Evolutionary Signatures amongst Disease Genes Permit Novel Methods for Gene Prioritization and Construction of Informative Gene-Based Networks

Genes involved in the same function tend to have similar evolutionary histories, in that their rates of evolution covary over time. This coevolutionary signature, termed Evolutionary Rate Covariation (ERC), is calculated using only gene sequences from a set of closely related species and has demonstrated potential as a computational tool for inferring functional relationships between genes. To further define applications of ERC, we first established that roughly 55% of genetic diseases posses an ERC signature between their contributing genes. At a false discovery rate of 5% we report 40 such diseases including cancers, developmental disorders and mitochondrial diseases. Given these coevolutionary signatures between disease genes, we then assessed ERC's ability to prioritize known disease genes out of a list of unrelated candidates. We found that in the presence of an ERC signature, the true disease gene is effectively prioritized to the top 6% of candidates on average. We then apply this strategy to a melanoma-associated region on chromosome 1 and identify MCL1 as a potential causative gene. Furthermore, to gain global insight into disease mechanisms, we used ERC to predict molecular connections between 310 nominally distinct diseases. The resulting “disease map” network associates several diseases with related pathogenic mechanisms and unveils many novel relationships between clinically distinct diseases, such as between Hirschsprung's disease and melanoma. Taken together, these results demonstrate the utility of molecular evolution as a gene discovery platform and show that evolutionary signatures can be used to build informative gene-based networks

Identification of Acquired Molecular Dependencies in Advanced Breast and Ovarian Cancers

Although individual cancers are driven by heterogeneous processes, cancer mortality has a near universal cause—therapy resistance, recurrence and eventual metastasis to vital organs. Despite great advancements in cancer therapies this past decade, outcomes in patients with advanced disease remain static. In these translational studies, using multiple cohorts of longitudinally collected tumor specimens, we test the hypothesis that relapsed cancers are molecularly distinct from primary disease and acquire druggable vulnerabilities throughout their life histories. As a preliminary study, a targeted gene expression analysis was performed to (1) determine differences in breast cancer (BrCa) intrinsic subtypes between primary tumors and matched brain metastases (BrM) and (2) explore if druggable targets are acquired in metastases. While BrM generally retain their intrinsic molecular subtypes, even after years of dormancy, nearly all gain expression of clinically actionable genes—most notably HER2 (35% of cases). To further assess molecular features acquired in metastases, exome-capture RNA-sequencing on decade-old and degraded tumor specimens was evaluated. Applying this technology, transcriptome-wide acquisitions in BrM were discovered, including highly recurrent expression gains in RET (38% of cases). Targeting RET or HER2 using in vitro, ex vivo, and in vivo models produced marked responses, suggesting RET and HER2-driven signaling as prime targets for patients with BrM. The same approach was applied to estrogen receptor [ER]-positive BrCa bone metastases, which discerned further site-specific acquisitions—such as shifts to Her2 and LumB phenotypes, temporally influenced expression evolution and druggable gains in CDK-Rb-E2F and FGFR-signaling pathways. To determine if these changes are consistent in non-metastatic samples, both RNA expression and DNA changes were assessed in a cohort of ER-positive local recurrences. Limited DNA-level changes, yet highly recurrent transcriptional remodeling events were observed—in particular, losses of ESR1, gains of NTRKs and upregulation of the cancer stem cell marker PROM1. Lastly, these findings were corroborated in ovarian cancer recurrences, where we show fusion RNA transcripts and recurrent outlier expression gains (NTRK2, INHBA and IGF1) are acquired in relapsed disease. Taken together, these studies establish that cancer recurrences commonly acquire multimodal and readily druggable molecular dependencies, unique from primary tumors, which may have profound clinical implications

Who's driving anyway? Herculean efforts to identify the drivers of breast cancer

The continuing advancement of sequencing technologies has made the systematic identification of all driving somatic events in cancer a possibility. In the June 2012 issue of Nature, five papers show some significant headway in this endeavor, each a herculean effort of genome sequencing, and transcriptome and copy number analysis resulting in data on thousands of breast cancers. Integrating these massive datasets, the authors were able to further subdivide breast cancer and identify a number of novel driver genes. While the studies represent a leap forward in describing the genomics of breast cancer, and clearly highlight the tremendous diversity between tumors, the studies only scrape the surface of molecular changes in breast tumors, with more granularity to come from the study of epigenomics, single cell sequencing, and so on. The immediate importance of the data to clinical care is currently unknown, and will depend upon detailed identification and functional analysis of driver mutations. © 2012 BioMed Central Ltd

Adaptive Learning Systems Between Optimization and Critique: An Interdisciplinary Media Constellation Analysis of Bettermarks

Die Entwicklung und der Einsatz datengetriebener adaptiver Lernsysteme finden in einem komplexen Spannungsfeld statt. Informatische Herangehensweisen treffen auf schulische Wirklichkeiten, die sie zu spezifischen Bedingungen modellieren. Dabei sind Entwicklung und Einsatz adaptiver Lernsysteme sowohl mit Optimierungserwartungen als auch mit Kritik seitens der schulischen Akteure verbunden. Der Artikel analysiert die adaptive Mathematik-Lernplattform bettermarks vor diesem Hintergrund als Medienkonstellation, in die sich informatische Modellierungs-, Prozessierungs- und Optimierungsprinzipien einschreiben und mit schulischen Unterrichtspraktiken und Subjektpositionen verflechten. Zentrale Aspekte sind dabei, dass sich in bettermarks Logiken des Computational Thinking, des Solutionismus, der Programmierung iterativer bedingter Schleifen und der profilbasierten Repräsentation von Schülerinnen und Schülern, strikte Lernzielorientierung und hierarchische Machtverhältnisse auffinden lassen. Dabei entsteht eine Medienkonstellation, die weniger auf eine Adaption des Lernsystems an sich als auf die behavioristisch anmutende Adaption des lernenden Subjekts im Sinne der optimierenden Anpassung an vorgegebene Leistungsprofile abzielt. Gleichzeitig wird jenseits der konkreten Modellierungen in bettermarks die informatische Modellierung selbst kritisch in den Blick genommen, insofern sie systematisch nicht-komputierbare Elemente ausschliesst. Für eine Gestaltung adaptiver Lernsysteme, die diese Ergebnisse kritisch-reflexiv einbezieht, wird abschliessend vorgeschlagen, medienwissenschaftliche und informatische Akteure in partizipativen Projekten mit Lehrenden und Lernenden zusammen zu bringen.The development and implementation of data-driven adaptive learning systems take place within a complex constellation. Principles from computer science come together with school realities that they transfer into specific formalized and computational models. In this context, the development and use of adaptive learning systems are associated with both optimization expectations and criticism on the part of school stakeholders. Against this backdrop, the paper analyzes the adaptive mathematics learning platform bettermarks as a media constellation that is pervaded by principles of modeling, processing and optimization that are typical for computer science and technology, and that are at the same time entangled with school practices of teaching and learning and established subject positions. Findings are that bettermarks is based on principles of computational thinking, solutionism, iterative conditional loops and student profiles that focus on learning objectives and hierarchical power relations. It constitutes a media constellation that is not primarily establishing an adaption of the system itself but a behavioristic adaption and optimization of the student according to given performance profiles. At the same time, beyond the concrete modelling in bettermarks, the informatics modelling itself is critically examined insofar as it systematically excludes non-computable elements. For a design of adaptive learning systems that critically and reflectively incorporates these results, it is finally proposed to bring together media science and informatics actors in participatory projects with teachers and learners

Targeted Mutation Detection in Advanced Breast Cancer Using MammaSeq Identifies RET as a Potential Contributor to Breast Cancer Metastasis

The lack of any reported breast cancer specific diagnostic NGS tests inspired the development of MammaSeq, an amplicon based NGS panel built specifically for use in advanced breast cancer. In a pilot study to define the clinical utility of the panel, 46 solid tumor samples, plus an additional 14 samples of circulating-free DNA (cfDNA) from patients with advanced breast cancer were sequenced and analyzed using the OncoKB precision oncology database. We identified 26 clinically actionable variants (levels 1-3) annotated by the OncoKB precision oncology database, distributed across 20 out of 46 solid tumor cases (40%), and 4 clinically actionable mutations distributed across 4 samples in the 14 cfDNA sample cohort (29%). The mutation allele (MAF) frequencies of ESR1-D538G and FOXA1-Y175C mutations correlated with CA.27.29 levels in patient-matched blood, indicating that MAF may be a reliable marker for disease burden. Interestingly, 4 of the mutations found in metastatic samples occurred in the gene RET, an oncogenic receptor tyrosine kinase. In an orthogonal study, the lab has recently identified RET as one of the most recurrently upregulated genes in breast cancer brain metastases. Interestingly, the ligand for RET is the family of glial-cell derived neurotrophic factors (GDNF), a growth factor secreted by glial cells of the central nervous system. This lead to the hypothesis that RET overexpression facilitates breast cancer brain metastasis in response to the high levels of GDNF, while RET activating point mutations increase metastatic capacity without specific organ tropism. While the effect of GDNF treatment on proliferation in 2D was limited, in ultra-low attachment (ULA) plates we saw a significant increase in anchorage independent growth of MCF-7 cells. To determine if GDNF acts as a chemoattractant for RET positive BrCa cells, we utilized a transwell migration assay, with GDNF as the sole chemoattractant. When RET was overexpressed, there was a visual increase in cell migration. Together, these studies demonstrate the clinical feasibility of using MammaSeq to detect clinically actionable mutations in breast cancer patients, and provides provisional data supporting the investigation of RET signaling as a potentially targetable mediator of breast cancer brain metastasis

Biomarkers of Targeted Therapy and Immuno-Oncology in Cancers Metastatic to the Breast.

The breast is a rare site for metastases, and their molecular characteristics have not been studied yet. Intrinsic molecular genetics, cancer characteristics, and breast tissue immune responses in diverse metastases to the breast have not been previously studied. We identified 64 patients with cancers metastatic to the breast: 51 carcinomas and 13 melanomas. Programmed death ligand 1 (PD-L1), steroid receptors, and HER2/neu expressions were evaluated using immunohistochemistry. Gene sequencing, copy number alterations, microsatellite instability, and tumor mutational burden were performed using next-generation sequencing platforms. The 3 most common primary sites for metastatic carcinomas were lung (37%), ovary (29%), and fallopian tubes/peritoneum (14%). TP53 mutations were commonly (50%) observed among the carcinoma cases, while other mutations were characteristic for the primary cancers (VHL in renal, BRCA1 in the fallopian tube, and BRAF in melanomas). High tumor mutational burden was detected in 5/14 carcinomas and 3/7 melanomas. Tumor cell PD-L1 expression was detected in 6 carcinomas, but not in any of the melanomas, whereas immune cells' expression of PD-L1 was seen in 17 carcinomas and 6 melanomas. Estrogen receptor status was positive in 13/49 carcinomas including 12 adenocarcinomas originating from the ovary and fallopian tube or peritoneum and 1 duodenal neuroendocrine carcinoma. No carcinoma was HER2/neu positive. Intrinsic genetic characteristics of the metastases to the breast followed the pattern commonly seen in primary tumors. Biomarkers of potential benefit to immune checkpoint inhibition therapy were limited to PD-L1-positive non-small cell lung cancer. No common characteristics of the heterogeneous group of tumor metastases to this organ were identified

ADAM22/LGI1 complex as a new actionable target for breast cancer brain metastasis

Background: Metastatic breast cancer is a major cause of cancer-related deaths in woman. Brain metastasis is a common and devastating site of relapse for several breast cancer molecular subtypes, including oestrogen receptor-positive disease, with life expectancy of less than a year. While efforts have been devoted to developing therapeutics for extra-cranial metastasis, drug penetration of blood–brain barrier (BBB) remains a major clinical challenge. Defining molecular alterations in breast cancer brain metastasis enables the identification of novel actionable targets.Methods: Global transcriptomic analysis of matched primary and metastatic patient tumours (n = 35 patients, 70 tumour samples) identified a putative new actionable target for advanced breast cancer which was further validated in vivo and in breast cancer patient tumour tissue (n = 843 patients). A peptide mimetic of the target's natural ligand was designed in silico and its efficacy assessed in in vitro, ex vivo and in vivo models of breast cancer metastasis.Results: Bioinformatic analysis of over-represented pathways in metastatic breast cancer identified ADAM22 as a top ranked member of the ECM-related druggable genome specific to brain metastases. ADAM22 was validated as an actionable target in in vitro, ex vivo and in patient tumour tissue (n = 843 patients). A peptide mimetic of the ADAM22 ligand LGI1, LGI1MIM, was designed in silico. The efficacy of LGI1MIM and its ability to penetrate the BBB were assessed in vitro, ex vivo and in brain metastasis BBB 3D biometric biohybrid models, respectively. Treatment with LGI1MIM in vivo inhibited disease progression, in particular the development of brain metastasis.Conclusion: ADAM22 expression in advanced breast cancer supports development of breast cancer brain metastasis. Targeting ADAM22 with a peptide mimetic LGI1MIM represents a new therapeutic option to treat metastatic brain disease

ESR1 mutant breast cancers show elevated basal cytokeratins and immune activation

Estrogen receptor alpha (ER/ESR1) is frequently mutated in endocrine resistant ER-positive (ER+) breast cancer and linked to ligand-independent growth and metastasis. Despite the distinct clinical features of ESR1 mutations, their role in intrinsic subtype switching remains largely unknown. Here we find that ESR1 mutant cells and clinical samples show a significant enrichment of basal subtype markers, and six basal cytokeratins (BCKs) are the most enriched genes. Induction of BCKs is independent of ER binding and instead associated with chromatin reprogramming centered around a progesterone receptor-orchestrated insulated neighborhood. BCK-high ER+ primary breast tumors exhibit a number of enriched immune pathways, shared with ESR1 mutant tumors. S100A8 and S100A9 are among the most induced immune mediators and involve in tumor-stroma paracrine crosstalk inferred by single-cell RNA-seq from metastatic tumors. Collectively, these observations demonstrate that ESR1 mutant tumors gain basal features associated with increased immune activation, encouraging additional studies of immune therapeutic vulnerabilities

Comparative analysis of the AIB1 interactome in breast cancer reveals MTA2 as a repressive partner which silences E-Cadherin to promote EMT and associates with a pro-metastatic phenotype

Funder: Breast Cancer Ireland GR 14-0883Abstract: Steroid regulated cancer cells use nuclear receptors and associated regulatory proteins to orchestrate transcriptional networks to drive disease progression. In primary breast cancer, the coactivator AIB1 promotes estrogen receptor (ER) transcriptional activity to enhance cell proliferation. The function of the coactivator in ER+ metastasis however is not established. Here we describe AIB1 as a survival factor, regulator of pro-metastatic transcriptional pathways and a promising actionable target. Genomic alterations and functional expression of AIB1 associated with reduced disease-free survival in patients and enhanced metastatic capacity in novel CDX and PDX ex-vivo models of ER+ metastatic disease. Comparative analysis of the AIB1 interactome with complementary RNAseq characterized AIB1 as a transcriptional repressor. Specifically, we report that AIB1 interacts with MTA2 to form a repressive complex, inhibiting CDH1 (encoding E-cadherin) to promote EMT and drive progression. We further report that pharmacological and genetic inhibition of AIB1 demonstrates significant anti-proliferative activity in patient-derived models establishing AIB1 as a viable strategy to target endocrine resistant metastasis. This work defines a novel role for AIB1 in the regulation of EMT through transcriptional repression in advanced cancer cells with a considerable implication for prognosis and therapeutic interventions

Comparative analysis of the AIB1 interactome in breast cancer reveals MTA2 as a repressive partner which silences E-Cadherin to promote EMT and associates with a pro-metastatic phenotype

Funder: Breast Cancer Ireland GR 14-0883Abstract: Steroid regulated cancer cells use nuclear receptors and associated regulatory proteins to orchestrate transcriptional networks to drive disease progression. In primary breast cancer, the coactivator AIB1 promotes estrogen receptor (ER) transcriptional activity to enhance cell proliferation. The function of the coactivator in ER+ metastasis however is not established. Here we describe AIB1 as a survival factor, regulator of pro-metastatic transcriptional pathways and a promising actionable target. Genomic alterations and functional expression of AIB1 associated with reduced disease-free survival in patients and enhanced metastatic capacity in novel CDX and PDX ex-vivo models of ER+ metastatic disease. Comparative analysis of the AIB1 interactome with complementary RNAseq characterized AIB1 as a transcriptional repressor. Specifically, we report that AIB1 interacts with MTA2 to form a repressive complex, inhibiting CDH1 (encoding E-cadherin) to promote EMT and drive progression. We further report that pharmacological and genetic inhibition of AIB1 demonstrates significant anti-proliferative activity in patient-derived models establishing AIB1 as a viable strategy to target endocrine resistant metastasis. This work defines a novel role for AIB1 in the regulation of EMT through transcriptional repression in advanced cancer cells with a considerable implication for prognosis and therapeutic interventions