# US 2018/0323049 A1 · Early Detection of Hepatocellular Carcinoma in High Risk Populations Using MALDI-TOF Mass Spectrometry

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- Number: US 2018/0323049 A1
- Document ID: US20180323049A1
- Status: Published application
- Kind: A1
- Application number: 16/020,183
- Filed: 2018-06-27
- Published: 2018-11-08
- Earliest claimed: 2014-12-03
- Assignees: Biodesix, Inc.
- Applicants: Biodesix, Inc.
- Inventors: Joanna Röder; Carlos Oliveira; Julia Grigorieva; Heinrich Röder
- Web page: https://patentgrep.com/patent/US20180323049A1

## CPC

- H01J 49/0036: {Step by step routines describing the handling of the data generated during a measurement}
- G01N 33/57525: of the liver or pancreas
- G01N 33/6851: {Methods of protein analysis involving laser desorption ionisation mass spectrometry}
- G06F 18/211: Selection of the most significant subset of features
- G06F 18/24147: {Distances to closest patterns, e.g. nearest neighbour classification}
- G06V 20/698: {Matching; Classification}
- G16H 10/40: for data related to laboratory analysis, e.g. patient specimen analysis
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- H01J 49/164: {Laser desorption/ionisation, e.g. matrix-assisted laser desorption/ionisation [MALDI] (sample holders H01J49/0418)}
- G06F 2218/12: Classification; Matching
- Y02A 90/10: Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
- H01J: ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS (spark-gaps H01T; electric arc lamps H05B31/00; particle accelerators H05H)
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- Y02A: TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE

## Abstract

Hepatocellular carcinoma (HCC) is detected in a patient with liver disease. Mass spectrometry data from a blood-based sample from the patient is compared to a reference set of mass-spectrometry data from a multitude of other patients with liver disease, including patients with and without HCC, in a general purpose computer configured as a classifier. The classifier generates a class label, such as HCC or No HCC, for the test sample. A laboratory system for early detection of HCC in patients with liver disease is also disclosed. Alternative testing strategies using AFP measurement and a reference set for classification in the form of class-labeled mass spectral data from blood-based samples of lung cancer patients are also described, including multi-stage testing.

## Claims

### Claim 1

A method of detecting a class label in a liver disease patient comprising:
a) conducting mass spectrometry on a blood-based sample of the patient and obtaining mass spectrometry data;
(b) obtaining integrated intensity values in the mass spectral data of a multitude of pre-determined mass-spectral features; and
(c) operating on the mass spectral data with a programmed computer implementing a classifier;
wherein in the operating step the classifier compares the integrated intensity values with feature values of a training set of class-labeled mass spectral data obtained from a multitude of other patients with liver disease with the values obtained in step (b) with a classification algorithm and detects a class label for the sample.

### Claim 2 (depends on 1)

The method of claim 1, wherein the mass spectrometry comprises performing MALDI-TOF mass spectrometry by subjecting the blood-based sample to at least 100,000 laser shots and acquiring mass spectral data.

### Claim 3 (depends on 1)

The method of claim 1, wherein the classifier is obtained from filtered mini-classifiers combined using a regularized combination method.

### Claim 4 (depends on 1)

The method of claim 1, wherein the obtaining step (b) comprises obtaining integrated intensity values of at least 50 features listed in one of the Appendices.

### Claim 5 (depends on 4)

The method of claim 4, wherein the obtaining step comprises obtaining integrated intensity values of at least 100 features listed in Example 1 Appendix A or Example 2 Appendix C.

### Claim 6 (depends on 4)

The method of claim 4, wherein the obtaining step comprises obtaining integrated intensity values of all the features of one of the Appendices.

### Claim 7 (depends on 1)

The method of claim 1, wherein the training set comprises all or a subset of a set of samples used to develop the classifier.

### Claim 8 (depends on 7)

The method of claim 7, wherein the training set is a subset of the set of samples used to develop the classifier pruned to remove those patients who have high AFP expression levels.

### Claim 9 (depends on 1)

The method of claim 1, wherein the feature values consist of the mass spectral features and AFP expression level.

### Claim 10 (depends on 3)

The method of claim 3, wherein the mini-classifiers perform classifications using single features, pairs of features, or single features, pairs of features and triplets of features in the feature values of a training set.