Systematic growth factor profiling platform for 3D tumor models reveals estradiol-responsive cellular mechanisms of immunotherapy resistance
Source abstract
Abstract
Abstract Current organoid culture systems face critical limitations: standardized growth factor formulations fail to capture patient-specific signaling requirements, while single-cell-type approaches overlook tumor-stromal interactions essential for understanding immunotherapy resistance. To address these challenges, we developed an automated biofabrication platform that systematically integrates patient-derived three-dimensional (3D) cultures with comprehensive growth factor profiling across 128 combinations. Through rigorous optimization of Matrigel concentration and gelation kinetics, we established standardized conditions achieving uniform signal distribution and quantitative reproducibility. Screening of 23 ovarian cancer patient samples identified universal growth factor combinations that consistently promoted robust cell growth while preserving parental tumor characteristics. Integration of growth factor response profiles with multi-scale genomic analysis revealed two estradiol-responsive cellular populations coordinating immunosuppression: a malignant cell fraction (MAL.PDCD5) that suppresses immune infiltration and a cancer-associated fibroblast fraction (FB.TNFSF10) that promotes immune exclusion through enhanced TGF- β signaling. Spatial transcriptomic validation demonstrated striking mutual exclusivity between FB.TNFSF10 cells and T/NK cells in native tissue architecture. Most significantly, FB.TNFSF10 abundance emerged as a robust predictor of immune checkpoint inhibitor therapy resistance across multiple cancer cohorts, independent of conventional biomarkers. This biofabrication platform provides a scalable, reproducible framework with broad applicability beyond oncology. The systematic optimization methodology is readily adaptable to other tissue types, disease models, and high-throughput drug screening applications, representing a significant advancement in functional tissue engineering for precision medicine.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Cancer Cells and Metastasis · 3D Printing in Biomedical Research · Single-cell and spatial transcriptomics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (83)
Show source-provided references
1. Biofabrication strategies for 3D in vitro models and regenerative medicine10.1038/s41578-018-0006-y ↗
2. Bioprinting using organ building blocks: spheroids, organoids, and assembloids10.1089/ten.tea.2023.0198 ↗
3. Is it time to start transitioning from 2D to 3D cell culture?10.3389/fmolb.2020.00033 ↗
4. The third dimension bridges the gap between cell culture and live tissue10.1038/nrm2236 ↗
5. Modeling development and disease with organoids10.1016/j.cell.2016.05.082 ↗
6. Cancer modeling meets human organoid technology10.1126/science.aaw6985 ↗
7. 3D bioprinting of tissues and organs10.1038/nbt.2958 ↗
8. The origins and the future of microfluidics10.1038/nature05058 ↗
9. Designer matrices for intestinal stem cell and organoid culture10.1038/nature20168 ↗
10. Organoids in cancer research10.1038/s41568-018-0007-6 ↗
11. Clonal heterogeneity and tumor evolution: past, present, and the future10.1016/j.cell.2017.01.018 ↗
12. The causes and consequences of genetic heterogeneity in cancer evolution10.1038/nature12625 ↗
13. Microenvironmental regulation of tumor progression and metastasis10.1038/nm.3394 ↗
14. Accessories to the crime: functions of cells recruited to the tumor microenvironment10.1016/j.ccr.2012.02.022 ↗
15. Cancer-associated fibroblasts: heterogeneity, cancer pathogenesis, and therapeutic targets10.1002/mco2.70292 ↗
16. A framework for advancing our understanding of cancer-associated fibroblasts10.1038/s41568-019-0238-1 ↗
17. The role of the estrogen pathway in the tumor microenvironment10.3390/ijms19020611 ↗
18. Patient-derived primary cancer-associated fibroblasts mediate resistance to anti-angiogenic drug in ovarian cancers10.3390/biomedicines11010112 ↗
19. Sarek: a portable workflow for whole-genome sequencing analysis of germline and somatic variants10.12688/f1000research.16665.2 ↗
20. Fast and accurate long-read alignment with Burrows-Wheeler transform10.1093/bioinformatics/btp698 ↗
21. Systematic comparison of somatic variant calling performance among different sequencing depth and mutation frequency10.1038/s41598-020-60559-5 ↗
22. Strelka2: fast and accurate calling of germline and somatic variants10.1038/s41592-018-0051-x ↗
23. The ensembl variant effect predictor10.1186/s13059-016-0974-4 ↗
24. Rapid and accurate identification of ribosomal RNA sequences via deep learning10.1093/nar/gkac112 ↗
25. STAR: ultrafast universal RNA-seq aligner10.1093/bioinformatics/bts635 ↗
26. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome10.1186/1471-2105-12-323 ↗
27. ComBat-seq: batch effect adjustment for RNA-seq count data10.1093/nargab/lqaa078 ↗
28. gprofiler2—an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler10.12688/f1000research.24956.2 ↗
29. Machine learning identifies stemness features associated with oncogenic dedifferentiation10.1016/j.cell.2018.03.034 ↗
30. xCell: digitally portraying the tissue cellular heterogeneity landscape10.1186/s13059-017-1349-1 ↗
31. Determining cell type abundance and expression from bulk tissues with digital cytometry10.1038/s41587-019-0114-2 ↗
32. Consensus on molecular subtypes of high-grade serous ovarian carcinoma10.1158/1078-0432.CCR-18-0784 ↗
33. Cancer-associated fibroblasts: key players in shaping the tumor immune microenvironment10.1111/imr.12982 ↗
34. The molecular signatures database (MSigDB) hallmark gene set collection10.1016/j.cels.2015.12.004 ↗
35. Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes10.1038/s41592-020-0820-1 ↗
36. Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference10.1186/s13059-019-1865-2 ↗
37. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation10.1038/nbt.4042 ↗
38. Dictionary learning for integrative, multimodal and scalable single-cell analysis10.1038/s41587-023-01767-y ↗
39. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression10.1186/s13059-019-1874-1 ↗
40. Fast, sensitive and accurate integration of single-cell data with Harmony10.1038/s41592-019-0619-0 ↗
41. PRIMUS: rapid reconstruction of pedigrees from genome-wide estimates of identity by descent10.1016/j.ajhg.2014.10.005 ↗
42. Longitudinal single-cell RNA-seq analysis reveals stress-promoted chemoresistance in metastatic ovarian cancer10.1126/sciadv.abm1831 ↗
43. Cell-type-specific co-expression inference from single cell RNA-sequencing data10.1101/2022.12.13.520181) ↗
44. WGCNA: an R package for weighted correlation network analysis10.1186/1471-2105-9-559 ↗
45. Single-cell dissection of cellular components and interactions shaping the tumor immune phenotypes in ovarian cancer10.1016/j.ccell.2021.04.004 ↗
46. Single-cell analysis reveals fibroblast clusters linked to immunotherapy resistance in cancer10.1158/2159-8290.CD-19-1384 ↗
47. Inference and analysis of cell-cell communication using CellChat10.1038/s41467-021-21246-9 ↗
48. An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics10.1016/j.cell.2018.02.052 ↗
49. DeepCC: a novel deep learning-based framework for cancer molecular subtype classification10.1038/s41389-019-0157-8 ↗
50. Comprehensive molecular characterization of clinical responses to PD-1 inhibition in metastatic gastric cancer10.1038/s41591-018-0101-z ↗
51. A single dose of neoadjuvant PD-1 blockade predicts clinical outcomes in resectable melanoma10.1038/s41591-019-0357-y ↗
52. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma10.1126/science.aad0095 ↗
53. Tumor and microenvironment evolution during immunotherapy with nivolumab10.1016/j.cell.2017.09.028 ↗
54. Analysis of immune signatures in longitudinal tumor samples yields insight into biomarkers of response and mechanisms of resistance to immune checkpoint blockade10.1158/2159-8290.CD-15-1545 ↗
55. Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma10.1038/s41591-019-0654-5 ↗
56. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma10.1016/j.cell.2016.02.065 ↗
57. Distinct immune cell populations define response to anti-PD-1 monotherapy and anti-PD-1/anti-CTLA-4 combined therapy10.1016/j.ccell.2019.01.003 ↗
58. Immune-related gene expression profiling after PD-1 blockade in non-small cell lung carcinoma, head and neck squamous cell carcinoma, and melanoma10.1158/0008-5472.CAN-16-3556 ↗
59. Synthetic lethality-mediated precision oncology via the tumor transcriptome10.1016/j.cell.2021.03.030 ↗
60. Genome-wide identification of differentially methylated promoters and enhancers associated with response to anti-PD-1 therapy in non-small cell lung cancer10.1038/s12276-020-00493-8 ↗
61. DNA methylation loss promotes immune evasion of tumours with high mutation and copy number load10.1038/s41467-019-12159-9 ↗
62. Immune gene signatures for predicting durable clinical benefit of anti-PD-1 immunotherapy in patients with non-small cell lung cancer10.1038/s41598-019-57218-9 ↗
63. Genomic correlates of response to immune checkpoint therapies in clear cell renal cell carcinoma10.1126/science.aan5951 ↗
64. Immune cell topography predicts response to PD-1 blockade in cutaneous T cell lymphoma10.1038/s41467-021-26974-6 ↗
65. TGFbeta attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells10.1038/nature25501 ↗
66. A scaling normalization method for differential expression analysis of RNA-seq data10.1186/gb-2010-11-3-r25 ↗
67. edgeR: a bioconductor package for differential expression analysis of digital gene expression data10.1093/bioinformatics/btp616 ↗
68. Hierarchical cluster analysis in clinical research with heterogeneous study population: highlighting its visualization with R10.21037/atm.2017.02.05 ↗
69. Estradiol induces epithelial to mesenchymal transition of human glioblastoma cells10.3390/cells9091930 ↗
70. Tumor microenvironment-derived R-spondins enhance antitumor immunity to suppress tumor growth and sensitize for immune checkpoint blockade therapy10.1158/2159-8290.CD-20-0833 ↗
71. Estrogen biosynthesis and action in ovarian cancer10.3389/fendo.2014.00192 ↗
72. Estrogen receptor-alpha mediates gene expression changes and growth response in ovarian cancer cells exposed to estrogen10.1677/erc.1.01039 ↗
73. The novel estrogen receptor GPER regulates the migration and invasion of ovarian cancer cells10.1007/s11010-013-1579-9 ↗
74. Integrated digital pathology and transcriptome analysis identifies molecular mediators of T-cell exclusion in ovarian cancer10.1038/s41467-020-19408-2 ↗
75. Estrogen signaling and its potential as a target for therapy in ovarian cancer10.3390/cancers12061647 ↗
76. Tumor cell-independent estrogen signaling drives disease progression through mobilization of myeloid-derived suppressor cells10.1158/2159-8290.CD-16-0502 ↗
77. Distinct populations of inflammatory fibroblasts and myofibroblasts in pancreatic cancer10.1084/jem.20162024 ↗
78. Cross-species single-cell analysis of pancreatic ductal adenocarcinoma reveals antigen-presenting cancer-associated fibroblasts10.1158/2159-8290.CD-19-0094 ↗
79. Impaired mitochondrial oxidative phosphorylation limits the self-renewal of T cells exposed to persistent antigen10.1038/s41590-020-0725-2 ↗
80. TGF-beta mediated immune evasion in cancer-spotlight on cancer-associated fibroblasts10.3390/cancers12123650 ↗
81. Regulatory T cells in cancer immunosuppression—implications for anticancer therapy10.1038/s41571-019-0175-7 ↗
82. Role of the tumor microenvironment in PD-L1/PD-1-mediated tumor immune escape10.1186/s12943-018-0928-4 ↗
83. Primary, adaptive, and acquired resistance to cancer immunotherapy10.1016/j.cell.2017.01.017 ↗