IP Library › Granted Patent US 12,215,388
Granted Patent B2
US 12,215,388 · App. 16/610,001 · Granted Feb 4, 2025

Theranostic tools for management of pancreatic cancer and its precursors

Inventors: Jennifer Permuth (Tampa, FL); Daniel Jeong (Tampa, FL); Jung Choi (Wesley Chapel, FL); Yoganand Balagurunathan (Tampa, FL); Dung-Tsa Chen (Tampa, FL); Mokenge Malafa (Tampa, FL)
Assignee: Lee Moffitt Cancer Center and Research Institute, Inc.
C12Q1/6886C12Q2600/112C12Q2600/158C12Q2600/178
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Quick Facts
Patent No.
US 12,215,388
App. No.
16/610,001
Granted
Feb 4, 2025
Kind
B2
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.

Claims (27)

1. 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.

2. The method of claim 1 , wherein the blood sample is a sample of whole blood, serum, or plasma.

3. The method of claim 1 , wherein the blood sample is plasma.

4. The method of claim 1 , wherein said detecting is carried out using a method selected from among microarray hybridization, RNA-Seq, or polymerase chain reaction.

5. The method of claim 1 , wherein the image is produced by a computed tomography (CT) scan or magnetic resonance imaging (MRI).

6. The method of claim 1 , wherein the quantitative radiomic features comprise each of the following radiomic features: Fourier Descriptor Layer 1, Histogram Energy Layer 1, Histogram Entropy Layer 1, Co-occurrence matrix features OF1G1 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.

7. The method of claim 1 , wherein the image is produced by a computed tomography (CT) scan, and wherein the blood sample is a sample of plasma.

8. 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.

9. The method of claim 8 , wherein the blood sample is a sample of whole blood, serum, or plasma.

10. The method of claim 8 , wherein the blood sample is plasma.

11. The method of claim 8 , wherein said detecting is carried out using a method selected from among microarray hybridization, RNA-Seq, or polymerase chain reaction.

12. The method of claim 8 , wherein the image is produced by a computed tomography (CT) scan or magnetic resonance imaging (MRI).

13. The method of claim 8 , wherein the image is produced by a computed tomography (CT) scan, and wherein the blood sample is a sample of plasma.

14. 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.

15. The method of claim 14 , wherein the image is produced by a computed tomography (CT) scan, and wherein the blood sample is a sample of plasma.

Assignments (2)
CONFIRMATORY LICENSE Recorded Sep 8, 2023
From: H. LEE MOFFITT CANCER CTR & RES INST
To: NATIONAL INSTITUTES OF HEALTH (NIH), U.S. DEPT. OF HEALTH AND HUMAN SERVICES (DHHS), U.S. GOVERNMENT
Reel/Frame 064852/0126 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 15, 2019
From: PERMUTH, JENNIFER; JEONG, DANIEL; CHOI, JUNG; BALAGURUNATHAN, YOGANAND; CHEN, DUNG-TSA; MALAFA, MOKENGE
To: H. LEE MOFFITT CANCER CENTER AND RESEARCH INSTITUTE, INC.
Reel/Frame 051015/0566 →
Continuity (2)
Provisional Application 62501040 · May 3, 2017
Related Publication 20200063215A1 · Feb 27, 2020
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