ESMO 2022

September 9, 2022

ESMO 2022

Serum-based colorectal cancer detection using orphan noncoding RNAs

Hani Goodarzi1, Jeffrey Wang2, Oluwadamilare I. Afolabi2, Lisa Fish2 , Helen Li2 , Kimberly H. Chau2, Patrick Arensdorf2, Fereydoun Hormozdiari2, Babak Alipanahi2

1UCSF School of Medicine, University of California, San Francisco, CA, 2Exai Bio Inc., Palo Alto, CA


  • Small non-coding RNAs (sncRNAs) have established roles as posttranscriptional regulators of cancer pathogenesis.
  • We previously reported a novel and previously unannotated class of sncRNAs that were found in breast cancer tissue but not in normal tissue adjacent to the tumor, which we termed orphan non-coding RNAs(oncRNAs).1 Since then, we have identified and validated novel oncRNAs in multiple cancer tissues, using data from The Cancer Genome Atlas (TCGA) and other independent cohorts.2
  • We recently showed that these oncRNAs can also be detected in sera and demonstrated prognostic value for treatment response among breast cancer patients.3
  • Early detection of colorectal cancer (CRC) can drastically improve survival odds, reduce treatment complexity and side effects, and improve patient quality of life.4
  • We hypothesize that oncRNAs can be used as biomarkers in a liquid biopsy strategy to detect CRC across a range of cancer stages and tumor sizes


  • Develop and validate a methodology that uses machine learning (ML) to accurately predict CRC status based on oncRNA profiles detected in patient sera.


  • Our study cohort consists of 191 frozen serum samples from clinically diagnosed colorectal cancer patients (n=96) and age- and sex-matched individuals from the general population with no known diagnosis of cancer (n=95). Samples were acquired from three commercial biobanks and processed for small RNA (smRNA) sequencing. Dates of blood draw for serum collection range from 2009 to 2022.
  • Subjects were treatment-naive at sample collection and were selected to represent all stages of CRC (I–IV) as well as a broad range of ages of onset, including patients <45 years old.
  • Patients had provided informed consent and contributing centers had obtained IRB approval.


  • RNA was extracted from frozen serum samples of ≤1.0ml volume and prepared for sequencing. Sample libraries were sequenced to an average depth of 18.8 million 50 bp single-end reads per sample.
  • oncRNAs were previously identified in multiple cancer tissues, using data from TCGA as a discovery cohort. Of this multicancer library of oncRNAs, 57,663 were significantly present in TCGA CRC samples. To refine our TCGA library of CRC-associated oncRNAs for applications in serum, we filtered out smRNA sequences found in sera of an independent non-cancer control cohort (N=31). OncRNAs that were detected in more than one control serum sample were filtered out, yielding a final set of 53,814 CRC-significant oncRNAs.
  • This filtered library of oncRNAs was used as a reference to generate oncRNA expression profiles by cataloguing and quantifying oncRNAs for each individual serum sample (N=191).
  • • These oncRNA expression profiles were used to build an ensemble of logistic regression models to make predictions of CRC vs. control. The ensemble model was trained and evaluated using a 5-fold cross-validation setup. Within each training fold only oncRNAs observed in >4% of samples and yielding an odds ratio for CRC >1 were used to train and validate the model.

oncRNA Library Creation and Profiling

Diagram illustrating oncRNA Library Creation and Profiling

Study Cohort

Chart of study cohort.

Result 1: oncRNA Content Differentiates Cancer Status

Figure 1. oncRNA Content in Control and Breast Cancer Serum Samples

  • Of the 53,814 TCGA CRCspecific oncRNA species, 36,282 (67.4%) were observed in the study cohort (N=191).
  • Total sequencing depthnormalized oncRNA content, the aggregate count of all detected oncRNAs within each sample, was significantly higher in cancer samples (one-sided Mann-Whitney U test, P=3.5e-14).
Figure 1 graphic of oncRNA Content in Cancer and Control Samples

Result 2: Prediction of Colorectal Cancer Status

Figure 2. ROC Curve of an Ensemble Model

  • A five-fold cross validation of an ensemble of logistic regression model’s CRC prediction performance on our study cohort (N=191).
  • On average, 3,285 oncRNAs were used as features within each fold.
  • Overall area under the ROC curve (AUC) across folds is 0.964 (95% CI: 0.938–0.99).
  • The model achieved an overall sensitivity (true positive rate) of 90.6% (95% CI: 82.9%–95.6%) with specificity set at 90% across folds.
Figure 2 graphic of ROC Curve of an Ensemble Model

Figures 3 & 4. Sensitivities for CRC Detection by Cancer Stage (I–IV) and Tumor T Category (T1–T4)

  • For each subgroup, using the model, sensitivity was calculated with specificity set at 90% (based on recent CMS reimbursement publication). 95% confidence intervals were calculated using the Clopper-Pearson method.
  • Sensitivities for CRC detection were similar and high across all stages and tumor T categories
Figures 3 and 4: sensitivities for CRC Detection by Cancer Stage (I–IV) and Tumor T Category (T1–T4)


  • Analyzing oncRNA data with machine learning models accurately predicted colorectal cancer (CRC) across all cancer stages (I–IV) and tumor categories (T1–T4).
  • This oncRNA-based liquid biopsy technology is compatible with standard sample requirements enabling integration into conventional clinical workflows.
  • The results will be validated prospectively in further population studies.


JW, OA, LF, HL, KC, FH are full-time employees of Exai Bio. BA and PA are cofounders, stockholders, and full-time employees of Exai Bio. HG is co-founder, stockholder, and advisor of Exai Bio.


  1. Fish L., et al. Nature Med. 2018;24:1743-51.
  2. Wang J, et al. AACR. 2022; 3353.
  3. Navickas A., et al. SABCS. 2021; PD9-04.
  4. Xi Y., et al. Transl Oncol. 2021;14(10):101174
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