HiTAIC: hierarchical tumor artificial intelligence classifier traces tissue of origin and tumor type in primary and metastasized tumors using DNA methylation

Abstract

Human cancers are heterogenous by their cell composition and origination site. Cancer metastasis generates the conundrum of the unknown origin of migrated tumor cells. Tracing tissue of origin and tumor type in primary and metastasized cancer is vital for clinical significance. DNA methylation alterations play a crucial role in carcinogenesis and mark cell fate differentiation, thus can be used to trace tumor tissue of origin. In this study, we employed a novel tumor-type-specific hierarchical model using genome-scale DNA methylation data to develop a multilayer perceptron model, HiTAIC, to trace tissue of origin and tumor type in 27 cancers from 23 tissue sites in data from 7735 tumors with high resolution, accuracy, and specificity. In tracing primary cancer origin, HiTAIC accuracy was 99% in the test set and 93% in the external validation data set. Metastatic cancers were identified with a 96% accuracy in the external data set. HiTAIC is a user-friendly web-based application through https://sites.dartmouth.edu/salaslabhitaic/. In conclusion, we developed HiTAIC, a DNA methylation-based algorithm, to trace tumor tissue of origin in primary and metastasized cancers. The high accuracy and resolution of tumor tracing using HiTAIC holds promise for clinical assistance in identifying cancer of unknown origin.

Recent Advancements in Research on DNA Methylation and Testicular Germ Cell Tumors: Unveiling the Intricate Relationship

“…In this hierarchy, TGCTs were classified in the first layer and showed a high precision, both in their test data set and the external validation data set. Furthermore, HiTAIC was able to accurately predict metastases to lung and lymph nodes, but only on a sample size of six patients [75].…”

“…The TCGA dataset was further used to develop an algorithm for identifying tissue of origin and tumor type in primary and metastasized cancer based on DNA methylation alterations [75]. The method developed by the authors, called HiTAIC, used a hierarchical model to identify 27 different cancers based on specific CpGs.…”

New techniques to identify the tissue of origin for cancer of unknown primary in the era of precision medicine: progress and challenges

“…Using genomic profiling data as an example, bioinformatics and ML algorithms are applied to score and rank the most relevant genes for creating tumor–gene associations and constructing TOO classifiers. Several ML algorithms to identify the TOO of CUP have been applied in this context [ 28 , 29 , 33 , 42 , 44 , 45 , 52–54 , 57 , 58 , 64 , 73 , 96 ] ( Supplementary Figure 3 and Table 2 ). These associations are subsequently assessed through independent validation sets, and the classifier’s efficacy is further verified with challenging clinical cases.…”

Tracing unknown tumor origins with a biological-pathway-based transformer model

“…Cancer metastasis, responsible for nearly 90% of cancer-related deaths, is considered the primary driver of cancer mortality. 1 , 2 Cancer metastasis occurs when late-stage tumor cells develop the ability to detach from the primary tumor tissue, travel through the circulatory and lymphatic systems, invade distant tissues, and proliferate at secondary sites. 1 , 3 Nonetheless, in 3%–5% of patients with cancer, metastatic tumors manifest for which conventional diagnostics cannot identify the primary tumor sites, resulting in a diagnosis of cancer of unknown primary (CUP).…”