A temporal in vivo catalog of chromatin accessibility and expression profiles in pineoblastoma reveals a prevalent role for repressor elements.

Pediatric pineoblastomas (PBs) are rare and aggressive tumors of grade IV histology. Although some oncogenic drivers are characterized, including germline mutations in RB1 and DICER1, the role of epigenetic deregulation and cis-regulatory regions in PB pathogenesis and progression is largely unknown. Here, we generated genome-wide gene expression, chromatin accessibility, and H3K27ac profiles covering key time points of PB initiation and progression from pineal tissues of a mouse model of CCND1-driven PB. We identified PB-specific enhancers and super-enhancers, and found that in some cases, the accessible genome dynamics precede transcriptomic changes, a characteristic that is underexplored in tumor progression. During progression of PB, newly acquired open chromatin regions lacking H3K27ac signal become enriched for repressive state elements and harbor motifs of repressor transcription factors like HINFP, GLI2, and YY1. Copy number variant analysis identified deletion events specific to the tumorigenic stage, affecting, among others, the histone gene cluster and Gas1, the growth arrest specific gene. Gene set enrichment analysis and gene expression signatures positioned the model used here close to human PB samples, showing the potential of our findings for exploring new avenues in PB management and therapy. Overall, this study reports the first temporal and in vivo cis-regulatory, expression, and accessibility maps in PB.

Whole genomes define concordance of matched primary, xenograft, and organoid models of pancreas cancer.

Pancreatic ductal adenocarcinoma (PDAC) has the worst prognosis among solid malignancies and improved therapeutic strategies are needed to improve outcomes. Patient-derived xenografts (PDX) and patient-derived organoids (PDO) serve as promising tools to identify new drugs with therapeutic potential in PDAC. For these preclinical disease models to be effective, they should both recapitulate the molecular heterogeneity of PDAC and validate patient-specific therapeutic sensitivities. To date however, deep characterization of the molecular heterogeneity of PDAC PDX and PDO models and comparison with matched human tumour remains largely unaddressed at the whole genome level. We conducted a comprehensive assessment of the genetic landscape of 16 whole-genome pairs of tumours and matched PDX, from primary PDAC and liver metastasis, including a unique cohort of 5 'trios' of matched primary tumour, PDX, and PDO. We developed a pipeline to score concordance between PDAC models and their paired human tumours for genomic events, including mutations, structural variations, and copy number variations. Tumour-model comparisons of mutations displayed single-gene concordance across major PDAC driver genes, but relatively poor agreement across the greater mutational load. Genome-wide and chromosome-centric analysis of structural variation (SV) events highlights previously unrecognized concordance across chromosomes that demonstrate clustered SV events. We found that polyploidy presented a major challenge when assessing copy number changes; however, ploidy-corrected copy number states suggest good agreement between donor-model pairs. Collectively, our investigations highlight that while PDXs and PDOs may serve as tractable and transplantable systems for probing the molecular properties of PDAC, these models may best serve selective analyses across different levels of genomic complexity.

A subgroup of microRNAs defines PTEN-deficient, triple-negative breast cancer patients with poorest prognosis and alterations in RB1, MYC, and Wnt signaling.

BACKGROUND: Triple-negative breast cancer (TNBC) represents a heterogeneous group of ER- and HER2-negative tumors with poor clinical outcome. We recently reported that Pten-loss cooperates with low expression of microRNA-145 to induce aggressive TNBC-like lesions in mice. To systematically identify microRNAs that cooperate with PTEN-loss to induce aggressive human BC, we screened for miRNAs whose expression correlated with PTEN mRNA levels and determined the prognostic power of each PTEN-miRNA pair alone and in combination with other miRs. METHODS: Publically available data sets with mRNA, microRNA, genomics, and clinical outcome were interrogated to identify miRs that correlate with PTEN expression and predict poor clinical outcome. Alterations in genomic landscape and signaling pathways were identified in most aggressive TNBC subgroups. Connectivity mapping was used to predict response to therapy. RESULTS: In TNBC, PTEN loss cooperated with reduced expression of hsa-miR-4324, hsa-miR-125b, hsa-miR-381, hsa-miR-145, and has-miR136, all previously implicated in metastasis, to predict poor prognosis. A subgroup of TNBC patients with PTEN-low and reduced expression of four or five of these miRs exhibited the worst clinical outcome relative to other TNBCs (hazard ratio (HR) = 3.91; P < 0.0001), and this was validated on an independent cohort (HR = 4.42; P = 0.0003). The PTEN-low/miR-low subgroup showed distinct oncogenic alterations as well as TP53 mutation, high RB1-loss signature and high MYC, PI3K, and ß-catenin signaling. This lethal subgroup almost completely overlapped with TNBC patients selected on the basis of Pten-low and RB1 signature loss or ß-catenin signaling-high. Connectivity mapping predicted response to inhibitors of the PI3K pathway. CONCLUSIONS: This analysis identified microRNAs that define a subclass of highly lethal TNBCs that should be prioritized for aggressive therapy.

Public data and open source tools for multi-assay genomic investigation of disease.

Molecular interrogation of a biological sample through DNA sequencing, RNA and microRNA profiling, proteomics and other assays, has the potential to provide a systems level approach to predicting treatment response and disease progression, and to developing precision therapies. Large publicly funded projects have generated extensive and freely available multi-assay data resources; however, bioinformatic and statistical methods for the analysis of such experiments are still nascent. We review multi-assay genomic data resources in the areas of clinical oncology, pharmacogenomics and other perturbation experiments, population genomics and regulatory genomics and other areas, and tools for data acquisition. Finally, we review bioinformatic tools that are explicitly geared toward integrative genomic data visualization and analysis. This review provides starting points for accessing publicly available data and tools to support development of needed integrative methods.

Genefu: an R/Bioconductor package for computation of gene expression-based signatures in breast cancer.

UNLABELLED: Breast cancer is one of the most frequent cancers among women. Extensive studies into the molecular heterogeneity of breast cancer have produced a plethora of molecular subtype classification and prognosis prediction algorithms, as well as numerous gene expression signatures. However, reimplementation of these algorithms is a tedious but important task to enable comparison of existing signatures and classification models between each other and with new models. Here, we present the genefu R/Bioconductor package, a multi-tiered compendium of bioinformatics algorithms and gene signatures for molecular subtyping and prognostication in breast cancer. AVAILABILITY AND IMPLEMENTATION: The genefu package is available from Bioconductor. http://www.bioconductor.org/packages/devel/bioc/html/genefu.html Source code is also available on Github https://github.com/bhklab/genefu CONTACT: bhaibeka@uhnresearch.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PharmacoGx: an R package for analysis of large pharmacogenomic datasets.

UNLABELLED: Pharmacogenomics holds great promise for the development of biomarkers of drug response and the design of new therapeutic options, which are key challenges in precision medicine. However, such data are scattered and lack standards for efficient access and analysis, consequently preventing the realization of the full potential of pharmacogenomics. To address these issues, we implemented PharmacoGx, an easy-to-use, open source package for integrative analysis of multiple pharmacogenomic datasets. We demonstrate the utility of our package in comparing large drug sensitivity datasets, such as the Genomics of Drug Sensitivity in Cancer and the Cancer Cell Line Encyclopedia. Moreover, we show how to use our package to easily perform Connectivity Map analysis. With increasing availability of drug-related data, our package will open new avenues of research for meta-analysis of pharmacogenomic data. AVAILABILITY AND IMPLEMENTATION: PharmacoGx is implemented in R and can be easily installed on any system. The package is available from CRAN and its source code is available from GitHub. CONTACT: bhaibeka@uhnresearch.ca or benjamin.haibe.kains@utoronto.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Medulloblastoma subgroups remain stable across primary and metastatic compartments.

Medulloblastoma comprises four distinct molecular variants with distinct genetics, transcriptomes, and outcomes. Subgroup affiliation has been previously shown to remain stable at the time of recurrence, which likely reflects their distinct cells of origin. However, a therapeutically relevant question that remains unanswered is subgroup stability in the metastatic compartment. We assembled a cohort of 12-paired primary-metastatic tumors collected in the MAGIC consortium, and established their molecular subgroup affiliation by performing integrative gene expression and DNA methylation analysis. Frozen tissues were collected and profiled using Affymetrix gene expression arrays and Illumina methylation arrays. Class prediction and hierarchical clustering were performed using existing published datasets. Our molecular analysis, using consensus integrative genomic data, establishes the unequivocal maintenance of molecular subgroup affiliation in metastatic medulloblastoma. We further validated these findings by interrogating a non-overlapping cohort of 19 pairs of primary-metastatic tumors from the Burdenko Neurosurgical Institute using an orthogonal technique of immunohistochemical staining. This investigation represents the largest reported primary-metastatic paired cohort profiled to date and provides a unique opportunity to evaluate subgroup-specific molecular aberrations within the metastatic compartment. Our findings further support the hypothesis that medulloblastoma subgroups arise from distinct cells of origin, which are carried forward from ontogeny to oncology.

Circulating tumour DNA detects somatic variants contributing to spatial and temporal intra-tumoural heterogeneity in head and neck squamous cell carcinoma.

Background: As circulating tumour DNA (ctDNA) liquid biopsy analysis is increasingly incorporated into modern oncological practice, establishing the impact of genomic intra-tumoural heterogeneity (ITH) upon data output is paramount. Despite advances in other cancer types the evidence base in head and neck squamous cell carcinoma (HNSCC) remains poor. We sought to investigate the utility of ctDNA to detect ITH in HNSCC. Methods: In a pilot cohort of 9 treatment-naïve HNSCC patients, DNA from two intra-tumoural sites (core and margin) was whole-exome sequenced. A 9-gene panel was designed to perform targeted sequencing on pre-treatment plasma cell-free DNA and selected post-treatment samples. Results: Rates of genomic ITH among the 9 patients was high. COSMIC variants from 19 TCGA HNSCC genes demonstrated an 86.9% heterogeneity rate (present in one tumour sub-site only). Across all patients, cell-free DNA (ctDNA) identified 12.9% (range 7.5-19.8%) of tumour-specific variants, of which 55.6% were specific to a single tumour sub-site only. CtDNA identified 79.0% (range: 55.6-90.9%) of high-frequency variants (tumour VAF>5%). Analysis of ctDNA in serial post-treatment blood samples in patients who suffered recurrence demonstrated dynamic changes in both tumour-specific and acquired variants that predicted recurrence ahead of clinical detection. Conclusion: We demonstrate that a ctDNA liquid biopsy identified spatial genomic ITH in HNSCC and reliably detected high-frequency driver mutations. Serial sampling allowed post-treatment surveillance and early identification of treatment failure.

The landscape of the prion protein's structural response to mutation revealed by principal component analysis of multiple NMR ensembles.

Prion Proteins (PrP) are among a small number of proteins for which large numbers of NMR ensembles have been resolved for sequence mutants and diverse species. Here, we perform a comprehensive principle components analysis (PCA) on the tertiary structures of PrP globular proteins to discern PrP subdomains that exhibit conformational change in response to point mutations and clade-specific evolutionary sequence mutation trends. This is to our knowledge the first such large-scale analysis of multiple NMR ensembles of protein structures, and the first study of its kind for PrPs. We conducted PCA on human (n = 11), mouse (n = 14), and wildtype (n = 21) sets of PrP globular structures, from which we identified five conformationally variable subdomains within PrP. PCA shows that different non-local patterns and rankings of variable subdomains arise for different pathogenic mutants. These subdomains may thus be key areas for initiating PrP conversion during disease. Furthermore, we have observed the conformational clustering of divergent TSE-non-susceptible species pairs; these non-phylogenetic clusterings indicate structural solutions towards TSE resistance that do not necessarily coincide with evolutionary divergence. We discuss the novelty of our approach and the importance of PrP subdomains in structural conversion during disease.

Overview of Bioinformatics Software and Databases for Metabolic Engineering.

The explosion of the "omics" era has introduced a growing number of sets and tools that facilitate molecular interrogation of the metabolome. These include various bioinformatics and pharmacogenomics resources that can be utilized independently or collectively to facilitate metabolic engineering across disease, clinical oncology, and understanding of molecular changes across larger systems. This review provides starting points for accessing publicly available data and computational tools that support assessment of metabolic profiles and metabolic regulation, providing both a depth-and-breadth approach toward understanding the metabolome. We focus in particular on pathway databases and tools, which provide in-depth analysis of metabolic pathways, which is at the heart of metabolic engineering.

Computational pharmacogenomic screen identifies drugs that potentiate the anti-breast cancer activity of statins.

Statins, a family of FDA-approved cholesterol-lowering drugs that inhibit the rate-limiting enzyme of the mevalonate metabolic pathway, have demonstrated anticancer activity. Evidence shows that dipyridamole potentiates statin-induced cancer cell death by blocking a restorative feedback loop triggered by statin treatment. Leveraging this knowledge, we develop an integrative pharmacogenomics pipeline to identify compounds similar to dipyridamole at the level of drug structure, cell sensitivity and molecular perturbation. To overcome the complex polypharmacology of dipyridamole, we focus our pharmacogenomics pipeline on mevalonate pathway genes, which we name mevalonate drug-network fusion (MVA-DNF). We validate top-ranked compounds, nelfinavir and honokiol, and identify that low expression of the canonical epithelial cell marker, E-cadherin, is associated with statin-compound synergy. Analysis of remaining prioritized hits led to the validation of additional compounds, clotrimazole and vemurafenib. Thus, our computational pharmacogenomic approach identifies actionable compounds with pathway-specific activities.

The anti-tumour activity of DNA methylation inhibitor 5-aza-2'-deoxycytidine is enhanced by the common analgesic paracetamol through induction of oxidative stress.

The DNA demethylating agent 5-aza-2'-deoxycytidine (DAC, decitabine) has anti-cancer therapeutic potential, but its clinical efficacy is hindered by DNA damage-related side effects and its use in solid tumours is debated. Here we describe how paracetamol augments the effects of DAC on cancer cell proliferation and differentiation, without enhancing DNA damage. Firstly, DAC specifically upregulates cyclooxygenase-2-prostaglandin E2 pathway, inadvertently providing cancer cells with survival potential, while the addition of paracetamol offsets this effect. Secondly, in the presence of paracetamol, DAC treatment leads to glutathione depletion and finally to accumulation of ROS and/or mitochondrial superoxide, both of which have the potential to restrict tumour growth. The benefits of combined treatment are demonstrated here in head and neck squamous cell carcinoma (HNSCC) and acute myeloid leukaemia cell lines, further corroborated in a HNSCC xenograft mouse model and through mining of publicly available DAC and paracetamol responses. The sensitizing effect of paracetamol supplementation is specific to DAC but not its analogue 5-azacitidine. In summary, the addition of paracetamol could allow for DAC dose reduction, widening its clinical usability and providing a strong rationale for consideration in cancer therapy.

Bioinformatics and computational approaches for analyzing patient-derived disease models in cancer research.

Patient-derived organoids (PDO) and patient-derived xenografts (PDX) continue to emerge as important preclinical platforms for investigations into the molecular landscape of cancer. While the advantages and disadvantage of these models have been described in detail, this review focuses in particular on the bioinformatics and state-of-the art techniques that accompany preclinical model development. We discuss the strength and limitations of currently used technologies, particularly 'omics profiling and bioinformatics analyses, in addressing the 'efficacy' of preclinical models, both for tumour characterization as well as their use in identifying potential therapeutics. We select pancreatic ductal adenocarcinoma (PDAC) as a case study to highlight the state of the art of the field, and address new avenues for improved bioinformatics characterization of preclinical models.

Modeling germline mutations in pineoblastoma uncovers lysosome disruption-based therapy.

Pineoblastoma is a rare pediatric cancer induced by germline mutations in the tumor suppressors RB1 or DICER1. Presence of leptomeningeal metastases is indicative of poor prognosis. Here we report that inactivation of Rb plus p53 via a WAP-Cre transgene, commonly used to target the mammary gland during pregnancy, induces metastatic pineoblastoma resembling the human disease with 100% penetrance. A stabilizing mutation rather than deletion of p53 accelerates metastatic dissemination. Deletion of Dicer1 plus p53 via WAP-Cre also predisposes to pineoblastoma, albeit with lower penetrance. In silico analysis predicts tricyclic antidepressants such as nortriptyline as potential therapeutics for both pineoblastoma models. Nortriptyline disrupts the lysosome, leading to accumulation of non-functional autophagosome, cathepsin B release and pineoblastoma cell death. Nortriptyline further synergizes with the antineoplastic drug gemcitabine to effectively suppress pineoblastoma in our preclinical models, offering new modality for this lethal childhood malignancy.

MetaGxData: Clinically Annotated Breast, Ovarian and Pancreatic Cancer Datasets and their Use in Generating a Multi-Cancer Gene Signature.

A wealth of transcriptomic and clinical data on solid tumours are under-utilized due to unharmonized data storage and format. We have developed the MetaGxData package compendium, which includes manually-curated and standardized clinical, pathological, survival, and treatment metadata across breast, ovarian, and pancreatic cancer data. MetaGxData is the largest compendium of curated transcriptomic data for these cancer types to date, spanning 86 datasets and encompassing 15,249 samples. Open access to standardized metadata across cancer types promotes use of their transcriptomic and clinical data in a variety of cross-tumour analyses, including identification of common biomarkers, and assessing the validity of prognostic signatures. Here, we demonstrate that MetaGxData is a flexible framework that facilitates meta-analyses by using it to identify common prognostic genes in ovarian and breast cancer. Furthermore, we use the data compendium to create the first gene signature that is prognostic in a meta-analysis across 3 cancer types. These findings demonstrate the potential of MetaGxData to serve as an important resource in oncology research, and provide a foundation for future development of cancer-specific compendia.

Correlating novel variable and conserved motifs in the Hemagglutinin protein with significant biological functions.

BACKGROUND: Variations in the influenza Hemagglutinin protein contributes to antigenic drift resulting in decreased efficiency of seasonal influenza vaccines and escape from host immune response. We performed an in silico study to determine characteristics of novel variable and conserved motifs in the Hemagglutinin protein from previously reported H3N2 strains isolated from Hong Kong from 1968-1999 to predict viral motifs involved in significant biological functions. RESULTS: 14 MEME blocks were generated and comparative analysis of the MEME blocks identified blocks 1, 2, 3 and 7 to correlate with several biological functions. Analysis of the different Hemagglutinin sequences elucidated that the single block 7 has the highest frequency of amino acid substitution and the highest number of co-mutating pairs. MEME 2 showed intermediate variability and MEME 1 was the most conserved. Interestingly, MEME blocks 2 and 7 had the highest incidence of potential post-translational modifications sites including phosphorylation sites, ASN glycosylation motifs and N-myristylation sites. Similarly, these 2 blocks overlap with previously identified antigenic sites and receptor binding sites. CONCLUSION: Our study identifies motifs in the Hemagglutinin protein with different amino acid substitution frequencies over a 31 years period, and derives relevant functional characteristics by correlation of these motifs with potential post-translational modifications sites, antigenic and receptor binding sites.

Consensus on Molecular Subtypes of High-Grade Serous Ovarian Carcinoma.

Purpose: The majority of ovarian carcinomas are of high-grade serous histology, which is associated with poor prognosis. Surgery and chemotherapy are the mainstay of treatment, and molecular characterization is necessary to lead the way to targeted therapeutic options. To this end, various computational methods for gene expression-based subtyping of high-grade serous ovarian carcinoma (HGSOC) have been proposed, but their overlap and robustness remain unknown.Experimental Design: We assess three major subtype classifiers by meta-analysis of publicly available expression data, and assess statistical criteria of subtype robustness and classifier concordance. We develop a consensus classifier that represents the subtype classifications of tumors based on the consensus of multiple methods, and outputs a confidence score. Using our compendium of expression data, we examine the possibility that a subset of tumors is unclassifiable based on currently proposed subtypes.Results: HGSOC subtyping classifiers exhibit moderate pairwise concordance across our data compendium (58.9%-70.9%; P < 10-5) and are associated with overall survival in a meta-analysis across datasets (P < 10-5). Current subtypes do not meet statistical criteria for robustness to reclustering across multiple datasets (prediction strength < 0.6). A new subtype classifier is trained on concordantly classified samples to yield a consensus classification of patient tumors that correlates with patient age, survival, tumor purity, and lymphocyte infiltration.Conclusions: A new consensus ovarian subtype classifier represents the consensus of methods and demonstrates the importance of classification approaches for cancer that do not require all tumors to be assigned to a distinct subtype. Clin Cancer Res; 24(20); 5037-47. ©2018 AACR.

Administration of Hypoxia-Activated Prodrug Evofosfamide after Conventional Adjuvant Therapy Enhances Therapeutic Outcome and Targets Cancer-Initiating Cells in Preclinical Models of Colorectal Cancer.

Purpose: Cancer-initiating cells (C-IC) have been described in multiple cancer types, including colorectal cancer. C-ICs are defined by their capacity to self-renew, thereby driving tumor growth. C-ICs were initially thought to be static entities; however, recent studies have determined these cells to be dynamic and influenced by microenvironmental cues such as hypoxia. If hypoxia drives the formation of C-ICs, then therapeutic targeting of hypoxia could represent a novel means to target C-ICs.Experimental Design: Patient-derived colorectal cancer xenografts were treated with evofosfamide, a hypoxia-activated prodrug (HAP), in combination with 5-fluorouracil (5-FU) or chemoradiotherapy (5-FU and radiation; CRT). Treatment groups included both concurrent and sequential dosing regimens. Effects on the colorectal cancer-initiating cell (CC-IC) fraction were assessed by serial passage in vivo limiting dilution assays. FAZA-PET imaging was utilized as a noninvasive method to assess intratumoral hypoxia.Results: Hypoxia was sufficient to drive the formation of CC-ICs and colorectal cancer cells surviving conventional therapy were more hypoxic and C-IC-like. Using a novel approach to combination therapy, we show that sequential treatment with 5-FU or CRT followed by evofosfamide not only inhibits tumor growth of xenografts compared with 5-FU or CRT alone, but also significantly decreases the CC-IC fraction. Furthermore, noninvasive FAZA-PET hypoxia imaging was predictive of a tumor's response to evofosfamide.Conclusions: Our data demonstrate a novel means to target the CC-IC fraction by adding a HAP sequentially after conventional adjuvant therapy, as well as the use of FAZA-PET as a biomarker for hypoxia to identify tumors that will benefit most from this approach. Clin Cancer Res; 24(9); 2116-27. ©2018 AACR.

Integrative Cancer Pharmacogenomics to Infer Large-Scale Drug Taxonomy.

Identification of drug targets and mechanism of action (MoA) for new and uncharacterized anticancer drugs is important for optimization of treatment efficacy. Current MoA prediction largely relies on prior information including side effects, therapeutic indication, and chemoinformatics. Such information is not transferable or applicable for newly identified, previously uncharacterized small molecules. Therefore, a shift in the paradigm of MoA predictions is necessary toward development of unbiased approaches that can elucidate drug relationships and efficiently classify new compounds with basic input data. We propose here a new integrative computational pharmacogenomic approach, referred to as Drug Network Fusion (DNF), to infer scalable drug taxonomies that rely only on basic drug characteristics toward elucidating drug-drug relationships. DNF is the first framework to integrate drug structural information, high-throughput drug perturbation, and drug sensitivity profiles, enabling drug classification of new experimental compounds with minimal prior information. DNF taxonomy succeeded in identifying pertinent and novel drug-drug relationships, making it suitable for investigating experimental drugs with potential new targets or MoA. The scalability of DNF facilitated identification of key drug relationships across different drug categories, providing a flexible tool for potential clinical applications in precision medicine. Our results support DNF as a valuable resource to the cancer research community by providing new hypotheses on compound MoA and potential insights for drug repurposing. Cancer Res; 77(11); 3057-69. ©2017 AACR.

MM2S: personalized diagnosis of medulloblastoma patients and model systems.

BACKGROUND: Medulloblastoma (MB) is a highly malignant and heterogeneous brain tumour that is the most common cause of cancer-related deaths in children. Increasing availability of genomic data over the last decade had resulted in improvement of human subtype classification methods, and the parallel development of MB mouse models towards identification of subtype-specific disease origins and signaling pathways. Despite these advances, MB classification schemes remained inadequate for personalized prediction of MB subtypes for individual patient samples and across model systems. To address this issue, we developed the Medullo-Model to Subtypes ( MM2S ) classifier, a new method enabling classification of individual gene expression profiles from MB samples (patient samples, mouse models, and cell lines) against well-established molecular subtypes [Genomics 106:96-106, 2015]. We demonstrated the accuracy and flexibility of MM2S in the largest meta-analysis of human patients and mouse models to date. Here, we present a new functional package that provides an easy-to-use and fully documented implementation of the MM2S method, with additional functionalities that allow users to obtain graphical and tabular summaries of MB subtype predictions for single samples and across sample replicates. The flexibility of the MM2S package promotes incorporation of MB predictions into large Medulloblastoma-driven analysis pipelines, making this tool suitable for use by researchers. RESULTS: The MM2S package is applied in two case studies involving human primary patient samples, as well as sample replicates of the GTML mouse model. We highlight functions that are of use for species-specific MB classification, across individual samples and sample replicates. We emphasize on the range of functions that can be used to derive both singular and meta-centric views of MB predictions, across samples and across MB subtypes. CONCLUSIONS: Our MM2S package can be used to generate predictions without having to rely on an external web server or additional sources. Our open-source package facilitates and extends the MM2S algorithm in diverse computational and bioinformatics contexts. The package is available on CRAN, at the following URL: https://cran.r-project.org/web/packages/MM2S/, as well as on Github at the following URLs: https://github.com/DGendoo and https://github.com/bhklab.