Patient-derived organoids as a predictive biomarker for treatment response in cancer patients

G Emerens Wensink, Sjoerd G Elias, Jasper Mullenders, Miriam Koopman, Sylvia F Boj, Onno W Kranenburg, Jeanine M L Roodhart, G Emerens Wensink, Sjoerd G Elias, Jasper Mullenders, Miriam Koopman, Sylvia F Boj, Onno W Kranenburg, Jeanine M L Roodhart

Abstract

Effective predictive biomarkers are needed to enable personalized medicine and increase treatment efficacy and survival for cancer patients, thereby reducing toxic side effects and treatment costs. Patient-derived organoids (PDOs) enable individualized tumour response testing. Since 2018, 17 publications have examined PDOs as a potential predictive biomarker in the treatment of cancer patients. We review and provide a pooled analysis of the results regarding the use of PDOs in individualized tumour response testing, focusing on evidence for analytical validity, clinical validity and clinical utility. We identify future perspectives to accelerate the implementation of PDOs as a predictive biomarker in the treatment of cancer patients.

Conflict of interest statement

S.F.B. is inventor on patents related to the Organoid Technology. S.F.B. and J.M. are employed by the Foundation Hubrecht Organoid Technology (HUB). The remaining authors declare no conflict of interest. M.K. Institutional financial instructs (IFI): Amgen, Bayer, BMS, Merck-Serono, Nordic Farma, Roche, Servier, Sirtex, Sanofi-Aventis; O.W.K. IFI: Genmab, Johnson & Johnson; J.M.L.R. IFI: Servier, Merck, Bayer. All grants were unrelated to the study and paid to the individual’s institution. All authors are involved in an ongoing prospective clinical trial evaluating the predictive role of organoids in mCRC patients.

Figures

Fig. 1. PDO-based individualized tumour response testing…
Fig. 1. PDO-based individualized tumour response testing as a predictive biomarker.
a Illustrates the tumour types for which patient-derived organoids (PDOs) have been tested for clinical validity (listed in full in Table 1). Personalized treatment strategies currently implemented in oncology treatment largely comprise of genomic biomarkers. However, this only results in a personalized treatment strategy for a minority of patients. Individualized tumour response testing using PDOs is a new biomarker which may be used in personalized treatment and increases access to personalized treatment. b For individualized tumour response testing, tissue from a patient’s tumour is obtained to culture organoids, perform drug screens and various read-outs can be obtained to define PDO drug screen response (including organoid size, viability and co-culture cytokine measurements). A predictive biomarker test is developed using the PDO drug screen results and clinical response seen in patients.
Fig. 2. Evidence landscape of PDO drug…
Fig. 2. Evidence landscape of PDO drug screen parameters and clinical response.
Illustrates the clinical validity results for PDOs as a predictive biomarker for treatment response (dark red: significant correlation and/or predictive value found, pink: trend for correlation or predictive value found, blue: no correlation and white: not tested), with the size of the circle representing the patient cohort size, specified per treatment and tumour type (y-axis) and ex vivo drug response parameter (x-axis). Abbreviations: 5-FU 5-fluorouracil, AC-T doxorubicin + cyclophosphamide + paclitaxel, AUC area under the curve, Capec. capecitabine, CAPIRI capecitabine + irinotecan, CRC colorectal cancer, EOX epirubicin + oxaliplatin + 5-FU, FOLFIRI 5-FU + irinotecan, FOLFOX 5-FU + oxaliplatin, GC gastric cancer, GOC gastroesophageal cancer, GR growth rate inhibition metrics, GR50 value with 50% viable GR, HNSCC head and neck squamous cell carcinoma, ICI immune checkpoint inhibitors, OMI optical metabolic imaging, PDO patient-derived organoid.
Fig. 3. Forest plots of sensitivity and…
Fig. 3. Forest plots of sensitivity and specificity (clinical validity pooled results).
A paired forest plot of the sensitivity and specificity of each study and treatment type is shown with 95% confidence intervals. A bivariate meta-analysis was performed to obtain a pooled summary estimate for sensitivity and specificity indicated in the forest plots (1: for all studies that reported results that could be included in this pooled analysis and 2: for studies with ≥5 responders/non-responders). The analysis was performed in R (Version 3.6.1) using the “mada” package. In blue and bold font (#) the studies were indicated which were included in the analysis for ≥5 responders/non-responders. Patients who contributed to multiple accuracy estimates: 2 patients received ICI and dabrefinib/trametinib; 3 patients received FOLFOX and irinotecan-based treatment; 3 patients had 2 PDOs each (before and after FOLFOX treatment) and 1 patient had a synchronous tumour (responder and non-responder). Abbreviations: capec. capecitabine, CAPIRI capecitabine + irinotecan, df degrees of freedom, EOX epirubicin + oxaliplatin + 5-FU, FOLFIRI 5-flouruoracil + irinotecan, FOLFOX 5-fluorouracil + oxaliplatin, HNSCC head & neck squamous cell carcinoma, ICI immune checkpoint inhibitors, mCRC metastatic colorectal cancer, RC rectal cancer, ref reference, resp. responder clinically, non-resp. non-responder clinically.

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