PubMed HealthSearch

PubMed · 77475

Quantitation of differential sensitivity of human-tumor stem cells to anticancer drugs.

Abstract

With a direct in vitro tumor-colony assay developed to measure sensitity of human-tumor stem cells to anticancer drugs, we performed 32 retrospective or prospective clinical studies in nine patients with myeloma and nine with ovarian cancer treated with standard agents that were tested in vitro. The results were clearly correlated (P is less than 0.00001). Unique patterns of sensitivity and resistance to the six drugs tested were observed for individual patients. In eight cases of myeloma and three of obarian carcinoma in vitro sensitivity corresponded with in vivo sensitivity whereas in one case of myeloma it did not. In vitro resistance correlated with clinical resistance in all five comparisons in myeloma and all 15 in ovarian cancer. We conclude that this assay shows sufficient promise to warrant larger-scale testing to determine its efficacy for selection of new agents and individualized cancer chemotherapy regimens.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

S E Salmon, A W Hamburger, B Soehnlen, B G Durie, D S Alberts, T E Moon. 1978-06-15. Quantitation of differential sensitivity of human-tumor stem cells to anticancer drugs.. https://doi.org/10.1056/nejm197806152982401

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Deep generative neural network for accurate drug response imputation.

Drug response differs substantially in cancer patients due to inter- and intra-tumor heterogeneity. Particularly, transcriptome context, especially tumor microenvironment, has been shown playing a significant role in shaping the actual treatment outcome. In this study, we develop a deep variational autoencoder (VAE) model to compress thousands of genes into latent vectors in a low-dimensional space. We then demonstrate that these encoded vectors could accurately impute drug response, outperform standard signature-gene based approaches, and appropriately control the overfitting problem. We apply rigorous quality assessment and validation, including assessing the impact of cell line lineage, cross-validation, cross-panel evaluation, and application in independent clinical data sets, to warrant the accuracy of the imputed drug response in both cell lines and cancer samples. Specifically, the expression-regulated component (EReX) of the observed drug response achieves high correlation across panels. Using the well-trained models, we impute drug response of The Cancer Genome Atlas data and investigate the features and signatures associated with the imputed drug response, including cell line origins, somatic mutations and tumor mutation burdens, tumor microenvironment, and confounding factors. In summary, our deep learning method and the results are useful for the study of signatures and markers of drug response.

Antineoplastic Agents