Estrogen receptor 1 chromatin profiling in human breast tumors reveals high inter-patient heterogeneity with enrichment of risk SNPs and enhancer activity at most-conserved regions.

Estrogen Receptor 1 (ESR1; also known as ERα, encoded by ESR1 gene) is the main driver and prime drug target in luminal breast cancer. ESR1 chromatin binding is extensively studied in cell lines and a limited number of human tumors, using consensi of peaks shared among samples. However, little is known about inter-tumor heterogeneity of ESR1 chromatin action, along with its biological implications. Here, we use a large set of ESR1 ChIP-seq data from 70 ESR1+ breast cancers to explore inter-patient heterogeneity in ESR1 DNA binding to reveal a striking inter-tumor heterogeneity of ESR1 action. Of note, commonly shared ESR1 sites show the highest estrogen-driven enhancer activity and are most engaged in long-range chromatin interactions. In addition, the most commonly shared ESR1-occupied enhancers are enriched for breast cancer risk SNP loci. We experimentally confirm SNVs to impact chromatin binding potential for ESR1 and its pioneer factor FOXA1. Finally, in the TCGA breast cancer cohort, we can confirm these variations to associate with differences in expression for the target gene. Cumulatively, we reveal a natural hierarchy of ESR1-chromatin interactions in breast cancers within a highly heterogeneous inter-tumor ESR1 landscape, with the most common shared regions being most active and affected by germline functional risk SNPs for breast cancer development.

Combined DNA Analysis from Stool and Blood Samples Improves Tumor Tracking and Assessment of Clonal Heterogeneity in Localized Rectal Cancer Patients.

Objectives: In this study, stool samples were evaluated for tumor mutation analysis via a targeted next generation sequencing (NGS) approach in a small patient cohort suffering from localized rectal cancer. Introduction: Colorectal cancer (CRC) causes the second highest cancer-related death rate worldwide. Thus, improvements in disease assessment and monitoring that may facilitate treatment allocation and allow organ-sparing "watch-and-wait" treatment strategies are highly relevant for a significant number of CRC patients. Methods: Stool-based results were compared with mutation profiles derived from liquid biopsies and the gold standard procedure of tumor biopsy from the same patients. A workflow was established that enables the detection of de-novo tumor mutations in stool samples of CRC patients via ultra-sensitive cell-free tumor DNA target enrichment. Results: Notably, only a 19% overall concordance was found in mutational profiles across the compared sample specimens of stool, tumor, and liquid biopsies. Conclusion: Based on these results, the analysis of stool and liquid biopsy samples can provide important additional information on tumor heterogeneity and potentially on the assessment of minimal residual disease and clonal tumor evolution.

Unveiling the Dynamics behind Glioblastoma Multiforme Single-Cell Data Heterogeneity.

Glioblastoma Multiforme is a brain tumor distinguished by its aggressiveness. We suggested that this aggressiveness leads single-cell RNA-sequence data (scRNA-seq) to span a representative portion of the cancer attractors domain. This conjecture allowed us to interpret the scRNA-seq heterogeneity as reflecting a representative trajectory within the attractor's domain. We considered factors such as genomic instability to characterize the cancer dynamics through stochastic fixed points. The fixed points were derived from centroids obtained through various clustering methods to verify our method sensitivity. This methodological foundation is based upon sample and time average equivalence, assigning an interpretative value to the data cluster centroids and supporting parameters estimation. We used stochastic simulations to reproduce the dynamics, and our results showed an alignment between experimental and simulated dataset centroids. We also computed the Waddington landscape, which provided a visual framework for validating the centroids and standard deviations as characterizations of cancer attractors. Additionally, we examined the stability and transitions between attractors and revealed a potential interplay between subtypes. These transitions might be related to cancer recurrence and progression, connecting the molecular mechanisms of cancer heterogeneity with statistical properties of gene expression dynamics. Our work advances the modeling of gene expression dynamics and paves the way for personalized therapeutic interventions.

Integrating spatial and single-cell transcriptomics reveals tumor heterogeneity and intercellular networks in colorectal cancer.

Single cell RNA sequencing (scRNA-seq), a powerful tool for studying the tumor microenvironment (TME), does not preserve/provide spatial information on tissue morphology and cellular interactions. To understand the crosstalk between diverse cellular components in proximity in the TME, we performed scRNA-seq coupled with spatial transcriptomic (ST) assay to profile 41,700 cells from three colorectal cancer (CRC) tumor-normal-blood pairs. Standalone scRNA-seq analyses revealed eight major cell populations, including B cells, T cells, Monocytes, NK cells, Epithelial cells, Fibroblasts, Mast cells, Endothelial cells. After the identification of malignant cells from epithelial cells, we observed seven subtypes of malignant cells that reflect heterogeneous status in tumor, including tumor_CAV1, tumor_ATF3_JUN | FOS, tumor_ZEB2, tumor_VIM, tumor_WSB1, tumor_LXN, and tumor_PGM1. By transferring the cellular annotations obtained by scRNA-seq to ST spots, we annotated four regions in a cryosection from CRC patients, including tumor, stroma, immune infiltration, and colon epithelium regions. Furthermore, we observed intensive intercellular interactions between stroma and tumor regions which were extremely proximal in the cryosection. In particular, one pair of ligands and receptors (C5AR1 and RPS19) was inferred to play key roles in the crosstalk of stroma and tumor regions. For the tumor region, a typical feature of TMSB4X-high expression was identified, which could be a potential marker of CRC. The stroma region was found to be characterized by VIM-high expression, suggesting it fostered a stromal niche in the TME. Collectively, single cell and spatial analysis in our study reveal the tumor heterogeneity and molecular interactions in CRC TME, which provides insights into the mechanisms underlying CRC progression and may contribute to the development of anticancer therapies targeting on non-tumor components, such as the extracellular matrix (ECM) in CRC. The typical genes we identified may facilitate to new molecular subtypes of CRC.

Pervasive structural heterogeneity rewires glioblastoma chromosomes to sustain patient-specific transcriptional programs.

Glioblastoma multiforme (GBM) encompasses brain malignancies marked by phenotypic and transcriptional heterogeneity thought to render these tumors aggressive, resistant to therapy, and inevitably recurrent. However, little is known about how the spatial organization of GBM genomes underlies this heterogeneity and its effects. Here, we compile a cohort of 28 patient-derived glioblastoma stem cell-like lines (GSCs) known to reflect the properties of their tumor-of-origin; six of these were primary-relapse tumor pairs from the same patient. We generate and analyze 5 kbp-resolution chromosome conformation capture (Hi-C) data from all GSCs to systematically map thousands of standalone and complex structural variants (SVs) and the multitude of neoloops arising as a result. By combining Hi-C, histone modification, and gene expression data with chromatin folding simulations, we explain how the pervasive, uneven, and idiosyncratic occurrence of neoloops sustains tumor-specific transcriptional programs via the formation of new enhancer-promoter contacts. We also show how even moderately recurrent neoloops can relate to patient-specific vulnerabilities. Together, our data provide a resource for dissecting GBM biology and heterogeneity, as well as for informing therapeutic approaches.

Heterogeneity and molecular landscape of melanoma: implications for targeted therapy.

Uveal cancer (UM) offers a complex molecular landscape characterized by substantial heterogeneity, both on the genetic and epigenetic levels. This heterogeneity plays a critical position in shaping the behavior and response to therapy for this uncommon ocular malignancy. Targeted treatments with gene-specific therapeutic molecules may prove useful in overcoming radiation resistance, however, the diverse molecular makeups of UM call for a patient-specific approach in therapy procedures. We need to understand the intricate molecular landscape of UM to develop targeted treatments customized to each patient's specific genetic mutations. One of the promising approaches is using liquid biopsies, such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), for detecting and monitoring the disease at the early stages. These non-invasive methods can help us identify the most effective treatment strategies for each patient. Single-cellular is a brand-new analysis platform that gives treasured insights into diagnosis, prognosis, and remedy. The incorporation of this data with known clinical and genomics information will give a better understanding of the complicated molecular mechanisms that UM diseases exploit. In this review, we focused on the heterogeneity and molecular panorama of UM, and to achieve this goal, the authors conducted an exhaustive literature evaluation spanning 1998 to 2023, using keywords like "uveal melanoma, "heterogeneity". "Targeted therapies"," "CTCs," and "single-cellular analysis".

Multiregional transcriptomics identifies congruent consensus subtypes with prognostic value beyond tumor heterogeneity of colorectal cancer.

Intra-tumor heterogeneity compromises the clinical value of transcriptomic classifications of colorectal cancer. We investigated the prognostic effect of transcriptomic heterogeneity and the potential for classifications less vulnerable to heterogeneity in a single-hospital series of 1093 tumor samples from 692 patients, including multiregional samples from 98 primary tumors and 35 primary-metastasis sets. We show that intra-tumor heterogeneity of the consensus molecular subtypes (CMS) is frequent and has poor-prognostic associations independently of tumor microenvironment markers. Multiregional transcriptomics uncover cancer cell-intrinsic and low-heterogeneity signals that recapitulate the intrinsic CMSs proposed by single-cell sequencing. Further subclassification identifies congruent CMSs that explain a larger proportion of variation in patient survival than intra-tumor heterogeneity. Plasticity is indicated by discordant intrinsic phenotypes of matched primary and metastatic tumors. We conclude that multiregional sampling reconciles the prognostic power of tumor classifications from single-cell and bulk transcriptomics in the context of intra-tumor heterogeneity, and phenotypic plasticity challenges the reconciliation of primary and metastatic subtypes.

Heterogeneity generating capacity in tumorigenesis and cancer therapeutics.

Cells of multicellular organisms generate heterogeneity in a controlled and transient fashion during embryogenesis, which can be reactivated in pathologies such as cancer. Although genomic heterogeneity is an important part of tumorigenesis, continuous generation of phenotypic heterogeneity is central for the adaptation of cancer cells to the challenges of tumorigenesis and response to therapy. Here I discuss the capacity of generating heterogeneity, hereafter called cell hetness, in cancer cells both as the activation of hetness oncogenes and inactivation of hetness tumor suppressor genes, which increase the generation of heterogeneity, ultimately producing an increase in adaptability and cell fitness. Transcriptomic high hetness states in therapy-tolerant cell states denote its importance in cancer resistance to therapy. The definition of the concept of hetness will allow the understanding of its origins, its control during embryogenesis, its loss of control in tumorigenesis and cancer therapeutics and its active targeting.

3D genomic mapping reveals multifocality of human pancreatic precancers.

Pancreatic intraepithelial neoplasias (PanINs) are the most common precursors of pancreatic cancer, but their small size and inaccessibility in humans make them challenging to study1. Critically, the number, dimensions and connectivity of human PanINs remain largely unknown, precluding important insights into early cancer development. Here, we provide a microanatomical survey of human PanINs by analysing 46 large samples of grossly normal human pancreas with a machine-learning pipeline for quantitative 3D histological reconstruction at single-cell resolution. To elucidate genetic relationships between and within PanINs, we developed a workflow in which 3D modelling guides multi-region microdissection and targeted and whole-exome sequencing. From these samples, we calculated a mean burden of 13 PanINs per cm3 and extrapolated that the normal intact adult pancreas harbours hundreds of PanINs, almost all with oncogenic KRAS hotspot mutations. We found that most PanINs originate as independent clones with distinct somatic mutation profiles. Some spatially continuous PanINs were found to contain multiple KRAS mutations; computational and in situ analyses demonstrated that different KRAS mutations localize to distinct cell subpopulations within these neoplasms, indicating their polyclonal origins. The extensive multifocality and genetic heterogeneity of PanINs raises important questions about mechanisms that drive precancer initiation and confer differential progression risk in the human pancreas. This detailed 3D genomic mapping of molecular alterations in human PanINs provides an empirical foundation for early detection and rational interception of pancreatic cancer.

Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm.

BACKGROUND AND OBJECTIVE: Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcome. METHODS: We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. Classification accuracy of each gene were compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity. RESULTS: WSO-SVM achieved 0.80 accuracy, 0.79 sensitivity, and 0.81 specificity for classifying EGFR; 0.71 accuracy, 0.70 sensitivity, and 0.72 specificity for classifying PDGFRA; 0.80 accuracy, 0.78 sensitivity, and 0.83 specificity for classifying PTEN; these results significantly outperformed the existing ML algorithms. Using SHAP, we found that the relative contributions of the five contrast images differ between genes, which are consistent with findings in the literature. The prediction maps revealed extensive intra-tumoral region-to-region heterogeneity within each individual tumor in terms of the alteration status of the three genes. CONCLUSIONS: This study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology.

Molecular Heterogeneity in Leiomyosarcoma and Implications for Personalised Medicine.

OPINION STATEMENT: Leiomyosarcoma (LMS) is one of the more common subtypes of soft tissue sarcomas (STS), accounting for about 20% of cases. Differences in anatomical location, risk of recurrence and histomorphological variants contribute to the substantial clinical heterogeneity in survival outcomes and therapy responses observed in patients. There is therefore a need to move away from the current one-size-fits-all treatment approach towards a personalised strategy tailored for individual patients. Over the past decade, tissue profiling studies have revealed key genomic features and an additional layer of molecular heterogeneity among patients, with potential utility for optimal risk stratification and biomarker-matched therapies. Furthermore, recent studies investigating intratumour heterogeneity and tumour evolution patterns in LMS suggest some key features that may need to be taken into consideration when designing treatment strategies and clinical trials. Moving forward, national and international collaborative efforts to aggregate expertise, data, resources and tools are needed to achieve a step change in improving patient survival outcomes in this disease of unmet need.

A Multiplexed Approach to Assess Small Cell Lung Cancer Subtype Heterogeneity in Primary and Patient-Derived Tumor Samples.

Unlocking the heterogeneity of cancers is crucial for developing therapeutic approaches that effectively eradicate disease. As our understanding of markers specific to cancer subclones or subtypes expands, there is a growing demand for advanced technologies that enable the simultaneous investigation of multiple targets within an individual tumor sample. Indeed, multiplex approaches offer distinct benefits, particularly when tumor specimens are small and scarce. Here we describe the utility of two fluorescence-based multiplex approaches; fluorescent Western blots, and multiplex immunohistochemistry (Opal™) staining to interrogate heterogeneity, using small cell lung cancer as an example. Critically, the coupling of Opal™ staining with advanced image quantitation, permits the dissection of cancer cell phenotypes at a single cell level. These approaches can be applied to patient biopsies and/or patient-derived xenograft (PDX) models and serve as powerful methodologies for assessing tumor cell heterogeneity in response to therapy or between metastatic lesions across diverse tissue sites.

Patterns of Chromosomal Instability and Clonal Heterogeneity in Luminal B Breast Cancer: A Pilot Study.

Chromosomal instability (CIN), defined by variations in the number or structure of chromosomes from cell to cell, is recognized as a distinctive characteristic of cancer associated with the ability of tumors to adapt to challenging environments. CIN has been recognized as a source of genetic variation that leads to clonal heterogeneity (CH). Recent findings suggest a potential association between CIN and CH with the prognosis of BC patients, particularly in tumors expressing the epidermal growth factor receptor 2 (HER2+). In fact, information on the role of CIN in other BC subtypes, including luminal B BC, is limited. Additionally, it remains unknown whether CIN in luminal B BC tumors, above a specific threshold, could have a detrimental effect on the growth of human tumors or whether low or intermediate CIN levels could be linked to a more favorable BC patient prognosis when contrasted with elevated levels. Clarifying these relationships could have a substantial impact on risk stratification and the development of future therapeutic strategies aimed at targeting CIN in BC. This study aimed to assess CIN and CH in tumor tissue samples from ten patients with luminal B BC and compare them with established clinicopathological parameters. The results of this study reveal that luminal B BC patients exhibit intermediate CIN and stable aneuploidy, both of which correlate with lymphovascular invasion. Our results also provide valuable preliminary data that could contribute to the understanding of the implications of CIN and CH in risk stratification and the development of future therapeutic strategies in BC.

Treatments, prognostic factors, and genetic heterogeneity in advanced cholangiocarcinoma: A multicenter real-world study.

BACKGROUND AND AIMS: Cholangiocarcinoma (CCA), a rare and aggressive hepatobiliary malignancy, presents significant clinical management challenges. Despite rising incidence and evolving treatment options, prognosis remains poor, motivating the exploration of real-world data for enhanced understanding and patient care. METHODS: This multicenter study analyzed data from 120 metastatic CCA patients at three institutions from 2016 to 2023. Kaplan-Meier curves assessed overall survival (OS), while univariate and multivariate analyses evaluated links between clinical variables (age, gender, tumor site, metastatic burden, ECOG performance status, response to first-line chemotherapy) and OS. Genetic profiling was conducted selectively. RESULTS: Enrolled patients had a median age of 68.5 years, with intrahepatic tumors predominant in 79 cases (65.8%). Among 85 patients treated with first-line chemotherapy, cisplatin and gemcitabine (41.1%) was the most common regimen. Notably, one-third received no systemic treatment. After a median 14-month follow-up, 81 CCA-related deaths occurred, with a median survival of 13.1 months. Two clinical variables independently predicted survival: response to first-line chemotherapy (disease control vs. no disease control; HR: 0.27; 95% CI: 0.14-0.50; p < 0.0001) and metastatic involvement (>1 site vs. 1 site; HR: 1.99; 95% CI: 1.04-3.80; p = 0.0366). The three most common genetic alterations involved the ARID1A, tp53, and CDKN2A genes. CONCLUSIONS: Advanced CCA displays aggressive clinical behavior, emphasizing the need for treatments beyond chemotherapy. Genetic diversity supports potential personalized therapies. Collaborative research and deeper CCA biology understanding are crucial to enhance patient outcomes in this challenging malignancy.

Information-incorporated sparse hierarchical cancer heterogeneity analysis.

Cancer heterogeneity analysis is essential for precision medicine. Most of the existing heterogeneity analyses only consider a single type of data and ignore the possible sparsity of important features. In cancer clinical practice, it has been suggested that two types of data, pathological imaging and omics data, are commonly collected and can produce hierarchical heterogeneous structures, in which the refined sub-subgroup structure determined by omics features can be nested in the rough subgroup structure determined by the imaging features. Moreover, sparsity pursuit has extraordinary significance and is more challenging for heterogeneity analysis, because the important features may not be the same in different subgroups, which is ignored by the existing heterogeneity analyses. Fortunately, rich information from previous literature (for example, those deposited in PubMed) can be used to assist feature selection in the present study. Advancing from the existing analyses, in this study, we propose a novel sparse hierarchical heterogeneity analysis framework, which can integrate two types of features and incorporate prior knowledge to improve feature selection. The proposed approach has satisfactory statistical properties and competitive numerical performance. A TCGA real data analysis demonstrates the practical value of our approach in analyzing data heterogeneity and sparsity.

Patient genetic heterogeneities acting as indicators of post-operative pain and opioid requirement in orthopedic surgery: A systematic review.

INTRODUCTION: Orthopedic surgical procedures are expected to increase annually, making it imperative to understand the correlations between patient genetic makeup and post-operative pain levels. METHODS: We performed a systematic literature review using PubMed and Cochrane databases in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A total of 299 articles were initially selected, 20 articles remained after title and abstract review, and nine articles were selected for inclusion upon full text review. RESULTS: Genetic risk factors identified included the A allele of the 5HT2A gene single nucleotide polymorphism, the AA genotype of the ADRB2 gene, the CG genotype of the IL6 gene, the genotypes CT and TT of the NTRK1 gene, genotypes AA and GA of the OPRM gene, and the AA and GA genotypes of the COMT gene. Additional studies in the review discuss statistical significance of other variants of the COMT gene. CONCLUSION: There have been genetic association studies performed on the patient heterogeneity and its relationship on patient pain levels, but more data need to be collected to understand the clinical utility of stratifying patients based on genomic sequence.

Genetic heterogeneity of familial hypercholesterolaemia in two populations from two different countries.

BACKGROUND: Familial hypercholesterolemia (FH) is a genetically determined monogenic disorder of predominantly autosomal dominant inheritance. A number of studies on differences in the genetic profile of patients with FH have demonstrated the importance of a more substantive evaluation of genetic features. The aim of this study was to evaluate the genetic profile of patients with clinical FH among Italian and Russian patients. METHODS: We included 144 Italian and 79 Russian FH patients; clinical diagnosis was based on the same criteria. Patients were divided in: positive to genetic test (one causative variant), inconclusive (only variants of uncertain clinical significance [VUS]), and negative (with likely benign/benign variants, heterozygous variants in LDLRAP1 gene, or without causative variants). RESULTS: The genetic test was positive in 76.4 % of the Italian patients and in 49.4 % of the Russian patients. The presence of VUS alone was detected in 7.6 % and in 19.0 % (p < 0.001), respectively. Among patients with positive genetic diagnosis, pre-treatment LDL-C levels were higher in the Russian cohort (353.5 ± 111.3 vs. 302.7 ± 52.1 mg/dL, p = 0.009), as well as the percentage of treated patients (53.8 % vs. 14.5 %, p < 0.001) and the prevalence of premature coronary heart disease (12.8 % vs. 3.6 %, p = 0.039). Among patients carrying only VUS, mean pre-treatment LDL-C levels were similar between the cohorts (299.5 ± 68.1 vs. 295.3 ± 46.8 mg/dL, p = 0.863). Among pathogenic/likely pathogenic variants and VUS, only 5 % and 4 % was shared between the two cohorts, respectively. CONCLUSION: The genetic background of patients clinically diagnosed with FH in two different countries is characterized by high variability.

Glioblastoma evolution and heterogeneity from a 3D whole-tumor perspective.

Treatment failure for the lethal brain tumor glioblastoma (GBM) is attributed to intratumoral heterogeneity and tumor evolution. We utilized 3D neuronavigation during surgical resection to acquire samples representing the whole tumor mapped by 3D spatial coordinates. Integrative tissue and single-cell analysis revealed sources of genomic, epigenomic, and microenvironmental intratumoral heterogeneity and their spatial patterning. By distinguishing tumor-wide molecular features from those with regional specificity, we inferred GBM evolutionary trajectories from neurodevelopmental lineage origins and initiating events such as chromothripsis to emergence of genetic subclones and spatially restricted activation of differential tumor and microenvironmental programs in the core, periphery, and contrast-enhancing regions. Our work depicts GBM evolution and heterogeneity from a 3D whole-tumor perspective, highlights potential therapeutic targets that might circumvent heterogeneity-related failures, and establishes an interactive platform enabling 360° visualization and analysis of 3D spatial patterns for user-selected genes, programs, and other features across whole GBM tumors.

Intratumor genetic heterogeneity and head and neck cancer relapse.

BACKGROUND: Head and neck squamous cell carcinomas are treated by surgery, radiotherapy (RT), chemoradiotherapy (CRT) or combinations thereof, but locoregional recurrences (LRs) occur in 30-40% of treated patients. We have previously shown that in approximately half of the LRs after CRT, cancer driver mutations are not shared with the index tumor. AIM: To investigate two possible explanations for these genetically unrelated relapses, treatment-induced genetic changes and intratumor genetic heterogeneity. METHODS: To investigate treatment-induced clonal DNA changes, we compared copy number alterations (CNAs) and mutations between primary and recurrent xenografted tumors after treatment with (C)RT. Intratumor genetic heterogeneity was studied by multi-region sequencing on DNA from 31 biopsies of 11 surgically treated tumors. RESULTS: Induction of clonal DNA changes by (C)RT was not observed in the xenograft models. Multi-region sequencing demonstrated variations in CNA profiles between paired biopsies of individual tumors, with copy number heterogeneity scores varying from 0.027 to 0.333. In total, 32 cancer driver mutations could be identified and were shared in all biopsies of each tumor. Remarkably, multi-clonal mutations in these same cancer driver genes were observed in 6 of 11 tumors. Genetically distinct heterogeneous cell cultures could also be established from single tumors, with different biomarker profiles and drug sensitivities. CONCLUSION: Intratumor genetic heterogeneity at the level of the cancer driver mutations might explain the discordant mutational profiles in LRs after CRT, while there are no indications in xenograft models that these changes are induced by CRT.