Cancer organoid-based diagnosis reactivity prediction (CODRP) index-based anticancer drug sensitivity test in ALK-rearrangement positive non-small cell lung cancer (NSCLC)

Abstract

Background: Recently, cancer organoid-based drug sensitivity tests have been studied to predict patient responses to anticancer drugs. The area under curve (AUC) or IC₅₀ value of the dose-response curve (DRC) is used to differentiate between sensitive and resistant patient's groups. This study proposes a multi-parameter analysis method (cancer organoid-based diagnosis reactivity prediction, CODRP) that considers the cancer stage and cancer cell growth rate, which represent the severity of cancer patients, in the sensitivity test.

Methods: On the CODRP platform, patient-derived organoids (PDOs) that recapitulate patients with lung cancer were implemented by applying a mechanical dissociation method capable of high yields and proliferation rates. A disposable nozzle-type cell spotter with efficient high-throughput screening (HTS) has also been developed to dispense a very small number of cells due to limited patient cells. A drug sensitivity test was performed using PDO from the patient tissue and the primary cancer characteristics of PDOs were confirmed by pathological comparision with tissue slides.

Results: The conventional index of drug sensitivity is the AUC of the DRC. In this study, the CODRP index for drug sensitivity test was proposed through multi-parameter analyses considering cancer cell proliferation rate, the cancer diagnosis stage, and AUC values. We tested PDOs from eight patients with lung cancer to verify the CODRP index. According to the anaplastic lymphoma kinase (ALK) rearrangement status, the conventional AUC index for the three ALK-targeted drugs (crizotinib, alectinib, and brigatinib) did not classify into sensitive and resistant groups. The proposed CODRP index-based drug sensitivity test classified ALK-targeted drug responses according to ALK rearrangement status and was verified to be consistent with the clinical drug treatment response.

Conclusions: Therefore, the PDO-based HTS and CODRP index drug sensitivity tests described in this paper may be useful for predicting and analyzing promising anticancer drug efficacy for patients with lung cancer and can be applied to a precision medicine platform.

Keywords: 3D cell culture; Cancer Organoid-based diagnosis reactivity prediction (CODRP) platform; High-throughput screening (HTS); Non-small cell Lung cancer (NSCLC); Patient-derived Organoid (PDO).

Fig. 1

A Schematic view of high-throughput screening (HTS) platform using patient-derived lung cancer organoids (PDOs). (A) Photographs of the proposed cell spotter (ASFA™ Spotter DN) with disposable nozzle and sample dispensing unit. (B) Schematic diagram of module parts for dispensing of ASFA™ Spotter DN. (C) 384-pillar/well plate photograph and 3D cell culture illustration. The lung cancer PDO is mixed with an ECM and is 3D cultured and drug screen on the surface of a 384-pillar plate. (D) Representative bright field (BF) and confocal images of PDOs from day 1 to 6. The lung cancer PDO grows by forming colonies

Fig. 2

Experimental procedures of HTS using lung cancer PDOs (A) Tumor tissue and malignant pleural effusion (MPE) samples derived from patients with lung cancer. (B) Preparation step for isolating cancer cells from patient-derived lung cancer tumor tissues and MPEs. (C) The pretreated patient-derived lung cancer samples filtered using a 40 μm strainer. (D) Patient-derived lung cancer cells mixed with the extracellular matrix (ECM) of hydrogel components. (E) Cancer cell and Matrigel mixtures dispensed on the surface of 384-pillar plate by ASFA™ Spotter DN. (F) The drug exposed by combining the 384-pillar plate, in which the cells have been dispensed, with the 384-well plates containing the fresh culture medium and drugs. (G) 3D live cell staining by replacing the 384-well plate with one filled with live cell-staining dye; images were taken after live cell staining. (H) Quantifying the percentage of live cells by evaluating scanned images and calculating the cell growth rate. (I) Cell viability measurement through CellTiter-Glo reagent treatment and luminescence value readout; drug sensitivity analysis based on AUC index from dose-response curve (DRC). (J) CODRP index predicted drug efficacy by considering the cell growth rate and cancer diagnosed stage in the AUC index. (K) Immunohistochemistry (IHC) analysis using patient-derived lung cancer tissues and cultured lung cancer organoid samples

Fig. 3

H&E and immunohistochemical staining images of patient-derived lung cancer tumor tissues and lung cancer PDOs. Results of IHC analysis using cancer tissues (A) and organoids (B) derived from the same patient with lung cancer; in lung cancer organoids, the expression patterns of lung cancer characteristic markers (TTF-1, P63, CK7 and Napsin A) were well maintained, the same as in patient lung cancer tissues

Fig. 4

Immunohistochemical staining and ALK-Fluorescence In Situ Hybridization Evaluation (FISH) scanning images of patient-derived lung cancer tumor tissues and lung cancer PDOs. (A - F) IHC analysis of tumor tissue and PDO from ALK-positive patients (LC_01T, 02T, 03PE, 04T, 08PE and 09PE) verified similar ALK-positive expression patterns, respectively. Fluorescence In Situ Hybridization Evaluation (FISH) evaluation of ALK-EML4 break apart/split signal is the gold standard to investigate ALK rearrangement status. Scanned images showing fused red/green signals representing normal copies of the ALK (yellow arrows) and single red and green signals (red and green arrows) indicating that chromosomal break occurred between the 3 and the 5 contigs. ALK-EML4 break apart was verified in patient tissue (analysis was not performed in LC_04T patient), and ALK-EML4 break apart was also verified in PDO derived from ALK-positive patients (analysis was not performed in LC_01T, 08PE and 09PE)

Fig. 5

Lung cancer PDO-based high-throughput screening (HTS) and cancer organoid-based diagnosis reactivity prediction (CODRP) index analysis to crizotinib. DRC-based drug response analysis using ALK-positive (A) and ALK-negative (B) patient-derived organoids to crizotinib. (C) Comparative analysis of drug response based on the AUC and CODRP indices for crizotinib; The CODRP index is calculated as a Z-score value based on different mean and standard deviation (SD) values of crizotinib for individual PDO, considering the AUC index, cancer stage, and PDO growth rate through multiple linear regression (Mean, SD, and sample number of Crizotinib: 0.71, 0.26, 17). In the conventional AUC index-based analysis for the crizotinib, the sensitivity was 40% and the specificity was 58.3%, but as a result of the CODRP index-based drug sensitivity analysis, the analysis performance improved to 100% sensitivity and 66.6% specificity

Fig. 6

Lung cancer PDO-based high-throughput screening (HTS) and cancer organoid-based diagnosis reactivity prediction (CODRP) index analysis to alectinib. DRC-based drug response analysis using ALK-positive (A) and ALK-negative (B) patient-derived organoids to alectinib. (C) Comparative analysis of drug response based on the AUC and CODRP indices for alectinib; The CODRP index is calculated as a Z-score value based on different mean and standard deviation (SD) values of alectinib for individual PDO, considering the AUC index, cancer stage, and PDO growth rate through multiple linear regression (Mean, SD, and sample number of alectinib: 0.8, 0.36, 15). In the conventional AUC index-based analysis for the alectinib, the sensitivity was 100% and the specificity was 58.3%, but as a result of the CODRP index-based drug sensitivity analysis, the analysis performance improved to 100% sensitivity and 83.3% specificity

Fig. 7

Lung cancer PDO-based high-throughput screening (HTS) and cancer organoid-based diagnosis reactivity prediction (CODRP) index analysis to brigatinib. DRC-based drug response analysis using ALK-positive (A) and ALK-negative (B) patient-derived organoids to brigatinib. (C) Comparative analysis of drug response based on the AUC and CODRP indices for brigatinib; The CODRP index is calculated as a Z-score value based on different mean and standard deviation (SD) values of brigatinib for individual PDO, considering the AUC index, cancer stage, and PDO growth rate through multiple linear regression (Mean, SD, and sample number of brigatinib: 0.71, 0.32, 17). In the conventional AUC index-based analysis for the brigatinib, the sensitivity was 80% and the specificity was 58.3%, but as a result of the CODRP index-based drug sensitivity analysis, the analysis performance improved to 100% sensitivity and 83.3% specificity. The cut-off value for classifying drug responses into sensitive and resistant groups to three ALK-targeted drugs is 0

Fig. 8

Clinical relevance of lung cancer PDO-based HTS analysis and CODRP index analysis. (A) The LC_01T patient was prescribed alectinib after the tumor recurred and showed a partial response (PR), and as a result of drug sensitivity test using LC_01T organoid derived from tumor tissue, showed a sensitive response to alectinib. (B) After being diagnosed with lung cancer accompanied by MPE, the LC_03PE patient was prescribed alectinib and showed PR; a drug sensitivity test using LC_03PE organoid derived from MPE showed a sensitive response to alectinib. (C) LC_02T patient was prescribed brigatinib after tumor tissue removal surgery and maintained stable disease (SD) without recurrence; drug sensitivity tests using LC_02T organoid derived from tumor tissues showed a sensitive response to brigatinib. (D) After being diagnosed with lung cancer accompanied by MPE and brain metastases, the LC_09PE patient was prescribed brigatinib and showed PR; a drug sensitivity test using LC_03PE organoid derived from MPE showed a sensitive response to brigatinib

Fig. 9

Evaluation of CODRP index-based drug sensitivity prediction platform by applying ALK-targeted drug-resistant patient sample. (A) Drug sensitivity test of three ALK-targeted drugs using LC_08PE organoid derived from patients with positive ALK. (B) The results of the drug sensitivity test based on the conventional AUC index predicted sensitive drug response to alectinib; in the proposed CODRP index-based drug sensitivity test, all three ALK-targeted drugs were predicted as a resistance group. (C) LC_08PE patient was prescribed alectinib after palliative surgery and showed resistance to alectinib; the drug sensitivity test using LC_08PE organoid derived from the MPE of LC_08PE patient showed resistance to alectinib

Fig. 10

Summary of RECIST and CODRP index-based drug sensitivity test results. (A) Comparison of CODRP index-based drug sensitivity test and patient’s RECIST-based drug treatment response assessment results. (B) LC_01T, 02T, 03PE and 09PE patients showed effects on the prescribed drugs (alectinib and brigatinib), and sensitive drug responses were also analyzed in organoid models; LC_08PE patient had no therapeutic effect on the prescribed drug (alectinib) and showed drug resistance in an organoid model