Inferring cancer tissue-of-origin and molecular classification are two critical problems in personalized cancer therapy. Around 5% of cancers are cancers of unknown primary (CUP) sites, and these kinds of patients are treated with empiric chemotherapy, which leads to a very low survival rate. Therefore, it is important to infer cancer tissue-of-origin.

However, experimental methods usually fail to identify the exact tissue-of-origin even after the death of a patient, which provides a need for computational methods, especially in the era of big biomedical data. Based on the finding that the gene expression of metastatic cancer cells is more similar to that of the original tissue, there have been a few computational methods developed in this area. However, the accuracy of these methods needs to be improved to assure clinical usage. In addition to CUP, inferring cancer tissue-of-origin is also important to avoid misdiagnosis even if the cancer origin is known. In addition, cancer molecular classification has been proven to be useful in optimizing treatment methods. With the accumulation of genetic and prognostic data, it is possible to mine a better molecular classification for most cancer types based on historical data, especially when more and more single-cell data are available. A better cancer molecular classification and a better set of markers are critical for developing more efficient cancer treatment and new drugs.

The aim of this Special Issue is to comprehensively discuss the topic of cancer tissue-of-origin and molecular classification. We welcome studies on many kinds of methods, algorithms, and tools to infer cancer origins and molecular classification, but we also encourage translational studies for cancer treatment in hospitals. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Inferring tissue-of-origin for cancers based on tissue data, including gene expression, methylation, acetylation, and somatic mutations
• Inferring tissue-of-origin for cancers based on liquid biopsy, including cell-free DNA, cell-free RNA, and circulating tumor cells
• Tissue and liquid biopsy-based methods for inferring tissue-of-origin for cancer subtypes
• Inferring cancer site-of-origin for multiple nodules in a specific tissue
• Machine learning methods for molecular classification of a specific cancer
• Clinical applications of cancer tissue-of-origin identification, including biomarkers for cancer subtype identification
• Clinical applications of cancer molecular classification for different treatment strategies
• The association between cancer molecular classification and immunotherapies
• The association between cancer molecular classification and other important cancer features like microsatellite instability (MSI), total number of mutations (TMB), and PD1-PDL1
• Single-cell analyses for tissue-of-origin and cancer molecular classification
• Medical image-based methods in cancer tissue-of-origin and molecular classification