has recently confirmed the transforming growth factor β (TGFβ) as the most recurrently mutated signal transduction pathway. Moreover, this transcriptional network analysis indicated that programs enriched with a macrophage specific signature have the most significant association with poor survival in this disease. Having demonstrated that TGFβ signaling inhibition is an active strategy in combination with classic chemotherapy in unselected advanced PDAC patient populations, the identification of biomarkers that could predict which patients would benefit more from these combination strategies remains of unique importance for the development of more personalized treatments. In this study, we explored the mechanisms responsible for the exceptional response to the inhibition of TGFβ signaling pathway of pancreatic tumors characterized by high levels of the chemokine Ccl3. We demonstrate that tumor-derived Ccl3 acts as a poor prognostic factor in PC by inducing tumor infiltration of immunosuppressive tumor associated macrophages (TAMs). Using in vitro and in vivo assays, we show that TGFβ acts as a mediator of Ccl3 in sustaining macrophage recruitment and their M2-polarization. We also provide evidence that targeting of TGFβ signaling potentiates the response of high-Ccl3 tumors to gemcitabine. Our findings suggest CCL3 as a valid biomarker to predict which patients would benefit more from these combination strategies, allowing the development of more personalized treatments for PDAC therapy.File | Dimensione | Formato | |
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PhD thesis Monica Bertolini.pdf
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https://hdl.handle.net/20.500.14242/181169
URN:NBN:IT:UNIVR-181169