Machine learning-based systems and methods for predicting liver cancer recurrence in liver transplant patients

Patent metadata

Field	Value
Publication number	US-2024404707-A1
Application number	US-202218688105-A
Country	US
Kind code	A1
Filing date	Sep 2, 2022
Priority date	Sep 2, 2021
Publication date	Dec 5, 2024
Grant date	—

Abstract

Described herein are prediction models based on the transcriptomic, exomic, and/or radiological analyses on tissue samples to predict the likelihood of the original cancer (such as Hepatocellular carcinoma (HCC)) recurrence into the liver transplant. An example computer implemented method for predicting the likelihood of liver cancer recurrence 5 into a liver transplant includes receiving gene expression data related to a liver tissue sample for a subject having a liver cancer, inputting the gene expression data into a trained machine learning model, and predicting, using the trained machine learning model, a risk of recurrence of the liver cancer in the subject after liver transplantation.

First claim

1 . A computer-implemented method comprising:
receiving gene expression data related to a liver tissue sample for a subject having a liver cancer; inputting the gene expression data into a trained machine learning model; and predicting, using the trained machine learning model, a risk of recurrence of the liver cancer in the subject after liver transplantation.

2 . The computer-implemented method of claim 1 , wherein the trained machine learning model is a supervised machine learning model.

3 . The computer-implemented method of claim 1 , wherein the trained machine learning model is configured to predict the risk of recurrence as a probability score.

4 . The computer-implemented method of claim 1 , wherein the trained machine learning model is configured to predict the risk of recurrence by classifying the subject into one of a plurality of categories.

5 . The computer-implemented method of claim 1 , wherein the trained machine learning model is a support vector machine (SVM), a random forest model, a logistic regression model, or a k-top scoring pairs (k-TSP) model.

6 . The computer-implemented method of claim 1 , wherein the gene expression data comprises respective gene expression levels for a top-n differentially expressed genes, where n is an integer greater or equal to 10 .

7 . The computer-implemented method of claim 6 , wherein n is greater than or equal to 50.

8 . The computer-implemented method of claim 6 , wherein the top-n differentially expressed genes comprise one or more of HOOK1, EFCAB7, CDC7, NUF2, UBE2T, HELLS, RRM1, SYT12, KIF21A, RACGAP1, PRIM1, PTGES3, YEATS4, CCT2, PARPBP, PPP1CC, KNTC1, TMED2, CDKN3, DLGAP5, BUB1B, NUSAP1, CCNB2, KIF23, FANCI, PRC1, CDC6, TOP2A, KPNA2, NDC80, RBBP8, NARS, BUB1, TOPBP1, SMC4, NCAPG, CENPE, PLK4, CENPU, CENPQ, TTK, FBX05, ANLN, MELK, DYNLT3, ZNF674, KIF4A, AMMECR1, ZNF449, and BRCC3.

9 . The computer-implemented method of claim 6 , wherein the trained machine learning model is a random forest model.

10 . The computer-implemented method of claim 1 , wherein the gene expression data comprises respective gene expression levels for a top-q pairs of differentially expressed genes, where q is an integer greater or equal to 10.

11 . The computer-implemented method of claim 10 , wherein q is 43.

12 . The computer-implemented method of claim 10 , wherein the top-q pairs of differentially expressed genes comprise one or more of BUB1B and SSH3, MCM8 and OGDHL, NUSAP1 and FNDC4, KIF21A and RAB43, CDC7 and CNGA1, MORF4L2 and ETFB, HELLS and HAMP, PPIL1 and ZCCHC24, MELK and GALNT15, BRCC3 and CCDC69, CCT6A and ASL, CDKN3 and SYT12, RBBP8 and TMPRSS2, KIF23 and LMF1, KPNA2 and SUN2, SMC4 and FXYD1, PPP1CC and FTCD, NUCB2 and NDRG2, PARP2 and IL11RA, VBP1 and AGTR1, TOP2A and TSPAN9, KTN1 and COL18A1, NCAPG and ADAMTS13, STT3B and CD14, SEC11C and C8G, CCNA2 and ADRA1A, CENPQ and UROC1, TTK and PLCH2, FANCI and SHBG, DEK and EGR1, RFC5 and APOF, PTGES3 and TAT, SNX7 and PGLYRP2, CCT2 and PIGR, PRC1 and MGMT, NARS and MASP1, RRM1 and MGLL, TOPBP1 and CTSF, F2 and ITIH4, ANLN and ZNF674, PRIM1 and SULTIA1, RARRES2 and HRG, and CENPU and NNMT.

13 . The computer-implemented method of claim 10 , wherein the trained machine learning model is a k-TSP model.

14 . The computer-implemented method of claim 1 , further comprising:
receiving mutation data related to the liver tissue sample; and inputting the mutation data into the trained machine learning model.

15 . The computer-implemented method of claim 14 , wherein the mutation data comprises a number of somatic mutations present in the liver tissue sample from the subject.

16 . The computer-implemented method of claim 14 , wherein the mutation data comprises a number of somatic mutations present in a top-m mutation pathways, where m is an integer greater or equal to 5 .

17 . The computer-implemented method of claim 16 , wherein m is 5.

18 . The computer-implemented method of claim 16 , wherein the top-m mutation pathways comprise one or more of GO_ENDONUCLEASE_ACTIVITY_ACTIVE_WITH_EITHER_RIBO_OR_DEOXYRIBON UCLEIC_ACIDS_AND_PRODUCING_3_PHOSPHOMONOESTERS, GO_GLUCOSE_BINDING, GO_PALMITOYL_COA_HYDROLASE_ACTIVITY, GO_PEPTIDE_N_ACETYLTRANSFERASE_ACTIVITY, and GO_DOPAMINE_BINDING.

19 . The computer-implemented method of claim 16 , wherein the trained machine learning model is a random forest model.

20 . The computer-implemented method of claim 14 , wherein the mutation data comprises a number of somatic mutations present in a top-r mutation pathway pairs, where r is an integer greater or equal to 3.

21 . The computer-implemented method of claim 20 , wherein r is 3.

22 . The computer-implemented method of claim 20 , wherein the top-r mutation pathways pairs comprise one or more of GO_SYNTAXIN_BINDING and GO_N_ACYLTRANSFERASE_ACTIVITY, REACTOME_GOLGI_ASSOCIATED_VESICLE_BIOGENESIS and GO_N_ACETYLTRANSFERASE_ACTIVITY, and GO_REGULATION_OF_HORMONE_METABOLIC_PROCESS and GO_PEPTIDE_N_ACETYLTRANSFERASE_ACTIVITY.

23 . The computer-implemented method of claim 20 , wherein the trained machine learning model is a k-top scoring pairs (k-TSP) model.

24 . The computer-implemented method of claim 14 , wherein the trained machine learning model is a support vector machine (SVM), a random forest model, a logistic regression model, or a k-top scoring pairs (k-TSP) model.

25 . The computer-implemented method of claim 1 , further comprising:
receiving a radiology-based parameter related to the liver cancer; and inputting the radiology-based parameter into the trained machine learning model.

26 . The computer-implemented method of claim 25 , wherein the radiology-based parameter is based on a size or number of tumor nodules associated with the liver cancer.

27 . The computer-implemented method of claim 25 , wherein the radiology-based parameter is Milan criteria.

28 . The computer-implemented method of claim 25 , wherein the trained machine learning model is a k-top scoring pairs (k-TSP) model.

29 . The computer-implemented method of claim 25 , wherein the trained machine learning model is a support vector machine (SVM), a random forest model, or a logistic regression model.

30 . The computer-implemented method of claim 1 , further comprising:
receiving mutation data related to the liver tissue sample and a radiology-based parameter related to the liver cancer; and inputting the mutation data and the radiology-based parameter into the trained machine learning model.

31 . The computer-implemented method of claim 30 , wherein the trained machine learning model is a random forest model.

32 . The computer-implemented method of claim 30 , wherein the trained machine learning model is a support vector machine (SVM), a logistic regression model, or a k-top scoring pairs (k-TSP) model.

33 . The computer-implemented method of claim 1 , further comprising providing a treatment recommendation based on the prediction.

34 . The computer-implemented method of claim 33 , wherein the treatment recommendation is to perform a liver transplant procedure on the subject.

35 . The computer-implemented method of claim 1 , wherein the liver cancer is hepatocellular carcinoma (HCC).

36 . A method comprisin

Assignees

• Univ Pittsburgh Commonwealth Sys Higher Education

Inventors

• Luo Jianhua
• Singhi Aatur Dilip
• Ren Baoguo
• Tseng Chien-Cheng
• Michalopoulos George
• Nalesnik Michael A
• Wood-Trageser Michelle A
• Liu Shuchang
• Yu Yanping

Classifications

• G16B40/20

  Supervised data analysis · CPC title

• G16B25/10

  Gene or protein expression profiling; Expression-ratio estimation or normalisation · CPC title

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G16H30/40

  for processing medical images, e.g. editing · CPC title