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Chromosomal instability (CIN)—which is a high rate of loss or gain of whole or parts of chromosomes—is a characteristic of most human cancers and a cause of tumour aneuploidy and intra‐tumour heterogeneity. CIN is associated with poor patient outcome and drug resistance, which could be mediated by evolutionary adaptation fostered by intra‐tumour heterogeneity. In this review, we discuss the clinical consequences of CIN and the challenges inherent to its measurement in tumour specimens. The relationship between CIN and prognosis supports assessment of CIN status in the clinical setting and suggests that stratifying tumours according to levels of CIN could facilitate clinical risk assessment. This review provides a much‐needed translational perspective into the issue of aneuploidy and chromosomal instability, discussing the prognostic value of CIN assessment in human tumours, methods to analyze it and how it could be therapeutically targeted.

Hepatoblastoma (HB) is a common paediatric liver malignancy characterised by significant intratumoral heterogeneity and a complex tumour microenvironment (TME). Using single‐cell RNA sequencing (scRNA‐seq), we analysed 43,592 cells from three tumour regions and adjacent normal tissue of an HB patient. Our study revealed distinct cellular compositions and varying degrees of malignancy across different tumour regions, with the T1 region showing the highest malignancy and overexpression of HMGB2 and TOP2A. Survival analysis demonstrated that high HMGB2 expression is associated with poor prognosis and increased recurrence, suggesting its potential as a prognostic marker. Additionally, we identified a diverse immune microenvironment enriched with regulatory T cells (Tregs) and CD8+ effector memory T cells (Tem), indicating potential immune evasion mechanisms. Notably, CTLA‐4 and PD‐1 were highly expressed in Tregs and Tem cells, highlighting their potential as immunotherapy targets. Myeloid cells, including Kupffer cells and dendritic cells, also exhibited distinct functional roles in different tumour regions. This study provides the first comprehensive single‐cell atlas of HB, revealing critical insights into its intratumoral heterogeneity and immune microenvironment. Our findings not only advance the understanding of HB biology but also offer new directions for precision medicine, including the development of targeted therapies and immunotherapeutic strategies to improve patient outcomes.

Prostate cancer (PCa) is an extremely heterogeneous cancer and is highly prevalent in the older male population. Since intra‐tumour heterogeneity (ITH) commonly results in PCa chemotherapy resistance and recurrence, it is critical to explore its effects on tumour behaviour. Prognostic genes related to ITH were identified, and a signature was constructed using Cox regression analyses and multiple machine learning algorithms. Single‐cell RNA sequencing data extracted from PCa and CRPC samples were analysed via sub‐clustering, pseudotime, cell communication and drug sensitivity approaches to elucidate their function. The oncogenic potential of hub genes was confirmed by immunohistochemistry and cell proliferation assays. An 11‐gene signature underlying a prostate cancer meta‐program (PCMP) was generated by selecting an optimal combination of machine learning methods. Survival assays and multivariate Cox regression analyses conducted in multiple cohorts revealed the superior prognostic value of the PCMP signature. Functional enrichment analyses indicated that it dysregulates the cell cycle. Using trajectory and cell–cell communication analyses, we illustrated that PCMP genes exert oncogenic effects by enhancing the proliferation and oxidative phosphorylation of epithelial cells. Intra‐cellular assays also demonstrated that CENPA and CKS1B had promising malignant potential. In summary, our research not only establishes the association between the PCMP signature and reveals its malignant characteristics, but also deepens our understanding of the mechanisms underlying PCa progression and ITH. It holds promise for the development of targeted therapeutic interventions, thereby offering clinical benefits to patients.

Today, clinical evaluation of tumor heterogeneity is an emergent issue to improve clinical oncology. In particular, intra-tumor heterogeneity (ITH) is closely related to cancer progression, resistance to therapy, and recurrences. It is interconnected with complex molecular mechanisms including spatial and temporal phenomena, which are often peculiar for every single patient. This review tries to describe all the types of ITH including morphohistological ITH, and at the molecular level clonal ITH derived from genomic instability and nonclonal ITH derived from microenvironment interaction. It is important to consider the different types of ITH as a whole for any patient to investigate on cancer progression, prognosis, and treatment opportunities. From a practical point of view, analytical methods that are widely accessible today, or will be in the near future, are evaluated to investigate the complex pattern of ITH in a reproducible way for a clinical application.

Despite advances in malignant pleural mesothelioma therapy, life expectancy of affected patients remains short. The limited efficiency of treatment options is mainly caused by inter- and intra-tumor heterogeneity of mesotheliomas. This diversity can be observed at the morphological and molecular levels. Molecular analyses reveal a high heterogeneity (i) between patients; (ii) within different areas of a given tumor in terms of different clonal compositions; and (iii) during treatment over time. The aim of the present review is to highlight this diversity and its therapeutic implications.

Breast tumor heterogeneity is a major challenge in the clinical management of breast cancer patients. Both inter-tumor and intra-tumor heterogeneity imply that each breast cancer (BC) could have different prognosis and would benefit from specific therapy. Breast cancer is a dynamic entity, changing during tumor progression and metastatization and this poses fundamental issues to the feasibility of a personalized medicine approach. The most effective therapeutic strategy for patients with recurrent disease should be assessed evaluating biopsies obtained from metastatic sites. Furthermore, the tumor progression and the treatment response should be strictly followed and radiogenomics and liquid biopsy might be valuable tools to assess BC heterogeneity in a non-invasive way.

Background Breast cancer is a highly heterogeneous disease, characterized by tumor and nontumor cells at various cell states. Ecotyper is an innovative machine learning framework that quantifies the tumor microenvironment and delineates the tumor ecosystem, demonstrating clinical significance. However, further validation is needed in breast cancer. Methods Ecotyper was applied to identify multiple cellular states and tumor ecotypes using large‐scale breast cancer bulk sequencing data, followed by a detailed analysis of their associations with clinical classification, molecular subtypes, survival prognosis, and immunotherapy response. Identified subtypes were further characterized using single‐cell and spatial data sets to reveal molecular profiles. Results In a comprehensive analysis of 6578 breast cancer samples from four data sets, Ecotyper identified 69 cellular states and 10 tumor ecotypes. Of these, 37 cellular states significantly correlated with overall survival. Notably, specific states within epithelial cells, macrophages/monocytes, and fibroblasts were linked to a worse prognosis. CE2 abundance was identified as the most significant marker indicating unfavorable prognosis and was further validated in an additional data set of 116 HER2‐negative patients. These biomarkers also indicated the efficacy of neoadjuvant immunotherapy in breast cancer. CE2‐high cancers were characterized by an abundance of basal‐like epithelial cells, scant lymphocytic infiltration, and activation of hypoxia signaling. Single‐cell analysis showed that CE2‐high areas were rich in SPP1‐positive tumor‐associated macrophages(TAM), basal‐like epithelial cells, and hypoxic cancer‐associated fibroblasts(CAF). Spatially, these regions were often peripheral in triple‐negative breast cancer, adjacent to fibrotic/necrotic zones. Multiplex immunofluorescence confirmed the enrichment of SPP1+CD68+TAM and HIF1A+SMA+CAF in hypoxic triple‐negative breast cancer (TNBC) regions. Conclusions Ecotyper identified novel biomarkers for breast cancer prognosis and treatment prediction. The CE2‐high region may represent a hypoxic immune‐suppressive niche. A novel breast cancer subtyping based on intra‐tumoral microenvironment heterogeneity predicts prognosis and immunotherapy response. The CE2 subtype, in particular, is linked to the worst outcomes and the most significant immunotherapy benefits, with distinct cellular and molecular features mediated by hypoxia.

Ovarian cancer encompasses a collection of neoplasms with distinct clinicopathological and molecular features and prognosis. Despite there being a variety of ovarian cancer subtypes, these are treated as a single disease. Tremendous efforts have been made to characterize these subtypes and identify tumoral pathways and potential biomarkers for therapeutic strategies. As in other cancer types, tumor heterogeneity appears to be very high across subtypes and within a single tumor, representing a major cause of treatment failure. We describe the morphological and molecular heterogeneity among ovarian cancers and discuss recent advances in our understanding of intratumor heterogeneity.

Lung cancer diagnosis is a challenge since it is also one of the most frequently diagnosed cancers. Diagnostic challenges are deeply related to the development of personalized therapy and molecular and precise histological characterizations of lung cancer. When addressing these features, it is very important to acknowledge the issue of tumour heterogeneity, as it imposes several questions. First of all, lung cancer is a very heterogeneous disease, at a cellular and histological level. Cellular and histological heterogeneity are addressed with emphasis on the diagnosis, pre-neoplastic lesions, and cell origin, trying to contribute to a better knowledge of carcinogenesis. Molecular intra-tumour and inter-tumour heterogeneity are also addressed as temporal heterogeneity. Lung cancer heterogeneity has implications in pathogenesis understanding, diagnosis, selection of tissue for molecular diagnosis, as well as therapeutic decision. The understanding of tumour heterogeneity is crucial and we must be aware of the implications and future developments regarding this field.

The facts that cancer represents tissues consisting of heterogeneous neoplastic, as well as reactive, cell populations and that cancers of the same histotype may show profound differences in clinical behavior have long been recognized. With the advent of new technologies and the demands of precision medicine, the investigation of tumor heterogeneity has gained much interest. An understanding of intertumoral heterogeneity in patients with the same disease entity is necessary to optimally guide personalized treatment. In addition, increasing evidence indicates that different tumor areas or primary tumors and metastases in an individual patient can show significant intratumoral heterogeneity on different levels. This phenomenon can be driven by genomic instability, epigenetic events, the tumor microenvironment, and stochastic variations in cellular function and antitumoral therapies. These mechanisms may lead to branched subclonal evolution from a common progenitor clone, resulting in spatial variation between different tumor sites, disease progression, and treatment resistance. This review addresses tumor heterogeneity in lymphomas from a pathologist's viewpoint. The relationship between morphologic, immunophenotypic, and genetic heterogeneity is exemplified in different lymphoma entities and reviewed in the context of high-grade transformation and transdifferentiation. In addition, factors driving heterogeneity, as well as clinical and therapeutic implications of lymphoma heterogeneity, will be discussed.