Patient-derived micro-organospheres enable clinical precision oncology

Abstract

Patient-derived xenografts (PDXs) and patient-derived organoids (PDOs) have been shown to model clinical response to cancer therapy. However, it remains challenging to use these models to guide timely clinical decisions for cancer patients. Here, we used droplet emulsion microfluidics with temperature control and dead-volume minimization to rapidly generate thousands of micro-organospheres (MOSs) from low-volume patient tissues, which serve as an ideal patient-derived model for clinical precision oncology. A clinical study of recently diagnosed metastatic colorectal cancer (CRC) patients using an MOS-based precision oncology pipeline reliably assessed tumor drug response within 14 days, a timeline suitable for guiding treatment decisions in the clinic. Furthermore, MOSs capture original stromal cells and allow T cell penetration, providing a clinical assay for testing immuno-oncology (IO) therapies such as PD-1 blockade, bispecific antibodies, and T cell therapies on patient tumors.

Keywords: adoptive cell therapy; bispecific antibody; colorectal cancer; droplet microfluidics; immune-oncology; lung cancer; micro-organosphere; precision medicine; precision oncology; tumorsphere.

Figure 1.

Establishing CRC MOS for drug screen and clinical validation. (A) Scheme of CRC MOS generation and drug screening. (B) Images of the microfluidic MOS chip. (C) Bright field microscope images of CRC MOS generated with different cell numbers per MOS. (D) Representative images of generated MOS from patient CRC tumor tissue and hematoxylin and eosin (H&E) staining of the primary CRC tumor tissue and derived MOS. (E) H&E staining of CRC MOS established from different patient tumor tissues. (F) Heat map of high throughput drug screen using CRC tumor-derived MOS indicates sensitivity to oxaliplatin and resistance to Irinotecan. (G) The same patient showed response to oxaliplatin after 6 months of treatment in clinic. (H) Schematic illustration of the clinical study design. MOS are established from CRC biopsy for drug testing. (I) Representative images of patient-derived MOS. (J) Survival outcomes from all eight CRC patients are correlated with MOS drug sensitivity. Scale bar: 100 um.

Figure 2.

Genomic and transcriptomic characterization of MOS generated from patient lung tumor. (A) Representative images of generated MOS from patient lung tumor tissue and H&E staining of the primary lung tumor tissue and derived MOS. (B) High-throughput drug screen demonstrates feasibility of using lung MOS to identify other targets in cancer therapy. (C) Copy number variation (CNV) profiles with correlations of lung tumor tissue and derived MOS. (D) Driver mutations in commonly mutated genes for lung cancer is largely preserved in MOS compared to respective original tissues. Grey: driver mutations present. White: driver mutations absent. (E) UMAP of cells from primary lung tumor tissue or derived MOS labeled by cell types. (F) Comparison of log-transformed relative abundance of each cell type for lung tumor samples and derived MOS. (G) Relative abundance of cell types represented in either tissue (n=3) or MOS (n=3) samples. Abundances reported as log1p(percentage out of 1).

Figure 3.

Differential gene analysis on lung tumor tissue vs. derived MOS. (A) Pseudo-bulk differential expression analysis comparing lung cancer primary tissue and derived MOS datasets (n=3). Genes with absolute log-fold change > 1.5 are labeled in red. (B) Volcano plots of differentially expressed genes from fibroblasts, lymphoid cells, myeloid cells, and tumor cells from lung tumor samples. (C) Expression of cancer-associated marker genes CD274 (PD-L1), PDCD1 (PD-1), and TGFB1 (TGF-beta) plotted as UMAPs. Cells are plotted on separate UMAPs depending on source: primary tissue (left) or MOS (right). (D) Top five identified conserved markers for each cell type and labeled by cell source.

Figure 4.

Immune cells preserved in MOS are responsive to immunotherapy. (A) Nivolumab induced significant cytotoxicity in tumorspheres within MOS. Incucyte images were taken every 2 hours for 4 days, and Annexin V Green dye was added to indicate apoptosis. (B) Representative images from Incucyte demonstrated Nivolumab induces cell apoptosis within MOS. (C) Established MOS (day 4) derived from lung tumor tissue. (D) Animation of how ESK1* TCB drug induces CTL-mediated killing in MOS. (E) HLA-A2 gene expression in lung tumor tissues. (F) HLA-A2 expression detected by flow cytometry in established MOS derived from lung tumor tissue. (G) ESK1* induced higher apoptosis signal (indicated by Annexin V signal) in MOS. (H) Representative images of apoptosis induced by ESK1* treatment. (I) ESK1* induced killing of lung cancer MOS in all eight lung cases (p<0.005).

Figure 5.

A MOS potency assay for T-Cell therapies. (A) TILs cannot penetrate traditional Matrigel. (B) TILs can penetrate MOS and adhere to tumor cells. Immune cells were stained with Cytolight Red dye before images taken using Incucyte. (C) Increased killing indicated by Annexin V was observed in MOS treated with autologous TILs. (D) Representative images of MOS killing by TILs in MOS (indicated by Annexin V dye). (E) Activated PBMCs induce MOS killing (indicated by Annexin V Green dye). (F) Representative images of MOS killing by PBMCs. (G) Representative images to illustrate an imaging analysis pipeline that identifies droplet area to minimize background noise from outside immune cells. (H) Quantification analysis suggest PBMCs induce MOS killing (indicated by Caspase 3/7 dye). (I) ESK1* enhanced PBMC-induced tumor cell killing compared to the DP47 (CD3 only TCB). (J) Representative images of induced death of ESK1* treated MOS combined with PBMCs. White arrows indicating lung cancer tumorspheres within MOS. Compared to ESK1*, the negative control TCB, DP47, did not induce significant apoptosis of tumorspheres within MOS. (K) Dotted plot suggests ESK1* induces PBMC-mediated lung tumor MOS death in seven patient cases (p<0.005). (M) Image of dsRed expressing vector infection on MOS derived from lung tumor (3 days post infection). Significant higher gene expression of HLA-A2 (N) and antigen expression (O) were observed in HLA-A2-infected MOS. (P) HLA-A2-infected MOS underwent higher cell death than matched uninfected MOS in the presence of ESK1* and activated PBMCs (as indicated by Annexin V dye).