December 6, 2023

SABCS 2023

Cell-free orphan noncoding RNAs and AI enable early detection of invasive breast cancer and ductal carcinoma in situ

Noura K. Tbeileh1, Taylor B. Cavazos1, Mehran Karimzadeh1, Jeffrey Wang1, Alice Huang1, Ti Lam1, Seda Kilinc1, Jieyang Wang1, Xuan Zhao1, Andy Pohl1, Helen Li1, Lisa Fish1, Kim H. Chau1, Marra S. Francis1, Lee S. Schwartzberg2, Pat A. Arensdorf1, Hani Goodarzi3, Fereydoun Hormozdiari1, Babak Alipanahi1

1Exai Bio Inc., Palo Alto, CA; 2Renown Health-Pennington Cancer Institute, Reno, NV; 3University of California San Francisco, San Francisco CA

Background

  • Earlier detection of breast cancer through mammography screening has reduced disease-specific mortality.
  • Confounding issues such as breast density, tumor size, and imaging anomalies can result in false negatives and ultimately later stage diagnosis.
  • We have previously demonstrated high sensitivity and specificity for early detection of invasive breast cancer (BC) by utilizing a novel category of cancer-associated small RNAs, termed orphan noncoding RNAs (oncRNAs), through a liquid biopsy platform.
  • In this study, we further assess the diagnostic ability of oncRNAs in a larger cohort of invasive BC and ductal carcinoma in situ (DCIS) for early cancer detection.

Goals

  • Further improve our ability to detect invasive BC in a larger, multi-source cohort and demonstrate that the same platform has the potential to also detect DCIS.
  • Establish that this next generation liquid biopsy platform has the ability to complement mammography and enable earlier detection of breast cancer.

Samples

  • The study cohort includes clinically diagnosed female breast cancer patients (N=279) and age- and sex-matched individuals with no known diagnosis of cancer (N=304).
  • Samples were sourced from Indivumed (Hamburg, Germany), Proteogenex (Inglewood, CA), and MT Group (Los Angeles, CA).
  • Breast cancer samples were acquired at time of diagnosis and were treatment-naive. All cancer and cancer-free samples were collected between 2010-2022.
  • Patients provided informed consent and contributing centers obtained IRB approval.

Methods

  • The small RNA content of all samples was sequenced at an average depth of 25.28 ± 9.37 million 50-bp single-end reads.
  • These reads were annotated using a library of 20,538 oncRNAs discovered through The Cancer Genome Atlas (TCGA) small RNA-seq database. These oncRNAs were found to be significantly enriched among 1,103 breast tumors compared to 349 normal tissue samples spanning multiple tissue sites through a female-specific analysis.
  • We detected 18,025 (87.8%) unique breast cancer-specific oncRNA species within at least one sample from the study cohort.
  • We then trained a generative AI model using 5-fold cross-validation to predict cancer status for all samples.

Overview of oncRNA Profiling and AI-Driven Model for Breast Cancer Prediction

Figure 1. Schematic of oncRNA Profiling and Modeling Pipeline

Figure 1. Schematic of oncRNA Profiling and Modeling Pipeline

Our generative AI model utilizes tumor-derived oncRNAs discovered in TCGA female tissue samples for downstream applications in serum.

Study Demographics

Chart of study demographics

Ability of oncRNA-Based Model for Prediction of Overall Breast Cancer Status in Serum

Figure 2. Overall Model Performance by ROC and Sensitivity at 90% Specificity

Figure 2. Overall Model Performance by ROC and Sensitivity at 90% Specificity

  • The ROC curve (A) demonstrated an AUC of 0.95 (95% CI: 0.94–0.97) in predicting the cancer status of both invasive BC and DCIS samples at a sensitivity of 0.85 (0.81–0.89).
  • Sensitivities at 90% specificity can be seen by cancer stage (B) and tumor T-category (C). 95% confidence intervals were calculated using the Clopper-Pearson method within each group.

Figure 3. Model ROC and Sensitivity in (A) DCIS and (B) Invasive BC

  • Prediction of cancer within individuals with invasive BC compared to cancer-free controls resulted in an AUC of 0.95 (95% CI, 0.93–0.97) with a sensitivity of 0.87 (0.82–0.91) at 90% specificity.
  • Within the DCIS cohort, we had an AUC of 0.93 (95% CI, 0.93– 0.97) with a sensitivity of 0.77 (0.82–0.91) at 90% specificity.
Figure 3. Model ROC and Sensitivity in (A) DCIS and (B) IBC

Conclusions

  • We have demonstrated the ability of an oncRNA-based liquid biopsy platform to effectively detect breast cancer.
  • Cancer status can be determined with high sensitivity and specificity in the earliest stages and smallest tumors of invasive BC with the potential for high sensitivity in DCIS.
  • This potential provides a novel opportunity to accurately detect breast cancer otherwise missed in a screening population and prevent unnecessary diagnostic work-ups.

Disclosures:

NT, TC, MK, JW , AH, TL, SK, JW, XZ, AP, HL, LF, KC, and FH are full-time employees of Exai Bio. BA and PA are co-founders, stockholders, and full-time employees of Exai Bio. HG is a co-founder, stockholder, and advisor of Exai Bio. LS is an unpaid advisor and MF is a paid advisor of Exai Bio.

References:

  1. Fish L, et al. Nature Med. 2018;24:1743-51.
  2. Wang J, et al. AACR. 2022; 3353.
  3. Wang J, et al. ESMO. 2022; 4635.
  4. Cavazos T, et al. SABCS. 2022; P1- 05-18.
  5. Karimzadeh M, et al. AACR 2023; 5711.
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