cancer immunotherapy

Overview

Cancer immunotherapy encompasses a broad class of therapeutic strategies that harness or augment the body's own immune system to recognize, target, and eliminate malignant cells. Unlike conventional treatments such as chemotherapy, radiotherapy, and surgery, immunotherapy operates through immune-specific mechanisms—activating cytotoxic T cells (CD8+ T cells), natural killer (NK) cells, dendritic cells, and other immune effectors against tumors—offering superior immune specificity and reduced off-target effects. The foundational principles rest on overcoming tumor immune evasion: cancer cells exploit checkpoints such as the PD-1/PD-L1 axis, suppress antigen presentation, and remodel the tumor microenvironment (TME) to exclude or exhaust infiltrating lymphocytes. Key pillars of modern cancer immunotherapy include checkpoint inhibitor (targeting PD-1, PD-L1, and CTLA-4), chimeric antigen receptor T cell (CAR-T) therapy, cancer vaccines, bispecific and trispecific antibody engagers, and innate immune pathway agonists such as activators of the cGAS-STING pathway.

The clinical impact of cancer immunotherapy has been transformative across multiple tumor types. Agents such as nivolumab, ipilimumab, atezolizumab, and durvalumab have demonstrated durable responses in patients with advanced melanoma, lung cancer, bladder cancer, and other malignancies. However, substantial inter-patient response heterogeneity limits benefit to specific subsets, driving intense investigation into predictive biomarkers, combination strategies, and novel delivery systems. Ongoing research seeks to extend immunotherapy's reach to immunologically "cold" tumors—those with sparse lymphocytic infiltration—by reprogramming the TME, enhancing antigen presentation, and synergizing immune activation with targeted agents and nanomedicine platforms.

Focus of Latest Publications

Recent publications have focused on identifying predictive biomarkers and patient stratification strategies to optimize cancer immunotherapy outcomes across multiple malignancies. In advanced and recurrent cervical cancer, peripheral blood biomarkers—including baseline CD4+ T-cell percentages and post-treatment levels of CA125, SCCA, CD8+ T-cell subsets, and PD-1 expression—were significantly associated with treatment response and prognosis following immunotherapy combined with chemoradiotherapy. Similarly, in liver cancer, computed tomography imaging features have been employed to identify distinct tumor subtypes with different prognostic profiles and immunotherapy responsiveness, suggesting that radiologic biomarkers may help predict treatment efficacy. These biomarker-driven approaches represent a minimally invasive strategy for personalizing immunotherapy selection.

Combination immunotherapy strategies are emerging as a key focus in recent research. In high-risk bladder cancer, the addition of immune checkpoint inhibitors—including durvalumab and atezolizumab—to Bacillus Calmette-Guérin (BCG) has been investigated, with trials (CREST and POTOMAC) demonstrating improvements in event-free or disease-free survival, though differential toxicity and patient attrition patterns may complicate interpretation of efficacy outcomes. Multi-omics analyses in colorectal cancer have identified distinct immune subtypes, with findings suggesting that immune-cold tumors characterized by high WNT pathway activation may benefit from combination immunotherapy with Targeted Cancer Therapy approaches. In pancreatic adenocarcinoma and biliary tract cancers, researchers are optimizing immunotherapy efficacy through combinations with other targeted approaches, including MEK inhibitors, though clinical translation remains challenging.

Emerging evidence highlights the role of the tumor microenvironment and systemic immune signatures in determining immunotherapy response. Multi-omics integration approaches, combining transcriptomics, genomics, methylation, and immune infiltration profiles, have generated novel tumor classifications—such as the Multi-Omics Tumor Immune Features-based Clusters (MotifCC) system for colorectal cancer—that stratify patients by immune phenotype and predicted responsiveness to treatment. In non-small cell lung cancer, artificial intelligence and radiomics have been applied to integrate multi-omics data, including pathomics and microbiomics, to predict immunotherapy efficacy and potential toxicities, advancing precision medicine approaches despite current challenges in data standardization and interpretability.

Surgical and mechanistic factors are increasingly recognized as determinants of immunotherapy efficacy. In biliary tract cancer and other solid tumors, excessive dissection of tumor-draining lymph nodes during surgical resection may impair subsequent immunotherapy outcomes by reducing adaptive immune response capacity, suggesting that lymph node preservation may be strategically important for maintaining antitumor immunity. Furthermore, emerging research in lung cancer has identified autophagy-ferroptosis crosstalk as a mechanism influencing immunotherapy response, with combination approaches targeting these pathways showing potential for synergistic efficacy, though most strategies remain in preclinical development and require further molecular elucidation.