基于基因组学特征分布对齐和药物结构信息的癌症药物敏感性预测方法
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国家自然科学基金(62176236)


Prediction of cancer drug sensitivity based on genomic feature distribution alignment and drug structure information
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    摘要:

    近年来精准医学在癌症治疗中得到了广泛的应用,其重点在于如何准确地预测不同的患者对药物治疗的反应。本研究设计了一种基于基因组学特征分布对齐和药物结构信息的癌症药物敏感性预测方法,该方法首先对齐来自细胞系的基因组学特征与来自患者的基因组学特征的分布并去除基因表达数据中的噪声,之后融合药物结构信息,使用多任务学习的方式进行患者药物敏感性预测。结果表明,在癌症相关药物敏感性基因组学数据集(genomics of drug sensitivity in cancer, GDSC)上,此方法的预测结果中均方误差降至0.905 2,相关系数提升至0.875 4,准确率提升至0.836 0,显著优于最近发表的方法,在癌症基因组图谱数据集(the cancer genome atlas, TCGA)上,此方法预测药物敏感性的平均召回率提升至0.571 4,F1-分数提升至0.658 0,表现出优秀的泛化性能。这展现了本方法未来用于辅助选择临床治疗方案的潜力。

    Abstract:

    In recent years, precision medicine has demonstrated wide applications in cancer therapy, and the focus of precision medicine lies in accurately predicting the responses of different patients to drug treatment. We propose a model for predicting cancer drug sensitivity based on genomic feature distribution alignment and drug structure information. This model initially aligns the genomic features from cell lines with those from patients and removes noise from gene expression data. Subsequently, it integrates drug structure features and employs multi-task learning to predict the drug sensitivity of patients. The experimental results on the genomics of drug sensitivity in cancer (GDSC) dataset indicates that this method achieved a reduced mean square error of 0.905 2, an increased correlation coefficient of 0.875 4, and an enhanced accuracy rate of 0.836 0 which significantly outperformed the recently published methods. On the cancer genome atlas (TCGA) dataset, this method demonstrates an improved average recall rate of 0.571 4 and an increased F1-score of 0.658 0 in predicting drug sensitivity, exhibiting excellent generalization performance. The result demonstrates the potential of this method to assist in the selection of clinical treatment plans in the future.

    参考文献
    [1] CHEN WQ, XIA CF, ZHENG RS, ZHOU MG, LIN CQ, ZENG HM, ZHANG SW, WANG LJ, YANG ZX, SUN KX, LI H, BROWN MD, ISLAMI F, BRAY F, JEMAL A, HE J. Disparities by province, age, and sex in site-specific cancer burden attributable to 23 potentially modifiable risk factors in China: a comparative risk assessment[J]. The Lancet Global Health, 2019, 7(2): e257-e269.
    [2] GAGAN J, van ALLEN EM. Next-generation sequencing to guide cancer therapy[J]. Genome medicine, 2015, 7: 1-10.
    [3] TATE JG, BAMFORD S, JUBB HC, SONDKA Z, BEARE DM, BINDAL N, BOUTSELAKIS H, COLE CG, CREATORE C, DAWSON E, FISH P, HARSHA B, HATHAWAY C, JUPE SC, KOK CY, NOBLE K, PONTING L, RAMSHAW CC, RYE CE, SPEEDY HE, et al. COSMIC: the catalogue of somatic mutations in cancer[J]. Nucleic Acids Research, 2019, 47(D1): D941-D947.
    [4] WEINSTEIN JN, COLLISSON EA, MILLS GB, SHAW KRM, OZENBERGER BA, ELLROTT K, SHMULEVICH I, SANDER C, STUART JM. The cancer genome atlas pan-cancer analysis project[J]. Nature genetics, 2013, 45(10): 1113-1120.
    [5] YANG WJ, SOARES J, GRENINGER P, EDELMAN EJ, LIGHTFOOT H, FORBES S, BINDAL N, BEARE D, SMITH JA, THOMPSON IR, RAMASWAMY S, FUTREAL PA, HABER DA, STRATTON MR, BENES C, MCDERMOTT U, GARNETT MJ. Genomics of drug sensitivity in cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells[J]. Nucleic Acids Research, 2013, 41(database issue): D955-D961.
    [6] GHANDI M, HUANG FW, JANÉ-VALBUENA J, KRYUKOV GV, LO CC, McDONALD ER 3rd, BARRETINA J, GELFAND ET, BIELSKI CM, LI HX, HU K, ANDREEV-DRAKHLIN AY, KIM J, HESS JM, HAAS BJ, AGUET F, WEIR BA, ROTHBERG MV, PAOLELLA BR, LAWRENCE MS, et al. Next-generation characterization of the cancer cell line encyclopedia[J]. Nature, 2019, 569(7757): 503-508.
    [7] CHEN YR, ZHANG LX. How much can deep learning improve prediction of the responses to drugs in cancer cell lines?[J]. Briefings in Bioinformatics, 2022, 23(1): 1-8.
    [8] 张乃千. 差异甲基化分析和抗癌药物敏感性预测中的计算模型[D]. 上海: 上海师范大学博士学位论文, 2016. ZHANG NQ. Computational models in differential methylation analysis and sensitivity prediction of anticancer drugs[D]. Shanghai: Doctoral Dissertation of Shanghai Normal University, 2016(in Chinese).
    [9] SUTHAHARAN S. Machine learning models and algorithms for big data classification[J]. Integrated Series in Information Systems, 2016, 36: 1-12.
    [10] LIAW A, WIENER M. Classification and regression by randomForest[J]. R News, 2002, 2(3): 18-22.
    [11] 李苗苗. 基于XG-BOOST和多数据源的药物重定位预测[J]. 软件导刊, 2020, 19(2): 110-113. LI MM. Drug reposition prediction based on XG-BOOST and multi-source data[J]. Software Guide, 2020, 19(2): 110-113(in Chinese).
    [12] AMMAD-UD-DIN M, KHAN SA, WENNERBERG K, AITTOKALLIO T. Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression[J]. Bioinformatics, 2017, 33(14): i359-i368.
    [13] ZHANG NQ, WANG HY, FANG Y, WANG J, ZHENG XQ, LIU XS. Predicting anticancer drug responses using a dual-layer integrated cell line-drug network model[J]. PLoS Computational Biology, 2015, 11(9): e1004498.
    [14] 杨晨雨, 刘振浩, 代培斌, 张钰, 黄鹏杰, 林勇, 谢鹭. 基于多组学数据的肿瘤药物敏感性预测[J]. 生物工程学报, 2022, 38(6): 2201-2212. YANG CY, LIU ZH, DAI PB, ZHANG Y, HUANG PJ, LIN Y, XIE L. Predicting tumor drug sensitivity with multi-omics data[J]. Chinese Journal of Biotechnology, 2022, 38(6): 2201-2212(in Chinese).
    [15] CHEN JY, ZHANG LX. A survey and systematic assessment of computational methods for drug response prediction[J]. Briefings in Bioinformatics, 2021, 22(1): 232-246.
    [16] LI M, WANG YK, ZHENG RQ, SHI XH, LI YH, WU FX, WANG JX. DeepDSC: a deep learning method to predic汴琠楤?瑵慧猠歳?汮敳慩牴湩楶湩杴孹?嵯???牡瑮楣晥楲挠楣慥汬??湬瑩敮汥汳楛杊敝渮挠敉?楅湅??敃摍椠捔楲湡敮???ぴ?????????は?????扴物?孮??崠?婩啯佬?婧剹??坮?丠??偯??????乡?塩坣??吠??串??????′???儵??中‵????卢坲渾敛琱??愠?撶整攬瀠?氎攬愠狦溉榳測朠?浓澆搮攠泺?晌潉牎?摓爭界朱‰爰攰獰炨濡滷猚旇?灁牅攭摘楇捂瑯楯潳湴?曕犄潋浯?揱懼渋掄旆狞※朧敛湊潝洮椠挟?珥椋杦溥愬琠甲爰攲猱?愠渳搷?挴漩洺瀠漱申渴搶?挱栳攵洹椮挠慌汕?獊瑘爬甠捃瑈畅牎攠獍嬬?嵑???????楙潕椠湘晑漮爠浐慲瑥楤捩獣???の㈠?????????????扥牤?季??嵬???剡乢?呬呩???????????乇?????????佯割乩?卨????剳??丠??丠???????匱吰唰到??????啲??坴???删?乩?乮??剛?偝??呃?佩?健即佥丠??創???啬传?塦??卩佯?剥?卨??????唬?儲匰???传刳?伨????匱唳刴??娱″??????乃????????丼佢?刾??????么?????刭??呇????吠????噏??協????十吠?嘬?乃协佌乌???????删呅?佔剅偒??匮??敏瑌?愺氠??卬祴獩琭敯浭慩瑣楳挠?楡摴敥渠瑩楮晴楥捧慲瑡楴潩湯?漠晷?杴敨渠潤浥楥捰?浮慥牵歲敡牬猠?潥晴?摯牲畫杳?獦敯湲猠楤瑲極癧椠瑲祥?楰湯?捳慥渠捰敲牥?捩散汴汩獯孮?嵊??丠慂瑩畯物敮???ね????????‰?????????戩爺?孩特?崱?卩唵?刹???乲??丱????久?剄?夠?义?删???佗剕匠???佘?卅???倠????????乴?呡佷???吠????啮?塯????佮啧????????噯?卥?????吠啲???????????卣???啮????????副???????删卣????乣???????唠?婥????佳乥??啲??????啬???乮??乣?????乮????坲????乮????匮??呡?????????????????剬婬佩乧????攬琠′愰氲????渺攠砸琷?札攸渹攲爮愼瑢楲漾湛′挰潝渠湃效捅瑎椠癊楙琬礠?流慎灇?????きぁ?灁汊愬琠晗潁牎浇?慑湅搬?瑌桉敕?時楑爬猠瑌?ㄠが??じさ??瀬爠潍晁椠汑攮猠孄?嵥???敲污汮???ひㄠ???????????ㄠ?????????敵????扳牰?孮??嵳???传畩浮汴?乧?乡??????創?佫??乮??????牬略札?獥畬獬挠敒灎瑁椭打楥汱椠瑤祡?灡牛敊摝椮挠瑎楡潴湵?慥朠慃楯湭獭瑵?慩?灡慴湩敯汮?漬映′搰爲甲本猠?申猺椠渶朴?欴攭父渵攰氶椮稼敢摲 ̄?愲礱敝猠楃慏湓?流甠汆琬椠瑤慥猠歇?汁敖慅爠湋椮渠杆孡?嵴???楩潧楨湢景潲牨浯慯瑤椠捳獵???ち?????ど??????楤????楮?????扲牮?孬?ぃ嵝?圯?乲????????塳娠??娠??乥?′?塴????佴?兲???浩灯牮潡癬攠摃?慮湦瑥楲捥慮湣捥攠牯?搠牉畮杴?牲敮獡灴潩湯獮敡?瀠牃敯摮楦捥瑲楥潮湣?椠湯?挠敍污汣?汩楮湥攠獌?畡獲楮湩杮?洮愠瑈牡楩硦?昬愠捉瑳潲牡楥穬愮琠楁潃湍?眠椲琰栱‰猺椠洲椵氵愭爲椶琲礮?牢敲朾畛氲愲牝椠穙慁瑎楇漠湃嬬?嵚???????愬渠捃效牅??㈠ず??????????ㄠ???扎片?孌??崠??啁?乇?之丬??婅??传?奇??坑?乁??????????儠?????乁?塇??偗??佃?塅???測琠楌捉愠湚挬攠牑?摎爠畗杘?爠敘獉灁漠湑猬攠?灁牎敇搠楘捎琬椠潗湁?楇渠?挬攠汈汁?汇椠湈敌献?畍獵楬湴杩?睲敥楧杩桯瑮攠摳?東牵慥灮档?牮敧朠畷汩慴牨椠穳数摡?浩慡瑬爠楩确?景慲捭瑡潴物楯穮愠瑥楮潡湢孬?嵳???潣汵敲捡畴汥愠牨?呴桥敲牯慧灥祮?乩畴捹氠敥楳捴??捡楴摩獯???の?????????????????扴物?孮??嵮????坥???卡??剥佲??卝吮删佇??????????乣????删?吰吲??删???????丱?????丰?丼????′?啝倠呄????????呌呏????删奃??乂??偏佄佉乎???匬??剉???偌????卒?么?????啳???剴??????奡???嘠?????啴???????佴????剤?????丠??却佳丠?????卨?乧?唭側周??????敵湴攠?敎硁瀠牳敥獱獵楥潮湣?扮慧獛敊摝?椠湎晵散牬敥湩捣攠?潣晩?捳愠湒捥敳牥?摲牣畨本?猲攰渰猸椬琠椳瘶椨琱礶嬩?崠??丰愵琭略爱攱??漼浢浲甾湛椲挴慝琠楍潏湒獔??休ざ?㈠??ㄠ??????き???????扣牃?孅??崬?乓?啈奁?乆?呅??乌?唠套?乌??呂吮??乡?啰奩?乧?呡??????????特慩灮桧?捭潡湭癭潡汬畩瑡楮漠湴慲污?湳散瑲睩潰牴歯獭?晳漠牢?搠牒畎杁?牓敥獱灛潊湝献攠?灡牴敵摲楥挠瑍楥潴湨孯?嵳???????????吶爲愱渭猶愲挸琮椼潢湲猾?漲渵??潔流灎甠瑍愮琠楐潲湥慤汩??楩潯汮漠杯祦?慡湮摴??楣潡楮湣晥潲爠浤慲瑵楧挠獲???は????????????????????戀爀?嬀??崀???唀?儀???唀?娀儀?????一??刀??娀?伀唀?????攀攀瀀??刀??愀?栀礀戀爀椀搀?最爀愀瀀栀?挀漀渀瘀漀氀甀琀椀漀渀愀氀?渀攀琀眀漀爀欀?昀漀爀?瀀爀攀搀椀挀琀椀渀最?挀愀渀挀攀爀?搀爀甀最?爀攀猀瀀漀渀猀攀嬀?崀???椀漀椀渀昀漀爀洀愀琀椀挀猀??? ? ?????猀甀瀀瀀氀攀洀攀渀琀开????椀????椀?????戀爀?嬀??崀?娀??一?????圀?一??????堀???一??夀?一??????????????渀漀瘀攀氀?栀攀琀攀爀漀最攀渀攀漀甀猀?渀攀琀眀漀爀欀?戀愀猀攀搀?洀攀琀栀漀搀?昀漀爀?搀爀甀最?爀攀猀瀀漀渀猀攀?瀀爀攀搀椀挀琀椀漀渀?椀渀?挀愀渀挀攀爀?挀攀氀氀?氀椀渀攀猀嬀?崀??匀挀椀攀渀琀椀昀椀挀?刀攀瀀漀爀琀猀??? ??????????????????戀爀?嬀??崀?倀?一??圀?????一?吀???????圀??倀爀攀搀椀挀琀椀渀最?搀爀甀最?爀攀猀瀀漀渀猀攀?戀愀猀攀搀?漀渀?洀甀氀琀椀?漀洀椀挀猀?昀甀猀椀漀渀?愀渀搀?最爀愀瀀栀?挀漀渀瘀漀氀甀琀椀漀渀嬀?崀????????漀甀爀渀愀氀?漀昀??椀漀洀攀搀椀挀愀氀?愀渀搀??攀愀氀琀栀??渀昀漀爀洀愀琀椀挀猀??? ??????????????????????戀爀?嬀??崀?吀?一??????一?唀?夀????唀?一???儀??匀????堀??娀??一???圀??唀渀戀椀愀猀攀搀?猀挀攀渀攀?最爀愀瀀栀?最攀渀攀爀愀琀椀漀渀?昀爀漀洀?戀椀愀猀攀搀?琀爀愀椀渀椀渀最嬀?崀??倀爀漀挀攀攀搀椀渀最猀?漀昀?琀栀攀???????嘀???漀渀昀攀爀攀渀挀攀?漀渀??漀洀瀀甀琀攀爀?嘀椀猀椀漀渀?愀渀搀?倀愀琀琀攀爀渀?刀攀挀漀最渀椀琀椀漀渀??? ? ????????????
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廉令航,杨旭华. 基于基因组学特征分布对齐和药物结构信息的癌症药物敏感性预测方法[J]. 生物工程学报, 2024, 40(7): 2235-2245

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  • 收稿日期:2023-12-29
  • 在线发布日期: 2024-07-08
  • 出版日期: 2024-07-25
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