Tweet
At the 2025 AACR (American Association for Cancer Research) Annual Meeting, a plenary session titled “Innovative Technologies Driving Advances in Cancer Research” brought attention to how emerging technologies are changing the way researchers conduct cancer research. Chaired by Alberto Bardelli of IFOM and the University of Turin, the session opened with a pointed thought experiment. He asked today’s researchers to imagine how their labs would function if PCR, next-generation sequencing, or advanced microscopy had never been invented. “These are just a few examples of how technology has transformed the way we do science,” he stated. Bardelli’s remarks opened the floor for four presentations on technologies making huge impacts on cancer research, including patient-derived organoid models, cell-free whole genome sequencing, CAR-T cell development, and improved strategies for expanding access to clinical trials.
Advances in Patient-Derived Organoid Models for Solid Tumors
The first talk was presented by Matthias P. Lütolf, Ph.D., from EPFL and Roche, who highlighted a series of advances in engineering next-generation patient-derived organoid models for studying tumor biology, with a focus on colorectal cancer. While organoids already serve as developing platforms for modeling cancer and testing therapies, they still fall short in important areas such as standardization, experimental control, and mimicking the tumor microenvironment. To address these challenges, Lütolf’s lab applied bioengineering strategies to guide organoid growth using defined geometries. These “scaffold-guided” systems produced tissues that resemble in vivo architecture and support long-term culture.
This model allows detailed manipulation of variables, including the induction of oncogenic mutations via optogenetics. Lütolf’s team was about to demonstrate spatially distinct tumor phenotypes and heterogeneity by triggering tumorigenesis in specific epithelial regions, even when tumors carried identical driver mutations. They also traced clonal dynamics using cell barcoding and single-cell sequencing, which showed that tumor clones tend to be larger and enriched in stem-like and cycling cells compared to healthy clones.
The system also allowed exploration of environmental and microenvironmental influences. The team further showed that bacterial metabolites and calorie intake modulate tumor growth, and that cancer-associated fibroblasts can promote invasion and immune evasion through IL-1β signaling. Additionally, results demonstrated that tumor-infiltrating lymphocytes could kill tumor cells in some patient-derived models, though this effect was suppressed by fibroblasts unless checkpoint blockade (e.g., Atezolizumab) was applied.
Finally, the team introduced a “mini lymph node” into the organoid platform to model cancer-immune interactions. This multi-compartment system included epithelial cells, fibroblasts, endothelial cells, and immune cell subsets. Over weeks, tumors could invade lymphatic structures and mimic aspects of metastasis, offering a highly tractable, patient-specific, in vitro platform for studying tumor progression, immune dynamics, and therapeutic response at organ-level complexity. Lütolf’s work demonstrates the development of an integrated organoid platform that brings tumor modeling closer to physiological relevance, providing new opportunities to study cancer biology and treatment response in a controlled, human-specific system.
Developing New CAR T Cells
In the following talk, Marcela V. Maus, Ph.D., of Massachusetts General Hospital, presented two unpublished studies focused on improving CAR T cell therapies. The first explored how modulating interferon gamma can reduce toxicity and improve expansion without compromising antitumor activity. In mouse models of leukemia and lymphoma, CAR T cells lacking interferon gamma or its receptor maintained tumor control while reducing cytokine release and associated toxicities. However, in solid tumor models, interferon gamma signaling in tumor cells was essential for effective CAR T-mediated killing, suggesting receptor knockout in T cells, but not tumors, may enhance efficacy in solid cancers. Interferon gamma receptor–deficient CAR T cells also demonstrated improved persistence, reduced apoptosis, and enhanced memory formation in these models.
The second part of the talk addressed how to identify the most promising CAR T cell modifications. Given the ease of generating many constructs but the challenge of selecting those worth clinical translation, Maus and collaborators developed an in vivo CRISPR screen using a restricted guide RNA library (the “Mario” library) targeting 135 genes. This dual-vector system enabled the testing of gene knockouts across multiple cytokine environments and tumor settings, with distinct hits emerging at different stages. Among them, CDKN1B knockout consistently improved CAR T persistence and tumor control across multiple myeloma models, outperforming other candidates such as PTPN2 and RASA2.
Maus proposed that patient-driven selection of CAR T variants during early-phase trials (tracking enrichment of edited cells in vivo) may be more efficient than the current one-at-a-time model. She argued this approach could better match the pace of innovation in cell therapies. To support this strategy, her team is also analyzing correlative data from glioblastoma CAR T trials using single-cell RNA-seq of patient CSF samples.
The Promise of Cell-Free Whole Genome Sequencing
Trevor J. Pugh of the University of Toronto shared collective efforts to use cell-free whole genome sequencing (cfWGS) across three key stages of the cancer journey, including early detection, treatment monitoring, and genome reconstruction. He emphasized that tumor genomes contain useful information at every point, and that cfDNA can serve as a real-time molecular readout when tissue is inaccessible or limited.
In hereditary cancer syndromes such as Li-Fraumeni and BRCA1/2, Pugh’s team found that cfWGS could detect somatic alterations months before clinical diagnosis. Combining mutation analysis, copy number variation, fragment size, and open chromatin features improved detection sensitivity. The CHARM (cfDNA in Hereditary And High-Risk Malignancies) Consortium has now launched a prospective study randomizing patients with hereditary cancer risk to receive cfDNA reports, which have already enabled off-cycle cancer detection and timely follow-up interventions.
The team also applied cfWGS to monitor treatment response in multiple myeloma. Tracking cfDNA for known bone marrow mutations allowed the researchers to detect signs of minimal residual disease and relapse sooner than traditional clinical methods could, including in patients who lacked new bone marrow samples.
In liver cancer patients undergoing transplant, the group used cfWGS to reconstruct tumor genomes when tissue was degraded by pre-transplant therapy. They trained a machine learning model using fragmentomic features, rather than relying solely on mutation calls, to classify cancer-derived fragments, enabling genome-level resolution from blood alone.
Pugh proposed a collaborative, global effort to scale these analyses, advocating for the use of cfWGS as a standard blood test tailored to individual cancer risk and disease stage. In closing, Pugh positioned cfDNA sequencing as a flexible, noninvasive tool capable of supporting early detection, treatment monitoring, and genome profiling across cancer types, particularly where tissue-based approaches fall short.
Improving Access to Clinical Trials
The final presentation by Muhammad Shaalan Beg, M.D., of the National Cancer Institute, focused on innovations in clinical trial design and delivery aimed at improving access, efficiency, and sustainability in the face of rapid advances in cancer biology and therapeutics. Beg started by explaining how precision oncology continues to become increasingly complex, which has led to the clinical trial infrastructure being under pressure, especially given the low prevalence of targetable mutations, geographic disparities in trial availability, and rising operational costs.
Beg emphasized the need to modernize trial approaches, highlighting pragmatic trial designs that reduce complexity by aligning study procedures with standard clinical care. These designs simplify eligibility criteria, minimize data collection, and focus on meaningful endpoints such as overall survival. He also described the increasing adoption of decentralized clinical trial methods, including telemedicine, mobile nursing, and direct-to-patient enrollment, which help extend trial access to underserved populations. Surveys of academic centers and industry groups show growing acceptance of these approaches, especially in trials involving subcutaneous or oral therapies.
The NCI has been actively piloting supportive infrastructure such as virtual clinical trial offices and telehealth research centers to assist with trial coordination, informed consent, and data reporting. Several case studies demonstrated that remote trial delivery can improve patient diversity and enrollment rates while maintaining compliance with regulatory standards. Beg also outlined the potential of artificial intelligence to streamline patient matching, automate document translation, and integrate trial protocols with electronic data systems. He noted, however, that data harmonization and standardization remain significant barriers to AI-driven trial matching.
The talk closed with Beg's call to action. Trial delivery must change to match the pace of discovery. Redefining feasibility, broadening geographic reach, and reducing participant burden are essential for ensuring that new therapies reach patients efficiently and equitably. Clinical trials, he emphasized, must be redesigned around the needs and realities of the people they aim to serve.