Unified framework for patient-derived, tumor-organoid-based predictive testing of standard-of-care therapies in metastatic colorectal cancer

Abstract

Predictive drug testing of patient-derived tumor organoids (PDTOs) holds promise for personalizing treatment of metastatic colorectal cancer (mCRC), but prospective data are limited to chemotherapy regimens with conflicting results. We describe a unified framework for PDTO-based predictive testing across standard-of-care chemotherapy and biologic and targeted therapy options. In an Australian community cohort, PDTO predictions based on treatment-naive patients (n = 56) and response rates from first-line mCRC clinical trials achieve 83% accuracy for forecasting responses in patients receiving palliative treatments (18 patients, 29 treatments). Similar assay accuracy is achieved in a prospective study of third-line or later mCRC treatment, AGITG FORECAST-1 (n = 30 patients). "Resistant" predictions are associated with inferior progression-free survival; misclassification rates are similar by regimen. Liver metastases are the optimal site for sampling, with testing achievable within 7 weeks for 68.8% cases. Our findings indicate that PDTO drug panel testing can provide predictive information for multifarious standard-of-care therapies for mCRC.

Keywords: colorectal cancer; patient-derived tumor organoid; precision medicine; predictive drug testing.

Graphical abstract

Figure 1

PDTO generation and histopathologic validation for patients with stage I–IV CRC enrolled in the ACCORD-CRC and TRACC registries (A) Flow chart indicating the number of patients with stage I–IV CRC included, the success rate of establishing cultures from patients, and the number of PDTO lines progressed to drug sensitivity testing. (B) Representative bright-field images and H&E-stained sections of PDTOs grown in LVM suspension culture medium illustrating heterogeneous morphologies ranging from solid/compact structures to thin-walled cystic structures. Scale bars, 200 μm. (C) Representative H&E staining and immunohistochemistry results for PDTOs from patients stained for Ki67, p53, and MUC2. Scale bars, 200 μm. (D) Correlation of tumor Ki67 expression and PDTO Ki67 expression, PDTO growth rate and tumor Ki67 expression, and PDTO growth rate and PDTO Ki67 expression (n = 21 tissues and n = 20 PDTOs). Statistical significance was attributed to values of p < 0.050 as determined by the correlation test. ∗p < 0.050. See also Table S1.

Figure 2

Genomic features of PDTOs and matched original tumor tissues from the community cohort as determined by whole-genome sequencing (A) Mutation profiles of 21 PDTOs and matched tumor tissues, including counts of SNVs and insertions or deletions (indels), proportions of nucleotide transitions and transversions, and overlap for genome-wide somatic SNVs. (B–D) Somatic mutation spectra for the most frequently mutated CRC genes (B), genome-wide relative DNA copy-number aberrations (C), and frequencies of DNA copy-number gains and losses for PDTOs and matched tumor tissues (D). T, tumor; O, patient-derived tumor organoid. See also Table S2.

Figure 3

PDTO drug sensitivity for patients with stage I–IV CRC enrolled in the ACCORD-CRC and TRACC registries (A) Schematic of the standardized workflow for semiautomated PDTO drug testing. Established tumoroids were dissociated and seeded as single cells, grown into small PDTOs over 3 days, and treated with drug for 7 days with daily bright-field imaging; viability was quantified by determination of mean PDTO size. (B) Representative images of PDTOs treated with increasing 5FU concentrations on day 10 of the assay. (C) Representative PDTO dose-response curve for 5FU single-agent treatment and viability matrix for 5FU-oxaliplatin combination treatment. (D) Drug sensitivity profiles for 56 PDTO lines from 56 chemo-naive patients with CRC for single-agent 5FU, oxaliplatin, SN38, regorafenib, TAS-102, erlotinib, gemcitabine, pemetrexed, and temozolomide treatments and combination 5FU-oxaliplatin and 5FU-SN38 treatments; indicated cutoffs are based on treatment-naive patients and response rates from clinical trials of first-line mCRC treatment; blue, resistant; red, sensitive. (E) Correlation of PDTO erlotinib versus cetuximab sensitivity (n = 8 PDTOs). Statistical significance was attributed to values of p < 0.050 as determined by the correlation test. (F) Comparison of erlotinib sensitivity between RAS^Mut (n = 28) and RAS^WT (n = 38) PDTOs. Statistical significance was attributed to values of p < 0.050 as determined by the Student’s t test. ∗p < 0.050. See also Figures S1 and S5 and Tables S1, S3, and S4.

Figure 4

PDTO-based prediction model for standard-of-care drug testing in mCRC (A) Flow chart indicating the number of PDTOs from treatment-naive community patients and response rate cutoffs from first-line mCRC clinical trials used for predictive model development and the number of PDTOs from an independent validation cohort of community patients used for prospective model evaluation. (B) Details of mCRC patient treatments, clinical outcomes, PDTO predictions, and matching status of predictions and responses (18 patients, 29 treatments). Pie charts detail model accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). See also Table S5.

Figure 5

AGITG FORECAST-1 study to determine the feasibility of PDTO-based response prediction in the third-line or later mCRC treatment setting (A) Flow chart indicating the number of patients recruited, the success rate of establishing and drug testing PDTO cultures, and the number of evaluable patients and clinical responses. (B) Representative report indicating PDTO assay results for single-agent treatments grouped into quintiles from low to high sensitivity and summary of respective moderated clinical interpretations; future versions of the report will indicate PDTO assay cutoff-based predictions of sensitivity and resistance. (C) Details of patient treatments, clinical outcomes, PDTO predictions, and matching status of predictions and responses (9 patients, 10 treatments). See also Figures S1 and S2 and Tables S4, S6, S7, and S8.

Figure 6

PDTO assay predictions for the combined AGITG FORECAST-1 and community mCRC patient cohorts (A) Pie charts detailing PDTO assay accuracy, sensitivity, specificity, PPV, and NPV for 27 patients with mCRC with 39 evaluable treatments. (B) Kaplan-Meier plot of progression-free survival divided into sensitive and resistant groups according to PDTO assay predictions. Statistical significance was attributed to values of p < 0.050 as determined by the log-rank test. (C) Comparison of drug sensitivity between PDTOs from chemo-naive patients (n = 56) and PDTOs from patients refractory to 5FU-based treatment (5FU, FOLFIRI, and FOLFOX; n = 18) for 5FU, FOLFIRI, FOLFOX, TAS-102, regorafenib, and erlotinib. Statistical significance was attributed to values of p < 0.050 as determined by Student’s t test. ∗p < 0.050. See also Figures S3 and S4.

Figure 7

PDTO assay miniaturization to reduce time from biopsy to drug screen result (A) PDTO assay sensitivity, specificity, PPV, NPV, and accuracy for decreasing drug titration window sizes from 5 to 2 single points for 27 patients with mCRC with 39 evaluable treatments from the combined AGITG FORECAST-1 and community mCRC patient cohorts. (B) Kaplan-Meier plot of progression-free survival divided into sensitive and resistant groups according to PDTO assay predictions for decreasing window sizes. Statistical significance was attributed to values of p < 0.050 as determined by the log rank test. ∗p < 0.050. (C) Growth curves of successfully assayed PDTOs from AGITG FORECAST-1 biopsy samples. Horizontal dashed lines indicate the number of cells required to test PDTOs for standard-of-care treatments for decreasing assay window sizes. Vertical dashed lines indicate time to PDTO assay commencement with reporting at 7 weeks. CRC liver metastases are shown in blue, and metastases from other sites are in gray. Percentages of biopsy samples estimated to be tested and reported within 7 weeks are shown for liver and other metastatic sites. See also Table S9.