Multicenter study correlating molecular characteristics and clinical outcomes of cancer cases with patient-derived organoids

Abstract

Background: 3D-spatial interaction between cancer cells influences tumor behavior, making it essential to replicate tumor structures for predicting patient outcomes.

Methods: We collected data from three multicenter prospective studies to evaluate the ability to establish Patient-Derived Organoids (PDOs) from different biological samples and timepoints, correlating their characteristics and drug sensitivity with clinical outcomes.

Results: From 184 patients (17 tumor types), 249 samples were collected: 149 (59.8%) from tumor tissue, 61 (24.5%) from peritoneal fluids, 39 (15.7%) from peripheral blood. Success rates for PDO establishment were 39.5%, 34.4%, and 25.6%, respectively. PDOs reproduced pathological and immunohistochemical patterns of source tumors, with pathogenic variants confirmed in 84% (21/25). In a series of 13 baseline and sequential PDOs from 9 patients undergoing treatment, responses to therapy mirrored patient responses during therapy.

Conclusions: PDOs preserve tumor features, reflect disease progression, and predict treatment responses, providing valuable models to complement molecular testing in precision medicine.

Keywords: Drug screening; Patient-derived organoids; Precision Medicine.

Fig. 1

Flow chart of samples collected for PDO establishment and characterization. Samples included: peripheral blood (obtained through venipuncture), tumor (obtained through biopsy or percutaneous punction) and peritoneal fluids (peritoneal washings along a surgical procedure or malignant ascites). The success rate of organoid establishment is depicted, alongside the analyses conducted: immunohistochemistry (IHC), determination by PCR of the presence of DNA pathogenic variants identified in the source tumor (PCR confirmation of original mutation), characterization of the immune cell populations present in the source tumor (Immuneprofiling), study of sensitivity to different drugs to compare in vitro response with the real outcome of patients (Drug screening). Partially created in BioRender.com

Fig. 2

Flow cytometry gating strategy for the study of the immune infiltrate of source tumors (A-B) and correlation with PDO establishment (C). A Gating strategy used for immune profiling by flow cytometry, showing the sequential steps to identify and quantify immune cell populations. Two different strategies to detect Tregs cells are shown. B Percentage of the different immune populations per patient. The different colors reflect the type of tumor as shown in legend (C) Immune populations with significant differences between samples that successfully generated organoids and those that did not. TILs, Tumor Infiltrating lymphocytes; LTh, T Healper lymphocytes; LTc, T cytotoxic lymphocytes; TCM, Central Memory T cells; TEM, Effector Memory T cells; TN, Naïve T cells; TEMRA, Terminal Effector Tcells. *p < 0.05; **p < 0.01

Fig. 3

Correlation of morphology and IHC patterns between PDOs and source tumors and between PDOs from paired samples (A) H&E staining of paraffin-embedded organoids and their source tumors (B) Immunohistochemical staining of ovarian cancer markers in PDOs and source tumors (PAX8, p53, and WT1). Scale bar = 100um

Fig. 4

Correlation of the genetic background of PDOs and source tumors The panel displays the presence or absence in PDOs of pathogenic variants originally identified in the source tumors. Also, clinical and pathological data of the patients from whom samples were collected are represented (sex, tumor type, disease stage, number of prior treatment lines, and the type of sample obtained (peripheral blood, tumor or peritoneal fluids)

Fig. 5

Timelines comparing PDO drug sensitivity with real outcome of source patients (cases #1–3). A-C Graphical summaries of source patients clinical and molecular characteristics and their clinical evolution and PDO drug sensitivity score

Fig. 6

Timelines comparing PDO drug sensitivity with real outcome of source patients (cases #4–6). A-C Graphical summaries of source patients clinical and molecular characteristics and their clinical evolution and PDO drug sensitivity score

Fig. 7

Timelines comparing PDO drug sensitivity with real outcome of source patients (cases #7–9). A-C Graphical summaries of source patients clinical and molecular characteristics and their clinical evolution and PDO drug sensitivity score

Fig. 8

Legend for the correct interpretation of Figs. 5, 6 and 7

Fig. 9

Drug sensitivity scores. A Drug sensitivity score of all PDOs (paired synchronous samples are presented within the same square; metachronous samples are separated by a red dot line; PDOs from different patients are separated by black dot lines). B and C respectively: olaparib and platinum derivates sensitivity scores of PDOs; orange and green circles represent platinum resistant and sensitive patients (respectively); naïve: PDOs established from patients that had not received the drug by the time of sample collection; exposed: PDOs established from patients that had already received the drug by the time of sample collection

Fig. 10

Immunoorganoids establishment and check point inhibitors testing. A Schematic representation of the experimental workflow: patient-derived organoids (PDOs) generation, tumor-infiltrating lymphocytes (TILs) isolation and expansion, followed by co-culture of PDOs and TILs to form the immuno-organoid. B Bright-field microscopy and hematoxylin–eosin (H&E) staining of generated PDOs, illustrating structural integrity. C Phenotypic characterization of expanded TILs using flow cytometry, identifying key immune subpopulations. D Cytotoxicity assessment: analysis of cell death ratio (PI/Hoechst) and treatment-specific organoid cell death (SOCD) after co-culturing PDOs with TILs at different ratios (1:10 and 1:20) in the presence or absence of Ipilimumab (38.8 μg/ml). Results are represented as the average of three technical replicates*p < 0.05; **p < 0.0015