Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: UMLS:C0699790 (colon cancer)
 28,837 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

A new generation of anticancer therapeutics called target drugs has quickly developed in the 21st century. These drugs are tailored to inhibit cancer cell growth, proliferation, and viability by specific interactions with one or a few target proteins. However, despite formally known molecular targets for every "target" drug, patient response to treatment remains largely individual and unpredictable. Choosing the most effective personalized treatment remains a major challenge in oncology and is still largely trial and error. Here we present a novel approach for predicting target drug efficacy based on the gene expression signature of the individual tumor sample(s). The enclosed bioinformatic algorithm detects activation of intracellular regulatory pathways in the tumor in comparison to the corresponding normal tissues. According to the nature of the molecular targets of a drug, it predicts whether the drug can prevent cancer growth and survival in each individual case by blocking the abnormally activated tumor-promoting pathways or by reinforcing internal tumor suppressor cascades. To validate the method, we compared the distribution of predicted drug efficacy scores for five drugs (Sorafenib, Bevacizumab, Cetuximab, Sorafenib, Imatinib, Sunitinib) and seven cancer types (Clear Cell Renal Cell Carcinoma, Colon cancer, Lung adenocarcinoma, non-Hodgkin Lymphoma, Thyroid cancer and Sarcoma) with the available clinical trials data for the respective cancer types and drugs. The percent of responders to a drug treatment correlated significantly (Pearson's correlation 0.77 p = 0.023) with the percent of tumors showing high drug scores calculated with the current algorithm.
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PMID:A method for predicting target drug efficiency in cancer based on the analysis of signaling pathway activation. 2632 Jan 81

ARID1A alterations would compromise mismatch repair pathway and increase the number of tumor-infiltrating lymphocytes and PD-L1 expression in some cancers, which would cooperate with immune checkpoint inhibitors (ICIs) treatment. However, a comprehensive analysis of ARID1A alteration frequency and its predictive value for ICI treatment outcome in cancers has not yet been investigated. Hence, we performed this pan-cancer analysis to evaluate the prevalence and predictive value of ARID1A alterations across >40,000 cases in multiple cancer types. We found a high frequency (6.2%) of ARID1A, which were associated with significantly higher tumor mutation burden level across various cancers. Importantly, patients with ARID1A alterations and advanced cancers had the substantially prolonged overall survival in ICI treatment cohort, suggesting it might be used to predict a survival benefit from ICI therapy across multiple cancer types. Notably, ARID1A alterations were correlated with markedly high immune infiltrates in endometrial, stomach and colon cancer. However, patients with ARID1A-mutant renal clear cell carcinoma had dramatically lower CD8+ T cell infiltrations than those without, indicating the association between ARID1A alterations and immune infiltrates was cancer-dependent. Collectively, our findings highlight the important value of ARID1A alterations as pan-cancer predictive biomarkers for ICI treatment.
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PMID:Pan-cancer analysis of ARID1A Alterations as Biomarkers for Immunotherapy Outcomes. 3194 79