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These studies suggest that cancer tumors are real and can be monitored and analyzed through various methods such as circulating tumor cells, real-time PCR, liquid biopsies, and the tumor microenvironment, although challenges in data integration and standardization remain.
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In the realm of lung cancer research, various methodologies have been developed to estimate disease progression using real-world data. These include manual abstraction of medical records, natural language processing (NLP) of clinical notes and radiology reports, treatment-based algorithms, changes in tumor volume, and delta radiomics-based approaches. Each method has its strengths and limitations, with manual abstraction showing strong correlations with overall survival (Spearman rank ρ = 0.61-0.84) and NLP approaches demonstrating high accuracy (area under the curve = 0.86-0.96). However, the lack of a standardized gold standard for comparison complicates the assessment of these methods' accuracy across different studies.
Circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) provide a non-invasive means to monitor cancer biology in real-time. These biomarkers, shed into the bloodstream by tumors, offer insights into cancer progression, response to treatment, and potential therapeutic targets. For instance, genes encoding ribosomal proteins in CTCs have been linked to metastatic progression and poor outcomes in patients, highlighting their potential as biomarkers for metastatic cancer therapies. The liquid biopsy approach, which involves analyzing ctDNA, allows for continuous monitoring of tumor genomes, aiding in the detection of early resistance to targeted therapies and providing accessible genetic biomarkers for cancer diagnosis and prognosis .
Advances in real-time PCR technology have revolutionized cancer diagnostics by enabling precise molecular profiling of tumors. This technology can detect gene duplications, deletions, and small mutations, providing critical information for prognosis and predicting responses to therapy. Real-time PCR is particularly advantageous due to its speed, accuracy, and ability to be multiplexed, making it a favorable option for routine clinical use in tumor profiling.
The physical properties of tumors, such as solid stress, interstitial fluid pressure, stiffness, and altered tissue microarchitecture, play significant roles in cancer progression and treatment resistance. Elevated solid stress can compress blood vessels, impairing drug delivery and promoting tumor invasiveness. Increased stiffness and altered microarchitecture disrupt normal tissue interactions, further fueling cancer progression. The tumor microenvironment, comprising immune cells, stromal cells, blood vessels, and extracellular matrix, actively promotes cancer progression through complex interactions with tumor cells. This dynamic environment supports tumor growth, local invasion, and metastatic dissemination.
The integration of real-world clinical data with genomic data from tumor profiling is crucial for advancing precision medicine. Electronic health records provide a rich repository of patient experiences, treatments, and outcomes, which, when linked with genomic data, can inform precision medicine interventions. However, the infrastructure required to ensure data quality and facilitate rapid learning from these composite data sets is complex. Establishing data standards and incentivizing data sharing are essential steps to harness the full potential of real-world evidence in oncology.
The integration of real-world data, real-time monitoring through liquid biopsies, and advanced molecular profiling techniques like real-time PCR are transforming cancer research and clinical practice. Understanding the physical traits of tumors and the tumor microenvironment further enhances our ability to develop targeted therapies and improve patient outcomes. As the field progresses, standardized methodologies and robust data integration will be key to fully realizing the potential of these innovative approaches in cancer care.
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