AIモデルは患者のがんがどこで発生したかを特定するのに役立つ(AI model can help determine where a patient’s cancer arose)


2023-08-07 マサチューセッツ工科大学(MIT)

◆一部のがん患者では、がんの発生源を特定できない場合があり、特定のがん種に対する適切な治療方法の選択が難しくなります。MITとDana-Farber Cancer Instituteの研究者が開発した新しいアプローチは、機械学習を使用して約400の遺伝子の配列を分析し、どの部位からの腫瘍であるかを予測する計算モデルを作成しました。


原発不明がんにおける遺伝学に基づく分類と治療効果予測のための機械学習 Machine learning for genetics-based classification and treatment response prediction in cancer of unknown primary

Intae Moon,Jaclyn LoPiccolo,Sylvan C. Baca,Lynette M. Sholl,Kenneth L. Kehl,Michael J. Hassett,David Liu,Deborah Schrag & Alexander Gusev
Nature Medicine  Published:07 August 2023

extended data figure 1


Cancer of unknown primary (CUP) is a type of cancer that cannot be traced back to its primary site and accounts for 3–5% of all cancers. Established targeted therapies are lacking for CUP, leading to generally poor outcomes. We developed OncoNPC, a machine-learning classifier trained on targeted next-generation sequencing (NGS) data from 36,445 tumors across 22 cancer types from three institutions. Oncology NGS-based primary cancer-type classifier (OncoNPC) achieved a weighted F1 score of 0.942 for high confidence predictions (≥0.9) on held-out tumor samples, which made up 65.2% of all the held-out samples. When applied to 971 CUP tumors collected at the Dana-Farber Cancer Institute, OncoNPC predicted primary cancer types with high confidence in 41.2% of the tumors. OncoNPC also identified CUP subgroups with significantly higher polygenic germline risk for the predicted cancer types and with significantly different survival outcomes. Notably, patients with CUP who received first palliative intent treatments concordant with their OncoNPC-predicted cancers had significantly better outcomes (hazard ratio (HR) = 0.348; 95% confidence interval (CI) = 0.210–0.570; P= 2.32×10-52.32×10-5). Furthermore, OncoNPC enabled a 2.2-fold increase in patients with CUP who could have received genomically guided therapies. OncoNPC thus provides evidence of distinct CUP subgroups and offers the potential for clinical decision support for managing patients with CUP.