Artificial intelligence (AI) and big data in cancer and precision oncology

Abstract

Artificial intelligence (AI) and machine learning have significantly influenced many facets of the healthcare sector. Advancement in technology has paved the way for analysis of big datasets in a cost- and time-effective manner. Clinical oncology and research are reaping the benefits of AI. The burden of cancer is a global phenomenon. Efforts to reduce mortality rates requires early diagnosis for effective therapeutic interventions. However, metastatic and recurrent cancers evolve and acquire drug resistance. It is imperative to detect novel biomarkers that induce drug resistance and identify therapeutic targets to enhance treatment regimes. The introduction of the next generation sequencing (NGS) platforms address these demands, has revolutionised the future of precision oncology. NGS offers several clinical applications that are important for risk predictor, early detection of disease, diagnosis by sequencing and medical imaging, accurate prognosis, biomarker identification and identification of therapeutic targets for novel drug discovery. NGS generates large datasets that demand specialised bioinformatics resources to analyse the data that is relevant and clinically significant. Through these applications of AI, cancer diagnostics and prognostic prediction are enhanced with NGS and medical imaging that delivers high resolution images. Regardless of the improvements in technology, AI has some challenges and limitations, and the clinical application of NGS remains to be validated. By continuing to enhance the progression of innovation and technology, the future of AI and precision oncology show great promise.

Keywords: AI, Artificial Intelligence; Artificial intelligence; Big datasets; CNV, Copy Number Variations; Deep learning; Diagnosis; Digital pathology; FFPE, Formalin-Fixed Paraffin-Embedded; LYNA, LYmph Node Assistant; ML, Machine Learning; Machine learning; Medical imaging; NGS and bioinformatics; NGS, Next Generation Sequencing; Precision oncology; Prognosis and drug discovery; TCGA, The Cancer Genome Atlas; Treatment; WSI, Whole Slide Imaging.

Graphical abstract

Fig. 1

An overview of the applications of artificial intelligence in some major sectors. Artificial intelligence (AI) and machine learning (ML) have important applications in healthcare and precision oncology. ML is a subset of AI that uses neural networks to solve healthcare problems and predict treatment outcomes by pattern recognition in patient datasets. The accuracy of the data is warranted by implementing deep learning of machines , , , , , , .

Fig. 2

The advancement of DNA sequencing. 1st generation sequencing or Sanger sequencing involves the fragmentation and cloning of the target DNA into plasmid vectors. The DNA is then sequenced using a cyclic chain termination method with either radio isotopically labelled or fluorescently labelled dNTPs. The 2nd generation sequencing technologies are all based on sequencing by synthesis. Two common methods used are emulsion PCR and bridge PCR. Following these methods, different platforms make use of different sequencing technologies. 3rd generation sequencing methods have been developed by many different companies and are based on different technologies. They all involve more direct examination of the target DNA .

Fig. 3

Artificial intelligence (AI) in cancer medical imaging. Deep learning algorithms in healthcare begins with the gathering of large amounts of data. The curation of this data is then used in the screening of patients to make better data driven diagnosis. Patients can be screened with medical imaging and the presence of biomarkers for disease. Image analysis involves the identification of image of interest and the areas of the image that are important. The application of information from datasets as well as the results of patient screening results in automated detection of malignant tumours. Through classification of different tumours, the application of AI algorithm’s will then allow for the use of specific treatments optimised for each individual patient .

Fig. 4

AI algorithms in digital pathology. The 3rd generation AI algorithms are the latest offerings in improved digital pathology compared to the current 1st and 2nd generation platforms. Whole slide imaging (WSI) has enhanced the standard glass slide preparation by producing high resolution scanned images of the entire slide. WSI is the mainstay of AI algorithms in digital pathology.

Fig. 5

Molecular basis of drug resistance in cancer. (A) Estrogen receptor positive breast cancers. Mutated estrogen receptor 1 (ESR1) has altered ligand-binding domains leading to alterations in PI3K/mTOR signalling pathways. (B) Drug resistance in ovarian cancer results from mutations or modified gene expression in the molecular pathways responsible for DNA repair process, p53 pathway, the P-glycoprotein and multi drug resistance-associated protein. Image .

Fig. 6

The use of sequencing in precision medicine. Targeted sequencing is the current standard of sequencing for clinical purposes. It involves the use of selected candidate genes, prevalent in specific cancers, such as BRCA1 and BRCA2 in breast cancer, p53 and PTEN in prostate cancer, KRAS in pancreatic cancer, BRAF in colorectal cancer and ERBB2 in lung cancer. Targeted sequencing has the advantage of higher sensitivity, high coverage and lower costs. It has the disadvantages of not identifying large genomic rearrangements or potential pathogenic mutations in genes that are not targeted. Whole genome sequencing has the advantage of allowing for mutations and changes in the whole genome .