子どもの脳腫瘍は10分のスキャンで診断可能、代謝シグネチャーに基づく(Children’s brain tumours could be diagnosed with 10 min scan, based on metabolic signature)

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2024-06-10 バーミンガム大学

新しい研究により、最も一般的な悪性脳腫瘍である髄芽腫を持つ子供たちの診断待ち時間が短縮される可能性があります。この研究は、バーミンガム大学とニューカッスル大学が主導し、86の腫瘍から細胞サンプルを取り、各腫瘍グループに特有の代謝マーカーを特定しました。MRIスキャンと機械学習を組み合わせて、侵襲的な生検なしで髄芽腫を評価でき、現在の3~4週間の診断待ち時間を大幅に短縮する可能性があります。研究により、髄芽腫の四つの異なるグループがそれぞれ特有の代謝プロファイルを持つことが確認され、迅速な診断と最適な治療が可能となります。この発見は、がん診断の迅速化と治療の質向上に寄与すると期待されています。

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髄芽腫の代謝物プロファイルから分子疾患群を迅速かつ非侵襲的に検出する Metabolite profiles of medulloblastoma for rapid and non-invasive detection of molecular disease groups

Sarah Kohe,Christopher Bennett,Florence Burté,Magretta Adiamah,Heather Rose,Lara Worthington,et al.
eBioMedicine  Published:January 06, 2024
DOI:https://doi.org/10.1016/j.ebiom.2023.104958

子どもの脳腫瘍は10分のスキャンで診断可能、代謝シグネチャーに基づく(Children’s brain tumours could be diagnosed with 10 min scan, based on metabolic signature)

Summary

Background
The malignant childhood brain tumour, medulloblastoma, is classified clinically into molecular groups which guide therapy. DNA-methylation profiling is the current classification ‘gold-standard’, typically delivered 3–4 weeks post-surgery. Pre-surgery non-invasive diagnostics thus offer significant potential to improve early diagnosis and clinical management. Here, we determine tumour metabolite profiles of the four medulloblastoma groups, assess their diagnostic utility using tumour tissue and potential for non-invasive diagnosis using in vivo magnetic resonance spectroscopy (MRS).

Methods
Metabolite profiles were acquired by high-resolution magic-angle spinning NMR spectroscopy (MAS) from 86 medulloblastomas (from 59 male and 27 female patients), previously classified by DNA-methylation array (WNT (n = 9), SHH (n = 22), Group3 (n = 21), Group4 (n = 34)); RNA-seq data was available for sixty. Unsupervised class-discovery was performed and a support vector machine (SVM) constructed to assess diagnostic performance. The SVM classifier was adapted to use only metabolites (n = 10) routinely quantified from in vivo MRS data, and re-tested. Glutamate was assessed as a predictor of overall survival.

Findings
Group-specific metabolite profiles were identified; tumours clustered with good concordance to their reference molecular group (93%). GABA was only detected in WNT, taurine was low in SHH and lipids were high in Group3. The tissue-based metabolite SVM classifier had a cross-validated accuracy of 89% (100% for WNT) and, adapted to use metabolites routinely quantified in vivo, gave a combined classification accuracy of 90% for SHH, Group3 and Group4. Glutamate predicted survival after incorporating known risk-factors (HR = 3.39, 95% CI 1.4–8.1, p = 0.025).

Interpretation
Tissue metabolite profiles characterise medulloblastoma molecular groups. Their combination with machine learning can aid rapid diagnosis from tissue and potentially in vivo. Specific metabolites provide important information; GABA identifying WNT and glutamate conferring poor prognosis.

Funding
Children with Cancer UK, Cancer Research UK, Children’s Cancer North and a Newcastle University PhD studentship.

医療・健康
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