日前,，科學(xué)家通過研究發(fā)現(xiàn),，多數(shù)抗癌藥藥效好壞取決于癌細(xì)胞的基因組成,，如果進(jìn)行針對(duì)性用藥,，提高藥物敏感度，癌癥療法的療效或可更上一個(gè)臺(tái)階,。相關(guān)兩份研究成果3月28日在線發(fā)表于《Nature》雜志上,。
科學(xué)家在一次研究中發(fā)現(xiàn)，同癌癥相關(guān)的基因在發(fā)生突變的時(shí)候,，藥物敏感性會(huì)隨之發(fā)生變化,。于是，科學(xué)家將遺傳識(shí)別同藥物測(cè)試緊密統(tǒng)一起來,，并且建立了詳實(shí)的資料庫,，從而能夠完整描繪出各類基因突變會(huì)對(duì)不同癌癥抗癌類藥物的藥性造成何種影響。在進(jìn)行分門別類的劃分和測(cè)定之后,，今后在治療癌癥時(shí),，就可根據(jù)病人的自身情況具體用藥，從而提高治療的成功率,。
據(jù)悉,，為了研究癌癥機(jī)理，并且尋找對(duì)應(yīng)的治療方法,，科學(xué)家在實(shí)驗(yàn)室里培養(yǎng)了各種各樣的癌細(xì)胞,。通過不斷的收集積累，科學(xué)家搞清楚了數(shù)百種癌細(xì)胞系的基因特征,，其中就包括同癌癥相關(guān)的基因突變,，以及基因活化的各種樣式。隨后,，科學(xué)家又將已獲準(zhǔn)生產(chǎn)的抗癌藥同這些基因特征進(jìn)行一一對(duì)應(yīng),，并且查看記錄癌細(xì)胞的不同反應(yīng)。
加拿大安大略癌癥研究所（Ontario Institute for Cancer Research）的主席湯姆-哈德森（Tom Hudson）表示：“這項(xiàng)研究成果具有非常重大的意義,。這樣一來,，我們?cè)卺槍?duì)不同的癌癥病人設(shè)計(jì)治療方案時(shí)，就能夠從中獲得有價(jià)值的信息,。同時(shí),，這項(xiàng)研究對(duì)于制藥產(chǎn)業(yè)而言也是一件好事，今后他們?cè)谶M(jìn)行藥物研發(fā)的時(shí)候能夠更加具有針對(duì)性,，并且有效降低研究經(jīng)費(fèi),。”據(jù)悉,，哈德森并未參與此項(xiàng)研究，因而他的意見十分客觀,。
波士頓達(dá)納-法伯癌癥研究所（Dana-Farber Cancer Institute）的癌癥研究員李維 加萊維（Levi Garraway）參與了此項(xiàng)研究,，他介紹稱，盡管目前科學(xué)家們已經(jīng)紛紛意識(shí)到,，就癌癥治療而言,，更應(yīng)該關(guān)注引發(fā)癌癥的基因變異而不是疾病本身，但是,，真正考慮到用藥時(shí),，仍面臨不少困難。其中之一就是,，制藥公司在進(jìn)行臨床試驗(yàn)之前,，他們也無法弄清楚自己生產(chǎn)的抗癌藥具體會(huì)對(duì)哪些病人有效。
近日,，一項(xiàng)新研究指出,，癌癥患者體內(nèi)同一腫瘤的不同部位具有不同的基因，并且轉(zhuǎn)移至身體別處的癌癥與原發(fā)癌癥的基因也具有一定差異,。正因?yàn)槿绱?，盡管藥物在不斷更新，但是一種藥物開始時(shí)有效,，患者使用一段時(shí)間后卻突然失效,，因而癌癥死亡率才會(huì)居高不下。
為了讓制藥公司能夠開發(fā)出更具針對(duì)性的抗癌藥,，加萊維同其他科研人員一起編撰了一部癌細(xì)胞系百科全書（Cancer Cell Line Encyclopedia） ,，里面收集整理了947個(gè)細(xì)胞系的基因信息，其中還包含479個(gè)細(xì)胞系的藥物敏感性測(cè)試結(jié)果以及24類抗癌藥的測(cè)試結(jié)果,。
同時(shí),，英國劍橋大學(xué)韋爾科姆基金會(huì)桑格學(xué)院（Wellcome Trust Sanger Institute）的馬修-加內(nèi)特（Mathew Garnett）帶領(lǐng)了另外一組科學(xué)家，他們也收集整理出了一部類似的百科全書,，其中包括639個(gè)腫瘤細(xì)胞系的基因信息,，以及130種抗癌藥的測(cè)試結(jié)果。目前,，這兩個(gè)科研團(tuán)隊(duì)所整理的癌細(xì)胞系百科全書都已公開,，制藥公司可以在互聯(lián)網(wǎng)上找到研究結(jié)果。休斯敦MD安德森癌癥研究中心(MD Anderson Cancer Center)的計(jì)算生物學(xué)家約翰-韋恩斯坦（John Weinstein）表示,，科研團(tuán)隊(duì)提供的這份百科全書會(huì)是一個(gè)非常有用的研究工具,。
不過，實(shí)驗(yàn)室培養(yǎng)的癌細(xì)胞也存在一定的局限性，那就是在培養(yǎng)器皿中生長(zhǎng)繁殖的細(xì)胞,，其周圍環(huán)境無法做到同人體內(nèi)部完全一致,，例如在針對(duì)實(shí)驗(yàn)室癌細(xì)胞用藥時(shí)，藥物會(huì)直接作用在細(xì)胞上,，而如果是在真實(shí)的人體環(huán)境下，藥物首先會(huì)在人體循環(huán)系統(tǒng)中流動(dòng),，最終才會(huì)到達(dá)目標(biāo)處,。因而，在臨床階段,，某些藥物的藥效有可能會(huì)出現(xiàn)同實(shí)驗(yàn)室模擬情況不大相符的結(jié)果,。這也許是科學(xué)家下一步需要研究的方向。（生物谷 bioon.com）
doi:10.1038/nature11005
PMC:
PMID:
Systematic identification of genomic markers of drug sensitivity in cancer cells
Mathew J. Garnett, Elena J. Edelman, Sonja J. Heidorn, Chris D. Greenman, Anahita Dastur, King Wai Lau, Patricia Greninger I. Richard Thompson, Xi Luo, Jorge Soares, Qingsong Liu, Francesco Iorio, Didier Surdez, Li Chen, Randy J. Milano, Graham R. Bignell, Ah T. Tam, Helen Davies, Jesse A. Stevenson, Syd Barthorpe, Stephen R. Lutz, Fiona Kogera, Karl Lawrence, Anne McLaren-Douglas, Xeni Mitropoulos, Tatiana Mironenko,1Helen Thi, Laura Richardson, Wenjun Zhou, Frances Jewitt, Tinghu Zhang, Patrick O’Brien, Jessica L. Boisvert, Stacey Price, Wooyoung Hur, Wanjuan Yang, Xianming Deng, Adam Butler, Hwan Geun Choi, Jae Won Chang, Jose Baselga, Ivan Stamenkovic, Jeffrey A. Engelman, Sreenath V. Sharma, Olivier Delattre, Julio Saez-Rodriguez, Nathanael S. Gray, Jeffrey Settleman, P. Andrew Futreal, Daniel A. Haber, Michael R. Stratton, Sridhar Ramaswamy, Ultan McDermott1, Cyril H. Benes et al
Clinical responses to anticancer therapies are often restricted to a subset of patients. In some cases, mutated cancer genes are potent biomarkers for responses to targeted agents. Here, to uncover new biomarkers of sensitivity and resistance to cancer therapeutics, we screened a panel of several hundred cancer cell lines—which represent much of the tissue-type and genetic diversity of human cancers—with 130 drugs under clinical and preclinical investigation. In aggregate, we found that mutated cancer genes were associated with cellular response to most currently available cancer drugs. Classic oncogene addiction paradigms were modified by additional tissue-specific or expression biomarkers, and some frequently mutated genes were associated with sensitivity to a broad range of therapeutic agents. Unexpected relationships were revealed, including the marked sensitivity of Ewing’s sarcoma cells harbouring the EWS (also known as EWSR1)-FLI1 gene translocation to poly(ADP-ribose) polymerase (PARP) inhibitors. By linking drug activity to the functional complexity of cancer genomes, systematic pharmacogenomic profiling in cancer cell lines provides a powerful biomarker discovery platform to guide rational cancer therapeutic strategies.
doi:10.1038/nature11003
PMC:
PMID:
The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
Jordi Barretina, Giordano Caponigro, Nicolas Stransky, Kavitha Venkatesan, Adam A. Margolin, Sungjoon Kim, Christopher J. Wilson, Joseph Lehár, Gregory V. Kryukov, Dmitriy Sonkin, Anupama Reddy, Manway Liu, Lauren Murray, Michael F. Berger, John E. Monahan, Paula Morais, Jodi Meltzer, Adam Korejwa, Judit Jané-Valbuena, Felipa A. Mapa, Joseph Thibault, Eva Bric-Furlong, Pichai Raman, Levi A. Garraway et al
The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available1. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of ‘personalized’ therapeutic regimens.
