The tumor microenvironment, or TME, is far more than a passive backdrop for malignant cells. It is a dense, dynamic ecosystem of endothelial cells, immune cells, neurons, stromal cells, and secreted signaling molecules, all woven into an extracellular matrix composed of structural proteins such as collagen and elastin, adhesive glycoproteins like fibronectin and laminin, and charged polysaccharide chains. This matrix does not merely hold cells in place; it actively determines how fast tumors progress, how readily they metastasize, and how they respond to therapy. Proteoglycans within the matrix, for example, can remodel the biochemical landscape to open pathways through which cancer cells invade the bloodstream, a metastatic step known as intravasation. Once malignant cells reach distant tissues, they may even enter a quiescent, dormant state in the G0 phase of the cell cycle, evading both immune surveillance and chemotherapy until conditions favor renewed growth.

Hydrogels, the focus of the new review, are three-dimensional polymer networks formed by chemically or physically crosslinking hydrophilic building blocks such as hydroxyl, carboxyl, sulfonic acid, and amine groups. Their capacity to absorb large quantities of water gives them a softness and fluidity remarkably similar to soft biological tissue, which is precisely why tissue engineers have embraced them as extracellular matrix mimics. The choice of polymer backbone and crosslinking strategy directly dictates the mechanical strength, porosity, and degradation rate of the resulting material, allowing researchers to tune stiffness and biochemistry with a precision that animal models and plastic dishes cannot match. Hydrogels enable cells to interact with their surroundings on all sides, preserving the dynamic cell-matrix conversations that drive proliferation, migration, and intercellular communication.

The review classifies hydrogels into natural, synthetic, smart, and hybrid categories, each with distinct trade-offs. Natural polymers, including collagen, fibrin, alginate, hyaluronic acid, and chitosan, offer inherent bioactivity and enzymatic biodegradability because they resemble the extracellular matrix the body already knows how to process. Collagen, the most abundant structural protein in skin, cartilage, and bone, remains the workhorse of natural hydrogel matrices. Synthetic polymers such as polyethylene glycol, polyvinyl alcohol, and polyacrylic acid, by contrast, provide adjustable stiffness, reproducible composition, and regulatory pedigree, but they lack natural cell-binding sites and must be decorated with adhesive proteins. Concerns also persist about cytotoxic degradation products; PLGA, for instance, breaks down into acidic by-products that can lower local pH and provoke inflammation, while PEG can yield reactive species under long-term oxidative stress.

Hybrid systems attempt to capture the best of both worlds. PEG-heparin hydrogels, which combine a synthetic backbone with an anionic glycosaminoglycan, have emerged as strong alternatives to Matrigel, the widely used but compositionally ill-defined protein mixture derived from tumor cells. Compared with Matrigel, PEG-heparin platforms offer defined composition, greater reproducibility, and easier modification, and several have already advanced into preclinical and early clinical studies for drug delivery and tissue regeneration. Crosslinking chemistry adds another layer of control: physical crosslinking produces reversible, stimuli-responsive, and cytocompatible networks with modest mechanical strength, whereas covalent strategies such as photopolymerization, Michael addition, click chemistry, and enzymatic crosslinking deliver superior structural integrity and more predictable degradation kinetics.

The practical payoff of these materials is already visible across the five major cancer types examined in the review. In lung cancer, which claimed roughly one hundred twenty thousand American lives in 2024, a temperature-sensitive hydrogel integrating realgar arsenic sulfide with Fe3O4 magnetic nanoparticles created a combined imaging and thermotherapy platform, the first hydrogel formulation of its kind for that compound. Separately, an injectable hydrogel delivering the anti-angiogenic drug anlotinib through a hyaluronic acid-tyramine matrix inhibited endothelial cell proliferation and produced tumor suppression rates ten to twenty percent higher than the free drug across all tested doses, while reducing systemic toxicity. Matrigel-based cultures have even been used to test combined MEK inhibition and anti-PD-L1 immunotherapy in patient-derived three-dimensional spheroids, probing the tumor immune landscape outside the body.

Liver cancer models show a similar leap in physiological relevance. A three-dimensional culture system built from decellularized liver extracellular matrix hydrogel enhanced the self-renewal, migration, and drug resistance of HepG2 cells, unmasking cancer stem cell properties that flat cultures conceal and opening a route to stem cell-targeted drug discovery. A thermosensitive Pluronic F127 hydrogel co-delivering resveratrol microspheres and cisplatin enabled localized intraperitoneal chemotherapy, with resveratrol counteracting cisplatin’s side effects and prolonging drug retention in the abdominal cavity. Synthetic PEG-cysteine hydrogels and organic-inorganic PDMS-TEOS scaffolds, meanwhile, boosted albumin synthesis and urea production in hepatocyte-like cultures, markers of genuine liver function that monolayer systems routinely fail to sustain.

In breast cancer, a chitosan-based thermosensitive hydrogel seeded with 4T1 mouse mammary carcinoma cells produced tumors in mice that grew approximately 2.5-fold larger and weighed twice as much as those from conventional two-dimensional injections, while markedly elevating the cancer stem cell markers CD44 and CD24, demonstrating that the hydrogel platform genuinely replicates the in vivo niche. pH-sensitive chitosan nanocarriers extended the bioactivity of curcumin, a polyphenol crippled by poor solubility, and an L-alanine-derived hydrogel released doxorubicin specifically at acidic pH values characteristic of tumor tissue. An injectable, temperature-sensitive hydrogel loaded with titanium carbide nanoparticles even enabled laser-triggered photothermal therapy, heating tumors locally to kill cancer cells while sparing surrounding tissue.

Prostate and colon cancer applications extend the technology from disease modeling to clinical devices and microbiome engineering. Injectable hydrogel spacers such as SpaceOAR, placed between the prostate and rectum during radiotherapy, physically shield the bowel from radiation for three to six months before dissolving harmlessly, reducing rectal pain, bleeding, and other treatment toxicities. Matching hydrogel stiffness to the bone-like mechanical environment of prostate metastases, around 25 to 40 kilopascals, has proven essential for studying invasion and drug resistance. In colorectal cancer, pH-sensitive alginate beads reinforced with hydroxyapatite and magnetic nanoparticles protected the chemotherapy drug 5-fluorouracil from stomach acid and released it in the intestine, while hyaluronan-based organoid co-cultures combining patient-derived tumor cells with cancer-associated fibroblasts delivered personalized drug-testing platforms. Perhaps most strikingly, a living hydrogel embedded with the bacterium Thiobacillus denitrificans metabolizes immunosuppressive hydrogen sulfide in the colon tumor microenvironment into harmless sulfate, normalizing tumor vasculature and amplifying the efficacy of co-delivered camptothecin.

The review’s authors are candid about the obstacles that stand between these laboratory triumphs and routine clinical use. Natural hydrogels suffer from batch-to-batch variability, weak mechanical strength, and rapid, uneven degradation; synthetic ones raise questions about long-term biodegradation and residual monomer toxicity. Smart, stimuli-responsive hydrogels depend on environmental triggers whose values shift between individuals, disease states, and tissue locations, making therapeutic outcomes difficult to predict. Reproducibility, standardization, and clinical validation remain stubborn hurdles, and manufacturing dual-responsive systems under Good Manufacturing Practice conditions is still rare. Yet the trajectory is unmistakable: 3D bioprinting and photopolymerization are giving researchers precise spatial control over tumor architecture, artificial intelligence is accelerating polymer design, and injectable hydrogels are proving they can localize chemotherapy, immunotherapy, and even engineered bacteria directly at tumor sites. As the authors conclude, hydrogels have evolved from simple extracellular matrix mimics into powerful platforms for decoding tumor biology, and the models they enable may well define the next generation of precision oncology.

Subject of Research: Hydrogel-based three-dimensional models of the tumor microenvironment in cancer research

Article Title: Hydrogel applications in tumor microenvironment modeling

Article References: Karakaya, M., Karataş, R. B., Ayaz, F., & Aydemir, E. (2025). Hydrogel applications in tumor microenvironment modeling. Discover Biotechnology, 2(1), Article 21. https://doi.org/10.1007/s44340-025-00029-8

Image Credits: AI Generated

DOI: 10.1007/s44340-025-00029-8

Keywords: hydrogels, tumor microenvironment, 3D cell culture, extracellular matrix, drug delivery, cancer models, organoids, bioprinting, immunotherapy, biomaterials, precision oncology, spheroids

Patient-derived organoids, or PDOs, are three-dimensional cultures grown directly from a patient’s own tumor tissue, often obtained through surgery, biopsy, or fine-needle aspiration. Embedded in basement membrane extract and nourished with tailored media, dissociated tumor cells reorganize themselves into miniature structures that recapitulate key genetic, histological, and functional features of the original cancer. Unlike conventional two-dimensional cell lines, which accumulate adaptations over years of laboratory passage, PDOs retain the copy-number variations, mutations, and gene-expression programs of the tumor from which they came. Unlike patient-derived xenografts implanted in mice, they can be established and drug-tested within weeks, at a scale that permits longitudinal study of the very same tumor over many treatment cycles. That combination of fidelity and throughput is precisely what makes them attractive for studying the dynamics of treatment failure.

The review’s most distinctive contribution is a dynamic conceptual map that integrates four determinants of drug response — genetic background, cell-state plasticity, cell-cycle state, and microenvironmental context — along three experimentally measurable dimensions: drug sensitivity, proliferative activity, and temporal reversibility. The authors emphasize that clonal evolution, drug-tolerant persister states, cancer stem cell plasticity, cell-cycle heterogeneity, and microenvironment-mediated protection have traditionally been discussed as separate mechanisms of resistance. In reality, they intersect and reinforce one another. A tumor cell may tolerate a targeted drug by entering a reversible, slow-cycling persister state; over months, those persister populations can provide the raw material for stable, genetically fixed resistance. Organoids, because they allow repeated drug exposure, clonal tracking, and single-cell profiling over time, make it possible to watch that transition happen rather than merely infer it from end-stage samples.

The distinction between tolerance, persistence, and frank resistance carries real clinical weight. Tolerance describes a reversible, non-genetic state in which cells survive drug exposure without dividing — the hallmark of drug-tolerant persisters, a subpopulation first characterized in melanoma and lung cancer models treated with targeted agents. Persistence describes the survival of such cells over longer periods and their eventual re-expansion. Resistance, by contrast, typically involves durable changes, such as secondary mutations in drug targets, activation of bypass signaling pathways, or selection of pre-existing resistant clones. The authors caution against conflating these categories, because interventions that work for one may fail for another. A reversible persister state might be dismantled by epigenetic inhibitors given alongside the primary therapy, whereas a genetically resistant clone demands a fundamentally different strategy. Organoids, which preserve both the genetic and the epigenetic heterogeneity of the source tumor, are uniquely suited to distinguishing the two in an individual patient’s cancer.

Technically, the toolkit available to organoid researchers has matured dramatically. Longitudinal drug exposure protocols now allow researchers to mimic the pulsed, cyclical nature of clinical chemotherapy regimens rather than applying constant drug concentrations. Clonal tracking with fluorescent barcoding reveals which lineages survive treatment and repopulate the culture. Single-cell RNA sequencing and single-cell ATAC sequencing resolve the transcriptional and chromatin states of surviving cells, exposing the epithelial-mesenchymal plasticity and stem-like programs that often accompany drug tolerance. Fluorescence ubiquitination cell cycle indicator, or FUCCI, imaging makes cell-cycle heterogeneity visible in real time, linking quiescent subpopulations to survival under cytotoxic pressure. Growth-rate-corrected pharmacological metrics, such as the GR50 and growth-rate-corrected area under the curve, correct for the confounding effects of differing proliferation rates, yielding more comparable drug sensitivity measurements across cultures. Together, these methods turn a static drug-screening assay into a dynamic record of evolutionary response.

Crucially, the review resists the temptation to treat chemotherapy, targeted therapy, and immunotherapy as interchangeable contexts. Evidence from cytotoxic chemotherapy, the authors note, reflects selection among genotypically diverse clones and the protection of quiescent cells, whereas targeted therapy failures frequently involve adaptive rewiring of signaling networks such as EGFR or downstream bypass pathways in a defined genetic background. Immunotherapy and cellular therapy, including CAR-T and CAR-NK approaches, introduce an entirely different dimension: the interplay between malignant cells and immune effectors. Organoid systems are only beginning to capture that dimension through co-culture with tumor-infiltrating lymphocytes, peripheral blood mononuclear cells, natural killer cells, and myeloid populations. Air-liquid interface cultures, which preserve tumor epithelium alongside native immune and stromal cells, have enabled testing of immune checkpoint blockade responses in vitro — a capability that classical immersed organoid cultures, which strip away most immune components, simply lack.

The tumor microenvironment receives particularly careful treatment. Cancer-associated fibroblasts secrete growth factors such as hepatocyte growth factor and heparin-binding EGF-like growth factor that can shield tumor cells from targeted drugs; tumor-associated macrophages and myeloid-derived suppressor cells blunt immune attack; and stromal cells transformed by oncogenic signaling — for example, pancreatic stellate cells in pancreatic ductal adenocarcinoma — can turn chemotherapy into an unintended pro-survival signal. Organoid co-cultures now allow researchers to reconstruct these interactions deliberately, adding defined stromal or immune populations and measuring how sensitivity shifts. Organ-on-chip platforms go further, introducing controlled fluid flow, drug gradients, and spatial organization that better approximate the physiological conditions of a tumor. Spatial transcriptomics and multiplexed imaging then map where resistant niches form within the culture, connecting cell state to physical location. The result, the authors suggest, is the ability to define resistance niches — specific microenvironments in which tumor cells are actively protected from therapy.

The translational ambition of the framework is equally clear. Patient-derived organoid-based drug sensitivity testing has already been explored prospectively across gastrointestinal cancers and other tumor types, with studies reporting meaningful concordance between organoid responses and patient outcomes in progression-free survival and overall survival. Negative predictive value — the ability to identify therapies that will not work — appears particularly strong, an attribute that could spare patients futile and toxic regimens. The authors argue that standardized functional testing, pathology-based interpretation, and careful pharmacometric normalization will be essential if organoid testing is to move from exploratory studies into routine clinical decision-making, potentially supporting adaptive clinical trial designs in which treatment is matched to live drug-response data. They are equally candid about limitations: organoid establishment rates vary by tumor type, culture conditions select for certain subclones, and immune and stromal components remain incompletely reconstructed in many systems.

What emerges from the review is a deliberate repositioning of the organoid itself. The authors insist that the value of their framework lies not in replacing existing models of resistance — clonal evolution, persister biology, stem cell plasticity — but in integrating them along measurable dimensions within a clinically relevant system. PDOs, in this vision, are not merely drug screening tools but living systems for mapping the dynamic architecture of therapy resistance: an experimental arena in which genetics, cell state, cell cycle, and microenvironment can be observed interacting under longitudinal drug pressure. As co-culture methods, organ-on-chip engineering, spatial transcriptomics, and artificial intelligence-assisted image analysis continue to converge, the prospect of predicting, monitoring, and ultimately preempting the evolution of resistance in an individual patient’s tumor moves from aspiration toward feasible clinical practice. The resistance problem in cancer may not have a single answer, but it may finally have a laboratory in which all of its faces can be studied at once.

Subject of Research: Patient-derived organoids for modeling therapy tolerance, persistence, and resistance in cancer

Article Title: Patient-derived organoids for modeling therapy tolerance, persistence, and resistance in cancer

Article References: Harada, K., Sakamoto, N., Ukai, S., & Ishikawa, S. (2026). Patient-derived organoids for modeling therapy tolerance, persistence, and resistance in cancer. Molecular Cancer. https://doi.org/10.1186/s12943-026-02800-9

Image Credits: AI Generated

DOI: 10.1186/s12943-026-02800-9

Keywords: patient-derived organoids, therapy resistance, drug-tolerant persisters, cancer models, tumor microenvironment, personalized medicine, drug sensitivity testing, cancer stem cells, targeted therapy, immunotherapy, single-cell analysis, Molecular Cancer

The central obstacle, the authors explain, is heterogeneity. Oesophageal adenocarcinoma is driven in large part by chromosomal instability, a process that generates large-scale genomic chaos rather than the tidy, single-gene mutations seen in some other cancers. This instability produces profound differences not only between patients but also between different regions of the same tumour and between the primary tumour and its metastases. Two cells sitting centimetres apart within one patient’s oesophagus may carry different copy-number landscapes, different mutational burdens and different vulnerabilities. A therapy that eradicates one subclone may simply clear the way for another, which is why responses to treatment are so variable and why resistance so often emerges. Any model that smooths over this complexity risks giving clinicians a misleading picture of how a real tumour will behave.

Precision oncology promises to match each treatment to the biology of each tumour, but the review makes clear that in oesophageal cancer this promise has been constrained by history. Conventional two-dimensional cell lines—the workhorses of cancer biology for decades—grow quickly, are cheap and are easy to manipulate genetically, yet decades of passaging in plastic have driven them far from the tumours they originally came from. They lack the three-dimensional architecture of real tissue, they have lost most of the stromal and immune cells that surround a tumour in the body, and their genomes often no longer reflect the patient’s disease. They remain useful for dissecting mechanisms, the authors concede, but as avatars of an individual patient they fall short of what translational medicine now demands.

The models that have attracted the most excitement in recent years are patient-derived organoids: miniature, self-organising tumour fragments grown from fresh biopsy or surgical tissue in a supportive extracellular matrix. Because they are established directly from a patient and expanded for only a limited number of passages, organoids preserve much of the genotype and phenotype of the parent tumour, including the copy-number aberrations that dominate oesophageal adenocarcinoma. Crucially, they can be grown in multi-well formats, meaning dozens of drugs and drug combinations can be tested against a patient’s own tumour cells within days to weeks—a time horizon that can genuinely inform clinical decision-making. Studies across multiple cancer types have shown that organoid drug responses can predict patient responses with encouraging accuracy, and the review highlights their potential as functional biomarkers for treatment selection in oesophageal cancer specifically.

Yet organoids have an inherent limitation: they usually contain only the epithelial cancer cells. The tumour microenvironment—the fibroblasts, immune cells, blood vessels and signalling molecules that bathe a tumour in vivo—is largely absent, and it is this microenvironment that determines whether immunotherapies work. To close that gap, researchers are developing co-culture systems that introduce cancer-associated fibroblasts or immune cells into organoid cultures, and the review singles out immune-augmented organoid platforms as one of the most promising frontiers. By embedding tumour organoids with autologous immune cells, laboratories can begin to run functional immunology readouts: measuring whether a patient’s own T cells recognise their tumour, whether immune checkpoint blockade reinvigorates an anti-tumour response, and whether resistance mechanisms are already at play. Such systems offer a glimpse of personalised immunotherapy testing—something barely imaginable a decade ago.

At the other end of the biological fidelity spectrum sit patient-derived xenografts, or PDX models, in which fragments of a patient’s tumour are implanted into immunodeficient mice. These models retain the three-dimensional architecture, stromal interactions and evolutionary dynamics of the original tumour, and because they grow inside a living organism they capture whole-body pharmacology—how a drug is absorbed, distributed, metabolised and cleared—that no dish can replicate. Orthotopic variants, implanted directly into the oesophagus, add anatomical realism, while humanised PDX mice, engrafted with a human immune system, allow immunotherapies to be studied in a living setting. The trade-off, the authors stress, is throughput and time: establishing a PDX line takes months, success rates vary, and the cost and animal requirements limit how many patients can be modelled at scale. PDX models therefore serve best as deep characterisation platforms and for studying evolutionary and pharmacological questions rather than as rapid diagnostic tools.

Between the dish and the mouse lies a class of models that the review treats with particular attention: ex vivo organotypic tissue slice platforms and histocultures. Rather than dissociating a tumour or passaging it, these approaches take fresh slices of the actual surgical specimen—preserving the full cellular ecosystem of cancer cells, stroma, vasculature and immune infiltrate—and keep them alive in culture for days to a few weeks. Because nothing is disrupted, these slices offer what may be the highest fidelity to the parent tumour of any platform, and their short turnaround makes them attractive for clinically aligned endpoints such as predicting a patient’s response to neoadjuvant chemotherapy or radiotherapy before treatment begins. The limitations are equally practical: slice viability is finite, oxygen and nutrient penetration constrain slice thickness, and standardisation across laboratories remains immature. Nonetheless, the authors argue that organotypic cultures, especially when paired with immune readouts, occupy a unique translational niche for short-horizon therapeutic testing.

The review then turns to a rapidly accelerating dimension of cancer modelling that involves no cells at all: computation. In silico inference pipelines now integrate whole-genome sequencing, transcriptomics, epigenetics and imaging data to infer tumour evolutionary history, predict vulnerabilities and stratify patients, while computational histopathology—increasingly powered by deep learning applied to routine pathology slides—can extract prognostic and predictive information at a scale no experimental model can match. Digital approaches offer unlimited scalability and near-instant results, and they can integrate multi-omic and imaging information that fragmented experimental systems capture only in part. But the authors are emphatic about a caveat: algorithms trained on retrospective data are only as good as their validation, and rigorous benchmarking against real patient outcomes and against experimental models is essential before computational predictions can safely guide therapy. The most credible future, they suggest, is not a single winning platform but a triangulation in which genomic inference, organoid and slice-based drug testing, and selective PDX experiments corroborate one another.

What emerges from the survey is a portfolio philosophy. No single model satisfies all the translationally relevant criteria the authors apply—fidelity to the parent tumour, representation of stromal and immune compartments, scalability, time-to-result and suitability for clinically aligned endpoints such as response prediction and resistance evolution. Organoids win on speed and scalability; organotypic slices win on microenvironmental fidelity and clinical turnaround; PDX models win on organism-level pharmacology and evolutionary context; and computational pipelines win on throughput and data integration. Used intelligently and in combination, these platforms could finally give oncologists what oesophageal adenocarcinoma has long denied them: a way to test, in advance and in the laboratory, whether a given therapy will work for a given patient, and to watch resistance evolve before it happens in the clinic.

The stakes could hardly be higher. As immune checkpoint inhibitors reshape frontline treatment of gastro-oesophageal cancers and a growing arsenal of targeted agents waits in the wings, the absence of reliable predictive biomarkers means many patients endure toxic therapies from which they derive little benefit, while potentially effective options go untried. The Birmingham team, whose work was supported by Cancer Research UK and the Sir Arthur Thomson Charitable Trust, frames its review as both a critical appraisal and a call to action: the model-building tools now exist, but the field must invest in head-to-head comparisons, standardisation and prospective validation against patient outcomes. If that work succeeds, the era of treating oesophageal adenocarcinoma by trial and error could give way to one in which a patient’s tumour is first grown, challenged and computationally interrogated in the laboratory—so that the first real experiment happens where it matters most, in the clinic, with the odds stacked in the patient’s favour.

Subject of Research: Preclinical and computational modelling of oesophageal adenocarcinoma for precision oncology and immunotherapy

Article Title: Modelling oesophageal adenocarcinoma for precision oncology and immunotherapy

Article References: Anwar, R., Rose, E., Swirsky, F., Kunene, V., & Contino, G. (2026). Modelling oesophageal adenocarcinoma for precision oncology and immunotherapy. Cancer Immunology, Immunotherapy. https://doi.org/10.1007/s00262-026-04447-3

Image Credits: AI Generated

DOI: 10.1007/s00262-026-04447-3

Keywords: oesophageal adenocarcinoma, tumour heterogeneity, patient-derived organoids, patient-derived xenografts, immunotherapy, precision oncology, drug sensitivity, tumour microenvironment, computational histopathology, chromosomal instability, personalised medicine, cancer models