2.2.3 Prediction of Treatment and Post-treatment
Altered protein levels, abnormal structural conformation and impaired function are the most immediate causes of aberrant signaling in tumorigenesis.Protein dynamics are dependent in part on genetic factors, transcriptional regulation and mRNA translation, hence genomic and transcriptome abnormalities are reflected in the proteome.Analyzing the protein composition of tumors has been the focus of immense initiatives such as CPTAC.These studies utilize large cancer and grade-specific cohorts to characterize the proteome of tumor specimens and identify patterns that are indicative of disease onset and progression.Among the challenges with most proteomic analyses are a poor representation of low grade, early stage samples as most patients are diagnosed following the presentation of symptoms that correlate with an advanced disease state.The Early Detection Research Network (EDRN) is another extensive NCI initiative focused on cancer biomarker discovery and validation with the goal of early detection.These multi-institution efforts emphasize the importance of proteomics in understanding disease etiology, but they also recognize the advantage of integrating genomic data thereby establishing proteogenomics as a prominent approach to cancer biomarker discovery [17, 18].
Despite continuous efforts on all fronts, omics areas have advanced at different rates with genomics experiencing a boom in sequencing data acquisition and analysis due to the introduction of NGS.In addition, RNA-seq has propelled transcriptomics and allowed for multiplexing and the detection of low abundance mRNA and regulatory miRNA.Proteomics has also evolved, but to a lesser degree as protein composition is a dynamic and low abundance species presenting a challenge for mass spectrometry methods, while antibody targeted approaches that allow for signal amplification are not optimal for biomarker discovery.Despite the vast amounts of available genomic and transcriptomic data, mapping the signaling networks that initiate carcinogenesis is challenging since protein composition is not in perfect agreement with mRNA abundance and genomic variants in non-coding regions are difficult to assess without knowing if and how they impact protein expression and function [4, 19].
Integration of “omics” data can identify genomic and transcriptomic alterations that impact the proteome and cell signaling, leading to tumorigenesis.In 2018, it is highlighted that Cancer SEEK as a blood test capable of detecting eight different cancer types through the integration of genomic and proteomic analysis [20].This proteogenomic approach combines targeted genomic analysis of ctDNA using a 61-amplicon panel with a set of eight validated protein cancer biomarkers.CancerSEEK is an excellent example of the superior diagnostic sensitivity and specificity achieved through the integration of two omics approaches.Multi-omics analyses are used to identify transcription factors responsible for onset of triple negative breast cancer (TNBC), which lacks expression of estrogen receptor, progesterone receptor and HER2 and presents with aggressive proliferation, large tumor size and rapid progression to metastasis.This study utilized TNBC genomic databases to identify a subset of differentially expressed genes and subsequently analyzed their transcription and protein expression profiles using TNBC transcriptome and protein-interaction databases.Comparing data across omics areas implicated upregulation of JAK-STAT and TNF signaling as key mechanisms contributing to TNBC onset and progression.Furthermore, proteogenomics is utilized by the multi-institution CPTAC initiative in an effort to characterize abnormal signaling across cancer types.Colon and rectal tumor samples previously subjected to RNA-seq analysis as part of the Cancer Genome Atlas study are subjected to tandem mass-spectrometry protein detection in an effort to correlate genomic and proteomic profiles.In addition to characterizing the proteome of individual biopsy specimens belonging to the same cancer class, this approach demonstrated that mRNA abundance does not directly predict protein levels.Discrepancy between the transcriptome and proteome emphasizes the impact post-transcriptional regulation has on cell signaling through mechanisms such as miRNA translational repression.This complex and indirect correlation between mRNA and protein levels stresses the need for multi-omics analysis and underscores the potential for misinterpretation when a single omic approach is used [20-22].
Tumor biomarker discovery most frequently originates in academic research settings, leading to the identification of thousands of protein and genetic markers.However, despite the continuously growing number of potential cancer biomarkers, there have been very few new cancer biomarker-based diagnostics.The discrepancy between the rate of biomarker discovery and implementation of new tests into the clinical lab can be attributed to several challenges associated with the translational process [23].
Transitioning “from bench to bedside” is the foundation of translational research and it requires large-scale validation studies and clinical trials that are difficult for a single academic lab or even institution.Translating biomarker discovery into clinical assays requires a large team, including academic researchers, clinicians, and industry, to define the clinical utility of the test, validation process and study design.Assembling such a team can be a daunting task that requires tremendous resources and poses a major challenge in translational research.The NCI EDRN initiative is a prime example of a successful multi-organization translational research program aimed at the discovery of cancer biomarkers for early detection and risk assessment, where collaboration among academic institutions, industry, and government has been able to address challenges including analytical and clinical validation that require large specimen cohorts and standardized sample preparation [24].(https://www.daowen.com)
Tumor biomarker discovery conducted in a research setting utilizes complex instrumentation and sample preparation techniques that are not compatible with day to day operations in a clinical lab,which require high throughput, reproducible methods that are straightforward to perform.For example, proteomic-based cancer biomarker discovery relies on mass spectrometers that are highly complex, have low throughput capabilities and require extensive personnel training, making them difficult to integrate in clinical labs with total automation and multiplexing testing platforms.The incompatibility of instruments used in research labs and those utilized for clinical testing, is a major challenge in translational research.Developing a method suitable for clinical testing can be difficult.In particular, while mass spectrometry might be highly specific, clinical laboratories may want to use an immunoassay platform that can achieve better analytical sensitivity and is easy to incorporate into a clinical setting, but has less specificity for a particular protein variant that serves as the disease biomarker.To further complicate the transition from research to clinical testing, a method must be validated to establish key performance parameters of the assay including a limit of detection, limit of quantitation, precision, accuracy and potential interferences by other substances present in a patient sample.Furthermore, this process requires a standardized operating procedure,validated standards, reference materials, and calibrators as well as a large specimen cohort.Interpretation of the results and how they will influence clinical decisions, dictates the performance and analytical requirements of the test such as necessary sensitivity, bias, and coefficient of variation at and near medical decision points [25, 26].
Analytical and clinical validation studies are essential for an assay to be submitted for the US FDA clearance or approval.This process can be time-consuming and difficult to achieve due to limited sample availability.Biospecimen repositories and biobanks are key to the translational process as access to patient specimens and detailed clinical history is crucial for the development of assays.The growing need for meticulously categorized specimens with respect to patient status and disease progression has spurred on initiatives to organize large repositories of patient samples and data such as the National Biospecimen Network .Availability of specimens is not enough for proper assay development, standardized sample collection and patient history are necessary for proper validation.Differences in specimen collection, storage and processing can impact results during the analytical validation process, which will pose additional challenges.Patient history and outcome data can be obtained either through prospective trials or retrospective studies, the latter allowing for a shorter validation timeline.Risk assessment biomarker assays further benefit from repositories comprised of specimens from patients and their relatives, such as the Breast Cancer Family Registry, which allows clinical validation through prospective trials that follow individuals with potential risk factors.Such familial repositories are invaluable for the discovery of risk and early diagnostic biomarkers, but unfortunately, they are not readily available for most malignancies [4, 27, 28].
Large sample size, detailed patient history and the need to account for sample variability require big data manipulation and superior statistical analysis capabilities.Although this may have been a major challenge in the past, cross-institution efforts such as EDRN have successfully overcome these limitations.The financial backing necessary to take a cancer biomarker from the discovery phase to clinical testing is another limitation that requires a multi-institution effort with ample resources.Overall, the complexity of translational research has presented a bottleneck for the clinical implementation of tests following cancer biomarker discovery, but in recent years tremendous efforts on multiple fronts and increased financial support have been able to address these challenges.Genomic cancer biomarkers have been able to transition from the research to the clinical lab, in part due to the development of NGS and its ability to meet the demands for clinical testing.Primary examples of genomic biomarkers that have successfully transitioned to clinical tests include the Oncotype Dx and Mammaprint assays used for assessment of breast cancer patients whose gene expression profile influences therapy decisions.NSCLC patients harboring resistance to tyrosine kinase inhibitor therapy due to the EGFR T790M mutation, have also benefitted from translational research where the presence of EGFR T790M can be detected through clinical tests and used to initiate therapy with osimertinib.Certain proteomic cancer biomarkers have also made a successful transition “from bench to bedside”, with OVA1 multivariate index assay being a prime example.OVA1 is used for risk assessment of ovarian cancer in patients with pelvic masses who are scheduled to undergo surgery.Risk of malignancy is determined through a combination of markers including CA125, prealbumin, apolipoprotein A1, beta-2-microglobulin,and transferrin.Combining multiple proteins, and even genomic attributes, to serve as a cancer biomarker has tremendous potential and is the current approach to diagnostic and prognostic testing,however, validation studies examining multiple analytes in a single assay can be difficult.One successful example is Cologuard, an US FDA-approved fecal screening test for colorectal cancer.Cologuard is a clinical assay for tumor-specific DNA changes, including aberrant methylated BMP3 and NDRG4, a mutant form of KRAS, beta-actin, and hemoglobin [4, 29].
Translating research discoveries into clinical assays has been challenging and a very small fraction of genomic and proteomic markers complete the transition to tests routinely offered in the clinical lab.Clinical utility is a major consideration when selecting candidate biomarkers, with patient care,early diagnosis and improved treatment outcome being key to deciding what biomarkers are applicable in a clinical setting.Additionally, the ability to develop a method that meets the clinical needs, can be integrated into a clinical lab and is cost-effective also impacts the selection of cancer biomarkers to be implemented in patient care.Increasing multi-institution efforts, collaborations among academia, government and industry and funding support have made translational research the focus of cancer initiatives in an effort to address unmet clinical needs and transition to Precision Medicine [4].