Weekly reads 23/2/26

When context controls cancer: oxygen, space, niches and chromatin

Mar 08, 2026

This week’s reads show how cancer biology is deeply shaped by context across multiple scales—from metabolism and tissue architecture to chromatin state and experimental models. One study overturns a long-standing paradigm by showing that systemic hypoxia suppresses tumor growth, not by activating canonical HIF programs but by crippling de novo purine synthesis and starving tumors of nucleotides. At the tissue level, the Wayfarer framework demonstrates that spatial gene expression patterns shift across length scales during lung cancer progression, revealing immune exclusion and tumor architecture changes invisible at any single resolution. New experimental platforms also expand how we study tumor heterogeneity and therapy response: GENEVA enables multiplexed in vivo pharmacogenomics by pooling diverse cancer models into mosaic tumors, uncovering mitochondrial hyperactivation as a mechanism of KRAS inhibitor killing and EMT-driven resistance. Computationally, REMAP reconstructs spatial tissue architecture from dissociated single-cell RNA-seq data, enabling spatial analysis of existing atlases without direct spatial measurements. Meanwhile, studies of early tumorigenesis and therapeutic resistance emphasize the importance of cellular plasticity and microenvironmental interactions, from fibroblast niche remodeling that determines whether nascent tumors persist, to chromatin-mediated EMT programs that drive resistance to KRAS-targeted therapies.

Preprints/articles that I managed to read this week

Systemic hypoxia suppresses solid tumor growth

Midha, A. D. et al. bioRxiv (2026). https://doi.org/10.64898/2026.02.09.704975

The paper in one sentence

Systemic hypoxia, reducing atmospheric oxygen to 8–11%, suppresses tumor growth across multiple cancer models by inhibiting de novo purine synthesis, an effect distinct from local tumor hypoxia, independent of HIF activation, and synergistic with chemotherapy and immunotherapy.

Summary

Local tumor hypoxia is a well-established negative prognostic factor, driving angiogenesis, therapy resistance, and aggressive progression. But what happens when the entire host is hypoxic? Midha and colleagues address this question across a remarkable range of models: syngeneic subcutaneous and orthotopic tumors (Panc02 pancreatic, E0771 breast), a genetically engineered KPC pancreatic cancer model, and a 20-cell-line mosaic xenograft pool (GENEVA) enabling lineage-resolved fitness measurements. Systemic hypoxia (8–11% O₂) consistently suppressed tumor growth, with orthotopic breast tumors in 8% O₂ growing ~60% slower than normoxic controls. Direct oxygen measurements confirmed tumors were indeed more hypoxic. Yet the mechanism defied obvious explanations: hypoglycemia (a known effect of hypoxia) was not responsible, glucose supplementation failed to rescue growth. Constitutive insulin signaling via PTEN knockout did not overcome suppression. And HIF activation, while present, was not required: ARNT-knockout tumors remained sensitive. The GENEVA platform revealed heterogeneous responses: most lines (18/20) showed reduced fitness, but two renal cell carcinoma lines (786O, Caki1) were resistant. By correlating transcriptional changes with fitness, the authors identified de novo purine synthesis genes (PPAT, MTHFD1, PAICS, ATIC) as strongly associated with sensitivity, their downregulation in hypoxia predicted growth suppression. In contrast, purine salvage genes showed no relationship. Metabolomics confirmed depletion of nucleotides (especially AMP, ADP, ATP) in hypoxic tumors and accumulation of the salvage intermediate hypoxanthine in tumor interstitial fluid. Stable isotope tracing with ¹⁵N-glutamine and ¹³C-adenine in vitro and in vivo demonstrated that hypoxia suppresses de novo purine synthesis while increasing reliance on salvage. This aligns with prior work showing de novo purine synthesis is essential for in vivo tumor growth but dispensable in culture. Systemic hypoxia proved durable: tumors serially reimplanted over four passages showed no resistance. It synergized with gemcitabine in pancreatic cancer and with anti-CTLA4 immunotherapy in breast cancer, with the triple combination nearly abolishing growth. Finally, the authors show that HypoxyStat, a small molecule that increases hemoglobin’s oxygen affinity, mimicking systemic hypoxia, recapitulates the tumor-suppressive effect.

Personal highlights

Paradoxical tumor suppression by systemic hypoxia: while local tumor hypoxia promotes aggression, systemic hypoxia (8–11% O₂) consistently suppresses growth across subcutaneous, orthotopic, genetic, and multiplexed xenograft models. This challenges the long-held paradigm that hypoxia uniformly supports cancer progression and reveals that the scale of hypoxia, local vs. systemic, dramatically alters its consequences.
GENEVA platform enables lineage-resolved fitness mapping: by pooling 20 human cancer cell lines into mosaic tumors and using SNP-based deconvolution, the authors measure relative fitness of each line under hypoxia versus normoxia. This reveals heterogeneous responses, most lines sensitive, two renal cell lines resistant—and enables correlation of transcriptional changes with fitness, pinpointing de novo purine synthesis as the key pathway.
De novo purine synthesis, not HIF or glucose, drives sensitivity: hypoxia-induced growth suppression persists despite glucose supplementation, constitutive insulin signaling (PTEN knockout), and HIF inactivation (ARNT knockout). Instead, metabolomics and stable isotope tracing show that systemic hypoxia suppresses de novo purine synthesis, shifting tumors toward salvage pathways. Genes in this pathway (PPAT, MTHFD1, PAICS, ATIC) are among those whose downregulation best predicts sensitivity.
Synergy with chemotherapy and immunotherapy, no acquired resistance: systemic hypoxia enhances the efficacy of gemcitabine in pancreatic cancer and anti-CTLA4 in breast cancer, with combination therapy nearly abolishing tumor growth. Serial reimplantation over four passages shows no evidence of resistance, tumors remain sensitive to hypoxia therapy.

Why should we care?

For decades, hypoxia has been cast as a villain in cancer—a driver of angiogenesis, therapy resistance, and poor outcomes. This work turns that narrative on its head by showing that systemic hypoxia does something fundamentally different from local tumor hypoxia. When the entire host experiences low oxygen, the tumor cannot rely on well-oxygenated pockets to supply nucleotides or salvageable substrates. The division of labor that sustains growth in heterogeneous tumors collapses. The mechanistic finding, suppression of de novo purine synthesis, is particularly compelling. Purine synthesis is energetically costly (4 ATP, 1 GTP per AMP), and cancer cells in vivo depend on it more than cells in culture. Systemic hypoxia seems to exploit this vulnerability, shifting tumors toward salvage pathways that cannot keep up with proliferative demand. The translational implications are substantial. Systemic hypoxia synergizes with both chemotherapy and immunotherapy, and the small molecule HypoxyStat offers a practical route to achieve it without altitude or hypoxic chambers. The epidemiological correlation with altitude, while correlational, adds a layer of real-world plausibility.

Wayfarer: A multiscale framework for spatial analysis of tumor progression

Moses, L. et al. bioRxiv (2026). https://doi.org/10.64898/2026.02.16.706245

The paper in one sentence

Wayfarer is a multiscale spatial analysis framework that tracks how spatial statistics, Moran's I, Lee's L, and MULTISPATI PCA, evolve across nested spatial aggregations, revealing that tumor progression in lung adenocarcinoma is accompanied by reproducible shifts in scale-dependent spatial patterns that are invisible at any single resolution.

Summary

Spatial biology operates across length scales, from subcellular organization to tissue architecture, yet most spatial transcriptomics analyses implicitly assume that biological patterns are scale-consistent, choosing a single resolution (e.g., Visium spots, 8 μm bins, or one neighborhood radius) and treating scale as a technical nuisance rather than a biological variable. The authors demonstrate that this assumption is false. Using Xenium data from a lung adenocarcinoma (LUAD) progression series (AIS A, AIS B, MIA C, IA), they systematically aggregate transcript counts into square bins ranging from 8 μm to 384 μm and compute how three spatial metrics—Moran’s I (spatial autocorrelation), Lee’s L (spatially informed correlation), and MULTISPATI PCA (spatially informed dimension reduction), change with scale. The results reveal that spatial patterns at fine and coarse scales can co-exist. For example, genes can exhibit bimodal Moran’s I curves, with peaks at both small and large bin sizes, indicating spatial structure at two distinct scales simultaneously, a phenomenon reproduced in synthetic data with sparse large spots and dense small spots. Lee’s L curves similarly show that gene co-expression relationships can be scale-dependent, with some gene pairs exhibiting flat curves that nonetheless conceal qualitatively different spatial organizations at different scales. Crucially, these scale-response profiles differ reproducibly between LUAD stages. Using linear mixed models with spline terms, the authors test whether Moran’s I or Lee’s L curves vary significantly across stages. Over 80% of genes show significant stage-dependent effects, not explained by differential expression alone. ERBB2, for instance, is not significantly differentially expressed between stages in pseudobulk, but its Moran’s I at fine scales is markedly higher in invasive adenocarcinoma (IA) than in earlier stages, reflecting coherent, homogeneous ERBB2-high tumor blocs that emerge only in late disease. This difference disappears at coarser (96 μm) resolutions comparable to Visium, suggesting that analyses at a single scale would miss this progression-associated phenotype. Immune markers reveal a fundamental restructuring of the tumor immune geography. ITGAE (CD103, tissue-resident memory T cells) shows minimal single-cell spatial autocorrelation across all stages, but at coarse scales, Moran’s I increases in IA, consistent with T cells becoming spatially restricted to boundaries rather than intermingled with tumor cells. GZMB (cytotoxic T cells) shifts from fine-scale infiltration to coarse-scale pockets at invasive margins. CXCL9, a T-cell-attracting chemokine, transitions from diffuse patterns in early stages to boundary-localized clustering in IA. These shifts from fine-scale mixing to coarse-scale exclusion would be invisible without multiscale analysis.

Personal highlights

Multiscale spatial analysis reveals co-existing patterns at different scales: by systematically aggregating Xenium data across bin sizes from 8 μm to 384 μm, the authors show that spatial autocorrelation (Moran’s I) and gene co-expression (Lee’s L) can exhibit multiple peaks or plateaus, indicating spatial structure at multiple scales simultaneously. Bimodal curves in real data, reproduced in synthetic data with sparse large spots and dense small spots, demonstrate that fine-scale and coarse-scale patterns are not mutually exclusive but can co-exist within the same tissue section.
Stage-specific spatial signatures invisible at single resolution: linear mixed models with spline terms reveal that over 80% of genes have Moran’s I curves that differ significantly between LUAD stages, independent of differential expression. ERBB2 exemplifies this: not differentially expressed in pseudobulk, but with markedly higher fine-scale Moran’s I in invasive adenocarcinoma (IA) than in earlier stages, reflecting coherent ERBB2-high tumor blocs that emerge only in late disease. This difference disappears at Visium-like resolutions, showing how single-scale analyses can miss progression-associated phenotypes.
Immune geography shifts from fine-scale infiltration to coarse-scale exclusion: T-cell markers (ITGAE, GZMB) and the chemokine CXCL9 exhibit scale-dependent changes with progression. In early stages, these markers show fine-scale spatial mixing with tumor cells; in IA, they become restricted to coarse-scale pockets at invasive margins. This shift from infiltration to exclusion, a hallmark of immune evasion, is only detectable by comparing how spatial statistics change across scales, not by any single-resolution measurement.
Gene co-expression relationships are scale-dependent and stage-specific: Lee’s L curves for gene pairs with known biological relevance (e.g., ERBB2-PRG4, SPP1-APOE, SPP1-CXCL9) show that correlation can change sign with scale and differ between stages. For ERBB2-PRG4, weak fine-scale correlation across all stages gives way to negative correlation in IA at coarse scales, reflecting PRG4 exclusion from ERBB2-high regions. For SPP1-APOE, positive correlation at fine scales transitions to negative correlation in IA, with local analysis revealing co-existing zones of positive and negative correlation that cancel out in global metrics—a phenomenon invisible without multiscale decomposition.

Why should we care?

Spatial transcriptomics has given us the ability to see where genes are expressed in tissue, but most analyses implicitly assume that biological patterns are scale-invariant, that the right resolution can be chosen once and applied universally. Wayfarer demonstrates that this assumption is not just oversimplified but actively misleading. The same tissue can contain spatial structures at multiple scales simultaneously, and the relationship between scales can change with disease progression in ways that single-resolution analyses completely miss. The implications are profound. When we choose a single bin size or neighborhood radius, whether by convention (Visium spots), convenience (8 μm Xenium bins), or algorithmic default, we are not just simplifying; we are selecting which biological phenomena we can see. The ERBB2 example shows that a key progression-associated phenotype (coherent ERBB2-high tumor blocs in invasive cancer) is detectable at fine scales but disappears at Visium-like resolutions. The immune geography examples show that the shift from T-cell infiltration to exclusion, a central mechanism of immune evasion, manifests as a change in scale-dependent behavior, not a simple change in cell counts or correlation magnitude.

The GENEVA platform models tumor mosaicism to reveal variations of responses to KRAS inhibitors and identify improved drug combinations

Yu, J. X. et al. Nature Cancer (2026). https://doi.org/10.1038/s43018-026-01130-5

The paper in one sentence

GENEVA is a scalable platform that pools multiple cancer cell lines or patient-derived models into mosaic tumors, enabling single-cell-resolution profiling of drug responses across diverse genetic backgrounds within a single in vivo experiment, revealing that KRAS-G12C inhibitors kill cancer cells via mitochondrial hyperactivation and that EMT is a prominent in vivo resistance mechanism.

Summary

Preclinical cancer drug development relies on xenograft models that are costly, labor-intensive, and difficult to scale, limiting the number of genetic backgrounds that can be tested before clinical trials, where efficacy across diverse patients determines success. Yu, Suh, and colleagues introduce GENEVA (GENetically diverse and Endogenously controlled phenotypic Variation Assay), a platform that addresses this by pooling tens of cell lines or patient-derived models into mosaic 3D cultures or xenograft tumors. After drug treatment, single-cell RNA-seq combined with SNP-based deconvolution and MULTI-seq hashing assigns every cell to its line of origin and treatment condition, enabling simultaneous measurement of sensitivity, cell cycle state, and transcriptomic response across models. Applying GENEVA to KRAS-G12C inhibitors (ARS-1620, sotorasib, adagrasib) across panels of lung cancer lines, patient-derived organoids, and xenografts, the authors uncover several unexpected findings. First, cells surviving inhibitor treatment have significantly lower mitochondrial transcript content. CRISPRi screens confirm that knockdown of mitoribosomal and mitochondrial genes confers resistance. Acute treatment rapidly increases mitochondrial membrane potential, spare respiratory capacity, and oxygen consumption—effects that precede caspase cleavage and are specific to KRAS-G12C inhibition (not seen with other chemotherapies). Inhibiting complex III with antimycin A rescues cell death, demonstrating that mitochondrial hyperactivation is a direct mechanism of on-target killing. Second, GENEVA identifies mTOR and EMT as key tolerance pathways. mTOR signature genes are upregulated in persister cells, and combining KRAS-G12C inhibitors with the mTOR inhibitor INK128 shows strong Bliss synergy in vitro and in vivo. EMT genes emerge strongly only in vivo—not in vitro, highlighting the importance of the tumor microenvironment. Combining ARS-1620 with the TGFβ receptor inhibitor galunisertib (targeting EMT) synergistically reduces tumor growth. Third, in vivo CRISPR screens validate GENEVA-prioritized targets: knocking down mTOR or EMT pathway genes sensitizes cells to KRAS-G12C inhibitors, while knocking down mitochondrial ribosomal genes protects them. Finally, GENEVA enables systematic mapping of drug combination synergies at single-cell resolution, revealing that mitochondrial genes are downregulated in synergistic combinations (ARS-1620 + galunisertib, ARS-1620 + INK128), consistent with the mechanism of action.

Personal highlights

Multiplexed in vivo pharmacogenomics at single-cell resolution: GENEVA pools tens of cell lines or patient-derived models into mosaic xenografts, then uses SNP-based deconvolution and MULTI-seq hashing to assign every sequenced cell to its line of origin and treatment condition. This enables simultaneous measurement of drug sensitivity, cell cycle effects, and transcriptomic responses across diverse genetic backgrounds within a single mouse, dramatically scaling preclinical pharmacogenomics while controlling for technical variation.
Mitochondrial hyperactivation as a mechanism of KRAS-G12C inhibitor killing: cells surviving treatment show reduced mitochondrial transcripts, and CRISPRi screens reveal that knocking down mitoribosomal or mitochondrial genes confers resistance. Acute inhibitor treatment rapidly increases mitochondrial membrane potential, spare respiratory capacity, and oxygen consumption, preceding caspase cleavage. Inhibiting complex III with antimycin A rescues cell death, demonstrating that KRAS-G12C inhibitors kill via on-target mitochondrial hyperactivation, a mechanism distinct from the canonical view of MAPK pathway suppression.
EMT emerges as an in vivo-specific resistance mechanism: while mTOR pathway upregulation is observed both in vitro and in vivo, epithelial-mesenchymal transition (EMT) signatures appear only in xenograft tumors, not in culture. This highlights the importance of the tumor microenvironment in shaping resistance and demonstrates GENEVA’s ability to capture in vivo-specific biology that would be missed by conventional in vitro screening.
Systematic mapping of drug combination synergies: GENEVA enables quantitative Bliss synergy analysis at single-cell resolution, revealing that ARS-1620 combinations with mTOR (INK128) or EMT (galunisertib) inhibitors show strong synergy across KRAS-G12C lines. Gene-level synergy modeling identifies mitochondrial genes as consistently downregulated in synergistic combinations, mechanistically linking the combination effect to the drug’s primary mechanism.

Why should we care?

The gap between preclinical cancer models and clinical outcomes is stark: drugs that work in mice often fail in humans, in part because we test them in too few models before moving to trials. Xenografts are expensive and labor-intensive, so we typically evaluate candidates in a handful of cell lines or patient-derived models, hoping they represent the diversity of human tumors. They don’t. GENEVA offers a way out. By pooling dozens of models into a single mouse, it scales in vivo pharmacogenomics by an order of magnitude, turning what was a 50-mouse experiment into a 1-mouse experiment. More importantly, it provides rich molecular data, not just cell counts but transcriptomes, cell cycle states, and gene expression responses, across all models simultaneously. This turns drug testing from a binary “sensitive/resistant” readout into a high-dimensional portrait of how different genetic backgrounds respond. The biological discoveries enabled by this approach are striking. The finding that KRAS-G12C inhibitors kill cells by hyperactivating mitochondria, not just suppressing MAPK signaling, changes our understanding of how these drugs work and suggests new combination strategies (like adding complex III inhibitors to enhance killing). The emergence of EMT as an in vivo-specific resistance mechanism underscores that we cannot rely on in vitro models alone; the tumor microenvironment fundamentally shapes drug response.

Reconstructing multi-scale tissue spatial architecture from single-cell RNA-seq with REMAP

Jiang, S. et al. bioRxiv (2026). https://doi.org/10.64898/2026.02.21.707167

The paper in one sentence

REMAP is a deep learning framework that reconstructs spatial locations of cells from dissociated single-cell RNA-seq data by integrating gene expression with neighborhood-level gene-gene covariance learned from one or multiple spatial transcriptomics references, enabling multi-scale spatial analysis of existing scRNA-seq atlases.

Summary

Single-cell RNA sequencing (scRNA-seq) provides transcriptomes at scale but loses spatial context; spatial transcriptomics (ST) preserves location but is costly and limited in gene coverage. REMAP bridges this gap by learning to predict where cells in scRNA-seq data likely originated, using ST data as a reference. The key innovation is the use of both first-order (individual gene expression) and second-order (gene-gene covariance within cellular neighborhoods) features. During training on ST data, REMAP identifies spatial neighbors for each cell, computes covariance among neighboring cells’ gene expression to capture local tissue context, and trains a neural network to predict coordinates from these combined features. For scRNA-seq inference, where neighbors are unknown, REMAP iteratively refines covariance estimates: an initial guess from ENVI, location prediction, then a second network refining covariance based on predicted locations, repeating to improve accuracy. When multiple ST references are available (e.g., covering different tissue regions), REMAP shifts from predicting absolute coordinates to predicting pairwise cell-cell distance matrices, which remain comparable across differently oriented slices. This enables reconstruction of global tissue relationships even from fragmented captures. Across extensive benchmarking—mouse brain (10x Visium HD vs. Xenium), human fetal cortex (MERFISH), colorectal cancer (Visium HD vs. Xenium), and seven cancer types—REMAP consistently outperforms existing methods (CeLery, iSORT, LUNA, CellContrast) in preserving pairwise distances, reconstructing fine structures (hippocampal subregions, V1/V2 cortical border, curved tumor architectures), and recovering cellular neighborhood (CN) networks. In a human multiple sclerosis atlas (15 samples, paired Visium and snRNA-seq), REMAP enabled spatial analysis of microglial neighborhoods. It revealed that inactive MS samples stratify into two subgroups: one resembling controls (minimal microglial self-colocalization), the other mirroring active MS (increased microglia-microglia interactions). Within a rare microglial subpopulation colocalized with astrocytes, REMAP identified a transitioning, pro-inflammatory state enriched for interferon signaling and MS-relevant markers (CHIT1, SIGLEC1)—insights invisible to snRNA-seq alone. Across five cancer types (cervical, ovarian, melanoma, lung, prostate), REMAP uncovered conserved spatial subtypes of cancer-associated fibroblasts (CAFs) based on neighborhood composition. These matched the s1–s4 CAF taxonomy: s1-CAFs tumor-adjacent (prognostic), s2-CAFs self-colocalized, s3-CAFs adjacent to macrophages, s4-CAFs near tertiary lymphoid structures. Transcriptional profiling confirmed distinct functional programs (ECM remodeling, stress response, antigen presentation), demonstrating REMAP’s ability to decode microenvironmental organization from dissociated cells

Personal highlights

Neighborhood covariance as a second-order spatial signal: REMAP goes beyond matching individual cell expression by learning gene-gene covariance within cellular neighborhoods, a proxy for local tissue context. This second-order information captures microenvironmental patterns (e.g., cell-type composition, signaling niches) that are more spatially informative than individual transcriptomes alone, enabling accurate reconstruction even when expression profiles alone are ambiguous.
Iterative refinement of latent spatial context: for scRNA-seq data where true neighbors are unknown, REMAP initializes covariance estimates using ENVI, then iteratively refines them: predict locations, then predict better covariance from those locations, then re-predict locations. This closed loop progressively aligns the latent spatial representation with the expression data, overcoming the initial lack of spatial information.
Multi-reference integration via pairwise distance prediction: When tissue samples exceed a single ST capture, common in practice, REMAP switches from absolute coordinates to pairwise distance matrices, which remain comparable across slices with different orientations. A grid-based downsampling strategy makes training tractable, and optional neighbor filtering scales inference to large datasets. This enables global tissue reconstruction from fragmented references.
Conserved CAF spatial subtypes across cancers: across five cancer types, REMAP recovered the s1–s4 CAF taxonomy from dissociated scRNA-seq data, based solely on predicted neighborhood compositions. These subtypes showed distinct transcriptional programs (ECM remodeling, stress response, antigen presentation) and spatial niches (tumor-adjacent, self-colocalized, macrophage-adjacent, TLS-adjacent), validating that microenvironmental organization can be inferred from single-cell data and revealing conserved principles of CAF architecture.

Precancerous niche remodelling dictates nascent tumour persistence

Skrupskelyte, G. et al. Nature (2026). https://doi.org/10.1038/s41586-026-10157-8

The paper in one sentence

A subset of nascent tumours in the mouse oesophagus survive by instructing local fibroblasts to form a fibronectin-rich stromal niche via an EGF-SOX9-FN1 signalling axis, and disrupting this interaction prevents tumour persistence.

Summary

Most studies of early tumorigenesis focus on mutations in cancer cells, but healthy tissues accumulate cancer-associated mutations with age, suggesting that additional factors determine whether mutant clones progress to tumours. Using a diethylnitrosamine (DEN) mouse model of upper gastrointestinal tract carcinogenesis, Skrupskelyte, Rojo Arias, and colleagues investigate why some nascent tumours persist while others are eliminated. At 10 days post-DEN, microscopic tumours (as few as 10 cells) fall into two categories: Niche⁻ lesions with no stromal reorganization, and Niche⁺ lesions where underlying PDGFRαⁱᵒʷ lamina propria fibroblasts form a supportive scaffold protruding into the epithelium. Over time, Niche⁻ tumours are progressively eliminated, while Niche⁺ tumours persist, enlarge, and become enriched in the tissue. By 8 months, 82% of surviving tumours are Niche⁺. Lineage tracing shows that niche fibroblasts derive from local PDGFRαⁱᵒʷ cells that clonally expand beneath persistent tumours. Single-cell RNA sequencing of microdissected tumours identifies a tumour-specific epithelial population (”Tumour 12”) characterized by high SOX9 expression and enrichment for EGF ligands (AREG, HBEGF) and ECM-interacting genes (LAMC2, ITGB6). This population signals to fibroblasts via EGF, promoting their migration and inducing a pro-fibrotic transcriptional program with marked upregulation of fibronectin (FN1) and other ECM components. Functional assays confirm that tumour-derived signals are sufficient: normal epithelium exposed to denuded tumour stroma acquires tumour-like features in 3D culture and shows enhanced engraftment in vivo. The EGF-SOX9-FN1 axis is functionally required: inhibiting EGFR signalling with gefitinib or blocking fibronectin fibrillogenesis with the FUD peptide reduces Niche⁺ tumour formation and overall tumour burden.

Personal highlights

Nascent tumours stratify by niche-forming ability within days: at 10 days post-carcinogen, microscopic tumours (≥10 cells) already segregate into Niche⁻ (no stromal change) and Niche⁺ (fibroblast scaffold) phenotypes. Longitudinal tracking shows Niche⁻ tumours are progressively eliminated, while Niche⁺ tumours persist and enlarge, demonstrating that fate is determined at the earliest stages by stromal interaction, not just mutation burden.
Local PDGFRαⁱᵒʷ fibroblasts form the niche via clonal expansion: lineage tracing using Colla2-Cre and Pdgfra-Cre mice reveals that niche fibroblasts derive from the lamina propria PDGFRαⁱᵒʷ population, not deeper submucosal fibroblasts. These cells clonally expand beneath persistent tumours, indicating that the niche is built by proliferation of local fibroblasts, not recruitment from distant sources.
A rare tumour-specific epithelial state (Tumour 12) drives niche formation: scRNA-seq identifies a distinct keratinocyte population (Tumour 12) enriched in persistent tumours, marked by high SOX9, EGF ligands (AREG, HBEGF), and ECM-interacting genes (LAMC2, ITGB6). This stress-associated state, not present in all tumour cells, is the signalling hub that instructs fibroblast recruitment and ECM remodelling.
EGF-SOX9-FN1 axis is necessary and sufficient for niche formation: chemoattractant assays show tumour-derived AREG stimulates fibroblast migration. 3D epithelioid-fibroblast cocultures demonstrate that expanding keratinocytes (high SOX9) induce fibroblast segregation, FN1 deposition, and vimentin upregulation, all blocked by EGFR inhibition. In vivo, gefitinib or the fibronectin assembly inhibitor FUD reduces Niche⁺ tumour formation and overall burden, validating the axis as a therapeutic target.

Why should we care?

For decades, we’ve thought of cancer as a disease of mutations, accumulate enough drivers, and a tumour forms. But recent findings that normal ageing tissues are riddled with cancer-associated mutations have forced a rethink: mutations are common, but tumours are rare. Something else determines which mutant clones cross the line. This study provides a compelling answer: the ability to remodel the microenvironment. Nascent tumours that survive do so because a subset of their cells activate a stress program (SOX9 high, EGF high) that recruits local fibroblasts and builds a fibronectin-rich supportive niche. Tumours that fail to do this are eliminated, despite presumably carrying similar mutations. Persistence is not about what mutations you have, but about how you talk to your neighbours.

mSWI/SNF complex inhibition sensitizes KRAS-mutant lung cancers to targeted therapies via epithelial-mesenchymal subversion

Gentile, C. et al. bioRxiv (2026). https://doi.org/10.64898/2026.02.27.708377

The paper in one sentence

Inhibiting mSWI/SNF chromatin remodeling complexes with the clinical-grade SMARCA4/2 inhibitor FHD-286 reverses EMT-driven resistance to KRAS inhibitors in lung cancer by suppressing AXL and mesenchymal programs, synergizing with KRAS-targeted therapies across multiple mutation subtypes and in patient-derived models.

Summary

KRAS-mutant lung cancers respond to targeted inhibitors like sotorasib and adagrasib, but responses are typically short-lived (<8 months) and resistance inevitably emerges. In nearly half of patients, resistance occurs without new mutations, implicating non-genetic mechanisms such as chromatin-mediated transcriptional plasticity.

Gentile, Feng, and colleagues identify mSWI/SNF (BAF) chromatin remodeling complexes as critical determinants of this adaptive resistance. Analyzing KRAS-mutant tumors and cell lines, they find mSWI/SNF genes among the top transcriptional regulators. Combining the clinical-grade SMARCA4/2 ATPase inhibitor FHD-286 with KRAS-G12C inhibitors (sotorasib, adagrasib) produces strong synergy in 5/8 cell lines tested—but only in those with mesenchymal signatures. Cell lines with epithelial phenotypes lack acute synergy but still show enhanced response durability over time. The mechanistic dissection is comprehensive. CUT&RUN and ATAC-seq reveal that mSWI/SNF complexes occupy distinct genomic sites in synergy versus non-synergy lines, with synergy-specific sites enriched for EMT, cytoskeletal organization, and cell migration genes. Combination treatment (sotorasib + FHD-286) uniquely downregulates these EMT programs and reduces chromatin accessibility at loci including the receptor tyrosine kinase AXL, a known driver of EMT-mediated resistance. AXL overexpression confers sotorasib resistance, which FHD-286 reverses. The synergy extends beyond G12C: FHD-286 sensitizes KRAS-G12S, -G12A, -G13D, and -G12D lines to the pan-RAS inhibitor RMC-6236 or the G12D-specific inhibitor MRTX-1133, with durable suppression of regrowth after drug washout. AXL and vimentin induction by RAS inhibitors is blunted by FHD-286 co-treatment, and the AXL inhibitor bemcentinib partially phenocopies the effect. In sotorasib-resistant H358 cells (H358SR), established by 3-month drug exposure, mSWI/SNF complexes retarget to new genomic loci enriched for EMT, integrin signaling, and TNFα pathways. FHD-286 alone reverts AXL and vimentin expression and resensitizes cells, but only when combined with MEK inhibition to block pERK rebound, revealing a vertical inhibition strategy. Patient-derived ex vivo tumor spheroids (DFCI486, DFCI491) and PDX models (PHLC239, PHLC194) confirm the synergy. In the G12Ci-resistant PDX_PHLC239, only combination treatment significantly reduced tumor volume over 56 days. In the initially sensitive PDX_PHLC194, tumors relapsed on sotorasib monotherapy but remained suppressed with FHD-286 co-treatment. The authors propose a model where mSWI/SNF complexes maintain mesenchymal chromatin states that enable adaptive resistance; their inhibition collapses this state, enhancing both depth and durability of KRAS inhibitor response.

Personal highlights

mSWI/SNF complexes as master regulators of EMT-mediated resistance: upstream regulator analysis of KRAS-mutant tumors ranked mSWI/SNF genes among top transcriptional regulators. CUT&RUN profiling revealed that synergy-specific mSWI/SNF occupancy sites are enriched for EMT, cytoskeletal organization, and cell migration genes, distinct from non-synergy lines where occupied sites enrich for WNT signaling and cell cycle. This establishes chromatin accessibility at EMT loci as a determinant of response.
FHD-286 synergy is predicted by EMT status, not STK11 mutation: While 4/5 synergy lines were STK11-mutant (”KL” subtype), CRISPR knockout or rescue experiments ruled out STK11 as the mechanism. Instead, synergy tracked with mesenchymal signature scores. Epithelial lines lacked acute synergy but still benefited from enhanced durability, suggesting mSWI/SNF inhibition blocks eventual EMT-mediated escape even when not immediately synergistic.
AXL as a critical downstream effector of mSWI/SNF-driven resistance: Combination treatment reduced chromatin accessibility at the AXL locus and downregulated its expression. AXL overexpression in H358 cells conferred sotorasib resistance, which FHD-286 reversed. The AXL inhibitor bemcentinib phenocopied FHD-286 effects in non-G12C lines, validating AXL as a key node. This positions mSWI/SNF inhibition as a strategy to target AXL in the absence of effective clinical AXL inhibitors.
Broad efficacy across KRAS mutation subtypes and inhibitor classes: FHD-286 sensitized KRAS-G12S, -G12A, -G13D, and -G12D lines to pan-RAS (RMC-6236) and G12D-specific (MRTX-1133) inhibitors. In H441 (KRAS-G12V), combination treatment prevented regrowth after drug washout, a durable response not seen with single agents. This suggests the mechanism is mutation-agnostic and targets a common adaptive program.

Why should we care?

KRAS inhibitors have transformed treatment for the one-third of lung cancer patients with KRAS mutations, but the excitement is tempered by reality: responses are rarely durable, and resistance almost always emerges. The field has focused on genetic bypass mechanisms, but nearly half of progressing patients lack new resistance mutations, pointing to non-genetic, adaptive plasticity as the culprit. This work identifies that plasticity as chromatin-mediated, driven by mSWI/SNF complexes maintaining a mesenchymal state permissive for resistance. The clinical-grade SMARCA4/2 inhibitor FHD-286 collapses that state, suppressing AXL and other EMT programs, and synergizes with KRAS inhibitors across multiple mutation subtypes—including those not covered by G12C-specific drugs.

Other papers that peeked my interest and were added to the purgatory of my “to read” pile

Thanks for reading.

Cheers,

Seb.

Sebcentrism

Discussion about this post

Ready for more?