# US 2022/0044762 A1 · Methods of assessing breast cancer using machine learning systems

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- Number: US 2022/0044762 A1
- Document ID: US20220044762A1
- Status: Published application
- Kind: A1
- Application number: 17/395,161
- Filed: 2021-08-05
- Published: 2022-02-10
- Earliest claimed: 2020-08-06
- Assignees: Agendia N.V.
- Applicants: Agendia NV
- Inventors: Bastiaan Van Der Baan; Annuska Maria Glas
- Web page: https://patentgrep.com/patent/US20220044762A1

## CPC

- G16B 25/10: Gene or protein expression profiling; Expression-ratio estimation or normalisation
- G16B 25/30: Microarray design
- G06N 20/00: Machine learning
- G06T 7/0012: {Biomedical image inspection}
- G16B 40/20: Supervised data analysis
- G16H 10/40: for data related to laboratory analysis, e.g. patient specimen analysis
- G16H 15/00: ICT specially adapted for medical reports, e.g. generation or transmission thereof
- G16H 20/10: relating to drugs or medications, e.g. for ensuring correct administration to patients
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/30: for calculating health indices; for individual health risk assessment
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- C12Q 1/6886: for cancer (immunoassay for cancer G01N33/575)
- C12Q 2600/158: Expression markers
- G06T 2207/30024: Cell structures in vitro; Tissue sections in vitro
- G06T 2207/30068: Mammography; Breast
- G06T 2207/30096: Tumor; Lesion
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- C12Q: MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS (immunoassay G01N33/53); COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES

## Abstract

The present disclosure provides methods and systems using machine learning to assess one or more of a patient's biomarkers to analyze various conditions, including cancers, such as breast cancer. The present systems and methods can be trained to analyze patient's biomarker data to form prognoses, diagnoses, and treatment suggestions. Further, the present systems and methods can use biomarker feature data and clinical feature data to create novel correlations in order to provide more accurate, patient-specific diagnoses, prognoses, and treatment suggestions.

## Claims

### Claim 1

A method of analyzing breast cancer, the method comprising:
training an analysis system to associate expression signatures with clinical outcomes;
determining expression levels of RNA molecules in a blood sample from a patient;
producing, from the determined expression levels, an RNA signature associated with said sample;
providing the signature as input to the trained analysis system; and
operating the analysis system to assess disease.

### Claim 2 (depends on 1)

The method of claim 1, wherein assessing disease comprises one of:
(i) predicting disease severity;
(ii) determining a diagnosis or stage of disease progression;
(iii) classifying cancer type; or
(iv) predicting a drug response.

### Claim 3 (depends on 2)

The method of claim 2, wherein the steps of the method are performed to diagnosis a tumor before the tumor is visible.

### Claim 4 (depends on 2)

The method of claim 2, further comprising selecting a treatment based on the predicted drug response.

### Claim 5 (depends on 4)

The method of claim 4, wherein the treatment comprises a multi-drug therapy.

### Claim 6 (depends on 1)

The method of claim 1, wherein the analysis system comprises a computer system hosting a supervised clustering algorithm.

### Claim 7 (depends on 6)

The method of claim 6, wherein the clustering algorithm classifies a breast cancer as Luminal, Basal, or HER2.

### Claim 8 (depends on 1)

The method of claim 1, further comprising providing genomic data to the analysis system as input and operating the analysis system to assess disease based on a combination of the determined expression levels and the genomic data.

### Claim 9 (depends on 8)

The method of claim 8, wherein the genomic data comprises a mutation status of a BRCA gene.

### Claim 10 (depends on 8)

The method of claim 8, wherein assessing disease comprises determining a risk of a distant metastatic event and/or selecting a course of treatment.

### Claim 11 (depends on 1)

The method of claim 1, further comprising analyzing genomic data or an image of tissue from the patient to support or refine a disease assessment, wherein the image comprises an image of a stained FFPE slide from a tumor from the patient.

### Claim 12 (depends on 1)

The method of claim 1, wherein the expression levels are determined for RNA molecules in one or more extracellular vesicles isolated from the blood sample.

### Claim 13 (depends on 1)

The method of claim 1, wherein the expression levels are determined by measuring transcripts from a pre-determined panel of one or more target genes and one or more reference genes.

### Claim 14 (depends on 13)

The method of claim 13, wherein the RNA signature comprises a weighted metric of levels of the target genes and the reference genes.

### Claim 15 (depends on 14)

The method of claim 14, wherein the target genes or reference genes are selected for inclusion in the RNA signature when they have average expression levels that is significantly above a pre-determined level of expression associated with background noise.

### Claim 16 (depends on 1)

The method of claim 1, wherein the analysis system reports a score indicating a risk of cancer recurrence or a distant metastasis event.

### Claim 17

An analysis method comprising:
providing training data to an analysis system, the training data comprising gene expression signatures and image data with known patient outcomes;
training the analysis system to correlate the gene expression signatures with the image data;
measuring RNA expression levels from a patient blood sample and providing the measured expression levels as input to the trained analysis system; and
operating the analysis system to assess disease based on learned correlations between gene expression signatures and the image data.

### Claim 18 (depends on 16)

The method of claim 16, wherein the image data comprises images of stained FFPE slides from tumors.

### Claim 19 (depends on 16)

The method of claim 16, wherein the analysis system generates a report assessing a risk of a distant metastasis event based on the learned correlations.