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The galectin family: 15 glycan readers reshaping cancer biology

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T hey bind sugar. They talk to tumours. And they may be the next frontier in cancer immunotherapy. Every cell in your body is coated in a dense forest of sugar chains — glycans. These structures are not decoration. They are read, constantly, by a class of proteins called lectins. Among them, the galectins stand out: a family of 15 human genes whose products specifically recognise β-galactoside motifs and translate glycan patterns into cellular decisions about growth, survival, and immune response. In the language of glycobiology, galectins are glycan readers — they do not build or break sugar chains (that is the job of glycosyltransferase writers and glycosidase erasers), but they interpret them. And increasingly, it is clear that what they read in tumours spells trouble for the immune system. The family tree: three structural archetypes All 15 members share a conserved carbohydrate recognition domain (CRD), but differ in how many CRDs they carry and how those domains are arranged. T...
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The SOX Family: Master Regulators of Development

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 Few protein families in molecular biology are as versatile — or as consequential — as the SOX transcription factors. From the earliest moments of embryonic development to the maintenance of adult tissues, these proteins quietly orchestrate some of the most fundamental decisions a cell ever makes. What Are SOX Factors? SOX proteins take their name from S RY- b ox, a reference to SRY ( Sex-determining Region Y ), the founding member of the family discovered in 1990. SRY turned out to encode a transcription factor with a distinctive DNA-binding domain called the HMG (High Mobility Group) box. When researchers began scanning the genome for proteins sharing this domain, they found not one or two relatives, but an entire family — 19 members in humans, now classified into subgroups A through H. What all SOX proteins share is that HMG box: a roughly 80-amino-acid domain that grips the minor groove of DNA and bends it sharply, sometimes by as much as 70–85 degrees. This bending is not i...
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Unlocking Cancer Insights: Introducing the R2 Platform

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Every year, millions of people are diagnosed with cancer — a disease that, despite its name, is not one condition but thousands. Understanding why tumors behave differently from patient to patient is one of the most pressing challenges in modern medicine. A major breakthrough came with The Cancer Genome Atlas (TCGA), a landmark project that molecularly mapped over 30 types of cancer across thousands of patients. But having data is only half the battle. Making sense of it — quickly, reliably, and without needing a team of bioinformaticians — is where progress stalls. That's the problem R2 was built to solve. R2 is a free, browser-based platform that puts powerful cancer genomics analysis in the hands of any researcher or clinician, no coding required. Drawing on TCGA RNA-seq and clinical data from 31 cancer types, R2 lets you ask — and answer — complex biological questions in minutes. Here's what you can do with it: Compare gene activity across cancer types and patient g...
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A Multi-Omics Resource for Neuroblastoma Research: Molecular Profiles and Drug Response Data for Classical NB Cell Lines

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Neuroblastoma remains one of the most challenging pediatric cancers to treat. Despite decades of research, high-risk disease still carries a poor prognosis, and finding effective therapies requires a deep understanding of the molecular landscape driving each tumor. To support that mission, we are excited to announce the release of a comprehensive multi-omics dataset covering the most widely used classical neuroblastoma cell lines — an openly accessible resource designed to accelerate discovery across the field. What Is in the Dataset? This resource brings together multiple layers of molecular and pharmacological data for a panel of classical neuroblastoma cell lines. In one place, researchers can now access: Transcriptomics (mRNA expression) Genome-wide gene expression profiles capturing the transcriptional state of each cell line. These data allow researchers to explore pathway activity, subtype classification, and gene regulatory networks. DNA copy number variation Genome-scal...
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"From Molecule to Medicine" — Using Target Actionability Reviews to Bridge Data and Drug Development

 Most research stories end with a publication. The best ones end with a treatment. Between those two endpoints lies a long and difficult journey: from a statistically significant finding in a patient cohort, through mechanistic validation, to a druggable target, through preclinical models, to a clinical trial. It's a journey that requires not just good science, but an organised, evidence-based case for why this particular target, in this particular cancer, deserves the investment of a drug development programme. R2's Target Actionability Review (TAR) module is a tool built specifically to support that case-building process — and it is unlike almost anything else in a genomics platform. A TAR is a manually curated, structured literature review focused on a single gene target in a single cancer context. It brings together evidence from multiple domains: the genomic prevalence of alterations in the target, the functional evidence linking it to disease biology, the availability...
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"Making It Yours" — Uploading Data, Building Tracks and Collaborating in R2

 At some point, the public datasets stop being enough. You've spent months characterising a cohort of your own — patient samples collected through your clinical network, cell lines you've treated and profiled, an in vivo experiment that generated a dataset unlike anything in the public domain. The analysis tools in R2 are exactly what you need. But your data isn't there yet. The Adapting R2 tools are what bridge that gap — and they're more capable than most users realise. Uploading your own dataset to R2 is a structured process, but a manageable one. You prepare your expression matrix and sample annotation file in the required formats, submit them through the platform's data addition workflow, and within a defined turnaround time, your dataset is live in your private R2 workspace — fully accessible through every analysis module the platform offers, while remaining invisible to anyone outside your authorised group. Once your data is in, you can enrich it with cu...
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"The Whole Genome Story" — Visualising Structural Variation with WGS Data

 Gene expression tells you what a cell is doing right now. But cancer is, at its heart, a disease of the genome — of broken chromosomes, rearranged sequences, amplified oncogenes and deleted tumour suppressors. To understand why a gene is expressed the way it is, sometimes you need to see the structural context: the copy number landscape, the chromosomal rearrangements, the mutations that preceded everything else. R2's WGS/NGS integration tools bring whole-genome sequencing data into the same analytical space as expression data, and the entry point is one of the most visually striking displays in the platform: the Circos plot . A Circos plot is a circular representation of the entire genome. Each chromosome occupies an arc of the circle, and lines drawn across the interior of the circle connect genomic regions that have been rearranged relative to each other — translocations, inversions, insertions of one chromosome into another. For complex cancer genomes, these plots can look ...
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