Carcinoma of unknown primary (CUP) is a type of metastatic cancer with tissue-of-origin (TOO) unidentifiable by traditional methods. CUP patients typically have poor prognosis but therapy targeting the original cancer tissue can significantly improve patients’ prognosis. Thus, it’s critical to develop accurate computational methods to infer cancer TOO. While qPCR or microarray-based methods are effective in inferring TOO for most cancer types, the overall prediction accuracy is yet to be improved. In this study, we propose a cross-cohort computational framework to trace TOO of 32 cancer types based on RNA sequencing (RNA-seq). Specifically, we employed logistic regression models to select 80 genes for each cancer type to create a combined 1356-gene set, based on transcriptomic data from 9911 tissue samples covering the 32 cancer types with known TOO from the Cancer Genome Atlas (TCGA). The selected genes are enriched in both tissue-specific and tissue-general functions. The cross-validation accuracy of our framework reaches 97.50% across all cancer types. Furthermore, we tested the performance of our model on the TCGA metastatic dataset and International Cancer Genome Consortium (ICGC) dataset, achieving an accuracy of 91.09% and 82.67%, respectively, despite the differences in experiment procedures and pipelines. In conclusion, we developed an accurate yet robust computational framework for identifying TOO, which holds promise for clinical applications. Our code is available at http://github.com/wangbo00129/classifybysklearn.

Background. Cancer of unknown primary (CUP) is a type of malignant tumor, which is histologically diagnosed as a metastatic carcinoma while the tissue‐of‐origin cannot be identified. CUP accounts for roughly 5% of all cancers. Traditional treatment for CUP is primarily broad‐spectrum chemotherapy; however, the prognosis is relatively poor. Thus, it is of clinical importance to accurately infer the tissue‐of‐origin of CUP. Methods. We developed a gradient boosting framework to trace tissue‐of‐origin of 20 types of solid tumors. Specifically, we downloaded the expression profiles of 20,501 genes for 7713 samples from The Cancer Genome Atlas (TCGA), which were used as the training data set. The RNA‐seq data of 79 tumor samples from 6 cancer types with known origins were also downloaded from the Gene Expression Omnibus (GEO) for an independent data set. Results. 400 genes were selected to train a gradient boosting model for identification of the primary site of the tumor. The overall 10‐fold cross‐validation accuracy of our method was 96.1% across 20 types of cancer, while the accuracy for the independent data set reached 83.5%. Conclusion. Our gradient boosting framework was proven to be accurate in identifying tumor tissue‐of‐origin on both training data and independent testing data, which might be of practical usage.

Carcinoma of unknown primary (CUP) is a type of metastatic cancer with tissue-of-origin (TOO) unidentifiable by traditional methods. Most CUP patients have poor prognosis since no therapy targeting TOO is allowed. Thus, it’s critical to develop accurate computational methods to infer TOO. While qPCR or microarray-based methods are effective in predicting TOO for most cancer types, the overall prediction accuracy is yet to be improved. Here, we propose a computational framework to trace TOO of 19 cancer types based on RNA sequencing (RNA-seq). Specifically, we download the RNA-seq data of 7000+ tissue samples covering 19 cancer types with known TOO from TCGA. By feature selection, 90 genes are finally selected to train a random forest model for TOO inference; the 90 genes are enriched in both tissue-specific functions and tissue-general functions. The cross-validation accuracy of our framework reaches 97.55% across all cancer types. Furthermore, we collected an independent cohort of samples in GEO as testing samples. The accuracy on the independent data is 74% despite the differences in experiment procedures and pipelines. In conclusion, we develop an accurate yet robust computational framework for identifying TOO, which might be promising in clinical applications.

Carcinoma of unknown primary (CUP) is a type of metastatic cancer with tissue-of-origin (TOO) unidentifiable by traditional methods. CUP patients typically have poor prognosis but therapy targeting the original cancer tissue can significantly improve patients’ prognosis. Thus, it’s critical to develop accurate computational methods to infer cancer TOO. While qPCR or microarray-based methods are effective in inferring TOO for most cancer types, the overall prediction accuracy is yet to be improved. In this study, we propose a cross-cohort computational framework to trace TOO of 32 cancer types based on RNA sequencing (RNA-seq). Specifically, we employed logistic regression models to select 80 genes for each cancer type to create a combined 1356-gene set, based on transcriptomic data from 9911 tissue samples covering the 32 cancer types with known TOO from the Cancer Genome Atlas (TCGA). The selected genes are enriched in both tissue-specific and tissue-general functions. The cross-validation accuracy of our framework reaches 97.50% across all cancer types. Furthermore, we tested the performance of our model on the TCGA metastatic dataset and International Cancer Genome Consortium (ICGC) dataset, achieving an accuracy of 91.09% and 82.67%, respectively, despite the differences in experiment procedures and pipelines. In conclusion, we developed an accurate yet robust computational framework for identifying TOO, which holds promise for clinical applications.