Profiling lung adenocarcinoma by liquid biopsy: can one size fit all?

Clifford, Harry; Cassidy, Amy P.; Vaughn, Courtney M.; Tsai, Evaline S.; Seres, Bianka; Patel, Nirmesh; O'Neill, Hannah L; Hewage, Emil; Cassidy, John W.

doi:10.1186/s12645-016-0023-8

Cited by 5 publications

(7 citation statements)

References 31 publications

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“…It is an EGFR protein kinase inhibitor designated to treat patients with NSCLC by deleting the 19 number exon. Li and colleagues also used another drug to treat metastasized renal carcinoma named sorafenib [140,143].…”

Section: Machine Learning In Oncologymentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Precision and Genomic Medicine

Quazi¹

2021

Preprint

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The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision-making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of data sets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.

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Section: Machine Learning In Oncologymentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Precision and Genomic Medicine

Quazi¹

2021

Preprint

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“…In one such case study, Li and colleagues built drug sensitivity models from cancer cell lines treated with erlotinib [an EGFR protein kinase inhibitor approved for NSCLC patients with activating mutations: exon 19 deletion (del19) or exon 21 (L858R) substitution] and sorafenib (a non-specific kinase inhibitor approved for advanced renal cell carcinoma) [48,51]. Models were then used to stratify patients in the BATTLE (Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination) clinical trial [48,52], with identified biomarkers backwards justified with knowledge of the mechanism of action of each kinase inhibitor drug.…”

Section: Predictive Biomarkers For Personalised Cancer Carementioning

confidence: 99%

Applications of Machine Learning in Drug Discovery II: Biomarker Discovery, Patient Stratification and Pharmacoeconomics

Cassidy¹

2020

Artificial Intelligence in Oncology Drug Discovery and Development

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Cancer remains a leading cause of morbidity and mortality around the world. Despite significant advances in our understanding of the pathology of the disease, and the substantial public and private investment into treatment development, late-stage patients often exhaust therapeutic options. Indeed, in the US alone, there were >1.7 million new cancer diagnoses and >600,000 cancerassociated deaths in 2019. As biology in general and cancer research in particular become ever richer in data, we explore the role of machine learning (ML) in changing the cancer drug development landscape. In the first part of this analysis, we focussed on ML for target identification and drug design. We discussed the growing need for ML-based analysis as we enter an age of clinical-omic data and provided a primer to ML-based techniques for the non-statistician/ mathematician. In this chapter, we will explore the problem of tumour heterogeneity together with the role of ML in the discovery and development of cancer biomarkers and for clinical trial design. We end with a brief consideration of the economics of personalised cancer treatment.

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“…These identified mutation genes or pathways might be drivers contributing to cancer (Youn and Simon, 2011; Dees et al, 2012; Hua et al, 2013; Merid et al, 2014; Leiserson et al, 2015) or potential diagnosis biomarkers for a cancer (Ece Solmaz et al, 2015; Clifford et al, 2016; Li et al, 2016; Sato et al, 2016). For example, Clifford et al identified a panel of 400 mutations covering more than 80% of the lung adenocarcinoma (LUAD) patients from The Cancer Genome Atlas (TCGA) database (Clifford et al, 2016). However, in an independent validation dataset, this panel of mutations only covered 55% of 183 patients (Clifford et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…For example, Clifford et al identified a panel of 400 mutations covering more than 80% of the lung adenocarcinoma (LUAD) patients from The Cancer Genome Atlas (TCGA) database (Clifford et al, 2016). However, in an independent validation dataset, this panel of mutations only covered 55% of 183 patients (Clifford et al, 2016). It is not surprising that the coverage drops so much in the validation dataset because the distribution of somatic mutations is highly heterogeneous (N.…”

Section: Introductionmentioning

confidence: 99%

“…The methods to extract sub-networks mainly combined mutations with copy number variations to identify modules related with diseases, such as HotNet (Leiserson et al, 2015) and MEMo (Ciriello et al, 2012). Panels of mutation genes have been reported to be a promising way to diagnose a specific cancer (Ece Solmaz et al, 2015; Clifford et al, 2016; Li et al, 2016; Sato et al, 2016). It would be of great significance if we could find sub-pathways mutated in almost all patients of a cancer.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Common and Subtype-Specific Mutated Sub-Pathways for a Cancer

et al. 2019

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The heterogeneity of cancer is a big obstacle for cancer diagnosis and treatment. Prioritizing combinations of driver genes that mutate in most patients of a specific cancer or a subtype of this cancer is a promising way to tackle this problem. Here, we developed an empirical algorithm, named PathMG, to identify common and subtype-specific mutated sub-pathways for a cancer. By analyzing mutation data of 408 samples (Lung-data1) for lung cancer, three sub-pathways each covering at least 90% of samples were identified as the common sub-pathways of lung cancer. These sub-pathways were enriched with mutated cancer genes and drug targets and were validated in two independent datasets (Lung-data2 and Lung-data3). Especially, applying PathMG to analyze two major subtypes of lung cancer, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LSCC), we identified 13 subtype-specific sub-pathways with at least 0.25 mutation frequency difference between LUAD and LSCC samples in Lung-data1, and 12 of the 13 sub-pathways were reproducible in Lung-data2 and Lung-data3. Similar analyses were done for colorectal cancer. Together, PathMG provides us a novel tool to identify potential common and subtype-specific sub-pathways for a cancer, which can provide candidates for cancer diagnoses and sub-pathway targeted treatments.

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Profiling lung adenocarcinoma by liquid biopsy: can one size fit all?

Cited by 5 publications

References 31 publications

Artificial Intelligence and Machine Learning in Precision and Genomic Medicine

Artificial Intelligence and Machine Learning in Precision and Genomic Medicine

Applications of Machine Learning in Drug Discovery II: Biomarker Discovery, Patient Stratification and Pharmacoeconomics

Identification of Common and Subtype-Specific Mutated Sub-Pathways for a Cancer

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