A tumor sorting protocol that enables enrichment of pancreatic adenocarcinoma cells and facilitation of genetic analyses

Systematic Generation of Patient-Derived Tumor Models in Pancreatic Cancer

In highly aggressive malignancies like pancreatic cancer (PC), patient-derived tumor models can serve as disease-relevant models to understand disease-related biology as well as to guide clinical decision-making. In this study, we describe a two-step protocol allowing systematic establishment of patient-derived primary cultures from PC patient tumors. Initial xenotransplantation of surgically resected patient tumors (n = 134) into immunodeficient mice allows for efficient in vivo expansion of vital tumor cells and successful tumor expansion in 38% of patient tumors (51/134). Expansion xenografts closely recapitulate the histoarchitecture of their matching patients' primary tumors. Digestion of xenograft tumors and subsequent in vitro cultivation resulted in the successful generation of semi-adherent PC cultures of pure epithelial cell origin in 43.1% of the cases. The established primary cultures include diverse pathological types of PC: Pancreatic ductal adenocarcinoma (86.3%, 19/22), adenosquamous carcinoma (9.1%, 2/22) and ductal adenocarcinoma with oncocytic IPMN (4.5%, 1/22). We here provide a protocol to establish quality-controlled PC patient-derived primary cell cultures from heterogeneous PC patient tumors. In vitro preclinical models provide the basis for the identification and preclinical assessment of novel therapeutic opportunities targeting pancreatic cancer.

A Novel Multiplex Droplet Digital PCR Assay to Identify and Quantify KRAS Mutations in Clinical Specimens

Recurrent activating point mutations in KRAS are critical drivers in pancreatic cancer and have been attributed to resistance to anti-epidermal growth factor receptor therapy in colorectal cancer. Although KRAS genotyping provides limited clinical utility in the diagnosis and management of pancreatic cancer patients at present, inferences about the fractional abundance of KRAS mutations may inform on tumor purity in traditionally challenging clinical specimens and their potential use in precision medicine. KRAS genetic testing has indeed become an essential tool to guide treatment decisions in colorectal cancer, but an unmet need for methods standardization exists. Here, we present a unique droplet digital PCR method that enables the simultaneous detection and quantification of KRAS exon 2, 3, and 4 point mutations and copy number alterations. We have validated 13 mutations (G12S, G12R, G12D, G12A, G12V, G12C, G13D, G60V, Q61H, Q61L, A146V, A146T, and A146P) and focal KRAS amplifications by conducting this assay in a cohort of 100 DNA samples extracted from fresh frozen tumor biopsies, formaldehyde-fixed, paraffin-embedded tissue, and liquid biopsy specimens. Despite its modest lower limit of detection (approximately 1%), this assay will be a rapid cost-effective means to infer the purity of biopsy specimens carrying KRAS mutations and can be used in noninvasive serial monitoring of circulating tumor DNA to evaluate clinical response and/or detect early signs of relapse.

SerpinB2 regulates stromal remodelling and local invasion in pancreatic cancer

Pancreatic cancer has a devastating prognosis, with an overall 5-year survival rate of ~8%, restricted treatment options and characteristic molecular heterogeneity. SerpinB2 expression, particularly in the stromal compartment, is associated with reduced metastasis and prolonged survival in pancreatic ductal adenocarcinoma (PDAC) and our genomic analysis revealed that SERPINB2 is frequently deleted in PDAC. We show that SerpinB2 is required by stromal cells for normal collagen remodelling in vitro, regulating fibroblast interaction and engagement with collagen in the contracting matrix. In a pancreatic cancer allograft model, co-injection of PDAC cancer cells and SerpinB2-/- mouse embryonic fibroblasts (MEFs) resulted in increased tumour growth, aberrant remodelling of the extracellular matrix (ECM) and increased local invasion from the primary tumour. These tumours also displayed elevated proteolytic activity of the primary biochemical target of SerpinB2-urokinase plasminogen activator (uPA). In a large cohort of patients with resected PDAC, we show that increasing uPA mRNA expression was significantly associated with poorer survival following pancreatectomy. This study establishes a novel role for SerpinB2 in the stromal compartment in PDAC invasion through regulation of stromal remodelling and highlights the SerpinB2/uPA axis for further investigation as a potential therapeutic target in pancreatic cancer.

PhyloOncology: Understanding cancer through phylogenetic analysis

Despite decades of research and an enormity of resultant data, cancer remains a significant public health problem. New tools and fresh perspectives are needed to obtain fundamental insights, to develop better prognostic and predictive tools, and to identify improved therapeutic interventions. With increasingly common genome-scale data, one suite of algorithms and concepts with potential to shed light on cancer biology is phylogenetics, a scientific discipline used in diverse fields. From grouping subsets of cancer samples to tracing subclonal evolution during cancer progression and metastasis, the use of phylogenetics is a powerful systems biology approach. Well-developed phylogenetic applications provide fast, robust approaches to analyze high-dimensional, heterogeneous cancer data sets. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

Precise inference of copy number alterations in tumor samples from SNP arrays

MOTIVATION

The accurate detection of copy number alterations (CNAs) in human genomes is important for understanding susceptibility to cancer and mechanisms of tumor progression. CNA detection in tumors from single nucleotide polymorphism (SNP) genotyping arrays is a challenging problem due to phenomena such as aneuploidy, stromal contamination, genomic waves and intra-tumor heterogeneity, issues that leading methods do not optimally address.

RESULTS

Here we introduce methods and software (PennCNV-tumor) for fast and accurate CNA detection using signal intensity data from SNP genotyping arrays. We estimate stromal contamination by applying a maximum likelihood approach over multiple discrete genomic intervals. By conditioning on signal intensity across the genome, our method accounts for both aneuploidy and genomic waves. Finally, our method uses a hidden Markov model to integrate multiple sources of information, including total and allele-specific signal intensity at each SNP, as well as physical maps to make posterior inferences of CNAs. Using real data from cancer cell-lines and patient tumors, we demonstrate substantial improvements in accuracy and computational efficiency compared with existing methods.

Cell sorting in cancer research--diminishing degree of cell heterogeneity

Increasing evidence of intratumor heterogeneity and its augmentation due to selective pressure of microenvironment and recent achievements in cancer therapeutics lead to the need to investigate and track the tumor subclonal structure. Cell sorting of heterogeneous subpopulations of tumor and tumor-associated cells has been a long established strategy in cancer research. Advancement in lasers, computer technology and optics has led to a new generation of flow cytometers and cell sorters capable of high-speed processing of single cell suspensions. Over the last several years cell sorting was used in combination with molecular biological methods, imaging and proteomics to characterize primary and metastatic cancer cell populations, minimal residual disease and single tumor cells. It was the principal method for identification and characterization of cancer stem cells. Analysis of single cancer cells may improve early detection of tumors, monitoring of circulating tumor cells, evaluation of intratumor heterogeneity and chemotherapeutic treatments. The aim of this review is to provide an overview of major cell sorting applications and approaches with new prospective developments such as microfluidics and microchip technologies.

Unlike pancreatic cancer cells pancreatic cancer associated fibroblasts display minimal gene induction after 5-aza-2'-deoxycytidine

Purpose

Cancer associated stromal fibroblasts (CAFs) undergo transcriptional and phenotypic changes that contribute to tumor progression, but the mechanisms responsible for these changes are not well understood. Aberrant DNA methylation is an important cause of transcriptional alterations in cancer cells but it is not known how important DNA methylation alterations are to CAF behavior.

Experimental Design

We used Affymetrix exon arrays to compare genes induced by the DNA methylation inhibitor 5-aza-dC in cultured pancreatic cancer associated fibroblasts, pancreatic control fibroblasts and pancreatic cancer cell lines.

Results

We found that pancreatic CAFs and control pancreatic fibroblasts were less responsive to 5-aza-dC-mediated gene reactivation than pancreatic cancer cells (mean+/−SD of genes induced ≥5-fold was 9±10 genes in 10 pancreatic CAF cultures, 17±14 genes in 3 control pancreatic fibroblast cultures, and 134±85 genes in 4 pancreatic cancer cell lines). We examined differentially expressed genes between CAFs and control fibroblasts for candidate methylated genes and identified the disintegrin and metalloprotease, ADAM12 as hypomethylated and overexpressed in pancreatic CAF lines and overexpressed in fibroblasts adjacent to primary pancreatic adenocarcinomas.

Conclusions

Compared to pancreatic cancer cells, few genes are reactivated by DNMT1 inhibition in pancreatic CAFs suggesting these cells do not harbor many functionally important alterations in DNA methylation. CAFs may also not be very responsive to therapeutic targeting with DNA methylation inhibitors.

Advancing a clinically relevant perspective of the clonal nature of cancer

Cancers frequently arise as a result of an acquired genomic instability and the subsequent clonal evolution of neoplastic cells with variable patterns of genetic aberrations. Thus, the presence and behaviors of distinct clonal populations in each patient's tumor may underlie multiple clinical phenotypes in cancers. We applied DNA content-based flow sorting to identify and isolate the nuclei of clonal populations from tumor biopsies, which was coupled with array CGH and targeted resequencing. The results produced high-definition genomic profiles of clonal populations from 40 pancreatic adenocarcinomas and a set of prostate adenocarcinomas, including serial biopsies from a patient who progressed to androgen-independent metastatic disease. The genomes of clonal populations were found to have patient-specific aberrations of clinical relevance. Furthermore, we identified genomic aberrations specific to therapeutically responsive and resistant clones arising during the evolution of androgen-independent metastatic prostate adenocarcinoma. We also distinguished divergent clonal populations within single biopsies and mapped aberrations in multiple aneuploid populations arising in primary and metastatic pancreatic adenocarcinoma. We propose that our high-definition analyses of the genomes of distinct clonal populations of cancer cells in patients in vivo can help guide diagnoses and tailor approaches to personalized treatment.

Quantifying the relative amount of mouse and human DNA in cancer xenografts using species-specific variation in gene length

Human cancer cell lines and xenografts are valuable samples for whole-genome sequencing of human cancer. Tumors can be maintained by serial xenografting in athymic (nude) or severe combined immunodeficient (SCID) mice. In the current study, we developed a molecular assay to quantify the relative contributions of human and mouse in mixed DNA samples. The assay was designed based on deletion/insertion variation between human and mouse genomes. The percentage of mouse DNA was calculated according to the relative peak heights of PCR products analyzed by capillary electrophoresis. Three markers from chromosomes 9 and 10 accurately predicted the mouse genome ratio and were combined into a multiplex PCR reaction. We used the assay to quantify the relative DNA amounts of 93 mouse xenografts used for a recently reported integrated genomic analysis of human pancreatic cancer. Of the 93 xenografts, the mean percentage of contaminating mouse DNA was 47%, ranging from 17% to 73%, with 43% of samples having >50% mouse DNA. We then comprehensively compared the human and mouse genomes to identify 370 additional candidate gene loci demonstrating human-mouse length variation. With increasing whole-genome sequencing of human cancers, this assay should be useful to monitor strategies to enrich human cancer cells from mixed human-mouse cell xenografts. Finally, we discuss how contaminating mouse DNA affects next-generation DNA sequencing.