Leveraging TCGA gene expression data to build predictive models for cancer drug response

BMC Bioinformatics

Table 1 Patient counts for each model by response

Model	Responder Count	Non-responder Count	Total Count
Fluorouracil pan-cancer^a	34	24	58
Gemcitabine pan-cancer^b	37	55	92

^aFluorouracil pan-cancer includes: colon adenocarcinoma, esophageal carcinoma, pancreatic adenocarcinoma, rectum adenocarcinoma, stomach adenocarcinoma
^bGemcitabine pan-cancer includes: bladder urothelial carcinoma, breast invasive carcinoma, cervical squamous cell carcinoma and endocervical adenocarcinoma, cholangiocarcinoma, head and neck squamous cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, sarcoma, skin cutaneous melanoma, testicular germ cell tumors, uterine corpus endometrial carcinoma

ISSN: 1471-2105