Circumvent the uncertainty in the applications of transcriptional signatures to tumor tissues sampled from different tumor sites

Abstract

The expression measurements of thousands of genes are correlated with the proportions of tumor epithelial cell (PTEC) in clinical samples. Thus, for a tumor diagnostic or prognostic signature based on a summarization of expression levels of the signature genes, the risk score for a patient may dependent on the tumor tissues sampled from different tumor sites with diverse PTEC for the same patient. Here, we proposed that the within-samples relative expression orderings (REOs) based gene pairs signatures should be insensitive to PTEC variations. Firstly, by analysis of paired tumor epithelial cell and stromal cell microdissected samples from 27 cancer patients, we showed that above 80% of gene pairs had consistent REOs between the two cells, indicating these REOs would be independent of PTEC in cancer tissues. Then, by simulating tumor tissues with different PTEC using each of the 27 paired samples, we showed that about 90% REOs of gene pairs in tumor epithelial cells were maintained in tumor samples even when PTEC decreased to 30%. Especially, the REOs of gene pairs with larger expression differences in tumor epithelial cells tend to be more robust against PTEC variations. Finally, as a case study, we developed a gene pair signature which could robustly distinguish colorectal cancer tissues with various PTEC from normal tissues. We concluded that the REOs-based signatures were robust against PTEC variations.

Transcriptional signatures for coupled predictions of stage II and III colorectal cancer metastasis and fluorouracil‐based adjuvant chemotherapy benefit

“…REO-based signatures are robust against experimental batch effects (24,48), the differences of measurement principles of different platforms (31), sampling locations in a tumor tissue (25,26), and partial RNA degradation (28). With the rapid decline of cost in microarray and RNA sequencing, it would be convenient to develop an RT-PCR kit or a specific gene panel, such as the 70-gene signature for predicting prognosis of estrogen receptor-positive breast cancer approved by the U.S. Food and Drug Administration (49) to measure the expression level of genes included in the 2 coupled signatures.…”

“…These risk score-based signatures are often unfit for clinical applications as a result of the requirement of data normalization to remove measurement batch effects, which needs a precollection of samples, whereas the risk score for a sample is influenced by the risk composition of other samples (24). In addition, gene expression measurement values would also be greatly affected by sampling locations in a tumor tissue (25,26) and partial RNA degradation during sample preparation (27,28), which introduces additional uncertainty for the risk score and risk classification of a patient. In contrast, the relative expression orderings (REOs) of gene pairs within a sample have been found to be robust against the above factors, which make it a promising approach for developing robust gene pair-based signatures (GPSs) (11,18,20,21).…”

A qualitative transcriptional signature for predicting the biochemical recurrence risk of prostate cancer patients after radical prostatectomy

“…More importantly, our previous studies have demonstrated that different from the signatures based on quantitative expression measurements of the signature genes, the REOs‐based qualitative signatures are rather insensitive to the tumor cell percentage difference of specimen sampled from different parts of the same tumor, 26 the inescapably partial RNA degradation 27,28 and amplification bias of low‐input RNA samples 29 . Based on these unique advantages of the within‐sample REOs, we have developed the qualitative REOs‐based signatures for predicting the prognosis of breast cancer, 30,31 colorectal cancer, 32 gastric cancer, 33 liver cancer, 34 and lung cancer 35 .…”

A qualitative transcriptional signature for the early diagnosis of colorectal cancer

“…[28][29][30][31] The robustness property of the REO enables researchers to integrate multiple datasets produced by the same or similar platforms for developing disease signatures or classifiers, 32,33 which makes it more likely to find robust signatures. 10,32,34 In addition, the qualitative transcriptional characteristics are highly robust against varied proportions of the tumor epithelial cell in specimens sampled from different tumor locations of the same patients, 26 partial RNA degradation during specimen preparation and storage, 25 and amplification bias for minimum specimens, 27 which are the common factors that lead to the failure of quantitative transcriptional signatures in clinical practice. Therefore, it is worth exploiting the within-sample REOs to identify a robust qualitative signature for the early diagnosis of CRC.…”