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Molecular Diagnostics for Cancer Treatment: Expanding beyond the Genome

NCI’s investments in cancer genomics research have transformed our understanding of cancer, advanced the conduct of clinical trials testing the efficacy of cancer therapies, and changed the way oncologists select treatments for patients. These investments have produced valuable resources such as The Cancer Genome Atlas (TCGA), a publicly available catalog of cancer genome information covering more than 30 cancer types.

The genome codes for proteins that carry out functions in cells—both normal and tumor cells. Mutations in specific genes that affect how the resulting proteins function, can contribute to the development and progression of cancer.

Aided by this knowledge and advances in genomic sequencing technology, oncologists are increasingly selecting therapies based on the specific genomic abnormalities identified in a patient’s tumor. This genomic information often comes from a molecular diagnostic test.

Molecular diagnostic tests detect specific biologic molecules, or biomarkers, in a patient’s tissue and fluid samples. Molecular diagnostic tests can be used to select a cancer therapy and/or to monitor the effects of a treatment based on characteristics of or changes in the biomarker.

This approach has revolutionized cancer diagnosis and treatment. However, many patients who are matched to a therapy based on the genomic profile of their tumors either do not respond to the therapy initially or do not experience a sustained response.

That may be, in part, because key information about the biology of the tumor is missing from looking at the genome alone. While the presence of mutations can be determined from sequencing a tumor’s genome, the effect of mutations on protein function cannot be fully understood without interrogating the proteome, including the many modifications that occur to proteins in a tumor. Changes that normally occur in proteins after they are made (post-translational modifications) can affect how proteins function or how long they are present in a cell.

The proteome is the entire complement of proteins that is or can be expressed by a cell, tissue, or organism. Cancer proteomics is the comprehensive characterization and quantification of the proteome of cancer cells.

A better understanding of cancer at the protein level will enhance cancer diagnosis and treatment by providing more details about what is happening in tumors. Furthermore, proteogenomics, the integration of proteomics with genomics, produces a clearer picture of a tumor’s biology and will enable the development of new molecular diagnostic tests. Yet, developing protein-based biomarkers for cancer treatment has been hampered, in part, by the technical challenges of working with proteins, which are more complex than nucleic acids (DNA and RNA).

NCI supports several large initiatives in cancer proteomics to overcome these challenges and pave the way for the next generation of molecular diagnostics for cancer. NCI has been supporting cancer proteomics at multiple levels across the research continuum, from the development and validation of methods for protein identification and quantification, to the basic research needed to better understand the cancer proteome, to the integration of proteomic assays, to applying the information uncovered in clinical trials and patient care.

With sustained investments in proteogenomics research, doctors will, in the future, be able to assemble a more complete picture of a patient’s tumor—one that informs diagnosis and treatment and improves outcomes.

Bolstering Proteomic Research through Collaboration and Data Sharing

The NCI-supported Clinical Proteomic Tumor Analysis Consortium (CPTAC) is a collaborative effort among academic institutions, industry, and several federal agencies to measure, in a rapid and large-scale manner, the entire complement of proteins in tumors and combine this information with genomic, imaging, and clinical data from patients. Efforts by the consortium have produced standardized methods for proteomics research; revealed new aspects of tumor biology that were not evident from genomic information alone; and identified additional targets for cancer treatment.

So far, proteogenomic characterizations have been completed for 11 tumor types, with four more anticipated in 2021. All the information assembled by CPTAC is made publicly available so that others can use it to make their own discoveries. With these data, NCI has developed some of the world’s largest public, open‐access repositories of data. These repositories include DNA, RNA, protein, and imaging data on many cancer types, and the list of cancer types is growing. These data “live” in the NCI Cancer Research Data Commons, a virtual platform for secure data storage and sharing. The Proteomic Data Commons went live in 2020, joining the Genomic Data Commons, and the Imaging Data Commons is anticipated to be available soon.

NCI and the Departments of Defense and Veterans Affairs have partnered to advance the clinical utility of proteogenomics. The tri-agency Applied Proteogenomics OrganizationaL Learning and Outcomes (APOLLO) network aims to incorporate proteomic and genomic analyses into patient care to identify targets for therapy and better predict how patients will respond to therapy.

Moving New Proteogenomic Diagnostic Approaches into the Clinic

Many challenges exist in translating a potential biomarker identified in the laboratory to one that can be used in the clinic. Combining multiple biomarkers, including genomic and proteomic information, adds more complexity and introduces additional hurdles to overcome.

NCI is supporting research through CPTAC and other initiatives to advance the identification and use of protein molecular markers and proteogenomic information for clinical purposes. For example, NCI-funded Proteogenomic Translational Research Centers are using samples previously collected from patients who participated in clinical trials to see if biomarkers derived from proteogenomic information can predict the outcomes observed at the end of the trials. If these biomarkers can accurately predict patient outcomes, they potentially could be used to design the next generation of prospective studies and new clinical trials to further test the biomarkers’ utility.

Understanding proteins—their expression, modifications, and abnormal functions in cancer development and progression—is critically important when developing drugs, selecting treatments, and predicting treatment response. Integration of proteomic information is the next step in precision oncology.

Aiding Drug Development

A pharmacodynamic (PD) marker is a measurable indicator of a drug’s effect on its target in the body. For example, blood glucose levels are a PD marker of the effect of insulin. PD biomarkers provide critical information about whether a drug is hitting its intended target and having its intended biological effect; thereby providing insights about the dose and timing of drug delivery. This type of information is important in drug development.

NCI-funded researchers at Fred Hutchinson Cancer Research Center and their industry collaborators applied a cutting-edge proteomic technology for the first time to guide the selection of a PD biomarker that could be used in patients. Using primary human cells and patient-derived models, they identified a protein-based biomarker that reflected the effect of an investigational drug that targets the ATM protein. ATM is an important protein in signaling DNA damage in a cell.

Further investigation showed that the biomarker was a useful indicator of the inhibition of a second protein called ATR. ATR works in concert with ATM to alert a cell that DNA damage has occurred. The investigators then developed a test intended for clinical use to measure this biomarker in patient samples in future phase 2 clinical studies testing drugs that inhibit ATM and ATR. A PD marker of this kind would greatly speed the development of these types of drugs for the treatment of cancer.

Selecting Treatment

The NCI-MATCH trial and NCI–COG Pediatric MATCH trial use genomic characterization of a patient’s tumor to match a targeted therapy to the patient based on a mutation in the tumor’s DNA. Proteogenomic characterization will enhance this concept to include protein characterization and treatment selection based on protein level, activation, or modification.

Recently, NCI-funded researchers from Baylor College of Medicine, the Pacific Northwest National Laboratory, and their colleagues performed proteogenomic analyses of samples from more than 100 people with colon cancer. By integrating the data from several types of genomic and proteomic analyses from individual patients, they created patient tumor‒specific proteogenomic atlases that revealed many new potential targets for personalized colon cancer treatment. One potential target they found is a protein called CDK2. Drugs that block the activity of CDK2 may potentially suppress the growth of colon cancer cells by restoring the normal function of a tumor suppressor gene.

Predicting Treatment Responses

One challenge in moving proteomics into the clinic alongside genomics is collecting enough tumor tissue. The amount of tumor typically collected during a biopsy is sufficient for DNA and RNA analysis, but more tissue is needed to analyze tumor proteins.

In 2020, NCI-funded researchers at the Broad Institute, Baylor College of Medicine, and their collaborators developed new methods of comprehensive proteomic analysis that require a smaller amount of tissue than has previously been possible. In a pilot study of patients with HER2-positive breast cancer, core needle biopsies of tumors obtained before and after treatment with the HER2-targeted therapy trastuzumab (Herceptin) were analyzed using the newly developed microscale approach. The analysis looked at both genomic and proteomic information to provide insights into why some patients’ tumors responded to treatment while others did not. The proteomic information suggested mechanisms of treatment response, lack of response, and drug resistance that would not have been evident with genomic information alone. This research also proved that these types of proteogenomic analyses can be performed with routine clinical samples.

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