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Quantitative differences in signal transduction are to date an understudied feature of tumour heterogeneity. The MAPK Erk pathway, which is activated in a large proportion of human tumours, is a prototypic example of distinct cell fates being driven by signal intensity. We have used primary hepatocyte precursors transformed with different dosages of an oncogenic form of Ras to model subclonal variations in MAPK signalling. Orthotopic allografts of Ras-transformed cells in immunocompromised mice gave rise to fast-growing aggressive tumours, both at the primary location and in the peritoneal cavity. Fluorescent labelling of cells expressing different oncogene levels, and consequently varying levels of MAPK Erk activation, highlighted the selection processes operating at the two sites of tumour growth. Indeed, significantly higher Ras expression was observed in primary as compared to secondary, metastatic sites, despite the apparent evolutionary trade-off of increased apoptotic death in the liver that correlated with high Ras dosage. Analysis of the immune tumour microenvironment at the two locations suggests that fast peritoneal tumour growth in the immunocompromised setting is abrogated in immunocompetent animals due to efficient antigen presentation by peritoneal dendritic cells. Furthermore, our data indicate that, in contrast to the metastatic-like outgrowth, strong MAPK signalling is required in the primary liver tumours to resist elimination by NK (natural killer) cells. Overall, this study describes a quantitative aspect of tumour heterogeneity and points to a potential vulnerability of a subtype of hepatocellular carcinoma as a function of MAPK Erk signalling intensity.

Background: Investigating molecular heterogeneity provides insights into tumour origin and metabolomics. The increasing amount of data gathered makes manual analyses infeasible—therefore, automated unsupervised learning approaches are utilised for discovering tissue heterogeneity. However, automated analyses require experience setting the algorithms' hyperparameters and expert knowledge about the analysed biological processes. Moreover, feature engineering is needed to obtain valuable results because of the numerous features measured. Results: We propose DiviK: a scalable stepwise algorithm with local data-driven feature space adaptation for segmenting high-dimensional datasets. The algorithm is compared to the optional solutions (regular k-means, spatial and spectral approaches) combined with different feature engineering techniques (None, PCA, EXIMS, UMAP, Neural Ions). Three quality indices: Dice Index, Rand Index and EXIMS score, focusing on the overall composition of the clustering, coverage of the tumour region and spatial cluster consistency, are used to assess the quality of unsupervised analyses. Algorithms were validated on mass spectrometry imaging (MSI) datasets—2D human cancer tissue samples and 3D mouse kidney images. DiviK algorithm performed the best among the four clustering algorithms compared (overall quality score 1.24, 0.58 and 162 for d(0, 0, 0), d(1, 1, 1) and the sum of ranks, respectively), with spectral clustering being mostly second. Feature engineering techniques impact the overall clustering results less than the algorithms themselves (partial η 2 effect size: 0.141 versus 0.345, Kendall's concordance index: 0.424 versus 0.138 for d(0, 0, 0)). Conclusions: DiviK could be the default choice in the exploration of MSI data. Thanks to its unique, GMM-based local optimisation of the feature space and deglomerative schema, DiviK results do not strongly depend on the feature engineering technique applied and can reveal the hidden structure in a tissue sample. Additionally, DiviK shows high scalability, and it can process at once the big omics data with more than 1.5 mln instances and a few thousand features. Finally, due to its simplicity, DiviK is easily generalisable to an even more flexible framework. Therefore, it is helpful for other -omics data (as single cell spatial transcriptomic) or tabular data in general (including medical images after appropriate embedding). A generic implementation is freely available under Apache 2.0 license at https://github.com/gmrukwa/divik. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is considered the second cause of cancer death worldwide. The early diagnosis plays a key role in patient prognosis and subsequently overall survival. Similar to several types of cancer, colorectal cancer is also characterised by drug resistance and heterogeneity that contribute to its complexity -especially at advanced stages. However, despite the extensive research related to the identification of biomarkers associated to early diagnosis, accurate prognosis and the management of CRC patients, little progress has been made thus far. Therefore, the mortality rates, especially at advanced stages, remain high. A large family of chemoattractant cytokines called chemokines are known for their significant role in inflammation and immunity. Chemokines released by the different tumorous cells play a key role in increasing the complexity of the tumour's microenvironment. The current review investigates the role of chemokines and chemokine receptors in colorectal cancer and their potential as clinical molecular signatures that could be effectively used as a personalised therapeutic approach. We discussed how chemokine and chemokine receptors regulate the microenvironment and lead to heterogeneity in CRC. An important aspect of chemokines is their role in drug resistance which has been extensively discussed. This review also provides an overview of the current advances in the search for chemokines and chemokine receptors in CRC. [ABSTRACT FROM AUTHOR]

Imaging mass cytometry (IMC) is a new technology integrating mass spectrometry, high-resolution laser ablation and immunohistochemistry/cytochemistry. A unique high-dimensional perspective comprehensively and accurately depicts the complex interaction of phenotype, signalling pathway and tumour microenvironment and is widely used in solid tumours. However, the application scenarios of IMC in basic medicine and clinical research in solid tumours lack systematic introduction and classification. This paper reviews the application of IMC in depicting the panorama of the tumour microenvironment, revealing tumour spatial heterogeneity, clarifying tumour pharmacological mechanisms, assisting in new drug development, and dynamically evaluating the efficacy of immunotherapy in solid tumours. • IMC is a new technology integrating mass spectrometry, high-resolution laser ablation and immunohistochemistry. • This paper reviews the application of IMC in basic laboratory studies, preclinical studies and clinical observations in solid tumours. • It can depict the complex phenotypes in tissue and tumour microenvironment. • IMC is a powerful tool for solid tumour transformation research and precision treatment as new technology. • The existing research cases are classified and summarized, meanwhile the clinical trials using IMC are listed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

After decades of relative stagnation lung cancer is emerging as a disease type where rapid progress is being made in diagnosis and therapy, as well as in our understanding of disease biology. Much of this progress is of immediate impact to diagnosticians, and more is likely to affect diagnostic practice in the near future. In this review we seek to briefly summarize several key areas of active research of immediate or probable imminent value to trainee and consultant pulmonary pathologists alike. We cover some major changes in tumour classification, grading, and patient stratification, as well as considering the state of the art in machine-assisted interpretation of lung cancer histology, and the use of genetically modified lung cancer models. [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. [ABSTRACT FROM AUTHOR]

The 2023 Annual Review Issue of The Journal of Pathology, Recent Advances in Pathology, contains 12 invited reviews on topics of current interest in pathology. This year, our subjects include immuno‐oncology and computational pathology approaches for diagnostic and research applications in human disease. Reviews on the tissue microenvironment include the effects of apoptotic cell‐derived exosomes, how understanding the tumour microenvironment predicts prognosis, and the growing appreciation of the diverse functions of fibroblast subtypes in health and disease. We also include up‐to‐date reviews of modern aspects of the molecular basis of malignancies, and our final review covers new knowledge of vascular and lymphatic regeneration in cardiac disease. All of the reviews contained in this issue are written by expert groups of authors selected to discuss the recent progress in their particular fields and all articles are freely available online (https://pathsocjournals.onlinelibrary.wiley.com/journal/10969896). © 2023 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Fenghai Liu, Meng Zhao, Shan Lu, Liqing Kang Department of Magnetic Resonance Imaging, Cangzhou Central Hospital, Cangzhou, Hebei Province, 061001, People’s Republic of ChinaCorrespondence: Liqing KangDepartment of Magnetic Resonance Imaging, Cangzhou Central Hospital, No. 16 of Xinhua West Road, Cangzhou, 061001, Hebei Province, People’s Republic of ChinaTel +86 13333367921Email 13333367921@163.comObjective: DCE-MRI is an imaging technique that reflects the blood perfusion status of the tissue’s microcirculation. The purpose of this article is to explore the clinical value of dynamic contrast-enhanced (DCE)-MRI in distinguishing benign and malignant tongue lesions and the internal heterogeneity of a tumour.Methods: The patients were divided into a tongue cancer group (22 patients) and a glossitis group (7 patients) based on the pathology results. All of the patients underwent DCE-MRI examination.Results: The results of this study showed that the volume transfer constant (Ktrans), rate constant (Kep), contrast enhancement ratio (CER) and initial area under the gadolinium contrast agent concentration time curve (IAUGG) values of the tongue cancer group were significantly higher than those of the glossitis group, and the difference was statistically significant (P < 0.05). However, the extravascular extracellular volume fraction (Ve), fractional plasma volume (fPV), maximum slope (MaxSlope), and bolus arrival time (BAT) values measured by DCE-MRI in the tongue cancer group were not significantly different from those in the glossitis group (P > 0.05). The results of this study showed that the Ktrans, Kep, and IAUGG values measured by DCE-MRI had a good ability to distinguish tongue inflammation from tumours and Ktrans threshold of 0.484 has the best discriminative ability among them. The mean Ktrans values of stage I–II lesions were significantly higher than that of stage III–IV lesion (p = 0.045).Conclusion: DCE-MRI is effective in distinguishing between benign and malignant tongue lesions and the internal heterogeneity of the tumour; it is worth following up in a larger study.Clinical Registration Number: Research registry 6393.Keywords: DCE-MRI, tongue cancer, tumour heterogeneity, tumour microenvironment, tumour precision diagnosis and treatment

• HNSCC demonstrates considerable methylomic intra-tumoural heterogeneity. • Plasma cell-free DNA potential to capture a select fraction of tumour-specific methylation. • Tumour specific enriched methylated gene pathways identified in cell-free DNA. • Cell-free DNA able to detect tumour-specific genomic copy number. • Temporal evolution in genomic copy number is partly visible in plasma cell-free DNA. Liquid biopsy profiling is gaining increasing promise towards biomarker-led identification and disease stratification of tumours, particularly for tumours displaying significant intra-tumoural heterogeneity (ITH). For head and neck squamous cell carcinoma (HNSCC), which display high levels of genetic ITH, identification of epigenetic modifications and methylation signatures has shown multiple uses in stratification of HNSCC for prognosis, treatment, and HPV status. In this study, we investigated the potential of liquid biopsy methylomics and genomic copy number to profile HNSCC. We conducted multi-region sampling of tumour core, tumour margin and normal adjacent mucosa, as well as plasma cell-free DNA (cfDNA) across 9 HNSCC patients. Collectively, our work highlights the prevalence of methylomic ITH in HNSCC, and demonstrates the potential of cfDNA methylation as a tool for ITH assessment and serial sampling. [ABSTRACT FROM AUTHOR]

Simple Summary: Triple Negative Breast Cancer represents a cancer type with an unmet clinical need. This type of breast cancer presents the worse clinical outcome due to its aggressiveness, high heterogeneity, and absence of therapeutic targets. Chemotherapy is still the standard of care for this type of cancer, but many patients develop resistance to treatment and metastatic disease. In this review, we highlight the existing challenges for effective treatment of triple negative breast cancer. We discuss the importance of the stratification into different molecular subtypes and the identification of resistant cells within tumours that is needed to guide future strategies for effective and precise therapies. The Triple Negative Breast Cancer (TNBC) subtype is known to have a more aggressive clinical course compared to other breast cancer subtypes. Targeted therapies for this type of breast cancer are limited and patients are mostly treated with conventional chemo- and radio-therapies which are not specific and do not target resistant cells. Therefore, one of the major clinical challenges is to find compounds that target the drug-resistant cell populations which are responsible for reforming secondary tumours. The molecular profiling of the different TNBC subtypes holds a promise for better defining these resistant cells specific to each tumour. To this end, a better understanding of TNBC heterogeneity and cancer stemness is required, and extensive genomic analysis can help to understand the disease complexity and distinguish new molecular drivers that can be targeted in the clinics. The use of persister cancer cell-targeting therapies combined with other therapies may provide a big advance to improve TNBC patients' survival. [ABSTRACT FROM AUTHOR]

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 < 0.001). For the subgroup of patients with only 18F-FES-positive lesions, low FES-HI patients experienced substantially longer PFS times than those with high FES-HI (26.5 months vs. 16.5 months, P = 0.004).Conclusions: 18F-FES-PET may provide a promising method for identifying and selecting candidate ER + /HER2- MBC patients who would most likely benefit from palbociclib combined with endocrine treatment and could serve as a predictive marker for treatment response. Trial registration NCT04992156, Date of registration: August 5, 2021 (retrospectively registered). [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Background and Aims: Colorectal cancer (CRC) is a heterogeneous disease with distinctive genetic pathways, such as chromosomal instability, microsatellite instability and methylator pathway. Our aim was to correlate clinical and genetic characteristics of CRC patients in order to understand clinical implications of tumour genotype. Methods: Single-institution retrospective cohort of patients who underwent curative surgery for CRC, from 2012 to 2014. RAS and BRAF mutations were evaluated with the real-time PCR technique Idylla®. Mismatch repair deficiency (dMMR) was characterized by absence of MLH1, MSH6, MSH2 and/or PMS2 expression, evaluated by tissue microarrays. Overall survival (OS) and disease-free survival (DFS) were assessed using survival analysis. Results: Overall, 242 patients were included (males 57.4%, age 69.3 ± 12.9 years; median follow-up 49 months). RAS-mutated tumours were associated with reduced DFS (p = 0.02) and OS (p = 0.045) in stage I–III CRC. BRAF-mutated tumours were more predominant in females and in the right colon, similarly to dMMR tumours. BRAF status did not influence OS (4 years)/DFS (3.5 years) in stage I–III disease. However, after relapse, length of survival was 3.5 months in BRAF-mutated tumours in contrast to 18.6 months in BRAF wild-type tumours (p = NS). No germline mutations in mismatch repair genes were so far identified in the patients with dMMR tumours. Molecular phenotype (RAS, BRAF and MMR) did not influence OS in metastatic patients. Our small sample size may be a limitation of the study. Conclusion: In our cohort, RAS-mutated tumours were associated with worse DFS and OS in early-stage CRC, whereas the remaining molecular variables had no prognostic influence.

Abstract Background High throughput single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for exploring cellular heterogeneity among complex human cancers. scRNA-Seq studies using fresh human surgical tissue are logistically difficult, preclude histopathological triage of samples, and limit the ability to perform batch processing. This hindrance can often introduce technical biases when integrating patient datasets and increase experimental costs. Although tissue preservation methods have been previously explored to address such issues, it is yet to be examined on complex human tissues, such as solid cancers and on high throughput scRNA-Seq platforms. Methods Using the Chromium 10X platform, we sequenced a total of ~ 120,000 cells from fresh and cryopreserved replicates across three primary breast cancers, two primary prostate cancers and a cutaneous melanoma. We performed detailed analyses between cells from each condition to assess the effects of cryopreservation on cellular heterogeneity, cell quality, clustering and the identification of gene ontologies. In addition, we performed single-cell immunophenotyping using CITE-Seq on a single breast cancer sample cryopreserved as solid tissue fragments. Results Tumour heterogeneity identified from fresh tissues was largely conserved in cryopreserved replicates. We show that sequencing of single cells prepared from cryopreserved tissue fragments or from cryopreserved cell suspensions is comparable to sequenced cells prepared from fresh tissue, with cryopreserved cell suspensions displaying higher correlations with fresh tissue in gene expression. We showed that cryopreservation had minimal impacts on the results of downstream analyses such as biological pathway enrichment. For some tumours, cryopreservation modestly increased cell stress signatures compared to freshly analysed tissue. Further, we demonstrate the advantage of cryopreserving whole-cells for detecting cell-surface proteins using CITE-Seq, which is impossible using other preservation methods such as single nuclei-sequencing. Conclusions We show that the viable cryopreservation of human cancers provides high-quality single-cells for multi-omics analysis. Our study guides new experimental designs for tissue biobanking for future clinical single-cell RNA sequencing studies.

Simple Summary: Tumour heterogeneity is a phenomenon where the cancer cells evolve diversely over the course of the disease. As a result of the evolution, the cancer cells can be found to be genetically, epigenetically and/or phenotypically different in order to survive in the human body. The tumour microenvironment also plays a crucial role during the evolution. This review compiles the recent developments in melanoma tumour heterogeneity and how heterogeneity impacts the evolution of the disease. The recent findings have greatly improved our knowledge on heterogeneity in melanoma, but there remain many unanswered questions. There is growing evidence that tumour heterogeneity has an imperative role in cancer development, evolution and resistance to therapy. Continuing advancements in biomedical research enable tumour heterogeneity to be observed and studied more critically. As one of the most heterogeneous human cancers, melanoma displays a high level of biological complexity during disease progression. However, much is still unknown regarding melanoma tumour heterogeneity, as well as the role it plays in disease progression and treatment response. This review aims to provide a concise summary of the importance of tumour heterogeneity in melanoma. [ABSTRACT FROM AUTHOR]

Abstract Background At diagnosis tumours are typically composed of a mixture of genomically distinct malignant cell populations. Bulk sequencing of tumour samples coupled with computational deconvolution can be used to identify these populations and study cancer evolution. Existing computational methods for populations deconvolution are slow and/or potentially inaccurate when applied to large datasets generated by whole genome sequencing data. Results We describe PyClone-VI, a computationally efficient Bayesian statistical method for inferring the clonal population structure of cancers. We demonstrate the utility of the method by analyzing data from 1717 patients from PCAWG study and 100 patients from the TRACERx study. Conclusions Our proposed method is 10–100× times faster than existing methods, while providing results which are as accurate. Software implementing our method is freely available https://github.com/Roth-Lab/pyclone-vi .

Gastrointestinal cancers refer to a group of deadly malignancies of the gastrointestinal tract and organs of the digestive system. Over the past decades, considerable amounts of medicinal plants have exhibited potent anticancer effects on different types of gastrointestinal cancers. OMICS, systems biology approaches covering genomics, transcriptomics, proteomics and metabolomics, are broadly applied to comprehensively reflect the molecular profiles in mechanistic studies of medicinal plants. Single- and multi-OMICS approaches facilitate the unravelling of signalling interaction networks and key molecular targets of medicinal plants with anti-gastrointestinal cancer potential. Hence, this review summarizes the applications of various OMICS and advanced bioinformatics approaches in examining therapeutic targets, signalling pathways, and the tumour microenvironment in response to anticancer medicinal plants. Advances and prospects in this field are also discussed.

Alternative splicing is a process by which a single gene is able to encode multiple different protein isoforms. It is regulated by the inclusion or exclusion of introns and exons that are joined in different patterns prior to protein translation, thus enabling transcriptomic and proteomic diversity. It is now widely accepted that alternative splicing is dysregulated across nearly all cancer types. This widespread dysregulation means that nearly all cellular processes are affected – these include processes synonymous with the hallmarks of cancer – evasion of apoptosis, tissue invasion and metastasis, altered cellular metabolism, genome instability and drug resistance. Emerging evidence indicates that the dysregulation of alternative splicing also promotes a permissive environment for increased tumour heterogeneity and cellular plasticity. These are fundamental regulators of a patient's response to therapy. In this Review, we introduce the mechanisms of alternative splicing and the role of aberrant splicing in cancer, with particular focus on newfound evidence of alternative splicing promoting tumour heterogeneity, cellular plasticity and altered metabolism. We discuss recent in vivo models generated to study alternative splicing and the importance of these for understanding complex tumourigenic processes. Finally, we review the effects of alternative splicing on immune evasion, cell death and genome instability, and how targeting these might enhance therapeutic efficacy.

Simple Summary: Most malignant tumours are highly heterogeneous at molecular and phenotypic levels. Tumour variability poses challenges for the management of patients, as it arises between patients and even evolves in space and time within a single patient. Currently, treatment-decision making usually relies on the molecular characteristics of a limited tumour tissue sample at the time of diagnosis or disease progression but does not take into account the complexity of the bulk tumours and their constant evolution over time. In this review, we explore the extent of tumour heterogeneity and report the mechanisms that promote and sustain this diversity in cancers. We summarise the clinical strikes of tumour diversity in the management of patients with cancer. Finally, we discuss the current material and technological approaches that are relevant to adequately appreciate tumour heterogeneity. Human solid malignancies harbour a heterogeneous set of cells with distinct genotypes and phenotypes. This heterogeneity is installed at multiple levels. A biological diversity is commonly observed between tumours from different patients (inter-tumour heterogeneity) and cannot be fully captured by the current consensus molecular classifications for specific cancers. To extend the complexity in cancer, there are substantial differences from cell to cell within an individual tumour (intra-tumour heterogeneity, ITH) and the features of cancer cells evolve in space and time. Currently, treatment-decision making usually relies on the molecular characteristics of a limited tumour tissue sample at the time of diagnosis or disease progression but does not take into account the complexity of the bulk tumours and their constant evolution over time. In this review, we explore the extent of tumour heterogeneity with an emphasis on ITH and report the mechanisms that promote and sustain this diversity in cancers. We summarise the clinical strikes of ITH in the management of patients with cancer. Finally, we discuss the current material and technological approaches that are relevant to adequately appreciate ITH. [ABSTRACT FROM AUTHOR]

Lentiviral vectors are unique and highly efficient genetic tools to incorporate genetic materials into the genome of a variety of cells whilst conserving biosafety. Their rapid acceptance made it necessary to improve existing protocols, including molecular engineering and cloning, production of purified lentiviral particles, and efficient infection of target cells. In addition to traditional protocols, which can be time-consuming, several biotechnology companies are providing scientists with commercially available lentiviral constructs and particles. However, these constructs are limited by their original form, tend to be costly, and lack the flexibility to re-engineer based on the ever-changing needs of scientific projects. Therefore, the current study organizes the existing methods and integrates them with novel ideas to establish a protocol that is simple and efficient to implement. In this study we, (i) generated an innovative site-directed nucleotide attachment/replacement and DNA insertion method using unique PCR primers, (ii) improved traditional methods by integrating plasmid clarification steps, (iii) utilized endogenous mRNA as a resource to construct new lentiviruses, and (iv) identified an existing purification method and incorporated it into an organized workflow to produce high-yield lentiviral particle collection. Finally, (v) we verified and demonstrated the functional validity of our methods using an infection strategy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Gastrointestinal cancers refer to a group of deadly malignancies of the gastrointestinal tract and organs of the digestive system. Over the past decades, considerable amounts of medicinal plants have exhibited potent anticancer effects on different types of gastrointestinal cancers. OMICS, systems biology approaches covering genomics, transcriptomics, proteomics and metabolomics, are broadly applied to comprehensively reflect the molecular profiles in mechanistic studies of medicinal plants. Single- and multi-OMICS approaches facilitate the unravelling of signalling interaction networks and key molecular targets of medicinal plants with anti-gastrointestinal cancer potential. Hence, this review summarizes the applications of various OMICS and advanced bioinformatics approaches in examining therapeutic targets, signalling pathways, and the tumour microenvironment in response to anticancer medicinal plants. Advances and prospects in this field are also discussed. [ABSTRACT FROM AUTHOR]

Aims: The aims of this work were to build a semi‐mechanistic tumour growth inhibition (TGI) model for metastatic colorectal cancer (mCRC) patients receiving either cetuximab + chemotherapy or chemotherapy alone and to identify early predictors of overall survival (OS). Methods: A total of 1716 patients from 4 mCRC clinical studies were included in the analysis. The TGI model was built with 8973 tumour size measurements where the probability of drop‐out was also included and modelled as a time‐to‐event variable using parametric survival models, as it was the case in the OS analysis. The effects of patient‐ and tumour‐related covariates on model parameters were explored. Results: Chemotherapy and cetuximab effects were included in an additive form in the TGI model. Development of resistance was found to be faster for chemotherapy (drug effect halved at wk 8) compared to cetuximab (drug effect halved at wk 12). KRAS wild‐type status and presenting a right‐sided primary lesion were related to a 3.5‐fold increase in cetuximab drug effect and a 4.7× larger cetuximab resistance, respectively. The early appearance of a new lesion (HR = 4.14), a large tumour size at baseline (HR = 1.62) and tumour heterogeneity (HR = 1.36) were the main predictors of OS. Conclusions: Semi‐mechanistic TGI and OS models have been developed in a large population of mCRC patients receiving chemotherapy in combination or not with cetuximab. Tumour‐related predictors, including a machine learning derived‐index of tumour heterogeneity, were linked to changes in drug effect, resistance to treatment or OS, contributing to the understanding of the variability in clinical response. [ABSTRACT FROM AUTHOR]

Bladder cancer is the most common malignant tumour of the urinary system that is characterised by significant intra-tumoural heterogeneity. While large-scale sequencing projects have provided a preliminary understanding of tumour heterogeneity, these findings are based on the average signals obtained from the pooled populations of diverse cells. Recent advances in single-cell sequencing (SCS) technologies have been critical in this regard, opening up new ways of understanding the nuanced tumour biology by identifying distinct cellular subpopulations, dissecting the tumour microenvironment, and characterizing cellular genomic mutations. By integrating these novel insights, SCS technologies are expected to make powerful and meaningful changes to the current diagnosis and treatment of bladder cancer through the identification and usage of novel biomarkers as well as targeted therapeutics. SCS can discriminate complex heterogeneity in a large population of tumour cells and determine the key molecular properties that influence clinical outcomes. Here, we review the advances in single-cell technologies and discuss their applications in cancer research and clinical practice, with a specific focus on bladder cancer. [ABSTRACT FROM AUTHOR]

BackgroundTumour samples containing distinct sub-populations of cancer and normal cells present challenges in the development of reproducible biomarkers, as these biomarkers are based on bulk signals from mixed tumour profiles. ISOpure is the only mRNA computational purification method to date that does not require a paired tumour-normal sample, provides a personalized cancer profile for each patient, and has been tested on clinical data. Replacing mixed tumour profiles with ISOpure-preprocessed cancer profiles led to better prognostic gene signatures for lung and prostate cancer.ResultsTo simplify the integration of ISOpure into standard R-based bioinformatics analysis pipelines, the algorithm has been implemented as an R package. The ISOpureR package performs analogously to the original code in estimating the fraction of cancer cells and the patient cancer mRNA abundance profile from tumour samples in four cancer datasets.ConclusionsThe ISOpureR package estimates the fraction of cancer cells and personalized patient cancer mRNA abundance profile from a mixed tumour profile. This open-source R implementation enables integration into existing computational pipelines, as well as easy testing, modification and extension of the model.

Circulating tumour cells (CTCs) are shed into the bloodstream from both primary and secondary tumours and provide a non-invasive means to study tumor progression and response to treatment. Assessment of ribonucleic acid (RNA) and monitoring dynamic changes in gene expression profiles of CTCs extends their clinical and prognostic power and establish their role in guiding treatment. Among these methods, droplet digital (RT-ddPCR) technique provides a high sensitivity and detectibility of CTCs. RNA-sequencing (RNAseq) is the most comprehensive method, that would allow the simultaneous measurement of a large number of genes and theoretically the whole transcriptome. Since CTCs are heterogeneous in nature, single cell RNAseq methods are very valuable in assessing population dynamics and functional states of CTCs. While RNA in situ hybridization (RNA-ISH) is used relatively less frequently, it also allows for the assessment of expression of multiple genes within individual CTCs. Epithelial to Mesenchymal Transition (EMT) or Plasticity (EMP) is a major contributor to metastasis, providing a mechanism to allow cells to become migratory and invasive, and to survive in the bloodstream. Monitoring CTCs undergoing EMT may lead to improvement in their prognostic and predictive power. Here, we review various RNA analysis of CTCs and those that undergo EMT and their application in diagnosis, prognosis and treatment of cancers.

Abstract Background The concept of head and neck cancers (HNSCC) having unique molecular signatures is well accepted but relating this to clinical presentation and disease behaviour is essential for patient benefit. Currently the clinical significance of HNSCC molecular subtypes is uncertain therefore personalisation of HNSCC treatment is not yet possible. Methods We performed meta-analysis on 8 microarray studies and identified six significantly up- (PLAU, FN1, CDCA5) and down-regulated (CRNN, CLEC3B and DUOX1) genes which were subsequently quantified by RT-qPCR in 100 HNSCC patient margin and core tumour samples. Results Retrospective correlation with sociodemographic and clinicopathological patient details identified two subgroups of opposing molecular signature (+q6 and -q6) that correlated to two recognised high-risk HNSCC populations in the UK. The +q6 group were older, male, and excessive alcohol users whilst the –q6 group were younger, female, paan-chewers and predominantly Bangladeshi. Additionally, all patients with tumour recurrence were in the latter subgroup. Conclusions We provide the first evidence linking distinct molecular signatures in HNSCC with clinical presentations. Prospective trials are required to determine the correlation between these distinct genotypes and disease progression or treatment response. This is an important step towards the ultimate goal of improving outcomes by utilising personalised molecular-signature-guided treatments for HNSCC patients.

Background: High throughput single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for exploring cellular heterogeneity among complex human cancers. scRNA-Seq studies using fresh human surgical tissue are logistically difficult, preclude histopathological triage of samples, and limit the ability to perform batch processing. This hindrance can often introduce technical biases when integrating patient datasets and increase experimental costs. Although tissue preservation methods have been previously explored to address such issues, it is yet to be examined on complex human tissues, such as solid cancers and on high throughput scRNA-Seq platforms. Methods: Using the Chromium 10X platform, we sequenced a total of ~ 120,000 cells from fresh and cryopreserved replicates across three primary breast cancers, two primary prostate cancers and a cutaneous melanoma. We performed detailed analyses between cells from each condition to assess the effects of cryopreservation on cellular heterogeneity, cell quality, clustering and the identification of gene ontologies. In addition, we performed single-cell immunophenotyping using CITE-Seq on a single breast cancer sample cryopreserved as solid tissue fragments. Results: Tumour heterogeneity identified from fresh tissues was largely conserved in cryopreserved replicates. We show that sequencing of single cells prepared from cryopreserved tissue fragments or from cryopreserved cell suspensions is comparable to sequenced cells prepared from fresh tissue, with cryopreserved cell suspensions displaying higher correlations with fresh tissue in gene expression. We showed that cryopreservation had minimal impacts on the results of downstream analyses such as biological pathway enrichment. For some tumours, cryopreservation modestly increased cell stress signatures compared to freshly analysed tissue. Further, we demonstrate the advantage of cryopreserving whole-cells for detecting cell-surface proteins using CITE-Seq, which is impossible using other preservation methods such as single nuclei-sequencing. Conclusions: We show that the viable cryopreservation of human cancers provides high-quality single-cells for multi-omics analysis. Our study guides new experimental designs for tissue biobanking for future clinical single-cell RNA sequencing studies. [ABSTRACT FROM AUTHOR]

There is considerable clinical and fundamental value in measuring the clonal heterogeneity of T and B cell expansions in tumors and tumor-associated lymphoid structures—along with the associated heterogeneity of the tumor neoantigen landscape—but such analyses remain challenging to perform. Here, we propose a straightforward approach to analyze the heterogeneity of immune repertoires between different tissue sections in a quantitative and controlled way, based on a beta-binomial noise model trained on control replicates obtained at the level of single-cell suspensions. This approach allows to identify local clonal expansions with high accuracy. We reveal in situ proliferation of clonal T cells in a mouse model of melanoma, and analyze heterogeneity of immunoglobulin repertoires between sections of a metastatically-infiltrated lymph node in human melanoma and primary human colon tumor. On the latter example, we demonstrate the importance of training the noise model on datasets with depth and content that is comparable to the samples being studied. Altogether, we describe here the crucial basic instrumentarium needed to facilitate proper experimental setup planning in the rapidly evolving field of intratumoral immune repertoires, from the wet lab to bioinformatics analysis.

Computed Tomography perfusion (CTp) is a func- tional imaging technique with a wide application in the oncologi- cal field. CTp allows detecting the presence of tumour abnormal hemodynamic patterns, by analysing the tissue temporal varia- tions occurring after the administration of the contrast medium. This work presents a novel approach to extract meaningful features from blood flow (BF) maps of lung cancers, which could act as a prognostic image-based biomarker.

Triple-negative breast cancer (TNBC) is characterized by a more aggressive clinical course with extensive inter- and intra-tumour heterogeneity. Combination of single-cell and bulk tissue transcriptome profiling allows the characterization of tumour heterogeneity and identifies the association of the immune landscape with clinical outcomes. We identified inter- and intra-tumour heterogeneity at a single-cell resolution. Tumour cells shared a high correlation amongst stemness, angiogenesis, and EMT in TNBC. A subset of cells with concurrent high EMT, stemness and angiogenesis was identified at the single-cell level. Amongst tumour-infiltrating immune cells, M2-like tumour-associated macrophages (TAMs) made up the majority of macrophages and displayed immunosuppressive characteristics. CIBERSORT was applied to estimate the abundance of M2-like TAM in bulk tissue transcriptome file from The Cancer Genome Atlas (TCGA). M2-like TAMs were associated with unfavourable prognosis in TNBC patients. A TAM-related gene signature serves as a promising marker for predicting prognosis and response to immunotherapy. Two commonly used machine learning methods, random forest and SVM, were applied to find the genes that were mostly associated with M2-like TAM densities in the gene signature. A neural network-based deep learning framework based on the TAM-related gene signature exhibits high accuracy in predicting the immunotherapy response. [ABSTRACT FROM AUTHOR]

Background: At diagnosis tumours are typically composed of a mixture of genomically distinct malignant cell populations. Bulk sequencing of tumour samples coupled with computational deconvolution can be used to identify these populations and study cancer evolution. Existing computational methods for populations deconvolution are slow and/or potentially inaccurate when applied to large datasets generated by whole genome sequencing data. Results: We describe PyClone-VI, a computationally efficient Bayesian statistical method for inferring the clonal population structure of cancers. We demonstrate the utility of the method by analyzing data from 1717 patients from PCAWG study and 100 patients from the TRACERx study. Conclusions: Our proposed method is 10–100× times faster than existing methods, while providing results which are as accurate. Software implementing our method is freely available https://github.com/Roth-Lab/pyclone-vi. [ABSTRACT FROM AUTHOR]

Purpose: Breast cancer (BC) risk factors have been differentially associated with BC subtypes, but quantification is still undefined. Therefore, we compared selected risk factors with BC subtypes, using a case-case approach. Methods: We retrieved 1321 invasive female BCs from the Piedmont Cancer Registry. Through record linkage of clinical records, we obtained data on estrogen (Er) and progesterone (Pr) receptors, Ki67 and HER2+ status, BC family history, breast imaging reporting and data system (BI-RADS) density, reproductive risk factors and education. We defined BC subtypes as follows : luminal A (Er+ and/or Pr+ , HER2− , low Ki67), luminal BH- (Er+ and/or Pr + , HER2− , Ki67 high), luminal BH+ (Er+ and/or Pr + , HER2+), HER2+ (Er − , Pr − , HER2+),) and triple negative (Er − , Pr − , HER2−). Using a multinomial regression model, we estimated the odds ratios (ORs) for selected BC risk factors considering luminal A as reference. Results: For triple negative, the OR for BC family history was 1.83 (95% confidence interval (CI) 1.13–2.97). Compared to BI-RADS 1, for triple negative, the OR for BI-RADS 2 was 0.56 (95% CI 0.27–1.14) and for BI-RADS 3–4 was 0.37 (95% CI 0.15–0.88); for luminal BH +, the OR for BI-RADS 2 was 2.36 (95% CI 1.08–5.11). For triple negative, the OR for high education was 1.78 (95% CI 1.03–3.07), and for late menarche, the OR was 1.69 (95% CI 1.02–2.81). For luminal BH + , the OR for parous women was 0.56 (95% CI 0.34–0.92). Conclusions: This study supported BC etiologic heterogeneity across subtypes, particularly for triple negative. [ABSTRACT FROM AUTHOR]

Cancers exhibit a remarkable degree of intratumoral heterogeneity (ITH), which results from complex cellular interactions amongst various cell types. This phenomenon provides an opportunity for clonal selection and growth advantages to aggressive cancer cell types, resulting in worse prognosis and challenges to anti-cancer therapy. Cell competition is a conserved mechanism operational in cellular and organ systems, which allows neighboring cells to compare their relative fitness levels and results in the elimination of viable but suboptimal cells. By abuse of this conserved homeostasis mechanism, aggressive cancer cell types gain an advantage over normal cell types by achieving traits like increased proliferation, de-differentiation, and stemness. This review presents recent evidence that cell competition mechanisms actively participate in the regulation of intratumoral cell-cell interactions and thus contribute to ITH, and this process is essential for cancer development and progression. [ABSTRACT FROM AUTHOR]

Purpose: Triple negative breast cancers (TNBCs) are enriched in cells bearing stem-like features, i.e., cancer stem cells (CSCs), which underlie cancer progression. Thus, targeting stemness may be an interesting treatment approach. The epigenetic machinery is crucial for maintaining the stemness phenotype. Bromodomain and extra-terminal domain (BET) epigenetic reader family members are emerging as novel targets for cancer therapy, and have already shown preclinical effects in breast cancer. Here, we aimed to evaluate the effect of the BET inhibitor JQ1 on stemness in TNBC. Methods: Transcriptomic, functional annotation and qRT-PCR studies were performed on JQ1-exposed TNBC cells in culture. The results obtained were confirmed in spheroids and spheroid-derived tumours. In addition, limiting dilution, secondary and tertiary tumour sphere formation, matrigel invasion, immunofluorescence and flow cytometry assays were performed to evaluate the effect of JQ1 on CSC features. For clinical outcome analyses, the online tool Kaplan-Meier Plotter and an integrated response database were used. Results: We found that JQ1 modified the expression of stemness-related genes in two TNBC-derived cell lines, MDA-MB-231 and BT549. Among these changes, the CD44 Antigen/CD24 Antigen (CD44/CD24) ratio and Aldehyde Dehydrogenase 1 Family Member A1 (ALDH1A1) expression level, i.e., both classical stemness markers, were found to be decreased by JQ1. Using a validated spheroid model to mimic the intrinsic characteristics of CSCs, we found that JQ1 decreased surface CD44 expression, inhibited self-renewal and invasion, and induced cell cycle arrest in G0/G1, thereby altering the stemness phenotype. We also found associations between four of the identified stemness genes, Gap Junction Protein Alpha 1 (GJA1), CD24, Epithelial Adhesion Molecule (EPCAM) and SRY-related HMG-box gene 9 (SOX9), and a worse TNBC patient outcome. The expression of another two of the stemness-related genes was found to be decreased by JQ1, i.e., ATP Binding Cassette Subfamily G Member 2 (ABCG2) and RUNX2, and predicted a low response to chemotherapy in TNBC patients, which supports a role for RUNX2 as a potential predictive marker for chemotherapy response in TNBC. Conclusions: We identified a stemness-related gene panel associated with JQ1 and describe how this inhibitor modifies the stemness landscape in TNBC. Therefore, we propose a novel role for JQ1 as a stemness-targeting drug. Loss of the stem cell phenotype via JQ1 treatment could lead to less aggressive and more chemo-sensitive tumours, reflecting a better patient prognosis. Thus, the identified gene panel may be of interest for the clinical management of patients with aggressive TNBC. [ABSTRACT FROM AUTHOR]

Although cancerous tumours usually originate from a single cell, they normally evolve into a remarkably heterogeneous agglomeration of cells. Heterogeneity is a pervasive and almost universal feature of tumours, but its origin and consequences remain poorly understood. Tumour heterogeneity has been usually associated with poor prognosis, but a better understanding of it may lead to more personalized diagnosis and therapy. Here, we study tumour heterogeneity developing a computational model in which different cell subpopulations compete for space. The model suggests that aggressive tumour subpopulations may become even more aggressive when they grow with a nonaggressive subpopulation. The model also provides a mechanistic explanation of how heterogeneity drives growth. In particular, we observed that even a mild heterogeneity in the proliferation rates of different cell subpopulations leads to a much faster overall tumour growth when compared to a homogeneous tumour. The proposed model may be a starting point to study tumour heterogeneity computationally and to suggest new hypotheses to be tested experimentally. [ABSTRACT FROM AUTHOR]

There is considerable clinical and fundamental value in measuring the clonal heterogeneity of T and B cell expansions in tumors and tumor-associated lymphoid structures—along with the associated heterogeneity of the tumor neoantigen landscape—but such analyses remain challenging to perform. Here, we propose a straightforward approach to analyze the heterogeneity of immune repertoires between different tissue sections in a quantitative and controlled way, based on a beta-binomial noise model trained on control replicates obtained at the level of single-cell suspensions. This approach allows to identify local clonal expansions with high accuracy. We reveal in situ proliferation of clonal T cells in a mouse model of melanoma, and analyze heterogeneity of immunoglobulin repertoires between sections of a metastatically-infiltrated lymph node in human melanoma and primary human colon tumor. On the latter example, we demonstrate the importance of training the noise model on datasets with depth and content that is comparable to the samples being studied. Altogether, we describe here the crucial basic instrumentarium needed to facilitate proper experimental setup planning in the rapidly evolving field of intratumoral immune repertoires, from the wet lab to bioinformatics analysis. [ABSTRACT FROM AUTHOR]

Recent clinical trials have shown that adaptive drug therapies can bemore efficient than a standard cancer treatment based on a continuous use of maximum tolerated doses (MTD). The adaptive therapy paradigmis not based on a preset schedule; instead, the doses are administered based on the current state of tumour. But the adaptive treatment policies examined so far have been largely ad hoc. We propose a method for systematically optimizing adaptive policies based on an evolutionary game theory model of cancer dynamics. Given a set of treatment objectives, we use the framework of dynamic programming to find the optimal treatment strategies. In particular, we optimize the total drug usage and time to recovery by solving a Hamilton–Jacobi–Bellman equation. We compare MTD-based treatment strategy with optimal adaptive treatment policies and show that the latter can significantly decrease the total amount of drugs prescribed while also increasing the fraction of initial tumour states from which the recovery is possible. We conclude that the use of optimal control theory to improve adaptive policies is a promising concept in cancer treatment and should be integrated into clinical trial design. [ABSTRACT FROM AUTHOR]