Cancer mutations are alterations in the DNA of tumour cells that are absent from healthy tissue. Finding these changes accurately is essential for understanding how cancers develop, tracking disease progression and selecting treatments tailored to individual patients. Yet the task is technically demanding. Tumour samples often contain a mixture of cancerous and normal cells, while mutations can occur in repetitive or structurally complex sections of the genome that are difficult to reconstruct from short fragments of DNA.

Most existing somatic-variant callers—the software tools used to distinguish tumour mutations from inherited genetic differences—were created primarily for short-read sequencing. Short-read platforms produce large numbers of highly accurate fragments, but each fragment covers only a small portion of the genome. Long-read sequencing, by contrast, generates much longer DNA molecules that can span repetitive sequences, structural rearrangements and other difficult regions. This broader view can reveal genomic changes that would otherwise remain hidden, although it also creates new computational challenges.

ClairS tackles these challenges with a neural-network architecture trained to interpret the complex signals produced by long-read tumour-normal sequencing. The system compares DNA data from a tumour with a matched normal sample and searches for small somatic variants, including single-nucleotide changes and short insertions or deletions. Rather than relying only on fixed rules, the model learns patterns associated with genuine tumour mutations, sequencing errors and differences caused by the proportion of cancer cells present in a sample.

One of the most innovative aspects of ClairS is the way its developers generated training data. High-quality tumour-normal datasets are scarce, expensive to produce and difficult to obtain in sufficient quantities. To overcome this limitation, the researchers mixed sequencing data from normal human samples to create synthetic tumour-normal pairs. The process allowed them to simulate a wide range of biological and technical conditions, including different tumour purities, sequencing depths and mutation burdens.

This synthetic-data strategy gives the model access to an effectively unlimited supply of realistic training examples. Tumour purity is particularly important because a mutation may appear in only a small fraction of the DNA molecules analysed. If the cancer cells represent a minor component of a biopsy, the signal from a true mutation can be overwhelmed by normal DNA. By exposing ClairS to simulated samples with varying levels of tumour purity, the researchers trained it to recognise weak but meaningful mutation signals under conditions that resemble real clinical specimens.

The team evaluated ClairS using datasets from several cancer types, including breast cancer, lung cancer, melanoma and pancreatic cancer cell lines. Across different sequencing conditions, the algorithm showed high accuracy in detecting small somatic mutations. Its performance was particularly important in regions where long reads provide an advantage, because the extended DNA fragments can preserve the genomic context needed to distinguish a true mutation from a technical artefact.

Unlike many experimental algorithms that remain confined to academic demonstrations, ClairS has already been incorporated into the official somatic-variant-calling workflow of Oxford Nanopore Technologies. The integration places the method inside a practical commercial analysis pipeline and could speed its adoption by researchers and clinical genomics laboratories. Although further validation will be needed before any tool is used routinely for patient diagnosis or treatment decisions, the development represents a significant step toward making long-read cancer analysis more accessible.

“Long-read sequencing is transforming how we study cancer genomes, especially in regions that were previously difficult to analyse,” said Professor Ruibang Luo, the study’s senior researcher and an Associate Professor at HKU’s School of Computing and Data Science. “ClairS makes it possible to train powerful AI models even when real cancer training data is limited, supporting more reliable cancer mutation discovery from long-read sequencing data.”

The work also illustrates a broader shift in biomedical AI. Many medical algorithms are limited not by a lack of computational power, but by the shortage of accurately labelled clinical data. ClairS demonstrates how carefully designed simulations can provide a practical bridge between limited real-world samples and the enormous diversity of conditions encountered in biology. By combining long-read sequencing with deep learning and scalable synthetic-data generation, the method could help researchers build more complete cancer genomes, uncover mutations missed by traditional approaches and advance the development of precision oncology. The study, published in Nature Methods, is open source, allowing the wider genomics community to inspect, reproduce and further develop the technology.

Subject of Research: Computational simulation/modeling

Article Title: ClairS: a deep-learning method for long-read tumor–normal pair somatic small variant calling

News Publication Date: 1 July 2026

Web References: https://github.com/HKU-BAL/ClairS

References: Nature Methods. DOI: 10.1038/s41592-026-03152-4

Image Credits: The University of Hong Kong

Keywords: ClairS, cancer mutations, long-read sequencing, deep learning, artificial intelligence, somatic variant calling, tumour genomics, precision medicine, bioinformatics, Oxford Nanopore Technologies

Traditional fluorescent probes have long been instrumental in biomedical imaging, yet their efficacy is often limited by tissue autofluorescence, photobleaching, and suboptimal penetration depth. CETIF6a leverages the advantages of near-infrared wavelengths, which minimize background interference and allow deeper tissue imaging. This development enables researchers to vividly illuminate tumors across diverse cancer types with exceptional clarity.

CETIF6a is engineered to bind selectively to tumor-associated proteins, acting as a bright beacon for malignant tissues. Unlike previous probes, its design facilitates simultaneous functional proteomic profiling, thereby providing not just visual confirmation but also detailed insights into protein activity within the tumor microenvironment. This dual capability has the potential to enhance understanding of tumor biology and heterogeneity.

In preclinical models, CETIF6a demonstrated remarkable sensitivity, effectively delineating tumor margins and enabling real-time monitoring of tumor progression and response to therapies. The fluorotag’s stability and brightness under physiological conditions mark a substantial improvement over existing fluorescent markers, which often suffer from rapid photobleaching.

Moreover, the application of CETIF6a extends beyond imaging. By enabling functional proteomic profiling in situ, scientists can identify key protein interactions and pathways driving tumor growth. This functional readout could inform personalized therapeutic strategies by revealing potential molecular targets.

The technology hinges on an optimized molecular scaffold that balances photostability, biocompatibility, and target affinity. The utilization of a NIR wavelength range (approximately 700-900 nm) enhances tissue penetration, while the fluorescence quantum yield has been tuned for maximal emission intensity. This bespoke fluorotag design ensures robust performance in complex biological environments.

Looking forward, CETIF6a holds promise for clinical translation, particularly in image-guided surgery and non-invasive diagnostics. By providing surgeons with real-time tumor visualization, the fluorotag could improve resection accuracy, sparing healthy tissue and reducing recurrence rates. Additionally, functional proteomic profiling may pave the way for companion diagnostic assays.

This pioneering work aligns with the broader trend of integrating imaging with molecular characterization, fostering a more holistic approach to cancer diagnostics and treatment. The capacity to illuminate tumors with high specificity while simultaneously probing their proteomic landscape represents a new frontier in oncological research.

As this technology advances toward clinical applications, it underscores the value of interdisciplinary innovation combining chemistry, molecular biology, and optical engineering. CETIF6a exemplifies how tailored fluorescent reporters can unlock deeper insights into disease mechanisms and support the development of precision medicine.

Subject of Research: Near-infrared fluorotag reporter for tumor imaging and functional proteomic profiling

Article Title: A NIR fluorotag reporter CETIF6a enables bright pan-tumor labeling and functional proteomic profiling

Article References:
Li, J., Zhang, F., Cheng, J. et al. A NIR fluorotag reporter CETIF6a enables bright pan-tumor labeling and functional proteomic profiling. Nat Commun (2026). https://doi.org/10.1038/s41467-026-75356-3

Image Credits: AI Generated

The hallmark of Burkitt lymphoma lies in its rapid cellular proliferation, often fueled by translocations involving the MYC oncogene. However, the full spectrum of genetic changes and how these alterations evolve in tandem within a tumor mass has remained elusive. By harnessing the power of single-cell sequencing, the researchers dissected the tumor genomes of individual malignant cells, circumventing the averaging effects of bulk sequencing methods. This approach revealed a mosaic of subclones within tumors, each exhibiting unique combinations of mutations and genomic rearrangements, yet converging on shared evolutionary trajectories that promote tumor expansion.

Delving into the detailed genomic architecture, the study illuminated multiple instances of convergent evolution—a phenomenon where distinct subclonal lineages independently acquire similar genetic changes. This phenomenon suggests strong selective pressures shaping the lymphoma’s genetic landscape, with key oncogenic pathways repeatedly targeted across different clones. Such redundancy in genetic strategies underscores the tumor’s adaptability and resilience, complicating treatment efforts that target single molecular alterations. By identifying these convergent mutations, the research opens new avenues for the development of therapies that can simultaneously disrupt multiple critical pathways, potentially overcoming the tumor’s evolutionary escape mechanisms.

The single-cell sequencing platform employed in this study combined whole-genome amplification with high-throughput sequencing technologies, enabling comprehensive detection of copy number variations, structural rearrangements, and point mutations at unprecedented resolution. This capability allowed the scientists to build phylogenetic trees charting the evolutionary relationships between subclones within individual patients. These trees not only map the chronological acquisition of mutations but also highlight key genomic events linked to the transition from indolent to aggressive disease states, thereby providing valuable prognostic markers for clinical management.

Furthermore, the study’s findings challenge the previously held notion that Burkitt lymphoma evolves primarily through linear expansion of a dominant clone. Instead, the data indicate a branching evolutionary pattern characterized by genetic diversification and competitive clonal selection. This complex tumoral heterogeneity may explain the variability in patient responses to chemotherapy and underscores the necessity for treatment regimens adaptable to intratumoral genetic diversity. The elucidation of these evolutionary pathways at a single-cell level thus promises to refine prognostic models and encourages the exploration of combination therapies tailored to intercept multiple evolutionary routes.

Importantly, the study also reveals the impact of the tumor microenvironment on shaping the evolutionary course of Burkitt lymphoma. Interactions between malignant cells and surrounding stromal or immune cells likely create dynamic selective landscapes that influence which genetic alterations confer survival advantages. Through single-cell profiling, subtle genetic adaptations aligned with microenvironmental pressures were detected, suggesting a co-evolutionary process that nurtures tumor growth and evasion. Understanding these reciprocal interactions might illuminate new therapeutic targets aimed at disrupting the supportive tumor niche.

The implications of this research extend beyond Burkitt lymphoma, setting a methodological and conceptual precedent for studying cancer evolution in other malignancies. It vividly demonstrates how single-cell genomics can decode complex evolutionary patterns obscured in bulk analyses, enabling a more granular understanding of tumor biology. This approach has the potential to revolutionize oncology by fostering the design of adaptive therapies informed by real-time evolutionary dynamics, ultimately improving patient outcomes.

Moreover, these findings resonate with the broader field of evolutionary biology, highlighting convergent evolution as a pivotal force in cancer adaptation. The repeated emergence of similar driver mutations across distinct subclones mirrors evolutionary strategies observed in nature, where unrelated species independently develop analogous traits in response to shared environmental challenges. This parallel accentuates the sophistication of cancer evolution and emphasizes the importance of evolutionary principles in guiding cancer research and treatment.

Beyond the laboratory, the translational potential of this work is immense. By pinpointing convergent mutations as common vulnerabilities, it may become feasible to develop pan-subclonal therapies that preemptively target the evolutionary “bottlenecks” leveraged by Burkitt lymphoma cells. Such precision therapies would represent a paradigm shift, moving away from the traditional one-size-fits-all model toward highly individualized, evolution-aware interventions designed to suppress multiple resistant clones simultaneously.

The research also paves the way for integrating single-cell genomics into routine clinical diagnostics. With the continuing refinement and cost reduction of these technologies, it is foreseeable that comprehensive single-cell genome profiling could become a standard tool for diagnosing Burkitt lymphoma, monitoring disease progression, and tailoring therapies with unprecedented specificity. These advances would represent a major leap forward in personalized oncology, offering hope for improved survival and quality of life for patients afflicted with this aggressive cancer.

In conclusion, Steemers, van Roosmalen, and Hagelaar’s study represents a tour de force in cancer genomics, unveiling the intricately convergent evolutionary landscape of Burkitt lymphoma at a single-cell resolution. This novel perspective highlights the adaptive complexity and heterogeneity inherent in cancer, revealing new molecular vulnerabilities and informing the future design of therapeutic strategies. As this research reshapes our understanding of tumor evolution, it brings us closer to a future where cancer treatment is informed by the evolutionary tactics of the disease itself, enabling more effective and durable responses for patients worldwide.

Subject of Research: Evolutionary dynamics of Burkitt lymphoma studied through single-cell whole-genome sequencing.

Article Title: Single-cell whole-genome sequencing reveals convergent evolution in Burkitt lymphoma.

Article References:

Steemers, A.S., van Roosmalen, M.J., Hagelaar, R. et al. Single-cell whole-genome sequencing reveals convergent evolution in Burkitt lymphoma.
Nat Commun (2026). https://doi.org/10.1038/s41467-026-74121-w

Image Credits: AI Generated

The investigation centers on the clinical imperative to distinguish between soft and hard PitNET consistency, a factor historically reliant on intraoperative tactile assessment. Accurate preoperative prediction of tumor consistency holds immense potential to tailor surgical strategies, minimize operative risks, and improve patient outcomes. Capitalizing on mpMRI, this study leverages the rich imaging data derived from sequences including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1-weighted imaging (CE-T1) to construct a multidimensional radiomic profile reflective of underlying tumor heterogeneity.

Drawing on a robust retrospective cohort of 137 patients who underwent preoperative mpMRI, the research stratified tumor consistency based on detailed neurosurgical records. The patient data were divided into a large training set and a carefully curated internal validation set to ensure rigorous model development and initial performance verification. Radiomics features extracted from both two-dimensional (2D) and three-dimensional (3D) regions of interest (ROI) were integral to the analytical framework, yielding tens of thousands of quantitative imaging biomarkers indicative of texture, shape, and intensity distribution.

Through a methodical feature selection process, the researchers distilled these extensive datasets down to the most predictive radiomics signatures: 28 key features from 2D ROIs and 15 from 3D ROIs. Logistic regression classifiers were then employed to build radiomics signatures, with the 3D multiparametric model—encompassing combined T1WI, T2WI, and CE-T1 imaging—demonstrating superior predictive performance. Quantitatively, this 3D multi-sequence radiomics signature achieved an area under the receiver operating characteristic curve (AUC) of approximately 0.79 in both training and internal validation data, reflecting a high degree of accuracy.

Recognizing that radiomics alone might not capture the full clinical complexity, the research further integrated significant clinical risk factors—identified through univariate and multivariate analyses—with radiomic features to form comprehensive clinical-radiomics models. Notably, models incorporating both 2D and 3D ROI features alongside clinical data outperformed others, achieving AUCs nearing 0.89 during training and maintaining robust validation performance with AUCs above 0.81.

The construction of a nomogram based on these clinical-radiomics models offers a practical and intuitive tool for clinicians to apply preoperative consistency predictions in real-world settings. Especially valuable is the model’s validation on external, multicenter datasets, which underscores its generalizability and potential for widespread clinical deployment across diverse patient populations and imaging platforms.

The implications of this research extend beyond immediate surgical planning. Preoperative knowledge of tumor consistency could influence the choice of surgical approach—whether endoscopic or microscopic transsphenoidal surgery—anticipate the need for adjunctive treatments, or even guide biopsy decisions. Soft tumors typically afford easier resection and reduced operative time, whereas hard tumors may necessitate more complex maneuvers, underscoring the prognostic utility of this imaging-based predictive capability.

From a technical standpoint, the implementation of multiparametric MRI sequences ensures comprehensive tissue characterization by harnessing differences in tumor cellularity, vascularity, and necrotic components. Radiomics quantitatively captures these features, transcending the subjective interpretations of conventional radiology through sophisticated algorithms capable of pattern recognition and statistical modeling.

This effort exemplifies the growing fusion of artificial intelligence, medical imaging, and clinical oncology, where data-rich radiomic analyses complement traditional diagnostic pathways. The use of logistic regression classifiers, alongside rigorous feature selection and validation protocols, provides methodological robustness that paves the way towards clinical translation and integration into decision support systems.

Importantly, the study highlights the distinct predictive efficiencies between 2D and 3D ROI-based radiomics models, advocating for a combined approach to leverage the strengths of both dimensional analyses. The 3D models, for instance, may better capture the volumetric heterogeneity and spatial distribution of tumor texture, while 2D features can provide finer resolution details within specific slices.

Given the increasing prevalence of PitNETs and their clinical challenge, particularly due to variable tumor textures influencing surgical morbidity, these findings herald a new era of precision medicine in pituitary surgery. Surgeons equipped with preoperative knowledge of tumor consistency may optimize operative tactics, potentially reducing complications such as cerebrospinal fluid leaks, hemorrhage, or incomplete resections.

Future research directions suggested by these investigators include prospective validation studies, expansion into other tumor types exhibiting consistency-related surgical challenges, and integration with other omics data streams such as genomics and proteomics to enhance predictive modeling further.

In conclusion, this multicenter study robustly demonstrates that multiparametric MRI radiomics is a powerful, non-invasive modality for the preoperative prediction of PitNET consistency. The combination of advanced imaging techniques, comprehensive feature extraction, and sophisticated statistical modeling underpins a clinical tool with significant potential to improve the management paradigms of pituitary neuroendocrine tumors.

Article Title: Preoperative prediction of pituitary neuroendocrine tumor consistency based on multiparametric MRI radiomics: a multicenter study

Article References: Yang, Q., Wang, Y., Wu, J. et al. Preoperative prediction of pituitary neuroendocrine tumor consistency based on multiparametric MRI radiomics: a multicenter study. BMC Cancer 25, 1501 (2025). https://doi.org/10.1186/s12885-025-14799-1

Image Credits: Scienmag.com

DOI: https://doi.org/10.1186/s12885-025-14799-1

Cancer drug resistance arises when a subpopulation of tumor cells acquires or possesses intrinsic mechanisms that allow them to survive despite the administration of potent chemotherapeutic agents or targeted therapies. Historically, unraveling the precise dynamics of how these resistant phenotypes emerge and evolve during treatment has been hindered by technological limitations. Conventional methods often fail to capture the temporal and spatial complexity of tumor heterogeneity, leaving scientists with an incomplete picture of resistance evolution. The study led by Whiting, Mossner, Gabbutt, and their colleagues addresses this gap through an innovative methodology that integrates genetic barcoding with quantitative phenotypic analysis.

Genetic barcoding involves tagging individual cancer cells with unique DNA sequences, effectively labeling each cell as it undergoes proliferation and evolution. By sequencing these barcodes over time, researchers can track the lineage and abundance of distinct cellular clones within a tumor population. This precise lineage tracing enables the detection of subtle shifts in subclonal composition as selective pressures, such as drug treatments, reshape the tumor landscape. The study capitalizes on this to illuminate how phenotype dynamics unfold in a living cancer ecosystem subjected to evolving drug stress.

One striking revelation from this work is the observation that cancer cell populations do not invariably evolve resistance through the expansion of pre-existing resistant clones alone. Instead, there is a dynamic interplay among diverse phenotypes, with some lineages adapting through gradual phenotypic plasticity, while others harness genetic mutations that confer robust drug tolerance. The ability to quantify these dynamics at an unprecedented resolution offers a detailed timeline of resistance evolution, illustrating the heterogeneity and plasticity underlying tumor adaptation.

The research team employed a sophisticated experimental model system, wherein human cancer cell lines were genetically barcoded and then exposed to clinically relevant dosages of chemotherapeutic drugs. Over multiple treatment cycles, the composition and behavior of hundreds of thousands of individual clones were monitored using high-throughput sequencing and single-cell phenotypic profiling. Computational algorithms integrated these data to reconstruct lineage trajectories and phenotypic distributions, creating a temporal map of resistance emergence.

One of the most compelling technical achievements is their development of a computational framework capable of disentangling the intertwined effects of genetic and non-genetic factors on phenotype dynamics. Traditional genetic analyses often overlook the role of epigenetics, transcriptional states, and microenvironmental cues. By incorporating single-cell phenotyping alongside lineage tracing, the researchers demonstrate how transient, non-heritable phenotypic states contribute substantially to the early phases of drug resistance, potentially setting the stage for stable genomic alterations.

Furthermore, the quantitative approach allowed the researchers to deconvolute complex drug response behaviors, revealing that the timing and sequence of phenotypic changes are critical determinants in whether resistance stabilizes or dissipates. Certain subclones exhibited reversible drug-tolerant states that could transiently survive treatment, whereas others accumulated mutations solidifying resistance. This nuanced understanding underscores the importance of therapeutic scheduling and dosing strategies to outmaneuver cancer’s adaptive capacities.

From a translational perspective, this research lays the groundwork for real-time monitoring of tumor evolution in patients. The genetic barcoding technology, although currently applied in preclinical models, promises to be adapted for in vivo applications, potentially via circulating tumor DNA sequencing or tumor biopsies. By profiling the evolving phenotypic landscape of a patient’s tumor during therapy, clinicians might soon predict emergent resistance pathways and personalize treatment regimens accordingly to forestall relapse.

The implications of these findings extend beyond cancer drug resistance. The framework introduced here paves the way for studying phenotypic evolution in other areas of medicine, such as infectious diseases where pathogens develop antibiotic resistance, or in regenerative medicine where tissue stem cells evolve phenotypic heterogeneity. The integration of lineage tracing with functional phenotype measurement represents a new frontier in biology, merging genetics, biophysics, and computational science.

Moreover, this study challenges prevailing dogmas that have dominated cancer biology for decades. By illustrating that drug resistance is not merely a product of fixed genetic mutations but a continuum involving dynamic phenotypic plasticity, it calls for a paradigm shift in both research priorities and therapeutic development. Drugs designed solely to target genetic mutations might fall short unless they also address the underlying reversible phenotypic states that enable initial survival.

Intricately detailed in the experimental design is the use of advanced single-cell technologies, including fluorescence-activated cell sorting (FACS) and high-resolution microscopy, to phenotype cells alongside barcode sequencing. This multimodal analysis revealed subtle morphological and metabolic traits correlated with resistance states, providing biomarkers that could be exploited for diagnostic or therapeutic interventions. The ability to link phenotype and genotype at single-cell resolution is a pivotal advancement made possible by this work.

The scientific community will undoubtedly be watching with keen interest how these findings influence ongoing clinical trials and the development of next-generation cancer treatments. While genetic barcoding has primarily been a research tool, its emerging clinical relevancy is exciting. Future iterations may include integrating it with immunotherapy research, where phenotypic adaptation of tumor cells to immune pressures similarly challenges treatment durability.

In summary, Whiting and colleagues have delivered a seminal contribution to cancer biology with their meticulous quantitative analysis of phenotype dynamics during the evolution of drug resistance. By harnessing the power of genetic barcoding and sophisticated phenotypic measurements, they expose the layered complexity of tumor adaptation, offering hope for new diagnostic and therapeutic strategies capable of outpacing cancer’s rapid evolution. This landmark study marks a decisive step forward in the endeavor to transform cancer from a deadly adversary into a manageable chronic condition.

The road ahead will require integrating these insights with clinical workflows and expanding the technology to heterogeneous patient populations and diverse cancer types. Nevertheless, the framework established in this research sets an inspiring precedent—one where the intricate dance of cellular evolution can be observed, understood, and ultimately controlled. As the fight against cancer continues, such innovative approaches herald a new era of precision oncology grounded in deep mechanistic understanding.

Subject of Research: Dynamics of cancer drug resistance evolution studied through genetic barcoding and quantitative phenotypic analysis.

Article Title: Quantitative measurement of phenotype dynamics during cancer drug resistance evolution using genetic barcoding.

Article References:
Whiting, F.J.H., Mossner, M., Gabbutt, C. et al. Quantitative measurement of phenotype dynamics during cancer drug resistance evolution using genetic barcoding. Nat Commun 16, 5282 (2025). https://doi.org/10.1038/s41467-025-59479-7

Image Credits: AI Generated

One of the overarching themes of City of Hope’s presentations is the integration of artificial intelligence (AI) and multiomics to decode the complex biology of tumors. Professor David W. Craig, an authority in integrative translational sciences, chairs the final plenary session titled “Opportunities in Predictive Oncology.” This session will explore emerging computational and biological strategies that leverage multi-level tumor data to refine precision oncology. Dr. Craig’s work particularly focuses on melding diverse data types—including genomic, proteomic, and spatial information—to dissect tumor heterogeneity and treatment resistance, fundamental barriers in effective cancer therapy.

Dr. Craig also helms an educational session dedicated to AI and data science, spotlighting how multi-scale, multi-modal integration enhances understanding of cancer’s genetic diversity. Highlighting methods such as spatial transcriptomics and single-cell genomics, this session illustrates how dissecting the spatial architecture within tumors reveals subclonal variations influencing tumor progression and therapeutic response. Graduate researcher Nina Song from Dr. Craig’s laboratory will present novel findings demonstrating the power of AI to fuse digital pathology with genomic data, offering unprecedented insights into aggressive cancers like glioblastoma, triple-negative breast cancer, and colorectal cancer.

Natural killer (NK) cells represent another focal point of City of Hope’s scientific agenda at AACR 2025. Michael A. Caligiuri, M.D., former president of City of Hope National Medical Center, chairs critical educational sessions on the biology and clinical translation of NK cells. These innate immune lymphocytes have emerged as potent anti-cancer effectors, capable of recognizing and eradicating transformed cells without prior sensitization. Dr. Caligiuri’s presentations will delve into molecular mechanisms regulating NK cell function and therapeutic strategies leveraging NK cells as immunotherapy agents, reflecting City of Hope’s leadership in harnessing innate immunity to combat cancer.

In addition to immunology, City of Hope scientists are pioneering research in precision medicine for underserved populations, an imperative often overlooked in cancer research. Postdoctoral scientist Francisco Carranza will unveil multi-omics analyses dissecting the MYC oncogene and WNT signaling pathway alterations in early-onset colorectal cancer among Hispanic/Latino patients. By integrating genomic and spatial transcriptomics technologies, this research elucidates the molecular underpinnings of cancer disparities and guides development of tailored diagnostics and treatments informed by ethnic diversity.

Precision artificial intelligence tools for clinical oncogenomics represent a further area of innovation. Assistant Professor Enrique Velazquez Villarreal and collaborators have developed PM-AI Agent, a conversational AI system designed to integrate extensive clinical, genomic, and social determinants of health data. This tool aims to facilitate equitable precision oncology by accounting for population-specific variables and social factors, providing clinicians with actionable insights that transcend traditional data silos. Such integrative approaches promise to reduce disparities and optimize therapeutic decision-making in complex cancer cases.

City of Hope’s clinical trial portfolio also features prominently at AACR 2025, highlighting advances in antibody-drug conjugates (ADCs) and immune checkpoint inhibitors. Hope Rugo, M.D., newly appointed director of the Women’s Cancers Program, will present on managing toxicities associated with emerging ADCs, which combine targeted antibodies with potent cytotoxins to selectively eliminate cancer cells. Her expertise also extends to discussions on biologics and T-cell engagers, signaling ongoing efforts to refine immune-based therapies in breast and other cancers.

Among other high-impact clinical presentations, Aditya Shreenivas, M.D., M.S., will report phase 3 trial results for Penpulimab, a humanized anti-PD-1 monoclonal antibody evaluated as first-line treatment for recurrent or metastatic nasopharyngeal carcinoma. These findings could redefine therapeutic options for this aggressive malignancy by improving survival and tolerability in diverse patient populations, reflecting City of Hope’s commitment to global oncology.

Research connecting fundamental biology to therapeutic resistance mechanisms will be illustrated by Kimya Karimi, a postdoctoral scholar investigating ways to overcome cell cycle inhibitor resistance in estrogen receptor-positive (ER+) breast cancer. By combining epigenetic and molecular analyses, this work aims to restore endocrine therapy efficacy, addressing a significant clinical challenge in breast cancer management.

The conference also spotlights the vital role of community engagement in translating scientific discoveries into health policy and patient outcomes. Kimlin Tam Ashing, Ph.D., will elaborate on frameworks for fostering community alliances and partnerships that promote equitable cancer care delivery. This integration of social science with biomedicine embodies City of Hope’s holistic vision of research impacting patients beyond the laboratory.

Highlighted poster sessions further reveal City of Hope’s versatile expertise. Senior research associate Jing Qian will present spatial transcriptomic data unmasking differences in tumor and immune microenvironments among high-grade serous ovarian cancers, providing insights into variable responses to checkpoint blockade immunotherapies. Similarly, hematology fellow Peter Zang will explore spatial proteomic distinctions in metastatic prostate cancer across ethnicities, informing biomarker development and personalized treatment strategies.

Lastly, cutting-edge computational approaches to cancer prognosis are represented by a team including postdoctoral fellow Sydney Grant and assistant professor Aritro Nath. Their application of survival-based variational autoencoders to integrate multimodal data advances predictive modeling of recurrence-free survival in breast cancer patients, potentially guiding individualized risk assessment and therapeutic planning.

City of Hope’s robust presence at AACR Annual Meeting 2025 exemplifies its unwavering commitment to integrating state-of-the-art technologies, clinical trials, and community-driven approaches. By harnessing artificial intelligence, multiomics, and immunotherapy research, their scientists and clinicians are shaping the future landscape of cancer care, striving to transform hope into tangible cures for patients worldwide.

Subject of Research: Advances in cancer research and treatment integrating artificial intelligence, multiomics, and immunotherapy at City of Hope.

Web References:

• City of Hope
• AACR Abstracts Portal

Image Credits: City of Hope