hIMB1636-MMAE, a Novel TROP2-Targeting Antibody-Drug Conjugate Exerting Potent Antitumor Efficacy in Pancreatic Cancer

Sun, Li-ping; Bai, Wei-qi; Zhou, Dan-dan; Wu, Xiao-fan; Zhang, Lan-wen; Cui, A-long; Xie, Zi-hui; Gao, Rui-juan; Zhen, Yong-su; Li, Zhuo-Rong; Miao, Qing-fang

doi:10.1021/acs.jmedchem.3c01210

Cited by 3 publications

(3 citation statements)

References 53 publications

(86 reference statements)

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“…Previous work has suggested that TROP2 over-expression occurs in these cancer types [59]. Others have also recently reported preclinical evidence of tumor reduction using another TROP2-targeting ADC in xenograft mouse models of pancreatic cancer [60], consistent with our finding.…”

Section: Tacstd2 Case Studysupporting

confidence: 92%

Machine learning enabled prediction of digital biomarkers from whole slide histopathology images

McCaw,

Shcherbina,

Shah

et al. 2024

Preprint

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Current predictive biomarkers generally leverage technologies such as immunohis-tochemistry or genetic analysis, which may require specialized equipment, be time-intensive to deploy, or incur human error. In this paper, we present an alternative approach for the development and deployment of a class of predictive biomarkers, leveraging deep learning on digital images of hematoxylin and eosin (H&E)-stained biopsy samples to simultaneously predict a range of molecular factors that are relevant to treatment selection and response. Our framework begins with the training of a pan-solid tumor H&E foundation model, which can generate a universal featurization of H&E-stained tissue images. This featurization becomes the input to machine learning models that perform multi-target, pan-cancer imputation. For a set of 352 drug targets, we show the ability to predict with high accuracy: copy number amplifications, target RNA expression, and an RNA-derived “amplification signature” that captures the transcriptional consequences of an amplification event. We facilitate exploratory analyses by making broad predictions initially. Having identified the subset of biomarkers relevant to a patient population of interest, we develop specialized machine learning models, built on the same foundational featurization, which achieve even higher performance for key biomarkers in tumor types of interest. Moreover, our models are robust, generalizing with minimal loss of performance across different patient populations. By generating imputations from tile-level featurizations, we enable spatial overlays of molecular annotations on top of whole-slide images. These annotation maps provide a clear means of interpreting the histological correlates of our model’s predictions, and align with features identified by expert pathologist review. Overall, our work demonstrates a flexible and scalable framework for imputing molecular measurements from H&E, providing a generalizable approach to the development and deployment of predictive biomarkers for targeted therapeutics in cancer.

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Section: Tacstd2 Case Studysupporting

confidence: 92%

Machine learning enabled prediction of digital biomarkers from whole slide histopathology images

McCaw,

Shcherbina,

Shah

et al. 2024

Preprint

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show abstract

“…Considering that ADCs entered cancer cells by receptor-mediated endocytosis, maybe the limited Trop2 expression in MCF-7 cells restricted numbers of ADC molecules that entered the cells. While lidamycin demonstrated 100 times or more potent cell-killing activity than SN-38 in vitro, which might explain why hIMB1636-LDP-AE displayed more potent antitumor activity in MCF-7 xenograft model [47][48][49][50] . These results were consistent with the reports that the use of ADCs bearing more highly potent payloads would increase the probability of delivering a therapeutic dose to tumor cells with low antigen expression 51 .…”

Section: Discussionmentioning

confidence: 99%

A new TROP2-targeting antibody-drug conjugate shows potent antitumor efficacy in breast and lung cancers

Zhou,

Zhai,

Zhang

et al. 2024

npj Precis. Onc.

Self Cite

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Trophoblast cell surface antigen 2 (Trop2) is considered to be an attractive therapeutic target in cancer treatments. We previously generated a new humanized anti-Trop2 antibody named hIMB1636, and designated it as an ideal targeting carrier for cancer therapy. Lidamycin (LDM) is a new antitumor antibiotic, containing an active enediyne chromophore (AE) and a noncovalently bound apoprotein (LDP). AE and LDP can be separated and reassembled, and the reassembled LDM possesses cytotoxicity similar to that of native LDM; this has made LDM attractive in the preparation of gene-engineering drugs. We herein firstly prepared a new fusion protein hIMB1636-LDP composed of hIMB1636 and LDP by genetic engineering. This construct showed potent binding activities to recombinant antigen with a KD value of 4.57 nM, exhibited binding to Trop2-positive cancer cells and internalization and transport to lysosomes, and demonstrated powerful tumor-targeting ability in vivo. We then obtained the antibody-drug conjugate (ADC) hIMB1636-LDP-AE by molecular reconstitution. In vitro, hIMB1636-LDP-AE inhibited the proliferation, migration, and tumorsphere formation of tumor cells with half-maximal inhibitory concentration (IC50) values at the sub-nanomolar level. Mechanistically, hIMB1636-LDP-AE induced apoptosis and cell-cycle arrest. In vivo, hIMB1636-LDP-AE also inhibited the growth of breast and lung cancers in xenograft models. Moreover, compared to sacituzumab govitecan, hIMB1636-LDP-AE showed more potent antitumor activity and significantly lower myelotoxicity in tumors with moderate Trop2 expression. This study fully revealed the potent antitumor efficacy of hIMB1636-LDP-AE, and also provided a new preparation method for LDM-based ADC, as well as a promising candidate for breast cancer and lung cancer therapeutics.

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“…The promising in vivo antitumor effects are attributable to at least three MOAs: ADCC, the inhibition of cancer cell proliferation and migration, and the induction of cell cycle arrest and apoptosis. Sun et al further constructed an ADC version of hIMB1636 (hIMB1636-MMAE), using a Valine-Citrulline linker to conjugate mAb with three to four molecules of MMAE 64 . Not only does hIMB1636 retain the MOAs of its parent antibody, but it also achieves enhanced tumor inhibition via bystander effect in pancreatic cancer.…”

Section: Trop2-targeted Therapiesmentioning

confidence: 99%

Trop2-targeted therapies in solid tumors: advances and future directions

Liu,

Ma,

et al. 2024

Theranostics

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hIMB1636-MMAE, a Novel TROP2-Targeting Antibody-Drug Conjugate Exerting Potent Antitumor Efficacy in Pancreatic Cancer

Cited by 3 publications

References 53 publications

Machine learning enabled prediction of digital biomarkers from whole slide histopathology images

Machine learning enabled prediction of digital biomarkers from whole slide histopathology images

A new TROP2-targeting antibody-drug conjugate shows potent antitumor efficacy in breast and lung cancers

Trop2-targeted therapies in solid tumors: advances and future directions

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