Overview on Clinical Relevance of Intra-Tumor Heterogeneity

Lectin-modified drug delivery systems - Recent applications in the oncology field

Chemotherapy with cytotoxic drugs remains the core treatment for cancer but, due to the difficulty to find general and usable biochemical differences between cancer cells and normal cells, many of these drugs are associated with lack of specificity, resulting in side effects and collateral cytotoxicity that impair patients' adherence to therapy. Novel cancer treatments in which the cytotoxic effect is maximized while adverse effects are reduced can be implemented by developing targeted therapies that exploit the specific features of cancer cells, such as the typical expression of aberrant glycans. Modification of drug delivery systems with lectins is one of the strategies to implement targeted chemotherapies, as lectins are able to specifically recognize and bind to cancer-associated glycans expressed at the surface of cancer cells, guiding the drug treatment towards these cells and not affecting healthy ones. In this paper, recent advances on the development of lectin-modified drug delivery systems for targeted cancer treatments are thoroughly reviewed, with a focus on their properties and performance in diverse applications, as well as their main advantages and limitations. The synthesis and analytical characterization of the cited lectin-modified drug delivery systems is also briefly described. A comparison with free-drug treatments and with antibody-modified drug delivery systems is presented, emphasizing the advantages of lectin-modified drug delivery systems. Main constraints and potential challenges of lectin-modified drug delivery systems, including key difficulties for clinical translation of these systems, and the required developments in this area, are also signalled.

The evolutionary theory of cancer: challenges and potential solutions

The clonal evolution model of cancer was developed in the 1950s-1970s and became central to cancer biology in the twenty-first century, largely through studies of cancer genetics. Although it has proven its worth, its structure has been challenged by observations of phenotypic plasticity, non-genetic forms of inheritance, non-genetic determinants of clone fitness and non-tree-like transmission of genes. There is even confusion about the definition of a clone, which we aim to resolve. The performance and value of the clonal evolution model depends on the empirical extent to which evolutionary processes are involved in cancer, and on its theoretical ability to account for those evolutionary processes. Here, we identify limits in the theoretical performance of the clonal evolution model and provide solutions to overcome those limits. Although we do not claim that clonal evolution can explain everything about cancer, we show how many of the complexities that have been identified in the dynamics of cancer can be integrated into the model to improve our current understanding of cancer.

Molecular subtypes of colorectal cancer in the era of precision oncotherapy: Current inspirations and future challenges

BACKGROUND

Colorectal cancer (CRC) is among the most hackneyed malignancies. Even patients with identical clinical symptoms and the same TNM stage still exhibit radically different clinical outcomes after receiving equivalent treatment regimens, indicating extensive heterogeneity of CRC. Myriad molecular subtypes of CRC have been exploited for decades, including the most compelling consensus molecular subtype (CMS) classification that has been broadly applied for patient stratification and biomarker-drug combination formulation. Encountering barriers to clinical translation, however, CMS classification fails to fully reflect inter- or intra-tumor heterogeneity of CRC. As a consequence, addressing heterogeneity and precisely managing CRC patients with unique characteristics remain arduous tasks for clinicians.

REVIEW

In this review, we systematically summarize molecular subtypes of CRC and further elaborate on their clinical applications, limitations, and future orientations.

CONCLUSION

In recent years, exploration of subtypes through cell lines, animal models, patient-derived xenografts (PDXs), organoids, and clinical trials contributes to refining biological insights and unraveling subtype-specific therapies in CRC. Therapeutic interventions including nanotechnology, clustered regulatory interspaced short palindromic repeat/CRISPR-associated nuclease 9 (CRISPR/Cas9), gut microbiome, and liquid biopsy are powerful tools with the possibility to shift the immunologic landscape and outlook for CRC precise medicine.

Exosomal noncoding RNAs: decoding their role in thyroid cancer progression

Exosomes, as pivotal entities within the tumor microenvironment, orchestrate intercellular communication through the transfer of diverse molecules, among which non-coding RNAs (ncRNAs) such as miRNAs, lncRNAs, and circRNAs play a crucial role. These ncRNAs, endowed with regulatory functions, are selectively incorporated into exosomes. Emerging evidence underscores the significance of exosomal ncRNAs in modulating key oncogenic processes in thyroid cancer (TC), including proliferation, metastasis, epithelial-mesenchymal transition (EMT), angiogenesis, and immunoediting. The unique composition of exosomes shields their cargo from enzymatic and chemical degradation, ensuring their integrity and facilitating their specific expression in plasma. This positions exosomal ncRNAs as promising candidates for novel diagnostic and prognostic biomarkers in TC. Moreover, the potential of exosomes in the therapeutic landscape of TC is increasingly recognized. This review aims to elucidate the intricate relationship between exosomal ncRNAs and TC, fostering a deeper comprehension of their mechanistic involvement. By doing so, it endeavors to propel forward the exploration of exosomal ncRNAs in TC, ultimately paving the way for innovative diagnostic and therapeutic strategies predicated on exosomes and their ncRNA content.

Biochemical hallmarks-targeting antineoplastic nanotherapeutics

Tumor microenvironments (TMEs) have received increasing attention in recent years as they play pivotal roles in tumorigenesis, progression, metastases, and resistance to the traditional modalities of cancer therapy like chemotherapy. With the rapid development of nanotechnology, effective antineoplastic nanotherapeutics targeting the aberrant hallmarks of TMEs have been proposed. The appropriate design and fabrication endow nanomedicines with the abilities for active targeting, TMEs-responsiveness, and optimization of physicochemical properties of tumors, thereby overcoming transport barriers and significantly improving antineoplastic therapeutic benefits. This review begins with the origins and characteristics of TMEs and discusses the latest strategies for modulating the TMEs by focusing on the regulation of biochemical microenvironments, such as tumor acidosis, hypoxia, and dysregulated metabolism. Finally, this review summarizes the challenges in the development of smart anti-cancer nanotherapeutics for TME modulation and examines the promising strategies for combination therapies with traditional treatments for further clinical translation.

Clinical Use of Molecular Biomarkers in Canine and Feline Oncology: Current and Future

Molecular biomarkers are central to personalised medicine for human cancer patients. It is gaining traction as part of standard veterinary clinical practice for dogs and cats with cancer. Molecular biomarkers can be somatic or germline genomic alterations and can be ascertained from tissues or body fluids using various techniques. This review discusses how these genomic alterations can be determined and the findings used in clinical settings as diagnostic, prognostic, predictive, and screening biomarkers. We showcase the somatic and germline genomic alterations currently available to date for testing dogs and cats in a clinical setting, discussing their utility in each biomarker class. We also look at some emerging molecular biomarkers that are promising for clinical use. Finally, we discuss the hurdles that need to be overcome in going 'bench to bedside', i.e., the translation from discovery of genomic alterations to adoption by veterinary clinicians. As we understand more of the genomics underlying canine and feline tumours, molecular biomarkers will undoubtedly become a mainstay in delivering precision veterinary care to dogs and cats with cancer.

Predicting drug response through tumor deconvolution by cancer cell lines

Large-scale cancer drug sensitivity data have become available for a collection of cancer cell lines, but only limited drug response data from patients are available. Bridging the gap in pharmacogenomics knowledge between in vitro and in vivo datasets remains challenging. In this study, we trained a deep learning model, Scaden-CA, for deconvoluting tumor data into proportions of cancer-type-specific cell lines. Then, we developed a drug response prediction method using the deconvoluted proportions and the drug sensitivity data from cell lines. The Scaden-CA model showed excellent performance in terms of concordance correlation coefficients (>0.9 for model testing) and the correctly deconvoluted rate (>70% across most cancers) for model validation using Cancer Cell Line Encyclopedia (CCLE) bulk RNA data. We applied the model to tumors in The Cancer Genome Atlas (TCGA) dataset and examined associations between predicted cell viability and mutation status or gene expression levels to understand underlying mechanisms of potential value for drug repurposing.

Cancer cell plasticity, stem cell factors, and therapy resistance: how are they linked?

Cellular plasticity can occur naturally in an organism and is considered an adapting mechanism during the developmental stage. However, abnormal cellular plasticity is observed in different diseased conditions, including cancer. Cancer cell plasticity triggers the stimuli of epithelial-mesenchymal transition (EMT), abnormal epigenetic changes, expression of stem cell factors and implicated signaling pathways, etc., and helps in the maintenance of CSC phenotype. Conversely, CSC maintains the cancer cell plasticity, EMT, and epigenetic plasticity. EMT contributes to increased cell migration and greater diversity within tumors, while epigenetic changes, stem cell factors (OCT4, NANOG, and SOX2), and various signaling pathways allow cancer cells to maintain various phenotypes, giving rise to intra- and inter-tumoral heterogeneity. The intricate relationships between cancer cell plasticity and stem cell factors help the tumor cells adopt drug-tolerant states, evade senescence, and successfully acquire drug resistance with treatment dismissal. Inhibiting molecules/signaling pathways involved in promoting CSCs, cellular plasticity, EMT, and epigenetic plasticity might be helpful for successful cancer therapy management. This review discussed the role of cellular plasticity, EMT, and stem cell factors in tumor initiation, progression, reprogramming, and therapy resistance. Finally, we discussed how the intervention in this axis will help better manage cancers and improve patient survivability.

A statistical learning method for simultaneous copy number estimation and subclone clustering with single-cell sequencing data

The availability of single-cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in SCS data using various clustering methods, as cells comprising a subpopulation are found to share a genetic profile. However, currently available methods may generate spurious results (e.g., falsely identified variants) in the procedure of CNA detection, thereby diminishing the accuracy of subclone identification within a large, complex cell population. In this study, we developed a subclone clustering method based on a fused lasso model, referred to as FLCNA, which can simultaneously detect CNAs in single-cell DNA sequencing (scDNA-seq) data. Spike-in simulations were conducted to evaluate the clustering and CNA detection performance of FLCNA, benchmarking it against existing copy number estimation methods (SCOPE, HMMcopy) in combination with commonly used clustering methods. Application of FLCNA to a scDNA-seq data set of breast cancer revealed different genomic variation patterns in neoadjuvant chemotherapy-treated samples and pretreated samples. We show that FLCNA is a practical and powerful method for subclone identification and CNA detection with scDNA-seq data.

Targeting Tumor Heterogeneity with Neoantigen-Based Cancer Vaccines

Neoantigen-based cancer vaccines have emerged as a promising immunotherapeutic approach to treat cancer. Nevertheless, the high degree of heterogeneity in tumors poses a significant hurdle for developing a vaccine that targets the therapeutically relevant neoantigens capable of effectively stimulating an immune response as each tumor contains numerous unique putative neoantigens. Understanding the complexities of tumor heterogeneity is crucial for the development of personalized neoantigen-based vaccines, which hold the potential to revolutionize cancer treatment and improve patient outcomes. In this review, we discuss recent advancements in the design of neoantigen-based cancer vaccines emphasizing the identification, validation, formulation, and targeting of neoantigens while addressing the challenges posed by tumor heterogeneity. The review highlights the application of cutting-edge approaches, such as single-cell sequencing and artificial intelligence to identify immunogenic neoantigens, while outlining current limitations and proposing future research directions to develop effective neoantigen-based vaccines.

Spatial modelling of the tumor microenvironment from multiplex immunofluorescence images: methods and applications

Spatial modelling methods have gained prominence with developments in high throughput imaging platforms. Multiplex immunofluorescence (mIF) provides the scope to examine interactions between tumor and immune compartment at single cell resolution using a panel of antibodies that can be chosen based on the cancer type or the clinical interest of the study. The markers can be used to identify the phenotypes and to examine cellular interactions at global and local scales. Several translational studies rely on key understanding of the tumor microenvironment (TME) to identify drivers of immune response in immunotherapy based clinical trials. To improve the success of ongoing trials, a number of retrospective approaches can be adopted to understand differences in response, recurrence and progression by examining the patient's TME from tissue samples obtained at baseline and at various time points along the treatment. The multiplex immunofluorescence (mIF) technique provides insight on patient specific cell populations and their relative spatial distribution as qualitative measures of a favorable treatment outcome. Spatial analysis of these images provides an understanding of the intratumoral heterogeneity and clustering among cell populations in the TME. A number of mathematical models, which establish clustering as a measure of deviation from complete spatial randomness, can be applied to the mIF images represented as spatial point patterns. These mathematical models, developed for landscape ecology and geographic information studies, can be applied to the TME after careful consideration of the tumor type (cold vs. hot) and the tumor immune landscape. The spatial modelling of mIF images can show observable engagement of T cells expressing immune checkpoint molecules and this can then be correlated with single-cell RNA sequencing data.

ConDoR: tumor phylogeny inference with a copy-number constrained mutation loss model

A tumor contains a diverse collection of somatic mutations that reflect its past evolutionary history and that range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). However, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs, complicating the inference of tumor phylogenies. We introduce a new evolutionary model, the constrained k-Dollo model, that uses SNVs as phylogenetic markers but constrains losses of SNVs according to clusters of cells. We derive an algorithm, ConDoR, that infers phylogenies from targeted scDNA-seq data using this model. We demonstrate the advantages of ConDoR on simulated and real scDNA-seq data.

Comprehensive pan‑cancer analysis of potassium voltage-gated channel Q4 (KCNQ4) gene across multiple human malignant tumors

A large number of studies indicate that Potassium Voltage-Gated Channel Q4 (KCNQ4) gene is the cause of non-syndromic hearing loss, but there are few studies investigating the role of KCNQ4 in cancers and scarcity of comprehensive analysis of its involvement in the diagnosis, methylation, mutation, prognosis of various cancer types. Therefore, the aim of this study is to examine the anticancerous and immune effects of KCNQ4 in various cancers and its potential value in breast cancer. In this study, we explored the potential role of KCNQ4 in cancers using public databases and the R software for bioinformatics analysis. The results showed that the low expression of KCNQ4 across specific cancer types was positively associated with low mutation frequency and methylation, and the improved survival. Eight small molecule compounds were identified that could potentially target KCNQ4. In addition, immunohistochemistry confirmed that the KCNQ4 expression was low in breast cancer. In vitro experiments confirmed that overexpression of KCNQ4 inhibited cell migration and invasion and promoted apoptosis. In summary, our comprehensive pan-cancer analysis highlights the potential of KCNQ4 as a cancer marker, and can be used as an auxiliary prognostic indicator and an indicator for immunotherapy in certain tumor types.

Strategy of targeting the tumor microenvironment via inhibition of fibroblast/fibrosis remodeling new era to cancer chemo-immunotherapy resistance

The use of repurposing drugs that may have neoplastic and anticancer effects increases the efficiency and decrease resistance to chemotherapy drugs through a biochemical and mechanical transduction mechanisms through modulation of fibroblast/fibrosis remodeling in tumor microenvironment (TME). Interestingly, fibroblast/fibrosis remodeling plays a vital role in mediating cancer metastasis and drug resistance after immune chemotherapy. The most essential hypothesis for induction of chemo-immunotherapy resistance is via activation of fibroblast/fibrosis remodeling and preventing the infiltration of T cells after is mainly due to the interference between cytoskeleton, mechanical, biochemical, metabolic, vascular, and remodeling signaling pathways in TME. The structural components of the tumor that can be targeted in the fibroblast/fibrosis remodeling include the depletion of the TME components, targeting the cancer-associated fibroblasts and tumor associated macrophages, alleviating the mechanical stress within the ECM, and normalizing the blood vessels. It has also been found that during immune-chemotherapy, TME injury and fibroblast/fibrosis remodeling causes the up-regulation of inhibitory signals and down-regulation of activated signals, which results in immune escape or chemo-resistance of the tumor. In this regard, repurposing or neo-adjuvant drugs with various transduction signaling mechanisms, including anti-fibrotic effects, are used to target the TME and fibroblast/fibrosis signaling pathway such as angiotensin 2, transforming growth factor-beta, physical barriers of the TME, cytokines and metabolic factors which finally led to the reverse of the chemo-resistance. Consistent to many repurposing drugs such as pirfenidone, metformin, losartan, tranilast, dexamethasone and pentoxifylline are used to decrease immune-suppression by abrogation of TME inhibitory signal that stimulates the immune system and increases efficiency and reduces resistance to chemotherapy drugs. To overcome immunosuppression based on fibroblast/fibrosis remodeling, in this review, we focus on inhibitory signal transduction, which is the physical barrier, alleviates mechanical stress and prevents mechano-metabolic activation.

Overview of Tumor Heterogeneity in High-Grade Serous Ovarian Cancers

Ovarian cancers encompass a group of neoplasms originating from germinal tissues and exhibiting distinct clinical, pathological, and molecular features. Among these, epithelial ovarian cancers (EOCs) are the most prevalent, comprising five distinct tumor histotypes. Notably, high-grade serous ovarian cancers (HGSOCs) represent the majority, accounting for over 70% of EOC cases. Due to their silent and asymptomatic behavior, HGSOCs are generally diagnosed in advanced stages with an evolved and complex genomic state, characterized by high intratumor heterogeneity (ITH) due to chromosomal instability that distinguishes HGSOCs. Histologically, these cancers exhibit significant morphological diversity both within and between tumors. The histologic patterns associated with solid, endometrioid, and transitional (SET) and classic subtypes of HGSOCs offer prognostic insights and may indicate specific molecular profiles. The evolution of HGSOC from primary to metastasis is typically characterized by clonal ITH, involving shared or divergent mutations in neoplastic sub-clones within primary and metastatic sites. Disease progression and therapy resistance are also influenced by non-clonal ITH, related to interactions with the tumor microenvironment and further genomic changes. Notably, significant alterations occur in nonmalignant cells, including cancer-associated fibroblast and immune cells, during tumor progression. This review provides an overview of the complex nature of HGSOC, encompassing its various aspects of intratumor heterogeneity, histological patterns, and its dynamic evolution during progression and therapy resistance.

Notch receptor/ligand diversity: contribution to colorectal cancer stem cell heterogeneity

Cancer cell heterogeneity is a key contributor to therapeutic failure and post-treatment recurrence. Targeting cell subpopulations responsible for chemoresistance and recurrence seems to be an attractive approach to improve treatment outcome in cancer patients. However, this remains challenging due to the complexity and incomplete characterization of tumor cell subpopulations. The heterogeneity of cells exhibiting stemness-related features, such as self-renewal and chemoresistance, fuels this complexity. Notch signaling is a known regulator of cancer stem cell (CSC) features in colorectal cancer (CRC), though the effects of its heterogenous signaling on CRC cell stemness are only just emerging. In this review, we discuss how Notch ligand-receptor specificity contributes to regulating stemness, self-renewal, chemoresistance and cancer stem cells heterogeneity in CRC.

Intratumoral metabolic heterogeneity of colorectal cancer

Although the metabolic phenotype within tumors is known to differ significantly from that of the surrounding normal tissue, the importance of this heterogeneity is just becoming widely recognized. Colorectal cancer (CRC) is often classified as the Warburg phenotype, a metabolic type in which the glycolytic system is predominant over oxidative phosphorylation (OXPHOS) in mitochondria for energy production. However, this dichotomy (glycolysis vs. OXPHOS) may be too simplistic and not accurately represent the metabolic characteristics of CRC. Therefore, in this review, we decompose metabolic phenomena into factors based on their source/origin and reclassify them into two categories: extrinsic and intrinsic. In the CRC context, extrinsic factors include those based on the environment, such as hypoxia, nutrient deprivation, and the tumor microenvironment, whereas intrinsic factors include those based on subpopulations, such as pathological subtypes and cancer stem cells. These factors form multiple layers inside and outside the tumor, affecting them additively, dominantly, or mutually exclusively. Consequently, the metabolic phenotype is a heterogeneous and fluid phenomenon reflecting the spatial distribution and temporal continuity of these factors. This allowed us to redefine the characteristics of specific metabolism-related factors in CRC and summarize and update our accumulated knowledge of their heterogeneity. Furthermore, we positioned tumor budding in CRC as an intrinsic factor and a novel form of metabolic heterogeneity, and predicted its metabolic dynamics, noting its similarity to circulating tumor cells and epithelial-mesenchymal transition. Finally, the possibilities and limitations of using human tumor tissue as research material to investigate and assess metabolic heterogeneity are discussed.

Intratumor heterogeneity in colorectal cancer: Distribution of tumor suppressor gene variants with regard to patient lymph node status

Intratumor heterogeneity (ITH) results from accumulation of somatic mutations in the fractions of successive cancer cell generations. We aimed to use deep sequencing to investigate ITH in colorectal tumors with particular emphasis on variants in oncogenes (ONC) and tumor suppressor genes (TSG). Samples were collected from 16 patients with colorectal cancer and negative or positive lymph node status (n = 8 each). We deep-sequenced a panel of 56 cancer-related genes in the central and peripheral locations of T3 size primary tumors and healthy mucosa. The central region of T3 tumors has a different frequency profile and composition of genetic variants. This mutation profile is capable of independently discriminating patients with different lymph node status (p = 0.028) in the central region. We noted an increasing number of mutations outside of the central region of the tumor and a higher number of mutations in tumors from node-positive patients. Unexpectedly, in the healthy mucosa, we identified somatic mutations with variant allele frequencies, characteristic not only of heterozygotes and homozygotes but also of other discrete peaks (e.g., around 10%, 20%), suggestive of clonal expansion of certain mutant alleles. We found differences in the distribution of variant allele frequencies in TSGs when comparing node-negative and node-positive tumors (p = 0.029), as well as central and peripheral regions (p = 0.00399). TSGs may play an important role in the escape of the tumor toward metastatic colonization.

Investigating genomic, proteomic, and post-transcriptional regulation profiles in colorectal cancer: a comparative study between primary tumors and associated metastases

INTRODUCTION

Approximately 50% of patients with primary colorectal carcinoma develop liver metastases. This study investigates the possible molecular discrepancies between primary colorectal cancer (pCRC) and their respective metastases.

METHODS

A total of 22 pairs of pCRC and metastases were tested. Mutation profiling of 26 cancer-associated genes was undertaken in 22/22primary-metastasis tumour pairs using next-generation sequencing, whilst the expression of a panel of six microRNAs (miRNAs) was investigated using qPCRin 21/22 pairs and 22 protein biomarkers was tested using Reverse Phase Protein Array (RPPA)in 20/22 patients' tumour pairs.

RESULTS

Among the primary and metastatic tumours the mutation rates for the individual genes are as follows:TP53 (86%), APC (44%), KRAS (36%), PIK3CA (9%), SMAD4 (9%), NRAS (9%) and 4% for FBXW7, BRAF, GNAS and CDH1. The primary-metastasis tumour mutation status was identical in 54/60 (90%) loci. However, there was discordance in heterogeneity status in 40/58 genetic loci (z-score = 6.246, difference = 0.3793, P < 0.0001). Furthermore, there was loss of concordance in miRNA expression status between primary and metastatic tumours, and 57.14-80.95% of the primary-metastases tumour pairs showed altered primary-metastasis relative expression in all the miRNAs tested. Moreover, 16 of 20 (80%) tumour pairs showed alteration in at least 3 of 6 (50%) of the protein biomarker pathways analysed.

CONCLUSION

The molecular alterations of primary colorectal tumours differ significantly from those of their matched metastases. These differences have profound implications for patients' prognoses and response to therapy.

Quantification of circulating tumour cells over time in dogs with appendicular osteosarcoma

Enumeration of circulating tumour cells (CTC) has shown promise for prognostication and guidance of therapeutic decisions in human cancers. The objective of this study was to enumerate CTC over time in dogs with naturally occurring osteosarcoma (OSA), and to determine correlation with patient outcome. Twenty-six dogs with OSA and no evidence of metastatic disease at the time of amputation were enrolled. Dogs were assessed for lung metastases and CTC prior to and following amputation, and at each chemotherapy visit. Twenty-one dogs completed the study. Nineteen dogs were euthanized and two were alive and free of metastases. Overall survival time ranged from 88 to 1058 days (median survival time (MST) 374 days). Increased serum alkaline phosphatase activity, advanced age, and higher body weight were significantly associated with lower MST. Dogs with OSA had a mean of 356 (0 to 4443) CTC/106 leukocytes. In 12 of 15 dogs that developed radiographic evidence of metastasis, a pre-metastatic CTC spike was retrospectively detectable on average 36.5 (1-100 days) days prior to metastasis and was associated with significantly shorter MST (301 ± 64 vs. 626 ± 55 days; p = .0107). In a multivariable analysis, dogs with a CTC spike were 10× more likely to die compared with those without. These results suggest that a spike in CTC frequency precedes detection of metastasis in dogs with OSA and is associated with shorter survival. More frequent enumeration of CTC in a larger cohort of dogs with OSA may be warranted.

Models to study CTCs and CTC culture methods

The vast majority of cancer-related deaths are due to the presence of disseminated disease. Understanding the metastatic process is key to achieving a reduction in cancer mortality. Particularly, there is a need to understand the molecular mechanisms that drive cancer metastasis, which will allow the identification of curative treatments for metastatic cancers. Liquid biopsies have arisen as a minimally invasive approach to gain insights into the biology of metastasis. Circulating tumour cells (CTCs), shed to the circulation from the primary tumour or metastatic lesions, are a key component of liquid biopsy. As metastatic precursors, CTCs hold the potential to unravel the mechanisms involved in metastasis formation as well as new therapeutic strategies for treating metastatic disease. However, the complex biology of CTCs together with their low frequency in circulation are factors hampering an in-depth mechanistic investigation of the metastatic process. To overcome these problems, CTC-derived models, including CTC-derived xenograft (CDX) and CTC-derived ex vivo cultures, in combination with more traditional in vivo models of metastasis, have emerged as powerful tools to investigate the biological features of CTCs facilitating cancer metastasis and uncover new therapeutic opportunities. In this chapter, we provide an up to date view of the diverse models used in different cancers to study the biology of CTCs, and of the methods developed for CTC culture and expansion, in vivo and ex vivo. We also report some of the main challenges and limitations that these models are facing.

Heterogeneity of the tumor immune microenvironment and clinical interventions

The tumor immune microenvironment (TIME) is broadly composed of various immune cells, and its heterogeneity is characterized by both immune cells and stromal cells. During the course of tumor formation and progression and anti-tumor treatment, the composition of the TIME becomes heterogeneous. Such immunological heterogeneity is not only present between populations but also exists on temporal and spatial scales. Owing to the existence of TIME, clinical outcomes can differ when a similar treatment strategy is provided to patients. Therefore, a comprehensive assessment of TIME heterogeneity is essential for developing precise and effective therapies. Facilitated by advanced technologies, it is possible to understand the complexity and diversity of the TIME and its influence on therapy responses. In this review, we discuss the potential reasons for TIME heterogeneity and the current approaches used to explore it. We also summarize clinical intervention strategies based on associated mechanisms or targets to control immunological heterogeneity.

Analyzing the invasive front of colorectal cancer - By punching tissue block or laser capture microdissection?

The aim of this study was to determine the advantages and limitations of two commonly used sampling techniques, i.e., punching tissue block (PTB) and laser capture microdissection (LCM) when investigating tumor cell-derived gene expression patterns at the invasive front of colorectal cancer (CRC). We obtained samples from 20 surgically removed CRCs at locations crucial for tumor progression, i.e., the central part, the expansive front and the infiltrative front exhibiting tumor budding (TB), using both sampling techniques. At each location, we separately analyzed the expressions of miR-200 family (miR-141, miR-200a, miR-200b, miR-200c and miR-429), known as reliable markers of epithelial-mesenchymal transition (EMT). We found significant downregulation of all members of miR-200 family at the infiltrative front in comparison to the central part regardless of the used sampling technique. However, when comparing miR-200 expression between the expansive and the infiltrative front, we found significant downregulation of all tested miR-200 at the infiltrative front only in samples obtained by LCM. Our results suggest that, PTB is an adequate technique for studying the differences in tumor gene expression between the central part and the invasive front of CRC, but is insufficient to analyze and compare morphologically distinct patterns along the invasive front including TB. For this purpose, the use of LCM is essential.

The Emerging Role of Radiotherapy in Oligoprogressive Non-Small Cell Lung Cancer

Oligoprogressive disease (OPD) is an emerging concept that describes patients who have progression of disease in a limited number of metastatic sites while on systemic therapy. Growing evidence has suggested the integration of local ablative therapy with systemic agents in patients with OPD further improves survival. In oligoprogressive non-small cell lung cancer, stereotactic body radiotherapy may have an important role in the effective local control of selective progressing metastases, which may translate to better patient outcomes. This review explores the treatment paradigm of this subset of patients and provides an update on the current existing literature on this topic.

Drug screening at single-organoid resolution via bioprinting and interferometry

High throughput drug screening is an established approach to investigate tumor biology and identify therapeutic leads. Traditional platforms use two-dimensional cultures which do not accurately reflect the biology of human tumors. More clinically relevant model systems such as three-dimensional tumor organoids can be difficult to scale and screen. Manually seeded organoids coupled to destructive endpoint assays allow for the characterization of treatment response, but do not capture transitory changes and intra-sample heterogeneity underlying clinically observed resistance to therapy. We present a pipeline to generate bioprinted tumor organoids linked to label-free, time-resolved imaging via high-speed live cell interferometry (HSLCI) and machine learning-based quantitation of individual organoids. Bioprinting cells gives rise to 3D structures with unaltered tumor histology and gene expression profiles. HSLCI imaging in tandem with machine learning-based segmentation and classification tools enables accurate, label-free parallel mass measurements for thousands of organoids. We demonstrate that this strategy identifies organoids transiently or persistently sensitive or resistant to specific therapies, information that could be used to guide rapid therapy selection.

Quantifying Cell Heterogeneity and Subpopulations Using Single Cell Metabolomics

Mass spectrometry (MS) has become an indispensable tool for metabolomics studies. However, due to the lack of applicable experimental platforms, suitable algorithm, software, and quantitative analyses of cell heterogeneity and subpopulations, investigating global metabolomics profiling at the single cell level remains challenging. We combined the Single-probe single cell MS (SCMS) experimental technique with a bioinformatics software package, SinCHet-MS (Single Cell Heterogeneity for Mass Spectrometry), to characterize changes of tumor heterogeneity, quantify cell subpopulations, and prioritize the metabolite biomarkers of each subpopulation. As proof of principle studies, two melanoma cancer cell lines, the primary (WM115; with a lower drug resistance) and the metastatic (WM266-4; with a higher drug resistance), were used as models. Our results indicate that after the treatment of the anticancer drug vemurafenib, a new subpopulation emerged in WM115 cells, while the proportion of the existing subpopulations was changed in the WM266-4 cells. In addition, metabolites for each subpopulation can be prioritized. Combining the SCMS experimental technique with a bioinformatics tool, our label-free approach can be applied to quantitatively study cell heterogeneity, prioritize markers for further investigation, and improve the understanding of cell metabolism in human diseases and response to therapy.

A statistical learning method for simultaneous copy number estimation and subclone clustering with single cell sequencing data

The availability of single cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in SCS data using various clustering methods, since cells comprising a subpopulation are found to share genetic profile. However, currently available methods may generate spurious results (e.g., falsely identified CNAs) in the procedure of CNA detection, hence diminishing the accuracy of subclone identification from a large complex cell population. In this study, we developed a CNA detection method based on a fused lasso model, referred to as FLCNA, which can simultaneously identify subclones in single cell DNA sequencing (scDNA-seq) data. Spike-in simulations were conducted to evaluate the clustering and CNA detection performance of FLCNA benchmarking to existing copy number estimation methods (SCOPE, HMMcopy) in combination with the existing and commonly used clustering methods. Interestingly, application of FLCNA to a real scDNA-seq dataset of breast cancer revealed remarkably different genomic variation patterns in neoadjuvant chemotherapy treated samples and pre-treated samples. We show that FLCNA is a practical and powerful method in subclone identification and CNA detection with scDNA-seq data.

Subtyping of advanced lung cancer based on PD-L1 expression, tumor histopathology and mutation burden (EGFR and KRAS): a study from North India

Immune checkpoint inhibitor (PD-L1) therapy of advanced non-small-cell lung cancer (NSCLC) has variable outcomes. Tumor subtypes based on PD-L1 expression, histopathology, mutation burden is required for patient stratification and formulation of treatment guidelines. Lung cancers (n=57) diagnosed at Pathology department, VPCI (2018-2021) were retrospectively analyzed. PD-L1(SP263) expressed by tumor cells [low (<1%), medium (1-49%), high (≥50%)] was correlated with histopathology, microenvironment, EGFR, KRAS expression. Patients were categorized into high and low risk based on their: i) gender: males (n=47, 30-89 years), females (n=10, 45-80 years); ii) smoking history: males 26/47 (45.61%), females 1/10 (10%); iii) tumor subtyping: squamous cell carcinoma 15/57 (26.32%), adenocarcinoma 6/57 (17.54%), NSCLC-undifferentiated 24/57 (42.10%), adenosquamous carcinoma 5/57 (8.77 %), carcinosarcoma 4/57 (7.02%), small cell carcinoma 1/57 (1.75%); iv) inflammatory tumor microenvironment/TILs 44/57 (77.1%); iv) PD-L1 positivity-31/57 (54.3%); v) concomitant EGFR/KRAS positivity. PD-L1positive cases showed squamous/undifferentiated histopathology, concomitant EGFR+ (9/20, 45%) and KRAS+ (8/15, 53.3%), smoking+ (21/31,67.74%).PD-L1 negative cases (26/57, 45.6%), were EGFR+ (2/14, 14.28%) and KRAS+ (6/19, 31.5%). The high-risk lung cancer subtypes show squamous/undifferentiated histopathology, inflammatory microenvironment, male preponderance, smoking history, higher concomitant PD-L1, KRAS and EGFR positivity. Lung cancer subtyping can predict clinical response/resistance of patients prior to initiation of PD-L1 inhibitor therapies and can be used to guide therapy.

Radiomics analysis of CT imaging improves preoperative prediction of cervical lymph node metastasis in laryngeal squamous cell carcinoma

OBJECTIVES

To investigate the role of CT radiomics for preoperative prediction of lymph node metastasis (LNM) in laryngeal squamous cell carcinoma (LSCC).

METHODS

LSCC patients who received open surgery and lymphadenectomy were enrolled and randomized into primary and validation cohorts at a ratio of 7:3 (325 vs. 139). In the primary cohort, we extracted radiomics features from whole intratumoral regions on venous-phase CT images and constructed a radiomics signature by least absolute shrinkage and selection operator (LASSO) regression. A radiomics model incorporating the radiomic signature and independent clinical factors was established via multivariable logistic regression and presented as a nomogram. Nomogram performance was compared with a clinical model and traditional CT report with respect to its discrimination and clinical usefulness. The radiomics nomogram was internally tested in an independent validation cohort.

RESULTS

The radiomics signature, composed of 9 stable features, was associated with LNM in both the primary and validation cohorts (both p < .001). A radiomics model incorporating independent predictors of LNM (the radiomics signature, tumor subsite, and CT report) showed significantly better discrimination of nodal status than either the clinical model or the CT report in the primary cohort (AUC 0.91 vs. 0.84 vs. 0.68) and validation cohort (AUC 0.89 vs. 0.83 vs. 0.70). Decision curve analysis confirmed that the radiomics nomogram was superior to the clinical model and traditional CT report.

CONCLUSIONS

The CT-based radiomics nomogram may improve preoperative identification of nodal status and help in clinical decision-making in LSCC.

KEY POINTS

• The radiomics model showed favorable performance for predicting LN metastasis in LSCC patients. • The radiomics model may help in clinical decision-making and define patient subsets benefiting most from neck treatment.

Zebrafish Cancer Avatars: A Translational Platform for Analyzing Tumor Heterogeneity and Predicting Patient Outcomes

The increasing number of available anti-cancer drugs presents a challenge for oncologists, who must choose the most effective treatment for the patient. Precision cancer medicine relies on matching a drug with a tumor's molecular profile to optimize the therapeutic benefit. However, current precision medicine approaches do not fully account for intra-tumoral heterogeneity. Different mutation profiles and cell behaviors within a single heterogeneous tumor can significantly impact therapy response and patient outcomes. Patient-derived avatar models recapitulate a patient's tumor in an animal or dish and provide the means to functionally assess heterogeneity's impact on drug response. Mouse xenograft and organoid avatars are well-established, but the time required to generate these models is not practical for clinical decision-making. Zebrafish are emerging as a time-efficient and cost-effective cancer avatar model. In this review, we highlight recent developments in zebrafish cancer avatar models and discuss the unique features of zebrafish that make them ideal for the interrogation of cancer heterogeneity and as part of precision cancer medicine pipelines.

Non-Association of Driver Alterations in PTEN with Differential Gene Expression and Gene Methylation in IDH1 Wildtype Glioblastomas

During oncogenesis, alterations in driver genes called driver alterations (DAs) modulate the transcriptome, methylome and proteome through oncogenic signaling pathways. These modulatory effects of any DA may be analyzed by examining differentially expressed mRNAs (DEMs), differentially methylated genes (DMGs) and differentially expressed proteins (DEPs) between tumor samples with and without that DA. We aimed to analyze these modulations with 12 common driver genes in Isocitrate Dehydrogenase 1 wildtype glioblastomas (IDH1-W-GBs). Using Cbioportal, groups of tumor samples with and without DAs in these 12 genes were generated from the IDH1-W-GBs available from "The Cancer Genomics Atlas Firehose Legacy Study Group" (TCGA-FL-SG) on Glioblastomas (GBs). For all 12 genes, samples with and without DAs were compared for DEMs, DMGs and DEPs. We found that DAs in PTEN were unassociated with any DEM or DMG in contrast to DAs in all other drivers, which were associated with several DEMs and DMGs. This contrasting PTEN-related property of being unassociated with differential gene expression or methylation in IDH1-W-GBs was unaffected by concurrent DAs in other common drivers or by the types of DAs affecting PTEN. From the lists of DEMs and DMGs associated with some common drivers other than PTEN, enriched gene ontology terms and insights into the co-regulatory effects of these drivers on the transcriptome were obtained. The findings from this study can improve our understanding of the molecular mechanisms underlying gliomagenesis with potential therapeutic benefits.

The Concept of Cancer Stem Cells: Elaborating on ALDH1B1 as an Emerging Marker of Cancer Progression

Cancer is a multifactorial, complex disease exhibiting extraordinary phenotypic plasticity and diversity. One of the greatest challenges in cancer treatment is intratumoral heterogeneity, which obstructs the efficient eradication of the tumor. Tumor heterogeneity is often associated with the presence of cancer stem cells (CSCs), a cancer cell sub-population possessing a panel of stem-like properties, such as a self-renewal ability and multipotency potential. CSCs are associated with enhanced chemoresistance due to the enhanced efflux of chemotherapeutic agents and the existence of powerful antioxidant and DNA damage repair mechanisms. The distinctive characteristics of CSCs make them ideal targets for clinical therapeutic approaches, and the identification of efficient and specific CSCs biomarkers is of utmost importance. Aldehyde dehydrogenases (ALDHs) comprise a wide superfamily of metabolic enzymes that, over the last years, have gained increasing attention due to their association with stem-related features in a wide panel of hematopoietic malignancies and solid cancers. Aldehyde dehydrogenase 1B1 (ALDH1B1) is an isoform that has been characterized as a marker of colon cancer progression, while various studies suggest its importance in additional malignancies. Here, we review the basic concepts related to CSCs and discuss the potential role of ALDH1B1 in cancer development and its contribution to the CSC phenotype.

ConDoR: Tumor phylogeny inference with a copy-number constrained mutation loss model

Tumors consist of subpopulations of cells that harbor distinct collections of somatic mutations. These mutations range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). While many approaches infer tumor phylogenies using SNVs as phylogenetic markers, CNAs that overlap SNVs may lead to erroneous phylogenetic inference. Specifically, an SNV may be lost in a cell due to a deletion of the genomic segment containing the SNV. Unfortunately, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs. For instance, recent targeted scDNA-seq technologies, such as Mission Bio Tapestri, measure SNVs with high fidelity in individual cells, but yield much less reliable measurements of CNAs. We introduce a new evolutionary model, the constrained k-Dollo model, that uses SNVs as phylogenetic markers and partial information about CNAs in the form of clustering of cells with similar copy-number profiles. This copy-number clustering constrains where loss of SNVs can occur in the phylogeny. We develop ConDoR (Constrained Dollo Reconstruction), an algorithm to infer tumor phylogenies from targeted scDNA-seq data using the constrained k-Dollo model. We show that ConDoR outperforms existing methods on simulated data. We use ConDoR to analyze a new multi-region targeted scDNA-seq dataset of 2153 cells from a pancreatic ductal adenocarcinoma (PDAC) tumor and produce a more plausible phylogeny compared to existing methods that conforms to histological results for the tumor from a previous study. We also analyze a metastatic colorectal cancer dataset, deriving a more parsimonious phylogeny than previously published analyses and with a simpler monoclonal origin of metastasis compared to the original study.

Code availability

Software is available at https://github.com/raphael-group/constrained-Dollo.

Translating Data Science Results into Precision Oncology Decisions: A Mini Review

While reviewing and discussing the potential of data science in oncology, we emphasize medical imaging and radiomics as the leading contextual frameworks to measure the impacts of Artificial Intelligence (AI) and Machine Learning (ML) developments. We envision some domains and research directions in which radiomics should become more significant in view of current barriers and limitations.

Prognostic value of genetic alterations and 18F-FDG PET/CT imaging features in diffuse large B cell lymphoma

The current standard front-line therapy for patients with diffuse large-B cell lymphoma (DLBCL)-rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP)-is found to be ineffective in up to one-third of them. Thus, their early identification is an important step towards testing alternative treatment options. In this retrospective study, we assessed the ability of ¹⁸F-FDG PET/CT imaging features (radiomic + PET conventional parameters) plus clinical data, alone or in combination with genomic parameters to predict complete response to first-line treatment. Imaging features were extracted from images prior treatment. Lesions were segmented as a whole to reflect tumor burden. Multivariate logistic regression predictive models for response to first-line treatment trained with clinical and imaging features, or with clinical, imaging, and genomic features were developed. For imaging feature selection, a manual selection approach or a linear discriminant analysis (LDA) for dimensionality reduction were applied. Confusion matrices and performance metrics were obtained to assess model performance. Thirty-three patients (median [range] age, 58 [49-69] years) were included, of whom 23 (69.69%) achieved long-term complete response. Overall, the inclusion of genomic features improved prediction ability. The best performance metrics were obtained with the combined model including genomic data and built applying the LDA method (AUC of 0.904, and 90% of balanced accuracy). The amplification of BCL6 was found to significantly contribute to explain response to first-line treatment in both manual and LDA models. Among imaging features, radiomic features reflecting lesion distribution heterogeneity (GLSZM_GrayLevelVariance, Sphericity and GLCM_Correlation) were predictors of response in manual models. Interestingly, when the dimensionality reduction was applied, the whole set of imaging features-mostly composed of radiomic features-significantly contributed to explain response to front-line therapy. A nomogram predictive for response to first-line treatment was constructed. In summary, a combination of imaging features, clinical variables and genomic data was able to successfully predict complete response to first-line treatment in DLBCL patients, with the amplification of BCL6 as the genetic marker retaining the highest predictive value. Additionally, a panel of imaging features may provide important information when predicting treatment response, with lesion dissemination-related radiomic features deserving especial attention.

Prognostic value of TMEM59L and its genomic and immunological characteristics in cancer

Background

TMEM59L is a newly discovered transmembrane protein; its functions in cancer remain unknown. This study was designed to reveal the prognostic value and the functional role of TMEM59L in cancer.

Methods

The gene expression profiles, methylation data, and corresponding clinical data of TMEM59L were retrieved from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression database. Survival analysis was employed to calculate the pan-cancer prognostic value of TMEM59L. The correlation between TMEM59L expression and tumor immune microenvironment, as well as DNA methylation dynamics and genomic heterogeneity across cancers were assessed based on data from TCGA.

Results

Our findings revealed that distinct differences of TMEM59L mRNA expression were observed in different cancer types and that higher TMEM59L expression was observed in the advanced pathological stage and associated with worse prognosis in kidney renal papillary cell carcinoma, bladder urothelial carcinoma, colon adenocarcinoma, and kidney renal clear cell carcinoma. Pathway analysis indicated that TMEM59L exerted a key influence in cancer development and in immune- and cancer-associated pathways such as epithelial-mesenchymal transition and TGF-β signaling. Moreover, correlation analysis hinted at a negative correlation of TMEM59L expression with CD8 T cells, activated CD4 T cells, and several immunomodulators, including IDO1, TIGIT, PD-L1, CTLA-4, and BTLA in various cancers. Survival analysis indicated that the hypermethylation of TMEM59L gene was associated with longer survival times. A significant correlation was also observed between TMEM59L expression and immunophenoscore, homologous recombination deficiency, loss of heterozygosity, tumor stemness score, and neoantigens in various cancers. Importantly, we also identified numerous potential agents that may target TMEM59L.

Conclusion

Our study revealed the prognostic value as well as the genomic and immunological characteristics of TMEM59L in cancers, highlighting the promising potential for TMEM59L as a prognostic cancer biomarker and a therapeutic target.

Imaging-based representation and stratification of intra-tumor heterogeneity via tree-edit distance

Personalized medicine is the future of medical practice. In oncology, tumor heterogeneity assessment represents a pivotal step for effective treatment planning and prognosis prediction. Despite new procedures for DNA sequencing and analysis, non-invasive methods for tumor characterization are needed to impact on daily routine. On purpose, imaging texture analysis is rapidly scaling, holding the promise to surrogate histopathological assessment of tumor lesions. In this work, we propose a tree-based representation strategy for describing intra-tumor heterogeneity of patients affected by metastatic cancer. We leverage radiomics information extracted from PET/CT imaging and we provide an exhaustive and easily readable summary of the disease spreading. We exploit this novel patient representation to perform cancer subtyping according to hierarchical clustering technique. To this purpose, a new heterogeneity-based distance between trees is defined and applied to a case study of prostate cancer. Clusters interpretation is explored in terms of concordance with severity status, tumor burden and biological characteristics. Results are promising, as the proposed method outperforms current literature approaches. Ultimately, the proposed method draws a general analysis framework that would allow to extract knowledge from daily acquired imaging data of patients and provide insights for effective treatment planning.

A computational modeling approach for predicting multicell spheroid patterns based on signaling-induced differential adhesion

Physiological and pathological processes including embryogenesis and tumorigenesis rely on the ability of individual cells to work collectively to form multicell patterns. In these heterogeneous multicell systems, cell-cell signaling induces differential adhesion between cells that leads to tissue-level patterning. However, the sensitivity of pattern formation to changes in the strengths of signaling or cell adhesion processes is not well understood. Prior work has explored these issues using synthetically engineered heterogeneous multicell spheroid systems, in which cell subpopulations engage in bidirectional intercellular signaling to regulate the expression of different cadherins. While engineered cell systems provide excellent experimental tools to observe pattern formation in cell populations, computational models of these systems may be leveraged to explore more systematically how specific combinations of signaling and adhesion parameters can drive the emergence of unique patterns. We developed and validated two- and three-dimensional agent-based models (ABMs) of spheroid patterning for previously described cells engineered with a bidirectional signaling circuit that regulates N- and P-cadherin expression. Systematic exploration of model predictions, some of which were experimentally validated, revealed how cell seeding parameters, the order of signaling events, probabilities of induced cadherin expression, and homotypic adhesion strengths affect pattern formation. Unsupervised clustering was also used to map combinations of signaling and adhesion parameters to these unique spheroid patterns predicted by the ABM. Finally, we demonstrated how the model may be deployed to design new synthetic cell signaling circuits based on a desired final multicell pattern.

Chemokines and cytokines: Axis and allies in prostate cancer pathogenesis

Chemokines are recognized as the major contributor to various tumorigenesis, tumor heterogeneity, and failures of current cancer therapies. The tumor microenvironment (TME) is enriched with chemokines and cytokines and plays a pivotal role in cancer progression. Chronic inflammation is also considered an instructive process of cancer progression, where chemokines are spatiotemporally secreted by malignant cells and leukocyte subtypes that initiate cell trafficking into the TME. In various cancers, prostate cancer (PCa) is reported as one of the leading cancers in the worldwide male population. The chemokines-mediated signaling pathways are intensively involved in PCa progression and metastasis. Emerging evidence suggests that chemokines and cytokines are responsible for the pleiotropic actions in cancer, including the growth, angiogenesis, endothelial mesenchymal transition, leukocyte infiltration, and hormone escape for advanced PCa and therapy resistance. Chemokine's system and immune cells represent a promising target to suppress tumorigenic environments and serve as potential therapy/immunotherapy for the PCa. In this review, an attempt has been made to shed light on the alteration of chemokine and cytokine profiles during PCa progression and metastasis. We also discussed the recent findings of the diverse molecular signaling of these circulating chemokines and their corresponding receptors that could become future targets for therapeutic management of PCa.

Gene expression-based dissection of inter-histotypes, intra-histotype and intra-tumor heterogeneity in pediatric tumors

Intra-tumor heterogeneity (ITH) fosters tumor evolution, resistance to therapy, and relapse. Recently, many evidence have been accumulated on the occurrence of genetic ITH in pediatric cancers. With this study we aimed to address the downstream effects that genetic and epigenetic ITH, and tumor-microenvironment interactions may produce within a tumor mass. To this aim, we investigated by high-throughput gene expression multiple samples of 5 hepatoblastomas, 5 neuroblastomas, 5 rhabdomyosarcomas, and 5 Wilms tumors. Principal component analysis, single sample hallmark gene sets analysis, and weighted gene co-expression network analysis were performed on gene expression data. We observed that the different tumors clustered by histotype, and then by case, and in addition, a variable degree of ITH was visible in all the investigated cases. The ITH highlighted in this study can represent a challenge in tumor treatment since we demonstrated that different druggable hallmarks and targets may be heterogeneously present within the same tumor mass, and this can potentially lead to therapeutic failure. Despite this heterogeneity, we could highlight some commonalities among the different histotypes investigated, supporting the feasibility to move in the clinic from a histotype-driven to a target-driven, sometimes agnostic, approach at least in some cases.

Targeting Endocytosis and Cell Communications in the Tumor Immune Microenvironment

The existence of multiple endocytic pathways is well known, and their exact biological effects in tumors have been intensively investigated. Endocytosis can affect the connection between tumor cells and determine the fate of tumor cells. Many relationships between endocytosis and tumor cells have been elucidated, but the mechanism of endocytosis between different types of cells in tumors needs to be explored in greater depth. Endocytic receptors sense the environment and are induced by specific ligands to trigger communication between tumor and immune cells. Crosstalk in the tumor microenvironment can occur through direct contact between cell adhesion molecules or indirectly through exosomes. So a better understanding of the endocytic pathways that control cell adhesion molecules and function is expected to lead to new candidates for cancer treatment. In additional, tumor-derived exosomes may changes immune cell function, which may be a key role for tumors to evade immune detection and response. The overall understanding of exosomes through endocytosis is also expected to bring new candidates for therapeutic regulation of tumor immune microenvironment. In this case, endocytic pathways coordinate cell adhesion molecules and exosomes and can be used as targets in the tumor immune microenvironment for cancer treatment.

Genetic and microenvironmental intra-tumor heterogeneity impacts colorectal cancer evolution and metastatic development

Colorectal cancer (CRC) is a highly diverse disease, where different genomic instability pathways shape genetic clonal diversity and tumor microenvironment. Although intra-tumor heterogeneity has been characterized in primary tumors, its origin and consequences in CRC outcome is not fully understood. Therefore, we assessed intra- and inter-tumor heterogeneity of a prospective cohort of 136 CRC samples. We demonstrate that CRC diversity is forged by asynchronous forms of molecular alterations, where mutational and chromosomal instability collectively boost CRC genetic and microenvironment intra-tumor heterogeneity. We were able to depict predictor signatures of cancer-related genes that can foresee heterogeneity levels across the different tumor consensus molecular subtypes (CMS) and primary tumor location. Finally, we show that high genetic and microenvironment heterogeneity are associated with lower metastatic potential, whereas late-emerging copy number variations favor metastasis development and polyclonal seeding. This study provides an exhaustive portrait of the interplay between genetic and microenvironment intra-tumor heterogeneity across CMS subtypes, depicting molecular events with predictive value of CRC progression and metastasis development.

Individual HLA heterogeneity and its implications for cellular immune evasion in cancer and beyond

Structural and functional variability of human leukocyte antigen (HLA) is the foundation for competent adaptive immune responses against pathogen and tumor antigens as it assures the breadth of the presented immune-peptidome, theoretically sustaining an efficient and diverse T cell response. This variability is presumably the result of the continuous selection by pathogens, which over the course of evolution shaped the adaptive immune system favoring the assortment of a hyper-polymorphic HLA system able to elaborate efficient immune responses. Any genetic alteration affecting this diversity may lead to pathological processes, perturbing antigen presentation capabilities, T-cell reactivity and, to some extent, natural killer cell functionality. A highly variable germline HLA genotype can convey immunogenetic protection against infections, be associated with tumor surveillance or influence response to anti-neoplastic treatments. In contrast, somatic aberrations of HLA loci, rearranging the original germline configuration, theoretically decreasing its variability, can facilitate mechanisms of immune escape that promote tumor growth and immune resistance. The purpose of the present review is to provide a unified and up-to-date overview of the pathophysiological consequences related to the perturbations of the genomic heterogeneity of HLA complexes and their impact on human diseases, with a special focus on cancer.

Spatiotemporal multi-scale modeling of radiopharmaceutical distributions in vascularized solid tumors

We present comprehensive mathematical modeling of radiopharmaceutical spatiotemporal distributions within vascularized solid tumors. The novelty of the presented model is at mathematical level. From the mathematical viewpoint, we provide a general modeling framework for the process of radiopharmaceutical distribution in the tumor microenvironment to enable an analysis of the effect of various tumor-related parameters on the distribution of different radiopharmaceuticals. We argue that partial differential equations (PDEs), beyond conventional methods, including ODE-based kinetic compartment modeling, can be used to evaluate radiopharmaceutical distribution in both time and space. In addition, we consider the spatially-variable dynamic structure of tumor microvascular networks to simulate blood flow distribution. To examine the robustness of the model, the effects of microvessel density (MVD) and tumor size, as two important factors in tumor prognosis, on the radiopharmaceutical distribution within the tumor are investigated over time (in the present work, we focus on the radiopharmaceutical [¹⁸F]FDG, yet the framework is broadly applicable to radiopharmaceuticals). Results demonstrate that the maximum total uptake of [¹⁸F]FDG at all time frames occurs in the tumor area due to the high capillary permeability and lack of a functional lymphatic system. As the MVD of networks increases, the mean total uptake in the tumor is also enhanced, where the rate of diffusion from vessel to tissue has the highest contribution and the rate of convection transport has the lowest contribution. The results of this study can be used to better investigate various phenomena and bridge a gap among cancer biology, mathematical oncology, medical physics, and radiology.

Toxicologic Concerns with Current Medical Nanoparticles

Nanotechnology is one of the scientific advances in technology. Nanoparticles (NPs) are small materials ranging from 1 to 100 nm. When the shape of the supplied nanoparticles changes, the physiological response of the cells can be very different. Several characteristics of NPs such as the composition, surface chemistry, surface charge, and shape are also important parameters affecting the toxicity of nanomaterials. This review covered specific topics that address the effects of NPs on nanomedicine. Furthermore, mechanisms of different types of nanomaterial-induced cytotoxicities were described. The distributions of different NPs in organs and their adverse effects were also emphasized. This review provides insight into the scientific community interested in nano(bio)technology, nanomedicine, and nanotoxicology. The content may also be of interest to a broad range of scientists.

Differential Expression of Genes Regulating Store-operated Calcium Entry in Conjunction With Mitochondrial Dynamics as Potential Biomarkers for Cancer: A Single-Cell RNA Analysis

Regulation of intracellular concentration of calcium levels is crucial for cell signaling, homeostasis, and in the pathology of diseases including cancer. Agonist-induced entry of calcium ions into the non-excitable cells is mediated by store-operated calcium channels (SOCs). This pathway is activated by the release of calcium ions from the endoplasmic reticulum and further regulated by the calcium uptake through mitochondria leading to calcium-dependent inactivation of calcium-release activated calcium channels (CARC). SOCs including stromal interaction molecules (STIM) and ORAI proteins have been implicated in tumor growth, progression, and metastasis. In the present study, we analyzed the mRNA and protein expression of genes mediating SOCs-STIM1, STIM2, ORAI1, ORAI2, ORAI3, TRPC1, TRPC3, TRPC4, TRPC5, TRPC6, TRPC7, TRPV1, TRPV2, TRPM1, and TRPM7 in head and neck squamous cell cancer (HNSC) patients using TCGA and CPTAC analysis. Further, our in silico analysis showed a significant correlation between the expression of SOCs and genes involved in the mitochondrial dynamics (MDGs) both at mRNA and protein levels. Protein-protein docking results showed lower binding energy for SOCs with MDGs. Subsequently, we validated these results using gene expression and single-cell RNA sequencing datasets retrieved from Gene Expression Omnibus (GEO). Single-cell gene expression analysis of HNSC tumor tissues revealed that SOCs expression is remarkably associated with the MDGs expression in both cancer and fibroblast cells.

Precision Medicine: An Optimal Approach to Patient Care in Renal Cell Carcinoma

Renal cell cancer (RCC) is a heterogeneous tumor that shows both intra- and inter-heterogeneity. Heterogeneity is displayed not only in different patients but also among RCC cells in the same tumor, which makes treatment difficult because of varying degrees of responses generated in RCC heterogeneous tumor cells even with targeted treatment. In that context, precision medicine (PM), in terms of individualized treatment catered for a specific patient or groups of patients, can shift the paradigm of treatment in the clinical management of RCC. Recent progress in the biochemical, molecular, and histological characteristics of RCC has thrown light on many deregulated pathways involved in the pathogenesis of RCC. As PM-based therapies are rapidly evolving and few are already in current clinical practice in oncology, one can expect that PM will expand its way toward the robust treatment of patients with RCC. This article provides a comprehensive background on recent strategies and breakthroughs of PM in oncology and provides an overview of the potential applicability of PM in RCC. The article also highlights the drawbacks of PM and provides a holistic approach that goes beyond the involvement of clinicians and encompasses appropriate legislative and administrative care imparted by the healthcare system and insurance providers. It is anticipated that combined efforts from all sectors involved will make PM accessible to RCC and other patients with cancer, making a tremendous positive leap on individualized treatment strategies. This will subsequently enhance the quality of life of patients.

Spatial heterogeneity and organization of tumor mutation burden with immune infiltrates within tumors based on whole slide images correlated with patient survival in bladder cancer

Background

High tumor mutation burden (TMB-H) could result in an increased number of neoepitopes from somatic mutations expressed by a patient's own tumor cell which can be recognized and targeted by neighboring tumor-infiltrating lymphocytes (TILs). Deeper understanding of spatial heterogeneity and organization of tumor cells and their neighboring immune infiltrates within tumors could provide new insights into tumor progression and treatment response.

Methods

Here we first developed computational approaches using whole slide images (WSIs) to predict bladder cancer patients' TMB status and TILs across tumor regions, and then investigate spatial heterogeneity and organization of regions harboring TMB-H tumor cells and TILs within tumors, as well as their prognostic utility. Results: In experiments using WSIs from The Cancer Genome Atlas (TCGA) bladder cancer (BLCA), our findings show that computational pathology can reliably predict patient-level TMB status and delineate spatial TMB heterogeneity and co-organization with TILs. TMB-H patients with low spatial heterogeneity enriched with high TILs show improved overall survival.

Conclusions

Computational approaches using WSIs have the potential to provide rapid and cost-effective TMB testing and TILs detection. Survival analysis illuminates potential clinical utility of spatial heterogeneity and co-organization of TMB and TILs as a prognostic biomarker in BLCA which warrants further validation in future studies.

Emergence of hybrid states of stem-like cancer cells correlates with poor prognosis in oral cancer

Cancer cell state transitions emerged as powerful mechanisms responsible for drug tolerance and overall poor prognosis; however, evidences were largely missing in oral cancer. Here, by multiplexing phenotypic markers of stem-like cancer cells (SLCCs); CD44, CD24 and aldehyde dehydrogenase (ALDH), we characterized diversity among multiple oral tumor tissues and cell lines. Two distinct patterns of spontaneous transitions with stochastic bidirectional interconversions on ‘ALDH-axis’, and unidirectional non-interconvertible transitions on ‘CD24-axis’ were observed. Interestingly, plastic ‘ALDH-axis’ was harnessed by cells to adapt to a Cisplatin tolerant state. Furthermore, phenotype-specific RNA sequencing suggested the possible maintenance of intermediate hybrid cell states maintaining stemness within the differentiating subpopulations. Importantly, survival analysis with subpopulation-specific gene sets strongly suggested that cell-state transitions may drive non-genetic heterogeneity, resulting in poor prognosis. Therefore, we have described the phenotypic-composition of heterogeneous subpopulations critical for global tumor behavior in oral cancer; which may provide prerequisite knowledge for treatment strategies.

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Demonstrated population trajectory driven non-genetic heterogeneity in oral cancer

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Created transition maps for subpopulations using discrete time Markov chain model

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Demonstrated maintenance of stemness in cells undergoing differentiation

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Uniquely expressed genes of these subpopulations associated with disease prognosis