Breast cancer remains the most commonly diagnosed cancer among women worldwide, with more than 2.3 million new cases each year according to the World Health Organization. Yet even as systemic therapies have advanced dramatically, from neoadjuvant chemotherapy to targeted agents and immunotherapy, a stubborn fraction of tumors simply refuse to respond. A new systematic review published in Medical Oncology argues that one of the most underappreciated culprits may be hiding inside the tumor itself: intratumor heterogeneity, or ITH, the coexistence of genetically, transcriptionally, and phenotypically distinct cell populations within a single cancer. The review, led by Aria Dianati and colleagues at Maastricht University Medical Centre, synthesizes evidence that this internal diversity is closely linked to chemotherapy resistance, residual disease, and early relapse in primary breast cancer.
The research team followed the PRISMA guidelines for systematic reviews, searching MEDLINE via PubMed, Web of Science, and Scopus through June 2026 under a protocol registered with PROSPERO. From an initial pool of 325 potentially relevant articles, the investigators ultimately included twelve studies that met strict eligibility criteria: original research on primary breast cancer that assessed heterogeneity through genomic, transcriptomic, radiomic, or immunologic methods and connected those measurements to chemotherapy response or clinical outcomes. The included cohorts spanned triple-negative breast cancer, HER2-positive disease, hormone receptor-positive tumors, and mixed populations, with sample sizes ranging from just six patients to nearly 1,600.
What makes this review particularly compelling is the sheer diversity of technologies the included studies deployed to peer inside tumors. Some used single-cell DNA and RNA sequencing to track clonal dynamics during treatment. Others relied on MRI-based radiomics, extracting quantitative features from medical images that reflect differences in tumor architecture, vascularization, and perfusion. Still others profiled long non-coding RNAs, mapped spatial immune infiltration, or combined imaging with genomic classifiers. Despite these methodological differences, a consistent pattern emerged: tumors with high or increasing heterogeneity, whether measured at the molecular level or through imaging, tended to show reduced sensitivity to chemotherapy.
The biological mechanisms underlying this resistance appear to be several and distinct. The most striking is adaptive clonal selection, the idea that chemotherapy does not create resistant cells so much as reveal them. In a landmark single-cell sequencing study included in the review, Kim and colleagues followed triple-negative breast tumors through treatment and found that certain clones pre-existed before therapy and survived the drug assault, effectively pruning sensitive cells while allowing resistant ones to dominate. Notably, a minority of pre-treatment cells appeared primed for survival, already expressing subsets of resistance-related genes before any drug exposure. Chemotherapy failure, in other words, may often be driven by the expansion of pre-existing resistant subclones rather than the acquisition of new mutations during treatment.
A second mechanism involves transcriptional plasticity, the capacity of cancer cells to rewire their gene expression programs into drug-tolerant states. Lusby and colleagues mapped the regulatory circuitry underlying chemoresistance in triple-negative disease, identifying super-enhancer-driven transcription factor networks that maintain a pool of resistant cells despite therapy, and deriving a twenty-gene signature predictive of response. Complementing this work, Shaath and colleagues profiled the long non-coding RNA landscape at single-cell resolution and implicated MALAT1, a well-known regulatory RNA, as a mechanistic driver of resistance to neoadjuvant chemotherapy. Functional CRISPR experiments reinforced the link, showing that MALAT1 influences drug sensitivity and regulatory networks, positioning non-coding RNA regulation as both a potential biomarker and a therapeutic avenue.
The tumor microenvironment adds yet another layer of complexity. Immune exclusion, in which regions of heterogeneous tumors lack effective infiltration by cytotoxic T lymphocytes, emerged as a clinically relevant dimension of heterogeneity. Gandhi and colleagues tested a cytokine-kinase mediated immune-priming regimen added to standard neoadjuvant chemotherapy in triple-negative breast cancer and observed encouraging safety, immune remodeling, enhanced CD8-positive T-cell infiltration, and a pathologic complete response rate of 55 percent in a small phase I setting. Meanwhile, other work suggests that growth factors such as HGF and NRG1β can actively preserve diverse basal, luminal, and mesenchymal phenotypes during treatment, sustaining phenotypic diversity rather than merely selecting for resistant clones. Heterogeneity, it seems, is not solely a property of tumor cells but is shaped by ongoing interactions with the surrounding microenvironment.
In HER2-positive disease, the review highlights genomic heterogeneity as a predictor of targeted therapy failure. Two independent studies found that tumors with heterogeneous HER2 amplification patterns, particularly those harboring low-level amplification alongside HER2-amplified subclones, were significantly less likely to achieve pathologic complete response after anti-HER2 neoadjuvant therapy. The coexistence of HER2-amplified and HER2-low cell populations may create a reservoir of intrinsically resistant cells before treatment even begins. Encouragingly, one of these studies showed that HER2 heterogeneity can be assessed using routine pathology data, suggesting that a predictive biomarker could be implemented without exotic new infrastructure.
Perhaps the most clinically provocative strand of evidence concerns imaging. Several included studies demonstrated that MRI-derived measures of heterogeneity can predict treatment response and recurrence risk non-invasively. Huang and colleagues developed an MRI-based heterogeneity nomogram that independently predicted both pathologic complete response and relapse-free survival in a multicenter design with external validation. Shi and colleagues combined ecological diversity concepts with radiomic analysis across multicenter cohorts and showed improved prediction of response. Chitalia and colleagues went further, demonstrating that early spatial-phenotypic shifts visible on MRI could predict recurrence and add predictive power beyond established genomic classifiers such as PAM50. Integrated radiomic-genomic models outperformed either modality alone, and rising heterogeneity during therapy, so-called delta-ITH, offered a promising early, non-invasive indicator of poor outcomes.
The review’s authors are careful to acknowledge significant limitations. The twelve included studies differed substantially in design, patient populations, heterogeneity assessment methods, and outcome definitions, which precluded formal meta-analysis. Most were retrospective, single-center, and based on relatively small cohorts, raising the risk of selection bias. Triple-negative and HER2-positive tumors were overrepresented while estrogen receptor-positive luminal disease was underrepresented, and there is no standardized way to measure heterogeneity across sequencing, radiomics, and immune profiling platforms. Quality assessment using the Newcastle-Ottawa Scale nonetheless rated eight of the twelve studies as high quality, and the consistency of findings across such disparate methods lends credibility to the central conclusion.
The implications for clinical practice are potentially profound but remain aspirational. If heterogeneity can be reliably measured before and during treatment, oncologists could one day stratify patients more accurately, identify those unlikely to benefit from a given regimen, and adapt therapy based on longitudinal heterogeneity signals captured through routine imaging. Such ITH-guided adaptive treatment remains a research question rather than current practice, and the authors emphasize that prospective, multicenter validation with standardized measurement approaches is essential before routine implementation. Still, the message of this synthesis is clear: the average tumor sampled by a single biopsy may be a misleading caricature of the diverse cellular ecosystem within it, and learning to read that diversity, through sequencing, spatial profiling, and the MRI scanner, could be key to finally overcoming chemotherapy resistance in breast cancer.
Subject of Research: Intratumor heterogeneity and chemotherapy resistance in primary breast cancer
Article Title: The impact of intratumor heterogeneity on outcome and resistance to therapy in primary breast cancer: a systematic review
Article References: The impact of intratumor heterogeneity on outcome and resistance to therapy in primary breast cancer: a systematic review. (n.d.). https://doi.org/10.1007/s12032-026-03426-z
Image Credits: AI Generated
DOI: 10.1007/s12032-026-03426-z
Keywords: intratumor heterogeneity, breast cancer, chemotherapy resistance, triple-negative breast cancer, HER2-positive, radiomics, MRI, single-cell sequencing, neoadjuvant chemotherapy, tumor microenvironment, MALAT1, systematic review
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Nathaniel Bowman. (October 3, 2026). Tumor Diversity Emerges as a Hidden Driver of Chemotherapy Failure in Breast Cancer. Scienmag. https://scienmag.com/tumor-diversity-emerges-as-a-hidden-driver-of-chemotherapy-failure-in-breast-cancer/
Nathaniel Bowman. “Tumor Diversity Emerges as a Hidden Driver of Chemotherapy Failure in Breast Cancer.” Scienmag, 3 October 2026, https://scienmag.com/tumor-diversity-emerges-as-a-hidden-driver-of-chemotherapy-failure-in-breast-cancer/. Accessed 3 October 2026.
Nathaniel Bowman. “Tumor Diversity Emerges as a Hidden Driver of Chemotherapy Failure in Breast Cancer.” Scienmag. October 3, 2026. https://scienmag.com/tumor-diversity-emerges-as-a-hidden-driver-of-chemotherapy-failure-in-breast-cancer/
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Tags: breast cancerbreast cancer tumor heterogeneitychemotherapy resistancechemotherapy resistance in breast cancergenomic and transcriptomic tumor analysisHER2-positiveimmunologic tumor heterogeneityimpact of tumor diversity on treatment outcomesintratumor genetic diversityintratumor heterogeneityMALAT1mechanisms of chemotherapy failureMRIneoadjuvant chemotherapyprimary breast cancer relapse factorsradiomic assessment of tumor heterogeneityradiomicssingle-cell sequencingsystematic reviewsystematic review of tumor heterogeneitytriple-negative breast cancertumor diversity as a driver of treatment resistancetumor microenvironmenttumor phenotypic variability
