Omics Profiling in Precision Oncology

Abstract

Cancer causes significant morbidity and mortality worldwide, and is the area most targeted in precision medicine. Recent development of high-throughput methods enables detailed omics analysis of the molecular mechanisms underpinning tumor biology. These studies have identified clinically actionable mutations, gene and protein expression patterns associated with prognosis, and provided further insights into the molecular mechanisms indicative of cancer biology and new therapeutics strategies such as immunotherapy. In this review, we summarize the techniques used for tumor omics analysis, recapitulate the key findings in cancer omics studies, and point to areas requiring further research on precision oncology.

Fig. 1.

Schematic diagram of omics modalities in precision oncology. Genomics, epigenomics, transcriptomics, proteomics, and metabolomics methods provide complementary information on the biology of tumorigenesis and cancer development.

Fig. 2.

Bisulfite sequencing identifies cytosines with and without methylation at a single nucleotide resolution. Unmethylated cytosines (represented by “C” in the sequence) are converted to uracil (represented by “U”) by bisulfite treatment, which will be sequenced as thymine (represented by “T”). In contrast, methylated cytosines (5-methylcytosine; represented by “C” with a small “m” on the top) are resistant to bisulfite conversion, and will be sequenced as they are. By comparing the bisulfite treated and untreated samples, researchers can identify the methylation status and methylation rate of each cytosine at a single nucleotide resolution.

Fig. 3.

A general workflow of RNA sequencing with reverse transcription to complementary DNA (cDNA). Coding mRNA molecules with poly-A tails are first isolated from the sample, and then reverse transcribed to cDNA. The cDNA is fragmented and sequenced, and the resulting sequence is mapped back onto the reference genome. The quantity of mapped sequences in each genic region is associated with the expression level of the gene. (Blue regions of the reference genome indicate introns, whereas red regions indicate exons.)

Fig. 4.

A schematic diagram of using machine learning methods to predict clinical phenotypes. First, a training data set is collected, subsets of features associated with the phenotype of interest are selected, and a statistical model is built by the training data. A previously untouched test set using the same omics profiling methods is collected and treated as new input to the established machine learning model. The model provides predictions on the test input. By comparing the model output and the actual clinical phenotypes of the patients in the test set, researchers can estimate the performance of the prediction model.

Fig. 5.

Protein array methods for proteomic profiling. A, Direct labeling method adds detectable markers to the proteins, and uses antibodies fixed on a solid surface to capture the proteins of interest. B, Sandwich method utilizes two types of antibodies to capture the proteins and to tag on the fluorescent molecules respectively. C. Reverse phase protein array method first prints protein lysate on a solid surface, and then uses antibodies to quantify the proteins of interest.

Fig. 6.

Omics applications in cancer immunotherapy. Cancer immunotherapy exploits the fact that genetic aberrations in the cancer genome can result in new antigens (neoantigens) not normally expressed in benign tissue. Researchers can sequence the tumor genome to identify potential neoantigens, use proteomic methods to characterize the expressed neoantigens, and design personalized cancer vaccines based on the identified neoantigens, which will elicit specific immune response against the tumor cells.