DNA-methylome-derived epigenetic fingerprint as an immunophenotype indicator of durable clinical immunotherapeutic benefits in head and neck squamous cell carcinoma.

Background: Cancer immunotherapy provides durable response and improves survival in a subset of head and neck squamous cell carcinoma (HNSC) patients, which may due to discriminative tumor microenvironment (TME). Epigenetic regulations play critical roles in HNSC tumorigenesis, progression, and activation of functional immune cells. This study aims to identify an epigenetic signature as an immunophenotype indicator of durable clinical immunotherapeutic benefits in HNSC patients.

CDS-DB, an omnibus for patient-derived gene expression signatures induced by cancer treatment.

Patient-derived gene expression signatures induced by cancer treatment, obtained from paired pre- and post-treatment clinical transcriptomes, can help reveal drug mechanisms of action (MOAs) in cancer patients and understand the molecular response mechanism of tumor sensitivity or resistance. Their integration and reuse may bring new insights. Paired pre- and post-treatment clinical transcriptomic data are rapidly accumulating.

DCABM-TCM: A Database of Constituents Absorbed into the Blood and Metabolites of Traditional Chinese Medicine.

Traditional Chinese medicine (TCM) not only maintains the health of Asian people but also provides a great resource of active natural products for modern drug development. Herein, we developed a Database of Constituents Absorbed into the Blood and Metabolites of TCM (DCABM-TCM), the first database systematically collecting blood constituents of TCM prescriptions and herbs, including prototypes and metabolites experimentally detected in the blood, together with the corresponding detailed detection conditions through manual literature mining. The DCABM-TCM has collected 1816 blood constituents with chemical structures of 192 prescriptions and 194 herbs and integrated their related annotations, including physicochemical, absorption, distribution, metabolism, excretion, and toxicity properties, and associated targets, pathways, and diseases.

An integrated computational strategy to predict personalized cancer drug combinations by reversing drug resistance signatures.

Drug resistance currently poses the greatest barrier to cancer treatments. To overcome drug resistance, drug combination therapy has been proposed as a promising treatment strategy. Herein, we present Re-Sensitizing Drug Prediction (RSDP), a novel computational strategy, for predicting the personalized cancer drug combination A + B by reversing the resistance signature of drug A.

CPDR: An R Package of Recommending Personalized Drugs for Cancer Patients by Reversing the Individual's Disease-Related Signature.

Due to cancer heterogeneity, only some patients can benefit from drug therapy. The personalized drug usage is important for improving the treatment response rate of cancer patients. The value of the transcriptome of patients has been recently demonstrated in guiding personalized drug use, and the Connectivity Map (CMAP) is a reliable computational approach for drug recommendation.