Cancer score for assessment and response prediction from biological fluids

1. A method of analyzing omics data and treating a patient having a cancer, the method comprising:

obtaining blood from the patient having or suspected to have the cancer;

obtaining, from the blood, omics data for a plurality of cancer-related genes, wherein the omics data comprise at least one of DNA sequence data, RNA sequence data, and RNA expression level data; providing an omics record computer system that includes at least one processor and at least one computer readable memory coupled to the processor and configured to digitally store the omics data for the plurality of cancer-related genes in the at least one memory;

calculating, in silico, a digital score from the digital omics data, wherein the digital score is calculated in silico by the sum of (i) counting the number of mutations of cancer-related genes, inflammation-related genes, and DNA-repair genes, (ii) counting changes in methylation or modifications in DNA of cancer-related genes and DNA-repair genes, (iii) counting upregulation or downregulation in expression levels of RNA of cancer-related genes, inflammation-related genes, and DNA-repair genes, (iv) counting the number of tumor- and patient-specific neoepitopes, (v) counting splice variants of cancer-related genes and DNA-repair genes, and (vi) counting changes in length of poly A tail of any cancer-related genes, inflammation-related genes, and DNA-repair genes;

associating the digital score with at least one of a health status, an omics error status, a cancer prognosis, a therapeutic recommendation, an effectiveness of a treatment; and

upon the digital score reaching a threshold value and a majority portion of the digital score is highly weighted as an overexpression of a specific neoepitope;

generating a personalized treatment option for the patient, wherein the personalized treatment option comprises a recombinant nucleic acid encoding one or more tumor- and patient-specific neoepitopes; and

treating the cancer by administering the personalized treatment option to the patient.

2. The method of claim 1 , wherein the plurality of cancer-related genes comprises at least one of a cancer-related gene, a cancer-specific gene, a DNA-repair gene, a neoepitope, and a gene not associated with a disease.

3. The method of claim 1 , wherein the DNA sequence data is selected from the group consisting of mutation data, copy number data duplication, loss of heterozygosity data, and epigenetic status.

4. The method of claim 1 , wherein the RNA sequence data is selected from the group consisting of mRNA sequence data and splice variant data.

5. The method of claim 1 , wherein the RNA expression level data is selected from the group consisting of a quantity of RNA transcript and a quantity of a small noncoding RNA.

6. The method of claim 1 , wherein DNA sequence data is obtained from circulating free DNA.

7. The method of claim 1 , wherein the RNA sequence data is obtained from the group consisting of circulating tumor RNA and circulating free RNA.

8. The method of claim 3 , wherein the plurality of cancer-related genes includes a cancer-specific gene, and the digital score is calculated based on a presence or an absence of a mutation in the cancer-specific gene.

9. The method of claim 8 , wherein the presence of the mutation in the cancer-specific gene weighs more than the presence of the mutation in the cancer-related genes other than the cancer-specific gene.

10. A method of determining prognosis of a cancer of a patient and treating the patient having the cancer, the method comprising:

obtaining blood from the patient having the cancer;

obtaining from the blood omics data of the cancer patient for a plurality of cancer genes, wherein the omics data comprise at least one of DNA sequence data, RNA sequence data, and RNA expression level;

providing an omics record computer system that includes at least one processor and at least one computer readable memory coupled to the processor and configured to digitally store the omics data for the plurality of cancer-related genes in the at least one memory;

analyzing, in silico, the digital omics data to obtain a digital cancer prognosis score, wherein the digital cancer prognosis score is calculated in silico by the sum of (i) counting the number of mutations of cancer-related genes, inflammation-related genes, and DNA-repair genes, (ii) counting changes in methylation or modifications in DNA of cancer-related genes and DNA-repair genes, (iii) counting upregulation or downregulation in expression levels of RNA of cancer-related genes, inflammation-related genes, and DNA-repair genes, (iv) counting the number of tumor-specific, patient specific neoepitopes, (v) counting splice variants of cancer-related genes and DNA-repair genes, and (vi) counting changes in length of poly A tail of any cancer-related genes, inflammation-related genes, and DNA-repair genes;

providing the prognosis of the cancer based on the digital cancer prognosis score; and

upon the digital cancer prognosis score reaching a threshold value and a majority portion of the digital cancer prognosis score is highly weighted as an overexpression of a specific neoepitope:

generating a personalized treatment option for the patient, wherein the personalized treatment option comprises a recombinant nucleic acid encoding one or more tumor- and patient-specific neoepitopes; and

treating the cancer by administering the personalized treatment option to the patient.

11. The method of claim 10 , wherein the plurality of cancer-related genes comprises at least one of a cancer-related gene, a cancer-specific gene, a DNA-repair gene, a neoepitope, and a gene not associated with a disease.

12. The method of claim 10 , wherein the DNA sequence data are selected from the group consisting of mutation data, copy number data duplication, loss of heterozygosity data, and epigenetic status.

13. The method of claim 10 , wherein the RNA sequence data are selected from the group consisting of mRNA sequence data and splice variant data.

14. The method of claim 10 , wherein the RNA expression level data are selected from the group consisting of a quantity of RNA transcript and a quantity of a small noncoding RNA.

15. The method of claim 10 , wherein DNA sequence data are obtained from circulating free DNA.

16. The method of claim 10 , wherein the RNA sequence data are obtained from the group consisting of circulating tumor RNA and circulating free RNA.

17. The method of claim 12 , wherein the plurality of cancer-related genes includes a cancer-specific gene, and the digital cancer prognosis score is calculated based on a presence or an absence of a mutation in the cancer-specific gene.

18. A method of predicting an outcome of a treatment for a cancer patient, the method comprising:

obtaining blood from a patient having a cancer; obtaining from the blood omics data of the cancer patient for a plurality of cancer genes, wherein the omics data comprise at least one of DNA sequence data, RNA sequence data, and RNA expression level;

providing an omics record computer system that includes at least one processor and at least one computer readable memory coupled to the processor and configured to digitally store the omics data for the plurality of cancer-related genes in the at least one memory;

analyzing, in silico, the omics data to generate a digital cancer gene score, wherein the digital cancer gene score is calculated in silico by the sum of (i) counting the number of mutations of cancer-related genes, inflammation-related genes, and DNA-repair genes, (ii) counting changes in methylation or modifications in DNA of cancer-related genes and DNA-repair genes, (iii) counting upregulation or downregulation in expression levels of RNA of cancer-related genes, inflammation-related genes, and DNA-repair genes, (iv) counting the number of tumor-specific, patient specific neoepitopes, (v) counting splice variants of cancer-related genes and DNA-repair genes, and (vi) counting changes in length of poly A tail of any cancer-related genes, inflammation-related genes, and DNA-repair genes; and

providing, in silico, a predicted outcome of the treatment based on the digital cancer gene score; and

upon the digital cancer gene score reaching a threshold value and a majority portion of the digital cancer gene score is highly weighted as an overexpression of a specific neoepitope:

generating a personalized treatment option for the patient, wherein the personalized treatment option comprises a recombinant nucleic acid encoding one or more tumor- and patient-specific neoepitopes; and

treating the cancer by administering the personalized treatment option to the patient.