Tumor Heterogeneity Research Articles

Establishment and Validation of Patient-Derived Non-Small Cell Lung Cancer Organoids as In Vitro Lung Cancer Models

Background: Recent advances in the personalized treatment of non-small cell lung cancer (NSCLC) require representative in vitro model systems that reflect tumor heterogeneity and maintain the characteristic genetic aberrations. We therefore aimed to establish patient-derived NSCLC organoids that offer a reliable platform for further investigations. Methods: NSCLC organoids were cultured between May 2020 and February 2022 from surgically resected NSCLC tissue specimens. After histological and immunohistochemical validation, genetic validation was performed by targeted next-generation sequencing of tissue and organoid specimens using the Oncomine Focus Assay (ThermoFisher Scientific). Results: From 37 resected NSCLC samples, 18 primary organoid cultures were successfully established and expanded during early passages. Upon histomorphological validation, organoids showed complementary characteristics when compared to the resected parental tumor, including adenocarcinoma, squamous cell carcinoma, mucoepidermoid carcinoma, and lung carcinoid differentiation. Among nine parental tumors, traceable genetic alterations were detected, and three corresponding organoids lines retained this mutational profile, including a KRAS p.Gly12Val mutation, KRAS p.Gly12Cys mutation, and RET-fusion. Conclusions: The establishment of primary NSCLC organoids from surgically resected tissue is feasible. Histological, immunohistochemical, and genetic validation is essential to identify representative NSCLC organoids that maintain the characteristics of the parental tumor. Overall, low establishment rates remain a challenge for broad clinical applications.

AI in Biomedical Imaging and Diagnostics

Advances in artificial intelligence (AI) and synthetic biology have profoundly influenced biomedical research, creating transformative opportunities in imaging, diagnostics, and therapeutic engineering. In biomedical imaging, AI-driven algorithms enhance precision and accuracy, enabling automated analysis of complex datasets, real-time imaging insights, and identification of disease biomarkers. Meanwhile, synthetic biology redefines cellular engineering, particularly in T-cell research, by enabling customized functionalities, such as precision-targeted antigen recognition and tunable immune responses. The integration of AI into T-cell engineering amplifies these capabilities, facilitating the design and optimization of synthetic circuits, predictive modeling of cellular behaviors, and dynamic monitoring of therapeutic outcomes. This interdisciplinary approach is revolutionizing diagnostics and immunotherapy by streamlining the identification of disease-specific markers, improving diagnostic accuracy, and enabling real-time modulation of T-cell functionality within the tumor microenvironment. By combining AI-powered insights with synthetic biology's ability to engineer living systems, this research aims to address critical challenges in disease treatment, including tumor heterogeneity and immune evasion. This work explores the synergistic application of AI and synthetic biology in biomedical imaging and T-cell engineering, highlighting state-of-the-art technologies, their therapeutic potential, and the future landscape of personalized medicine.

Genomic, epigenomic and transcriptomic inter- and intra-tumor heterogeneity in desmoid tumors.

Desmoid tumors are bland fibroblastic tumors that do not metastasize but have a high rate of local recurrence. Previously published studies proposed two different transcriptomic signatures to predict relapse. Molecular heterogeneity has been well established in high-grade sarcomas but little is known about molecular variability within locally aggressive tumors such as desmoids. We performed transcriptomic profiling of 31 specimens from 20 primary desmoid tumors to identify genes predictive of relapse. We also performed multi-omic analysis including DNA methylation, copy number alterations, point mutations and gene expression on 24 specimens from different regions of primary and recurrent desmoid tumors from 3 patients (7-9 specimens per patient). We observed highly variable expression of transcriptomic prognostic signatures, both in patients who did or did not progress. Signatures associated with favorable and unfavorable outcome were detected in different regions within the same tumor. Further multi-omic studies showed remarkable intra- and inter-tumor heterogeneity of genomic, epigenomic and transcriptomic patterns. The transcriptomic profiles showed the highest degree of variability within tumors and between primary and recurrent tumors from the same patient. This study shows an unexpected degree of intra- and inter-tumor heterogeneity in desmoid tumors. Our analysis indicates that molecular analysis of a single tumor biopsy may underestimate the magnitude of molecular alterations in desmoid tumors. Our study also shows that recurrent desmoid tumors acquire multiple new molecular alterations. Thus, molecular heterogeneity is an important consideration in drug development and validation of prognostic and predictive biomarkers for desmoid tumors.

Personalized cancer vaccine design using AI-powered technologies

Immunotherapy has ushered in a new era of cancer treatment, yet cancer remains a leading cause of global mortality. Among various therapeutic strategies, cancer vaccines have shown promise by activating the immune system to specifically target cancer cells. While current cancer vaccines are primarily prophylactic, advancements in targeting tumor-associated antigens (TAAs) and neoantigens have paved the way for therapeutic vaccines. The integration of artificial intelligence (AI) into cancer vaccine development is revolutionizing the field by enhancing various aspect of design and delivery. This review explores how AI facilitates precise epitope design, optimizes mRNA and DNA vaccine instructions, and enables personalized vaccine strategies by predicting patient responses. By utilizing AI technologies, researchers can navigate complex biological datasets and uncover novel therapeutic targets, thereby improving the precision and efficacy of cancer vaccines. Despite the promise of AI-powered cancer vaccines, significant challenges remain, such as tumor heterogeneity and genetic variability, which can limit the effectiveness of neoantigen prediction. Moreover, ethical and regulatory concerns surrounding data privacy and algorithmic bias must be addressed to ensure responsible AI deployment. The future of cancer vaccine development lies in the seamless integration of AI to create personalized immunotherapies that offer targeted and effective cancer treatments. This review underscores the importance of interdisciplinary collaboration and innovation in overcoming these challenges and advancing cancer vaccine development.

The concept of the liquid biopsy in the treatment of malignant eye tumours

The liquid biopsy is acutting-edge technique that involves analysing non-solid biological tissues, primarily blood but also ocular fluids, for the presence of cancer cells or fragments of tumour DNA. Unlike traditional biopsies, liquid biopsies are usually minimally invasive and can be performed more frequently, enabling continuous monitoring of disease progression and treatment efficacy. This article (and the associated series of articles) outlines the key developments in liquid biopsy, which include the analysis of circulating tumor DNA (ctDNA), circulating tumor cells (CTC) and exosomal RNA and protein biomarkers. Techniques, such as digital droplet PCR and next-generation sequencing (NGS) have made it possible to detect even very low levels of ctDNA, which is crucial for early cancer detection and monitoring minimal residual disease. The detection of rare CTCs is enhanced by techniques, such as microfluidic devices and immunomagnetic separation. Multiomic approaches, whereby exosomal RNA, protein and ctDNA analyses are combined, help to create amore comprehensive picture of tumour biology, including insights into tumour heterogeneity, potentially leading to better diagnostic and prognostic tools and helping to predict treatment response and resistance. The challenges of liquid biopsy application, which will be described in the following article, include (a)standardization, (b)cost and accessibility, (c)validation and clinical utility. However, the liquid biopsy represents apromising frontier in the application of precision ocular oncology, with ongoing research likely to expand its applications and improve its effectiveness in the coming years.

Sensitive Detection of Gynecological Cancer Recurrence Using Circulating Tumor DNA and Digital PCR: A Comparative Study with Serum Biochemical Markers

Early detection of recurrences in gynecological cancers is crucial for women’s health. Circulating tumor DNA (ctDNA) analysis through liquid biopsy offers a promising approach for monitoring disease progression and identifying relapses. This study investigated the utility of digital Polymerase Chain Reaction (dPCR) for ctDNA detection in three gynecological cancer patients with clinically confirmed relapses during a two-year post-surgical follow-up. Patient-specific tumor mutations were identified through whole-exome sequencing (WES) and confirmed via Sanger sequencing. dPCR probes targeting these mutations were used to quantify the ctDNA levels in plasma samples collected throughout the follow-up period, and the findings were compared with standard serum biochemical markers. In two patients, persistent positive dPCR signals for the selected mutations were detected after tumor removal, with ctDNA levels progressively increasing even after post-surgical chemotherapy. Notably, dPCR identified elevated ctDNA levels before an increase in the cancer antigen 125 (CA125) biochemical marker was observed. In the third patient, no ctDNA signals from the two selected mutations were detected despite clinical evidence of recurrence, suggesting the emergence of new mutations. While this study highlights the promise of dPCR for early recurrence detection in gynecological cancers, it also underscores the critical need for comprehensive mutation panels to overcome the inherent challenges posed by tumor heterogeneity and the emergence of new mutations during disease progression.

Murine colon cancer derived cells exhibit heterogeneous resistance profiles against an oncolytic virus

Oncolytic virotherapy has shown efficacy in various animal models and a few human cancers. However, there are still significant limitations for the implementation of these therapies. One such limitation is the emergence of cellular resistances, which may appear rapidly considering the high genetic heterogeneity of most tumors. We previously showed that cellular resistance to an oncolytic virus can be mediated by the chronic activation of innate immunity. Here, we explored the existence of additional resistance mechanisms in murine colon cancer-derived cells. For this purpose, we isolated two cellular clones that were resistant to the oncolytic virus VSV-D51. While one of the clones showed a strong resistance profile associated with increased cytokine-mediated antiviral responses, the other clone showed a lower level of resistance that involves cytoskeletal reorganization, signaling by small GTPases, and cell structural changes. These results demonstrate the capacity of tumor cells to deploy heterogeneous mechanisms of resistance to oncolytic viruses.

Prognostic characteristics and drug sensitivity analysis of hepatocellular carcinoma based on histone modification-related genes: a multi-omics integrated study revealing potential therapeutic targets and individualized treatment strategies

BackgroundHepatocellular carcinoma (HCC) ranks among the most prevalent and lethal malignancies worldwide. Histone modifications (HMs) play a pivotal role in the initiation and progression of HCC. However, our understanding of HMs in HCC remains limited due to the disease’s heterogeneity and the complexity of HMs.MethodsWe integrated multi-omics data from multiple cohorts, including single-cell RNA sequencing, bulk RNA sequencing, and clinical information. Weighted gene co-expression network analysis (WGCNA) and consensus clustering were employed to identify histone-related genes. We developed a histone modification-related signature (HMRS) using 117 machine learning methods. Comprehensive analyses of molecular characteristics, immune landscape, and drug sensitivity associated with the HMRS were performed.ResultsThrough integrative analysis, we defined 110 histone-related genes and identified 45 HCC-HM-related genes (HCC-HMRgenes). The HMRS demonstrated robust prognostic value across multiple cohorts. Patients with high HMRS scores exhibited distinct genomic alterations, including higher tumor heterogeneity and TP53 mutations. The high-risk group showed enrichment in cell cycle, DNA repair, and metabolic pathways. Immune landscape analysis revealed significant differences in immune cell infiltration and pathway activities between high- and low-risk groups. Drug sensitivity prediction suggested potential therapeutic strategies for different risk groups.ConclusionOur study provides a comprehensive understanding of HMs in HCC and establishes a robust prognostic signature. The HMRS not only stratifies patients into distinct risk groups but also offers insights into underlying molecular mechanisms, immune characteristics, and potential therapeutic strategies, paving the way for personalized medicine in HCC.

Spatial and Single Cell Analyses Reveal Heterogeneity of DNAM-1 Receptor-Ligand Interactions that Instructs Intratumoral γδT-Cell Activity.

The dynamic interplay between tumor cells and γδT cells within the tumor microenvironment (TME) significantly influences disease progression and immunotherapy outcome. Here, we delved into the modulation of γδT-cell activation by tumor cell ligands CD112 and CD155, which interact with the activating receptor DNAM-1 on γδT cells. Spatial and single cell RNA sequencing (scRNA-seq), as well as spatial metabolome analysis, from neuroblastoma (NB) revealed that the expression levels and localization of CD112 and CD155 varied across and within tumors, correlating with differentiation status, metabolic pathways, and ultimately disease prognosis and patient survival. Both in vivo tumor xenograft experiments and in vitro co-culture experiments demonstrated that a high CD112/CD155 expression ratio in tumors enhanced γδT-cell-mediated cytotoxicity, while a low-ratio fostered tumor resistance. Mechanistically, CD112 sustained DNAM-1-mediated γδT-cell activation, whereas CD155 downregulated DNAM-1 expression via TRIM21-mediated ubiquitin proteasomal degradation. By interacting with tumor cells differentially expressing CD112 and CD155, intratumoral γδT cells exhibited varying degrees of activation and DNAM-1 expression, representing three major functional subsets. This study underscores the complexity of tumor-immune crosstalk, offering insights into how tumor heterogeneity shapes the immune landscape.

Spatial transcriptomics reveals strong association between SFRP4 and extracellular matrix remodeling in prostate cancer

Prostate tumor heterogeneity is a major obstacle when studying the biological mechanisms of molecular markers. Increased gene expression levels of secreted frizzled-related protein 4 (SFRP4) is a biomarker in aggressive prostate cancer. To understand how SFRP4 relates to prostate cancer we performed comprehensive spatial and multiomics analysis of the same prostate cancer tissue samples. The experimental workflow included spatial transcriptomics, bulk transcriptomics, proteomics, DNA methylomics and tissue staining. SFRP4 mRNA was predominantly located in cancer stroma, produced by fibroblasts and smooth muscle cells, and co-expressed with extracellular matrix components. We also confirmed that higher SFRP4 gene expression is associated with cancer aggressiveness. Gene expression of SFRP4 was affected by gene promotor methylation. Surprisingly, the high mRNA levels did not reflect SFRP4 protein levels, which was much lower. This study contributes previously unknown insights of SFRP4 mRNA in the prostate tumor environment that potentially can improve diagnosis and treatment.

Targeting heterogeneous tumor microenvironments in pancreatic cancer mouse models of metastasis by TGF-β depletion.

The dual tumor-suppressive and -promoting functions of TGF-β signaling has made its targeting challenging. We examined the effects of TGF-β depletion by AVID200/BMS-986416 (TGF-β-TRAP), a TGF-β ligand trap, on the tumor microenvironment of pancreatic ductal adenocarcinoma (PDAC) murine models with different organ-specific metastasis. Our study demonstrated that TGF-β-TRAP potentiates the efficacy of anti-programmed cell death 1 (anti-PD-1) in a PDAC orthotopic murine model with liver metastasis tropism, significantly reducing liver metastases. We further demonstrated the heterogeneous response of cytotoxic effector T cells to combination TGF-β-TRAP and anti-PD-1 treatment across several tumor models. Single-nuclear RNA sequencing suggested that TGF-β-TRAP modulates cancer-associated fibroblast (CAF) heterogeneity and suppresses neutrophil degranulation and CD4+ T cell response to neutrophil degranulation. Ligand-receptor analysis indicated that TGF-β-TRAP may modulate the CCL5/CCR5 axis as well as costimulatory and checkpoint signaling from CAFs and myeloid cells. Notably, the most highly expressed ligands of CCR5 shifted from the immunosuppressive CCL5 to CCL7 and CCL8, which may mediate the immune agonist activity of CCR5 following TGF-β-TRAP and anti-PD-1 combination treatment. This study suggested that TGF-β depletion modulates CAF heterogeneity and potentially reprograms CAFs and myeloid cells into antitumor immune agonists in PDAC, supporting the validation of such effects in human specimens.

Assessment of Glioblastoma Multiforme Tumor Heterogeneity via MRI-Derived Shape and Intensity Features

Assessment of Glioblastoma Multiforme Tumor Heterogeneity via MRI-Derived Shape and Intensity Features

Predictive value of pre-treatment circulating tumor DNA genomic landscape in patients with relapsed/refractory multiple myeloma undergoing anti-BCMA CAR-T therapy: Insights from tumor cells and T cells.

B-cell maturation antigen (BCMA)-directed chimeric antigen receptor T (CAR-T) therapy yield remarkable responses in patients with relapsed/refractory multiple myeloma (R/RMM). Circulating tumor DNA (ctDNA) reportedly exhibits distinct advantages in addressing the challenges posed by tumor heterogeneity in the distribution and genetic variations in R/RMM. Herein, the ctDNA of 108 peripheral blood plasma samples from patients with R/RMM was thoroughly investigated before administration of anti-BCMA CAR-T therapy to establish its predictive potential. Flow cytometry is used primarily to detect subgroups of T cells or CAR-T cells. In this study, several tumor and T cell effector-mediated factors were considered to be related to treatment failure by an integrat analysis, including higher percentages of multiple myeloma (MM) cells in the bone marrow (P=0.013), lower percentages of CAR-T cells in the peripheral blood at peak (P=0.037), and higher percentages of CD8+ T cells (P=0.034). Furthermore, there is a substantial correlation between high ctDNA level (>143ng/mL) and shorter progression-free survival (PFS) (P=0.007). Multivariate Cox regression analysis showed that high levels of ctDNA (>143ng/mL), MM-driven high-risk mutations (including IGLL5 [P=0.004], IRF4 [P=0.024], and CREBBP [P=0.041]), number of multisite mutations, and resistance-related mutation (ERBB4, P=0.040) were independent risk factors for PFS. Finally, a ctDNA-based risk model was built based on the above independent risk factors, which serves as an adjunct non-invasive measure of substantial tumor burden and a prognostic genetic feature that can assist in predicting the response to anti-BCMA CAR-T therapy. Chinese Clinical Trial Registry (ChiCTR2100046474) and National Clinical Trial (NCT04670055, NCT05430945).

From Genomic Exploration to Personalized Treatment: Next-Generation Sequencing in Oncology

Next-generation sequencing (NGS) has revolutionized personalized oncology care by providing exceptional insights into the complex genomic landscape. NGS offers comprehensive cancer profiling, which enables clinicians and researchers to better understand the molecular basis of cancer and to tailor treatment strategies accordingly. Targeted therapies based on genomic alterations identified through NGS have shown promise in improving patient outcomes across various cancer types, circumventing resistance mechanisms and enhancing treatment efficacy. Moreover, NGS facilitates the identification of predictive biomarkers and prognostic indicators, aiding in patient stratification and personalized treatment approaches. By uncovering driver mutations and actionable alterations, NGS empowers clinicians to make informed decisions regarding treatment selection and patient management. However, the full potential of NGS in personalized oncology can only be realized through bioinformatics analyses. Bioinformatics plays a crucial role in processing raw sequencing data, identifying clinically relevant variants, and interpreting complex genomic landscapes. This comprehensive review investigates the diverse NGS techniques, including whole-genome sequencing (WGS), whole-exome sequencing (WES), and single-cell RNA sequencing (sc-RNA-Seq), elucidating their roles in understanding the complex genomic/transcriptomic landscape of cancer. Furthermore, the review explores the integration of NGS data with bioinformatics tools to facilitate personalized oncology approaches, from understanding tumor heterogeneity to identifying driver mutations and predicting therapeutic responses. Challenges and future directions in NGS-based cancer research are also discussed, underscoring the transformative impact of these technologies on cancer diagnosis, management, and treatment strategies.

New Insights into Colorectal Cancer through the Lens of Precision Oncology and Personalized Medicine: Multi-Omics Helps Aging of Predisposed People.

Recently, there has been a significant evolution in our understanding of the molecular pathways causing the genesis and progression of cancer via the inter-individual variations. Thus, one-size-fits-all methods for cancer treatment have been replaced by precision oncology (PO) targeting individual cancer symptoms, offering increased effectiveness, and decreased safety concerns and cost load. The identification of novel actionable indications, rapid, precise, and comprehensive detection of complex phenotypes in every individual, pioneering clinical trial projects with enhanced response feedback, and widespread availability of innovative targeted anticancer management for every patient are vital for the effective implementation of next-generation precision oncology. Additionally, the emergence of precision medicine has altered the perspective of oncologic biomarkers, drug discovery, drug development, and, improvements for cancer patients. This paper narratively reviewed to identify actionable abnormalities, Genomic profiling of tumors employing clinical next-generation sequencing (NGS) from both tumor tissues and liquid biopsies along with the multi-omics strategies as the key component of PO. Our increasing information on tumor biology, specifically microenvironment and heterogeneity- associated data, would improve our understanding of the resistance of targeted drugs and specific mechanisms of action, as well as help enhance existing metastatic colorectal cancer (mCRC) treatment strategies. Collectively, this paper indicated the current and innovative strategies for prognosis, diagnosis, and treatment of various cancer types based on PO overview with a groundbreaking emphasis on CRC suggesting the integrations of multi-omics, highlighting Genomics, and utilizing AL and ML algorithms with targeted therapies. Notably, these findings can help improve the life-span and ageing of the predisposed people.

Deciphering the topological landscape of glioma using a network theory framework

Glioma stem cells have been recognized as key players in glioma recurrence and therapeutic resistance, presenting a promising target for novel treatments. However, the limited understanding of the role glioma stem cells play in the glioma hierarchy has drawn controversy and hindered research translation into therapies. Despite significant advances in our understanding of gene regulatory networks, the dynamics of these networks and their implications for glioma remain elusive. This study employs a systemic theoretical perspective to integrate experimental knowledge into a core endogenous network model for glioma, thereby elucidating its energy landscape through network dynamics computation. The model identifies two stable states corresponding to astrocytic-like and oligodendrocytic-like tumor cells, connected by a transition state with the feature of high stemness, which serves as one of the energy barriers between astrocytic-like and oligodendrocytic-like states, indicating the instability of glioma stem cells in vivo. We also obtained various stable states further supporting glioma’s multicellular origins and uncovered a group of transition states that could potentially induce tumor heterogeneity and therapeutic resistance. This research proposes that the transition states linking both glioma stable states are central to glioma heterogeneity and therapy resistance. Our approach may contribute to the advancement of glioma therapy by offering a novel perspective on the complex landscape of glioma biology.

Deciphering normal and cancer stem cell niches by spatial transcriptomics: opportunities and challenges.

Cancer stem cells (CSCs) often exhibit stem-like attributes that depend on an intricate stemness-promoting cellular ecosystem within their niche. The interplay between CSCs and their niche has been implicated in tumor heterogeneity and therapeutic resistance. Normal stem cells (NSCs) and CSCs share stemness features and common microenvironmental components, displaying significant phenotypic and functional plasticity. Investigating these properties across diverse organs during normal development and tumorigenesis is of paramount research interest and translational potential. Advancements in next-generation sequencing (NGS), single-cell transcriptomics, and spatial transcriptomics have ushered in a new era in cancer research, providing high-resolution and comprehensive molecular maps of diseased tissues. Various spatial technologies, with their unique ability to measure the location and molecular profile of a cell within tissue, have enabled studies on intratumoral architecture and cellular cross-talk within the specific niches. Moreover, delineation of spatial patterns for niche-specific properties such as hypoxia, glucose deprivation, and other microenvironmental remodeling are revealed through multilevel spatial sequencing. This tremendous progress in technology has also been paired with the advent of computational tools to mitigate technology-specific bottlenecks. Here we discuss how different spatial technologies are used to identify NSCs and CSCs, as well as their associated niches. Additionally, by exploring related public data sets, we review the current challenges in characterizing such niches, which are often hindered by technological limitations, and the computational solutions used to address them.

Establishment and drug resistance characterization of paired organoids using human primary colorectal cancer and matched tumor deposit specimens

Tumor deposits (TDs) represent a specific form tumor metastasis observed in colorectal cancer (CRC). The lack of successfully established cell lines for TDs, as well as the molecular mechanisms by which TDs occur remain largely unknown. Here, we established paired CRC organoids, including a human primary cancer organoid and its TD organoid, from a 46-year-old male patient with CRC. Further analysis revealed that, compared with primary tumor-derived cells, TD-derived cells exhibited enhanced proliferative, invasive and metastatic capabilities, and increased expression of stemness-related proteins. Furthermore, the present findings also demonstrated that TD-derived cells were more resistant to oxaliplatin or 5-FU. Transcriptomic profiling and qPCR revealed that TD-derived cells exhibited more alterations in fatty acid metabolism signaling and enhanced lipid synthesis ability compared to primary tumor-derived cells. Inhibition of lipid synthesis markedly decreased resistance to oxaliplatin in TD-derived cells. Taken together, the paired organoids established using CRC primary tumor and its TD specimens will provide valuable tools to study tumorigenicity, metastasis and chemoresistance in CRC. Notably, these models will provide novel insights to study tumor heterogeneity and lipid metabolism in CRC.

Tumour assessment of ROR1 levels in various adult leukaemia and lymphoma types.

Receptor tyrosine kinase-like orphan receptor 1 (ROR1) is a tumour target currently used for the development of novel therapeutic modalities, such as antibody-drug conjugates, chimeric antigen receptor T-cell therapies, and others. Success of these new drugs depends on the selection of relevant indications based on ROR1 tumour prevalence, staining heterogeneity, and subcellular localization, among other parameters. We investigated ROR1 immunophenotype using validated antibody clones for immunohistochemistry (IHC) and flow cytometry (FC), analyzing 292 tumour specimens from 7 haematological malignancies and triple negative breast cancer (TNBC) as a reference solid tumour indication. ROR1 prevalence varied significantly across distinct tumour types, showing 100% of ROR1 positivity in all chronic lymphocytic leukaemia (n = 48) and hairy cell leukaemia (n = 14) specimens analyzed via FC with ranges between 1.1-99.8% and 0.8-62.1%, respectively. Samples analysed via IHC showed ROR1 membrane/cytoplasmic positivity in 44% of mantle cell lymphoma tumour samples (n = 27; H-score range: 10-285 in positive cases); 30% in TNBC (n = 46; H-score range: 1-200); 15% in diffuse large B-cell lymphoma (n = 45; H-score: 40-250); and 11% in follicular lymphoma (n = 34; H-score: 2-300). Finally, all acute myeloid leukaemia (n = 52) and most T-cell non-Hodgkin lymphoma (n = 31/32) tested samples were negative for ROR1 via IHC. In conclusion, ROR1 shows a heterogeneous tumour cell expression profile across multiple leukaemias and lymphomas, making it a tumour target that would require different patient selection strategies to develop novel therapeutic modalities.

Transient Regression of Breast Carcinoma After Diagnostic Biopsy and Tumor Heterogeneity: A Case Report

Transient Regression of Breast Carcinoma After Diagnostic Biopsy and Tumor Heterogeneity: A Case Report