Inhibition of the Replication Stress Response Is a Synthetic Vulnerability in SCLC That Acts Synergistically in Combination with Cisplatin

Small cell lung cancer (SCLC) is generally regarded as very difficult to treat, mostly due to the development of metastases early in the disease and a quick relapse with resistant disease. SCLC patients initially show a good response to treatment with the DNA damaging agents cisplatin and etoposide. This is, however, quickly followed by the development of resistant disease, which urges the development of novel therapies for this type of cancer. In this study, we set out to compile a comprehensive overview of the vulnerabilities of SCLC. A functional genome-wide screen where all individual genes were knocked out was performed to identify novel vulnerabilities of SCLC. By analysis of the knockouts that were lethal to these cancer cells, we identified several processes to be synthetic vulnerabilities in SCLC. We were able to validate the vulnerability to inhibition of the replication stress response machinery by use of Chk1 and ATR inhibitors. Strikingly, SCLC cells were more sensitive to these inhibitors than nontransformed cells. In addition, these inhibitors work synergistically with either etoposide and cisplatin, where the interaction is largest with the latter. ATR inhibition by VE-822 treatment in combination with cisplatin also outperforms the combination of cisplatin with etoposide in vivo Altogether, our study uncovered a critical dependence of SCLC on the replication stress response and urges the validation of ATR inhibitors in combination with cisplatin in a clinical setting.

Comparative oncogenomics identifies combinations of driver genes and drug targets in BRCA1-mutated breast cancer

BRCA1-mutated breast cancer is primarily driven by DNA copy-number alterations (CNAs) containing large numbers of candidate driver genes. Validation of these candidates requires novel approaches for high-throughput in vivo perturbation of gene function. Here we develop genetically engineered mouse models (GEMMs) of BRCA1-deficient breast cancer that permit rapid introduction of putative drivers by either retargeting of GEMM-derived embryonic stem cells, lentivirus-mediated somatic overexpression or in situ CRISPR/Cas9-mediated gene disruption. We use these approaches to validate Myc, Met, Pten and Rb1 as bona fide drivers in BRCA1-associated mammary tumorigenesis. Iterative mouse modeling and comparative oncogenomics analysis show that MYC-overexpression strongly reshapes the CNA landscape of BRCA1-deficient mammary tumors and identify MCL1 as a collaborating driver in these tumors. Moreover, MCL1 inhibition potentiates the in vivo efficacy of PARP inhibition (PARPi), underscoring the therapeutic potential of this combination for treatment of BRCA1-mutated cancer patients with poor response to PARPi monotherapy.

A role for the unfolded protein response stress sensor ERN1 in regulating the response to MEK inhibitors in KRAS mutant colon cancers

Background

Mutations in KRAS are frequent in human cancer, yet effective targeted therapeutics for these cancers are still lacking. Attempts to drug the MEK kinases downstream of KRAS have had limited success in clinical trials. Understanding the specific genomic vulnerabilities of KRAS-driven cancers may uncover novel patient-tailored treatment options.

Methods

We first searched for synthetic lethal (SL) genetic interactions with mutant RAS in yeast with the ultimate aim to identify novel cancer-specific targets for therapy. Our method used selective ploidy ablation, which enables replication of cancer-specific gene expression changes in the yeast gene disruption library. Second, we used a genome-wide CRISPR/Cas9-based genetic screen in KRAS mutant human colon cancer cells to understand the mechanistic connection between the synthetic lethal interaction discovered in yeast and downstream RAS signaling in human cells.

Results

We identify loss of the endoplasmic reticulum (ER) stress sensor IRE1 as synthetic lethal with activated RAS mutants in yeast. In KRAS mutant colorectal cancer cell lines, genetic ablation of the human ortholog of IRE1, ERN1, does not affect growth but sensitizes to MEK inhibition. However, an ERN1 kinase inhibitor failed to show synergy with MEK inhibition, suggesting that a non-kinase function of ERN1 confers MEK inhibitor resistance. To investigate how ERN1 modulates MEK inhibitor responses, we performed genetic screens in ERN1 knockout KRAS mutant colon cancer cells to identify genes whose inactivation confers resistance to MEK inhibition. This genetic screen identified multiple negative regulators of JUN N-terminal kinase (JNK) /JUN signaling. Consistently, compounds targeting JNK/MAPK8 or TAK1/MAP3K7, which relay signals from ERN1 to JUN, display synergy with MEK inhibition.

Conclusions

We identify the ERN1-JNK-JUN pathway as a novel regulator of MEK inhibitor response in KRAS mutant colon cancer. The notion that multiple signaling pathways can activate JUN may explain why KRAS mutant tumor cells are traditionally seen as highly refractory to MEK inhibitor therapy. Our findings emphasize the need for the development of new therapeutics targeting JUN activating kinases, TAK1 and JNK, to sensitize KRAS mutant cancer cells to MEK inhibitors.

Electronic supplementary material

The online version of this article (10.1186/s13073-018-0600-z) contains supplementary material, which is available to authorized users.

Role of variant allele fraction and rare SNP filtering to improve cellular DNA repair endpoint association

Background

Large cancer genome studies continue to reveal new players in treatment response and tumorigenesis. The discrimination of functional alterations from the abundance of passenger genetic alterations still poses challenges and determines DNA sequence variant selection procedures. Here we evaluate variant selection strategies that select homozygous variants and rare SNPs and assess its value in detecting tumor cells with DNA repair defects.

Methods

To this end we employed a panel of 29 patient-derived head and neck squamous cell carcinoma (HNSCC) cell lines, of which a subset harbors DNA repair defects. Mitomycin C (MMC) sensitivity was used as functional endpoint of DNA crosslink repair deficiency. 556 genes including the Fanconi anemia (FA) and homologous recombination (HR) genes, whose products strongly determine MMC response, were capture-sequenced.

Results

We show a strong association between MMC sensitivity, thus loss of DNA repair function, and the presence of homozygous and rare SNPs in the relevant FA/HR genes. Excluding such selection criteria impedes the discrimination of crosslink repair status by mutation analysis. Applied to all KEGG pathways, we find that the association with MMC sensitivity is strongest in the KEGG FA pathway, therefore also demonstrating the value of such selection strategies for exploratory analyses. Variant analyses in 56 clinical samples demonstrate that homozygous variants occur more frequently in tumor suppressor genes than oncogenes further supporting the role of a homozygosity criterion to improve gene function association or tumor suppressor gene identification studies.

Conclusion

Together our data show that the detection of relevant genes or of repair pathway defected tumor cells can be improved by the consideration of allele zygosity and SNP allele frequencies.

Molecular characterization of breast and lung tumors by integration of multiple data types with functional sparse-factor analysis

Effective cancer treatment is crucially dependent on the identification of the biological processes that drive a tumor. However, multiple processes may be active simultaneously in a tumor. Clustering is inherently unsuitable to this task as it assigns a tumor to a single cluster. In addition, the wide availability of multiple data types per tumor provides the opportunity to profile the processes driving a tumor more comprehensively. Here we introduce Functional Sparse-Factor Analysis (funcSFA) to address these challenges. FuncSFA integrates multiple data types to define a lower dimensional space capturing the relevant variation. A tailor-made module associates biological processes with these factors. FuncSFA is inspired by iCluster, which we improve in several key aspects. First, we increase the convergence efficiency significantly, allowing the analysis of multiple molecular datasets that have not been pre-matched to contain only concordant features. Second, FuncSFA does not assign tumors to discrete clusters, but identifies the dominant driver processes active in each tumor. This is achieved by a regression of the factors on the RNA expression data followed by a functional enrichment analysis and manual curation step. We apply FuncSFA to the TCGA breast and lung datasets. We identify EMT and Immune processes common to both cancer types. In the breast cancer dataset we recover the known intrinsic subtypes and identify additional processes. These include immune infiltration and EMT, and processes driven by copy number gains on the 8q chromosome arm. In lung cancer we recover the major types (adenocarcinoma and squamous cell carcinoma) and processes active in both of these types. These include EMT, two immune processes, and the activity of the NFE2L2 transcription factor. We validate the breast cancer findings on the METABRIC set and demonstrate the translatability of the TCGA breast cancer factors to METABRIC. In summary, FuncSFA is a robust method to perform discovery of key driver processes in a collection of tumors through unsupervised integration of multiple molecular data types and functional annotation.

In order to select effective cancer treatment, we need to determine which biological processes are active in a tumor. To this end, tumors have been quantified by high dimensional molecular measurements such as RNA sequencing and DNA copy number profiling. In order to support decision making, these measurements need to be condensed into interpretable summaries. Such summaries can be made interpretable by connecting them to biological processes. Biological process activity is continuous and multiple biological processes are taking place in a single tumor. Therefore, the biological processes associated with a tumor are misrepresented by clustering, which tries to put every tumor in a single cluster. In the method introduced in this paper (funcSFA), molecular measurements are summarized into a small number factors. A factor is a continuous value per tumor that aims to represent the activity of a biological process. When applied to breast and lung cancer, funcSFA identifies factors covering well known biology of these tumor types. FuncSFA also finds novel factors covering biology whose importance is not yet widely recognized in these tumor types. Some of the factors suggest treatment opportunities that can be further investigated in cell lines and mice.

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.

Mouse models in the era of large human tumour sequencing studies

Cancer is a complex disease in which cells progressively accumulate mutations disrupting their cellular processes. A fraction of these mutations drive tumourigenesis by affecting oncogenes or tumour suppressor genes, but many mutations are passengers with no clear contribution to tumour development. The advancement of DNA and RNA sequencing technologies has enabled in-depth analysis of thousands of human tumours from various tissues to perform systematic characterization of their (epi)genomes and transcriptomes in order to identify (epi)genetic changes associated with cancer. Combined with considerable progress in algorithmic development, this expansion in scale has resulted in the identification of many cancer-associated mutations, genes and pathways that are considered to be potential drivers of tumour development. However, it remains challenging to systematically identify drivers affected by complex genomic rearrangements and drivers residing in non-coding regions of the genome or in complex amplicons or deletions of copy-number driven tumours. Furthermore, functional characterization is challenging in the human context due to the lack of genetically tractable experimental model systems in which the effects of mutations can be studied in the context of their tumour microenvironment. In this respect, mouse models of human cancer provide unique opportunities for pinpointing novel driver genes and their detailed characterization. In this review, we provide an overview of approaches for complementing human studies with data from mouse models. We also discuss state-of-the-art technological developments for cancer gene discovery and validation in mice.

Loss of androgen receptor signaling in prostate cancer-associated fibroblasts (CAFs) promotes CCL2- and CXCL8-mediated cancer cell migration

Fibroblasts are abundantly present in the prostate tumor microenvironment (TME), including cancer‐associated fibroblasts (CAFs) which play a key role in cancer development. Androgen receptor (AR) signaling is the main driver of prostate cancer (PCa) progression, and stromal cells in the TME also express AR. High‐grade tumor and poor clinical outcome are associated with low AR expression in the TME, which suggests a protective role of AR signaling in the stroma against PCa development. However, the mechanism of this relation is not clear. In this study, we isolated AR‐expressing CAF‐like cells. Testosterone (R1881) exposure did not affect CAF‐like cell morphology, proliferation, or motility. PCa cell growth was not affected by culturing in medium from R1881‐exposed CAF‐like cells; however, migration of PCa cells was inhibited. AR chromatin immune precipitation sequencing (ChIP‐seq) was performed and motif search suggested that AR in CAF‐like cells bound the chromatin through AP‐1‐elements upon R1881 exposure, inducing enhancer‐mediated AR chromatin interactions. The vast majority of chromatin binding sites in CAF‐like cells were unique and not shared with AR sites observed in PCa cell lines or tumors. AR signaling in CAF‐like cells decreased expression of multiple cytokines; most notably CCL2 and CXCL8 and both cytokines increased migration of PCa cells. These results suggest direct paracrine regulation of PCa cell migration by CAFs through AR signaling.

MAP3K1 and MAP2K4 mutations are associated with sensitivity to MEK inhibitors in multiple cancer models

Activation of the mitogen-activated protein kinase (MAPK) pathway is frequent in cancer. Drug development efforts have been focused on kinases in this pathway, most notably on RAF and MEK. We show here that MEK inhibition activates JNK-JUN signaling through suppression of DUSP4, leading to activation of HER Receptor Tyrosine Kinases. This stimulates the MAPK pathway in the presence of drug, thereby blunting the effect of MEK inhibition. Cancers that have lost MAP3K1 or MAP2K4 fail to activate JNK-JUN. Consequently, loss-of-function mutations in either MAP3K1 or MAP2K4 confer sensitivity to MEK inhibition by disabling JNK-JUN-mediated feedback loop upon MEK inhibition. In a panel of 168 Patient Derived Xenograft (PDX) tumors, MAP3K1 and MAP2K4 mutation status is a strong predictor of response to MEK inhibition. Our findings suggest that cancers having mutations in MAP3K1 or MAP2K4, which are frequent in tumors of breast, prostate and colon, may respond to MEK inhibitors. Our findings also suggest that MAP3K1 and MAP2K4 are potential drug targets in combination with MEK inhibitors, in spite of the fact that they are encoded by tumor suppressor genes.

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.

Integrative Modeling Identifies Key Determinants of Inhibitor Sensitivity in Breast Cancer Cell Lines

Cancer cell lines differ greatly in their sensitivity to anticancer drugs as a result of different oncogenic drivers and drug resistance mechanisms operating in each cell line. Although many of these mechanisms have been discovered, it remains a challenge to understand how they interact to render an individual cell line sensitive or resistant to a particular drug. To better understand this variability, we profiled a panel of 30 breast cancer cell lines in the absence of drugs for their mutations, copy number aberrations, mRNA, protein expression and protein phosphorylation, and for response to seven different kinase inhibitors. We then constructed a knowledge-based, Bayesian computational model that integrates these data types and estimates the relative contribution of various drug sensitivity mechanisms. The resulting model of regulatory signaling explained the majority of the variability observed in drug response. The model also identified cell lines with an unexplained response, and for these we searched for novel explanatory factors. Among others, we found that 4E-BP1 protein expression, and not just the extent of phosphorylation, was a determinant of mTOR inhibitor sensitivity. We validated this finding experimentally and found that overexpression of 4E-BP1 in cell lines that normally possess low levels of this protein is sufficient to increase mTOR inhibitor sensitivity. Taken together, our work demonstrates that combining experimental characterization with integrative modeling can be used to systematically test and extend our understanding of the variability in anticancer drug response.Significance: By estimating how different oncogenic mutations and drug resistance mechanisms affect the response of cancer cells to kinase inhibitors, we can better understand and ultimately predict response to these anticancer drugs.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/78/15/4396/F1.large.jpg Cancer Res; 78(15); 4396-410. ©2018 AACR.

Comparative genomic analysis of oral versus laryngeal and pharyngeal cancer

OBJECTIVE

Locally advanced oral squamous cell carcinoma (OSCC) shows lower locoregional control and disease specific survival rates than laryngeal and pharyngeal squamous cell carcinoma (L/P-SCC) after definitive chemoradiotherapy treatment. Despite clinical factors, this can point towards a different tumor biology that could impact chemoradiotherapy response rates. This prompted us to compare the mutational profiles of OSCC with L/P-SCC.

METHODS

We performed target capture DNA sequencing on 111 HPV-negative HNSCC samples (NKI dataset), 55 oral and 56 laryngeal/pharyngeal, and identified somatic point mutations and copy number aberrations. We next expanded our analysis with 276 OSCC and 134 L/P-SCC sample data from The Cancer Genome Atlas (TCGA dataset). We focused our analyses on genes that are frequently mutated in HNSCC.

RESULTS

The mutational profiles of OSCC and L/P-SCC showed many similarities. However, OSCC was significantly enriched for CASP8 (NKI: 15% vs 0%; TCGA: 17% vs 2%) and HRAS (TCGA: 10% vs 1%) mutations. LAMA2 (TCGA: 5% vs 19%) and NSD1 (TCGA: 7% vs 25%) mutations were enriched in L/P-SCC. Overall, we find that OSCC had fewer somatic point mutations and copy number aberrations than L/P-SCC. Interestingly, L/P-SCC scored higher in mutational and genomic scar signatures associated with homologous recombination DNA repair defects.

CONCLUSION

Despite showing a similar mutational profile, our comparative genomic analysis revealed distinctive features in OSCC and L/P-SCC. Some of these genes and cellular processes are likely to affect the cellular response to radiation or cisplatin. Genomic characterizations may guide or enable personalized treatment in the future.

Fanconi anemia and homologous recombination gene variants are associated with functional DNA repair defects in vitro and poor outcome in patients with advanced head and neck squamous cell carcinoma

Mutations in Fanconi Anemia or Homologous Recombination (FA/HR) genes can cause DNA repair defects and could therefore impact cancer treatment response and patient outcome. Their functional impact and clinical relevance in head and neck squamous cell carcinoma (HNSCC) is unknown. We therefore questioned whether functional FA/HR defects occurred in HNSCC and whether they are associated with FA/HR variants. We assayed a panel of 29 patient-derived HNSCC cell lines and found that a considerable fraction is hypersensitive to the crosslinker Mitomycin C and PARP inhibitors, a functional measure of FA/HR defects. DNA sequencing showed that these hypersensitivities are associated with the presence of bi-allelic rare germline and somatic FA/HR gene variants. We next questioned whether such variants are associated with prognosis and treatment response in HNSCC patients. DNA sequencing of 77 advanced stage HNSCC tumors revealed a 19% incidence of such variants. Importantly, these variants were associated with a poor prognosis (p = 0.027; HR = 2.6, 1.1–6.0) but favorable response to high cumulative cisplatin dose. We show how an integrated in vitro functional repair and genomic analysis can improve the prognostic value of genetic biomarkers. We conclude that repair defects are marked and frequent in HNSCC and are associated with clinical outcome.

Identification of a Druggable Pathway Controlling Glioblastoma Invasiveness

Diffuse and uncontrollable brain invasion is a hallmark of glioblastoma (GBM), but its mechanism is understood poorly. We developed a 3D ex vivo organotypic model to study GBM invasion. We demonstrate that invading GBM cells upregulate a network of extracellular matrix (ECM) components, including multiple collagens, whose expression correlates strongly with grade and clinical outcome. We identify interferon regulatory factor 3 (IRF3) as a transcriptional repressor of ECM factors and show that IRF3 acts as a suppressor of GBM invasion. Therapeutic activation of IRF3 by inhibiting casein kinase 2 (CK2)-a negative regulator of IRF3-downregulated the expression of ECM factors and suppressed GBM invasion in ex vivo and in vivo models across a panel of patient-derived GBM cell lines representative of the main molecular GBM subtypes. Our data provide mechanistic insight into the invasive capacity of GBM tumors and identify a potential therapy to inhibit GBM invasion.

Bayesian data integration for quantifying the contribution of diverse measurements to parameter estimates

Motivation

Computational models in biology are frequently underdetermined, due to limits in our capacity to measure biological systems. In particular, mechanistic models often contain parameters whose values are not constrained by a single type of measurement. It may be possible to achieve better model determination by combining the information contained in different types of measurements. Bayesian statistics provides a convenient framework for this, allowing a quantification of the reduction in uncertainty with each additional measurement type. We wished to explore whether such integration is feasible and whether it can allow computational models to be more accurately determined.

Results

We created an ordinary differential equation model of cell cycle regulation in budding yeast and integrated data from 13 different studies covering different experimental techniques. We found that for some parameters, a single type of measurement, relative time course mRNA expression, is sufficient to constrain them. Other parameters, however, were only constrained when two types of measurements were combined, namely relative time course and absolute transcript concentration. Comparing the estimates to measurements from three additional, independent studies, we found that the degradation and transcription rates indeed matched the model predictions in order of magnitude. The predicted translation rate was incorrect however, thus revealing a deficiency in the model. Since this parameter was not constrained by any of the measurement types separately, it was only possible to falsify the model when integrating multiple types of measurements. In conclusion, this study shows that integrating multiple measurement types can allow models to be more accurately determined.

Availability and implementation

The models and files required for running the inference are included in the Supplementary information.

Contact

l.wessels@nki.nl.

Supplementary information

Supplementary data are available at Bioinformatics online.

Identifying transposon insertions and their effects from RNA-sequencing data

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.

Endogenous androgen receptor proteomic profiling reveals genomic subcomplex involved in prostate tumorigenesis

Androgen receptor (AR) is a key player in prostate cancer development and progression. Here we applied immunoprecipitation mass spectrometry of endogenous AR in LNCaP cells to identify components of the AR transcriptional complex. In total, 66 known and novel AR interactors were identified in the presence of synthetic androgen, most of which were critical for AR-driven prostate cancer cell proliferation. A subset of AR interactors required for LNCaP proliferation were profiled using chromatin immunoprecipitation assays followed by sequencing, identifying distinct genomic subcomplexes of AR interaction partners. Interestingly, three major subgroups of genomic subcomplexes were identified, where selective gain of function for AR genomic action in tumorigenesis was found, dictated by FOXA1 and HOXB13. In summary, by combining proteomic and genomic approaches we reveal subclasses of AR transcriptional complexes, differentiating normal AR behavior from the oncogenic state. In this process, the expression of AR interactors has key roles by reprogramming the AR cistrome and interactome in a genomic location-specific manner.

Complementary Value of Contralateral Parenchymal Enhancement on DCE-MRI to Prognostic Models and Molecular Assays in High-risk ER+/HER2- Breast Cancer

Purpose: To determine whether markers of healthy breast stroma are able to select a subgroup of patients at low risk of death or metastasis from patients considered at high risk according to routine markers of the tumor.Experimental Design: Patients with ER⁺/HER2^- breast cancer were consecutively included for retrospective analysis. The contralateral parenchyma was segmented automatically on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), where upon the average of the top-10% late enhancement was calculated. This contralateral parenchymal enhancement (CPE) was analyzed with respect to routine prognostic models and molecular assays (Nottingham Prognostic Index, Dutch clinical chemotherapy-selection guidelines, 70-gene signature, and 21-gene recurrence score). CPE was split in tertiles and tested for overall and distant disease-free survival. CPE was adjusted for patient and tumor characteristics, as well as systemic therapy, using inverse probability weighting (IPW). Subanalyses were performed in patients at high risk according to prognostic models and molecular assays.Results: Four-hundred-and-fifteen patients were included, constituting the same group in which the association between CPE and survival was discovered. Median follow-up was 85 months, 34/415(8%) patients succumbed. After IPW-adjustment for patient and tumor characteristics, patients with high CPE had significantly better overall survival than those with low CPE in groups at high risk according to the Nottingham Prognostic Index [HR (95% CI): 0.08 (0.00-0.40), P < 0.001]; Dutch clinical guidelines [HR (95% CI): 0.22 (0.00-0.81), P = 0.021]; and 21-gene recurrence score [HR (95% CI): 0.14 (0.00-0.84), P = 0.030]. One group showed a trend [70-gene signature: HR (95% CI): 0.25 (0.00-1.02), P = 0.054].Conclusions: In patients at high risk based on the tumor, subgroups at relatively low risk were identified using pretreatment enhancement of the stroma on breast DCE-MRI. Clin Cancer Res; 23(21); 6505-15. ©2017 AACR.

TANDEM: a two-stage approach to maximize interpretability of drug response models based on multiple molecular data types

MOTIVATION

Clinical response to anti-cancer drugs varies between patients. A large portion of this variation can be explained by differences in molecular features, such as mutation status, copy number alterations, methylation and gene expression profiles. We show that the classic approach for combining these molecular features (Elastic Net regression on all molecular features simultaneously) results in models that are almost exclusively based on gene expression. The gene expression features selected by the classic approach are difficult to interpret as they often represent poorly studied combinations of genes, activated by aberrations in upstream signaling pathways.

RESULTS

To utilize all data types in a more balanced way, we developed TANDEM, a two-stage approach in which the first stage explains response using upstream features (mutations, copy number, methylation and cancer type) and the second stage explains the remainder using downstream features (gene expression). Applying TANDEM to 934 cell lines profiled across 265 drugs (GDSC1000), we show that the resulting models are more interpretable, while retaining the same predictive performance as the classic approach. Using the more balanced contributions per data type as determined with TANDEM, we find that response to MAPK pathway inhibitors is largely predicted by mutation data, while predicting response to DNA damaging agents requires gene expression data, in particular SLFN11 expression.

AVAILABILITY AND IMPLEMENTATION

TANDEM is available as an R package on CRAN (for more information, see http://ccb.nki.nl/software/tandem).

CONTACT

m.michaut@nki.nl or l.wessels@nki.nl

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Mammary tumor-derived CCL2 enhances pro-metastatic systemic inflammation through upregulation of IL1β in tumor-associated macrophages

Patients with primary solid malignancies frequently exhibit signs of systemic inflammation. Notably, elevated levels of neutrophils and their associated soluble mediators are regularly observed in cancer patients, and correlate with reduced survival and increased metastasis formation. Recently, we demonstrated a mechanistic link between mammary tumor-induced IL17-producing γδ T cells, systemic expansion of immunosuppressive neutrophils and metastasis formation in a genetically engineered mouse model for invasive breast cancer. How tumors orchestrate this systemic inflammatory cascade to facilitate dissemination remains unclear. Here we show that activation of this cascade relies on CCL2-mediated induction of IL1β in tumor-associated macrophages. In line with these findings, expression of CCL2 positively correlates with IL1Β and macrophage markers in human breast tumors. We demonstrate that blockade of CCL2 in mammary tumor-bearing mice results in reduced IL17 production by γδ T cells, decreased neutrophil expansion and enhanced CD8⁺ T cell activity. These results highlight a new role for CCL2 in facilitating the breast cancer-induced pro-metastatic systemic inflammatory γδ T cell – IL17 – neutrophil axis.

Systematic functional perturbations uncover a prognostic genetic network driving human breast cancer

Prognostic classifiers conceivably comprise biomarker genes that functionally contribute to the oncogenic and metastatic properties of cancer, but this has not been investigated systematically. The transcription factor Fra-1 not only has an essential role in breast cancer, but also drives the expression of a highly prognostic gene set. Here, we systematically perturbed the function of 31 individual Fra-1-dependent poor-prognosis genes and examined their impact on breast cancer growth in vivo. We find that stable shRNA depletion of each of nine individual signature genes strongly inhibits breast cancer growth and aggressiveness. Several factors within this nine-gene set regulate each others expression, suggesting that together they form a network. The nine-gene set is regulated by estrogen, ERBB2 and EGF signaling, all established breast cancer factors. We also uncover three transcription factors, MYC, E2F1 and TP53, which act alongside Fra-1 at the core of this network. ChIP-Seq analysis reveals that a substantial number of genes are bound, and regulated, by all four transcription factors. The nine-gene set retains significant prognostic power and includes several potential therapeutic targets, including the bifunctional enzyme PAICS, which catalyzes purine biosynthesis. Depletion of PAICS largely cancelled breast cancer expansion, exemplifying a prognostic gene with breast cancer activity. Our data uncover a core genetic and prognostic network driving human breast cancer. We propose that pharmacological inhibition of components within this network, such as PAICS, may be used in conjunction with the Fra-1 prognostic classifier towards personalized management of poor prognosis breast cancer.

Abstract PD1-07: Comprehensive characterization of matched pre-treatment biopsies and residual disease of doxorubicin treated breast cancer

Background

Neoadjuvant chemotherapy is standard of care for locally advanced breast cancer. Unfortunately not all patients benefit from this treatment. Even after decades of research, we still cannot predict which tumor will or will not respond. This may in part be due to tumor heterogeneity, as the sample taken before treatment not necessarily represents the tumor cell population that causes therapy resistance.

Methods

To test this hypothesis, we collected pre-treatment biopsies, resection specimens, and matched blood from 21 breast cancer patients treated with doxorubicin and cyclophosphamide in a neoadjuvant setting. Specifically, tumors were selected with a tumor percentage &gt;50% after chemotherapy to enrich for resistant samples and ensure high quality data. RNA and whole exome sequencing were performed to characterize somatic mutations, copy number alterations and gene expression profiles. Histopathological characteristics were determined to obtain a comprehensive profile of all tumor samples.

Results

The comparisons of somatic variants and copy number alterations revealed a very diverse image: in several cases, high-level amplifications, large genomic gains or losses, and mutations in known oncogenes or tumor suppressors such as MAP3K1 and RUNX1 were either lost or gained during treatment, while in other cases no such changes were detected. We observed a remarkable number of genetic alterations involved in cell cycle progression and DNA damage checkpoints, including amplification of MDM2, CCND1 and CDK4, and copy number loss or mutations in CDKN1B and ATM. Strikingly, both cases of CDKN1B loss were identified in pre-treatment biopsies and no longer detectable in the surgery specimen. In contrast, CCND1, CDK4 and MDM2 amplifications were retained, although CCND1 expression decreased significantly in CCND1 amplified tumors.

In addition, eighty percent of tumors showed a decreased cell proliferation after chemotherapy, where the high-proliferative ER+ (Luminal B) tumors were most strongly affected. This trend was also visible in a validation cohort of 94 ER+ samples, but the prognosis of Luminal B tumors that showed a decrease in proliferation was still significantly worse than that of Luminal A tumors that did not show an altered proliferation rate.

Conclusion

Our results confirm that biologically relevant genomic alterations can differ between pre- and post-treatment samples, which greatly impacts biomarker discovery. In addition, our findings emphasize the chemotherapy insensitivity of CCND1 amplified ER+ breast cancers, and stress the need for better treatment regimens for these patients. In contrast, genomic loss of CDKN1B may be a marker for sensitivity to doxorubicin.

Citation Format: Lips EH, Hoogstraat M, Mulder L, Nederlof PM, Sonke GS, Rodenhuis S, Wesseling J, Wessels LFA. Comprehensive characterization of matched pre-treatment biopsies and residual disease of doxorubicin treated breast cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr PD1-07.

A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence

In cancer, mutually exclusive or co-occurring somatic alterations across genes can suggest functional interactions. Existing tests for such patterns make the unrealistic assumption of identical gene alteration probabilities across tumors. We present Discrete Independence Statistic Controlling for Observations with Varying Event Rates (DISCOVER), a novel test that is more sensitive than other methods and controls its false positive rate. A pan-cancer analysis using DISCOVER finds no evidence for widespread co-occurrence, and most co-occurrences previously detected do not exceed expectation by chance. Many mutual exclusivities are identified involving well-known genes related to cell cycle and growth factor signaling, as well as lesser known regulators of Hedgehog signaling.

BCM: toolkit for Bayesian analysis of Computational Models using samplers

BACKGROUND

Computational models in biology are characterized by a large degree of uncertainty. This uncertainty can be analyzed with Bayesian statistics, however, the sampling algorithms that are frequently used for calculating Bayesian statistical estimates are computationally demanding, and each algorithm has unique advantages and disadvantages. It is typically unclear, before starting an analysis, which algorithm will perform well on a given computational model.

RESULTS

We present BCM, a toolkit for the Bayesian analysis of Computational Models using samplers. It provides efficient, multithreaded implementations of eleven algorithms for sampling from posterior probability distributions and for calculating marginal likelihoods. BCM includes tools to simplify the process of model specification and scripts for visualizing the results. The flexible architecture allows it to be used on diverse types of biological computational models. In an example inference task using a model of the cell cycle based on ordinary differential equations, BCM is significantly more efficient than existing software packages, allowing more challenging inference problems to be solved.

CONCLUSIONS

BCM represents an efficient one-stop-shop for computational modelers wishing to use sampler-based Bayesian statistics.

Deletion of Polycomb Repressive Complex 2 From Mouse Intestine Causes Loss of Stem Cells

BACKGROUND & AIMS

The polycomb repressive complex 2 (PRC2) regulates differentiation by contributing to repression of gene expression and thereby stabilizing the fate of stem cells and their progeny. PRC2 helps to maintain adult stem cell populations, but little is known about its functions in intestinal stem cells. We studied phenotypes of mice with intestine-specific deletion of the PRC2 proteins embryonic ectoderm development (EED) (a subunit required for PRC2 function) and enhancer of zeste homolog 2 (EZH2) (a histone methyltransferase).

METHODS

We performed studies of AhCre;EedLoxP/LoxP (EED knockout) mice and AhCre;Ezh2LoxP/LoxP (EZH2 knockout) mice, which have intestine-specific disruption in EED and EZH2, respectively. Small intestinal crypts were isolated and subsequently cultured to grow organoids. Intestines and organoids were analyzed by immunohistochemical, in situ hybridization, RNA sequence, and chromatin immunoprecipitation methods.

RESULTS

Intestines of EED knockout mice had massive crypt degeneration and lower numbers of proliferating cells compared with wild-type control mice. Cdkn2a became derepressed and we detected increased levels of P21. We did not observe any differences between EZH2 knockout and control mice. Intestinal crypts from EED knockout mice had signs of aberrant differentiation of uncommitted crypt cells-these differentiated toward the secretory cell lineage. Furthermore, crypts from EED-knockout mice had impaired Wnt signaling and concomitant loss of intestinal stem cells, this phenotype was not reversed upon ectopic stimulation of Wnt and Notch signaling in organoids. Analysis of gene expression patterns from intestinal tissues of EED knockout mice showed dysregulation of several genes involved in Wnt signaling. Wnt signaling was regulated directly by PRC2.

CONCLUSIONS

In intestinal tissues of mice, PRC2 maintains small intestinal stem cells by promoting proliferation and preventing differentiation in the intestinal stem cell compartment. PRC2 controls gene expression in multiple signaling pathways that regulate intestinal homeostasis. Sequencing data are available in the genomics data repository GEO under reference series GSE81578; RNA sequencing data are available under subseries GSE81576; and ChIP sequencing data are available under subseries GSE81577.