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Immunotherapy for Cancer: Current Advances and Future Directions

Immunotherapy delivers a fundamental shift in the treatment of multiple cancers, offering hope for patients with cancer. Unlike conventional therapies, it leverages the patient’s immune system to precisely identify and destroy cancer cells. The remarkable journey of immunotherapy’s has revolutionized oncology and paved the way for the development of modern immunotherapies. Several approaches are applied for immunotherapy, including immune checkpoint inhibitors, adoptive cell therapies, cancer vaccines, tumor microenvironment reprogramming, monoclonal antibodies, cytokine therapies, oncolytic virus therapy and combination strategies. Various immunotherapies are being used for different types of cancers like skin, lung, bladder, kidney, liver, breast and blood cancers. Despite providing an effective cancer treatment, immunotherapies face some challenges in their clinical implementation.

Erratum to “BRD2 induces drug resistance through activation of the RasGRP1/Ras/ERK signaling pathway in adult T-cell lymphoblastic lymphoma”

Following publication of this article, the authors noticed that the WB bands of c-Myc in Figure 7D were inadvertently misplaced [1]. After carefully checking the original data, the authors confirmed the error occurred during figure assembly. This error does not affect the conclusions of this article. The figure has been corrected in the original HTML and PDF, and Fig. 7D is provided below. The authors would like to apologize for any inconvenience caused.

Spatial Proteomics for Precision Oncology

Tumors are complex cellular ecosystems in which cancer cell phenotypes, their spatial organization, and their interactions with immune and stromal compartments determine disease progression and therapy response. To resolve this complexity, the use of highly multiplexed Imaging Mass Cytometry™ (IMC™) together with multiomic profiling and artificial intelligence (AI) can identify clinically relevant features of the tumor microenvironment, providing a rich framework for patient stratification and targeted therapy in precision oncology.

In this GEN webinar, Bernd Bodenmiller, PhD (University of Zürich), illustrates this integrated approach through two recent examples. In a study of 323 epithelial ovarian cancers, tumors with more diverse cell phenotypes and spatial organization recurred faster. His lab also identified a therapy-resistant tumor cell phenotype (TUBB3+/pRB+) associated with recurrence and poor response to standard therapy. Spatial transcriptomics and ex vivo drug screening of 3D sphere models and patient samples showed that these cells resist standard-of-care agents but are sensitive to specific inhibitors. In 498 clear cell renal cell carcinomas, the use of AI revealed three survival ecotypes that predict survival better than basic clinical data and molecular stratification models. Each ecotype has its own genomic and metabolic features and is predictive of treatment outcome. Deep learning predicted the ecotypes directly from routine H&E slides, and the findings were validated across five cohorts comprising more than 2,500 patients.

Webinar attendees will learn how integrating spatial single-cell proteomics by IMC with multiomics and AI identifies therapy-resistant cell populations and prognostic tumor ecosystems. This approach provides a detailed framework for patient stratification and targeted therapy in precision oncology.

OpenAI’s Greg Brockman Says AI Will Allow Death To Become A Choice, Echoing Elon Musk’s Take That “Longevity Or Semi-Immortality Is An Extremely Solvable Problem”

Elon Musk made the claim in January 2026, then declared in October that “longevity is coming,” a view that OpenAI’s Brockman now echoes.