2019
DOI: 10.1101/724310
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Tissue-guided LASSO for prediction of clinical drug response using preclinical samples

Abstract: 1Prediction of clinical drug response (CDR) of cancer patients, based on their clinical and 2 molecular profiles obtained prior to administration of the drug, can play a significant role 3 in individualized medicine. Machine learning models have the potential to address this 4 issue, but training them requires data from a large number of patients treated with each 5 drug, limiting their feasibility. While large databases of drug response and molecular 6 profiles of preclinical in-vitro cancer cell lines (CCLs)… Show more

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Cited by 6 publications
(23 citation statements)
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“…For drug repurposing, there is an added need for accurate tissue-specific drug efficacy predictions to study the efficacy of a drug in a relevant tissue-of-origin. Recent models, such as tissue-guided LASSO, make use of information on samples' tissue-of-origin to improve in vivo prediction performance [116]. It was shown that tissue-guided LASSO improves the clinical predictions and was able to distinguish resistant and sensitive patients for selected drugs.…”
Section: Algorithms For Cell and Tissue-based Drug Response Predictionsmentioning
confidence: 99%
“…For drug repurposing, there is an added need for accurate tissue-specific drug efficacy predictions to study the efficacy of a drug in a relevant tissue-of-origin. Recent models, such as tissue-guided LASSO, make use of information on samples' tissue-of-origin to improve in vivo prediction performance [116]. It was shown that tissue-guided LASSO improves the clinical predictions and was able to distinguish resistant and sensitive patients for selected drugs.…”
Section: Algorithms For Cell and Tissue-based Drug Response Predictionsmentioning
confidence: 99%
“…We used data corresponding to RECIST CDR of TCGA patients carefully collected in [10] and identified 14 drugs that satisfy two conditions: 1) there were at least 20 patients with known CDR values for each drug in TCGA database and 2) the ln(IC50) response of these drugs were measured in the GDSC database. Similar to previous studies [9, 14], we transformed the CDR of these tumours into a Boolean label in which “resistant” referred to patients with CDR of “stable disease” or “progressive disease” and “sensitive” referred to patients with CDR of “complete response” or “partial response”. These CDR values were used to evaluate the predicted drug response values using TINDL and other algorithms but were not used for training them.…”
Section: Resultsmentioning
confidence: 99%
“…On the other hand, large databases of molecular profiles of hundreds of in-vitro cancer cell lines (CCLs) and their response to hundreds of drugs [35] have enabled development of various ML algorithms for prediction of drug response [68]. Unfortunately, these models, even though accurate in predicting the drug response of held-out CCLs, usually do not generalize well to predicting the CDR of real tumours from cancer patients, and their prediction performance significantly deteriorates due to the major biological and statistical differences between CCLs and tumours [9].…”
Section: Introductionmentioning
confidence: 99%
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