Understanding Tumor Immunology

Identifying markers in body fluids via liquid biopsy holds great promise for detecting diseases (such as cancer) at early stages, following disease progression and population heterogeneity, and, importantly, predicting a response to therapy with minimal patient inconvenience. Liquid biopsy relies on easy access to a multitude of molecular entities present in blood, but focus mainly on nucleic acids, DNA, and RNA. However, challenges persist. Current liquid biopsy approaches, such as circulating tumor DNA (ctDNA) analysis isolated from plasma, do not provide information on how the host immune system responds to the tumor, which plays a vital role in successful anti-tumor immune responses. Indeed, the immune system recognizes tumors early during carcinogenesis, long before clinical manifestations. It is now widely acknowledged that tumor and immune system maintain a very dynamic relationship so that many tumors are eliminated throughout life and cancer disease is the result of tumors highly selected for their ability to evade immune attack (1).

Whole blood RNA next generation sequencing provides information on global immune cell gene expression, including the active molecular and cellular pathways driving the integrated systemic anti-tumor immune responses. However, another challenge lies in obtaining consistent results with RNA tests: variances can be caused by differences in how testing is conducted, natural variations, and inadequate sample size. Using whole-blood RNA offers a complete view of gene expression in circulating immune cells, reflecting immune fitness, and responsiveness, as long as the RNA is immediately stabilized after blood sampling. This approach avoids technical issues in gene expression caused by external handling conditions. Additionally, traditional screening methods, such as blood detection in stools, face challenges with compliance as well as sensitivity and specificity, indicating a pressing need for more reliable and convenient options.

To enhance cancer treatment, particularly in immuno-oncology, there is an urgent need to discover new biomarkers for accurately predicting patient responses to different therapies. Though immunotherapy has significantly transformed cancer care, this success is limited in many clinical settings by a lack of capturing tumor heterogeneity and its evolution over time. Cancer affects the entire body and causes a fluctuation of changes in the immune system’s function and composition.

To dynamically understand immunosurveillance of cancer, we must look at the whole immune system – not just the tumor microenvironment throughout the course of the disease and therapeutic intervention. The peripheral immune system is vital for initiating both natural and treatment-induced anticancer immune responses. Additionally, the movement of immune cells and microvesicles to and from tumor sites provides valuable information about the overall anti-tumor immune response.

All of these challenges create demand for a cancer screening system that identifies markers and predicts responses to immunotherapy treatments, including early reactivation for the immune mediators or safety signals. Researchers across the globe have been looking for a solution, which so far seems to reside in AI and deep learning tools. This trend is supported by review studies on big data for RNA biology (2) and research on AI cell annotation for RNA sequencing data (3).

AI-powered screening solutions are now starting to reach the market, including a non-invasive blood biopsy that uses AI-powered RNA analysis – enhancing blood RNAseq and analysis by integrating standards, quality control measures, and advanced machine learning techniques (4). This system was originally tailored for early cancer detection in blood where weak signals have to be detected, boosting the signal-to-noise ratio in blood RNAseq analysis – allowing us to assess systemic immunity and patient response to therapy.

Looking ahead, this technology – supported by machine learning – holds promise in sensitive early detection of lung carcinoma. Low-dose computed tomography (LDCT), chest X-rays, and sputum cytology have been studied as screening tools, with LDCT shown to reduce mortality risk. However, LDCT can produce false positive results, leading to unnecessary procedures and overdiagnosis. With further research, technologies in LDCT-screened populations might provide valuable non-radiological biomarkers for accurately monitoring high-risk lung cancer groups.

As more technological advancements make their way into clinics, we can look forward to a revolution in conventional genomic tests and liquid biopsies, helping us all meet the evolving challenges in cancer diagnosis and treatment, and ultimately improving outcomes for patients worldwide.