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The foremost feature of glioblastoma (GBM), the most frequent malignant brain tumours in adults, is a remarkable degree of intra- and inter-tumour heterogeneity reflecting the coexistence within the tumour bulk of different cell populations displaying distinctive genetic and transcriptomic profiles. GBM with primitive neuronal component (PNC), recently identified by DNA methylation-based classification as a peculiar GBM subtype (GBM-PNC), is a poorly recognized and aggressive GBM variant characterised by nodules containing cells with primitive neuronal differentiation along with conventional GBM areas. In addition, the presence of a PNC component has been also reported in IDH-mutant high-grade gliomas (HGGs), and to a lesser extent to other HGGs, suggesting that regardless from being IDH-mutant or IDH-wildtype, peculiar genetic and/or epigenetic events may contribute to the phenotypic skewing with the emergence of the PNC phenotype. However, a clear hypothesis on the mechanisms responsible for this phenotypic skewing is still lacking. We assumed that the biphasic nature of these entities represents a unique model to investigate the relationships between genetic alterations and their phenotypic manifestations. In this study we show that in HGGs with PNC features both components are highly enriched in genetic alterations directly causing cell cycle deregulation (RB inactivation or CDK4 amplification) and p53 pathway inactivation (TP53 mutations or MDM2/4 amplification). However, the PNC component displays further upregulation of transcriptional pathways associated with proliferative activity, including overexpression of MYC target genes. Notably, the PNC phenotype relies on the expression of EBF3, an early neurogenic transcription factor, which is directly controlled by MYC transcription factors in accessible chromatin sites. Overall our findings indicate that the concomitant presence of genetic alterations, impinging on both cell cycle and p53 pathway control, strongly predisposes GBM to develop a concomitant poorly differentiated primitive phenotype depending on MYC-driven EBF3 transcription in a subset of glioma stem-like progenitor cells. [ABSTRACT FROM AUTHOR]

Simple Summary: HER2-positive breast cancer is an aggressive form of cancer and is often resistant to trastuzumab, a key FDA-approved therapy. The following study explores how the heterogeneity of ITGB3 contributes to trastuzumab resistance by affecting TGF-β signaling and migration markers. Using resistant breast cancer cell lines, researchers found that ITGB3 expression varied widely and was correlated with increased migration and signaling activity. Combining trastuzumab with cilengitide, an integrin β3 inhibitor, reduced these effects, particularly in cells with high ITGB3 levels. Targeting ITGβ3 could aid in overcoming trastuzumab resistance and improve treatment outcomes for HER2-positive breast cancer patients. HER2-positive breast cancer has an aggressive tumour progression among breast cancers characterized by the overexpression of HER2. Trastuzumab is an FDA-approved drug and has significantly improved outcomes for patients; however, drug resistance remains a major challenge. Tumour heterogeneity, describing genetic, epigenetic, and phenotypic differences within and between tumours, complicates tumour treatment and contributes to drug resistance. Understanding the mechanisms underlying Trastuzumab resistance, such as tumour heterogeneity, is crucial for developing new and effective therapeutic strategies. This study investigates the role of ITGB3 heterogeneity in Trastuzumab resistance, focusing on its impact on TGF-β signalling and migration marker response. It also evaluates the potential of combining Trastuzumab with the integrin β3 inhibitor cilengitide to overcome resistance associated with ITGB3 levels. Trastuzumab-resistant HER2-positive HCC1954 and SKBR3 breast cancer cell lines were generated and analysed for ITGB3 expression heterogeneity. The impact of ITGB3 on TGF-β-responsive genes (WWP1, CARM1, RASGRP1, THBS1, KCTD5, SGCA, EIF3S6, MCAM, FXR2, MTMR3, SOCS3, SLC2A4RG, MMP2, MMP9, and HSP47) and cell migration (Col4a1, fibronectin, ICAM1, Timp2, and vimentin) was analysed using luciferase reporter assays and real-time PCR. The effects of combined treatment with Trastuzumab and cilengitide were also evaluated via wound closure assay. ITGB3 expression varied significantly among resistant clones, correlating with increased expression of TGF-β-responsive genes and enhanced migration markers. Combined treatment with Trastuzumab and cilengitide significantly reduced TGF-β signalling and migration-related gene expression, particularly in high ITGB3-expressing cells. ITGB3 plays a critical role in Trastuzumab resistance through the modulation of TGF-β signalling, migration, and contributing to tumour heterogeneity. Targeting ITGβ3, alone or in combination with cilengitide, offers a promising strategy to resensitize resistant HER2-positive breast cancer cells to Trastuzumab. These findings provide valuable insights into the mechanisms of Trastuzumab resistance and suggest potential therapeutic avenues for improving patient outcomes. [ABSTRACT FROM AUTHOR]

Anaplastic thyroid carcinoma is arguably the most lethal human malignancy. It often co-occurs with differentiated thyroid cancers, yet the molecular origins of its aggressivity are unknown. We sequenced tumor DNA from 329 regions of thyroid cancer, including 213 from patients with primary anaplastic thyroid carcinomas. We also whole genome sequenced 9 patients using multi-region sequencing of both differentiated and anaplastic thyroid cancer components. Using these data, we demonstrate thatanaplastic thyroid carcinomas have a higher burden of mutations than other thyroid cancers, with distinct mutational signatures and molecular subtypes. Further, different cancer driver genes are mutated in anaplastic and differentiated thyroid carcinomas, even those arising in a single patient. Finally, we unambiguously demonstrate that anaplastic thyroid carcinomas share a genomic origin with co-occurring differentiated carcinomas and emerge from a common malignant field through acquisition of characteristic clonal driver mutations.

Precision medicine has revolutionised targeted cancer treatments; however, its implementation in ovarian cancer remains challenging. Diverse tumour biology and extensive heterogeneity in ovarian cancer can limit the translatability of genetic profiling and contribute to a lack of biomarkers of treatment response. This review addresses the barriers in precision medicine for ovarian cancer, including obtaining adequate and representative tissue samples for analysis, developing functional and standardised screening methods, and navigating data infrastructure and management. Ethical concerns related to patient consent, data privacy and health equity are also explored. We highlight the socio-economic complexities for precision medicine and propose strategies to overcome these challenges with an emphasis on accessibility and education amongst patients and health professionals and the development of regulatory frameworks to support clinical integration. Interdisciplinary collaboration is essential to drive progress in precision medicine to improve disease management and ovarian cancer patient outcomes.

Abstract Tumours exhibit high genotypic and transcriptional heterogeneity. Both affect cancer progression and treatment, but have been predominantly studied separately in follicular lymphoma. To comprehensively investigate the evolution and genotype-to-phenotype maps in follicular lymphoma, we introduce CaClust, a probabilistic graphical model integrating deep whole exome, single-cell RNA and B-cell receptor sequencing data to infer clone genotypes, cell-to-clone mapping, and single-cell genotyping. CaClust outperforms a state-of-the-art model on simulated and patient data. In-depth analyses of single cells from four samples showcase effects of driver mutations, follicular lymphoma evolution, possible therapeutic targets, and single-cell genotyping that agrees with an independent targeted resequencing experiment.

Simple Summary: Separately sequencing morphologically distinct regions within large, high-risk uveal melanomas provides deeper insights into tumour biology and identifies additional targets for therapy. Uveal melanomas are rare intraocular malignancies that exhibit significant heterogeneity, with macroscopic and microscopic differences between tumour regions. This study used microdissection and targeted sequencing to analyse melanotic and amelanotic regions within the same tumours. In four of seven cases, distinct molecular profiles were identified, including a MET exon 14 skipping transcript predictive of sensitivity to crizotinib. Additional variants were detected in genes relevant to active clinical trials. These findings highlight the importance of analysing spatially distinct tumour regions to identify actionable genetic alterations that may be missed in whole tumour aggregate analyses. This approach has implications for the molecular testing of uveal melanomas, guiding personalised therapies, and expanding eligibility for clinical trials in patients with advanced disease. Background/Objectives: Uveal melanomas (UMs) are rare but often deadly malignancies that urgently require viable treatment options. UMs often exhibit tumour heterogeneity, with macroscopic and microscopic differences in morphology between different regions of the same tumour. However, to date, the clinical significance of this and how it may help guide personalised therapy have not been realised. Methods: Using targeted DNA and RNA sequencing of a small case series of large, high-risk primary UMs, we explored whether morphologically distinct regions of the same tumour were associated with distinct molecular profiles. Results: In four of the seven tumours analysed, we detected different sets of genetic variants following the separate analysis of microdissected melanotic and amelanotic regions of the same tumour. These included a MET exon 14 skipping RNA transcript that predicts sensitivity to crizotinib and variants in other genes that are important in active clinical trials for patients with UM and advanced solid tumours. The integration of TCGA data also identified recurrent mutational events in genes that were not previously implicated in UM development (FANCA, SLX4, BRCA2, and ATRX). Conclusions: Our findings show that the molecular analysis of spatially separated and morphologically distinct regions of the same tumour may yield additional, therapeutically relevant genetic variants in uveal melanomas and have implications for the future molecular testing of UMs to identify targeted therapies. [ABSTRACT FROM AUTHOR]

Precision medicine has revolutionised targeted cancer treatments; however, its implementation in ovarian cancer remains challenging. Diverse tumour biology and extensive heterogeneity in ovarian cancer can limit the translatability of genetic profiling and contribute to a lack of biomarkers of treatment response. This review addresses the barriers in precision medicine for ovarian cancer, including obtaining adequate and representative tissue samples for analysis, developing functional and standardised screening methods, and navigating data infrastructure and management. Ethical concerns related to patient consent, data privacy and health equity are also explored. We highlight the socio-economic complexities for precision medicine and propose strategies to overcome these challenges with an emphasis on accessibility and education amongst patients and health professionals and the development of regulatory frameworks to support clinical integration. Interdisciplinary collaboration is essential to drive progress in precision medicine to improve disease management and ovarian cancer patient outcomes. [ABSTRACT FROM AUTHOR]

Background: Pituitary neuroendocrine tumours (PitNETs) are common intracranial tumours that are highly heterogeneous with unpredictable growth patterns. The driver genes and mechanisms that are crucial for tumour progression in somatotroph PitNETs are poorly understood. Methods: In this study, we performed integrative spatial transcriptomics (ST) and single‐cell RNA sequencing (scRNA‐seq) analysis on somatotroph tumours and normal pituitary samples to comprehensively characterize the differences in cellular characteristics. Results: By analyzing combined copy number variations (CNVs), tumour tissues were divided into two regions, which included the CNVhigh and CNVlow areas. The protumour genes DLK1 and RCN1 were highly expressed in the CNVhigh area, which might be related to tumour progression and could be targeted for precision therapy. We also found that the transforming growth factor beta signalling pathway participated in tumour progression and identified heterogeneity in the expression profiles of key genes. We assessed the intertumoral and intratumoral heterogeneity in somatotroph PitNETs and emphasized the importance of individualized treatment. Conclusion: In summary, we visualized the cellular distribution and transcriptional differences in normal pituitary and somatotroph PitNETs by ST and scRNA‐seq for the first time. This study provides a strong theoretical foundation to comprehensively understand the crucial mechanisms involved in tumour progression and develop new strategies to treat somatotroph PitNETs. Key points: The first‐ever visualization of cellular distributions in normal and tumor pituitary tissues.The inter‐ and intra‐tumoral transcriptomic heterogeneity of somatotroph PitNETs was comprehensively revealed.Identification of potential protumor factors and critical signaling pathways, opening new avenues for therapeutic intervention. [ABSTRACT FROM AUTHOR]

Opinion Statement: Soft tissue sarcomas (STS) are a rare and heterogeneous group of cancers. Treatment options have changed little in the past thirty years, and the role of neoadjuvant chemotherapy is controversial. Accurate risk stratification is crucial in STS in order to facilitate clinical discussions around peri-operative treatment. Current risk stratification tools used in clinic, such as Sarculator, use clinicopathological characteristics and may be specific to anatomical site or to histology. More recently, risk stratification tools have been developed using molecular or immunological data. Combining Sarculator with other risk stratification tools may identify novel patient groups with differential clinical outcomes. There are several considerations when translating risk stratification tools into widespread clinical use, including establishing clinical utility, health economic value, being applicable to existing clinical pathways, having strong real-world performance, and being supported by investment into infrastructure. Future work may include incorporation of novel modalities and data integration techniques. [ABSTRACT FROM AUTHOR]

Abstract Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal human malignancies. Tissue microarrays (TMA) are an established method of high throughput biomarker interrogation in tissues but may not capture histological features of cancer with potential biological relevance. Topographic TMAs (T-TMAs) representing pathophysiological hallmarks of cancer were constructed from representative, retrospective PDAC diagnostic material, including 72 individual core tissue samples. The T-TMA was interrogated with tissue hybridization-based experiments to confirm the accuracy of the topographic sampling, expression of pro-tumourigenic and immune mediators of cancer, totalling more than 750 individual biomarker analyses. A custom designed Next Generation Sequencing (NGS) panel and a spatial distribution-specific transcriptomic evaluation were also employed. The morphological choice of the pathophysiological hallmarks of cancer was confirmed by protein-specific expression. Quantitative analysis identified topography-specific patterns of expression in the IDO/TGF-β axis; with a heterogeneous relationship of inflammation and desmoplasia across hallmark areas and a general but variable protein and gene expression of c-MET. NGS results highlighted underlying genetic heterogeneity within samples, which may have a confounding influence on the expression of a particular biomarker. T-TMAs, integrated with quantitative biomarker digital scoring, are useful tools to identify hallmark specific expression of biomarkers in pancreatic cancer.

Kidney renal clear cell carcinoma (KIRC) pathogenesis intricately involves immune system dynamics, particularly the role of T cells within the tumour microenvironment. Through a multifaceted approach encompassing single‐cell RNA sequencing, spatial transcriptome analysis and bulk transcriptome profiling, we systematically explored the contribution of infiltrating T cells to KIRC heterogeneity. Employing high‐density weighted gene co‐expression network analysis (hdWGCNA), module scoring and machine learning, we identified a distinct signature of infiltrating T cell‐associated genes (ITSGs). Spatial transcriptomic data were analysed using robust cell type decomposition (RCTD) to uncover spatial interactions. Further analyses included enrichment assessments, immune infiltration evaluations and drug susceptibility predictions. Experimental validation involved PCR experiments, CCK‐8 assays, plate cloning assays, wound‐healing assays and Transwell assays. Six subpopulations of infiltrating and proliferating T cells were identified in KIRC, with notable dynamics observed in mid‐ to late‐stage disease progression. Spatial analysis revealed significant correlations between T cells and epithelial cells across varying distances within the tumour microenvironment. The ITSG‐based prognostic model demonstrated robust predictive capabilities, implicating these genes in immune modulation and metabolic pathways and offering prognostic insights into drug sensitivity for 12 KIRC treatment agents. Experimental validation underscored the functional relevance of PPIB in KIRC cell proliferation, invasion and migration. Our study comprehensively characterizes infiltrating T‐cell heterogeneity in KIRC using single‐cell RNA sequencing and spatial transcriptome data. The stable prognostic model based on ITSGs unveils infiltrating T cells' prognostic potential, shedding light on the immune microenvironment and offering avenues for personalized treatment and immunotherapy. [ABSTRACT FROM AUTHOR]

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal human malignancies. Tissue microarrays (TMA) are an established method of high throughput biomarker interrogation in tissues but may not capture histological features of cancer with potential biological relevance. Topographic TMAs (T-TMAs) representing pathophysiological hallmarks of cancer were constructed from representative, retrospective PDAC diagnostic material, including 72 individual core tissue samples. The T-TMA was interrogated with tissue hybridization-based experiments to confirm the accuracy of the topographic sampling, expression of pro-tumourigenic and immune mediators of cancer, totalling more than 750 individual biomarker analyses. A custom designed Next Generation Sequencing (NGS) panel and a spatial distribution-specific transcriptomic evaluation were also employed. The morphological choice of the pathophysiological hallmarks of cancer was confirmed by protein-specific expression. Quantitative analysis identified topography-specific patterns of expression in the IDO/TGF-β axis; with a heterogeneous relationship of inflammation and desmoplasia across hallmark areas and a general but variable protein and gene expression of c-MET. NGS results highlighted underlying genetic heterogeneity within samples, which may have a confounding influence on the expression of a particular biomarker. T-TMAs, integrated with quantitative biomarker digital scoring, are useful tools to identify hallmark specific expression of biomarkers in pancreatic cancer. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Simple Summary: Clear cell renal cell carcinoma (ccRCC) accounts for at least 80% of renal tumours worldwide. The grading of clear cell carcinoma is crucial for its management; therefore, it is important to distinguish the ccRCC grade pre-operatively. The aim of this research is to differentiate high- from low-grade ccRCC non-invasively using machine learning (ML) and radiomics features extracted from pre-operative computed tomography (CT) scans, taking into consideration the tumour sub-region that offers the greatest accuracy when grading. Furthermore, radiomics and machine learning were compared with biopsy-determined grading in a sub-group with resection histopathology as a reference standard. Background: Renal cancers are among the top ten causes of cancer-specific mortality, of which the ccRCC subtype is responsible for most cases. The grading of ccRCC is important in determining tumour aggressiveness and clinical management. Objectives: The objectives of this research were to predict the WHO/ISUP grade of ccRCC pre-operatively and characterise the heterogeneity of tumour sub-regions using radiomics and ML models, including comparison with pre-operative biopsy-determined grading in a sub-group. Methods: Data were obtained from multiple institutions across two countries, including 391 patients with pathologically proven ccRCC. For analysis, the data were separated into four cohorts. Cohorts 1 and 2 included data from the respective institutions from the two countries, cohort 3 was the combined data from both cohort 1 and 2, and cohort 4 was a subset of cohort 1, for which both the biopsy and subsequent histology from resection (partial or total nephrectomy) were available. 3D image segmentation was carried out to derive a voxel of interest (VOI) mask. Radiomics features were then extracted from the contrast-enhanced images, and the data were normalised. The Pearson correlation coefficient and the XGBoost model were used to reduce the dimensionality of the features. Thereafter, 11 ML algorithms were implemented for the purpose of predicting the ccRCC grade and characterising the heterogeneity of sub-regions in the tumours. Results: For cohort 1, the 50% tumour core and 25% tumour periphery exhibited the best performance, with an average AUC of 77.9% and 78.6%, respectively. The 50% tumour core presented the highest performance in cohorts 2 and 3, with average AUC values of 87.6% and 76.9%, respectively. With the 25% periphery, cohort 4 showed AUC values of 95.0% and 80.0% for grade prediction when using internal and external validation, respectively, while biopsy histology had an AUC of 31.0% for the classification with the final grade of resection histology as a reference standard. The CatBoost classifier was the best for each of the four cohorts with an average AUC of 80.0%, 86.5%, 77.0% and 90.3% for cohorts 1, 2, 3 and 4 respectively. Conclusions: Radiomics signatures combined with ML have the potential to predict the WHO/ISUP grade of ccRCC with superior performance, when compared to pre-operative biopsy. Moreover, tumour sub-regions contain useful information that should be analysed independently when determining the tumour grade. Therefore, it is possible to distinguish the grade of ccRCC pre-operatively to improve patient care and management. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is the prevailing subtype of hepatocellular malignancy. While previous investigations have evidenced a robust link with programmed cell death (PCD) and tumorigenesis, a comprehensive inquiry targeting the relationship between multiple PCDs and HCC remains scant. Our aim was to develop a predictive model for different PCD patterns in order to investigate their impact on survival rates, prognosis and drug response rates in HCC patients. We performed functional annotation and pathway analysis on identified PCD‐related genes (PCDRGs) using multiple bioinformatics tools. The prognostic value of these PCDRGs was verified through a dataset obtained from GEO. Consensus clustering analysis was utilized to elucidate the correlation between diverse PCD clusters and pertinent clinical characteristics. To comprehensively uncover the distinct PCD regulatory patterns, our analysis integrated gene expression profiling, immune cell infiltration and enrichment analysis. To predict survival differences in HCC patients, we established a PCD model. To enhance the clinical applicability for the model, we developed a highly accurate nomogram. To address the treatment of HCC, we identified several promising chemotherapeutic agents and novel targeted drugs. These drugs may be effective in treating HCC and could improve patient outcomes. To develop a cell death feature for HCC patients, we conducted an analysis of 12 different PCD mechanisms using eligible data obtained from public databases. Through this analysis, we were able to identify 1254 PCDRGs likely to contribute to cell death on HCC. Further analysis of 1254 PCDRGs identified 37 genes with prognostic value in HCC patients. These genes were then categorized into two PCD clusters A and B. The categorization was based on the expression patterns of the genes in the different clusters. Patients in PCD cluster B had better survival probabilities. This suggests that PCD mechanisms, as represented by the genes in cluster B, may have a protective effect against HCC progression. Furthermore, the expression of PCDRGs was significantly higher in PCD cluster A, indicating that this cluster may be more closely associated with PCD mechanisms. Furthermore, our observations indicate that patients exhibiting elevated tumour mutation burden (TMB) are at an augmented risk of mortality, in comparison to those displaying low TMB and low‐risk statuses, who are more likely to experience prolonged survival. In addition, we have investigated the potential distinctions in the susceptibility of diverse risk cohorts towards emerging targeted therapies, designed for the treatment of HCC. Moreover, our investigation has shown that AZD2014, SB505124, LJI308 and OSI‐207 show a greater efficacy in patients in the low‐risk category. Conversely, for the high‐risk group patients, PD173074, ZM447439 and CZC24832 exhibit a stronger response. Our findings suggest that the identification of risk groups and personalized treatment selection could lead to better clinical outcomes for patients with HCC. Furthermore, significant heterogeneity in clinical response to ICI therapy was observed among HCC patients with varying PCD expression patterns. This novel discovery underscores the prospective usefulness of these expression patterns as prognostic indicators for HCC patients and may aid in tailoring targeted treatment for those of distinct risk strata. Our investigation introduces a novel prognostic model for HCC that integrates diverse PCD expression patterns. This innovative model provides a novel approach for forecasting prognosis and assessing drug sensitivity in HCC patients, driving a more personalized and efficacious treatment paradigm, elevating clinical outcomes. Nonetheless, additional research endeavours are required to confirm the model's precision and assess its potential to inform clinical decision‐making for HCC patients. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) represents a major global health burden and is responsible for a significant number of cancer‐related fatalities. Its complex nature, characterized by heterogeneity and aggressive behaviour, poses considerable challenges for effective diagnosis and treatment. Single‐cell RNA sequencing (scRNA‐seq) has emerged as an important technique, offering unprecedented precision and depth in gene expression profiling at the cellular level. By facilitating the identification of distinct cell populations, rare cells and dynamic transcriptional changes within GC, scRNA‐seq has yielded valuable insights into tumour progression and potential therapeutic targets. Moreover, this technology has significantly improved our comprehension of the tumour microenvironment (TME) and its intricate interplay with immune cells, thereby opening avenues for targeted therapeutic strategies. Nonetheless, certain obstacles, including tumour heterogeneity and technical limitations, persist in the field. Current endeavours are dedicated to refining protocols and computational tools to surmount these challenges. In this narrative review, we explore the significance of scRNA‐seq in GC, emphasizing its advantages, challenges and potential applications in unravelling tumour heterogeneity and identifying promising therapeutic targets. Additionally, we discuss recent developments, ongoing efforts to overcome these challenges, and future prospects. Although further enhancements are required, scRNA‐seq has already provided valuable insights into GC and holds promise for advancing biomedical research and clinical practice. [ABSTRACT FROM AUTHOR]

The median age of patients with pancreatic ductal adenocarcinoma (PDAC) at diagnosis is 71 years; however, around 10% present with early‐onset pancreatic cancer (EOPC), i.e., before age 50. The molecular mechanisms underlying such an early onset are unknown. We assessed the role of common PDAC drivers (KRAS, TP53, CDKN2A and SMAD4) and determined their mutational status and protein expression in 90 formalin‐fixed, paraffin‐embedded tissues, including multiple primary and matched metastases, from 37 EOPC patients. KRAS was mutated in 88% of patients; p53 was altered in 94%, and p16 and SMAD4 were lost in 86% and 71% of patients, respectively. Meta‐synthesis showed a higher rate of p53 alterations in EOPC than in late‐onset PDAC (94% vs. 69%, P = 0.0009) and significantly higher loss of SMAD4 (71% vs. 44%, P = 0.0025). The majority of EOPC patients accumulated aberrations in all four drivers; in addition, high tumour heterogeneity was observed across all tissues. The cumulative effect of an exceptionally high rate of alterations in all common PDAC driver genes combined with high tumour heterogeneity suggests an important mechanism underlying the early onset of PDAC.

Summary: Background: The high heterogeneity of tumour and the complexity of tumour microenvironment (TME) greatly impacted the tumour development and the prognosis of cancer in the era of immunotherapy. In this study, we aimed to portray the single cell-characterised landscape of lung adenocarcinoma (LUAD), and develop an integrated signature incorporating both tumour heterogeneity and TME for prognosis stratification. Methods: Single-cell tagged reverse transcription sequencing (STRT-seq) was performed on tumour tissues and matched normal tissues from 14 patients with LUAD for immune landscape depiction and candidate key genes selection for signature construction. Kaplan–Meier survival analyses and in-vitro cell experiments were conducted to confirm the gene functions. The transcriptomic profile of 1949 patients from 11 independent cohorts including nine public datasets and two in-house cohorts were obtained for validation. Findings: We selected 11 key genes closely related to cell-to-cell interaction, tumour development, T cell phenotype transformation, and Ma/Mo cell distribution, including HLA-DPB1, FAM83A, ITGB4, OAS1, FHL2, S100P, FSCN1, SFTPD, SPP1, DBH-AS1, CST3, and established an integrated 11-gene signature, stratifying patients to High-Score or Low-Score group for better or worse prognosis. Moreover, the prognostically-predictive potency of the signature was validated by 11 independent cohorts, and the immunotherapeutic predictive potency was also validated by our in-house cohort treated by immunotherapy. Additionally, the in-vitro cell experiments and drug sensitivity prediction further confirmed the gene function and generalizability of this signature across the entire RNA profile spectrum. Interpretation: This single cell-characterised 11-gene signature might offer insights for prognosis stratification and potential guidance for treatment selection. Funding: Support for the study was provided by National key research and development project (2022YFC2505004, 2022YFC2505000 to Z.W. and J.W.), Beijing Natural Science Foundation (7242114 to J.X.), National Natural Science Foundation of China of China (82102886 to J.X., 81871889 and 82072586 to Z.W.), Beijing Nova Program (20220484119 to J.X.), NSFC general program (82272796 to J.W.), NSFC special program (82241229 to J.W.), CAMS Innovation Fund for Medical Sciences (2021-1-I2M-012, 2022-I2M-1-009 to Z.W. and J.W.), Beijing Natural Science Foundation (7212084 to Z.W.), CAMS Key lab of translational research on lung cancer (2018PT31035 to J.W.), Aiyou Foundation (KY201701 to J.W.). Medical Oncology Key Foundation of Cancer Hospital Chinese Academy of Medical Sciences (CICAMS-MOCP2022003 to J.X.)

The median age of patients with pancreatic ductal adenocarcinoma (PDAC) at diagnosis is 71 years; however, around 10% present with early‐onset pancreatic cancer (EOPC), i.e., before age 50. The molecular mechanisms underlying such an early onset are unknown. We assessed the role of common PDAC drivers (KRAS, TP53, CDKN2A and SMAD4) and determined their mutational status and protein expression in 90 formalin‐fixed, paraffin‐embedded tissues, including multiple primary and matched metastases, from 37 EOPC patients. KRAS was mutated in 88% of patients; p53 was altered in 94%, and p16 and SMAD4 were lost in 86% and 71% of patients, respectively. Meta‐synthesis showed a higher rate of p53 alterations in EOPC than in late‐onset PDAC (94% vs. 69%, P = 0.0009) and significantly higher loss of SMAD4 (71% vs. 44%, P = 0.0025). The majority of EOPC patients accumulated aberrations in all four drivers; in addition, high tumour heterogeneity was observed across all tissues. The cumulative effect of an exceptionally high rate of alterations in all common PDAC driver genes combined with high tumour heterogeneity suggests an important mechanism underlying the early onset of PDAC. [ABSTRACT FROM AUTHOR]

Precision oncology is a rapidly evolving concept that holds great promise in cancer treatment. However, a cancer complexity attributed to genomic and acquired tumour heterogeneity limits treatment effectiveness and increases toxicity. These limitations refer to both systemic therapies and radiotherapy, which are two mainstays of non-invasive cancer treatment. By understanding cancer heterogeneity and utilising advanced tools to personalise treatment strategies, precision oncology has the potential to revolutionise cancer care. In this article, we review the current status of precision oncology in solid tumours, specifically focusing on the impact of tumour heterogeneity and genomic patient features on systemic therapies and radiation. We also discuss the implementation of novel tools, such as next-generation sequencing and liquid biopsies, to overcome this problem. [ABSTRACT FROM AUTHOR]

Thyroid cancer, a prevalent form of endocrine malignancy, has witnessed a substantial increase in occurrence in recent decades. To gain a comprehensive understanding of thyroid cancer at the single-cell level, this narrative review evaluates the applications of single-cell RNA sequencing (scRNA-seq) in thyroid cancer research. ScRNA-seq has revolutionised the identification and characterisation of distinct cell subpopulations, cell-to-cell communications, and receptor interactions, revealing unprecedented heterogeneity and shedding light on novel biomarkers for therapeutic discovery. These findings aid in the construction of predictive models on disease prognosis and therapeutic efficacy. Altogether, scRNA-seq has deepened our understanding of the tumour microenvironment immunologic insights, informing future studies in the development of effective personalised treatment for patients. Challenges and limitations of scRNA-seq, such as technical biases, financial barriers, and ethical concerns, are discussed. Advancements in computational methods, the advent of artificial intelligence (AI), machine learning (ML), and deep learning (DL), and the importance of single-cell data sharing and collaborative efforts are highlighted. Future directions of scRNA-seq in thyroid cancer research include investigating intra-tumoral heterogeneity, integrating with other omics technologies, exploring the non-coding RNA landscape, and studying rare subtypes. Overall, scRNA-seq has transformed thyroid cancer research and holds immense potential for advancing personalised therapies and improving patient outcomes. Efforts to make this technology more accessible and cost-effective will be crucial to ensuring its widespread utilisation in healthcare. [ABSTRACT FROM AUTHOR]

Background & Aims: Hepatocellular adenomas (HCAs) are rare, benign, liver tumours classified at the clinicopathological, genetic, and proteomic levels. The β-catenin-activated (b-HCA) subtypes harbour several mutation types in the β-catenin gene (CTNNB1) associated with different risks of malignant transformation or bleeding. Glutamine synthetase is a surrogate marker of β-catenin pathway activation associated with the risk of malignant transformation. Recently, we revealed an overexpression of glutamine synthetase in the rims of exon 3 S45-mutated b-HCA and exon 7/8-mutated b-HCA compared with the rest of the tumour. A difference in vascularisation was found in this rim shown by diffuse CD34 staining only at the tumour centre. Here, we aimed to characterise this tumour heterogeneity to better understand its physiopathological involvement. Methods: Using mass spectrometry imaging, genetic, and proteomic analyses combined with laser capture microdissection, we compared the tumour centre with the tumour rim and with adjacent non-tumoural tissue. Results: The tumour rim harboured the same mutation as the tumour centre, meaning both parts belong to the same tumour. Mass spectrometry imaging showed different spectral profiles between the rim and the tumour centre. Proteomic profiling revealed the significant differential expression of 40 proteins at the rim compared with the tumour centre. The majority of these proteins were associated with metabolism, with an expression profile comparable with a normal perivenous hepatocyte expression profile. Conclusions: The difference in phenotype between the tumour centres and tumour rims of exon 3 S45-mutated b-HCA and exon 7/8-mutated b-HCA does not depend on CTNNB1 mutational status. In a context of sinusoidal arterial pathology, tumour heterogeneity at the rim harbours perivenous characteristics and could be caused by a functional peripheral venous drainage. Impact and implications: Tumour heterogeneity was revealed in β-catenin-mutated hepatocellular adenomas (b-HCAs) via the differential expression of glutamine synthase at tumour rims. The combination of several spatial approaches (mass spectrometry imaging, genetic, and proteomic analyses) after laser capture microdissection allowed identification of a potential role for peripheral venous drainage underlying this difference. Through this study, we were able to illustrate that beyond a mutational context, many factors can downstream regulate gene expression and contribute to different clinicopathological phenotypes. We believe that the combinations of spatial analyses that we used could be inspiring for all researchers wanting to access heterogeneity information of liver tumours.

The development of cancer begins with cells transitioning from their multicellular nature to a state akin to unicellular organisms. This shift leads to a breakdown in the crucial regulators inherent to multicellularity, resulting in the emergence of diverse cancer cell subpopulations that have enhanced adaptability. The presence of different cell subpopulations within a tumour, known as intratumoural heterogeneity (ITH), poses challenges for cancer treatment. In this review, we delve into the dynamics of the shift from multicellularity to unicellularity during cancer onset and progression. We highlight the role of genetic and non-genetic factors, as well as tumour microenvironment, in promoting ITH and cancer evolution. Additionally, we shed light on the latest advancements in omics technologies that allow for in-depth analysis of tumours at the single-cell level and their spatial organization within the tissue. Obtaining such detailed information is crucial for deepening our understanding of the diverse evolutionary paths of cancer, allowing for the development of effective therapies targeting the key drivers of cancer evolution. [ABSTRACT FROM AUTHOR]

Introduction: Widespread intra-peritoneal metastases is a main feature of high grade serous ovarian carcinoma (HGSOC). Recently, the extent of tumour heterogeneity was used to evaluate the cancer genomes among multi-regions in HGSOC. However, there is no consensus on the effect of tumour heterogeneity on the evolution of the tumour metastasis process in HGSOC. Objectives: We performed whole-exome sequencing in multiple regions of matched primary and metastatic HGSOC specimens to reveal the genetic mechanisms of ovarian tumourigenesis and malignant progression. Methods: 63 samples (including ovarian carcinoma, omentum metastasis, and normal tissues) were used. We analyzed the genomic heterogeneity, traced the subclone dissemination and establishment history and compared the different genetic characters of cancer evolutionary models in HGSOC. Results: We found that HGSOC had substantial intra-tumour heterogeneity (median 54.2, range 0 ∼ 106.7), high inter-patient heterogeneity (P

Abstract Background Colorectal cancer (CRC) is one of the most prevalent cancers, with over one million new cases per year. Overall, prognosis of CRC largely depends on the disease stage and metastatic status. As precision oncology for patients with CRC continues to improve, this study aimed to integrate genomic, transcriptomic, and proteomic analyses to identify significant differences in expression during CRC progression using a unique set of paired patient samples while considering tumour heterogeneity. Methods We analysed fresh-frozen tissue samples prepared under strict cryogenic conditions of matched healthy colon mucosa, colorectal carcinoma, and liver metastasis from the same patients. Somatic mutations of known cancer-related genes were analysed using Illumina's TruSeq Amplicon Cancer Panel; the transcriptome was assessed comprehensively using Clariom D microarrays. The global proteome was evaluated by liquid chromatography-coupled mass spectrometry (LC‒MS/MS) and validated by two-dimensional difference in-gel electrophoresis. Subsequent unsupervised principal component clustering, statistical comparisons, and gene set enrichment analyses were calculated based on differential expression results. Results Although panomics revealed low RNA and protein expression of CA1, CLCA1, MATN2, AHCYL2, and FCGBP in malignant tissues compared to healthy colon mucosa, no differentially expressed RNA or protein targets were detected between tumour and metastatic tissues. Subsequent intra-patient comparisons revealed highly specific expression differences (e.g., SRSF3, OLFM4, and CEACAM5) associated with patient-specific transcriptomes and proteomes. Conclusion Our research results highlight the importance of inter- and intra-tumour heterogeneity as well as individual, patient-paired evaluations for clinical studies. In addition to changes among groups reflecting CRC progression, we identified significant expression differences between normal colon mucosa, primary tumour, and liver metastasis samples from individuals, which might accelerate implementation of precision oncology in the future.

Chromosome instability (CIN) is a cancer hallmark that drives tumour heterogeneity, phenotypic adaptation, drug resistance and poor prognosis. High-grade serous ovarian cancer (HGSOC), one of the most chromosomally unstable tumour types, has a 5-year survival rate of only ~30% — largely due to late diagnosis and rapid development of drug resistance, e.g., via CIN-driven ABCB1 translocations. However, CIN is also a cell cycle vulnerability that can be exploited to specifically target tumour cells, illustrated by the success of PARP inhibitors to target homologous recombination deficiency (HRD). However, a lack of appropriate models with ongoing CIN has been a barrier to fully exploiting disease-specific CIN mechanisms. This barrier is now being overcome with the development of patient-derived cell cultures and organoids. In this review, we describe our progress building a Living Biobank of over 120 patient-derived ovarian cancer models (OCMs), predominantly from HGSOC. OCMs are highly purified tumour fractions with extensive proliferative potential that can be analysed at early passage. OCMs have diverse karyotypes, display intra- and inter-patient heterogeneity and mitotic abnormality rates far higher than established cell lines. OCMs encompass a broad-spectrum of HGSOC hallmarks, including a range of p53 alterations and BRCA1/2 mutations, and display drug resistance mechanisms seen in the clinic, e.g., ABCB1 translocations and BRCA2 reversion. OCMs are amenable to functional analysis, drug-sensitivity profiling, and multi-omics, including single-cell next-generation sequencing, and thus represent a platform for delineating HGSOC-specific CIN mechanisms. In turn, our vision is that this understanding will inform the design of new therapeutic strategies. [ABSTRACT FROM AUTHOR]

Tumour heterogeneity is one of the main characteristics of cancer and can be categorised into inter‐ or intratumour heterogeneity. This heterogeneity has been revealed as one of the key causes of treatment failure and relapse. Precision oncology is an emerging field that seeks to design tailored treatments for each cancer patient according to epidemiological, clinical and omics data. This discipline relies on bioinformatics tools designed to compute scores to prioritise available drugs, with the aim of helping clinicians in treatment selection. In this review, we describe the current approaches for therapy selection depending on which type of tumour heterogeneity is being targeted and the available next‐generation sequencing data. We cover intertumour heterogeneity studies and individual treatment selection using genomics variants, expression data or multi‐omics strategies. We also describe intratumour dissection through clonal inference and single‐cell transcriptomics, in each case providing bioinformatics tools for tailored treatment selection. Finally, we discuss how these therapy selection workflows could be integrated into the clinical practice.

The choice of materials challenges the development of Magnetic Resonance Imaging (MRI) phantoms and, to date, is mainly limited to water-filled compartments or gel-based components. Recently, solid materials have been introduced through additive manufacturing (AM) to mimic complex geometrical structures. Nonetheless, no such manufactured solid materials are available with controllable MRI contrast to mimic organ substructures or lesion heterogeneities. Here, we present a novel AM design that allows MRI contrast manipulation by varying the partial volume contribution to a ROI/voxel of MRI-visible material within an imaging object. Two sets of 11 cubes and three replicates of a spherical tumour model were designed and printed using AM. Most samples presented varying MRI-contrast in standard MRI sequences, based mainly on spin density and partial volume signal variation. A smooth and continuous MRI-contrast gradient could be generated in a single-compartment tumour model. This concept supports the development of more complex MRI phantoms that mimic the appearance of heterogeneous tumour tissues.

[Display omitted] • Heterogeneity represents a vital character of ovarian cancer. • Homologous recombination deficiency was correlated with HGSOC. • A dualistic classification of HGSOC was proposed based on spatial heterogeneity. • Star topology group showed higher heterogeneity than tree topology group. • Patient within tree topology model undertook more mutations than the star topology model. • More driver events were observed in tree topology group than in star topology group. Widespread intra-peritoneal metastases is a main feature of high grade serous ovarian carcinoma (HGSOC). Recently, the extent of tumour heterogeneity was used to evaluate the cancer genomes among multi-regions in HGSOC. However, there is no consensus on the effect of tumour heterogeneity on the evolution of the tumour metastasis process in HGSOC. We performed whole-exome sequencing in multiple regions of matched primary and metastatic HGSOC specimens to reveal the genetic mechanisms of ovarian tumourigenesis and malignant progression. 63 samples (including ovarian carcinoma, omentum metastasis, and normal tissues) were used. We analyzed the genomic heterogeneity, traced the subclone dissemination and establishment history and compared the different genetic characters of cancer evolutionary models in HGSOC. We found that HGSOC had substantial intra-tumour heterogeneity (median 54.2, range 0 ∼ 106.7), high inter-patient heterogeneity (P < 0.001), but relatively limited intra-patient heterogeneity (P = 0.949). Two COSMIC mutational signatures were identified in HGSOCs: signature 3 was related to homologous recombination, and signature 1 was associated with aging. Two scenarios were identified by phylogenetic reconstruction in our study: 3 cases (33.3 %) showed star topology, and the other 6 cases (66.7 %) displayed tree topology. Compared with star topology group, more driver events were identified in tree topology group (P < 0.001), and occurred more frequently in early stage than in late stage of clonal evolution (P < 0.001). Moreover, compared with the star topology group, the tree topology group showed higher rate of intra-tumour heterogeneity (P = 0.045). A dualistic classification model was proposed for the classification of HGSOC based on spatial heterogeneity, which may contribute to better managing patients and providing individual treatment for HGSOC patients. [ABSTRACT FROM AUTHOR]

Mantle cell lymphoma is a well-defined B-cell lymphoma entity with characteristic morphological, immunophenotypical and molecular findings. However, in the last decades significant heterogeneity of the disease has been identified with important consequences for the clinical management of the disease. Importantly, some mantle cell lymphomas show an indolent behaviour, which might be overtreated with traditionally used intensive chemoimmunotherapy regimens for this entity. In this article the clinical heterogeneity and current pathological concepts in mantle cell lymphoma are reviewed, with a focus on diagnostic hematopathology. [ABSTRACT FROM AUTHOR]

Abstract Background Tumour mutation burden (TMB), defined as the number of somatic mutations per megabase within the sequenced region in the tumour sample, has been used as a biomarker for predicting response to immune therapy. Several studies have been conducted to assess the utility of TMB for various cancer types; however, methods to measure TMB have not been adequately evaluated. In this study, we identified two sources of bias in current methods to calculate TMB. Methods We used simulated data to quantify the two sources of bias and their effect on TMB calculation, we down-sampled sequencing reads from exome sequencing datasets from TCGA to evaluate the consistency in TMB estimation across different sequencing depths. We analyzed data from ten cancer cohorts to investigate the relationship between inferred TMB and sequencing depth. Results We found that TMB, estimated by counting the number of somatic mutations above a threshold frequency (typically 0.05), is not robust to sequencing depth. Furthermore, we show that, because only mutations with an observed frequency greater than the threshold are considered, the observed mutant allele frequency provides a biased estimate of the true frequency. This can result in substantial over-estimation of the TMB, when the cancer sample includes a large number of somatic mutations at low frequencies, and exacerbates the lack of robustness of TMB to variation in sequencing depth and tumour purity. Conclusion Our results demonstrate that care needs to be taken in the estimation of TMB to ensure that results are unbiased and consistent across studies and we suggest that accurate and robust estimation of TMB could be achieved using statistical models that estimate the full mutant allele frequency spectrum.

Abstract Background Predictive biomarkers are needed to identify oestrogen receptor-positive, human epidermal growth factor receptor 2-negative (ER + /HER2-) metastatic breast cancer (MBC) patients who would likely benefit from cyclin-dependent kinase 4 and 6 inhibitors combined with endocrine therapy. Therefore, we performed an exploratory study to evaluate the tumour heterogeneity parameters based on 16α-18F-fluoro-17β-oestradiol (18F-FES)-PET imaging as a potential marker to predict progression-free survival (PFS) in MBC patients receiving palbociclib combined with endocrine therapy. Methods Fifty-six ER + MBC patients underwent 18F-FES-PET/CT before the initiation of palbociclib. 18F-FES uptake was quantified and expressed as the standardized uptake value (SUV). Interlesional heterogeneity was qualitatively identified according to the presence or absence of 18F-FES-negative lesions. Intralesional heterogeneity was measured by the SUV-based heterogeneity index (HI = SUVmax/SUVmean). Association with survival was evaluated using the Cox proportional hazards model. Results A total of 551 metastatic lesions were found in 56 patients: 507 lesions were identified as 18F-FES-positive, 38 lesions were distributed across 10 patients without 18F-FES uptake, and the remaining 6 were liver lesions. Forty-three patients obtained a clinical benefit, and 13 developed progressive disease (PD) within 24 weeks. Nine out of 10 patients with an 18F-FES-negative site developed PD, and the median PFS was only 2.4 months. Among 46 patients with only 18F-FES-positive lesions, only four patients had PD, and the median PFS was 23.6 months. There were statistically significant differences between the two groups (P

Background and Purpose: Intratumour heterogeneity frequently leads to drug resistance, which is a major issue in drug discovery. Drug distribution is one of the key factors for elucidating the resistance mechanism; however, quantitative and regional drug measurement is challenging. Here, we developed a novel ultra‐sensitive analytical method and applied it to HER3‐targeting antibody–drug conjugate patritumab deruxtecan (HER3‐DXd), aiming to explore its payload (DXd) distribution within heterogeneous tissues. Experimental Approach: The developed analytical method is named LDMS‐CE‐MS, a capillary electrophoresis‐mass spectrometry (CE‐MS) coupled with a novel sample preconcentration/separation method called "large‐volume dual‐sample stacking by micelle collapse and sweeping (LDMS)". First, the analytical performance of LDMS‐CE‐MS for DXd detection was evaluated. Subsequently, we evaluated the bystander effect of HER3‐DXd, where tumour tissues were excised from xenograft models and clinical specimens after administration of HER3‐DXd. HER3‐high expression, adjacent, and HER3‐low expression regions were then sampled by laser microdissection to quantify the released DXd. Key Results: LDMS concentrated DXd by 1000‐fold and separated it from the hydrophilic bio‐matrix through continuous capture and release by the charged micelles, allowing quantification at sub‐attomole‐level. DXd concentrations decreased in the order of antigen‐high expression > adjacent > antigen‐low expression regions in the tumour xenograft model, whereas in clinical specimens, adjacent and antigen‐high expression regions had approximately the same concentration. These distributions represent a bystander effect. Conclusions and Implications: Our LDMS‐CE‐MS successfully visualized the attomole‐level drug distributions in heterogeneous clinical specimens. This new platform opens a new era of quantitative pharmacokinetic analysis, facilitating drug discovery and development. [ABSTRACT FROM AUTHOR]

Simple Summary: Neuroendocrine tumours, recently reclassified as neuroendocrine neoplasms (NENs), are a heterogeneous group of tumours with variability in their disease course and outcome. Complex mechanisms involving spatial and temporal changes in tumour biology affect their treatment response and survival. Treatment strategies are often based on information regarding tumour stage and grade. Tumour heterogeneity, however, is a common phenomenon in NENs, and it is not uncommon for these neoplasms to show intra- and inter-tumour heterogeneity that may lead to incomplete understanding of their tumour biology and behaviour. This review summarises the available evidence on gastroenteropancreatic NEN heterogeneity and its impact on diagnosis and clinical management. Tumour heterogeneity is a common phenomenon in neuroendocrine neoplasms (NENs) and a significant cause of treatment failure and disease progression. Genetic and epigenetic instability, along with proliferation of cancer stem cells and alterations in the tumour microenvironment, manifest as intra-tumoural variability in tumour biology in primary tumours and metastases. This may change over time, especially under selective pressure during treatment. The gastroenteropancreatic (GEP) tract is the most common site for NENs, and their diagnosis and treatment depends on the specific characteristics of the disease, in particular proliferation activity, expression of somatostatin receptors and grading. Somatostatin receptor expression has a major role in the diagnosis and treatment of GEP-NENs, while Ki-67 is also a valuable prognostic marker. Intra- and inter-tumour heterogeneity in GEP-NENS, however, may lead to inaccurate assessment of the disease and affect the reliability of the available diagnostic, prognostic and predictive tests. In this review, we summarise the current available evidence of the impact of tumour heterogeneity on tumour diagnosis and treatment of GEP-NENs. Understanding and accurately measuring tumour heterogeneity could better inform clinical decision making in NENs. [ABSTRACT FROM AUTHOR]

Simple Summary: Lactic acidosis is a prominent feature of the tumour microenvironment and a key player in cancer metabolism. This review is aimed at combining the mechanisms through which lactic acidosis alters the metabolism of cancer cells, and determining how this effect could bring valuable contribution to the current understanding of the metabolism of whole tumours. This work also highlights the therapeutic perspectives that advances in lactic acidosis understanding open up. Lactic acidosis, a hallmark of solid tumour microenvironment, originates from lactate hyperproduction and its co-secretion with protons by cancer cells displaying the Warburg effect. Long considered a side effect of cancer metabolism, lactic acidosis is now known to play a major role in tumour physiology, aggressiveness and treatment efficiency. Growing evidence shows that it promotes cancer cell resistance to glucose deprivation, a common feature of tumours. Here we review the current understanding of how extracellular lactate and acidosis, acting as a combination of enzymatic inhibitors, signal, and nutrient, switch cancer cell metabolism from the Warburg effect to an oxidative metabolic phenotype, which allows cancer cells to withstand glucose deprivation, and makes lactic acidosis a promising anticancer target. We also discuss how the evidence about lactic acidosis' effect could be integrated in the understanding of the whole-tumour metabolism and what perspectives it opens up for future research. [ABSTRACT FROM AUTHOR]

Background: Cancer-associated fibroblasts (CAFs) are a diverse subset of cells, that is recently gaining in popularity and have the potential to become a new target for breast cancer (BC) therapy; however, broader research is required to understand their mechanisms and interactions with breast cancer cells. The goal of the study was to isolate CAFs from breast cancer tumour and characterise isolated cell lines. We concentrated on numerous CAF biomarkers that would enable their differentiation. Materials and methods: Flow cytometry, immunofluorescence, and reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) were used to phenotype the primary CAFs. Results/Conclusions: According to our findings, there was no significant pattern in the classification of cancer-associated fibroblasts. The results of biomarkers expression were heterogeneous, thus no specific subtypes were identified. Furthermore, a comparison of cancer-associated fibroblasts derived from different BC subtypes (luminal A and B, triple-negative, HER2 positive) did not reveal any clear trend of expression. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) is one of the most prevalent cancers, with over one million new cases per year. Overall, prognosis of CRC largely depends on the disease stage and metastatic status. As precision oncology for patients with CRC continues to improve, this study aimed to integrate genomic, transcriptomic, and proteomic analyses to identify significant differences in expression during CRC progression using a unique set of paired patient samples while considering tumour heterogeneity. Methods: We analysed fresh-frozen tissue samples prepared under strict cryogenic conditions of matched healthy colon mucosa, colorectal carcinoma, and liver metastasis from the same patients. Somatic mutations of known cancer-related genes were analysed using Illumina's TruSeq Amplicon Cancer Panel; the transcriptome was assessed comprehensively using Clariom D microarrays. The global proteome was evaluated by liquid chromatography-coupled mass spectrometry (LC‒MS/MS) and validated by two-dimensional difference in-gel electrophoresis. Subsequent unsupervised principal component clustering, statistical comparisons, and gene set enrichment analyses were calculated based on differential expression results. Results: Although panomics revealed low RNA and protein expression of CA1, CLCA1, MATN2, AHCYL2, and FCGBP in malignant tissues compared to healthy colon mucosa, no differentially expressed RNA or protein targets were detected between tumour and metastatic tissues. Subsequent intra-patient comparisons revealed highly specific expression differences (e.g., SRSF3, OLFM4, and CEACAM5) associated with patient-specific transcriptomes and proteomes. Conclusion: Our research results highlight the importance of inter- and intra-tumour heterogeneity as well as individual, patient-paired evaluations for clinical studies. In addition to changes among groups reflecting CRC progression, we identified significant expression differences between normal colon mucosa, primary tumour, and liver metastasis samples from individuals, which might accelerate implementation of precision oncology in the future. [ABSTRACT FROM AUTHOR]

Introduction: To determine whether heterogeneity in colorectal liver metastases (CRLM) 18 F fluorodeoxyglucose [ 18 F]FDG distribution is predictive of disease-free survival (DFS) and overall survival (OS) following liver transplantation (LT) for unresectable CRLM., Methods: The preoperative [ 18 F]FDG positron emission tomography/computed tomography examinations of all patients in the secondary cancer 1 and 2 studies were retrospectively assessed. Maximum standardized uptake value (SUV max ), metabolic tumour volume (MTV), and six texture heterogeneity parameters (joint entropy GLCM, dissimilarity GLCM, grey level variance SZM, size zone variance SZM, and zone percentage SZM , and morphological feature convex deficiency) were obtained. DFS and OS for patients over and under the median value for each of these parameters were compared by using the Kaplan Meier method and log rank test., Results: Twenty-eight out of 40 patients who underwent LT for unresectable CRLM had liver metastases with uptake above liver background and were eligible for inclusion. Low MTV (p < 0.001) and dissimilarity GLCM (p = 0.016) were correlated to longer DFS. Low MTV (p < 0.001) and low values of the texture parameters dissimilarity GLCM (p = 0.038), joint entropy GLCM (p = 0.015) and zone percentage SZM (p = 0.037) were significantly correlated to longer OS. SUV max was not correlated to DFS and OS., Conclusion: Although some texture parameters were able to significantly predict DFS and OS, MTV seems to be superior to predict both DFS and OS following LT for unresectable CRLM., (© 2024 The Author(s). Clinical Physiology and Functional Imaging published by John Wiley & Sons Ltd on behalf of Scandinavian Society of Clinical Physiology and Nuclear Medicine.)

Abstract Folliculostellate cells are S100B-expressing cells with numerous functions in the normal anterior pituitary. These cells have also been identified in pituitary neuroendocrine tumours (PitNETs), where their precise role remains elusive. Here, we aimed to build a refined cartography of S100B-expressing cells to characterise their interpatient and intratumoural spatial distribution, and to start identifying their potential functions in PitNETs. High-throughput histological analysis of S100B-stained tumour sections of 54 PitNETs revealed a significant decrease in S100B + cells in PitNETs compared to the normal anterior pituitary. A Ki67 index ≥ 3, a mitosis count > 2/10 per high power fields, and a proliferative status, were all associated with fewer S100B + cells in gonadotroph tumours. Gonadotroph tumours also showed interpatient and intratumoural heterogeneity in the spatial distribution of S100B + cells. The existence of an intratumoural heterogeneity was further confirmed by the incorporation to our spatial analysis of additional markers: Ki67, FSH, LH, ERα and SSTR2. The tumour areas with fewer S100B + cells displayed a higher percentage of Ki67 + cells, whereas strong positive correlations were observed between S100B + , FSH + , and ERα + cells. Such spatial associations suggest that S100B + folliculostellate cells could play a role in gonadotroph tumorigenesis, and may contribute to the maintenance of tumour cells in a low proliferating, FSH + /ERα + differentiated state. Albeit, further in-depth functional studies are required to decipher the mechanisms underlying these spatial associations and to potentially identify a therapeutic use.

Objective: Colorectal cancer (CRC) patients respond differently to treatments and are sub-classified by different approaches. We evaluated a deep learning model, which adopted endoscopic knowledge learnt from AI-doscopist, to characterise CRC patients by histopathological features. Results: Data of 461 patients were collected from TCGA-COAD database. The proposed framework was able to 1) differentiate tumour from normal tissues with an Area Under Receiver Operating Characteristic curve (AUROC) of 0.97; 2) identify certain gene mutations (MYH9, TP53) with an AUROC > 0.75; 3) classify CMS2 and CMS4 better than the other subtypes; and 4) demonstrate the generalizability of predicting KRAS mutants in an external cohort. Conclusions: Artificial intelligent can be used for on-site patient classification. Although KRAS mutants were commonly associated with therapeutic resistance and poor prognosis, subjects with predicted KRAS mutants in this study have a higher survival rate in 30 months after diagnoses.