The study, published in Nature Genetics, describes a clinically annotated, population-scale single-cell atlas built from bone marrow samples of 341 patients, spanning the full continuum of the disease from precursor conditions through newly diagnosed myeloma to relapsed and refractory disease after multiple lines of therapy. Using single-cell RNA sequencing enriched for plasma cells and the surrounding CD45-positive immune compartment, the researchers captured the transcriptomes of tens of thousands of individual cells, allowing them to dissect the malignant compartment cell by cell rather than averaging signals across bulk tumor tissue, which has historically masked the very heterogeneity that drives treatment failure.

Technically, the effort was formidable. Samples were processed using the MARS-seq platform and an updated version, MARS-seq2.0, with rigorous quality control on mitochondrial content, unique molecular identifier counts and detected genes. Cells were annotated through a computational pipeline combining scVI latent-space integration, uniform manifold approximation and projection embeddings, and inferCNV-based inference of copy number alterations to distinguish malignant plasma cells from their normal counterparts. The team then applied non-negative matrix factorization to decompose malignant gene expression into recurrent transcriptional programs, validated for stability through hundreds of bootstrapped iterations. This dual-layer analytical strategy allowed the investigators to separate two largely independent axes of tumor biology: what kind of myeloma a patient has, and how fast that myeloma is growing.

The first axis yielded five recurrent malignant transcriptional archetypes, designated MM1 through MM5, each corresponding to a stable pattern of gene expression anchored in distinct biological pathways. These archetypes align with known myeloma biology, including immunoglobulin heavy chain translocations such as t(11;14) with its cyclin D and BCL-2 dependencies, t(4;14) with NSD2 dysregulation, MAF and MAFB associated programs, and features reflecting unfolded protein response burden and bone marrow niche interactions. Crucially, the archetypes were not merely descriptive. They correlated with genomic features, therapeutic sensitivity patterns and clinical outcomes, and the team demonstrated that the classification could be ported to independent bulk RNA datasets, including the Blueprint cohort and the Multiple Myeloma Research Foundation’s CoMMpass cohort of treatment-naive patients, confirming that the single-cell-defined signatures retain prognostic power even when measured on standard clinical platforms.

The second axis, orthogonal to the archetypes, is a proliferative program. By scoring single-cell proliferation signatures and characterizing plasmablastic cells, the rapidly dividing precursors of antibody-secreting plasma cells, the researchers quantified the fraction of malignant cells actively cycling in each patient’s marrow. Proliferation has long been recognized as a poor prognostic marker in myeloma, measured historically by crude methods such as plasma cell labeling indices. The new work refines this concept at single-cell resolution, showing that the proportion of proliferating malignant plasma cells stratifies patients within every archetype, revealing intra-archetypal heterogeneity that earlier bulk approaches could not detect. In relapsed and refractory patients, higher proliferative fractions predicted shorter progression-free survival, and the effect persisted in multivariate Cox regression models adjusting for cytogenetic risk, age and prior treatment lines.

Combining the two axes produced an improved risk stratifier that outperformed existing molecular subtyping schemes. Patients could be placed into joint archetype-proliferation subgroups with meaningfully distinct progression-free and overall survival, and the framework added prognostic information beyond standard clinical variables including high-risk cytogenetics and chromosome 1p deletion. The validation in CoMMpass, one of the largest longitudinally followed myeloma cohorts in the world, demonstrated robustness and portability across sequencing platforms, an essential prerequisite for clinical translation. In principle, a myeloma patient’s tumor could one day be assigned to an archetype and proliferation state from a routine biopsy, guiding intensity of upfront therapy and informing decisions about transplantation, novel agents or early escalation.

Perhaps the most clinically electrifying result, however, came from the atlas’s use as a target-discovery engine. The team built a computational pipeline that ranked every protein-coding gene by a composite score integrating malignant enrichment, specificity for malignant plasma cells relative to normal plasma cells, and restriction across healthy tissues, the latter being critical to minimize off-tumor toxicity for any future immunotherapy. This screen surfaced FCRL2, an Fc receptor-like molecule with established roles in B cell biology, as a surface target expressed by malignant plasma cells but largely restricted to the B cell lineage elsewhere in the body. The atlas approach meant the researchers could verify not just that myeloma cells express FCRL2, but that expression is preserved across archetypes and proliferation states, addressing the antigen escape problem that plagues current myeloma immunotherapies.

The translational proof followed swiftly. The researchers engineered chimeric antigen receptor T cells directed against FCRL2 and tested them against myeloma cell lines with varying levels of target expression. In vitro, FCRL2-redirected CAR-T cells killed antigen-positive myeloma cells in an antigen-specific manner, with luciferase-based co-culture assays showing progressive suppression of tumor cell growth compared to non-transduced controls, and detailed immunophenotyping confirming proper CAR expression and memory-phenotype differentiation of the engineered cells. In mouse models, FCRL2-targeted CAR-T cells conferred a significant survival benefit. Given that existing myeloma immunotherapies targeting BCMA and GPRC5D eventually fail through antigen loss and relapse, a third lineage-restricted target backed by a genome-wide, single-cell-verified prioritization pipeline offers a credible path toward combination or sequential immunotherapy strategies.

For patients, the near-term significance is prognostic rather than therapeutic: an archetype and proliferation score could refine risk assessment well before relapse, when treatment decisions matter most. For the field, the study establishes a template for how population-scale single-cell atlases can move beyond description into actionable classification and target nomination. All of the underlying data, including the full scRNA-seq dataset deposited in the Gene Expression Omnibus and the analysis code released openly by the Amit lab, are publicly available, ensuring that other groups can interrogate, extend and challenge the framework. As single-cell sequencing costs fall and clinical grade assays mature, the line between research atlases and routine diagnostics grows thinner, and this myeloma atlas may be remembered as a turning point where that line was crossed for a historically intractable cancer.

Subject of Research: Single-cell transcriptomic atlas of multiple myeloma defining malignant archetypes, proliferative states, and the immunotherapy target FCRL2

Article Title: A single-cell atlas of multiple myeloma defines malignant archetypes and proliferative states

Article References: Zada, M., Kurilovich, A., Shapira, N., Wang, S.-Y., Sharet-Eshed, R., Kfir-Erenfeld, S., Schlossberg, M., Zorde, E., Asherie, N., Gur, C., Chalan, P., Shalita, R., Ben Yehuda, M., Zwicky, P., von Locquenghien, M., Ingelfinger, F., Mazuz, K., David, E., Gurevich-Shapiro, A., … Amit, I. (2026). A single-cell atlas of multiple myeloma defines malignant archetypes and proliferative states. Nature Genetics, 58(9), 2254-2269. https://doi.org/10.1038/s41588-026-02725-5

Image Credits: AI Generated

DOI: 10.1038/s41588-026-02725-5

Keywords: multiple myeloma, single-cell RNA sequencing, transcriptional archetypes, proliferation, FCRL2, CAR-T cell therapy, risk stratification, plasma cells, Nature Genetics, precision medicine, CoMMpass cohort, target discovery

The scale of the problem is considerable. Breast cancer accounted for approximately 2.308 million new cases worldwide in 2022, representing 11.6 percent of all new cancer diagnoses and making it the second most common cancer globally. Chemotherapy remains a cornerstone of treatment for early-stage disease, but its toxicities—ranging from severe neutropenia to peripheral neuropathy—can force dose reductions, treatment interruptions, or complete discontinuation, ultimately compromising prognosis and quality of life.

The review, led by researchers at The First Affiliated Hospital of Jinzhou Medical University in China, synthesizes evidence on how body composition indicators relate to chemotherapy toxicity. The authors trace the evolution of assessment methods from crude anthropometric surrogates—such as the five percent weight-loss threshold once used to mark cachexia in the 1970s—to today’s sophisticated imaging tools. Computed tomography, magnetic resonance imaging, dual-energy X-ray absorptiometry (DXA), and bioelectrical impedance analysis (BIA) now allow clinicians to quantify skeletal muscle, visceral fat, subcutaneous fat, and muscle quality with remarkable precision, and artificial intelligence is increasingly automating these analyses.

Central to the discussion is the distinction between lean body mass and fat-free mass, terms that are chemically similar but historically defined differently—lean body mass includes polar lipids, while fat-free mass does not. The authors recommend prioritizing fat-free mass in research to improve scientific rigor. They also highlight the third lumbar vertebra skeletal muscle index (L3-SMI), calculated from a single CT slice as skeletal muscle area at L3 divided by height squared, which was first reported in 2008 as an independent predictor of chemotherapy toxicity. Notably, DXA-derived appendicular lean mass indices and CT-based L3-SMI correlate only moderately (r = 0.66, p < 0.001), meaning the two metrics are not directly interchangeable—a source of ongoing confusion in the literature.

The mechanistic story is where the review becomes particularly compelling. Muscle is highly vascularized and metabolically active, so patients with greater lean mass tend to metabolize and clear drugs more efficiently. Pharmacokinetic studies bear this out: each additional kilogram of lean body mass increased doxorubicin clearance by roughly 19 percent in an exploratory study, and lower muscle mass was associated with reduced volume of distribution and higher peak plasma concentrations of paclitaxel. Low lean mass can also reduce creatinine production, causing the Cockcroft-Gault formula to overestimate renal function—a hazard flagged by a creatinine clearance to glomerular filtration rate ratio of 1.23 as a warning threshold for carboplatin overdose and severe thrombocytopenia.

Fat tells a different, sometimes paradoxical story. Lipophilic agents such as paclitaxel and docetaxel distribute into adipose compartments, while hydrophilic drugs like fluoruracil and cyclophosphamide prefer water-rich lean tissue. Visceral fat volume was positively correlated with doxorubicin exposure (r² = 0.324, P < 0.001) and grade 4 leukopenia in Asian breast cancer patients. Experimental work suggests adipocytes can increase anthracycline levels by 30 percent by upregulating CBR1 and AKR metabolic enzymes, sustaining the release of toxic metabolites. Visceral fat-derived free fatty acids also reach the liver through the portal circulation, potentially inducing hepatic steatosis and impairing drug metabolism—liver attenuation on CT, inversely related to fat content, predicted epirubicin exposure in one analysis.

The clinical correlations are striking. In early-stage breast cancer patients, higher fat mass increased the risk of toxicity-induced modification of treatment—dose reductions, interruptions, cessation, or regimen changes—while higher relative lean mass reduced that risk. Obese patients (BMI ≥ 30 kg/m²) experienced docetaxel dose reductions at 18 percent versus 5 percent in nonobese patients (p = 0.008), along with lower pathological complete response rates and shorter disease-free survival. Sarcopenia, which affects an estimated 40 to 45 percent of breast cancer patients, independently predicted severe toxicity: sarcopenic patients receiving epirubicin-cyclophosphamide experienced severe laboratory adverse events at 70 percent versus 22.2 percent (OR 7.9, p = 0.004). Myosteatosis—fat infiltration within muscle, visible as lower Hounsfield units on CT—was associated with reduced relative dose intensity and with dose reductions, early treatment interruption, and hospitalization.

Perhaps the most alarming phenotype is sarcopenic obesity, the coexistence of excessive adiposity with reduced muscle mass and impaired function, as defined by the ESPEN-EASO consensus. In early-stage breast cancer patients receiving anthracycline and taxane chemotherapy, sarcopenic obesity independently predicted severe toxicity, tripling the risk of grade 3–4 hematological toxicity and raising the risk of neutropenia 3.5-fold. Prevalence estimates vary widely—from 0.8 to 22.3 percent in general populations—partly because diagnostic thresholds remain inconsistent, with more than 14 sarcopenia cutoffs reported across oncology studies.

The review does not shy away from the field’s contradictions. Adipose tissue can exert bidirectional effects: in one study of 120 patients receiving neoadjuvant chemotherapy, higher fat percentage correlated with reduced neurotoxicity risk, though no significant interaction appeared in platinum-containing regimens. Chemotherapy itself alters body composition over time, and most studies rely only on baseline measurements, potentially underestimating true toxicity risk. The authors also point to the LEANOX randomized controlled trial as proof of concept: lean body mass-based oxaliplatin dosing at 3.09 mg/kg increased the proportion of patients free of grade ≥ 2 peripheral neurotoxicity from 42.1 to 67.2 percent, without compromising long-term survival—evidence that composition-guided dosing is clinically practicable, at least for some drugs.

Looking forward, the authors call for regimen-specific pharmacokinetic modeling across anthracyclines, taxanes, and platinum agents; risk stratification that integrates breast cancer subtypes with visceral-to-subcutaneous fat ratios and muscle indices; prospective trials of nutritional optimization and resistance training in high-risk patients; and international consensus on definitions and cutoffs, potentially enriched with multi-omics biomarkers. They suggest DXA, a low-radiation whole-body scan, could eventually replace CT for routine body composition assessment in early cancer, where L3-level CT scans are not standard. Until prospective, breast cancer-specific studies validate these approaches, body surface area dosing will remain the norm—but the writing is on the wall, and it is written in muscle and fat.

Subject of Research: The relationship between body composition and chemotherapy-related toxicity in early-stage breast cancer

Article Title: Body composition and chemotherapy-related toxicities in early-stage breast cancer: implications for personalized treatment strategies

Article References: Body composition and chemotherapy-related toxicities in early-stage breast cancer: implications for personalized treatment strategies. (n.d.). https://doi.org/10.1007/s44178-026-00287-4

Image Credits: AI Generated

DOI: 10.1007/s44178-026-00287-4

Keywords: breast cancer, body composition, chemotherapy toxicity, sarcopenia, sarcopenic obesity, lean body mass, visceral fat, myosteatosis, pharmacokinetics, personalized dosing, DXA, CT imaging

Cyclin-dependent kinase 4/6 inhibitors, known as CDK4/6 inhibitors, work by blocking two enzymes that drive the cell division cycle. In hormone receptor-positive breast cancer, tumor cells rely heavily on signaling through cyclin D and the kinases CDK4 and CDK6 to proliferate, and pairing one of these inhibitors with endocrine therapy such as an aromatase inhibitor or fulvestrant has been shown in landmark phase III trials—including PALOMA, MONALEESA, MONARCH, and DAWNA—to dramatically extend the time patients live without their disease progressing. Yet pivotal clinical trials enroll carefully selected patients under tightly controlled conditions, and the outcomes of ordinary patients in routine clinical practice, who are often older, have more comorbidities, or fall outside trial eligibility criteria, can differ substantially. That gap between trial efficacy and real-world effectiveness is precisely what the new study was designed to address.

The retrospective multicenter analysis drew on records from patients treated at 20 cancer centers across central China, making it one of the most geographically diverse real-world datasets of its kind. CDK4/6 inhibitors were used as first-line therapy in 65.68 percent of the cohort and as second-line treatment in 24.60 percent, with the remainder receiving the drugs later in their treatment course. The primary endpoint was progression-free survival, the length of time a patient lives without evidence of tumor growth or spread, assessed using imaging criteria and Kaplan–Meier statistical methods. The findings confirmed a striking advantage for earlier use: median progression-free survival reached 38.0 months in patients who received a CDK4/6 inhibitor as their first systemic treatment for metastatic disease, compared with 18.8 months among those who began the drugs only after prior lines of therapy had failed, a difference that was highly statistically significant with a P value below 0.001. In other words, patients who received the drugs first lived roughly twice as long without progression.

Beyond treatment timing, the investigators used multivariable Cox regression analysis to identify which patient characteristics independently shaped prognosis. Cox regression is a statistical technique that estimates the effect of multiple variables simultaneously on the risk of an event such as disease progression, while accounting for the fact that not all patients have been followed for the same length of time. Three factors emerged as adverse prognostic markers: having the Luminal B molecular subtype of breast cancer, which tends to be more aggressive than Luminal A disease; the presence of liver metastases, a known indicator of higher disease burden; and receiving the CDK4/6 inhibitor as second-line rather than first-line treatment. Conversely, two features were associated with better outcomes: tumors with HER2 immunohistochemistry score of 1+, a faint level of HER2 protein expression sometimes called HER2-low, and a longer disease-free interval between the initial diagnosis and the development of metastatic disease. Each of these findings aligns with, and extends, signals from smaller studies conducted in Europe, Japan, and North America.

To translate these population-level findings into a tool usable at the bedside, the team split patients receiving first- or second-line CDK4/6 inhibitors into a training cohort and a validation cohort in a seven-to-three ratio. On the training data they built a conventional Cox regression model and seven distinct machine learning algorithms designed for survival data: gradient boosting machines (GBM), random survival forests (RSF), Lasso-Cox, CoxBoost, XGBoost, super principal component analysis (SuperPC), and partial least squares regression for Cox data (plsRcox). These methods differ in how they handle complexity. Random survival forests, for example, grow many decision trees on bootstrap samples of the data and average them to capture non-linear relationships, while gradient boosting builds an ensemble of weak learners sequentially, each correcting the errors of the last. Lasso-Cox applies a penalty that shrinks coefficients and performs variable selection automatically, guarding against overfitting in datasets with many correlated predictors.

Model performance was evaluated using three complementary approaches: time-dependent area under the receiver operating characteristic curve (AUC), which measures discrimination, meaning the ability to correctly rank patients who progress sooner above those who progress later; calibration plots, which test whether predicted probabilities match observed outcomes; and decision curve analysis, which quantifies the clinical net benefit of acting on the model’s predictions at various risk thresholds. The conventional Cox model achieved respectable discrimination, with AUCs of 0.731, 0.719, and 0.704, values that indicate clinically meaningful predictive accuracy without reaching the level of certainty that would justify replacing clinician judgment. Among the machine learning approaches, gradient boosting machines and random survival forests showed the highest discrimination in the training cohort but settled into only moderate performance when tested on the held-out validation cohort, a pattern that reflects the classic challenge of overfitting, in which flexible algorithms memorize quirks of the training data that do not generalize to new patients.

The comparison between the Cox model and the machine learning alternatives carries a broader lesson for the field of computational oncology. Machine learning methods are often assumed to outperform classical regression simply because they are more sophisticated, but the evidence from survival prediction research is mixed, and recent systematic reviews have found that the two approaches frequently perform comparably when applied to modest-sized clinical datasets. The authors of the new study conclude that both the Cox model and the machine learning frameworks enable individualized prognostic prediction for CDK4/6 inhibitor therapy, but they emphasize that the GBM and RSF models performed relatively better and that external validation in independent patient populations remains essential before any of the tools can be deployed in routine clinical practice. This cautious stance mirrors the standards set by the TRIPOD reporting guidelines, which require transparent documentation of prediction model development and validation.

The study’s real-world effectiveness data carry important implications for treatment sequencing guidelines. Because median progression-free survival was double in the first-line setting, the findings reinforce the strategy of deploying CDK4/6 inhibitors upfront in combination with endocrine therapy rather than reserving them for later lines, consistent with the design of trials such as PALOMA-2, MONALEESA-2, MONARCH 3, and DAWNA-2. The finding that HER2-low tumors fared better adds to a growing body of evidence that the HER2-low subgroup, which was historically lumped together with HER2-zero disease, may represent a biologically and clinically distinct entity, with consequences for eligibility for novel antibody-drug conjugates as well. Meanwhile, the adverse prognostic weight of liver metastases and Luminal B biology provides clinicians with concrete variables to weigh when counseling patients and planning surveillance intensity.

The research also has significance for Chinese and other Asian patient populations, where locally relevant real-world evidence has historically been thinner than in Western Europe and North America. The cohort included patients treated with agents available in China, and the treatment patterns observed—first-line use in roughly two-thirds of patients—suggest substantial but incomplete uptake of guideline-concordant sequencing. The study protocol was registered at ClinicalTrials.gov, conducted under the Declaration of Helsinki, and approved by the Ethics Committee of Hunan Cancer Hospital, which waived the requirement for individual written informed consent given the retrospective, anonymized nature of the data. Funding came from the Hunan Provincial Natural Science Foundation, Hunan Cancer Hospital programs, and two Chinese medical foundations, and the authors declared no competing interests.

For patients with hormone receptor-positive, HER2-negative metastatic breast cancer, the most immediate message is one of cautious optimism: in the messy reality of everyday oncology, CDK4/6 inhibitors deliver substantial benefit, with first-line patients in this large cohort living a median of more than three years without progression. For the oncology community, the study demonstrates a rigorous template for building prognostic tools from real-world data, combining the interpretability of classical survival regression with the flexibility of modern machine learning. And for the rapidly expanding field of AI-assisted medicine, it serves as a measured reminder that predictive power must be validated, calibrated, and externally confirmed before an algorithm earns a place in the clinic. As external validation cohorts are assembled, the models described in this work may eventually help oncologists answer one of the most practical questions in metastatic breast cancer care: which patient, with which tumor, is likely to benefit most, and for how long, from these transformative drugs.

The platform relies on patient-derived organoids, three-dimensional clusters of tumor cells grown in a supportive gel that recapitulate key architectural and molecular features of the original cancer. Organoids have generated enormous enthusiasm in precision oncology because they allow clinicians to screen multiple drugs against a living surrogate of a patient’s tumor before committing that patient to a regimen. Yet the conventional route to building them, which begins with surgically resected or biopsied tissue, carries substantial drawbacks. Tissue acquisition is invasive, often requires a procedure that may not be clinically justified, and yields samples with low tumor cellularity. The resulting cultures can be contaminated with stromal and immune cells that dilute the tumor-specific signal, and establishment rates for pancreatic cancer organoids have historically been frustratingly low.

The Yonsei University team, led by researchers from the Division of Gastroenterology in collaboration with the Departments of Pathology and Hepatobiliary and Pancreatic Surgery at Severance Hospital, took a different route entirely. Rather than solid tissue, they started with malignant effusions, the pleural fluid that accumulates around the lungs and the ascitic fluid that pools in the abdomen of patients with advanced pancreatic ductal adenocarcinoma. These fluids are collected routinely for symptom management through minimally invasive drainage procedures, meaning that the raw material for organoid culture is essentially a clinical byproduct. Because the fluid already contains free-floating tumor cells shed from metastatic deposits, the researchers reasoned that it could serve as a rich, relatively pure starting inoculum.

Their reasoning proved correct. Fluid-derived organoids, or FDOs, established from these effusions grew faster than organoids generated from matched tissue samples, showed a higher establishment success rate, and carried markedly less non-tumor contamination. The comparison was not simply a matter of convenience. The team performed extensive quality control to demonstrate that FDOs faithfully mirror the biology of the parental tumors. Histopathological examination of hematoxylin and eosin stained sections showed that the organoids retained the glandular architecture characteristic of pancreatic ductal adenocarcinoma. Immunostaining for cytokeratin 7, an epithelial marker expressed in pancreatic ductal cells, confirmed ductal origin. Critically, mutation analysis confirmed that the organoids carried the same KRAS driver mutations as the original tumors. Since activating mutations in KRAS, most commonly at codon 12, occur in the vast majority of pancreatic cancers and anchor much of the field’s targeted drug development, this genetic concordance is essential for the model to have any translational value.

To characterize organoid morphology and drug response in fine detail without destructive processing, the researchers turned to holotomography, a label-free imaging technique that uses coherent light to reconstruct three-dimensional refractive index maps of living cells. This allowed quantitative measurement of cellular and organoid morphology and of how the structures changed in response to drug exposure, complementing conventional viability assays.

One of the most clinically significant demonstrations involved MRTX1133, a selective inhibitor of the KRAS G12D mutant protein. KRAS G12D is among the most common KRAS variants in pancreatic cancer, and MRTX1133 has emerged as a preclinical benchmark for direct KRAS targeting in this tumor type. In the study, FDOs harboring the KRAS G12D mutation showed marked sensitivity to the inhibitor, confirming that the fluid-derived platform can reproduce the drug-response behavior expected of a genetically defined tumor. The result establishes a proof of concept that FDOs can serve as a rapid and scalable test bed for emerging targeted agents, potentially shortening the path from genetic diagnosis to an individualized treatment decision.

The second major contribution of the study goes beyond the platform itself and into the molecular roots of chemotherapy failure. Gemcitabine, a nucleoside analog that has anchored pancreatic cancer chemotherapy for years, frequently stops working as tumors evolve resistance. To understand why, the team performed transcriptomic profiling, comparing gene expression in FDOs that responded to chemotherapy with expression in those that did not. Gene set enrichment and differential expression analysis converged on three genes that were consistently upregulated in the resistant cultures: CEMIP, which encodes cell migration inducing hyaluronidase 1; CALB2, which encodes calbindin 2, also known as the heart and neural crest derivatives expressed protein; and LY6D, a member of the lymphocyte antigen 6 family of glycosylphosphatidylinositol-anchored cell surface proteins.

Expression alone does not prove causation, so the researchers moved to functional validation. When they manipulated the activity of these genes in pancreatic cancer cell lines, the results were unambiguous: elevated CEMIP, CALB2, and LY6D suppressed apoptosis, the programmed cell death pathway that gemcitabine is designed to trigger, and thereby conferred resistance to the drug. CEMIP in particular has been previously implicated in hyaluronic acid metabolism and epithelial-mesenchymal transition, processes that pancreatic tumors exploit to remodel their microenvironment and escape cytotoxic stress. The new findings place all three genes squarely in the mechanistic chain linking cellular stress to survival.

The clinical implications of the three-gene signature were reinforced by outcome data. In analyses of patient cohorts, high expression of the CEMIP, CALB2, and LY6D signature correlated with worse progression-free survival and worse overall survival, indicating that the same genes that protect organoids from gemcitabine in a dish are associated with poorer outcomes in patients. This dual role, as both a mechanistic driver and a prognostic marker, is what gives the finding its translational weight. A test measuring the three-gene signature could in principle identify patients unlikely to benefit from standard chemotherapy, steering them toward alternative regimens or clinical trials of targeted and resistance-overcoming strategies. The genes themselves also represent candidate therapeutic targets, since interfering with their activity might restore sensitivity to apoptosis-inducing drugs.

The work also carries broader implications for how organoid models are built across oncology. Effusions are not unique to pancreatic cancer; malignant pleural and peritoneal effusions arise in ovarian, gastric, lung, and breast cancers, among others. A methodology that converts a routine drainage procedure into a high-fidelity drug-screening platform within days rather than weeks could be adapted widely, particularly for patients with advanced disease for whom tissue biopsy is impractical or unsafe. The scalability of the approach addresses one of the persistent bottlenecks of precision oncology: the sheer logistics of generating a personalized model quickly enough for it to influence a treatment decision made under time pressure.

The study was conducted under ethical approval from the Institutional Review Board of Yonsei University with written informed consent from all patients, and it was supported by grants from the National Research Foundation of Korea and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute. The research article was published as an accepted, citable open-access version carrying a permanent digital object identifier, with the final version of record to follow.

Taken together, the findings advance pancreatic cancer research on two fronts simultaneously. They provide a minimally invasive, rapid, and genetically faithful organoid platform derived from malignant effusions, validated against a state-of-the-art KRAS targeted inhibitor. And they expose a concrete molecular mechanism of chemotherapy resistance, distilled into a three-gene signature with demonstrated prognostic power. For a disease in which treatment options remain scarce and clinical timelines are unforgiving, tools that accelerate both drug selection and biomarker discovery are welcome indeed. The next steps, which the researchers and the field more broadly will be watching closely, involve prospective validation of the gene signature in larger patient cohorts and exploration of whether targeting CEMIP, CALB2, or LY6D can resensitize resistant tumors to gemcitabine and other cytotoxic agents.

The approach addresses a central challenge in modern medicine: patients generate large amounts of clinically important data, but those data are frequently stored in separate systems and interpreted independently. A CT scan may reveal the physical structure of a tumour, while genomic testing may indicate the molecular pathways driving its growth and clinical notes may document symptoms, treatment history and adverse reactions. Conventional artificial intelligence tools often analyse only one of these sources, limiting their ability to capture the biological complexity of cancer. PolyU’s system instead uses multimodal data integration, allowing algorithms to examine relationships among imaging features, biomarkers, genomic profiles and treatment outcomes.

At the centre of the platform is a patient-centric “digital twin”—a computational model that is updated as new information becomes available. In principle, this model can reflect changes in disease status, laboratory measurements, symptoms and other clinically relevant signals. Its purpose is not simply to archive information, but to support predictive analysis. When a patient’s condition changes, the system can help healthcare professionals assess possible future trajectories and compare the potential effects of alternative treatment plans. Such simulations could be particularly valuable in cancer and critical care, where disease progression may be rapid, therapeutic options may carry serious risks and decisions often require input from multiple medical specialties.

The platform includes tools for healthcare professionals as well as a mobile application for patients. Doctors can use the clinical interface to assemble a more comprehensive view of a patient’s history and to support diagnosis, monitoring and treatment assessment. The system is also intended to facilitate multidisciplinary consultations and referrals by making relevant information easier to review across clinical teams. Through the patient-facing application, individuals can upload medical records, record daily symptoms and follow changes in their health status. This design aims to shift patients from passive recipients of care toward active participants in managing their conditions, while giving clinicians access to a more continuous stream of patient-reported information.

Data exchange is supported by an encrypted Deep Feature QR code, which the team says can help transfer medical information securely across clinics, hospitals and devices. The technology is intended to improve interoperability without abandoning privacy protections, a critical requirement for any system handling genomic and clinical data. However, secure transmission is only one part of responsible medical AI. Systems used in healthcare must also address data quality, consent, cybersecurity, algorithmic bias and the interpretability of predictions. PolyU’s platform is being developed as a decision-support tool rather than a replacement for clinical judgment, with healthcare professionals remaining responsible for interpreting results in the context of each patient’s circumstances.

To demonstrate the system’s potential in oncology, the researchers introduced a clinical, data-driven, multiscale framework for predicting responses to immunotherapy in people with non-small cell lung cancer (NSCLC). The framework, called the Visual-Global Relation Fusion Network, or ViGNet, is designed for digital pathology and combines histopathological image features with clinical information. Its inputs include gene-expression profiles and cancer-related text, allowing the model to connect microscopic tissue patterns with molecular and clinical factors associated with treatment response. This is technically important because immunotherapy outcomes can depend on interactions between tumour biology, the surrounding immune environment and patient-specific clinical characteristics.

ViGNet uses a multiscale visual encoder to examine pathology images at different levels of detail. At a fine scale, the model can identify cellular and tissue-level patterns; at broader scales, it can assess the organisation of tumour regions and their surrounding microenvironment. A separate gene-driven encoder processes molecular information, while the fusion architecture seeks to establish relationships between visual and genomic representations. By combining these data streams, the model is intended to identify features that may be difficult to recognise when pathology images or molecular profiles are analysed in isolation. The result is a prediction framework aimed at distinguishing patients more likely or less likely to benefit from immunotherapy.

In qualitative and quantitative evaluations, the researchers reported that ViGNet outperformed baseline approaches in response classification, achieving a reported discrimination performance of 82.55% in predicting immunotherapy response. The result suggests that multimodal integration may improve the ability of machine-learning systems to extract clinically relevant signals from complex cancer datasets. At the same time, a performance figure alone does not establish that a model is ready for routine clinical use. Independent validation across hospitals, patient populations and imaging platforms will be necessary, along with prospective studies examining whether AI-supported predictions actually improve treatment decisions and patient outcomes. The reliability of such systems also depends on the quality and representativeness of the data used to train them.

Prof. Lawrence Chan, associate professor in PolyU’s Department of Health Technology and Informatics and leader of the research team, described the AI Virtual Patient Simulation System as a platform combining diagnosis, monitoring and treatment assessment. According to Chan, the system may help identify subtle pathological relationships across multimodal datasets and act as a monitoring “sentinel” by alerting care teams when biomarkers or symptoms become abnormal. Earlier recognition of clinically significant changes could help shorten assessment times and support more precise treatment planning, although the practical value of these alerts will depend on careful clinical validation and integration into existing workflows.

The project was recently showcased at Mobile World Congress 2026 in Barcelona, where it was shortlisted as a finalist for the 2026 Global Mobile Awards in the category of Best Mobile Innovation for Connected Health and Wellbeing. PolyU reports that the work has received support from its Micro Fund and Seed Fund, as well as the Greater Bay Area Innovation and Entrepreneurship Incubation Programme. The project has also been conditionally accepted into the Hong Kong Science and Technology Park’s Incubation Programme and is moving toward commercialisation and industrialisation. If clinical deployment expands, the resulting real-world data could support drug development, clinical trials and treatment optimisation. The research team’s ViGNet study has been published in Medical Image Analysis, adding a peer-reviewed foundation to a broader effort to make personalised, continuously updated cancer intelligence part of future healthcare.

Subject of Research: Artificial intelligence, digital twins, multimodal medical data integration, digital pathology and personalised cancer treatment

Article Title: ViGNet: A clinical data-supported deep learning approach for NSCLC immunotherapy response prediction in digital pathology

Web References: https://www.sciencedirect.com/science/article/pii/S1361841526002239

References: Medical Image Analysis

Image Credits: PolyU

Keywords

Artificial intelligence, virtual patient, digital twin, cancer, non-small cell lung cancer, immunotherapy, digital pathology, medical imaging, genomics, biomarkers, personalised medicine, machine learning, multimodal data, clinical decision support

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

Cancer immunotherapy has transformed oncology by shifting part of the fight against tumors from conventional chemotherapy and radiation toward the patient’s own immune system. Among the most important advances are immune checkpoint inhibitors, which release molecular brakes that restrain T cells, and engineered or expanded T-cell therapies designed to target malignant cells. Yet these treatments remain ineffective for many patients. Some tumors lack the biological signals needed for T-cell recognition, while others create a hostile microenvironment that suppresses immune activity or evolve rapidly enough to escape attack.

T cells are specialized immune cells capable of identifying abnormal proteins displayed on the surface of cancer cells. After recognizing their targets, they form a close contact zone with the tumor cell, known as an immunological synapse, and release toxic molecules that can trigger the cancer cell to die. This process depends on a series of precisely coordinated interactions between the T cell and the tumor. If any part of that process is weakened—whether because the tumor hides its identifying markers, blocks immune signaling or resists cell death—the immune response may fail even when large numbers of T cells are present.

To search for ways to overcome these barriers, Puig-Saus’ laboratory has developed a drug screening platform capable of testing thousands of chemical compounds. Such platforms allow scientists to observe how individual molecules influence interactions between immune cells and cancer cells. Rather than examining only whether a drug kills tumor cells directly, the UCLA team can investigate whether a compound changes the biological relationship between the tumor and the immune system. This distinction is important because many promising immunotherapy-enhancing drugs may not be effective as standalone cancer treatments.

The screening effort has identified two leading candidates with complementary effects. One compound appears to strengthen the physical and functional interaction between T cells and cancer cells. By improving the formation or stability of the cellular contact needed for immune attack, the drug could help T cells deliver their destructive signals more effectively. This type of intervention may be especially valuable in tumors where immune cells reach the cancer but fail to establish a sufficiently strong or sustained response.

The second candidate acts primarily on tumor cells rather than directly modifying T cells. Preliminary findings suggest that it makes cancer cells more vulnerable to destruction by T cells. In technical terms, the drug may alter pathways controlling tumor-cell survival, stress responses or susceptibility to the molecular machinery released by activated immune cells. The compound could therefore increase the “killability” of cancer cells without requiring researchers to permanently reprogram or intensify the immune cells themselves, potentially offering a different route to improving treatment efficacy.

The new grant will support experiments in preclinical melanoma models to determine whether either compound can boost existing immunotherapies. Researchers will evaluate combinations with immune checkpoint inhibitors and T-cell-based treatments, measuring tumor growth, immune-cell activity, treatment durability and possible toxic effects. They will also study how the compounds work at the molecular level, seeking to identify the cellular pathways responsible for improved immune recognition or tumor destruction. Understanding those mechanisms will be essential for selecting appropriate patients and designing safe clinical trials.

Melanoma provides a particularly important testing ground because it can carry a high number of mutations, creating abnormal proteins that immune cells may recognize. Despite this vulnerability, melanoma can still suppress immune responses and develop resistance after an initial treatment benefit. A drug that restores the effectiveness of T cells or exposes a tumor’s hidden weaknesses could help extend responses in patients who do not benefit from current therapies or whose cancers return after treatment. The researchers will need to establish whether the compounds work broadly across genetically different melanomas or only in tumors with particular biological features.

“If successful, these drugs could significantly improve the effectiveness of current immunotherapies and help more patients benefit from these treatments,” Puig-Saus said. She is an associate professor of microbiology, immunology and molecular genetics and surgical oncology at the David Geffen School of Medicine at UCLA. She is also a member of the UCLA Broad Stem Cell Research Center and the UCLA Parker Institute for Cancer Immunotherapy. Because the compounds are being developed as partners for existing treatments rather than replacements for them, the strategy could potentially be adapted to other cancers in which immune evasion and resistance limit therapeutic success.

The project remains at the preclinical stage, and its compounds have not yet been established as safe or effective treatments for people. Many candidates that show promise in laboratory systems ultimately fail because they produce unexpected toxicity, lose activity in complex tumors or cannot be delivered at useful doses. The UCLA team’s upcoming studies will therefore examine both therapeutic benefit and safety while tracing the precise mechanisms involved. If the candidates continue to perform well, they could provide a foundation for future clinical development and offer a new way to make the immune system’s attack on cancer more precise, persistent and effective.

Subject of Research: Cancer immunotherapy enhancement using drug-based strategies for melanoma and potentially other cancers

Article Title: UCLA Team Receives $3.9 Million Grant to Develop Drugs That Could Strengthen Cancer Immunotherapy

Web References: https://www.uclahealth.org/cancer/members/cristina-puig-saus; https://www.uclahealth.org/cancer

Keywords: Immunotherapy, cancer immunology, immune system, immune response, cancer research, cancer, melanoma, skin cancer, T-cell therapy, immune checkpoint inhibitors

CUP is not a single disease but a clinical condition in which cancer has spread while its original site remains hidden. In many patients, doctors use imaging, pathology, immunohistochemistry and molecular tests to search for the primary tumour. Even after extensive investigation, however, the source may remain unknown. This creates a major treatment challenge because cancer therapies are often selected according to the tissue in which a tumour originated. A cancer that began in the lung, breast, bowel or pancreas may respond to very different drugs, yet CUP can prevent clinicians from making that distinction with confidence.

The CUP-COMP trial is focused on a technology known as liquid biopsy. Instead of requiring a tumour sample obtained through surgery or an invasive biopsy, liquid biopsy analyses biological material circulating in the blood. Tumour cells can release fragments of DNA into the bloodstream, including circulating tumour DNA, or ctDNA. These fragments may contain mutations, copy-number changes and other molecular abnormalities that reflect the biology of the cancer. By sequencing this material, researchers can search for patterns that may help classify a tumour, reveal potentially targetable alterations or indicate how the disease is changing over time.

The central question is not simply whether such a test can produce a technically impressive molecular profile. The trial is designed to investigate whether blood-based profiling can be delivered reliably and usefully in routine clinical pathways for patients with CUP. That distinction is crucial. A test may work in a laboratory but prove difficult to implement at a national scale if blood samples arrive too late, contain too little tumour-derived DNA, fail quality-control checks or produce results that clinicians cannot interpret within the time available for treatment decisions.

A prospective design allows the researchers to evaluate these issues as they occur, rather than relying only on stored samples or retrospective records. Patients can be followed through the process of consent, blood collection, sample transport, laboratory analysis and clinical reporting. This approach can reveal where delays and failures arise and whether the information generated is available at a moment when it might influence patient care. It also provides a framework for measuring how often blood samples yield an interpretable molecular profile and how consistently testing can be integrated across participating centres.

Technically, the approach may combine several layers of genomic information. DNA sequencing can identify mutations in genes that drive tumour growth or create vulnerabilities to targeted drugs. Copy-number analysis can detect gains and losses of DNA segments, while broader molecular signatures may offer clues about tumour lineage. Some platforms can also estimate the fraction of DNA in a blood sample that originates from the tumour, a measurement known as tumour fraction. When this fraction is low, a negative result may not mean that a mutation is absent; it may simply indicate that the test did not receive enough tumour-derived material to detect it.

That limitation is particularly important in CUP, where disease biology can vary widely and metastatic deposits may release unequal amounts of DNA into the circulation. Tumour burden, the location of metastases, treatment exposure and the biology of individual cancers can all affect ctDNA levels. Blood-based profiling therefore does not eliminate the need for clinical assessment, imaging or tissue pathology. Instead, it is being evaluated as an additional source of evidence that could complement established diagnostic methods and potentially reduce the time required to obtain molecular information.

The precision-medicine element of CUP-COMP reflects a broader shift in cancer treatment. Rather than assigning therapy solely according to the organ where a tumour started, oncologists increasingly seek molecular features that can be targeted directly. Alterations in genes involved in DNA repair, cell signalling or immune regulation may appear across cancers from different organs. If these abnormalities are detected in a patient with CUP, they could provide a rationale for considering a targeted therapy or an immunotherapy, although the clinical value of any proposed treatment must still be assessed through evidence, eligibility criteria and multidisciplinary review.

The study also addresses an important question of equity and scalability. Advanced molecular testing is not useful if it is available only at specialist institutions or to patients who can access highly centralised services. A national trial can test whether samples collected in different hospitals can be processed through a coordinated system and whether results can be returned in a consistent format. The findings may help establish the logistical requirements for wider adoption, including laboratory capacity, data interpretation, reporting standards and communication between molecular scientists and treating teams.

For patients facing a diagnosis in which the primary tumour cannot be found, the promise of a blood-based test is therefore measured not only in scientific novelty but in speed, accessibility and clinical clarity. CUP-COMP is evaluating whether molecular information can be obtained from a relatively simple blood draw and incorporated into real-world decision-making. Its significance will ultimately depend on whether the approach produces dependable results, identifies actionable biology and fits the demanding timelines of cancer care. By testing those questions prospectively, the study may help determine whether liquid biopsy can become a practical component of precision medicine for one of oncology’s most uncertain and difficult diagnoses.

Subject of Research: Blood-based molecular profiling and precision medicine for patients with Cancer of Unknown Primary (CUP).

Article Title: A prospective national precision medicine trial evaluating the feasibility of blood-based molecular profiling in patients with Cancer of Unknown Primary (CUP-COMP).

Article References: Conway, AM., Robinson, M., Concannon, M. et al. A prospective national precision medicine trial evaluating the feasibility of blood-based molecular profiling in patients with Cancer of Unknown Primary (CUP-COMP). Br J Cancer (2026). https://doi.org/10.1038/s41416-026-03519-6

Image Credits: AI Generated

DOI: 10.1038/s41416-026-03519-6

Keywords: Cancer of Unknown Primary, CUP, liquid biopsy, circulating tumour DNA, molecular profiling, precision medicine, cancer genomics, oncology, blood-based testing.

Yousefian, S., Schubert, ML., Minafra, A.R. et al. A distinct CAR-T cell phenotype mediates therapeutic response at limited doses.
Nat Commun 17, 7589 (2026). https://doi.org/10.1038/s41467-026-76068-4

https://doi.org/10.1038/s41467-026-76068-4

AMP represents professionals who develop and perform molecular testing used to guide diagnosis, treatment, and disease monitoring. In a field where laboratory decisions can directly affect clinical outcomes, leadership is closely tied to scientific rigor, education, and community-wide standards for quality.

Tafe is known for expertise spanning thoracic and gynecologic cancers and for applying molecular diagnostics to improve patient stratification. She leads the Laboratory for Clinical Genomics and Advanced Technologies (CGAT) at Dartmouth Hitchcock and directs the molecular tumor board at the Dartmouth Cancer Center, where genomic findings are translated into actionable clinical insights.

Her work reflects a broader shift in oncology: moving from single-gene assumptions toward multigene, high-dimensional profiles that capture tumor heterogeneity. In practice, this requires careful interpretation frameworks, laboratory validation strategies, and continuous coordination between clinicians and molecular testing experts.

“I attended my first AMP meeting as a resident and was immediately smitten with the organization and the field of molecular diagnostics,” Tafe said, describing how the community shaped her professional path. She joined AMP in 2007 and later contributed nearly continuously through committees and elected roles, culminating in serving as AMP president in 2023.

Beyond organizational service, Tafe has contributed to AMP’s scientific output by reviewing papers for The Journal of Molecular Diagnostics since 2010. AMP leadership emphasizes not only governance, but also technical stewardship—helping ensure that education and standards evolve alongside emerging assays and analytical methods.

AMP Chief Executive Officer Laurie Menser, CAE, said Tafe’s leadership has strengthened educational programming, strategic initiatives, and the organization’s culture. Menser highlighted her commitment to mentoring future leaders as the field rapidly advances.

The Jeffrey A. Kant Leadership Award is named for the late Jeffrey A. Kant, M.D., Ph.D., one of AMP’s founding members, its first president, and the first recipient of the AMP Leadership Award. Tafe will receive the award and a medallion at the AMP 2026 Annual Meeting & Expo in November in Seattle.

Subject of Research: Molecular diagnostics leadership in oncology
Article Title: Laura J. Tafe Receives AMP 2026 Jeffrey A. Kant Leadership Award
Web References: https://www.amp.org
Image Credits: Courtesy of Laura J. Tafe, M.D
Keywords: molecular diagnostics, oncology, leadership award, tumor board, clinical genomics