Julian R. de Ruiter

Replication fork stability confers chemoresistance in BRCA-deficient cells

Cells deficient in the Brca1 and Brca2 genes have reduced capacity to repair DNA double-strand breaks by homologous recombination and consequently are hypersensitive to DNA-damaging agents, including cisplatin and poly(ADP-ribose) polymerase (PARP) inhibitors. Here we show that loss of the MLL3/4 complex protein, PTIP, protects Brca1/2-deficient cells from DNA damage and rescues the lethality of Brca2-deficient embryonic stem cells. However, PTIP deficiency does not restore homologous recombination activity at double-strand breaks. Instead, its absence inhibits the recruitment of the MRE11 nuclease to stalled replication forks, which in turn protects nascent DNA strands from extensive degradation. More generally, acquisition of PARP inhibitors and cisplatin resistance is associated with replication fork protection in Brca2-deficient tumour cells that do not develop Brca2 reversion mutations. Disruption of multiple proteins, including PARP1 and CHD4, leads to the same end point of replication fork protection, highlighting the complexities by which tumour cells evade chemotherapeutic interventions and acquire drug resistance.

CopywriteR: DNA copy number detection from off-target sequence data

Current methods for detection of copy number variants (CNV) and aberrations (CNA) from targeted sequencing data are based on the depth of coverage of captured exons. Accurate CNA determination is complicated by uneven genomic distribution and non-uniform capture efficiency of targeted exons. Here we present CopywriteR, which eludes these problems by exploiting ‘off-target’ sequence reads. CopywriteR allows for extracting uniformly distributed copy number information, can be used without reference, and can be applied to sequencing data obtained from various techniques including chromatin immunoprecipitation and target enrichment on small gene panels. CopywriteR outperforms existing methods and constitutes a widely applicable alternative to available tools.

Mechanisms of Therapy Resistance in Patient-Derived Xenograft Models of BRCA1-Deficient Breast Cancer.

BACKGROUND Although BRCA1-deficient tumors are extremely sensitive to DNA-damaging drugs and poly(ADP-ribose) polymerase (PARP) inhibitors, recurrences do occur and, consequently, resistance to therapy remains a serious clinical problem. To study the underlying mechanisms, we induced therapy resistance in patient-derived xenograft (PDX) models of BRCA1-mutated and BRCA1-methylated triple-negative breast cancer. METHODS A cohort of 75 mice carrying BRCA1-deficient breast PDX tumors was treated with cisplatin, melphalan, nimustine, or olaparib, and treatment sensitivity was determined. In tumors that acquired therapy resistance, BRCA1 expression was investigated using quantitative real-time polymerase chain reaction and immunoblotting. Next-generation sequencing, methylation-specific multiplex ligation-dependent probe amplification (MLPA) and Target Locus Amplification (TLA)-based sequencing were used to determine mechanisms of BRCA1 re-expression in therapy-resistant tumors. RESULTS BRCA1 protein was not detected in therapy-sensitive tumors but was found in 31 out of 42 resistant cases. Apart from previously described mechanisms involving BRCA1-intragenic deletions and loss of BRCA1 promoter hypermethylation, a novel resistance mechanism was identified in four out of seven BRCA1-methylated PDX tumors that re-expressed BRCA1 but retained BRCA1 promoter hypermethylation. In these tumors, we found de novo gene fusions that placed BRCA1 under the transcriptional control of a heterologous promoter, resulting in re-expression of BRCA1 and acquisition of therapy resistance. CONCLUSIONS In addition to previously described clinically relevant resistance mechanisms in BRCA1-deficient tumors, we describe a novel resistance mechanism in BRCA1-methylated PDX tumors involving de novo rearrangements at the BRCA1 locus, demonstrating that BRCA1-methylated breast cancers may acquire therapy resistance via both epigenetic and genetic mechanisms.

Insertional mutagenesis identifies drivers of a novel oncogenic pathway in invasive lobular breast carcinoma

Invasive lobular carcinoma (ILC) is the second most common breast cancer subtype and accounts for 8–14% of all cases. Although the majority of human ILCs are characterized by the functional loss of E-cadherin (encoded by CDH1), inactivation of Cdh1 does not predispose mice to develop mammary tumors, implying that mutations in additional genes are required for ILC formation in mice. To identify these genes, we performed an insertional mutagenesis screen using the Sleeping Beauty transposon system in mice with mammary-specific inactivation of Cdh1. These mice developed multiple independent mammary tumors of which the majority resembled human ILC in terms of morphology and gene expression. Recurrent and mutually exclusive transposon insertions were identified in Myh9, Ppp1r12a, Ppp1r12b and Trp53bp2, whose products have been implicated in the regulation of the actin cytoskeleton. Notably, MYH9, PPP1R12B and TP53BP2 were also frequently aberrated in human ILC, highlighting these genes as drivers of a novel oncogenic pathway underlying ILC development.

PTEN Loss in E-Cadherin-Deficient Mouse Mammary Epithelial Cells Rescues Apoptosis and Results in Development of Classical Invasive Lobular Carcinoma

Summary Invasive lobular carcinoma (ILC) is an aggressive breast cancer subtype with poor response to chemotherapy. Besides loss of E-cadherin, a hallmark of ILC, genetic inactivation of PTEN is frequently observed in patients. Through concomitant Cre-mediated inactivation of E-cadherin and PTEN in mammary epithelium, we generated a mouse model of classical ILC (CLC), the main histological ILC subtype. While loss of E-cadherin induced cell dissemination and apoptosis, additional PTEN inactivation promoted cell survival and rapid formation of invasive mammary tumors that recapitulate the histological and molecular features, estrogen receptor (ER) status, growth kinetics, metastatic behavior, and tumor microenvironment of human CLC. Combined inactivation of E-cadherin and PTEN is sufficient to cause CLC development. These CLCs showed significant tumor regression upon BEZ235-mediated inhibition of PI3K signaling. In summary, this mouse model provides important insights into CLC development and suggests inhibition of phosphatidylinositol 3-kinase (PI3K) signaling as a potential therapeutic strategy for targeting CLC.

BRCA-deficient mouse mammary tumor organoids to study cancer-drug resistance

Poly(ADP-ribose) polymerase inhibition (PARPi) is a promising new therapeutic approach for the treatment of cancers that show homologous recombination deficiency (HRD). Despite the success of PARPi in targeting HRD in tumors that lack the tumor suppressor function of BRCA1 or BRCA2, drug resistance poses a major obstacle. We developed three-dimensional cancer organoids derived from genetically engineered mouse models (GEMMs) for BRCA1- and BRCA2-deficient cancers. Unlike conventional cell lines or mammospheres, organoid cultures can be efficiently derived and rapidly expanded in vitro. Orthotopically transplanted organoids give rise to mammary tumors that recapitulate the epithelial morphology and preserve the drug response of the original tumor. Notably, GEMM-tumor-derived organoids can be easily genetically modified, making them a powerful tool for genetic studies of tumor biology and drug resistance.

Selective Loss of PARG Restores PARylation and Counteracts PARP Inhibitor-Mediated Synthetic Lethality

Inhibitors of poly(ADP-ribose) (PAR) polymerase (PARPi) have recently entered the clinic for the treatment of homologous recombination (HR)-deficient cancers. Despite the success of this approach, drug resistance is a clinical hurdle, and we poorly understand how cancer cells escape the deadly effects of PARPi without restoring the HR pathway. By combining genetic screens with multi-omics analysis of matched PARPi-sensitive and -resistant Brca2-mutated mouse mammary tumors, we identified loss of PAR glycohydrolase (PARG) as a major resistance mechanism. We also found the presence of PARG-negative clones in a subset of human serous ovarian and triple-negative breast cancers. PARG depletion restores PAR formation and partially rescues PARP1 signaling. Importantly, PARG inactivation exposes vulnerabilities that can be exploited therapeutically.

Identifying transposon insertions and their effects from RNA-sequencing data.

Abstract Insertional mutagenesis using engineered transposons is a potent forward genetic screening technique used to identify cancer genes in mouse model systems. In the analysis of these screens, transposon insertion sites are typically identified by targeted DNA-sequencing and subsequently assigned to predicted target genes using heuristics. As such, these approaches provide no direct evidence that insertions actually affect their predicted targets or how transcripts of these genes are affected. To address this, we developed IM-Fusion, an approach that identifies insertion sites from gene-transposon fusions in standard single- and paired-end RNA-sequencing data. We demonstrate IM-Fusion on two separate transposon screens of 123 mammary tumors and 20 B-cell acute lymphoblastic leukemias, respectively. We show that IM-Fusion accurately identifies transposon insertions and their true target genes. Furthermore, by combining the identified insertion sites with expression quantification, we show that we can determine the effect of a transposon insertion on its target gene(s) and prioritize insertions that have a significant effect on expression. We expect that IM-Fusion will significantly enhance the accuracy of cancer gene discovery in forward genetic screens and provide initial insight into the biological effects of insertions on candidate cancer genes.

Identifying biomarkers of anti-cancer drug synergy using multi-task learning

Combining anti-cancer drugs has the potential to increase treatment efficacy. Because patient responses to drug combinations are highly variable, predictive biomarkers of synergy are required to identify which patients are likely to benefit from a drug combination. To aid biomarker identification, the DREAM challenge consortium has recently released data from a screen containing 85 cell lines and 167 drug combinations. The main challenge of these data is the low sample size: per drug combination, a median of 14 cell lines have been screened. We found that widely used methods in single drug response prediction, such as Elastic Net regression per drug, are not predictive in this setting. Instead, we propose to use multi-task learning: training a single model simultaneously on all drug combinations, which we show results in increased predictive performance. In contrast to other multi-task learning approaches, our approach allows for the identification of biomarkers, by using a modified random forest variable importance score, which we illustrate using artificial data and the DREAM challenge data. Notably, we find that mutations in MYO15A are associated with synergy between ALK / IGFR dual inhibitors and PI3K pathway inhibitors in triple-negative breast cancer. Author summary Combining drugs is a promising strategy for cancer treatment. However, it is often not known which patients will benefit from a particular drug combination. To identify patients that are likely to benefit, we need to identify biomarkers, such as mutations in the tumor’s DNA, that are associated with favorable response to the drug combination. In this work, we identified such biomarkers using the drug combination data released by the DREAM challenge consortium, which contain 85 tumor cell lines and 167 drug combinations. The main challenge of these data is the extremely low sample size: a median of 14 cell lines have been screened per drug combination. We found that traditional methods to identify biomarkers for monotherapy response, which analyze each drug separately, are not suitable in this low sample size setting. Instead, we used a technique called multi-task learning to jointly analyze all drug combinations in a single statistical model. In contrast to existing multi-task learning algorithms, which are black-box methods, our method allows for the identification of biomarkers. Notably, we find that, in a subset of breast cancer cell lines, MYO15A mutations associate with response to the combination of ALK / IGFR dual inhibitors and PI3K pathway inhibitors.

Transcriptomics and Transposon Mutagenesis Identify Multiple Mechanisms of Resistance to the FGFR Inhibitor AZD4547

In human cancers, FGFR signaling is frequently hyperactivated by deregulation of FGF ligands or by activating mutations in the FGFR receptors such as gene amplifications, point mutations, and gene fusions. As such, FGFR inhibitors are considered an attractive therapeutic strategy for patients with mutations in FGFR family members. We previously identified Fgfr2 as a key driver of invasive lobular carcinoma (ILC) in an in vivo insertional mutagenesis screen using the Sleeping Beauty transposon system. Here we explore whether these FGFR-driven ILCs are sensitive to the FGFR inhibitor AZD4547 and use transposon mutagenesis in these tumors to identify potential mechanisms of resistance to therapy. Combined with RNA sequencing-based analyses of AZD4547-resistant tumors, our in vivo approach identified several known and novel potential resistance mechanisms to FGFR inhibition, most of which converged on reactivation of the canonical MAPK-ERK signaling cascade. Observed resistance mechanisms included mutations in the tyrosine kinase domain of FGFR2, overexpression of MET, inactivation of RASA1, and activation of the drug-efflux transporter ABCG2. ABCG2 and RASA1 were identified only from de novo transposon insertions acquired during AZD4547 treatment, demonstrating that insertional mutagenesis in mice is an effective tool for identifying potential mechanisms of resistance to targeted cancer therapies.Significance: These findings demonstrate that a combined approach of transcriptomics and insertional mutagenesis in vivo is an effective method for identifying potential targets to overcome resistance to therapy in the clinic. Cancer Res; 78(19); 5668-79. ©2018 AACR.