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Gemcitabine and nab-paclitaxel with or without the VDR agonist paricalcitol for metastatic pancreatic cancer: a randomized, multiarm, run-in phase trial

Nature Cancer (2026) Cite this article

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Abstract

Vitamin D receptor (VDR) agonists promote quiescence of cancer-associated fibroblasts and improve efficacy of chemotherapy in preclinical models of pancreatic cancer. We conducted a run-in phase trial with primary endpoint of safety when the VDR agonist paricalcitol is given with first-line gemcitabine and albumin-bound paclitaxel (GA) in patients with metastatic pancreatic cancer. Secondary endpoints included pharmacodynamic analyses. Thirty-six patients were randomized to GA plus placebo, GA plus intravenous paricalcitol or GA plus oral paricalcitol with pretreatment and on-treatment tumor biopsies. Paricalcitol was safely administered with GA, although five patients (42%) receiving oral paricalcitol had grade 2–4 hypercalcemia and required dose reduction. Nuclear VDR protein expression was heterogeneous across patients, and VDR was expressed in tumor, immune and stromal cells. Compared to pretreatment specimens, on-treatment biopsies had decreased proportion of αSMA+ fibroblasts, altered fibroblast VDR activation signature and greater density and spatial colocalization of CD8+ T cells with tumor cells in the GA-plus-paricalcitol arms. VDR expression was predictive of tumor response in the GA-plus-paricalcitol arms. Paricalcitol can be safely administered with chemotherapy to patients with metastatic pancreatic cancer, and on-treatment biopsies indicated favorable modulation of the tumor microenvironment by paricalcitol as predicted by preclinical models. ClinicalTrials.gov identifier: NCT03520790.

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Fig. 1: CONSORT diagram.
The alternative text for this image may have been generated using AI.
Fig. 2: Study overview.
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Fig. 3: Patient characteristics by baseline tumor VDR protein expression.
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Fig. 4: Fibroblast subtypes and expression programs in pretreatment and on-treatment tumor biopsies.
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Fig. 5: Immune cell infiltration and spatial organization in pretreatment and on-treatment tumor biopsies.
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Fig. 6: Tumor cell phenotype, circulating markers and patient outcomes.
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Data availability

The mIF data generated in this study are publicly available via Zenodo at https://doi.org/10.5281/zenodo.17969216 (ref. 65). The spatial transcriptional profiling data generated in this study are publicly available in the National Center for Biotechnology Information Gene Expression Omnibus database under accession code GSE313748. Source data are provided with this paper.

Code availability

The code generated in this study for analysis of mIF and spatial transcriptional profiling date is available via GitHub at https://github.com/wolpin-nowak-lab/paricalcitol-trial-nature-cancer.

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Acknowledgements

The primary support for the work described herein was provided by (1) Stand Up to Cancer–Cancer Research United Kingdom–Lustgarten Foundation Superenhancer Dream Team award, (2) Stand Up to Cancer–American Association for Cancer Research–Lustgarten Foundation VDR Agonist Team award and (3) Dana-Farber Cancer Institute Hale Family Center for Pancreatic Cancer Research. Other support to investigators includes S.R.: NIH/NCI grant no. K08 CA260442, and grants from the Claudia Adams Barr Program in Innovative Basic Cancer Research and the Hale Family Center for Pancreatic Cancer Research. R.M.E. holds the March of Dimes Chair in Molecular and Developmental Biology at the Salk Institute and is funded by grants from the Lustgarten Foundation for Pancreatic Cancer Research (grant no. 122215393), the Don and Lorraine Freeberg Foundation, the David C Copley Foundation, the Wasily Family Foundation, The Paul M. Angell Family Foundation and NIH grant nos. P30 014195 and P01 CA265762. B.M.W.: grant nos. U01 CA210171 and P50 CA127003 from the National Institutes of Health and funding from the Hale Family Center for Pancreatic Cancer Research, the Lustgarten Foundation Dedicated Laboratory Program, Stand Up To Cancer, the Pancreatic Cancer Action Network, the Noble Effort Fund, the Wexler Family Fund, the Promises for Purple and the Bob Parsons Fund. The authors would like to thank D. Tuveson for his support with this project since its inception. They would also like to thank the members of the clinical trial office at Dana-Farber Cancer Institute and University of Pennsylvania, Abramson Cancer Center, for their efforts in coordinating and maintaining this multi-institutional effort. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Kimberly J. Perez, Andressa Dias Costa.

  2. These authors jointly supervised this work: Andrew Aguirre, Jonathan A. Nowak, Brian M. Wolpin.

Authors and Affiliations

  1. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Kimberly J. Perez, Andressa Dias Costa, Alexander Jordan, Dalia Elganainy, Suryun Kim, Chen Yuan, Dan Y. Gui, Sung Chul Hong, Xi Wang, Emma Coleman, C. Sloane Furniss, Lauren Brais, Alexandra Bird, Josh Remland, Vasilena Gocheva, Jennifer S. Thalappillil, Mark Anderson, James M. Cleary, Andrea Enzinger, Marios Giannakis, Kimmie Ng, Douglas A. Rubinson, Benjamin Schlechter, Rishi Surana, Harshabad Singh, Thomas Abrams, Eliezer Allen, Srivatsan Raghavan, Andrew Aguirre, Jonathan A. Nowak & Brian M. Wolpin

  2. Department of Medical Oncology, Harvard Medical School, Boston, MA, USA

    Kimberly J. Perez, Andressa Dias Costa, Alexander Jordan, Dalia Elganainy, Suryun Kim, Chen Yuan, Dan Y. Gui, Sung Chul Hong, Xi Wang, Emma Coleman, C. Sloane Furniss, Lauren Brais, Alexandra Bird, Josh Remland, Vasilena Gocheva, Jennifer S. Thalappillil, Mark Anderson, James M. Cleary, Andrea Enzinger, Marios Giannakis, Kimmie Ng, Douglas A. Rubinson, Benjamin Schlechter, Rishi Surana, Harshabad Singh, Thomas Abrams, Eliezer Allen, Srivatsan Raghavan, Andrew Aguirre & Brian M. Wolpin

  3. Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Kimberly J. Perez, Alexander Jordan, Dan Y. Gui, Mark Anderson, James M. Cleary, Andrea Enzinger, Marios Giannakis, Kimmie Ng, Douglas A. Rubinson, Benjamin Schlechter, Rishi Surana, Harshabad Singh, Thomas Abrams, Eliezer Allen, Srivatsan Raghavan, Andrew Aguirre & Brian M. Wolpin

  4. Department of Medicine, Harvard Medical School, Boston, MA, USA

    Kimberly J. Perez, Alexander Jordan, Dan Y. Gui, Mark Anderson, James M. Cleary, Andrea Enzinger, Marios Giannakis, Kimmie Ng, Douglas A. Rubinson, Benjamin Schlechter, Rishi Surana, Harshabad Singh, Thomas Abrams, Eliezer Allen, Srivatsan Raghavan, Andrew Aguirre & Brian M. Wolpin

  5. Department of Medicine, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, USA

    Thomas B. Karasic, Ursina Teitelbaum, Natallia Izgur & Peter O’Dwyer

  6. Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA

    Runzi Tan & Simona Cristea

  7. Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Simona Cristea

  8. Salk Institute for Biological Studies, La Jolla, CA, USA

    Morgan Truitt, Tae Gyu Oh, Michael Downes & Ronald M. Evans

  9. Massachusetts General Hospital, Boston, MA, USA

    Hui Zheng

  10. Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA

    Peter S. Winter, Srivatsan Raghavan & Andrew Aguirre

  11. University of North Carolina School of Medicine, Chapel Hill, NC, USA

    Jen Jen Yeh

  12. Translational Genomics Research Institute, Phoenix, AZ, USA

    Daniel Von Hoff

  13. Storr Liver Centre, Westmead Institute for Medical Research, Westmead, New South Wales, Australia

    Christopher Liddle

  14. University of Sydney School of Medicine, Westmead, New South Wales, Australia

    Christopher Liddle

  15. Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA

    Jonathan A. Nowak

  16. Department of Pathology, Harvard Medical School, Boston, MA, USA

    Jonathan A. Nowak

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  1. Kimberly J. Perez
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  2. Andressa Dias Costa
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  17. C. Sloane Furniss
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  22. Jennifer S. Thalappillil
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  39. Daniel Von Hoff
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  40. Christopher Liddle
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  41. Michael Downes
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  42. Ronald M. Evans
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  43. Peter O’Dwyer
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  44. Andrew Aguirre
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  45. Jonathan A. Nowak
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  46. Brian M. Wolpin
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K.J.P., A.D.C., J.A.N. and B.M.W. conceived and designed the study. K.J.P., A.D.C., J.A.N. and B.M.W. collected and assembled data. K.J.P., A.D.C., A.J., T.B.K., D.E., S.K., C.Y., D.Y.G., R.T., S.C.H., X.W., S.C., E.C., M.T., T.G.O., H.Z., C.S.F., L.B., A.B., J.R., V.G., J.S.T., M.A., J.M.C., A.E., M.G., K.N., D.A.R., B.S., R.S., H.S., T.A., U.T., N.I., E.V.A., P.S.W., S.R., J.J.Y., D.V.H., C.L., M.D., R.M.E., P.O., A.A., J.A.N. and B.M.W. analyzed and interpreted data. K.J.P., A.D.C., A.J., T.B.K., D.E., S.K., C.Y., D.Y.G., R.T., S.C.H., X.W., S.C., E.C., M.T., T.G.O., H.Z., C.S.F., L.B., A.B., J.R., V.G., J.S.T., M.A., J.M.C., A.E., M.G., K.N., D.A.R., B.S., R.S., H.S., T.A., U.T., N.I., E.V.A., P.S.W., S.R., J.J.Y., D.V.H., C.L., M.D., R.M.E., P.O., A.A., J.A.N. and B.M.W. wrote the manuscript. K.J.P., A.D.C., A.J., T.B.K., D.E., S.K., C.Y., D.Y.G., R.T., S.C.H., X.W., S.C., E.C., M.T., T.G.O., H.Z., C.S.F., L.B., A.B., J.R., V.G., J.S.T., M.A., J.M.C., A.E., M.G., K.N., D.A.R., B.S., R.S., H.S., T.A., U.T., N.I., E.V.A., P.S.W., S.R., J.J.Y., D.V.H., C.L., M.D., R.M.E., P.O., A.A., J.A.N. and B.M.W. gave final approval of the manuscript. K.J.P., A.D.C., A.J., T.B.K., D.E., S.K., C.Y., D.Y.G., R.T., S.C.H., X.W., S.C., E.C., M.T., T.G.O., H.Z., C.S.F., L.B., A.B., J.R., V.G., J.S.T., M.A., J.M.C., A.E., M.G., K.N., D.A.R., B.S., R.S., H.S., T.A., U.T., N.I., E.V.A., P.S.W., S.R., J.J.Y., D.V.H., C.L., M.D., R.M.E., P.O., A.A., J.A.N. and B.M.W. were accountable for all aspects of the work.

Corresponding authors

Correspondence to Kimberly J. Perez, Jonathan A. Nowak or Brian M. Wolpin.

Ethics declarations

Competing interests

The authors declare the following competing interests: K.J.P.: The authors held a one-time advisory role for Ipsen, Exelixis and Novartis. J.M.C.: The author receives research funding to his institution from Amgen, Merus, Servier and Bristol Myers Squibb. He receives research support from Merck, AstraZeneca, Esperas Pharma, Bayer, Tesaro, Arcus Biosciences and Pyxis; he has also received honoraria for being on the advisory boards of Incyte and Blueprint Medicines and for serving on the data safety monitoring committee for AstraZeneca. He has given educational talks sponsored by Bayer, Bristol Myers Squibb, Lilly, Merck, AstraZeneca and Genentech. A.E.: The author receives research funding from Medtronic, Inc. Spouse has been a consultant for and has received honoraria from ALX oncology, Arcus Bioscience, Astellas, AstraZeneca, Blueprint Medicines, Bristol Myers Squibb, Chimeric Therapeutics, Celgene, Coherus, Daiichi-Sankyo, Five Prime, Ideaya, Istari, Legend, Lily, Loxo, Merck, Novartis, Ono, Servier, Taiho, Takeda, Turning Point Therapeutics, Xencor and Zymeworks. D.A.R.: The author receives research funding Instylla, Boston Scientific, Axial Therapeutics, Taiho, Sirtex. H.S.: The author receives research funding from AstraZeneca, has consulted for Dewpoint Therapeutics, Inc. and Merck Sharp & Dohme, LLC. Travel and accommodation from Dava Oncology. Honoraria from UpToDate. T.A.: The author receives research funding to his institution from Lilly, Sanofi; receives consulting fees from Eisai and AstraZeneca; stock ownership in Merck. P.S.W.: Receives research support from Microsoft and reports compensation for consulting from The Engine Ventures unrelated to this work. S.R.: Research support from Microsoft, equity in Amgen. T.B.K.: Research support to institution from AstraZeneca, Eli Lilly, Tempest Therapeutics, Xencor, Genentech, Totus Medicines, Boehringer Ingelheim, Replimune, BioNTech, PMV Pharma and Frontier Medicines. Consulting/advisory boards for Pfizer, Merus, Incyte, Nucorion Pharmaceuticals and Synnovation Therapeutics. P.O.D.: Research Support: Pfizer, BMS, AZ, GSK, Five Prime, Merck, Syndax, BBI, Novartis, Celgene, Incyte, Lilly/Imclone, array, h3biomedicine, Taiho, Minneamrata, pharmacyclics/abbvie, Mirati. J.A.N.: The author receives research funding from Natera and serves as a consultant for Leica Biosystems. B.M.W.: Research funding to institution: Amgen, AstraZeneca, BMS/Celgene, BreakThrough Cancer, Eli Lilly, Harbinger Health, Lustgarten Foundation, NIH/NCI, Novartis, Pancreatic Cancer Action Network, Revolution Medicines, Servier/Agios, Stand Up to Cancer. Advisory boards/consulting: Agenus, BMS/Mirati, EcoR1 Capital, GRAIL, Harbinger Health, Ipsen, Lustgarten Foundation, Revolution Medicines, Tango Therapeutics, Third Rock Ventures. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Tissue biopsy analysis flow diagram.

Flowchart illustrating biopsy sample allocation by time point (N = 35 pre-treatment; N = 25 on-treatment). Biopsy samples that passed quality control metrics underwent analysis by spatial transcriptomics or multiplex IF. Abbreviations: GA: Gemcitabine and albumin-bound (nab-) paclitaxel; miF fib: multiplex immunofluorescence fibroblast subtyping assay; mIF VDR: multiplex immunofluorescence vitamin D receptor assay; Spatial WTA: spatial transcriptomics with whole transcriptome analysis (GeoMx).

Extended Data Fig. 2 VDR expression landscape in pre-treatment tumor biopsies.

(a) Barplots showing nuclear VDR protein expression across all cells in pre-treatment samples for the GA plus placebo (N = 9) and GA plus paricalcitol arms (N = 23) assessed by multiplex IF. (b) Scatter plot and Spearman correlation coefficient for plasma levels of albumin-corrected calcium and nuclear protein expression of VDR in pre-treatment samples (N = 32) (P = 0.53). (c) Scatter plot and Spearman correlation coefficient for circulating levels of PTH and nuclear protein expression of VDR in pre-treatment samples (N = 32) (P = 0.87). (d) Nuclear protein VDR expression across cell types in pre-treatment samples (N = 32) (tumor vs immune cell, P < 2e-16; tumor vs stromal cell, P < 2e-16; immune vs stromal cell, P = 6e-7). (e) Boxplots depicting changes in fractional abundance of nuclear VDR protein expression assessed by multiplex IF and stratified by treatment arm (GA + PO placebo pre (N = 9) vs on-treatment (N = 8), P = 4e-6; GA + IV/PO paricalcitol pre (N = 23) vs on-treatment (N = 10), P = 0.13; P-interaction = 0.09). P values computed using LMM for panel (e), with P-values for intra-arm comparisons of pre-treatment and on-treatment values (left, right) and P-interaction for comparison of changes between the arms (center). Abbreviations: GA: gemcitabine and albumin–bound(nab-) paclitaxel; PO: per os; IV: intravenous; VDR: vitamin D receptor; PTH: parathyroid hormone; IF: immunofluorescence; LMM: linear mixed-effect model.

Source data

Extended Data Fig. 3 Nearest neighbor distance measurements and immune checkpoint expression in pre-treatment and on-treatment tumor biopsies.

(a) Boxplots showing comparison between the CD8+ T cells and tumor cells NND by time-point and stratified by treatment arm (GA + PO placebo pre (N = 9) vs on-treatment (N = 8), P = 0.43; GA + IV/PO paricalcitol pre (N = 23) vs on-treatment (N = 10), P = 3e-5). (b) Representative multiplex IF and cell phenotype images from pre and on-treatment biopsies of GA plus placebo and GA plus paricalcitol arms. The dashed blue lines represent distance between CD8+ T cells and tumor cells. (c) Boxplots depicting changes in immune checkpoint RNA expression in stromal ring areas within segmented tissue by spatial transcriptomics and stratified by treatment arm (PD-1: GA + PO placebo pre (N = 9) vs on-treatment (N = 7), GA + IV/PO paricalcitol pre (N = 22) vs on-treatment (N = 10), P-interaction = 0.37; CTLA-4: GA + PO placebo pre (N = 9) vs on-treatment (N = 7), P = 0.43, GA + IV/PO paricalcitol pre (N = 22) vs on-treatment (N = 11), P = 0.04, P-interaction = 0.43; LAG-3: GA + PO placebo pre (N = 9) vs on-treatment (N = 7); GA + IV/PO paricalcitol pre (N = 22) vs on-treatment (N = 11); P-interaction = 0.50; TIM-3: GA + PO placebo pre (N = 9) vs on-treatment (N = 7); GA + IV/PO paricalcitol pre (N = 22) vs on-treatment (N = 11); P-interaction = 0.12; TIGIT: GA + PO placebo pre (N = 9) vs on-treatment (N = 7), P = 0.64, GA + IV/PO paricalcitol pre (N = 22) vs on-treatment (N = 11), P = 0.003, P-interaction = 0.11). P-values for all panels computed using LMM, with P-values for intra-arm comparisons of pre-treatment and on-treatment values (left, right) and P-interaction for comparison of changes between the arms (center). Abbreviations: GA: gemcitabine and albumin–bound (nab) paclitaxel; NND: nearest neighbor distance; IF: immunofluorescence; LMM: linear mixed-effect model.

Source data

Extended Data Fig. 4 Progression-free survival by treatment arm in the GA plus placebo, GA plus IV paricalcitol and GA plus PO paricalcitol arms.

Barplots indicating progression-free survival, colored by treatment arm and including reasons for stopping treatment. Abbreviations: GA: gemcitabine and albumin-bound (nab-) paclitaxel; PO: per os; IV: intravenous.

Source data

Extended Data Fig. 5 Comparison between monoplex immunohistochemistry and monoplex immunofluorescence for protein markers included in the vitamin D panel.

Abbreviations: panCK; pan-cytokeratin; VDR, vitamin D receptor.

Extended Data Fig. 6 Flow chart of the cyclic immunofluorescence procedure.

Steps shaded in yellow were performed using a Leica BOND RX Research Stainer.

Extended Data Fig. 7 Multiplex immunofluorescence analysis of vitamin D receptor expression in different cellular compartments.

(a) Unmixed multiplex immunofluorescence image of the vitamin D receptor panel. (b) Tissue segmentation. (c) Cell segmentation. (d) Cell phenotyping. (e) Combinatorial expression of cell subset markers. Scale bar in panel A represents 50 µm.

Extended Data Fig. 8 Quality metrics and filtering for spatial transcriptional profiling data.

(a) Flowchart illustrating quality control filtering for areas of illumination.(b-f) Distribution of trimmed read percentages, stitched reads, aligned reads, sequencing saturation, and nuclei count distributions by type of area of illumination. (g) Distribution of genes detected.

Extended Data Fig. 9 Probe-level filtering for spatial transcriptional profiling data.

(a) Flowchart illustrating probe-level filtering. The geometric probe ratio is defined as the geometric mean of one probe’s counts from all segments divided by the geometric mean of all probe counts covering the target(gene) from all the segments in the dataset. (b) Barplot depicting total number of probes remaining in the dataset if different thresholds are selected (1%, 2.5%, 5%, 10%, 20%, 30%, and 50%).

Supplementary information

Reporting Summary (download PDF )

Supplementary Information (download PDF )

Supplementary data Tables 1–10.

Supplementary Information (download PDF )

Clinical trial protocol.

Supplementary Data (download PDF )

Checklist of information included in the reported clinical trial.

Source data

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Perez, K.J., Dias Costa, A., Jordan, A. et al. Gemcitabine and nab-paclitaxel with or without the VDR agonist paricalcitol for metastatic pancreatic cancer: a randomized, multiarm, run-in phase trial. Nat Cancer (2026). https://doi.org/10.1038/s43018-026-01165-8

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