Pan-cancer inference of intra-tumor heterogeneity reveals associations with different forms of genomic instability

Comprehensive review of drug resistance in mammalian cancer stem cells: implications for cancer therapy

Cancer remains a significant global challenge, and despite the numerous strategies developed to advance cancer therapy, an effective cure for metastatic cancer remains elusive. A major hurdle in treatment success is the ability of cancer cells, particularly cancer stem cells (CSCs), to resist therapy. These CSCs possess unique abilities, including self-renewal, differentiation, and repair, which drive tumor progression and chemotherapy resistance. The resilience of CSCs is linked to certain signaling pathways. Tumors with pathway-dependent CSCs often develop genetic resistance, whereas those with pathway-independent CSCs undergo epigenetic changes that affect gene regulation. CSCs can evade cytotoxic drugs, radiation, and apoptosis by increasing drug efflux transporter activity and activating survival mechanisms. Future research should prioritize the identification of new biomarkers and signaling molecules to better understand drug resistance. The use of cutting-edge approaches, such as bioinformatics, genomics, proteomics, and nanotechnology, offers potential solutions to this challenge. Key strategies include developing targeted therapies, employing nanocarriers for precise drug delivery, and focusing on CSC-targeted pathways such as the Wnt, Notch, and Hedgehog pathways. Additionally, investigating multitarget inhibitors, immunotherapy, and nanodrug delivery systems is critical for overcoming drug resistance in cancer cells.

Metabolic pathways, genomic alterations, and post-translational modifications in pulmonary hypertension and cancer as therapeutic targets and biomarkers

Background

Pulmonary hypertension (PH) can lead to right ventricular hypertrophy, significantly increasing mortality rates. This study aims to clarify PH-specific metabolites and their impact on genomic and post-translational modifications (PTMs) in cancer, evaluating DHA and EPA's therapeutic potential to mitigate oxidative stress and inflammation.

Methods

Data from 289,365 individuals were analyzed using Mendelian randomization to examine 1,400 metabolites' causal roles in PH. Anti-inflammatory and antioxidative effects of DHA and EPA were tested in RAW 264.7 macrophages and cancer cell lines (A549, HCT116, HepG2, LNCaP). Genomic features like CNVs, DNA methylation, tumor mutation burden (TMB), and PTMs were analyzed. DHA and EPA's effects on ROS production and cancer cell proliferation were assessed.

Results

We identified 57 metabolites associated with PH risk and examined key tumor-related pathways through promoter methylation analysis. DHA and EPA significantly reduced ROS levels and inflammatory markers in macrophages, inhibited the proliferation of various cancer cell lines, and decreased nuclear translocation of SUMOylated proteins during oxidative stress and inflammatory responses. These findings suggest a potential anticancer role through the modulation of stress-related nuclear signaling, as well as a regulatory function on cellular PTMs.

Conclusion

This study elucidates metabolic and PTM changes in PH and cancer, indicating DHA and EPA's role in reducing oxidative stress and inflammation. These findings support targeting these pathways for early biomarkers and therapies, potentially improving disease management and patient outcomes.

Development of a prognostic model related to homologous recombination deficiency in glioma based on multiple machine learning

Background

Despite advances in neuro-oncology, treatments of glioma and tools for predicting the outcome of patients remain limited. The objective of this research is to construct a prognostic model for glioma using the Homologous Recombination Deficiency (HRD) score and validate its predictive capability for glioma.

Methods

We consolidated glioma datasets from TCGA, various cancer types for pan-cancer HRD analysis, and two additional glioma RNAseq datasets from GEO and CGGA databases. HRD scores, mutation data, and other genomic indices were calculated. Using machine learning algorithms, we identified signature genes and constructed an HRD-related prognostic risk model. The model's performance was validated across multiple cohorts. We also assessed immune infiltration and conducted molecular docking to identify potential therapeutic agents.

Results

Our analysis established a correlation between higher HRD scores and genomic instability in gliomas. The model, based on machine learning algorithms, identified seven key genes, significantly predicting patient prognosis. Moreover, the HRD score prognostic model surpassed other models in terms of prediction efficacy across different cancers. Differential immune cell infiltration patterns were observed between HRD risk groups, with potential implications for immunotherapy. Molecular docking highlighted several compounds, notably Panobinostat, as promising for high-risk patients.

Conclusions

The prognostic model based on the HRD score threshold and associated genes in glioma offers new insights into the genomic and immunological landscapes, potentially guiding therapeutic strategies. The differential immune profiles associated with HRD-risk groups could inform immunotherapeutic interventions, with our findings paving the way for personalized medicine in glioma treatment.

Modeling metastatic progression from cross-sectional cancer genomics data

MOTIVATION

Metastasis formation is a hallmark of cancer lethality. Yet, metastases are generally unobservable during their early stages of dissemination and spread to distant organs. Genomic datasets of matched primary tumors and metastases may offer insights into the underpinnings and the dynamics of metastasis formation.

RESULTS

We present metMHN, a cancer progression model designed to deduce the joint progression of primary tumors and metastases using cross-sectional cancer genomics data. The model elucidates the statistical dependencies among genomic events, the formation of metastasis, and the clinical emergence of both primary tumors and their metastatic counterparts. metMHN enables the chronological reconstruction of mutational sequences and facilitates estimation of the timing of metastatic seeding. In a study of nearly 5000 lung adenocarcinomas, metMHN pinpointed TP53 and EGFR as mediators of metastasis formation. Furthermore, the study revealed that post-seeding adaptation is predominantly influenced by frequent copy number alterations.

AVAILABILITY AND IMPLEMENTATION

All datasets and code are available on GitHub at https://github.com/cbg-ethz/metMHN.

Hallmarks of cancer resistance

This review explores the hallmarks of cancer resistance, including drug efflux mediated by ATP-binding cassette (ABC) transporters, metabolic reprogramming characterized by the Warburg effect, and the dynamic interplay between cancer cells and mitochondria. The role of cancer stem cells (CSCs) in treatment resistance and the regulatory influence of non-coding RNAs, such as long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs), are studied. The chapter emphasizes future directions, encompassing advancements in immunotherapy, strategies to counter adaptive resistance, integration of artificial intelligence for predictive modeling, and the identification of biomarkers for personalized treatment. The comprehensive exploration of these hallmarks provides a foundation for innovative therapeutic approaches, aiming to navigate the complex landscape of cancer resistance and enhance patient outcomes.

Modulatory effects of cancer stem cell-derived extracellular vesicles on the tumor immune microenvironment

Cancer stem cells (CSCs), accounting for only a minor cell proportion (< 1%) within tumors, have profound implications in tumor initiation, metastasis, recurrence, and treatment resistance due to their inherent ability of self-renewal, multi-lineage differentiation, and tumor-initiating potential. In recent years, accumulating studies indicate that CSCs and tumor immune microenvironment act reciprocally in driving tumor progression and diminishing the efficacy of cancer therapies. Extracellular vesicles (EVs), pivotal mediators of intercellular communications, build indispensable biological connections between CSCs and immune cells. By transferring bioactive molecules, including proteins, nucleic acids, and lipids, EVs can exert mutual influence on both CSCs and immune cells. This interaction plays a significant role in reshaping the tumor immune microenvironment, creating conditions favorable for the sustenance and propagation of CSCs. Deciphering the intricate interplay between CSCs and immune cells would provide valuable insights into the mechanisms of CSCs being more susceptible to immune escape. This review will highlight the EV-mediated communications between CSCs and each immune cell lineage in the tumor microenvironment and explore potential therapeutic opportunities.

Metabolic Insight into Glioma Heterogeneity: Mapping Whole Exome Sequencing to In Vivo Imaging with Stereotactic Localization and Deep Learning

Intratumoral heterogeneity (ITH) complicates the diagnosis and treatment of glioma, partly due to the diverse metabolic profiles driven by underlying genomic alterations. While multiparametric imaging enhances the characterization of ITH by capturing both spatial and functional variations, it falls short in directly assessing the metabolic activities that underpin these phenotypic differences. This gap stems from the challenge of integrating easily accessible, colocated pathology and detailed genomic data with metabolic insights. This study presents a multifaceted approach combining stereotactic biopsy with standard clinical open-craniotomy for sample collection, voxel-wise analysis of MR images, regression-based GAM, and whole-exome sequencing. This work aims to demonstrate the potential of machine learning algorithms to predict variations in cellular and molecular tumor characteristics. This retrospective study enrolled ten treatment-naïve patients with radiologically confirmed glioma. Each patient underwent a multiparametric MR scan (T1W, T1W-CE, T2W, T2W-FLAIR, DWI) prior to surgery. During standard craniotomy, at least 1 stereotactic biopsy was collected from each patient, with screenshots of the sample locations saved for spatial registration to pre-surgical MR data. Whole-exome sequencing was performed on flash-frozen tumor samples, prioritizing the signatures of five glioma-related genes: IDH1, TP53, EGFR, PIK3CA, and NF1. Regression was implemented with a GAM using a univariate shape function for each predictor. Standard receiver operating characteristic (ROC) analyses were used to evaluate detection, with AUC (area under curve) calculated for each gene target and MR contrast combination. Mean AUC for five gene targets and 31 MR contrast combinations was 0.75 ± 0.11; individual AUCs were as high as 0.96 for both IDH1 and TP53 with T2W-FLAIR and ADC, and 0.99 for EGFR with T2W and ADC. These results suggest the possibility of predicting exome-wide mutation events from noninvasive, in vivo imaging by combining stereotactic localization of glioma samples and a semi-parametric deep learning method. The genomic alterations identified, particularly in IDH1, TP53, EGFR, PIK3CA, and NF1, are known to play pivotal roles in metabolic pathways driving glioma heterogeneity. Our methodology, therefore, indirectly sheds light on the metabolic landscape of glioma through the lens of these critical genomic markers, suggesting a complex interplay between tumor genomics and metabolism. This approach holds potential for refining targeted therapy by better addressing the genomic heterogeneity of glioma tumors.

Resolving tumor evolution: a phylogenetic approach

The evolutionary dynamics of cancer, characterized by its profound heterogeneity, demand sophisticated tools for a holistic understanding. This review delves into tumor phylogenetics, an essential approach bridging evolutionary biology with oncology, offering unparalleled insights into cancer's evolutionary trajectory. We provide an overview of the workflow, encompassing study design, data acquisition, and phylogeny reconstruction. Notably, the integration of diverse data sets emerges as a transformative step, enhancing the depth and breadth of evolutionary insights. With this integrated perspective, tumor phylogenetics stands poised to redefine our understanding of cancer evolution and influence therapeutic strategies.

Molecular characteristics of early-onset pancreatic ductal adenocarcinoma

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

Genetic and immune landscape evolution in MMR-deficient colorectal cancer

Mismatch repair-deficient (MMRd) colorectal cancers (CRCs) have high mutation burdens, which make these tumours immunogenic and many respond to immune checkpoint inhibitors. The MMRd hypermutator phenotype may also promote intratumour heterogeneity (ITH) and cancer evolution. We applied multiregion sequencing and CD8 and programmed death ligand 1 (PD-L1) immunostaining to systematically investigate ITH and how genetic and immune landscapes coevolve. All cases had high truncal mutation burdens. Despite pervasive ITH, driver aberrations showed a clear hierarchy. Those in WNT/β-catenin, mitogen-activated protein kinase, and TGF-β receptor family genes were almost always truncal. Immune evasion (IE) drivers, such as inactivation of genes involved in antigen presentation or IFN-γ signalling, were predominantly subclonal and showed parallel evolution. These IE drivers have been implicated in immune checkpoint inhibitor resistance or sensitivity. Clonality assessments are therefore important for the development of predictive immunotherapy biomarkers in MMRd CRCs. Phylogenetic analysis identified three distinct patterns of IE driver evolution: pan-tumour evolution, subclonal evolution, and evolutionary stasis. These, but neither mutation burdens nor heterogeneity metrics, significantly correlated with T-cell densities, which were used as a surrogate marker of tumour immunogenicity. Furthermore, this revealed that genetic and T-cell infiltrates coevolve in MMRd CRCs. Low T-cell densities in the subgroup without any known IE drivers may indicate an, as yet unknown, IE mechanism. PD-L1 was expressed in the tumour microenvironment in most samples and correlated with T-cell densities. However, PD-L1 expression in cancer cells was independent of T-cell densities but strongly associated with loss of the intestinal homeobox transcription factor CDX2. This explains infrequent PD-L1 expression by cancer cells and may contribute to a higher recurrence risk of MMRd CRCs with impaired CDX2 expression. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Improving susceptibility of neuroendocrine tumors to radionuclide therapies: personalized approaches towards complementary treatments

Radionuclide therapies are an important tool for the management of patients with neuroendocrine neoplasms (NENs). Especially [131I]MIBG and [177Lu]Lu-DOTA-TATE are routinely used for the treatment of a subset of NENs, including pheochromocytomas, paragangliomas and gastroenteropancreatic tumors. Some patients suffering from other forms of NENs, such as medullary thyroid carcinoma or neuroblastoma, were shown to respond to radionuclide therapy; however, no general recommendations exist. Although [131I]MIBG and [177Lu]Lu-DOTA-TATE can delay disease progression and improve quality of life, complete remissions are achieved rarely. Hence, better individually tailored combination regimes are required. This review summarizes currently applied radionuclide therapies in the context of NENs and informs about recent advances in the development of theranostic agents that might enable targeting subgroups of NENs that previously did not respond to [131I]MIBG or [177Lu]Lu-DOTA-TATE. Moreover, molecular pathways involved in NEN tumorigenesis and progression that mediate features of radioresistance and are particularly related to the stemness of cancer cells are discussed. Pharmacological inhibition of such pathways might result in radiosensitization or general complementary antitumor effects in patients with certain genetic, transcriptomic, or metabolic characteristics. Finally, we provide an overview of approved targeted agents that might be beneficial in combination with radionuclide therapies in the context of a personalized molecular profiling approach.

Multi-site pre-therapeutic biopsies demonstrate genetic heterogeneity in patients with newly diagnosed diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, both regarding clinical presentation, response to treatment and outcome. Recently, subclassification of DLBCL based on mutational profile has been suggested, and next generation sequencing (NGS) analysis may be relevant as part of the diagnostic workflow. This will, however, often be based on analysis of one tumor biopsy. Here, we present a prospective study where multi-site sampling was performed prior to treatment in patients with newly diagnosed DLBCL. Two spatially different biopsies from 16 patients were analyzed using NGS with an in-house 59-gene lymphoma panel. In 8/16 (50%) patients, mutational differences were found between the two biopsy sites, including differences in TP53 mutational status. Our data indicate that a biopsy from the extra-nodal site may represent the most advanced clone, and an extra-nodal biopsy should be preferred for analysis, if safely accessible. This will help ensure a standardized stratification and treatment decision.

Intratumoral Heterogeneity in Lung Cancer

The diagnosis and treatment of lung cancer (LC) is always a challenge. The difficulty in the decision of therapeutic schedule and diagnosis is directly related to intratumoral heterogeneity (ITH) in the progression of LC. It has been proven that most tumors emerge and evolve under the pressure of their living microenvironment, which involves genetic, immunological, metabolic, and therapeutic components. While most research on ITH revealed multiple mechanisms and characteristic, a systemic exposition of ITH in LC is still hard to find. In this review, we describe how ITH in LC develops from the perspective of space and time. We discuss elaborate details and affection of every aspect of ITH in LC and the relationship between them. Based on ITH in LC, we describe a more accurate multidisciplinary therapeutic strategy on LC and provide the newest opinion on the potential approach of LC therapy.

Applying single cell multi-omic analyses to understand treatment resistance in pediatric high grade glioma

Despite improvements in cancer patient outcomes seen in the past decade, tumor resistance to therapy remains a major impediment to achieving durable clinical responses. Intratumoral heterogeneity related to genetic, epigenetic, transcriptomic, proteomic, and metabolic differences between individual cancer cells has emerged as a driver of therapeutic resistance. This cell to cell heterogeneity can be assessed using single cell profiling technologies that enable the identification of tumor cell clones that exhibit similar defining features like specific mutations or patterns of DNA methylation. Single cell profiling of tumors before and after treatment can generate new insights into the cancer cell characteristics that confer therapeutic resistance by identifying intrinsically resistant sub-populations that survive treatment and by describing new cellular features that emerge post-treatment due to tumor cell evolution. Integrative, single cell analytical approaches have already proven advantageous in studies characterizing treatment-resistant clones in cancers where pre- and post-treatment patient samples are readily available, such as leukemia. In contrast, little is known about other cancer subtypes like pediatric high grade glioma, a class of heterogeneous, malignant brain tumors in children that rapidly develop resistance to multiple therapeutic modalities, including chemotherapy, immunotherapy, and radiation. Leveraging single cell multi-omic technologies to analyze naïve and therapy-resistant glioma may lead to the discovery of novel strategies to overcome treatment resistance in brain tumors with dismal clinical outcomes. In this review, we explore the potential for single cell multi-omic analyses to reveal mechanisms of glioma resistance to therapy and discuss opportunities to apply these approaches to improve long-term therapeutic response in pediatric high grade glioma and other brain tumors with limited treatment options.

Defining multiple layers of intratumor heterogeneity based on variations of perturbations in multi-omics profiling

BACKGROUND

Intratumor heterogeneity (ITH) plays a crucial role in tumor progression, relapse, immune evasion, and drug resistance. Existing ITH quantification methods based on a single molecular level are inadequate to capture ITH evolving from genotype to phenotype.

METHODS

We designed a set of information entropy (IE)-based algorithms for quantifying ITH at the genome (somatic copy number alterations and mutations), mRNA, microRNA (miRNA), long non-coding RNA (lncRNA), protein, and epigenome level, respectively. We evaluated the performance of these algorithms by analyzing the correlations between their ITH scores and ITH-associated molecular and clinical features in 33 TCGA cancer types. Moreover, we evaluated the correlations between the ITH measures at different molecular levels by Spearman correlation and clustering analysis.

RESULTS

The IE-based ITH measures had significant correlations with unfavorable prognosis, tumor progression, genomic instability, antitumor immunosuppression, and drug resistance. The mRNA ITH showed stronger correlations with the miRNA, lncRNA, and epigenome ITH than with the genome ITH, supporting the regulatory relationships of miRNA, lncRNA, and DNA methylation towards mRNA. The protein-level ITH displayed stronger correlations with the transcriptome-level ITH than with the genome-level ITH, supporting the central dogma of molecular biology. Clustering analysis based on the ITH scores identified four subtypes of pan-cancer showing significantly different prognosis. Finally, the ITH integrating the seven ITH measures displayed more prominent properties of ITH than that at a single level.

CONCLUSIONS

This analysis provides landscapes of ITH at various molecular levels. Combining the ITH observation from different molecule levels will improve personalized management for cancer patients.

A multispecies framework for modeling adaptive immunity and immunotherapy in cancer

Predator-prey theory is commonly used to describe tumor growth in the presence of selective pressure from the adaptive immune system. These interactions are mediated by the tumor immunopeptidome (what the tumor "shows" the body) and the T-cell receptor (TCR) repertoire (how well the body "sees" cancer cells). The tumor immunopeptidome comprises neoantigens which can be gained and lost throughout tumorigenesis and treatment. Heterogeneity in the immunopeptidome is predictive of poor response to immunotherapy in some tumor types, suggesting that the TCR repertoire is unable to support a fully polyclonal response against every neoantigen. Importantly, while tumor and T-cell populations are known to compete with each other for intratumoral resources, whether between-lineage competition among peripheral T cells influences the TCR repertoire is unknown and difficult to interrogate experimentally. Computational models may offer a way to investigate these phenomena and deepen our understanding of the tumor-immune axis. Here, we construct a predator-prey-like model and calibrate it to preclinical and clinical data to describe tumor growth and immunopeptidome diversification. Simultaneously, we model the expansion of antigen-specific T-cell lineages and their consumption of both lineage-specific antigenic resources and lineage-agnostic, shared resources. This predator-prey-like framework accurately described clinically observed immunopeptidomes; recapitulated response-associated effects of immunotherapy, including immunoediting; and allowed exploration of treatment of tumors with varying growth and mutation rates.

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.

Metabolic rearrangements and intratumoral heterogeneity for immune response in hepatocellular carcinoma

Liver cancer is one of the most common malignant tumors globally. Not only is it difficult to diagnose, but treatments are scarce and the prognosis is generally poor. Hepatocellular carcinoma (HCC) is the most common type of liver cancer. Aggressive cancer cells, such as those found in HCC, undergo extensive metabolic rewiring as tumorigenesis, the unique feature, ultimately causes adaptation to the neoplastic microenvironment. Intratumoral heterogeneity (ITH) is defined as the presence of distinct genetic features and different phenotypes in the same tumoral region. ITH, a property unique to malignant cancers, results in differences in many different features of tumors, including, but not limited to, tumor growth and resistance to chemotherapy, which in turn is partly responsible for metabolic reprogramming. Moreover, the different metabolic phenotypes might also activate the immune response to varying degrees and help tumor cells escape detection by the immune system. In this review, we summarize the reprogramming of glucose metabolism and tumoral heterogeneity and their associations that occur in HCC, to obtain a better understanding of the mechanisms of HCC oncogenesis.

The combined tumor-nodal glycolytic entropy improves survival stratification in nonsmall cell lung cancer with locoregional disease

OBJECTIVE

To investigate whether combining primary tumor and metastatic nodal glycolytic heterogeneity on 18 F-fluorodeoxyglucose PET ( 18 F-FDG PET) improves prognostic prediction in nonsmall cell lung cancer (NSCLC) with locoregional disease.

METHODS

We retrospectively analyzed 18 F-FDG PET-derived features from 94 patients who had undergone curative treatments for regional nodal metastatic NSCLC. Overall survival (OS) and progression-free survival (PFS) were analyzed using univariate and multivariate Cox regression models. We used the independent prognosticators to construct models to predict survival.

RESULTS

Combined entropy (entropy derived from the combination of the primary tumor and metastatic nodes) and age independently predicted OS (both P = 0.008) and PFS ( P = 0.007 and 0.050, respectively). At the same time, the Eastern Cooperative Oncology Group status was another independent risk factor for unfavorable OS ( P = 0.026). Our combined entropy-based models outperformed the traditional staging system (c-index = 0.725 vs. 0.540, P < 0.001 for OS; c-index = 0.638 vs. 0.511, P = 0.003 for PFS) and still showed prognostic value in subgroups according to sex, histopathology, and different initial curative treatment strategies.

CONCLUSION

Combined primary tumor-nodal glycolytic heterogeneity independently predicted survival outcomes. In combination with clinical risk factors, our models provide better survival predictions and may enable tailored treatment strategies for NSCLC with locoregional disease.

Spatiotemporal evolution of the clear cell renal cell carcinoma microenvironment links intra-tumoral heterogeneity to immune escape

BACKGROUND

Intratumoral heterogeneity (ITH) is a hallmark of clear cell renal cell carcinoma (ccRCC) that reflects the trajectory of evolution and influences clinical prognosis. Here, we seek to elucidate how ITH and tumor evolution during immune checkpoint inhibitor (ICI) treatment can lead to therapy resistance.

METHODS

Here, we completed a single-arm pilot study to examine the safety and feasibility of neoadjuvant nivolumab in patients with localized RCC. Primary endpoints were safety and feasibility of neoadjuvant nivolumab. Then, we spatiotemporally profiled the genomic and immunophenotypic characteristics of 29 ccRCC patients, including pre- and post-therapy samples from 17 ICI-treated patients. Deep multi-regional whole-exome and transcriptome sequencing were performed on 29 patients at different time points before and after ICI therapy. T cell repertoire was also monitored from tissue and peripheral blood collected from a subset of patients to study T cell clonal expansion during ICI therapy.

RESULTS

Angiogenesis, lymphocytic infiltration, and myeloid infiltration varied significantly across regions of the same patient, potentially confounding their utility as biomarkers of ICI response. Elevated ITH associated with a constellation of both genomic features (HLA LOH, CDKN2A/B loss) and microenvironmental features, including elevated myeloid expression, reduced peripheral T cell receptor (TCR) diversity, and putative neoantigen depletion. Hypothesizing that ITH may itself play a role in shaping ICI response, we derived a transcriptomic signature associated with neoantigen depletion that strongly associated with response to ICI and targeted therapy treatment in several independent clinical trial cohorts.

CONCLUSIONS

These results argue that genetic and immune heterogeneity jointly co-evolve and influence response to ICI in ccRCC. Our findings have implications for future biomarker development for ICI response across ccRCC and other solid tumors and highlight important features of tumor evolution under ICI treatment.

TRIAL REGISTRATION

The study was registered on ClinicalTrial.gov (NCT02595918) on November 4, 2015.

Direct cell-to-cell transfer in stressed tumor microenvironment aggravates tumorigenic or metastatic potential in pancreatic cancer

Pancreatic cancer exhibits a characteristic tumor microenvironment (TME) due to enhanced fibrosis and hypoxia and is particularly resistant to conventional chemotherapy. However, the molecular mechanisms underlying TME-associated treatment resistance in pancreatic cancer are not fully understood. Here, we developed an in vitro TME mimic system comprising pancreatic cancer cells, fibroblasts and immune cells, and a stress condition, including hypoxia and gemcitabine. Cells with high viability under stress showed evidence of increased direct cell-to-cell transfer of biomolecules. The resulting derivative cells (CD44^high/SLC16A1^high) were similar to cancer stem cell-like-cells (CSCs) with enhanced anchorage-independent growth or invasiveness and acquired metabolic reprogramming. Furthermore, CD24 was a determinant for transition between the tumorsphere formation or invasive properties. Pancreatic cancer patients with CD44^low/SLC16A1^low expression exhibited better prognoses compared to other groups. Our results suggest that crosstalk via direct cell-to-cell transfer of cellular components foster chemotherapy-induced tumor evolution and that targeting of CD44 and MCT1(encoded by SLC16A1) may be useful strategy to prevent recurrence of gemcitabine-exposed pancreatic cancers.

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.

ATAXIC: An Algorithm to Quantify Transcriptomic Perturbation Heterogeneity in Single Cancer Cells

The single-cell RNA sequencing (scRNA-seq) has recently been widely utilized to quantify transcriptomic profiles in single cells of bulk tumors. The transcriptomic profiles in single cells facilitate the investigation of intratumor heterogeneity that is unlikely confounded by the nontumor components. We proposed an algorithm (ATAXIC) to quantify the heterogeneity of transcriptomic perturbations (TPs) in single cancer cells. ATAXIC calculated the TP heterogeneity level of a single cell based on the standard deviations of the absolute z-scored gene expression values for tens of thousands of genes, reflecting the asynchronous degree of transcriptomic alterations relative to the central (mean) tendency. By analyzing scRNA-seq datasets for eight cancer types, we revealed that ATAXIC scores were likely to correlate positively with the enrichment scores of various proliferation and oncogenic signatures, DNA damage repair, treatment resistance, and unfavorable phenotypes and outcomes in cancer. The ATAXIC scores varied among different cancer types, with lung cancer and melanoma having the lowest average scores and clear cell renal cell carcinoma having the highest average scores. The low TP heterogeneity in lung cancer and melanoma could bestow relatively higher response rates to immune checkpoint inhibitors on both cancer types. In conclusion, ATAXIC is a useful algorithm to quantify the TP heterogeneity in single cancer cells, as well as providing new insights into tumor biology.

Novel GIRlncRNA Signature for Predicting the Clinical Outcome and Therapeutic Response in NSCLC

Background: Non–small cell lung cancer (NSCLC) is highly malignant with driver somatic mutations and genomic instability. Long non-coding RNAs (lncRNAs) play a vital role in regulating these two aspects. However, the identification of somatic mutation-derived, genomic instability-related lncRNAs (GIRlncRNAs) and their clinical significance in NSCLC remains largely unexplored.

Methods: Clinical information, gene mutation, and lncRNA expression data were extracted from TCGA database. GIRlncRNAs were screened by a mutator hypothesis-derived computational frame. Co-expression, GO, and KEGG enrichment analyses were performed to investigate the biological functions. Cox and LASSO regression analyses were performed to create a prognostic risk model based on the GIRlncRNA signature (GIRlncSig). The prediction efficiency of the model was evaluated by using correlation analyses with mutation, driver gene, immune microenvironment contexture, and therapeutic response. The prognostic performance of the model was evaluated by external datasets. A nomogram was established and validated in the testing set and TCGA dataset.

Results: A total of 1446 GIRlncRNAs were selected from the screen, and the established GIRlncSig was used to classify patients into high- and low-risk groups. Enrichment analyses showed that GIRlncRNAs were mainly associated with nucleic acid metabolism and DNA damage repair pathways. Cox analyses further identified 19 GIRlncRNAs to construct a GIRlncSig-based risk score model. According to Cox regression and stratification analyses, 14 risk lncRNAs (AC023824.3, AC013287.1, AP000829.1, LINC01611, AC097451.1, AC025419.1, AC079949.2, LINC01600, AC004862.1, AC021594.1, MYRF-AS1, LINC02434, LINC02412, and LINC00337) and five protective lncRNAs (LINC01067, AC012645.1, AL512604.3, AC008278.2, and AC089998.1) were considered powerful predictors. Analyses of the model showed that these GIRlncRNAs were correlated with somatic mutation pattern, immune microenvironment infiltration, immunotherapeutic response, drug sensitivity, and survival of NSCLC patients. The GIRlncSig risk score model demonstrated good predictive performance (AUCs of ROC for 10-year survival was 0.69) and prognostic value in different NSCLC datasets. The nomogram comprising GIRlncSig and tumor stage exhibited improved robustness and feasibility for predicting NSCLC prognosis.

Conclusion: The newly identified GIRlncRNAs are powerful biomarkers for clinical outcome and prognosis of NSCLC. Our study highlights that the GIRlncSig-based score model may be a useful tool for risk stratification and management of NSCLC patients, which deserves further evaluation in future prospective studies.

Ordered and deterministic cancer genome evolution after p53 loss

Although p53 inactivation promotes genomic instability1 and presents a route to malignancy for more than half of all human cancers2,3, the patterns through which heterogenous TP53 (encoding human p53) mutant genomes emerge and influence tumorigenesis remain poorly understood. Here, in a mouse model of pancreatic ductal adenocarcinoma that reports sporadic p53 loss of heterozygosity before cancer onset, we find that malignant properties enabled by p53 inactivation are acquired through a predictable pattern of genome evolution. Single-cell sequencing and in situ genotyping of cells from the point of p53 inactivation through progression to frank cancer reveal that this deterministic behaviour involves four sequential phases-Trp53 (encoding mouse p53) loss of heterozygosity, accumulation of deletions, genome doubling, and the emergence of gains and amplifications-each associated with specific histological stages across the premalignant and malignant spectrum. Despite rampant heterogeneity, the deletion events that follow p53 inactivation target functionally relevant pathways that can shape genomic evolution and remain fixed as homogenous events in diverse malignant populations. Thus, loss of p53-the 'guardian of the genome'-is not merely a gateway to genetic chaos but, rather, can enable deterministic patterns of genome evolution that may point to new strategies for the treatment of TP53-mutant tumours.

Systematic investigation of the prognostic impact of clonal status of somatic mutations across multiple cancer types

Tumors are genetically heterogeneous and many mutations are actually present in subclonal populations. The clonal status of mutations is valuable for accurate prognosis, clinical management. The aim of this study was to identify the clonal status of somatic mutations and systematically evaluate their prognostic values across various cancer types. We totally identified 227 clonal and 432 subclonal mutations contributed to prognosis and demonstrated the importance of clonal status in improving mutation-related clinical guidance. We further developed a customized multi-step approach to identify gene-specific prognostic patterns of clonal status at pan-cancer level and found some cancer-specific prognostic patterns. The 'subclonal-dependent risk' subpattern was one of the most common subpatterns, it usually accompanied by high genomic in-stability and high extent of intra-tumor heterogeneity and could be used to improve the accuracy of prognostic analysis. Our results revealed the importance of clonal status, especially subclonal mutation in clinical survival.

Tumor heterogeneity reshapes the tumor microenvironment to influence drug resistance

Tumor heterogeneity is one of the hallmarks of cancer and a challenge in the field of oncology. Tumor heterogeneity is the main cause of drug resistance, leading to therapeutic failure. Mechanically, tumor heterogeneity either directly affects therapeutic targets or shapes the tumor microenvironment (TME) by defining transcriptomic and phenotypic profiles to influence drug resistance. Tumor heterogeneity evolves spatially and temporally during tumor development, leading to the constant reprogramming of the TME. Advances in molecular profiling technologies and precision oncology platforms have allowed us to uncover the impact of tumor heterogeneity on drug resistance in the context of the TME. In this review, we focus on the processes during which genomic mutations drive tumor heterogeneity and the mechanisms through which tumor heterogeneity reprograms the TME to affect drug resistance and patient prognosis.

Heterogeneity of the tumor immune microenvironment and its clinical relevance

During the course of tumorigenesis and subsequent metastasis, malignant cells gradually diversify and become more heterogeneous. Consequently, the tumor mass might be infiltrated by diverse immune-related components, including the cytokine/chemokine environment, cytotoxic activity, or immunosuppressive elements. This immunological heterogeneity is universally presented spatially or varies temporally along with tumor evolution or therapeutic intervention across almost all solid tumors. The heterogeneity of anti-tumor immunity shows a profound association with the progression of disease and responsiveness to treatment, particularly in the realm of immunotherapy. Therefore, an accurate understanding of tumor immunological heterogeneity is essential for the development of effective therapies. Facilitated by multi-regional and -omics sequencing, single cell sequencing, and longitudinal liquid biopsy approaches, recent studies have demonstrated the potential to investigate the complexity of immunological heterogeneity of the tumors and its clinical relevance in immunotherapy. Here, we aimed to review the mechanism underlying the heterogeneity of the immune microenvironment. We also explored how clinical assessments of tumor heterogeneity might facilitate the development of more effective personalized therapies.

Dissecting and analyzing the Subclonal Mutations Associated with Poor Prognosis in Diffuse Glioma

The prognostic and therapeutic implications in diffuse gliomas are still challenging. In this study, we first performed an integrative framework to infer the clonal status of mutations in glioblastomas (GBMs) and low-grade gliomas (LGGs) by using exome sequencing data from TCGA and observed both clonal and subclonal mutations for most mutant genes. Based on the clonal status of a given gene, we systematically investigated its prognostic value in GBM and LGG, respectively. Focusing on the subclonal mutations, our results showed that they were more likely to contribute to the poor prognosis, which could be hardly figured out without considering clonal status. These risk subclonal mutations were associated with some specific genomic features, such as genomic instability and intratumor heterogeneity, and their accumulation could enhance the prognostic value. By analyzing the regulatory mechanisms underlying the risk subclonal mutations, we found that the subclonal mutations of AHNAK and AHNAK2 in GBM and those of NF1 and PTEN in LGG could influence some important molecules and functions associated with glioma progression. Furthermore, we dissected the role of risk subclonal mutations in tumor evolution and found that advanced subclonal mutations showed poorer overall survival. Our study revealed the importance of clonal status in prognosis analysis, highlighting the role of the subclonal mutation in glioma prognosis.

APOBEC Mutagenesis Inhibits Breast Cancer Growth through Induction of T cell-Mediated Antitumor Immune Responses

The APOBEC family of cytidine deaminases is one of the most common endogenous sources of mutations in human cancer. Genomic studies of tumors have found that APOBEC mutational signatures are enriched in the HER2 subtype of breast cancer and are associated with immunotherapy response in diverse cancer types. However, the direct consequences of APOBEC mutagenesis on the tumor immune microenvironment have not been thoroughly investigated. To address this, we developed syngeneic murine mammary tumor models with inducible expression of APOBEC3B. We found that APOBEC activity induced antitumor adaptive immune responses and CD4+ T cell-mediated, antigen-specific tumor growth inhibition. Although polyclonal APOBEC tumors had a moderate growth defect, clonal APOBEC tumors were almost completely rejected, suggesting that APOBEC-mediated genetic heterogeneity limits antitumor adaptive immune responses. Consistent with the observed immune infiltration in APOBEC tumors, APOBEC activity sensitized HER2-driven breast tumors to anti-CTLA-4 checkpoint inhibition and led to a complete response to combination anti-CTLA-4 and anti-HER2 therapy. In human breast cancers, the relationship between APOBEC mutagenesis and immunogenicity varied by breast cancer subtype and the frequency of subclonal mutations. This work provides a mechanistic basis for the sensitivity of APOBEC tumors to checkpoint inhibitors and suggests a rationale for using APOBEC mutational signatures and clonality as biomarkers predicting immunotherapy response in HER2-positive (HER2+) breast cancers.

Experimental challenges to modeling prostate cancer heterogeneity

Tumor heterogeneity plays a key role in prostate cancer prognosis, therapy selection, relapse, and acquisition of treatment resistance. Prostate cancer presents a heterogeneous diversity at inter- and intra-tumor and inter-patient levels which are influenced by multiple intrinsic and/or extrinsic factors. Recent studies have started to characterize the complexity of prostate tumors and these different tiers of heterogeneity. In this review, we discuss the most common factors that contribute to tumoral diversity. Moreover, we focus on the description of the in vitro and in vivo approaches, as well as high-throughput technologies, that help to model intra-tumoral diversity. Further understanding tumor heterogeneities and the challenges they present will guide enhanced patient risk stratification, aid the design of more precise therapies, and ultimately help beat this chameleon-like disease.

Quantification of tumor heterogeneity: from data acquisition to metric generation

Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care.

Pushing Past the Blockade: Advancements in T Cell-Based Cancer Immunotherapies

Successful cancer immunotherapies rely on a replete and functional immune compartment. Within the immune compartment, T cells are often the effector arm of immune-based strategies due to their potent cytotoxic capabilities. However, many tumors have evolved a variety of mechanisms to evade T cell-mediated killing. Thus, while many T cell-based immunotherapies, such as immune checkpoint inhibition (ICI) and chimeric antigen receptor (CAR) T cells, have achieved considerable success in some solid cancers and hematological malignancies, these therapies often fail in solid tumors due to tumor-imposed T cell dysfunctions. These dysfunctional mechanisms broadly include reduced T cell access into and identification of tumors, as well as an overall immunosuppressive tumor microenvironment that elicits T cell exhaustion. Therefore, novel, rational approaches are necessary to overcome the barriers to T cell function elicited by solid tumors. In this review, we will provide an overview of conventional immunotherapeutic strategies and the various barriers to T cell anti-tumor function encountered in solid tumors that lead to resistance. We will also explore a sampling of emerging strategies specifically aimed to bypass these tumor-imposed boundaries to T cell-based immunotherapies.

Epithelial-to-Mesenchymal Transition-Derived Heterogeneity in Head and Neck Squamous Cell Carcinomas

Simple Summary

Head and neck squamous cell carcinomas (HNSCC) are common malignancies with considerable morbidity and a high death toll worldwide. Resistance towards multi-modal therapy modalities composed of surgery, irradiation, chemo- and immunotherapy represents a major obstacle in the efficient treatment of HNSCC patients. Patients frequently show nodal metastases at the time of diagnosis and endure early relapses, oftentimes in the form of local recurrences. Differentiation programs such as the epithelial-to-mesenchymal transition (EMT) allow individual tumor cells to adopt cellular functions that are central to the development of metastases and treatment resistance. In the present review article, the molecular basis and regulation of EMT and its impact on the progression of HNSCC will be addressed.

Abstract

Head and neck squamous cell carcinomas (HNSCC) are common tumors with a poor overall prognosis. Poor survival is resulting from limited response to multi-modal therapy, high incidence of metastasis, and local recurrence. Treatment includes surgery, radio(chemo)therapy, and targeted therapy specific for EGFR and immune checkpoint inhibition. The understanding of the molecular basis for the poor outcome of HNSCC was improved using multi-OMICs approaches, which revealed a strong degree of inter- and intratumor heterogeneity (ITH) at the level of DNA mutations, transcriptome, and (phospho)proteome. Single-cell RNA-sequencing (scRNA-seq) identified RNA-expression signatures related to cell cycle, cell stress, hypoxia, epithelial differentiation, and a partial epithelial-to-mesenchymal transition (pEMT). The latter signature was correlated to nodal involvement and adverse clinical features. Mechanistically, shifts towards a mesenchymal phenotype equips tumor cells with migratory and invasive capacities and with an enhanced resistance to standard therapy. Hence, gradual variations of EMT as observed in HNSCC represent a potent driver of tumor progression that could open new paths to improve the stratification of patients and to innovate approaches to break therapy resistance. These aspects of molecular heterogeneity will be discussed in the present review.

Bioinformatics-based identification of SPNS3 (Spinster homolog 3) as a prognostic biomarker of apoptosis resistance in acute myeloid leukemia

Spinster homolog 3 (SPNS3) belongs to the Spinster (SPNS) family which participates in sphingolipid transportation through the cell membrane. However, the functions of SPNS3 in acute myeloid leukemia (AML) are unknown. This study obtained SPNS3 from a gene set that was related to AML relapse and evaluate whether high SPNS3 expression induced apoptosis resistance in an AML cell line, which is consistent with the role of SPNS3 as a marker of poor prognosis in the clinic. Moreover, internal tandem duplication of FMS-like tyrosine kinase 3 (FLT3-ITD) mutation and the AC127521.1/ MIR-139/SPNS3 competing endogenous RNA axis were found to regulate SPNS3 expression. In addition, we noted that SPNS3 may play an important role in the Sphingosine-1-phosphate signal pathway that is involved in the maintenance of the AML microenvironment. These results highlight the anti-apoptosis effect of SPNS3 in AML, and the potential mechanism mediating this effect was explored through bioinformatics.

Abbreviations: AML: acute myeloid leukemia; FLT3-ITD: internal tandem duplication of FMS-like tyrosine kinase 3; SPNS3: spinster homolog 3; SPNS1: spinster homolog 1; SPNS2: spinster homolog 2; GO: gene ontology; S1P: sphingosine-1-phosphate; ceRNA: competing endogenous RNA; dAML: acute myeloid leukemia at diagnosis; iAML: acute myeloid leukemia after induction chemotherapy; rAML: acute myeloid leukemia at relapse; DEGs: differentially expressed genes; BP: biological processes; CC: cellular components; MF: molecular functions; MRD: minimal residual disease; EFS: event-free survival; OS: overall survival; KEGG: Kyoto Encyclopedia of Genes and Genomes; SPHK: Sphingosine kinase.

Does large NGS panel analysed using exome tumour sequencing improve the management of advanced non-small-cell lung cancers?

INTRODUCTION

Non-small-cell lung cancer (NSCLC) is one of the most common and deadly cancers. Several molecular drivers of oncogene addiction are now known to be strong predictive biomarkers for target therapies. Advances in large Next Generation Sequencing (LNGS) have improved the ability to detect potentially targetable mutations. However, the integration of LNGS into clinical management in an individualized manner remains challenging.

METHODS

In this single-center observational study we included all patients with advanced NSCLC who underwent LNGS. Somatic and germline exome analysis was performed with a restriction on 323 cancer related genes. Variants were classified and Molecular Tumour Board (MTB) made therapeutic propositions.

RESULTS

We performed LNGS analysis in 281 patients with advanced NSCLC between March 2015 and January 2018. Technical failure occurred in only 3% of cases. Three hundred and fifty-six targetable mutations were detected. At least one targetable mutation was found in 209 patients. For all these patients, the MTB was able to recommend treatment with a targeted agent based on the evaluation of the tumour's genetic profile and treatment history. Twenty-nine patients (13.9%) were subsequently treated with an MTB-recommended targeted therapy. We did not observe any improvement in terms of clinical benefit for these patients.

CONCLUSIONS

In this case series, we show that including LNGS into routine clinical management was feasible but does not appear to provide clinical benefit in the management of patients with advanced NSCLC.

Genetic Determinants of Somatic Selection of Mutational Processes in 3,566 Human Cancers

The somatic landscape of the cancer genome results from different mutational processes represented by distinct "mutational signatures." Although several mutagenic mechanisms are known to cause specific mutational signatures in cell lines, the variation of somatic mutational activities in patients, which is mostly attributed to somatic selection, is still poorly explained. Here, we introduce a quantitative trait, mutational propensity (MP), and describe an integrated method to infer genetic determinants of variations in the mutational processes in 3,566 cancers with specific underlying mechanisms. As a result, we report 2,314 candidate determinants with both significant germline and somatic effects on somatic selection of mutational processes, of which, 485 act via cancer gene expression and 1,427 act through the tumor-immune microenvironment. These data demonstrate that the genetic determinants of MPs provide complementary information to known cancer driver genes, clonal evolution, and clinical biomarkers. SIGNIFICANCE: The genetic determinants of the somatic mutational processes in cancer elucidate the biology underlying somatic selection and evolution of cancers and demonstrate complementary predictive power across cancer types.

DITHER: an algorithm for Defining IntraTumor Heterogeneity based on EntRopy

Intratumor heterogeneity (ITH) is associated with tumor development, prognosis, immune evasion and therapeutic effects. We proposed the Defining ITH based on EntRopy (DITHER) algorithm for evaluating ITH. We first evaluated the entropies of somatic mutation profiles and copy number alteration (CNA) profiles in a tumor, respectively, and defined their average as the ITH level for the tumor. Using DITHER, we analyzed 33 cancer types from The Cancer Genome Atlas (TCGA) program. We demonstrated that the ITH defined by DITHER had the typical properties of ITH, namely its strong correlations with tumor progression, unfavorable phenotype, genomic instability and immune evasion. Compared with two other ITH evaluation methods: MATH and PhyloWGS, the DITHER ITH had more prominent characteristics of ITH. Moreover, different from MATH and PhyloWGS, DITHER scores were positively correlated with tumor purity, suggesting that DITHER tends to capture the ITH between tumor cells. Interestingly, microsatellite instability (MSI)-high tumors had significantly lower DITHER scores than microsatellite stability (MSS)/MSI-low tumors, although the former had significantly higher tumor mutation loads than the latter. It suggests that the hypermutability of MSI is homogeneous between different cellular populations in bulk tumors. The DITHER ITH may provide novel insights into tumor biology and potential clinical applications.

Epithelial cell plasticity: breaking boundaries and changing landscapes

Epithelial tissues respond to a wide variety of environmental and genotoxic stresses. As an adaptive mechanism, cells can deviate from their natural paths to acquire new identities, both within and across lineages. Under extreme conditions, epithelial tissues can utilize "shape-shifting" mechanisms whereby they alter their form and function at a tissue-wide scale. Mounting evidence suggests that in order to acquire these alternate tissue identities, cells follow a core set of "tissue logic" principles based on developmental paradigms. Here, we review the terminology and the concepts that have been put forward to describe cell plasticity. We also provide insights into various cell intrinsic and extrinsic factors, including genetic mutations, inflammation, microbiota, and therapeutic agents that contribute to cell plasticity. Additionally, we discuss recent studies that have sought to decode the "syntax" of plasticity-i.e., the cellular and molecular principles through which cells acquire new identities in both homeostatic and malignant epithelial tissues-and how these processes can be manipulated for developing novel cancer therapeutics.

DNA damage and mitochondria in cancer and aging

Age and DNA repair deficiencies are strong risk factors for developing cancer. This is reflected in the comorbidity of cancer with premature aging diseases associated with DNA damage repair deficiencies. Recent research has suggested that DNA damage accumulation, telomere dysfunction and the accompanying mitochondrial dysfunction exacerbate the aging process and may increase the risk of cancer development. Thus, an area of interest in both cancer and aging research is the elucidation of the dynamic crosstalk between the nucleus and the mitochondria. In this review, we discuss current research on aging and cancer with specific focus on the role of mitochondrial dysfunction in cancer and aging as well as how nuclear to mitochondrial DNA damage signaling may be a driving factor in the increased cancer incidence with aging. We suggest that therapeutic interventions aimed at the induction of autophagy and mediation of nuclear to mitochondrial signaling may provide a mechanism for healthier aging and reduced tumorigenesis.

Genomic heterogeneity and copy number variant burden are associated with poor recurrence-free survival and 11q loss in human papillomavirus-positive squamous cell carcinoma of the oropharynx

BACKGROUND

Human papillomavirus-positive (HPV+) squamous cell carcinoma of the oropharynx (OPSCC) is the most prevalent HPV-associated malignancy in the United States. Favorable treatment outcomes have led to increased interest in treatment de-escalation to reduce treatment morbidity as well as the development of prognostic markers to identify appropriately low-risk patients. Intratumoral genomic heterogeneity and copy number alteration burden have been demonstrated to be predictive of poor outcomes in many other cancers; therefore, we sought to determine whether intratumor heterogeneity and genomic instability are associated with poor outcomes in HPV+ OPSCC.

METHODS

Tumor heterogeneity estimates were made based on targeted exome sequencing of 45 patients with HPV+ OPSCC tumors. Analysis of an additional cohort of HPV+ OPSCC tumors lacking matched normal sequencing allowed copy number analysis of 99 patient tumors.

RESULTS

High intratumorally genomic heterogeneity and high numbers of copy number alterations were strongly associated with worse recurrence-free survival. Tumors with higher heterogeneity and frequent copy number alterations were associated with loss of distal 11q, which encodes key genes related to double-strand break repair, including ATM and MRE11A.

CONCLUSIONS

Both intratumor genomic heterogeneity and high-burden copy number alterations are strongly associated with poor recurrence-free survival in patients with HPV+ OPSCC. The drivers of genomic instability and heterogeneity in these tumors remains to be elucidated. However, 11q loss and defective DNA double-strand break repair have been associated with genomic instability in other solid tumors. Copy number alteration burden and intratumoral heterogeneity represent promising avenues for risk stratification of patients with HPV+OPSCC.

Histological diversity and molecular characteristics in gastric cancer: relation of cancer stem cell-related molecules and receptor tyrosine kinase molecules to mixed histological type and more histological patterns

BACKGROUND

Gastric cancers (GCs) are still one of the leading causes of cancer-related mortality. The histological and molecular features of GC may differ widely from area to area within the same tumor. Intratumoral heterogeneity has been considered a major obstacle to an efficient diagnosis and successful molecular treatment.

METHODS

We selected and reevaluated 842 GC cases and analyzed the relationship between numbers or composites of histological patterns within tumors, and clinicopathological parameters in mucosal and invasive areas. In addition, we searched for the GC-associated molecules or molecular subtypes marking histological diversities.

RESULTS

GC cases with more histological numbers or mixed types in invasive areas showed significantly higher T grade and staging, whereas those in mucosal areas did not show any significant associations. GCs with histological diversities showed poorer prognosis and characteristically expressed cancer stem cell-related molecules (CD44, CD133 or ALDH1) and receptor tyrosine kinase molecules (HER2, EGFR or c-MET) as well as Helicobacter pylori infection. Expressions of CD44, HER2, c-MET, laminin 5·2 or retained E-cadherin in mucosal areas were predictive of more histological numbers and mixed types in invasive areas. In addition, the chromosomal instability subtype of GC showed significant associations with more histological numbers and mixed histological type, whereas the genomic stability subtype of GC showed a significant relationship with pure type.

CONCLUSIONS

We displayed the relationship between histological diversity and molecular features in GC, and we hope that the present data can contribute to the early diagnosis and prevention, and effective treatment of GC.

Intratumoral heterogeneity in cancer progression and response to immunotherapy

Most (if not all) tumors emerge and progress under a strong evolutionary pressure imposed by trophic, metabolic, immunological, and therapeutic factors. The relative impact of these factors on tumor evolution changes over space and time, ultimately favoring the establishment of a neoplastic microenvironment that exhibits considerable genetic, phenotypic, and behavioral heterogeneity in all its components. Here, we discuss the main sources of intratumoral heterogeneity and its impact on the natural history of the disease, including sensitivity to treatment, as we delineate potential strategies to target such a detrimental feature of aggressive malignancies.

Heterogeneity and Clonal Evolution of Acquired PARP Inhibitor Resistance in TP53- and BRCA1-Deficient Cells

Homologous recombination (HR)-deficient cancers are sensitive to poly-ADP ribose polymerase inhibitors (PARPi), which have shown clinical efficacy in the treatment of high-grade serous cancers (HGSC). However, the majority of patients will relapse, and acquired PARPi resistance is emerging as a pressing clinical problem. Here we generated seven single-cell clones with acquired PARPi resistance derived from a PARPi-sensitive TP53 ^-/- and BRCA1 ^-/- epithelial cell line generated using CRISPR/Cas9. These clones showed diverse resistance mechanisms, and some clones presented with multiple mechanisms of resistance at the same time. Genomic analysis of the clones revealed unique transcriptional and mutational profiles and increased genomic instability in comparison with a PARPi-sensitive cell line. Clonal evolutionary analyses suggested that acquired PARPi resistance arose via clonal selection from an intrinsically unstable and heterogenous cell population in the sensitive cell line, which contained preexisting drug-tolerant cells. Similarly, clonal and spatial heterogeneity in tumor biopsies from a clinical patient with BRCA1-mutant HGSC with acquired PARPi resistance was observed. In an imaging-based drug screening, the clones showed heterogenous responses to targeted therapeutic agents, indicating that not all PARPi-resistant clones can be targeted with just one therapy. Furthermore, PARPi-resistant clones showed mechanism-dependent vulnerabilities to the selected agents, demonstrating that a deeper understanding on the mechanisms of resistance could lead to improved targeting and biomarkers for HGSC with acquired PARPi resistance. SIGNIFICANCE: This study shows that BRCA1-deficient cells can give rise to multiple genomically and functionally heterogenous PARPi-resistant clones, which are associated with various vulnerabilities that can be targeted in a mechanism-specific manner.

Mutational Signatures Driven by Epigenetic Determinants Enable the Stratification of Patients with Gastric Cancer for Therapeutic Intervention

DNA mismatch repair deficiency (dMMR) is associated with the microsatellite instability (MSI) phenotype and leads to increased mutation load, which in turn may impact anti-tumor immune responses and treatment effectiveness. Various mutational signatures directly linked to dMMR have been described for primary cancers. To investigate which mutational signatures are associated with prognosis in gastric cancer, we performed a de novo extraction of mutational signatures in a cohort of 787 patients. We detected three dMMR-related signatures, one of which clearly discriminates tumors with MLH1 gene silencing caused by promoter hypermethylation (area under the curve = 98%). We then demonstrated that samples with the highest exposure of this signature share features related to better prognosis, encompassing clinical and molecular aspects and altered immune infiltrate composition. Overall, the assessment of the prognostic value and of the impact of modifications in MMR-related genes on shaping specific dMMR mutational signatures provides evidence that classification based on mutational signature exposure enables prognosis stratification.

MLH1 Deficiency-Triggered DNA Hyperexcision by Exonuclease 1 Activates the cGAS-STING Pathway

Tumors with defective mismatch repair (dMMR) are responsive to immunotherapy because of dMMR-induced neoantigens and activation of the cGAS-STING pathway. While neoantigens result from the hypermutable nature of dMMR, it is unknown how dMMR activates the cGAS-STING pathway. We show here that loss of the MutLα subunit MLH1, whose defect is responsible for ~50% of dMMR cancers, results in loss of MutLα-specific regulation of exonuclease 1 (Exo1) during DNA repair. This leads to unrestrained DNA excision by Exo1, which causes increased single-strand DNA formation, RPA exhaustion, DNA breaks, and aberrant DNA repair intermediates. Ultimately, this generates chromosomal abnormalities and the release of nuclear DNA into the cytoplasm, activating the cGAS-STING pathway. In this study, we discovered a hitherto unknown MMR mechanism that modulates genome stability and has implications for cancer therapy.

Targeting Cancer Heterogeneity with Immune Responses Driven by Oncolytic Peptides

Accumulating preclinical and clinical evidence indicates that high degrees of heterogeneity among malignant cells constitute a considerable obstacle to the success of cancer therapy. This calls for the development of approaches that operate - or enable established treatments to operate - despite such intratumoral heterogeneity (ITH). In this context, oncolytic peptides stand out as promising therapeutic tools based on their ability to drive immunogenic cell death associated with robust anticancer immune responses independently of ITH. We review the main molecular and immunological pathways engaged by oncolytic peptides, and discuss potential approaches to combine these agents with modern immunotherapeutics in support of superior tumor-targeting immunity and efficacy in patients with cancer.

Inter and intra-tumor somatostatin receptor 2 heterogeneity influences peptide receptor radionuclide therapy response

Patients with neuroendocrine tumors (NETs) can be treated with peptide receptor radionuclide therapy (PRRT). Here, the somatostatin analogue octreotate radiolabeled with lutetium-177 is targeted to NET cells by binding to the somatostatin receptor subtype 2 (SST₂). During radioactive decay, DNA damage is induced, leading to NET cell death. Although the therapy proves to be effective, mortality rates remain high. To appropriately select more optimal treatment strategies, it is essential to first better understand the radiobiological responses of tumor cells to PRRT.

Methods: We analyzed PRRT induced radiobiological responses in SST₂ expressing cells and xenografted mice using SPECT/MRI scanning and histological and molecular analyses. We measured [¹⁷⁷Lu]Lu-DOTA-TATE uptake and performed analyses to visualize induction of DNA damage, cell death and other cellular characteristics.

Results: The highest accumulation of radioactivity was measured in the tumor and kidneys. PRRT induced DNA damage signaling and repair in a time-dependent manner. We observed intra-tumor heterogeneity of DNA damage and apoptosis, which was not attributed to proliferation or bioavailability. We found a strong correlation between high DNA damage levels and high SST₂ expression. PRRT elicited a different therapeutic response between models with different SST₂ expression levels. Heterogeneous SST₂ expression levels were also confirmed in patient NETs.

Conclusion: Heterogeneous SST₂ expression levels within NETs cause differentially induced DNA damage levels, influence recurrent tumor phenotypes and impact the therapeutic response in different models and potentially in patients. Our results contribute to a better understanding of PRRT effects, which might impact future therapeutic outcome of NET patients.

Cancer evolution: A means by which tumors evade treatment

Although various methods have been tried to study and treat cancer, the cancer remains a major challenge for human medicine today. One important reason for this is the presence of cancer evolution. Cancer evolution is a process in which tumor cells adapt to the external environment, which can suppress the human immune system's ability to recognize and attack tumors, and also reduce the reproducibility of cancer research. Among them, heterogeneity of the tumor provides intrinsic motivation for this process. Recently, with the development of related technologies such as liquid biopsy, more and more knowledge about cancer evolution has been gained and interest in this topic has also increased. Therefore, starting from the causes of tumorigenesis, this paper introduces several tumorigenesis processes and pathways, as well as treatment options for different targets.