An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics.

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.

Spatial molecular profiling of mixed invasive ductal and lobular breast cancers reveals heterogeneity in intrinsic molecular subtypes, oncogenic signatures, and mutations.

Mixed invasive ductal and lobular carcinoma (MDLC) is a rare histologic subtype of breast cancer displaying both E-cadherin positive ductal and E-cadherin negative lobular morphologies within the same tumor, posing challenges with regard to anticipated clinical management. It remains unclear whether these distinct morphologies also have distinct biology and risk of recurrence. Our spatially resolved transcriptomic, genomic, and single-cell profiling revealed clinically significant differences between ductal and lobular tumor regions including distinct intrinsic subtype heterogeneity - e.g., MDLC with triple-negative breast cancer (TNBC) or basal ductal and estrogen receptor positive (ER+) luminal lobular regions, distinct enrichment of cell cycle arrest/senescence and oncogenic (ER and MYC) signatures, genetic and epigenetic CDH1 inactivation in lobular but not ductal regions, and single-cell ductal and lobular subpopulations with unique oncogenic signatures further highlighting intraregional heterogeneity. Altogether, we demonstrated that the intratumoral morphological/histological heterogeneity within MDLC is underpinned by intrinsic subtype and oncogenic heterogeneity which may result in prognostic uncertainty and therapeutic dilemma.

Transcriptomic congruence analysis for evaluating model organisms.

Model organisms are instrumental substitutes for human studies to expedite basic, translational, and clinical research. Despite their indispensable role in mechanistic investigation and drug development, molecular congruence of animal models to humans has long been questioned and debated. Little effort has been made for an objective quantification and mechanistic exploration of a model organism's resemblance to humans in terms of molecular response under disease or drug treatment. We hereby propose a framework, namely Congruence Analysis for Model Organisms (CAMO), for transcriptomic response analysis by developing threshold-free differential expression analysis, quantitative concordance/discordance scores incorporating data variabilities, pathway-centric downstream investigation, knowledge retrieval by text mining, and topological gene module detection for hypothesis generation. Instead of a genome-wide vague and dichotomous answer of "poorly" or "greatly" mimicking humans, CAMO assists researchers to numerically quantify congruence, to dissect true cross-species differences from unwanted biological or cohort variabilities, and to visually identify molecular mechanisms and pathway subnetworks that are best or least mimicked by model organisms, which altogether provides foundations for hypothesis generation and subsequent translational decisions.

Systems approach for congruence and selection of cancer models towards precision medicine.

Cancer models are instrumental as a substitute for human studies and to expedite basic, translational, and clinical cancer research. For a given cancer type, a wide selection of models, such as cell lines, patient-derived xenografts, organoids and genetically modified murine models, are often available to researchers. However, how to quantify their congruence to human tumors and to select the most appropriate cancer model is a largely unsolved issue. Here, we present Congruence Analysis and Selection of CAncer Models (CASCAM), a statistical and machine learning framework for authenticating and selecting the most representative cancer models in a pathway-specific manner using transcriptomic data. CASCAM provides harmonization between human tumor and cancer model omics data, systematic congruence quantification, and pathway-based topological visualization to determine the most appropriate cancer model selection. The systems approach is presented using invasive lobular breast carcinoma (ILC) subtype and suggesting CAMA1 followed by UACC3133 as the most representative cell lines for ILC research. Two additional case studies for triple negative breast cancer (TNBC) and patient-derived xenograft/organoid (PDX/PDO) are further investigated. CASCAM is generalizable to any cancer subtype and will authenticate cancer models for faithful non-human preclinical research towards precision medicine.

Research autopsy programmes in oncology: shared experience from 14 centres across the world.

While there is a great clinical need to understand the biology of metastatic cancer in order to treat it more effectively, research is hampered by limited sample availability. Research autopsy programmes can crucially advance the field through synchronous, extensive, and high-volume sample collection. However, it remains an underused strategy in translational research. Via an extensive questionnaire, we collected information on the study design, enrolment strategy, study conduct, sample and data management, and challenges and opportunities of research autopsy programmes in oncology worldwide. Fourteen programmes participated in this study. Eight programmes operated 24 h/7 days, resulting in a lower median postmortem interval (time between death and start of the autopsy, 4 h) compared with those operating during working hours (9 h). Most programmes (n = 10) succeeded in collecting all samples within a median of 12 h after death. A large number of tumour sites were sampled during each autopsy (median 15.5 per patient). The median number of samples collected per patient was 58, including different processing methods for tumour samples but also non-tumour tissues and liquid biopsies. Unique biological insights derived from these samples included metastatic progression, treatment resistance, disease heterogeneity, tumour dormancy, interactions with the tumour micro-environment, and tumour representation in liquid biopsies. Tumour patient-derived xenograft (PDX) or organoid (PDO) models were additionally established, allowing for drug discovery and treatment sensitivity assays. Apart from the opportunities and achievements, we also present the challenges related with postmortem sample collections and strategies to overcome them, based on the shared experience of these 14 programmes. Through this work, we hope to increase the transparency of postmortem tissue donation, to encourage and aid the creation of new programmes, and to foster collaborations on these unique sample collections. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Hrq1/RECQL4 regulation is critical for preventing aberrant recombination during DNA intrastrand crosslink repair and is upregulated in breast cancer.

Human RECQL4 is a member of the RecQ family of DNA helicases and functions during DNA replication and repair. RECQL4 mutations are associated with developmental defects and cancer. Although RECQL4 mutations lead to disease, RECQL4 overexpression is also observed in cancer, including breast and prostate. Thus, tight regulation of RECQL4 protein levels is crucial for genome stability. Because mammalian RECQL4 is essential, how cells regulate RECQL4 protein levels is largely unknown. Utilizing budding yeast, we investigated the RECQL4 homolog, HRQ1, during DNA crosslink repair. We find that Hrq1 functions in the error-free template switching pathway to mediate DNA intrastrand crosslink repair. Although Hrq1 mediates repair of cisplatin-induced lesions, it is paradoxically degraded by the proteasome following cisplatin treatment. By identifying the targeted lysine residues, we show that preventing Hrq1 degradation results in increased recombination and mutagenesis. Like yeast, human RECQL4 is similarly degraded upon exposure to crosslinking agents. Furthermore, over-expression of RECQL4 results in increased RAD51 foci, which is dependent on its helicase activity. Using bioinformatic analysis, we observe that RECQL4 overexpression correlates with increased recombination and mutations. Overall, our study uncovers a role for Hrq1/RECQL4 in DNA intrastrand crosslink repair and provides further insight how misregulation of RECQL4 can promote genomic instability, a cancer hallmark.

iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data.

Multi-omics sequencing is poised to revolutionize clinical care in the coming decade. However, there is a lack of effective and interpretable genome-wide modeling methods for the rational selection of patients for personalized interventions. To address this, we present iGenSig-Rx, an integral genomic signature-based approach, as a transparent tool for modeling therapeutic response using clinical trial datasets. This method adeptly addresses challenges related to cross-dataset modeling by capitalizing on high-dimensional redundant genomic features, analogous to reinforcing building pillars with redundant steel rods. Moreover, it integrates adaptive penalization of feature redundancy on a per-sample basis to prevent score flattening and mitigate overfitting. We then developed a purpose-built R package to implement this method for modeling clinical trial datasets. When applied to genomic datasets for HER2 targeted therapies, iGenSig-Rx model demonstrates consistent and reliable predictive power across four independent clinical trials. More importantly, the iGenSig-Rx model offers the level of transparency much needed for clinical application, allowing for clear explanations as to how the predictions are produced, how the features contribute to the prediction, and what are the key underlying pathways. We anticipate that iGenSig-Rx, as an interpretable class of multi-omics modeling methods, will find broad applications in big-data based precision oncology. The R package is available: https://github.com/wangxlab/iGenSig-Rx .

Liver tropism of ER mutant breast cancer is characterized by unique molecular changes and immune infiltration.

PURPOSE: Hotspot estrogen receptor alpha (ER/ESR1) mutations are recognized as the driver for both endocrine resistance and metastasis in advanced ER-positive (ER+) breast cancer, but their contributions to metastatic organ tropism remain insufficiently understood. In this study, we aim to comprehensively profile the organotropic metastatic pattern for ESR1 mutant breast cancer. METHODS: The organ-specific metastatic pattern of ESR1 mutant breast cancer was delineated using multi-omics data from multiple publicly available cohorts of ER+ metastatic breast cancer patients. Gene mutation/copy number variation (CNV) and differential gene expression analyses were performed to identify the genomic and transcriptomic alterations uniquely associated with ESR1 mutant liver metastasis. Upstream regulator, downstream pathway, and immune infiltration analysis were conducted for subsequent mechanistic investigations. RESULTS: ESR1 mutation-driven liver tropism was revealed by significant differences, encompassing a higher prevalence of liver metastasis in patients with ESR1 mutant breast cancer and an enrichment of mutations in liver metastatic samples. The significant enrichment of AGO2 copy number amplifications (CNAs) and multiple gene expression changes were revealed uniquely in ESR1 mutant liver metastasis. We also unveiled alterations in downstream signaling pathways and immune infiltration, particularly an enrichment of neutrophils, suggesting potential therapeutic vulnerabilities. CONCLUSION: Our data provide a comprehensive characterization of the behaviors and mechanisms of ESR1 mutant liver metastasis, paving the way for the development of personalized therapy to target liver metastasis for patients with ESR1 mutant breast cancer.

RET signaling in breast cancer therapeutic resistance and metastasis.

RET, a single-pass receptor tyrosine kinase encoded on human chromosome 10, is well known to the field of developmental biology for its role in the ontogenesis of the central and enteric nervous systems and the kidney. In adults, RET alterations have been characterized as drivers of non-small cell lung cancer and multiple neuroendocrine neoplasms. In breast cancer, RET signaling networks have been shown to influence diverse functions including tumor development, metastasis, and therapeutic resistance. While RET is known to drive the development and progression of multiple solid tumors, therapeutic agents selectively targeting RET are relatively new, though multiple multi-kinase inhibitors have shown promise as RET inhibitors in the past; further, RET has been historically neglected as a potential therapeutic co-target in endocrine-refractory breast cancers despite mounting evidence for a key pathologic role and repeated description of a bi-directional relationship with the estrogen receptor, the principal driver of most breast tumors. Additionally, the recent discovery of RET enrichment in breast cancer brain metastases suggests a role for RET inhibition specific to advanced disease. This review assesses the status of research on RET in breast cancer and evaluates the therapeutic potential of RET-selective kinase inhibitors across major breast cancer subtypes.

Mixed invasive ductal lobular carcinoma is clinically and pathologically more similar to invasive lobular than ductal carcinoma.

BACKGROUND: Mixed invasive ductal lobular carcinoma (mDLC) remains a poorly understood subtype of breast cancer composed of coexisting ductal and lobular components. METHODS: We sought to describe clinicopathologic characteristics and determine whether mDLC is clinically more similar to invasive ductal carcinoma (IDC) or invasive lobular carcinoma (ILC), using data from patients seen at the University of Pittsburgh Medical Center. RESULTS: We observed a higher concordance in clinicopathologic characteristics between mDLC and ILC, compared to IDC. There is a trend for higher rates of successful breast-conserving surgery after neoadjuvant chemotherapy in patients with mDLC compared to patients with ILC, in which it is known to be lower than in those with IDC. Metastatic patterns of mDLC demonstrate a propensity to develop in sites characteristic of both IDC and ILC. A meta-analysis evaluating mDLC showed shared features with both ILC and IDC with significantly more ER-positive and fewer high grades in mDLC compared to IDC, although mDLCs were significantly smaller and included fewer late-stage tumours compared to ILC. CONCLUSIONS: These findings support clinicopathologic characteristics of mDLC driven by individual ductal vs lobular components and given the dominance of lobular pathology, mDLC features are often more similar to ILC than IDC. This study exemplifies the complexity of mixed disease.

Semi-deconvolution of bulk and single-cell RNA-seq data with application to metastatic progression in breast cancer.

MOTIVATION: Identifying cell types and their abundances and how these evolve during tumor progression is critical to understanding the mechanisms of metastasis and identifying predictors of metastatic potential that can guide the development of new diagnostics or therapeutics. Single-cell RNA sequencing (scRNA-seq) has been especially promising in resolving heterogeneity of expression programs at the single-cell level, but is not always feasible, e.g. for large cohort studies or longitudinal analysis of archived samples. In such cases, clonal subpopulations may still be inferred via genomic deconvolution, but deconvolution methods have limited ability to resolve fine clonal structure and may require reference cell type profiles that are missing or imprecise. Prior methods can eliminate the need for reference profiles but show unstable performance when few bulk samples are available. RESULTS: In this work, we develop a new method using reference scRNA-seq to interpret sample collections for which only bulk RNA-seq is available for some samples, e.g. clonally resolving archived primary tissues using scRNA-seq from metastases. By integrating such information in a Quadratic Programming framework, our method can recover more accurate cell types and corresponding cell type abundances in bulk samples. Application to a breast tumor bone metastases dataset confirms the power of scRNA-seq data to improve cell type inference and quantification in same-patient bulk samples. AVAILABILITY AND IMPLEMENTATION: Source code is available on Github at https://github.com/CMUSchwartzLab/RADs.

Fusion-associated carcinomas of the breast: Diagnostic, prognostic, and therapeutic significance.

Recurrent gene fusions comprise a class of viable genetic targets in solid tumors that have culminated several recent breakthrough cancer therapies. Their role in breast cancer, however, remains largely underappreciated due to the complexity of genomic rearrangements in breast malignancy. Just recently, we and others have identified several recurrent gene fusions in breast cancer with important clinical and biological implications. Examples of the most significant recurrent gene fusions to date include (1) ESR1::CCDC170 gene fusions in luminal B and endocrine-resistant breast cancer that exert oncogenic function via modulating the HER2/HER3/SRC Proto-Oncogene (SRC) complex, (2) ESR1 exon 6 fusions in metastatic disease that drive estrogen-independent estrogen-receptor transcriptional activity, (3) BCL2L14::ETV6 fusions in a more aggressive form of the triple-negative subtype that prime epithelial-mesenchymal transition and endow paclitaxel resistance, (4) the ETV6::NTRK3 fusion in secretory breast carcinoma that constitutively activates NTRK3 kinase, (5) the oncogenic MYB-NFIB fusion as a genetic driver underpinning adenoid cystic carcinomas of the breast that activates MYB Proto-Oncogene (MYB) pathway, and (6) the NOTCH/microtubule-associated serine-threonine (MAST) kinase gene fusions that activate NOTCH and MAST signaling. Importantly, these fusions are enriched in more aggressive and lethal breast cancer presentations and appear to confer therapeutic resistance. Thus, these gene fusions could be utilized as genetic biomarkers to identify patients who require more intensive treatment and surveillance. In addition, kinase fusions are currently being evaluated in breast cancer clinical trials and ongoing mechanistic investigation is exposing therapeutic vulnerabilities in patients with fusion-positive disease.

Molecular Biology Information Service: an innovative medical library-based bioinformatics support service for biomedical researchers.

Biomedical researchers are increasingly reliant on obtaining bioinformatics training in order to conduct their research. Here we present a model that academic institutions may follow to provide such training for their researchers, based on the Molecular Biology Information Service (MBIS) of the Health Sciences Library System, University of Pittsburgh (Pitt). The MBIS runs a four-facet service with the following goals: (1) identify, procure and implement commercially licensed bioinformatics software, (2) teach hands-on workshops using bioinformatics tools to solve research questions, (3) provide in-person and email consultations on software/databases and (4) maintain a web portal providing overall guidance on the access and use of bioinformatics resources and MBIS-created webtools. This paper describes these facets of MBIS activities from 2006 to 2018, including outcomes from a survey measuring attitudes of Pitt researchers about MBIS service and performance.

Inhibition of RPS6K reveals context-dependent Akt activity in luminal breast cancer cells.

Aberrant signaling through insulin (Ins) and insulin-like growth factor I (IGF1) receptors contribute to the risk and advancement of many cancer types by activating cell survival cascades. Similarities between these pathways have thus far prevented the development of pharmacological interventions that specifically target either Ins or IGF1 signaling. To identify differences in early Ins and IGF1 signaling mechanisms, we developed a dual receptor (IGF1R & InsR) computational response model. The model suggested that ribosomal protein S6 kinase (RPS6K) plays a critical role in regulating MAPK and Akt activation levels in response to Ins and IGF1 stimulation. As predicted, perturbing RPS6K kinase activity led to an increased Akt activation with Ins stimulation compared to IGF1 stimulation. Being able to discern differential downstream signaling, we can explore improved anti-IGF1R cancer therapies by eliminating the emergence of compensation mechanisms without disrupting InsR signaling.

IGF1R constitutive activation expands luminal progenitors and influences lineage differentiation during breast tumorigenesis.

Breast tumors display tremendous heterogeneity in part due to varying molecular alterations, divergent cells of origin, and differentiation. Understanding where and how this heterogeneity develops is likely important for effective breast cancer eradication. Insulin-like growth factor (IGF) signaling is critical for normal mammary gland development and function, and has an established role in tumor development and resistance to therapy. Here we demonstrate that constitutive activation of the IGF1 receptor (IGF1R) influences lineage differentiation during mammary tumorigenesis. Transgenic IGF1R constitutive activation promotes tumors with mixed histologies, multiple cell lineages and an expanded bi-progenitor population. In these tumors, IGF1R expands the luminal-progenitor population while influencing myoepithelial differentiation. Mammary gland transplantation with IGF1R-infected mammary epithelial cells (MECs) resulted in hyperplastic, highly differentiated outgrowths and attenuated reconstitution. Restricting IGF1R constitutive activation to luminal versus myoepithelial lineage-sorted MECs resulted in ductal reconstitutions co-expressing high IGF1R levels in the opposite lineage of origin. Using in vitro models, IGF1R constitutively activated MCF10A cells showed increased mammosphere formation and CD44+/CD24-population, which was dependent upon Snail and NFκB signaling. These results suggest that IGF1R expands luminal progenitor populations while also stimulating myoepithelial cell differentiation. This ability to influence lineage differentiation may promote heterogeneous mammary tumors, and have implications for clinical treatment.

Acquired mutations and transcriptional remodeling in long-term estrogen-deprived locoregional breast cancer recurrences.

BACKGROUND: Endocrine therapy resistance is a hallmark of advanced estrogen receptor (ER)-positive breast cancer. In this study, we aimed to determine acquired genomic changes in endocrine-resistant disease. METHODS: We performed DNA/RNA hybrid-capture sequencing on 12 locoregional recurrences after long-term estrogen deprivation and identified acquired genomic changes versus each tumor's matched primary. RESULTS: Despite being up to 7 years removed from the primary lesion, most recurrences harbored similar intrinsic transcriptional and copy number profiles. Only two genes, AKAP9 and KMT2C, were found to have single nucleotide variant (SNV) enrichments in more than one recurrence. Enriched mutations in single cases included SNVs within transcriptional regulators such as ARID1A, TP53, FOXO1, BRD1, NCOA1, and NCOR2 with one local recurrence gaining three PIK3CA mutations. In contrast to DNA-level changes, we discovered recurrent outlier mRNA expression alterations were common-including outlier gains in TP63 (n = 5 cases [42%]), NTRK3 (n = 5 [42%]), NTRK2 (n = 4 [33%]), PAX3 (n = 4 [33%]), FGFR4 (n = 3 [25%]), and TERT (n = 3 [25%]). Recurrent losses involved ESR1 (n = 5 [42%]), RELN (n = 5 [42%]), SFRP4 (n = 4 [33%]), and FOSB (n = 4 [33%]). ESR1-depleted recurrences harbored shared transcriptional remodeling events including upregulation of PROM1 and other basal cancer markers. CONCLUSIONS: Taken together, this study defines acquired genomic changes in long-term, estrogen-deprived disease; highlights the importance of longitudinal RNA profiling; and identifies a common ESR1-depleted endocrine-resistant breast cancer subtype with basal-like transcriptional reprogramming.

Robust and accurate deconvolution of tumor populations uncovers evolutionary mechanisms of breast cancer metastasis.

MOTIVATION: Cancer develops and progresses through a clonal evolutionary process. Understanding progression to metastasis is of particular clinical importance, but is not easily analyzed by recent methods because it generally requires studying samples gathered years apart, for which modern single-cell sequencing is rarely an option. Revealing the clonal evolution mechanisms in the metastatic transition thus still depends on unmixing tumor subpopulations from bulk genomic data. METHODS: We develop a novel toolkit called robust and accurate deconvolution (RAD) to deconvolve biologically meaningful tumor populations from multiple transcriptomic samples spanning the two progression states. RAD uses gene module compression to mitigate considerable noise in RNA, and a hybrid optimizer to achieve a robust and accurate solution. Finally, we apply a phylogenetic algorithm to infer how associated cell populations adapt across the metastatic transition via changes in expression programs and cell-type composition. RESULTS: We validated the superior robustness and accuracy of RAD over alternative algorithms on a real dataset, and validated the effectiveness of gene module compression on both simulated and real bulk RNA data. We further applied the methods to a breast cancer metastasis dataset, and discovered common early events that promote tumor progression and migration to different metastatic sites, such as dysregulation of ECM-receptor, focal adhesion and PI3k-Akt pathways. AVAILABILITY AND IMPLEMENTATION: The source code of the RAD package, models, experiments and technical details such as parameters, is available at https://github.com/CMUSchwartzLab/RAD. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Patient treatment and outcome after breast cancer orbital and periorbital metastases: a comprehensive case series including analysis of lobular versus ductal tumor histology.

BACKGROUND: Breast cancer is the most common malignancy to spread to the orbit and periorbit, and the invasive lobular carcinoma (ILC) histologic subtype of breast cancer has been reported to form these ophthalmic metastases (OM) more frequently than invasive ductal carcinomas (IDC). We herein report our single academic institution experience with breast cancer OM with respect to anatomical presentation, histology (lobular vs. ductal), treatment, and survival. METHODS: We employed the natural language processing platform, TIES (Text Information Extraction System), to search 2.3 million de-identified patient pathology and radiology records at our institution in order to identify patients with OM secondary to breast cancer. We then compared the resultant cohort, the "OM cohort," to two other representative metastatic breast cancer patient (MBC) databases from our institution. Histological analysis of selected patients was performed. RESULTS: Our TIES search and manual refinement ultimately identified 28 patients who were diagnosed with breast cancer between 1995 and 2016 that subsequently developed OM. Median age at diagnosis was 54 (range 28-77) years of age. ER, PR, and HER2 status from the 28 patients with OM did not differ from other patients with MBC from our institution. The relative proportion of patients with ILC was significantly higher in the OM cohort (32.1%) than in other MBC patients in our institution (11.3%, p = 0.007). Median time to first OM in the OM cohort was 46.7 months, and OM were the second most frequent first metastases after bony metastases. After diagnosis of the first distant metastasis of any kind, median survival of patients with ILC (21.4 months) was significantly shorter than that of patients with IDC (55.3 months, p = 0.03). Nine patients developed bilateral OM. We observed a significant co-occurrence of OM and central nervous system metastases (p = 0.0053). The histological analysis revealed an interesting case in which the primary tumor was of a mixed ILC/IDC subtype, while only ILC was present in the OM. CONCLUSIONS: OM from breast cancer are illustrative of the difference in metastatic behavior of ILC versus IDC and should be considered when treating patients with ILC, especially in those with complaints of visual acuity changes.

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.

Network-guided prediction of aromatase inhibitor response in breast cancer.

Prediction of response to specific cancer treatments is complicated by significant heterogeneity between tumors in terms of mutational profiles, gene expression, and clinical measures. Here we focus on the response of Estrogen Receptor (ER)+ post-menopausal breast cancer tumors to aromatase inhibitors (AI). We use a network smoothing algorithm to learn novel features that integrate several types of high throughput data and new cell line experiments. These features greatly improve the ability to predict response to AI when compared to prior methods. For a subset of the patients, for which we obtained more detailed clinical information, we can further predict response to a specific AI drug.