Precision Medicine Study

The number of genetic alterations found in the genome through genetic sequencing and comparison to the most common genetic sequence. A given variant may describe an alteration that is benign, pathogenic, or of unknown significance.

Total number of somatic insertions (INS) per patient. The number of instances where nucleotides have been erroneously added to the genome, as determined by genetic sequencing and comparison to the most common genetic sequence.

The number of instances where nucleotides that have been erroneously omitted from the genome, as determined by genetic sequencing and comparison to the most common genetic sequence.

The number of genetic alterations detected in MM tumor cells through sequencing and comparison to the most common genetic sequence.

The number of instances where nucleotides have been erroneously added to the MM tumor genome, per length of DNA, as determined by genetic sequencing and comparison to the most common genetic sequence.

The number of instances where nucleotides that have been erroneously omitted from the MM tumor genome, per length of DNA, as determined by genetic sequencing and comparison to the most common genetic sequence.

The number of genetic alterations detectable in >1 % or <1 % of the population, per length of DNA, among multiple myeloma (MM) subgroups, as determined by genetic sequencing and comparison to the most common genetic sequence.

The number of genetic alterations detectable in >1 % or <1 % of the population, per length of DNA, by genetic region (i.e., promoter, coding region, and termination sequence), for all MM and mutational subgroups, as determined by genetic sequencing and comparison to the most common genetic sequence.

The number and type of genetic alterations detectable in >1 % or <1 % of the population identified, as determined by genetic sequencing and comparison to the most common genetic sequence.

. The numbers and types of chromosomal abnormalities identified, as determined by genetic sequencing and comparison to the most common genetic sequence.

Number and type of sets of biomolecular features identified that could be useful in predicting the course of disease or response to therapeutic intervention among patients with MM and other cancers, as determined by sequencing, and gene set variation and targeted drug analysis.

The number and type of established prognostic markers identified. Evaluation of biological characteristics known to be useful in predicting the course of disease or response to therapeutic intervention among patients with MM and other cancers, as determined by sequencing and comparison to databases of known prognostic markers.

The number and type of genetic alterations found in the genome that could be treated with FDA-approved therapies, as determined by sequencing and comparison to databases of known targets and associated FDA-approved drugs.

Number and type of mutations known to lead to cancer cell transformation, growth, and spread in the body, as determined by genetic sequencing and comparison to the most common genetic sequence, and to databases of known cancer driver mutations. These mutations will be categorized as follows: those that are known targets of FDA-approved drugs, those that may be targets of drugs under development that are not yet FDA-approved, and those that may serve as targets for novel therapies.

Number and type of transcriptome variations identified with potential for the development of novel therapeutics (cell-surface expressed proteins that appear amenable to vaccine development), as determined by sequencing, network modeling, and cancer transcriptome profiling.

Number and type of germline mutations identified in cancer predisposition genes, as determined by genomic sequencing and comparison to the most common genetic sequence.

The number and type of FDA-approved drugs available that block enzymes produced in those pathways identified by comparison of genomic and transcriptomic findings to databases of known FDA-approved drugs and associated targets.

A listing of recommended treatments as determined by sequencing, analysis of the tumor microenvironment, and computational analysis.

The results of whole exome sequencing of the germline genome.

The results of whole exome sequencing of the tumor genome.

The results of RNA sequencing of the tumor transcriptome.

The results of single cell sequencing analysis.

The results of the cytometry by time of flight (CyTOF) analysis.

Signaling Pathways associated with each gene mutation, chromosomal abnormality and molecular signature, i.e. aging, defective DNA repair, and apolipoprotein B editing complex (APOBEC)/activation-induced deaminase activity, identified in Aim 1, as determined by sequencing and computational analysis.

Enzymes associated with each signaling pathway identified as determined by sequencing and computational analysis.

Use of artificial intelligence computing to implement cancer sequencing-based recommended therapies and improve accuracy of treatment prediction, to allow better interpretation of cancer sequencing data and advancement of the development of personalized and precision cancer therapies. Improvement will be measured by tracking the precision and accuracy of machine learning and evaluating the resulting data using statistical analysis.