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

Mouse Ovarian Cancer Models Recapitulate the Human Tumor Microenvironment and Patient Response to Treatment

Although there are many prospective targets in the tumor microenvironment (TME) of high-grade serous ovarian cancer (HGSOC), pre-clinical testing is challenging, especially as there is limited information on the murine TME. Here, we characterize the TME of six orthotopic, transplantable syngeneic murine HGSOC lines established from genetic models and compare these to patient biopsies. We identify significant correlations between the transcriptome, host cell infiltrates, matrisome, vasculature, and tissue modulus of mouse and human TMEs, with several stromal and malignant targets in common. However, each model shows distinct differences and potential vulnerabilities that enabled us to test predictions about response to chemotherapy and an anti-IL-6 antibody. Using machine learning, the transcriptional profiles of the mouse tumors that differed in chemotherapy response are able to classify chemotherapy-sensitive and -refractory patient tumors. These models provide useful pre-clinical tools and may help identify subgroups of HGSOC patients who are most likely to respond to specific therapies.

Analysis of high-throughput biological data using their rank values

High-throughput biological technologies are routinely used to generate gene expression profiling or cytogenetics data. To achieve high performance, methods available in the literature become more specialized and often require high computational resources. Here, we propose a new versatile method based on the data-ordering rank values. We use linear algebra, the Perron-Frobenius theorem and also extend a method presented earlier for searching differentially expressed genes for the detection of recurrent copy number aberration. A result derived from the proposed method is a one-sample Student's t-test based on rank values. The proposed method is to our knowledge the only that applies to gene expression profiling and to cytogenetics data sets. This new method is fast, deterministic, and requires a low computational load. Probabilities are associated with genes to allow a statistically significant subset selection in the data set. Stability scores are also introduced as quality parameters. The performance and comparative analyses were carried out using real data sets. The proposed method can be accessed through an R package available from the CRAN (Comprehensive R Archive Network) website: https://cran.r-project.org/web/packages/fcros .

BRCA1185delAG tumors may acquire therapy resistance through expression of RING-less BRCA1

Heterozygous germline mutations in breast cancer 1 (BRCA1) strongly predispose women to breast cancer. BRCA1 plays an important role in DNA double-strand break (DSB) repair via homologous recombination (HR), which is important for tumor suppression. Although BRCA1-deficient cells are highly sensitive to treatment with DSB-inducing agents through their HR deficiency (HRD), BRCA1-associated tumors display heterogeneous responses to platinum drugs and poly(ADP-ribose) polymerase (PARP) inhibitors in clinical trials. It is unclear whether all pathogenic BRCA1 mutations have similar effects on the response to therapy. Here, we have investigated mammary tumorigenesis and therapy sensitivity in mice carrying the Brca1185stop and Brca15382stop alleles, which respectively mimic the 2 most common BRCA1 founder mutations, BRCA1185delAG and BRCA15382insC. Both the Brca1185stop and Brca15382stop mutations predisposed animals to mammary tumors, but Brca1185stop tumors responded markedly worse to HRD-targeted therapy than did Brca15382stop tumors. Mice expressing Brca1185stop mutations also developed therapy resistance more rapidly than did mice expressing Brca15382stop. We determined that both murine Brca1185stop tumors and human BRCA1185delAG breast cancer cells expressed a really interesting new gene domain-less (RING-less) BRCA1 protein that mediated resistance to HRD-targeted therapies. Together, these results suggest that expression of RING-less BRCA1 may serve as a marker to predict poor response to DSB-inducing therapy in human cancer patients.

Multi-omic profiling of MYCN-amplified neuroblastoma cell-lines

Neuroblastoma is the most common pediatric cancer, arising from the neural crest cells of the sympathetic nervous system. Its most aggressive subtype, characterized by the amplification of the MYCN oncogene, has a dismal prognosis and no effective treatment is available. Understanding the alterations induced by the tumor on the various layers of gene expression is therefore important for a complete characterization of this neuroblastoma subtype and for the discovery of new therapeutic opportunities. Here we describe the profiling of 13 MYCN-amplified neuroblastoma cell lines at the genome (copy number), transcriptome, translatome and miRome levels (GEO series GSE56654, GSE56552 and GSE56655). We provide detailed experimental and data analysis procedures by means of which we derived the results described in [1].

Translational compensation of genomic instability in neuroblastoma

Cancer-associated gene expression imbalances are conventionally studied at the genomic, epigenomic and transcriptomic levels. Given the relevance of translational control in determining cell phenotypes, we evaluated the translatome, i.e., the transcriptome engaged in translation, as a descriptor of the effects of genetic instability in cancer. We performed this evaluation in high-risk neuroblastomas, which are characterized by a low frequency of point mutations or known cancer-driving genes and by the presence of several segmental chromosomal aberrations that produce gene-copy imbalances that guide aggressiveness. We thus integrated genome, transcriptome, translatome and miRome profiles in a representative panel of high-risk neuroblastoma cell lines. We identified a number of genes whose genomic imbalance was corrected by compensatory adaptations in translational efficiency. The transcriptomic level of these genes was predictive of poor prognosis in more than half of cases, and the genomic imbalances found in their loci were shared by 27 other tumor types. This homeostatic process is also not limited to copy number-altered genes, as we showed the translational stoichiometric rebalance of histone genes. We suggest that the translational buffering of fluctuations in these dose-sensitive transcripts is a potential driving process of neuroblastoma evolution.

Genome instability model of metastatic neuroblastoma tumorigenesis by a dictionary learning algorithm

Background

Metastatic neuroblastoma (NB) occurs in pediatric patients as stage 4S or stage 4 and it is characterized by heterogeneous clinical behavior associated with diverse genotypes. Tumors of stage 4 contain several structural copy number aberrations (CNAs) rarely found in stage 4S. To date, the NB tumorigenesis is not still elucidated, although it is evident that genomic instability plays a critical role in the genesis of the tumor. Here we propose a mathematical approach to decipher genomic data and we provide a new model of NB metastatic tumorigenesis.

Method

We elucidate NB tumorigenesis using Enhanced Fused Lasso Latent Feature Model (E-FLLat) modeling the array comparative chromosome hybridization (aCGH) data of 190 metastatic NBs (63 stage 4S and 127 stage 4). This model for aCGH segmentation, based on the minimization of functional dictionary learning (DL), combines several penalties tailored to the specificities of aCGH data. In DL, the original signal is approximated by a linear weighted combination of atoms: the elements of the learned dictionary.

Results

The hierarchical structures for stage 4S shows at the first level of the oncogenetic tree several whole chromosome gains except to the unbalanced gains of 17q, 2p and 2q. Conversely, the high CNA complexity found in stage 4 tumors, requires two different trees. Both stage 4 oncogenetic trees are marked diverged, up to five sublevels and the 17q gain is the most common event at the first level (2/3 nodes). Moreover the 11q deletion, one of the major unfavorable marker of disease progression, occurs before 3p loss indicating that critical chromosome aberrations appear at early stages of tumorigenesis. Finally, we also observed a significant (p = 0.025) association between patient age and chromosome loss in stage 4 cases.

Conclusion

These results led us to propose a genome instability progressive model in which NB cells initiate with a DNA synthesis uncoupled from cell division, that leads to stage 4S tumors, primarily characterized by numerical aberrations, or stage 4 tumors with high levels of genome instability resulting in complex chromosome rearrangements associated with high tumor aggressiveness and rapid disease progression.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-015-0132-y) contains supplementary material, which is available to authorized users.

Identification of Copy Number Aberrations in Breast Cancer Subtypes Using Persistence Topology

DNA copy number aberrations (CNAs) are of biological and medical interest because they help identify regulatory mechanisms underlying tumor initiation and evolution. Identification of tumor-driving CNAs (driver CNAs) however remains a challenging task, because they are frequently hidden by CNAs that are the product of random events that take place during tumor evolution. Experimental detection of CNAs is commonly accomplished through array comparative genomic hybridization (aCGH) assays followed by supervised and/or unsupervised statistical methods that combine the segmented profiles of all patients to identify driver CNAs. Here, we extend a previously-presented supervised algorithm for the identification of CNAs that is based on a topological representation of the data. Our method associates a two-dimensional (2D) point cloud with each aCGH profile and generates a sequence of simplicial complexes, mathematical objects that generalize the concept of a graph. This representation of the data permits segmenting the data at different resolutions and identifying CNAs by interrogating the topological properties of these simplicial complexes. We tested our approach on a published dataset with the goal of identifying specific breast cancer CNAs associated with specific molecular subtypes. Identification of CNAs associated with each subtype was performed by analyzing each subtype separately from the others and by taking the rest of the subtypes as the control. Our results found a new amplification in 11q at the location of the progesterone receptor in the Luminal A subtype. Aberrations in the Luminal B subtype were found only upon removal of the basal-like subtype from the control set. Under those conditions, all regions found in the original publication, except for 17q, were confirmed; all aberrations, except those in chromosome arms 8q and 12q were confirmed in the basal-like subtype. These two chromosome arms, however, were detected only upon removal of three patients with exceedingly large copy number values. More importantly, we detected 10 and 21 additional regions in the Luminal B and basal-like subtypes, respectively. Most of the additional regions were either validated on an independent dataset and/or using GISTIC. Furthermore, we found three new CNAs in the basal-like subtype: a combination of gains and losses in 1p, a gain in 2p and a loss in 14q. Based on these results, we suggest that topological approaches that incorporate multiresolution analyses and that interrogate topological properties of the data can help in the identification of copy number changes in cancer.

Stratifying tumour subtypes based on copy number alteration profiles using next-generation sequence data

MOTIVATION

The role of personalized medicine and target treatment in the clinical management of cancer patients has become increasingly important in recent years. This has made the task of precise histological substratification of cancers crucial. Increasingly, genomic data are being seen as a valuable classifier. Specifically, copy number alteration (CNA) profiles generated by next-generation sequencing (NGS) can become a determinant for tumours subtyping. The principle purpose of this study is to devise a model with good prediction capability for the tumours histological subtypes as a function of both the patients covariates and their genome-wide CNA profiles from NGS data.

RESULTS

We investigate a logistic regression for modelling tumour histological subtypes as a function of the patients' covariates and their CNA profiles, in a mixed model framework. The covariates, such as age and gender, are considered as fixed predictors and the genome-wide CNA profiles are considered as random predictors. We illustrate the application of this model in lung and oral cancer datasets, and the results indicate that the tumour histological subtypes can be modelled with a good fit. Our cross-validation indicates that the logistic regression exhibits the best prediction relative to other classification methods we considered in this study. The model also exhibits the best agreement in the prediction between smooth-segmented and circular binary-segmented CNA profiles.

AVAILABILITY AND IMPLEMENTATION

An R package to run a logistic regression is available in http://www1.maths.leeds.ac.uk/~arief/R/CNALR/.

CONTACT

a.gusnanto@leeds.ac.uk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Lack of genomic heterogeneity at high-resolution aCGH between primary breast cancers and their paired lymph node metastases

Lymph-node metastasis (LNM) predict high recurrence rates in breast cancer patients. Systemic treatment aims to eliminate (micro)metastatic cells. However decisions regarding systemic treatment depend largely on clinical and molecular characteristics of primary tumours. It remains, however, unclear to what extent metastases resemble the cognate primary breast tumours, especially on a genomic level, and as such will be eradicated by the systemic therapy chosen. In this study we used high-resolution aCGH to investigate DNA copy number differences between primary breast cancers and their paired LNMs. To date, no recurrent LNM-specific genomic aberrations have been identified using array comparative genomic hybridization (aCGH) analysis. In our study we employ a high-resolution platform and we stratify on different breast cancer subtypes, both aspects that might have underpowered previously performed studies.To test the possibility that genomic instability in triple-negative breast cancers (TNBCs) might cause increased random and potentially also recurrent copy number aberrations (CNAs) in their LNMs, we studied 10 primary TNBC–LNM pairs and 10 ER-positive (ER+) pairs and verified our findings adding additionally 5 TNBC-LNM and 22 ER+-LNM pairs. We found that all LNMs clustered nearest to their matched tumour except for two cases, of which one was due to the presence of two distinct histological components in one tumour. We found no significantly altered CNAs between tumour and their LNMs in the entire group or in the subgroups. Within the TNBC subgroup, no absolute increase in CNAs was found in the LNMs compared to their primary tumours, suggesting that increased genomic instability does not lead to more CNAs in LNMs. Our findings suggest a high clonal relationship between primary breast tumours and its LNMs, at least prior to treatment, and support the use of primary tumour characteristics to guide adjuvant systemic chemotherapy in breast cancer patients.

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.

Palb2 synergizes with Trp53 to suppress mammary tumor formation in a model of inherited breast cancer

Germ-line mutations in PALB2 lead to a familial predisposition to breast and pancreatic cancer or to Fanconi Anemia subtype N. PALB2 performs its tumor suppressor role, at least in part, by supporting homologous recombination-type double strand break repair (HR-DSBR) through physical interactions with BRCA1, BRCA2, and RAD51. To further understand the mechanisms underlying PALB2-mediated DNA repair and tumor suppression functions, we targeted Palb2 in the mouse. Palb2-deficient murine ES cells recapitulated DNA damage defects caused by PALB2 depletion in human cells, and germ-line deletion of Palb2 led to early embryonic lethality. Somatic deletion of Palb2 driven by K14-Cre led to mammary tumor formation with long latency. Codeletion of both Palb2 and Tumor protein 53 (Trp53) accelerated mammary tumor formation. Like BRCA1 and BRCA2 mutant breast cancers, these tumors were defective in RAD51 focus formation, reflecting a defect in Palb2 HR-DSBR function, a strongly suspected contributor to Brca1, Brca2, and Palb2 mammary tumor development. However, unlike the case of Brca1-mutant cells, Trp53bp1 deletion failed to rescue the genomic instability of Palb2- or Brca2-mutant primary lymphocytes. Therefore, Palb2-driven DNA damage control is, in part, distinct from that executed by Brca1 and more similar to that of Brca2. The mechanisms underlying Palb2 mammary tumor suppression functions can now be explored genetically in vivo.

A preclinical mouse model of invasive lobular breast cancer metastasis

Metastatic disease accounts for more than 90% of cancer-related deaths, but the development of effective antimetastatic agents has been hampered by the paucity of clinically relevant preclinical models of human metastatic disease. Here, we report the development of a mouse model of spontaneous breast cancer metastasis, which recapitulates key events in its formation and clinical course. Specifically, using the conditional K14cre;Cdh1(F/F);Trp53(F/F) model of de novo mammary tumor formation, we orthotopically transplanted invasive lobular carcinoma (mILC) fragments into mammary glands of wild-type syngeneic hosts. Once primary tumors were established in recipient mice, we mimicked the clinical course of treatment by conducting a mastectomy. After surgery, recipient mice succumbed to widespread overt metastatic disease in lymph nodes, lungs, and gastrointestinal tract. Genomic profiling of paired mammary tumors and distant metastases showed that our model provides a unique tool to further explore the biology of metastatic disease. Neoadjuvant and adjuvant intervention studies using standard-of-care chemotherapeutics showed the value of this model in determining therapeutic agents that can target early- and late-stage metastatic disease. In obtaining a more accurate preclinical model of metastatic lobular breast cancer, our work offers advances supporting the development of more effective treatment strategies for metastatic disease.

BRCA1 RING function is essential for tumor suppression but dispensable for therapy resistance

Hereditary breast cancers are frequently caused by germline BRCA1 mutations. The BRCA1(C61G) mutation in the BRCA1 RING domain is a common pathogenic missense variant, which reduces BRCA1/BARD1 heterodimerization and abrogates its ubiquitin ligase activity. To investigate the role of BRCA1 RING function in tumor suppression and therapy response, we introduced the Brca1(C61G) mutation in a conditional mouse model for BRCA1-associated breast cancer. In contrast to BRCA1-deficient mammary carcinomas, tumors carrying the Brca1(C61G) mutation responded poorly to platinum drugs and PARP inhibition and rapidly developed resistance while retaining the Brca1(C61G) mutation. These findings point to hypomorphic activity of the BRCA1-C61G protein that, although unable to prevent tumor development, affects response to therapy.

Fancf-deficient mice are prone to develop ovarian tumours

Fanconi anaemia (FA) is a rare recessive disorder marked by developmental abnormalities, bone marrow failure, and a high risk for the development of leukaemia and solid tumours. The inactivation of FA genes, in particular FANCF, has also been documented in sporadic tumours in non-FA patients. To study whether there is a causal relationship between FA pathway defects and tumour development, we have generated a mouse model with a targeted disruption of the FA core complex gene Fancf. Fancf-deficient mouse embryonic fibroblasts displayed a phenotype typical for FA cells: they showed an aberrant response to DNA cross-linking agents as manifested by G(2) arrest, chromosomal aberrations, reduced survival, and an inability to monoubiquitinate FANCD2. Fancf homozygous mice were viable, born following a normal Mendelian distribution, and showed no growth retardation or developmental abnormalities. The gonads of Fancf mutant mice functioned abnormally, showing compromised follicle development and spermatogenesis as has been observed in other FA mouse models and in FA patients. In a cohort of Fancf-deficient mice, we observed decreased overall survival and increased tumour incidence. Notably, in seven female mice, six ovarian tumours developed: five granulosa cell tumours and one luteoma. One mouse had developed tumours in both ovaries. High-resolution array comparative genomic hybridization (aCGH) on these tumours suggests that the increased incidence of ovarian tumours correlates with the infertility in Fancf-deficient mice and the genomic instability characteristic of FA pathway deficiency.

A computational index derived from whole-genome copy number analysis is a novel tool for prognosis in early stage lung squamous cell carcinoma

Squamous cell carcinoma of the lung is remarkable for the extent to which the same chromosomal abnormalities are detected in individual tumours. We have used next generation sequencing at low coverage to produce high resolution copy number karyograms of a series of 89 non-small cell lung tumours specifically of the squamous cell subtype. Because this methodology is able to create karyograms from formalin-fixed paraffin-embedded material, we were able to use archival stored samples for which survival data were available and correlate frequently occurring copy number changes with disease outcome. No single region of genomic change showed significant correlation with survival. However, adopting a whole-genome approach, we devised an algorithm that relates to total genomic damage, specifically the relative ratios of copy number states across the genome. This algorithm generated a novel index, which is an independent prognostic indicator in early stage squamous cell carcinoma of the lung.