Biomarker identification of hepatocellular carcinoma using a methodical literature mining strategy

The Role of Artificial Intelligence in the Detection and Implementation of Biomarkers for Hepatocellular Carcinoma: Outlook and Opportunities

Liver cancer is a leading cause of cancer-related death worldwide, and its early detection and treatment are crucial for improving morbidity and mortality. Biomarkers have the potential to facilitate the early diagnosis and management of liver cancer, but identifying and implementing effective biomarkers remains a major challenge. In recent years, artificial intelligence has emerged as a promising tool in the cancer sphere, and recent literature suggests that it is very promising in facilitating biomarker use in liver cancer. This review provides an overview of the status of AI-based biomarker research in liver cancer, with a focus on the detection and implementation of biomarkers for risk prediction, diagnosis, staging, prognostication, prediction of treatment response, and recurrence of liver cancers.

Real-world biomarker testing rate and positivity rate in NSCLC in Spain: Prospective Central Lung Cancer Biomarker Testing Registry (LungPath) from the Spanish Society of Pathology (SEAP)

AIM

The aim of this study was to describe the testing rate and frequency of molecular alterations observed in the Lung Cancer Biomarker Testing Registry (LungPath).

METHODS

A descriptive study of NSCLC biomarker determinations collected from March 2018 to January 2019, from 38 Spanish hospitals, was carried out. Only adenocarcinoma and not otherwise specified histologies were included for epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), c-ros oncogene 1 (ROS1) and programmed death ligand-1 (PD-L1) expression. The testing rate and the positivity rate were calculated. Multivariate logistic regression was used to explore the joint relationship between independent explanatory factors and both testing and positivity rates. Two models were adjusted: one with sample type and histology as independent factors, and the other adding the testing rate or the positivity rate of the other biomarkers.

RESULTS

3226 patient samples were analysed, where EGFR, ALK, ROS1 and PD-L1 information was collected (a total of 12 904 determinations). Overall, 9118 (71.4%) determinations were finally assessed. EGFR (91.4%) and ALK (80.1%) were the mainly tested biomarkers. Positivity rates for EGFR, ALK, ROS1 and PD-L1 were 13.6%, 3.4%, 2.0% and 49.2%, respectively. Multivariate models showed a lower testing rate for ALK in surgical pieces, fine-needle aspiration or other types of samples versus biopsies.

CONCLUSIONS

Despite the high testing rate in EGFR and ALK in NSCLC, the real-world evidence obtained from the LungPath demonstrates that ROS1 and PD-L1 were not determined in a significant portion of patients. LungPath provides crucial information to improve the coverage in molecular testing in lung cancer, to monitor the positivity rate and the introduction of new biomarker testing in clinical practice.

Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization

Background

Gastrointestinal (GI) cancer including colorectal cancer, gastric cancer, pancreatic cancer, etc., are among the most frequent malignancies diagnosed annually and represent a major public health problem worldwide.

Methods

This paper reports an aided curation pipeline to identify potential influential genes for gastrointestinal cancer. The curation pipeline integrates biomedical literature to identify named entities by Bi-LSTM-CNN-CRF methods. The entities and their associations can be used to construct a graph, and from which we can compute the sets of co-occurring genes that are the most influential based on an influence maximization algorithm.

Results

The sets of co-occurring genes that are the most influential that we discover include RARA - CRBP1, CASP3 - BCL2, BCL2 - CASP3 – CRBP1, RARA - CASP3 – CRBP1, FOXJ1 - RASSF3 - ESR1, FOXJ1 - RASSF1A - ESR1, FOXJ1 - RASSF1A - TNFAIP8 - ESR1. With TCGA and functional and pathway enrichment analysis, we prove the proposed approach works well in the context of gastrointestinal cancer.

Conclusions

Our pipeline that uses text mining to identify objects and relationships to construct a graph and uses graph-based influence maximization to discover the most influential co-occurring genes presents a viable direction to assist knowledge discovery for clinical applications.

Identification of pharmacodynamic biomarker hypotheses through literature analysis with IBM Watson

BACKGROUND

Pharmacodynamic biomarkers are becoming increasingly valuable for assessing drug activity and target modulation in clinical trials. However, identifying quality biomarkers is challenging due to the increasing volume and heterogeneity of relevant data describing the biological networks that underlie disease mechanisms. A biological pathway network typically includes entities (e.g. genes, proteins and chemicals/drugs) as well as the relationships between these and is typically curated or mined from structured databases and textual co-occurrence data. We propose a hybrid Natural Language Processing and directed relationships-based network analysis approach using IBM Watson for Drug Discovery to rank all human genes and identify potential candidate biomarkers, requiring only an initial determination of a specific target-disease relationship.

METHODS

Through natural language processing of scientific literature, Watson for Drug Discovery creates a network of semantic relationships between biological concepts such as genes, drugs, and diseases. Using Bruton's tyrosine kinase as a case study, Watson for Drug Discovery's automatically extracted relationship network was compared with a prominent manually curated physical interaction network. Additionally, potential biomarkers for Bruton's tyrosine kinase inhibition were predicted using a matrix factorization approach and subsequently compared with expert-generated biomarkers.

RESULTS

Watson's natural language processing generated a relationship network matching 55 (86%) genes upstream of BTK and 98 (95%) genes downstream of Bruton's tyrosine kinase in a prominent manually curated physical interaction network. Matrix factorization analysis predicted 11 of 13 genes identified by Merck subject matter experts in the top 20% of Watson for Drug Discovery's 13,595 ranked genes, with 7 in the top 5%.

CONCLUSION

Taken together, these results suggest that Watson for Drug Discovery's automatic relationship network identifies the majority of upstream and downstream genes in biological pathway networks and can be used to help with the identification and prioritization of pharmacodynamic biomarker evaluation, accelerating the early phases of disease hypothesis generation.