Theranostic tools for management of pancreatic cancer and its precursors
AssigneeLee Moffitt Cancer Center and Research Institute, Inc.
InventorsJennifer Permuth, Daniel Jeong, Jung Choi, Yoganand Balagurunathan, Dung-Tsa Chen, Mokenge Malafa
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Abstract
The present invention concerns materials and methods for identifying and classifying pancreatic ductal adenocarcinoma (PDAC) precursors or intraductal papillary mucinous neoplasm (IPMN) using messenger RNAs, microRNAs, long non-coding RNAs, radiomic features, radiologic measures of abdominal/visceral obesity, and combinations thereof, as diagnostic markers for integration with clinical management and interventions for personalized care.
Claims15
A method for detecting microRNAs (miRNA) and long non-coding RNAs (IncRNAs) in human blood and assessing radiomic features, from a human subject having an intraductal papillary mucinous neoplasm (IPMN) comprising:
- detecting a level of each of the following miRNAs in a blood sample from the human subject:
- miR-200a-3p, miR-1185-5p, miR-33a-5p, miR-574-4p, and miR-663b;
- wherein said detecting is carried out using a method selected from among hybridization assay, RNA sequencing, or amplification assay;
- detecting a level of each of the following lncRNAs in the blood sample from the human subject:
- ADARB2-AS1, ANRIL, GLIS3-AS1, LINC00472, MEG3, PANDA, PVT1, and UCA1;
- and
- extracting quantitative radiomic features from an image of abdominal visceral fat from the human subject;
- wherein the quantitative radiomic features comprise one or more of the following:
- Fourier Descriptor Layer 1, Histogram Energy Layer 1, Histogram Entropy Layer 1, Co-occurrence matrix features OF1G1 CONTRAST Layer 1, Run-length features G1 DO HGRE Layer 1, Run-length features G1 DO LGRE Layer 1, Laws features E5 E5 Energy Layer 1, Laws features L5 S5 Energy Layer 1, Laws features R5 E5 Energy Layer 1, Wavelet decomposition P1 L3 C1 Layer 1, Wavelet decomposition P1 L3 C2 Layer 1, Border length (Pxl), Width (Pxl), and Radius of largest enclosed ellipse.
A method of classifying an intraductal papillary mucinous neoplasm (IPMN) in a human subject as malignant, the method comprising:
- detecting a level of each of the following miRNAs in a blood sample from the human subject:
- miR-200a-3p, miR-1185-5p, miR-33a-5p, miR-574-4p, and miR-663b, wherein said detecting is carried out using a method selected from among hybridization assay, RNA sequencing, or amplification assay;
- detecting a level of each of the following lncRNAs in the blood sample from the human subject:
- ADARB2-AS1, ANRIL, GLIS3-AS1, LINC00472, MEG3, PANDA, PVT1, and UCAI;
- extracting each of the following quantitative radiomic features from an image of abdominal visceral fat from the human subject:
- Fourier Descriptor Layer 1, Histogram Energy Layer 1, Histogram Entropy Layer 1, Co-occurrence matrix features OF 1 G1 CONTRAST Layer 1, Run-length features G1DO HGRE Layer 1, Run-length features G1 DO LGRE Layer 1, Laws features E5E5 Energy Layer 1, Laws features L5S5 Energy Layer 1, Laws features R5E5 Energy Layer 1, Wavelet decomposition P1 L3 C1 Layer 1, Wavelet decomposition P1 L3 C2 Layer 1, Border length (Pxl), Width (Pxl), and Radius of largest enclosed ellipse;
- classifying the IPMN in the human subject as malignant using a classifier that integrates the level of each of said miRNAs, the level of each of said lncRNAs, and each of said quantitative radiomic features, wherein said classifier has an AUC of 0.90.
A method of classifying an intraductal papillary mucinous neoplasm (IPMN) in a human subject as malignant and treating the malignant IPMN, the method comprising:
- detecting a level of each of the following miRNAs in a blood sample from the human subject:
- miR-200a-3p, miR-1185-5p, miR-33a-5p, miR-574-4p, and miR-663b, wherein said detecting is carried out using a method selected from among hybridization assay, RNA sequencing, or amplification assay;
- detecting a level of each of the following lncRNAs in the blood sample from the human subject:
- ADARB2-AS1, ANRIL, GLIS3-AS1, LINC00472, MEG3, PANDA, PVT1, and UCAI;
- extracting each of the following quantitative radiomic features from an image of abdominal visceral fat from the human subject:
- Fourier Descriptor Layer 1, Histogram Energy Layer 1, Histogram Entropy Layer 1, Co-occurrence matrix features OF 1 G1 CONTRAST Layer 1, Run-length features G1DO HGRE Layer 1, Run-length features G1 DO LGRE Layer 1, Laws features E5E5 Energy Layer 1, Laws features L5S5 Energy Layer 1, Laws features R5E5 Energy Layer 1, Wavelet decomposition P1 L3 C1 Layer 1, Wavelet decomposition P1 L3 C2 Layer 1, Border length (Pxl), Width (Pxl), and Radius of largest enclosed ellipse;
- classifying the IPMN in the human subject as malignant using a classifier that integrates the level of each of said miRNAs, the level of each of said lncRNAs, and each of said quantitative radiomic features, wherein said classifier has an AUC of 0.90;
- and
- treating the malignant IPMN in the human subject with a surgical intervention, radiation therapy, or anti-cancer agent.
Dependencies are inferred from the claim text.