Use of Anti-Tumor MABs for Diagnosis and Immunotherapy of Human Tumors

The effectiveness of L-lysine-alpha oxidase Trichoderma cf. Aureoviride Rifai BKMF-4268D (LO) on models of subcutaneous xenografts of human tumors in athymic mice as well as the effectiveness of combination therapy with known antitumor drugs: cisplatin, irinotecan, etoposide on models of P388 lymphocytic leukemia, Lewis lung (LLC) and B16 melanoma was evaluated. The intraperitoneal injection of LO in a discrete regime at doses of 150-75-75-75-75 E/kg demonstrated inhibition of growth of all studied xenografts of human tumors in athymic mice. The combination of irinotecan+LO on the LLC model gave a significant summative therapeutic benefit with an increase in mouse life expectancy up to 35%. Cisplatin and LO realized a significant (p

Download Full-text

Antitumor activity of a novel nucleoside, 2′-C-cyano-2′-deoxy-1-β-d-arabinofuranosylcytosine (CNDAC) against murine and human tumors

Cancer Letters ◽

10.1016/0304-3835(92)90024-p ◽

1992 ◽

Vol 64 (1) ◽

pp. 67-74 ◽

Cited By ~ 44

Author(s):

Motohiro Tanaka ◽

Akira Matsuda ◽

Tomoko Terao ◽

Takuma Sasaki

Keyword(s):

Antitumor Activity ◽

Human Tumors

Download Full-text

Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidates for targeted treatment

Nature Communications ◽

10.1038/s41467-021-25177-3 ◽

2021 ◽

Vol 12 (1) ◽

Cited By ~ 1

Author(s):

Hua Sun ◽

Song Cao ◽

R. Jay Mashl ◽

Chia-Kuei Mo ◽

Simone Zaccaria ◽

...

Keyword(s):

Human Tumors ◽

Multiple Time ◽

Cancer Treatments ◽

Whole Genome Duplications ◽

Patient Derived Xenograft ◽

Genome Duplications ◽

Genomic Characterization ◽

Cancer Types ◽

Multiple Time Points ◽

Pdx Models

AbstractDevelopment of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established the genomic landscapes of 536 patient-derived xenograft (PDX) models across 25 cancer types, together with mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared with human tumors, PDXs typically have higher purity and fit to investigate dynamic driver events and molecular properties via multiple time points from same case PDXs. Here, we report on dynamic genomic landscapes and pharmacogenomic associations, including associations between activating oncogenic events and drugs, correlations between whole-genome duplications and subclone events, and the potential PDX models for NCI-MATCH trials. Lastly, we provide a web portal having comprehensive pan-cancer PDX genomic profiles and source code to facilitate identification of more druggable events and further insights into PDXs’ recapitulation of human tumors.

Download Full-text

Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors

Cancer Informatics ◽

10.1177/11769351211002494 ◽

2021 ◽

Vol 20 ◽

pp. 117693512110024

Author(s):

Jason D Wells ◽

Jacqueline R Griffin ◽

Todd W Miller

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Human Tumors ◽

Molecular Characteristics ◽

Survival Times ◽

Chemotherapy Drugs ◽

Testing Dataset ◽

Cancer Models ◽

Cancer Types ◽

Pan Cancer

Motivation: Despite increasing understanding of the molecular characteristics of cancer, chemotherapy success rates remain low for many cancer types. Studies have attempted to identify patient and tumor characteristics that predict sensitivity or resistance to different types of conventional chemotherapies, yet a concise model that predicts chemosensitivity based on gene expression profiles across cancer types remains to be formulated. We attempted to generate pan-cancer models predictive of chemosensitivity and chemoresistance. Such models may increase the likelihood of identifying the type of chemotherapy most likely to be effective for a given patient based on the overall gene expression of their tumor. Results: Gene expression and drug sensitivity data from solid tumor cell lines were used to build predictive models for 11 individual chemotherapy drugs. Models were validated using datasets from solid tumors from patients. For all drug models, accuracy ranged from 0.81 to 0.93 when applied to all relevant cancer types in the testing dataset. When considering how well the models predicted chemosensitivity or chemoresistance within individual cancer types in the testing dataset, accuracy was as high as 0.98. Cell line–derived pan-cancer models were able to statistically significantly predict sensitivity in human tumors in some instances; for example, a pan-cancer model predicting sensitivity in patients with bladder cancer treated with cisplatin was able to significantly segregate sensitive and resistant patients based on recurrence-free survival times ( P = .048) and in patients with pancreatic cancer treated with gemcitabine ( P = .038). These models can predict chemosensitivity and chemoresistance across cancer types with clinically useful levels of accuracy.

Download Full-text