Jorma J. de Ronde

REV7 counteracts DNA double-strand break resection and affects PARP inhibition

Error-free repair of DNA double-strand breaks (DSBs) is achieved by homologous recombination (HR), and BRCA1 is an important factor for this repair pathway. In the absence of BRCA1-mediated HR, the administration of PARP inhibitors induces synthetic lethality of tumour cells of patients with breast or ovarian cancers. Despite the benefit of this tailored therapy, drug resistance can occur by HR restoration. Genetic reversion of BRCA1-inactivating mutations can be the underlying mechanism of drug resistance, but this does not explain resistance in all cases. In particular, little is known about BRCA1-independent restoration of HR. Here we show that loss of REV7 (also known as MAD2L2) in mouse and human cell lines re-establishes CTIP-dependent end resection of DSBs in BRCA1-deficient cells, leading to HR restoration and PARP inhibitor resistance, which is reversed by ATM kinase inhibition. REV7 is recruited to DSBs in a manner dependent on the H2AX–MDC1–RNF8–RNF168–53BP1 chromatin pathway, and seems to block HR and promote end joining in addition to its regulatory role in DNA damage tolerance. Finally, we establish that REV7 blocks DSB resection to promote non-homologous end-joining during immunoglobulin class switch recombination. Our results reveal an unexpected crucial function of REV7 downstream of 53BP1 in coordinating pathological DSB repair pathway choices in BRCA1-deficient cells.

Concordance of clinical and molecular breast cancer subtyping in the context of preoperative chemotherapy response

ER, PR and HER2 status in breast cancer are important markers for the selection of drug therapy. By immunohistochemistry (IHC), three major breast cancer subtypes can be distinguished: Triple negative (TNIHC), HER2+IHC and LuminalIHC (ER+IHC/HER2−IHC). By using the intrinsic gene set defined by Hu et al. five molecular subtypes (BasalmRNA, HER2+mRNA, Luminal AmRNA, Luminal BmRNA and Normal-likemRNA) can be defined. We studied the concordance between analogous subtypes and their prediction of response to neoadjuvant chemotherapy. We classified 195 breast tumors by both IHC and mRNA expression analysis of patients who received neoadjuvant treatment at the Netherlands Cancer institute for Stage II–III breast cancer between 2000 and 2007. The pathological complete remission (pCR) rate was used to assess chemotherapy response. The IHC and molecular subtypes showed high concordance with the exception of the HER2+IHC group. 60% of the HER2+IHC tumors were not classified as HER2+mRNA. The HER2+IHC/Luminal A or BmRNA group had a low response rate to a trastuzumab-chemotherapy combination with a pCR rate of 8%, while the HER2+mRNA group had a pCR rate of 54%. The Luminal AmRNA and Luminal BmRNA groups showed similar degrees of response to chemotherapy. Neither the PR status nor the endocrine responsiveness index subdivided the ER+IHC tumors accurately into Luminal AmRNA and Luminal BmRNA groups. Molecular subtyping suggests the existence of a HER2+IHC/LuminalmRNA group that responds poorly to trastuzumab-based chemotherapy. For LuminalIHC and triple negativeIHC tumors, further subdivision into molecular subgroups does not offer a clear advantage in treatment selection.

Gene expression profiles from formalin fixed paraffin embedded breast cancer tissue are largely comparable to fresh frozen matched tissue.

Background and Methods Formalin Fixed Paraffin Embedded (FFPE) samples represent a valuable resource for cancer research. However, the discovery and development of new cancer biomarkers often requires fresh frozen (FF) samples. Recently, the Whole Genome (WG) DASL (cDNA-mediated Annealing, Selection, extension and Ligation) assay was specifically developed to profile FFPE tissue. However, a thorough comparison of data generated from FFPE RNA and Fresh Frozen (FF) RNA using this platform is lacking. To this end we profiled, in duplicate, 20 FFPE tissues and 20 matched FF tissues and evaluated the concordance of the DASL results from FFPE and matched FF material. Methodology and Principal Findings We show that after proper normalization, all FFPE and FF pairs exhibit a high level of similarity (Pearson correlation >0.7), significantly larger than the similarity between non-paired samples. Interestingly, the probes showing the highest correlation had a higher percentage G/C content and were enriched for cell cycle genes. Predictions of gene expression signatures developed on frozen material (Intrinsic subtype, Genomic Grade Index, 70 gene signature) showed a high level of concordance between FFPE and FF matched pairs. Interestingly, predictions based on a 60 gene DASL list (best match with the 70 gene signature) showed very high concordance with the MammaPrint® results. Conclusions and Significance We demonstrate that data generated from FFPE material with the DASL assay, if properly processed, are comparable to data extracted from the FF counterpart. Specifically, gene expression profiles for a known set of prognostic genes for a specific disease are highly comparable between two conditions. This opens up the possibility of using both FFPE and FF material in gene expressions analyses, leading to a vast increase in the potential resources available for cancer research.

Impact of Intertumoral Heterogeneity on Predicting Chemotherapy Response of BRCA1-Deficient Mammary Tumors

The lack of markers to predict chemotherapy responses in patients poses a major handicap in cancer treatment. We searched for gene expression patterns that correlate with docetaxel or cisplatin response in a mouse model for breast cancer associated with BRCA1 deficiency. Array-based expression profiling did not identify a single marker gene predicting docetaxel response, despite an increase in Abcb1 (P-glycoprotein) expression that was sufficient to explain resistance in several poor responders. Intertumoral heterogeneity explained the inability to identify a predictive gene expression signature for docetaxel. To address this problem, we used a novel algorithm designed to detect differential gene expression in a subgroup of the poor responders that could identify tumors with increased Abcb1 transcript levels. In contrast, standard analytical tools, such as significance analysis of microarrays, detected a marker only if it correlated with response in a substantial fraction of tumors. For example, low expression of the Xist gene correlated with cisplatin hypersensitivity in most tumors, and it also predicted long recurrence-free survival of HER2-negative, stage III breast cancer patients treated with intensive platinum-based chemotherapy. Our findings may prove useful for selecting patients with high-risk breast cancer who could benefit from platinum-based therapy.

The T7-Primer Is a Source of Experimental Bias and Introduces Variability between Microarray Platforms

Eberwine(-like) amplification of mRNA adds distinct 6–10 bp nucleotide stretches to the 5′ end of amplified RNA transcripts. Analysis of over six thousand microarrays reveals that probes containing motifs complementary to these stretches are associated with aberrantly high signals up to a hundred fold the signal observed in unaffected probes. This is not observed when total RNA is used as target source. Different T7 primer sequences are used in different laboratories and platforms and consequently different T7 primer bias is observed in different datasets. This will hamper efforts to compare data sets across platforms.

KC-SMARTR: An R package for detection of statistically significant aberrations in multi-experiment aCGH data

BackgroundMost approaches used to find recurrent or differential DNA Copy Number Alterations (CNA) in array Comparative Genomic Hybridization (aCGH) data from groups of tumour samples depend on the discretization of the aCGH data to gain, loss or no-change states. This causes loss of valuable biological information in tumour samples, which are frequently heterogeneous. We have previously developed an algorithm, KC-SMART, that bases its estimate of the magnitude of the CNA at a given genomic location on kernel convolution (Klijn et al., 2008). This accounts for the intensity of the probe signal, its local genomic environment and the signal distribution across multiple samples.ResultsHere we extend the approach to allow comparative analyses of two groups of samples and introduce the R implementation of these two approaches. The comparative module allows for a supervised analysis to be performed, to enable the identification of regions that are differentially aberrated between two user-defined classes.We analyzed data from a series of B- and T-cell lymphomas and were able to retrieve all positive control regions (VDJ regions) in addition to a number of new regions. A t-test employing segmented data, that we implemented, was also able to locate all the positive control regions and a number of new regions but these regions were highly fragmented.ConclusionsKC-SMARTR offers recurrent CNA and class specific CNA detection, at different genomic scales, in a single package without the need for additional segmentation. It is memory efficient and runs on a wide range of machines. Most importantly, it does not rely on data discretization and therefore maximally exploits the biological information in the aCGH data.The program is freely available from the Bioconductor website http://www.bioconductor.org/ under the terms of the GNU General Public License.

Platform comparisons for identification of breast cancers with a BRCA-like copy number profile

Previously, we employed bacterial artificial chromosome (BAC) array comparative genomic hybridization (aCGH) profiles from BRCA1 and -2 mutation carriers and sporadic tumours to construct classifiers that identify tumour samples most likely to harbour BRCA1 and -2 mutations, designated ‘BRCA1 and -2-like’ tumours, respectively. The classifiers are used in clinical genetics to evaluate unclassified variants, and patients for which no good quality germline DNA is available. Furthermore, we have shown that breast cancer patients with BRCA-like tumour aCGH profiles benefit substantially from platinum-based chemotherapy, potentially due to their inability to repair DNA double strand breaks (DSB), providing a further important clinical application for the classifiers. The BAC array technology has been replaced with oligonucleotide arrays. To continue clinical use of existing classifiers, we mapped oligonucleotide aCGH data to the BAC domain, such that the oligonucleotide profiles can be employed as in the BAC classifier. We demonstrate that segmented profiles derived from oligonucleotide aCGH show high correlation with BAC aCGH profiles. Furthermore, we trained a support vector machine score to objectify aCGH profile quality. Using the mapped oligonucleotide aCGH data, we show equivalence in classification of biologically relevant cases between BAC and oligonucleotide data. Furthermore, the predicted benefit of DSB inducing chemotherapy due to a homologous recombination defect is retained. We conclude that oligonucleotide aCGH data can be mapped to and used in the previously developed and validated BAC aCGH classifiers. Our findings suggest that it is possible to map copy number data from any other technology in a similar way.

Molecular subtyping of breast cancer: ready to use?

306 www.thelancet.com/oncology Vol 11 April 2010 Further, unlike previous reports, Friedrichs and colleagues show that, after 9 years’ follow-up there is a trend toward longer overall survival in patients receiving bone marrow rather than peripheral blood. So what do we advise our patients, and how do we give them confi dence that we know what we are doing? For instance, it seems that patients with CML obtain real benefi t from PBSC, but with second and third generation targeted therapies producing extraordinary disease-free survival benefi t, allografting is now a rarity, and one does not want to end up trying to use BMT for end-stage, older patients. We need an eff ective molecular screen at diagnosis to pick the right patients for transplantation. Reduced intensity chemotherapy (RIC) allografting, in which less toxic doses of drugs (or irradiation) are given, now seems the best way forward. In myeloma, it is hoped that RIC will fi nd a role among the expected plethora of approved targeted therapies. In the case of AML, it is a sad inditement of the lack of new treatment options available, that full myeloablative conditioning is still routinely used, usually after failed fi rst remission, in spite of no improvement in outcomes for three decades. We particularly need to optimise RIC treatment for AML, this relatively common haematological malignancy. The key might be the use of refi ned selected monoclonal antibodies to provide a window for the allogeneic eff ect to eradicate the malignant cells, without the attendant morbidity of GvHD and fragility of the graft. Independent research, such as the EBMT study described here, with surrogate short-term predictors of 10-year disease-free survival, are the way to deliver this progress.

Breast cancer subtype specific classifiers of response to neoadjuvant chemotherapy do not outperform classifiers trained on all subtypes.

Introduction Despite continuous efforts, not a single predictor of breast cancer chemotherapy resistance has made it into the clinic yet. However, it has become clear in recent years that breast cancer is a collection of molecularly distinct diseases. With ever increasing amounts of breast cancer data becoming available, we set out to study if gene expression based predictors of chemotherapy resistance that are specific for breast cancer subtypes can improve upon the performance of generic predictors. Methods We trained predictors of resistance that were specific for a subtype and generic predictors that were not specific for a particular subtype, i.e. trained on all subtypes simultaneously. Through a rigorous double-loop cross-validation we compared the performance of these two types of predictors on the different subtypes on a large set of tumors all profiled on the same expression platform (n = 394). We evaluated predictors based on either mRNA gene expression or clinical features. Results For HER2+, ER− breast cancer, subtype specific predictor based on clinical features outperformed the generic, non-specific predictor. This can be explained by the fact that the generic predictor included HER2 and ER status, features that are predictive over the whole set, but not within this subtype. In all other scenarios the generic predictors outperformed the subtype specific predictors or showed equal performance. Conclusions Since it depends on the specific context which type of predictor – subtype specific or generic- performed better, it is highly recommended to evaluate both specific and generic predictors when attempting to predict treatment response in breast cancer.

Abstract 4834: Microarray gene expression analysis on formalin-fixed, paraffin embedded material is feasible and the resulting profiles are comparable to profiles from fresh frozen matched material

Formalin Fixed Paraffin Embedded (FFPE) samples represent a valuable resource for cancer research and clinical setting. However, the discovery and development of new cancer biomarkers often requires fresh frozen (FF) samples. In this study, we profiled, in duplicate, 20 FFPE breast cancer tissues and 20 matched FF tissues and evaluated the concordance of the gene expression results from FFPE and matched FF material using the whole Genome (WG) DASL assay. We show that clinically relevant biomarkers developed on FF samples can be applied to FFPE samples. We show that after proper normalization, all FFPE and FF pairs exhibited a high level of similarity (Pearson correlation > 0.7), significantly larger than the similarity between non-paired samples. Interestingly, the probes showing the highest correlation had a higher G/C content percentage and were enriched for cell cycle genes. Predictions of gene expression signatures developed on frozen material (Intrinsic subtype, Genomic Grade Index, 70 gene prognosis signature) show a high level of concordance between FFPE and FF matched pairs. Predictions based on a 60 gene DASL list (best match with the 70 gene signature) show very high concordance with MammaPrint® results. We demonstrate that data generated from FFPE material with the DASL assay, if properly processed, are comparable to data extracted from the FF counterpart. This opens up the possibility of using both FFPE and FF material in gene expressions analyses, leading to a vast increase in the potential resources available for cancer research. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4834. doi:10.1158/1538-7445.AM2011-4834