Incorporating methylation genome information improves prediction accuracy for drug treatment responses

Xia, Xiaoxuan; Weng, Haoyi; Men, Ruoting; Sun, Rui; Zee, Benny; Chong, Ka Chun; Wang, Maggie Haitian

doi:10.1186/s12863-018-0644-5

Cited by 4 publications

(1 citation statement)

References 23 publications

(23 reference statements)

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“…As only a small subset of alterations identified from the whole genome, whole exome or DNA methylation sequencing are driver mutations that are clinically actionable [1], it is necessary to ensure that these actionable alterations are recorded in the knowledge base; otherwise patients may receive genetic tests but receive little or no pharmacotherapy suggestions. An increasing number of studies have revealed the importance of epigenome as well as other omics factors in explaining the influence on drug responses, especially in anti-cancer drug responses [2][3][4][5], thereby incorporating genomic, epigenomic, and other omics technologies that may improve prediction accuracy of drug treatment responses. In addition, some pharmacotherapy drugs are still in the clinical trial stage and thus are not available to the public or have been approved by administration agencies but the prices are unaffordable for some cancer patients.…”

Section: Introductionmentioning

confidence: 99%

OncoPDSS: an evidence-based clinical decision support system for oncology pharmacotherapy at the individual level

Xu¹,

Zhai²,

Huo³

et al. 2020

BMC Cancer

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Background: Precision oncology pharmacotherapy relies on precise patient-specific alterations that impact drug responses. Due to rapid advances in clinical tumor sequencing, an urgent need exists for a clinical support tool that automatically interprets sequencing results based on a structured knowledge base of alteration events associated with clinical implications. Results: Here, we introduced the Oncology Pharmacotherapy Decision Support System (OncoPDSS), a web server that systematically annotates the effects of alterations on drug responses. The platform integrates actionable evidence from several well-known resources, distills drug indications from anti-cancer drug labels, and extracts cancer clinical trial data from the ClinicalTrials.gov database. A therapy-centric classification strategy was used to identify potentially effective and noneffective pharmacotherapies from user-uploaded alterations of multi-omics based on integrative evidence. For each potentially effective therapy, clinical trials with faculty information were listed to help patients and their health care providers find the most suitable one. Conclusions: OncoPDSS can serve as both an integrative knowledge base on cancer precision medicine, as well as a clinical decision support system for cancer researchers and clinical oncologists. It receives multi-omics alterations as input and interprets them into pharmacotherapy-centered information, thus helping clinicians to make clinical pharmacotherapy decisions. The OncoPDSS web server is freely accessible at https://oncopdss.capitalbiobigdata.com.

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Section: Introductionmentioning

confidence: 99%

OncoPDSS: an evidence-based clinical decision support system for oncology pharmacotherapy at the individual level

Xu¹,

Zhai²,

Huo³

et al. 2020

BMC Cancer

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MeinteR: A framework to prioritize DNA methylation aberrations based on conformational and cis-regulatory element enrichment

Malousi

Kouidou

Tsagiopoulou

et al. 2019

Sci Rep

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DNA methylation studies have been reformed with the advent of single-base resolution arrays and bisulfite sequencing methods, enabling deeper investigation of methylation-mediated mechanisms. In addition to these advancements, numerous bioinformatics tools address important computational challenges, covering DNA methylation calling up to multi-modal interpretative analyses. However, contrary to the analytical frameworks that detect driver mutational signatures, the identification of putatively actionable epigenetic events remains an unmet need. The present work describes a novel computational framework, called MeinteR, that prioritizes critical DNA methylation events based on the following hypothesis: critical aberrations of DNA methylation more likely occur on a genomic substrate that is enriched in cis-acting regulatory elements with distinct structural characteristics, rather than in genomic “deserts”. In this context, the framework incorporates functional cis-elements, e.g. transcription factor binding sites, tentative splice sites, as well as conformational features, such as G-quadruplexes and palindromes, to identify critical epigenetic aberrations with potential implications on transcriptional regulation. The evaluation on multiple, public cancer datasets revealed significant associations between the highest-ranking loci with gene expression and known driver genes, enabling for the first time the computational identification of high impact epigenetic changes based on high-throughput DNA methylation data.

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Accurate Prediction of Neoadjuvant Chemotherapy Pathological Complete Remission (pCR) for the Four Sub-Types of Breast Cancer

et al. 2019

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Neoadjuvant chemotherapy (NAC) has become the main treatment option for breast cancer. Its adverse drug reactions (ADRs) make NAC painful both physiologically and psychologically. The factor pathological complete remission (pCR) describes how well a series of six or more chemotherapeutic treatments works on a patient. This study investigated the possibility of predicting pCR using only the nodal sizes of the first three treatments. A best feature combination for each breast cancer subtype was screened from the real nodal sizes of the first three treatments and the nodal sizes' of the next three treatments predicted from those of the first three ones. The prediction was evaluated by the metrics Avc = (sensitivity + specificity)/2. A triple-negative breast cancer (TN) patient may have an estimation of pCR Avc = 0.8696 after taking just three treatments. At least Avc = 0.7594 was achieved for all the four breast cancer subtypes investigated in this study.INDEX TERMS Pathological complete response (pCR), breast cancer, neoadjuvant chemotherapy, biomarker detection, feature selection.

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Incorporating methylation genome information improves prediction accuracy for drug treatment responses

Cited by 4 publications

References 23 publications

OncoPDSS: an evidence-based clinical decision support system for oncology pharmacotherapy at the individual level

OncoPDSS: an evidence-based clinical decision support system for oncology pharmacotherapy at the individual level

MeinteR: A framework to prioritize DNA methylation aberrations based on conformational and cis-regulatory element enrichment

Accurate Prediction of Neoadjuvant Chemotherapy Pathological Complete Remission (pCR) for the Four Sub-Types of Breast Cancer

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