AbstractCancer cells require high levels of iron for rapid proliferation, leading to significant upregulation of cell-surface transferrin receptor 1 (TfR1), which mediates iron uptake by binding to the iron-carrying protein transferrin1,2,3. Leveraging this phenomenon and the fast endocytosis rate of TfR1 (refs.

摘要癌细胞需要高水平的铁才能快速增殖，导致细胞表面转铁蛋白受体1（TfR1）的显着上调，TfR1通过与携带铁的蛋白转铁蛋白1,2,3结合来介导铁的摄取。利用这种现象和TfR1的快速内吞率（参考文献）。

4,5), we developed transferrin receptor targeting chimeras (TransTACs), a heterobispecific antibody modality for membrane protein degradation. TransTACs are engineered to drive rapid co-internalization of a target protein of interest and TfR1 from the cell surface, and to enable target protein entry into the lysosomal degradation pathway.

4,5），我们开发了转铁蛋白受体靶向嵌合体（TransTACs），这是一种用于膜蛋白降解的异源双特异性抗体形式。TransTACs被设计为驱动目标蛋白和TfR1从细胞表面快速共内化，并使目标蛋白进入溶酶体降解途径。

We show that TransTACs can efficiently degrade a diverse range of single-pass, multi-pass, native or synthetic membrane proteins, including epidermal growth factor receptor, programmed cell death 1 ligand 1, cluster of differentiation 20 and chimeric antigen receptor. In example applications, TransTACs enabled the reversible control of human primary chimeric antigen receptor T cells and the targeting of drug-resistant epidermal growth factor receptor-driven lung cancer with the exon 19 deletion/T790M/C797S mutations in a mouse xenograft model.

我们表明，TransTACs可以有效降解多种单程，多程，天然或合成膜蛋白，包括表皮生长因子受体，程序性细胞死亡1配体1，分化簇20和嵌合抗原受体。在实例应用中，TransTACs能够可逆地控制人原代嵌合抗原受体T细胞，并在小鼠异种移植模型中靶向具有外显子19缺失/T790M/C797S突变的耐药表皮生长因子受体驱动的肺癌。

TransTACs represent a promising new family of bifunctional antibodies for precise manipulation of membrane proteins and targeted cancer therapy..

TransTACs代表了一种有前途的新的双功能抗体家族，用于精确操纵膜蛋白和靶向癌症治疗。。

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Fig. 1: TransTAC overview.Fig. 2: Designing TransTAC.Fig. 3: Degrading diverse membrane proteins with TransTACs.Fig. 4: Targeting TKI-resistant NSCLC with an EGFR TransTAC.Fig. 5: In vivo characterization.

图1:TransTAC概述。图2：设计TransTAC。图3：用TransTACs降解多种膜蛋白。图4：用EGFR-TransTAC靶向TKI抗性NSCLC。图5：体内表征。

Data availability

数据可用性

TFRC transcriptomics analyses were conducted using data from the MERAV database (http://merav.wi.mit.edu) and the DICE database (https://dice-database.org/). Source data are provided with this paper.

TFRC转录组学分析是使用MERAV数据库的数据进行的(http://merav.wi.mit.edu)和骰子数据库(https://dice-database.org/)。本文提供了源数据。

Code availability

代码可用性

Python scripts for TFRC transcriptomics analysis are available at https://github.com/garykbrixi/TFRC_analysis.

https://github.com/garykbrixi/TFRC_analysis.

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Download referencesAcknowledgementsWe thank M. Bao, P. Budde and Zhou laboratory members for their input on the paper; P. Gokhale, K. Soroko, J. Alberta, the DFCI Experimental Therapeutics Core and DFCI Animal Research Facility for assistance with the animal experiments; J. Li for providing lung cancer cell lines; and E.

下载参考文献致谢我们感谢M.Bao，P.Budde和Zhou实验室成员在论文中的投入；P、 Gokhale，K。Soroko，J。Alberta，DFCI实验治疗核心和DFCI动物研究设施，用于协助动物实验；J、 李提供肺癌细胞系；和E。

Chouchani and S. Shin for the intact protein liquid chromatography–mass spectrometry experiment and data analysis. F.F. acknowledges funding support from the Fondation ARC pour la Recherche sur le Cancer. K.R. acknowledges funding support from a Chemical Biology T32 Training grant no. NIH T32GM139775.

Chouchani和S.Shin进行完整蛋白质液相色谱-质谱实验和数据分析。F、 F.感谢癌症研究基金会的资金支持。K、 R.感谢化学生物学T32培训资助号NIH T32GM139775的资金支持。

P.A.J. acknowledges funding support from grant no. NIH R35 CA220497 and American Cancer Society (grant no. CRP-17-111-01-CDD). X.Z. acknowledges funding support from grant nos. NIH R00EB030587 and NIH DP2GM154013 and the DFCI Helen Gurley Brown Foundation. Figs. 1a,b, 2a,d–g, 3a–d, 4a,e and 5a,e,g and Extended Data Figs.

P、 A.J.感谢资助号NIH R35 CA220497和美国癌症协会（资助号CRP-17-111-01-CDD）的资助。十、 Z.感谢NIH R00EB030587和NIH DP2GM154013以及DFCI Helen Gurley Brown基金会的资助。图1a，b，2a，d–g，3a–d，4a，e和5a，e，g和扩展数据图。

2a,b, 5a,b, 6a–e, 7a, 8a,c, 9a, 11a and 12d,h were created with BioRender.com.Author informationAuthors and AffiliationsDepartment of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USADingpeng Zhang, Jhoely Duque-Jimenez, Kaitlin Rhee & Xin ZhouDepartment of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USADingpeng Zhang, Kaitlin Rhee & Xin ZhouDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USAFrancesco Facchinetti, William W.

2a，b，5a，b，6a-e，7a，8a，c，9a，11a和12d，h是通过BioRender.com创建的。作者信息作者和附属机构癌症生物学系，达纳-法伯癌症研究所，波士顿，马萨诸塞州，美国，张丁鹏，朱利·杜克·希门尼斯，凯特林·Rhee和忻州哈佛医学院生物化学和分子药理学系，波士顿，马萨诸塞州，美国马萨诸塞州，达纳-法伯癌症研究所，美国弗朗西斯科·法奇内蒂，威廉·W。

Feng & Pasi A. JänneDepartment of Medicine, Harvard Medical School, Boston, MA, USAFrancesco Facchinetti, William W. Feng & Pasi A. JänneLowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Boston, MA, USAFrancesco Facchinetti, William W. Feng & Pasi A. JänneHarvard University, Boston, MA, USAGaryk BrixiBelfer Cen.

Feng＆Pasi A.Jänne哈佛医学院医学系，马萨诸塞州波士顿，USAFrancesco Facchinetti，William W.Feng＆Pasi A.JänneLowe胸腔肿瘤中心，达纳-法伯癌症研究所，马萨诸塞州波士顿，USAFrancesco Facchinetti，William W.Feng＆Pasi A.JänneHarvard University，波士顿，马萨诸塞州，USAGaryk BrixiBelfer Cen。

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PubMed Google ScholarContributionsD.Z. designed and performed the experiments unless otherwise stated. J.D.-J. performed cloning and protein expression, conducted the imaging colocalization analysis and western quantifications, and organized data. F.F. performed and analysed the CellTiter-Glo assays with the PC9 and DFCI243 cell lines.

PubMed谷歌学术贡献SD。Z、 除非另有说明，否则设计并进行实验。J、 D.-J.进行了克隆和蛋白质表达，进行了成像共定位分析和western定量，并组织了数据。F、 F.用PC9和DFCI243细胞系进行并分析了CellTiter-Glo测定。

G.B. carried out the transcriptomics analysis. D.Z., J.D.-J., F.F., K.R. and X.Z. cowrote the paper. F.F., W.W.F. and P.A.J. provided input on the lung cancer research. X.Z. conceived the study and supervised the project.Corresponding authorCorrespondence to.

G、 B.进行转录组学分析。D、 Z.，J.D.-J.，F.F.，K.R.和X.Z.共同撰写了这篇论文。F、 F.，W.W.F.和P.A.J.为肺癌研究提供了投入。十、 Z.构思了这项研究并监督了该项目。对应作者对应。

Xin Zhou.Ethics declarations

。道德宣言

Competing interests

相互竞争的利益

X.Z., D.Z. and K.R. have filed patent applications for the TransTAC technology. P.A.J. has received consulting fees from AstraZeneca, Boehringer-Ingelheim, Pfizer, Roche/Genentech, Takeda Oncology, ACEA Biosciences, Eli Lilly and Company, Araxes Pharma, Ignyta, Mirati Therapeutics, Novartis, LOXO Oncology, Daiichi Sankyo, Sanofi Oncology, Voronoi, SFJ Pharmaceuticals, Takeda Oncology, Transcenta, Silicon Therapeutics, Syndax, Nuvalent, Bayer, Esai, Biocartis, Allorion Therapeutics, Accutar Biotech, Monte Rosa, Scorpion Therapeutics, Merus, Frontier Medicines, Hongyun Biotechnology, Duality, Dizal Pharma and Abbvie; post-marketing royalties from DFCI owned intellectual property on EGFR mutations licensed to Lab Corp; sponsored research agreements with AstraZeneca, Daichi-Sankyo, PUMA, Boehringer-Ingelheim, Eli Lilly and Company, Revolution Medicines and Astellas Pharmaceuticals; and stock ownership in Gatekeeper Pharmaceuticals..

十、 Z.，D.Z.和K.R.已经为TransTAC技术提交了专利申请。P、 A.J.已从阿斯利康、勃林格殷格翰、辉瑞、罗氏/基因泰克、武田肿瘤学、ACEA生物科学、礼来公司、阿拉克斯制药、伊格尼塔、米拉蒂治疗公司、诺华、洛索肿瘤学、第一三共、赛诺菲肿瘤学、沃罗诺、SFJ制药、武田肿瘤学、Transcenta、硅治疗学、Syndax、Nuvalent、拜耳、Esai、生物艺术、Allorion治疗学、Accutar Biotech、Monte Rosa、蝎子治疗学、Merus、Frontier Medicines获得咨询费，红云生物技术，Duality，Dizal Pharma和Abbvie；DFCI拥有授权给Lab Corp的EGFR突变知识产权的上市后版税；赞助与阿斯利康，Daichi Sankyo，PUMA，勃林格殷格翰，礼来公司，Revolution Medicines和Astellas Pharmaceuticals的研究协议；。。

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同行评审信息

Nature thanks Manuel Penichet and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

《自然》杂志感谢曼努埃尔·佩尼切特（ManuelPenichet）和另一位匿名审稿人对这项工作的同行评议做出的贡献。

Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Extended data figures and tablesExtended Data Fig. 1 TFRC expression analysis.(a) t-statistics, P values, and n of samples for the overall and pairwise comparison of TFRC expression in healthy and tumor tissues.

Additional informationPublisher的注释Springer Nature在已发布的地图和机构隶属关系中的管辖权主张方面保持中立。扩展数据图和表扩展数据图1 TFRC表达分析。（a） 样本的t统计量，P值和n，用于健康和肿瘤组织中TFRC表达的整体和成对比较。

t-statistics and P values were determined by unpaired two-tailed Welch’s t-tests. t-statistic less than 0 means that tumor has higher expression of TFRC than normal tissues. Overall, and in 14 out of 19 tissue pairs, tumor tissue TFRC expression is significantly higher than in normal tissue. Female reproductive tissues include the endometrium, cervix, fallopian tubes, myometrium, ovary, placenta, and uterus.

t统计量和P值由不成对的两尾韦尔奇t检验确定。t统计量小于0意味着肿瘤比正常组织具有更高的TFRC表达。总体而言，在19个组织对中的14个中，肿瘤组织TFRC表达显着高于正常组织。女性生殖组织包括子宫内膜、子宫颈、输卵管、子宫肌层、卵巢、胎盘和子宫。

Central nervous system tissues include the basal ganglia, brainstem, cerebral cortex, hippocampus, spinal cord, and vestibular nuclei superior. Brain tissues include the hypothalamus, pituitary gland, thalamus, ganglia, and ganglion nodose. (b) Box plots of relative TFRC mRNA expression levels in different subsets of immune cells.

中枢神经系统组织包括基底神经节，脑干，大脑皮层，海马，脊髓和前庭上核。脑组织包括下丘脑，垂体，丘脑，神经节和神经节结节。（b） 免疫细胞不同亚群中相对TFRC mRNA表达水平的箱形图。

TFRC is upregulated by approximately 6-fold in activated CD4+ and CD8+ T cells compared to inactivated T cells. Data are presented as mean values ± s.d. (c) t-statistics, P values, and n of samples for comparison of TFRC expression in CD4+ and CD8+ T cells. t-statistics and P values were determined by unpaired two-tailed Welch’s t-tests.

与灭活的T细胞相比，TFRC在活化的CD4+和CD8+T细胞中上调约6倍。数据表示为样品的平均值±s.d.（c）t统计量，P值和n，用于比较CD4+和CD8+t细胞中TFRC的表达。t统计量和P值由不成对的两尾韦尔奇t检验确定。

t-statistic greater than 0 means that activated cells have higher TFRC than non-activated cells. In Extended Data Fig. 1b and Fig. 1d box plots, the minima and maxima are represented by whiskers that extend to values within 1.5 times the interquartile range (IQR) from the quartiles, with outliers depicted as individual points.

t统计量大于0意味着活化细胞比未活化细胞具有更高的TFRC。在扩展数据图1b和图1d箱形图中，最小值和最大值由晶须表示，晶须延伸到四分位数四分位数间距（IQR）的1.5倍以内，异常值表示为单个点。

The IQR, spanning.

IQR，跨越。

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