Effect of breast tissue density on cell-free orphan non-coding RNAs secreted by breast cancers.

Authors

Jeremy Ku

Jeremy Ku

Exai Bio Inc., Palo Alto, CA

Jeremy Ku , Akshaya Krishnan , Kim Hue Mai , Alice Huang , Dang D Nguyen , Selina Chen , Rose Hanna , Noura Tbeileh , Jieyang Wang , Xuan Zhao , Nae-Chyun Chen , Helen Li , Anna Hartwig , Fereydoun Hormozdiari , Pat Arensdorf , Lee S. Schwartzberg , Babak Alipanahi , Hani Goodarzi

Organizations

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

Research Funding

No funding sources reported

Background: Early detection of breast cancer is crucial for improved patient outcomes but cannot always be achieved through mammography for the more than 40% of women with dense breast tissue (BI-RADS C & D). Breast density masks the appearance of tumors, reducing mammographic sensitivity, which can result in false negatives and delayed diagnosis. Orphan non-coding RNA (oncRNAs) are a novel category of small RNAs that are present in tumors and largely absent in healthy tissue. We have recently demonstrated that oncRNAs secreted from breast cancer cells into the bloodstream enable early detection of breast cancer with high sensitivity and specificity. Here, we examine whether these oncRNAs are unaffected by breast tissue density, making them an effective blood-based biomarker in women with dense breast tissue. Methods: We prospectively collected serum samples and demographic/baseline characteristics from women being screened for breast cancer and analyzed samples from patients with documented breast cancer. We first assayed the cell-free small RNA content of every sample at an average depth of 50 million 50-bp single-end reads. Reads were then annotated using our proprietary RNA database to quantify oncRNA burden (expressed as the count of oncRNA reads per million mapped, unique reads). A previously developed oncRNA-based AI model for breast cancer detection was applied to calculate an oncRNA score for every sample. We compared both the oncRNA burden and the oncRNA score between breast density groups of fatty breast tissue (BI-RADS A & B) vs. dense breast tissue (BI-RADS C & D) using the Mann-Whitney (MW) U test and the Student’s t-test. Results: Of 68 women with breast cancer, 25 women had fatty breast tissue and 43 had dense breast tissue. Cohort characteristics were similar between women with fatty and dense breast tissue (cohort mean age: 58.9±11.6yr, BMI: 30.11±7.57, 74% Caucasian/White, 18% Hispanic, and 9% Black. In the overall cohort, all cancer stages were represented (I: n=36, II: n=18, III: n=10, IV: n=4), T-stage (T1: n=36, T2: n=23, T3: n=4, T4: n=5). The proportion of early stage (I/II) patients is comparable between women in the two breast density groups: fatty breast tissue, (21/25 = 84%) and dense breast tissue (33/43 = 77%). We did not observe a statistically significant difference in oncRNA burden (mean 56,452 counts per million or CPM vs 59,429 CPM in fatty vs. dense tissue, MW p=0.52; t=-0.59, p=0.56) nor in oncRNA score (mean 0.496 vs 0.554 in fatty vs. dense tissue, MW p=0.42; t=-0.79, p=0.43) between breast density groups. Conclusions: Taken together, our results indicate that unlike mammograms, the use of oncRNAs for detecting breast cancer are not influenced by breast density. Further research will utilize oncRNA score in determining breast cancer early detection in women with dense breast tissue.

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Abstract Details

Meeting

2024 ASCO Annual Meeting

Session Type

Poster Session

Session Title

Developmental Therapeutics—Molecularly Targeted Agents and Tumor Biology

Track

Developmental Therapeutics—Molecularly Targeted Agents and Tumor Biology

Sub Track

Molecular Diagnostics and Imaging

Citation

J Clin Oncol 42, 2024 (suppl 16; abstr 3065)

DOI

10.1200/JCO.2024.42.16_suppl.3065

Abstract #

3065

Poster Bd #

210

Abstract Disclosures

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