# US 2022/0044764 A1 · Treatment of cancer by risk stratification of patients based on comordidities

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- Number: US 2022/0044764 A1
- Document ID: US20220044764A1
- Status: Published application
- Kind: A1
- Application number: 17/451,482
- Filed: 2021-10-19
- Published: 2022-02-10
- Earliest claimed: 2017-06-28
- Inventors: Balaji Narayana Reddy; Madanika Subhash
- Web page: https://patentgrep.com/patent/US20220044764A1

## CPC

- C12Q 1/6883: for diseases caused by alterations of genetic material
- G16B 40/00: ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
- C12Q 1/6886: for cancer (immunoassay for cancer G01N33/575)
- G01N 33/57595: {involving intracellular compounds}
- G16B 20/00: ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
- G16B 25/10: Gene or protein expression profiling; Expression-ratio estimation or normalisation
- G16B 50/00: ICT programming tools or database systems specially adapted for bioinformatics
- G16H 20/10: relating to drugs or medications, e.g. for ensuring correct administration to patients
- G16H 20/40: relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/50: for simulation or modelling of medical disorders
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- G16H 70/00: ICT specially adapted for the handling or processing of medical references
- C12Q 2600/118: Prognosis of disease development
- C12Q 2600/158: Expression markers
- G01N 2800/52: Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis
- 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
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- 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)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

A method is provided of identifying genes associated with poor clinical outcomes for a particular cancer. Genes encoding a comorbidity associated with a poor clinical outcomes for a particular cancer are identified in a cohort of patients with the cancer. Gene alterations and mutations associated with the comorbidities are determined. Gene expression level associated with the comorbidities are normalized against the expression level of a reference set of RNA transcripts in patients without the cancer or comorbidity. A statistical analysis compares the pathological gene expression level with normal gene expression level to create a database of statistically significant genes wherein the expression level of the abnormal genes is negatively associated with worse outcomes for the particular cancer. The method can be used to stage cancer, estimate prognosis, and for the design of therapeutic interventions by treating the comorbidity.

## Claims

### Claim 1

A method of identifying genes associated with poor clinical outcomes for a particular cancer, comprising a cohort of patients with the said cancer, identifying at least one comorbid medical condition, determining the gene alterations associated with at least one comorbidity, determining the gene expression level associated with at least one comorbidity, normalizing said gene expression level against the expression level of a reference set of RNA transcripts in patients without the cancer or comorbidity, performing a statistical analysis comparing the pathological gene expression level with normal gene expression level, and creating a database of statistically significant genes wherein the expression level of said genes encoding a comorbidity are associated with poor clinical outcomes for the particular cancer, and wherein an outcome for the cancer can be graded from the expression of genes in the database.

### Claim 2 (depends on 1)

The method of claim 1 wherein the groups may comprise cancer in different stages grouped into two or more groups.

### Claim 3 (depends on 1)

The method of claim 1, wherein the comorbidity is selected from one or more of essential hypertension, obesity, diabetes type 1, diabetes type 2, metabolic syndrome, endocrinopathies, chronic obstructive pulmonary disease, chronic kidney disease, coronary artery disease, stroke, depression, dysthymia, anxiety disorders, bipolar disorders, drug abuse, alcohol abuse, smoking Parkinson's Disease, Alzheimer's Disease.

### Claim 4 (depends on 1)

The method of claim 1 wherein the genes include one or more of the genes listed in either table 1 or table 2.

### Claim 5 (depends on 1)

The method of claim 1 wherein the gene alteration and gene expression is quantified.

### Claim 6 (depends on 1)

The method of claim 1 wherein the genes identified as significant are further assessed for their relevance to oncogenes.

### Claim 7

A method of treating cancer in a patient suffering from cancer by treating abnormalities in at least one gene encoding a comorbidity associated with a particular cancer, comprising identifying genes encoding a comorbidity associated with a poor clinical outcomes for a particular cancer, identifying a cohort of patients with the said cancer, identifying at least one comorbid medical condition, determining the gene alterations associated with the at least one comorbidities, determining the gene expression level associated with the said comorbidity, normalizing said gene expression level against the expression level of a reference set of RNA transcripts in patients without the cancer or comorbidity, performing a statistical analysis comparing the pathological gene expression level with normal gene expression level, creating a database of statistically significant genes wherein the expression level of said genes is negatively associated with worse outcomes for the particular cancer, and treating the cancer by prescribing therapies that inhibit the expression of said genes.

### Claim 8 (depends on 7)

The method of claim 7, wherein the treatment comprises a treatment selected from surgery, radiation, chemotherapy, watchful waiting, active surveillance, immunotherapy, thermotherapy, embolization and cryotherapy.

### Claim 9 (depends on 7)

The method of claim 7 wherein the genes and their products may be blocked by using a suitable drug thereby achieving either a cure, or a delay in progression of cancer.

### Claim 10 (depends on 7)

The method of claim 7, wherein the comorbidity is hypertension.

### Claim 11

A method of treating cancer in a patient suffering from cancer by treating abnormalities in at least one gene encoding a comorbidity associated with a particular cancer, comprising
a. selecting a cohort of patients suffering with the particular cancer;
b. identifying a subset of patients having comorbidities;
c. dividing the cohort into a training set of patients of approximately two-thirds of the cohort, and a validation set of patients of approximately one-third of the cohort;
d. further stratify the training set stratify the training set by factors associated with cancer propensity;
e. identifying genes encoding comorbidities in the training set;
f. identifying mutations, alterations, or differential gene expression in the genes encoding comorbidities in each member of the training set and normalize the gene expression level against the reference set in patients without the cancer or comorbidity;
g. correlating mutations in the genes encoding comorbidities in the training set with the severity of the cancer for each member of the training set by determining the gene expression level associated with each comorbidity and normalizing the gene expression level against the expression level of a reference set of RNA transcripts in patients without the cancer or comorbidity;
h. applying the correlations from step (g) to the validation set and determining if the correlations are statistically significant to cancer severity in the validation set;
i. if the results from step (h) are statistically significant, then apply the analysis to a further one or more patients not in the cohort; and
j. treating the cancer by prescribing therapies that inhibit the expression of the one or more genes encoding comorbidities.

### Claim 12 (depends on 11)

The method of claim 11, wherein the division of the cohort is by randomly assigning patients into each of the sets.

### Claim 13 (depends on 11)

The method of claim 11, wherein the cohort is further stratified or based on one or more factors associated with cancer propensity.

### Claim 14 (depends on 11)

The method of claim 11 wherein the membership of training set and validation set are shuffled after step (g), and repeating the analysis from step (e).

### Claim 15 (depends on 14)

The method claim 14 wherein the analysis from step (e) is repeated two or more times.