Early Detection of Lung Cancer using small RNAs

BACKGROUND: Lung cancer remains the deadliest cancer in the world and survival is heavily dependent on tumor stage at the time of detection. Low-dose computed tomography (LDCT) screening can significantly reduce mortality, however, annual screening is limited by low adherence in the USA and still not broadly implemented in Europe. As a result, <10% of lung cancers are detected through existing programs. Thus, there is great need for additional screening tests, such as a blood test that could be deployed in the primary care setting. METHODS: We prospectively recruited 1,384 individuals meeting the NLST demographic eligibility criteria for lung cancer and collected stabilized whole blood to enable the pipetting-free collection of material, thus minimizing pre-analytical noise. Ultra-deep small RNA sequencing (20 million reads/sample) was performed with the addition of a method to remove highly abundant erythroid RNAs, and thus open bandwidth for the detection of less abundant species originating from plasma or the immune cellular compartment. We utilized 100 random data splits to train and evaluate an ensemble of logistic regression classifiers using small RNA expression of 943 individuals, discovered an 18-small RNA feature consensus signature (miLung), and validated this signature in an independent cohort (441 individuals). Blood cell sorting and tumor tissue sequencing were performed to deconvolve small RNAs into their source of origin. RESULTS: We generated diagnostic models and report a median ROC AUC of 0.86 (95% CI 0.84-0.86) in the discovery cohort, and generalized performance of 0.83 in the validation cohort. Diagnostic performance increased in a stage-dependent manner ranging from 0.73 (95% CI 0.71-0.76) for Stage I to 0.90 (95% CI 0.89-0.90) for Stage IV in the discovery cohort, and from 0.76 to 0.86 in the validation cohort. We identified a tumor-shed, plasma-bound ribosomal RNA fragment of the L1 stalk as a dominant predictor of lung cancer. The fragment is decreased following surgery with curative intent. In additional experiments, dried blood spot collection and sequencing demonstrated that small RNA analysis could potentially be conducted via home-sampling. CONCLUSION: These data suggest the potential of a small RNA-based blood test as a viable alternative to LDCT screening for early detection of smoking-associated lung cancer.

• Sikosek, T.
• Horos, R.
• Trudzinski, F.
• Jehn, J.
• Frank, M.
• Rajakumar, T.
• Klotz, L. V.
• Mercaldo, N.
• Kahraman, M.
• Heuvelman, M.
• Taha, Y.
• Gerwing, J.
• Skottke, J.
• Daniel-Moreno, A.
• Sanchez-Delgado, M.
• Bender, S.
• Rudolf, C.
• Hinkfoth, F.
• Tikk, K.
• Schenz, J.
• Weigand, M. A.
• Feindt, P.
• Schumann, C.
• Christopoulos, P.
• Winter, H.
• Kreuter, M.
• Schneider, M. A.
• Muley, T.
• Walterspacher, S.
• Schuler, M.
• Darwiche, K.
• Taube, C.
• Hegedus, B.
• Rabe, K. F.
• Rieger-Christ, K.
• Jacobsen, F. L.
• Aigner, C.
• Reck, M.
• Bankier, A. A.
• Sharma, A.
• Steinkraus, B. R.