Organoid Profiling Identifies Common Responders to Chemotherapy in Pancreatic Cancer

Abstract

Pancreatic cancer is the most lethal common solid malignancy. Systemic therapies are often ineffective, and predictive biomarkers to guide treatment are urgently needed. We generated a pancreatic cancer patient-derived organoid (PDO) library that recapitulates the mutational spectrum and transcriptional subtypes of primary pancreatic cancer. New driver oncogenes were nominated and transcriptomic analyses revealed unique clusters. PDOs exhibited heterogeneous responses to standard-of-care chemotherapeutics and investigational agents. In a case study manner, we found that PDO therapeutic profiles paralleled patient outcomes and that PDOs enabled longitudinal assessment of chemosensitivity and evaluation of synchronous metastases. We derived organoid-based gene expression signatures of chemosensitivity that predicted improved responses for many patients to chemotherapy in both the adjuvant and advanced disease settings. Finally, we nominated alternative treatment strategies for chemorefractory PDOs using targeted agent therapeutic profiling. We propose that combined molecular and therapeutic profiling of PDOs may predict clinical response and enable prospective therapeutic selection.Significance: New approaches to prioritize treatment strategies are urgently needed to improve survival and quality of life for patients with pancreatic cancer. Combined genomic, transcriptomic, and therapeutic profiling of PDOs can identify molecular and functional subtypes of pancreatic cancer, predict therapeutic responses, and facilitate precision medicine for patients with pancreatic cancer. Cancer Discov; 8(9); 1112-29. ©2018 AACR.See related commentary by Collisson, p. 1062This article is highlighted in the In This Issue feature, p. 1047.

Conflict of interest statement

Conflicts of Interest: The Authors declare no conflicts of interest.

©2018 American Association for Cancer Research.

Figures

Figure 1. Genomic landscape of pancreatic cancer PDO

A. Isolation efficiency rate of PDOs from total samples, biopsies (hF), and resected surgical specimens (hT). B. PDO morphology in brightfield microscopy. Scale bars are 1000 or 500 μm as indicated. C. Single nucleotide variants in the PDO library. Mutation frequency indicated in both cancer and normal organoids (cancer % (left) / normal % (right)). Only mutations reported in COSMIC were included. Patient staging and type of mutation are denoted by a color-coded key. FS = Frameshift, Del. = Deletion, Ins. = Insertion, IF = In Frame, NA = Not Available. D. Copy number alterations (−2.0 through −0.235 and 0.235 through 2.0 log2 copy number ratio color key) in the PDO library. The cancer stages of the patients are indicated.

Figure 2. Deep molecular clarity obtained from PDO genetic analyses

A. Purity, ploidy, concordance and percent of the primary tumor mutations found in the PDO cultures using whole genome SNVs of the PDO and matched primary tumor specimens following germline variant removal. Representative Venn diagrams are shown of PDO and Primary Tumor SNVs. B. CNA in representative matched primary tumor specimens and corresponding PDO. Two representative cases with differing degrees of primary tumor purity are shown. C. Circos plots demonstrating CNA (red and blue CNA inner circles) and gross chromosomal rearrangements (connecting lines) in representative, matched primary tumor and PDOs following germline variant removal.

Figure 3. Transcriptomic profiling of PDOs reveals distinct subtypes

A. Principal component analysis of organoids isolated from different cancer stages and normal healthy controls. B Clustering of PDO culture RNA-seq data reveals concordance with Classical and Basal-like subtypes. Patient staging and subtype are indicated. C. Clustering using Non-negative Matrix Factorization defines two distinct clusters of PDO cultures, C1 and C2. Patient staging and subtype are indicated. D. GSEA of genes differentially expressed genes between C1 and C2. Three Hallmark pathways are shown to be enriched in C1 compared to C2 (top panels), and three are enriched in C2 (lower panels, negative enrichment C1/C2).

Figure 4. Pharmacotyping of PDOs reveals heterogeneity of chemotherapy response

A–E: Dose-response curves and normalized AUC distribution for Gemcitabine (A), Paclitaxel (B), SN-38 (C), 5-FU (D), and Oxaliplatin (E) on PDO cultures (n = 63 – 66). The blue portion represents the 33% most sensitive samples, the red portion the 34% most resistant samples, and the middle portion intermediate drug responses.

Figure 5. Longitudinal, spatial and genetic influences on PDO response

A. AUC distribution of hM1A, E and F PDO longitudinal series. B. AUC distribution of hM19 A, B, C, and D PDOs from the same patient but different metastatic sites. C–E. AUC distribution and genotype correlation of Afatinib (C), Olaparib (D), and Everolimus (E) responders.

Figure 6. PDO-derived Gemcitabine sensitivity signature stratifies pancreatic cancer patients with improved response to adjuvant Gemcitabine

A. The Gemcitabine sensitivity prediction signature was used to cluster the PDO RNA-seq data. Additional data regarding the Pharmacotyping AUC response (log2 transformed z-score), C1/C2 subtype, Basal/Classical subtype, and stage are shown. B. The Gemcitabine sensitivity prediction signature was applied to RNA-seq data from patients who received single-agent Gemcitabine (ICGC-CA). Additional data regarding the Pharmacotyping AUC response (log2 transformed z-score), C1/C2 subtype, Basal/Classical subtype, and stage are shown. C. Kaplan-Meier analysis of PFS of Gemcitabine-sensitive and non-sensitive patients as identified in B. D. Kaplan-Meier analysis of PFS of Gemcitabine-sensitive and non-sensitive untreated patients. Log-rank (Mantel-Cox) test P value and log-rank Hazard Ratio are shown.

Figure 7. PDO-derived Oxaliplatin sensitivity signature stratifies advanced pancreatic cancer patients with improved response to FOLFIRINOX

A. The PDO-derived sensitivity signatures were applied to the RNA-seq data from 73 patients enrolled on the COMPASS trial that received either m-FOLFIRINOX or Gemcitabine with nab-Paclitaxel. B. A waterfall plot of the patients with RECIST criteria at 8 weeks post baseline that received FOLFIRINOX. Oxaliplatin signature significantly correlated with response (r = −0.396, P = 0.0078). Additional data regarding the mean chemotherapeutic signature scores, C1/C2 subtype, and Basal/Classical subtype are shown. C. The overall survival of patients receiving m-FOLFIRINOX segregated by their enrichment of the Oxaliplatin signature. Log-rank (Mantel-Cox) test P value.