A user guide for the online exploration and visualization of PCAWG data

Blending space and time to talk about cancer in extended reality

We introduce a proof-of-concept extended reality (XR) environment for discussing cancer, presenting genomic information from multiple tumour sites in the context of 3D tumour models generated from CT scans. This tool enhances multidisciplinary discussions. Clinicians and cancer researchers explored its use in oncology, sharing perspectives on XR's potential for use in molecular tumour boards, clinician-patient communication, and education. XR serves as a universal language, fostering collaborative decision-making in oncology.

The cytidine deaminase APOBEC3C has unique sequence and genome feature preferences

APOBEC proteins are cytidine deaminases that restrict the replication of viruses and transposable elements. Several members of the APOBEC3 family, APOBEC3A, APOBEC3B, and APOBEC3H-I, can access the nucleus and cause what is thought to be indiscriminate deamination of the genome, resulting in mutagenesis and genome instability. Although APOBEC3C is also present in the nucleus, the full scope of its deamination target preferences is unknown. By expressing human APOBEC3C in a yeast model system, I have defined the APOBEC3C mutation signature, as well as the preferred genome features of APOBEC3C targets. The APOBEC3C mutation signature is distinct from those of the known cancer genome mutators APOBEC3A and APOBEC3B. APOBEC3C produces DNA strand-coordinated mutation clusters, and APOBEC3C mutations are enriched near the transcription start sites of active genes. Surprisingly, APOBEC3C lacks the bias for the lagging strand of DNA replication that is seen for APOBEC3A and APOBEC3B. The unique preferences of APOBEC3C constitute a mutation profile that will be useful in defining sites of APOBEC3C mutagenesis in human genomes.

Intragenic Rearrangement Burden Associates with Immune Cell Infiltration and Response to Immune Checkpoint Blockade in Cancer

Immune checkpoint blockade (ICB) can induce durable cancer remission. However, only a small subset of patients gains benefits. While tumor mutation burden (TMB) differentiates responders from nonresponders in some cases, it is a weak predictor in tumor types with low mutation rates. Thus, there is an unmet need to discover a new class of genetic aberrations that predict ICB responses in these tumor types. Here, we report analyses of pan-cancer whole genomes which revealed that intragenic rearrangement (IGR) burden is significantly associated with immune infiltration in breast, ovarian, esophageal, and endometrial cancers, particularly with increased M1 macrophage and CD8+ T-cell signatures. Multivariate regression against spatially counted tumor-infiltrating lymphocytes in breast, endometrial, and ovarian cancers suggested that IGR burden is a more influential covariate than other genetic aberrations in these cancers. In the MEDI4736 trial evaluating durvalumab in esophageal adenocarcinoma, IGR burden correlated with patient benefits. In the IMVigor210 trial evaluating atezolizumab in urothelial carcinoma, IGR burden increased with platinum exposure and predicted patient benefit among TMB-low, platinum-exposed tumors. Altogether, we have demonstrated that IGR burden correlates with T-cell inflammation and predicts ICB benefit in TMB-low, IGR-dominant tumors, and in platinum-exposed tumors.

The Digital World of Cytogenetic and Cytogenomic Web Resources

The dynamic growth of technological capabilities at the cellular and molecular level has led to a rapid increase in the amount of data on the genes and genomes of organisms. In order to store, access, compare, validate, classify, and understand the massive data generated by different researchers, and to promote effective communication among research communities, various genome and cytogenetic online databases have been established. These data platforms/resources are essential not only for computational analyses and theoretical syntheses but also for helping researchers select future research topics and prioritize molecular targets. Furthermore, they are valuable for identifying shared recurrent genomic patterns related to human diseases and for avoiding unnecessary duplications among different researchers. The website interface, menu, graphics, animations, text layout, and data from databases are displayed by a front end on the screen of a monitor or smartphone. A database front-end refers to the user interface or application that enables accessing tabular, structured, or raw data stored in the database. The Internet makes it possible to reach a greater number of users around the world and gives them quick access to information stored in databases. The number of ways of presenting this data by front-ends increases as well. This requires unifying the ways of operating and presenting information by front-ends and ensuring contextual switching between front-ends of different databases. This chapter aims to present selected cytogenetic and cytogenomic Internet resources in terms of obtaining the needed information and to indicate how to increase the efficiency of access to stored information. Through a brief introduction of these databases and by providing examples of their usage in cytogenetic analyses, we aim to bridge the gap between cytogenetics and molecular genomics by encouraging their utilization.

Identification of enhancer RNA AC005515.1 as a novel biomarker for prognosis in esophageal cancer and predictors of immunotherapy response

Background

The enhancer RNA (eRNA) signature shows excellent promise in the prognostic role of many malignancies, but its value has not been fully explored in esophageal cancer (ESCA).

Methods

We comprehensively analyzed 33 oncogene expression matrices and clinical data from The Cancer Genome Atlas (TCGA) and identified ESCA prognostic-related key eRNAs by Kaplan-Meier and co-expression analysis. We also investigated the prognostic role of the key eRNA using a series of bioinformatics approaches, including immune infiltration, immune function, immune subtypes, and the tumor microenvironment. Finally, the tumor immune dysfunction and exclusion (TIDE) score was used to predict the immune response to immune checkpoint blockade (ICB) therapy.

Results

We identified eRNA AC005515.1, AC012368.1, AP003469.2, Clorf61, and WDFY3-AS2 were associated with the prognosis of ESCA. AC005515.1 is a critical prognostic-related eRNA in ESCA and was significantly co-expressed with immune checkpoint genes (CTLA4, CD274, etc.). In the pan-cancer analysis, AC005515.1 was also associated with the prognosis of seven cancers, including kidney renal papillary cell carcinoma and low-grade brain glioma. It was also found to be co-expressed with immune checkpoint genes in these tumors. Moreover, high expression of AC005515.1 was associated with CD8+ T cells and M1 macrophages infiltration, and the AC005515.1 high-expression group had a higher TIDE score in ESCA.

Conclusions

Overall, eRNA AC005515.1 is associated with the local immune environment of ESCA and may become a new biomarker of ESCA prognosis and immunotherapy response.

Systematic analysis of Mendelian disease-associated gene variants reveals new classes of cancer-predisposing genes

BACKGROUND

Despite the acceleration of somatic driver gene discovery facilitated by recent large-scale tumor sequencing data, the contribution of inherited variants remains largely unexplored, primarily focusing on previously known cancer predisposition genes (CPGs) due to the low statistical power associated with detecting rare pathogenic variant-phenotype associations.

METHODS

Here, we introduce a generalized log-regression model to measure the excess of pathogenic variants within genes in cancer patients compared to control samples. It aims to measure gene-level cancer risk enrichment by collapsing rare pathogenic variants after controlling the population differences across samples.

RESULTS

In this study, we investigate whether pathogenic variants in Mendelian disease-associated genes (OMIM genes) are enriched in cancer patients compared to controls. Utilizing data from PCAWG and the 1,000 Genomes Project, we identify 103 OMIM genes demonstrating significant enrichment of pathogenic variants in cancer samples (FDR 20%). Through an integrative approach considering three distinct properties, we classify these CPG-like OMIM genes into four clusters, indicating potential diverse mechanisms underlying tumor progression. Further, we explore the function of PAH (a key metabolic enzyme associated with Phenylketonuria), the gene exhibiting the highest prevalence of pathogenic variants in a pan-cancer (1.8%) compared to controls (0.6%).

CONCLUSIONS

Our findings suggest a possible cancer progression mechanism through metabolic profile alterations. Overall, our data indicates that pathogenic OMIM gene variants contribute to cancer progression and introduces new CPG classifications potentially underpinning diverse tumorigenesis mechanisms.

ReConPlot: an R package for the visualization and interpretation of genomic rearrangements

MOTIVATION

Whole-genome sequencing studies of human tumours have revealed that complex forms of structural variation, collectively known as complex genome rearrangements (CGRs), are pervasive across diverse cancer types. Detection, classification, and mechanistic interpretation of CGRs requires the visualization of complex patterns of somatic copy number aberrations (SCNAs) and structural variants (SVs). However, there is a lack of tools specifically designed to facilitate the visualization and study of CGRs.

RESULTS

We present ReConPlot (REarrangement and COpy Number PLOT), an R package that provides functionalities for the joint visualization of SCNAs and SVs across one or multiple chromosomes. ReConPlot is based on the popular ggplot2 package, thus allowing customization of plots and the generation of publication-quality figures with minimal effort. Overall, ReConPlot facilitates the exploration, interpretation, and reporting of CGR patterns.

AVAILABILITY AND IMPLEMENTATION

The R package ReConPlot is available at https://github.com/cortes-ciriano-lab/ReConPlot. Detailed documentation and a tutorial with examples are provided with the package.

InDEP: an interpretable machine learning approach to predict cancer driver genes from multi-omics data

Cancer driver genes are critical in driving tumor cell growth, and precisely identifying these genes is crucial in advancing our understanding of cancer pathogenesis and developing targeted cancer drugs. Despite the current methods for discovering cancer driver genes that mainly rely on integrating multi-omics data, many existing models are overly complex, and it is difficult to interpret the results accurately. This study aims to address this issue by introducing InDEP, an interpretable machine learning framework based on cascade forests. InDEP is designed with easy-to-interpret features, cascade forests based on decision trees and a KernelSHAP module that enables fine-grained post-hoc interpretation. Integrating multi-omics data, InDEP can identify essential features of classified driver genes at both the gene and cancer-type levels. The framework accurately identifies driver genes, discovers new patterns that make genes as driver genes and refines the cancer driver gene catalog. In comparison with state-of-the-art methods, InDEP proved to be more accurate on the test set and identified reliable candidate driver genes. Mutational features were the primary drivers for InDEP's identifying driver genes, with other omics features also contributing. At the gene level, the framework concluded that substitution-type mutations were the main reason most genes were identified as driver genes. InDEP's ability to identify reliable candidate driver genes opens up new avenues for precision oncology and discovering new biomedical knowledge. This framework can help advance cancer research by providing an interpretable method for identifying cancer driver genes and their contribution to cancer pathogenesis, facilitating the development of targeted cancer drugs.

PanDrugs2: prioritizing cancer therapies using integrated individual multi-omics data

Genomics studies routinely confront researchers with long lists of tumor alterations detected in patients. Such lists are difficult to interpret since only a minority of the alterations are relevant biomarkers for diagnosis and for designing therapeutic strategies. PanDrugs is a methodology that facilitates the interpretation of tumor molecular alterations and guides the selection of personalized treatments. To do so, PanDrugs scores gene actionability and drug feasibility to provide a prioritized evidence-based list of drugs. Here, we introduce PanDrugs2, a major upgrade of PanDrugs that, in addition to somatic variant analysis, supports a new integrated multi-omics analysis which simultaneously combines somatic and germline variants, copy number variation and gene expression data. Moreover, PanDrugs2 now considers cancer genetic dependencies to extend tumor vulnerabilities providing therapeutic options for untargetable genes. Importantly, a novel intuitive report to support clinical decision-making is generated. PanDrugs database has been updated, integrating 23 primary sources that support >74K drug-gene associations obtained from 4642 genes and 14 659 unique compounds. The database has also been reimplemented to allow semi-automatic updates to facilitate maintenance and release of future versions. PanDrugs2 does not require login and is freely available at https://www.pandrugs.org/.

CUB Domain-Containing Protein 1 (CDCP1) is a rational target for the development of imaging tracers and antibody-drug conjugates for cancer detection and therapy

Rationale: An antibody-drug conjugate (ADC) is a targeted therapy consisting of a cytotoxic payload that is linked to an antibody which targets a protein enriched on malignant cells. Multiple ADCs are currently used clinically as anti-cancer agents significantly improving patient survival. Herein, we evaluated the rationale of targeting the cell surface oncoreceptor CUB domain-containing protein 1 (CDCP1) using ADCs and assessed the efficacy of CDCP1-directed ADCs against a range of malignant tumors.

Methods: CDCP1 mRNA expression was evaluated using large transcriptomic datasets of normal/tumor samples for 23 types of cancer and 15 other normal organs, and CDCP1 protein expression was examined in 34 normal tissues, >300 samples from six types of cancer, and in 49 cancer cell lines. A recombinant human/mouse chimeric anti-CDCP1 antibody (ch10D7) was labelled with ⁸⁹Zirconium or monomethyl auristatin E (MMAE) and tested in multiple pre-clinical cancer models including 36 cancer cell lines and three mouse xenograft models.

Results: Analysis of CDCP1 expression indicates elevated CDCP1 expression in the majority of the cancers and restricted expression in normal human tissues. Antibody ch10D7 demonstrates a high affinity and specificity for CDCP1 inducing cell signalling via Src accompanied by rapid internalization of ch10D7/CDCP1 complexes in cancer cells.⁸⁹Zirconium-labelled ch10D7 accumulates in CDCP1 expressing cells enabling detection of pancreatic cancer xenografts in mice by PET imaging. Cytotoxicity of MMAE-labelled ch10D7 against kidney, colorectal, lung, ovarian, pancreatic and prostate cancer cells in vitro, correlates with the level of CDCP1 on the plasma membrane. ch10D7-MMAE displays robust anti-tumor effects against mouse xenograft models of pancreatic, colorectal and ovarian cancer.

Conclusion: CDCP1 directed imaging agents will be useful for selecting cancer patients for personalized treatment with cytotoxin-loaded CDCP1 targeting agents including antibody-drug conjugates.

A TCR mimic CAR T cell specific for NDC80 is broadly reactive with solid tumors and hematologic malignancies

Target identification for chimeric antigen receptor (CAR) T-cell therapies remains challenging due to the limited repertoire of tumor-specific surface proteins. Intracellular proteins presented in the context of cell surface HLA provide a wide pool of potential antigens targetable through T-cell receptor mimic antibodies. Mass spectrometry (MS) of HLA ligands from 8 hematologic and nonhematologic cancer cell lines identified a shared, non-immunogenic, HLA-A*02-restricted ligand (ALNEQIARL) derived from the kinetochore-associated NDC80 gene. CAR T cells directed against the ALNEQIARL:HLA-A*02 complex exhibited high sensitivity and specificity for recognition and killing of multiple cancer types, especially those of hematologic origin, and were efficacious in mouse models against a human leukemia and a solid tumor. In contrast, no toxicities toward resting or activated healthy leukocytes as well as hematopoietic stem cells were observed. This shows how MS can inform the design of broadly reactive therapeutic T-cell receptor mimic CAR T-cell therapies that can target multiple cancer types currently not druggable by small molecules, conventional CAR T cells, T cells, or antibodies.

The m6A-Related Long Noncoding RNA Signature Predicts Prognosis and Indicates Tumor Immune Infiltration in Ovarian Cancer

Background: OV is the most lethal gynecological malignancy. M6A and lncRNAs have a great impact on OV development and patient immunotherapy response. In this paper, we decided to establish a reliable signature of mRLs. Method: The lncRNAs associated with m6A in OV were analyzed and obtained by co-expression analysis of the TCGA-OV database. Univariate, LASSO and multivariate Cox regression analyses were employed to establish the model of mRLs. K-M analysis, PCA, GSEA and nomogram based on the TCGA-OV and GEO database were conducted to prove the predictive value and independence of the model. The underlying relationship between the model and TME and cancer stemness properties were further investigated through immune feature comparison, consensus clustering analysis and pan-cancer analysis. Results: A prognostic signature comprising four mRLs, WAC-AS1, LINC00997, DNM3OS and FOXN3-AS1, was constructed and verified for OV according to the TCGA and GEO database. The expressions of the four mRLs were confirmed by qRT-PCR in clinical samples. Applying this signature, one can identify patients more effectively. The samples were divided into two clusters, and the clusters had different overall survival rates, clinical features and tumor microenvironments. Finally, pan-cancer analysis further demonstrated that the four mRLs were significantly related to immune infiltration, TME and cancer stemness properties in various cancer types. Conclusions: This study provided an accurate prognostic signature for patients with OV and elucidated the potential mechanism of the mRLs in immune modulation and treatment response, giving new insights into identifying new therapeutic targets.

CCAS: One-stop and comprehensive annotation system for individual cancer genome at multi-omics level

Due to the explosion of cancer genome data and the urgent needs for cancer treatment, it is becoming increasingly important and necessary to easily and timely analyze and annotate cancer genomes. However, tumor heterogeneity is recognized as a serious barrier to annotate cancer genomes at the individual patient level. In addition, the interpretation and analysis of cancer multi-omics data rely heavily on existing database resources that are often located in different data centers or research institutions, which poses a huge challenge for data parsing. Here we present CCAS (Cancer genome Consensus Annotation System, https://ngdc.cncb.ac.cn/ccas/#/home), a one-stop and comprehensive annotation system for the individual patient at multi-omics level. CCAS integrates 20 widely recognized resources in the field to support data annotation of 10 categories of cancers covering 395 subtypes. Data from each resource are manually curated and standardized by using ontology frameworks. CCAS accepts data on single nucleotide variant/insertion or deletion, expression, copy number variation, and methylation level as input files to build a consensus annotation. Outputs are arranged in the forms of tables or figures and can be searched, sorted, and downloaded. Expanded panels with additional information are used for conciseness, and most figures are interactive to show additional information. Moreover, CCAS offers multidimensional annotation information, including mutation signature pattern, gene set enrichment analysis, pathways and clinical trial related information. These are helpful for intuitively understanding the molecular mechanisms of tumors and discovering key functional genes.

Visual Art Design of Digital Works Guided by Big Data

With the rapid development of digital technology, the development speed of digital media is also relatively fast. Digital media technology has a great impact on people’s lifestyles and aesthetic concepts, and it also has a greater impact on visual art, creative thinking communication methods, and expression methods. In this study, the quality enhancement of digital images has been intensively studied based on the guidance of big data of eye-movement gaze points. A large amount of visual data are collected from public social resources, and the optimization research of image sensory quality is carried out in-depth using the acquired big data. Next, the region of interest (ROI) is obtained by combining the data with a two-dimensional Gaussian distribution model-fitting method, and the obtained data clustered and improved based on the K-means clustering algorithm to obtain ROI fixation points. Finally, discontinuities in the choice of sharpness in graphics and video playback are pointed out, and the final fixation data analysis is utilized. Results show that targeted optimization is very effective in improving the quality of digital images and saving space, enabling users to enjoy higher-quality visual digital images. The proposed method can be used to improve the dynamic resolution of images and videos.

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.

Identification of EMT-Related Genes and Prognostic Signature With Significant Implications on Biological Properties and Oncology Treatment of Lower Grade Gliomas

The epithelial-mesenchymal transition (EMT) is an important process that drives progression, metastasis, and oncology treatment resistance in cancers. Also, the adjacent non-tumor tissue may affect the biological properties of cancers and have potential prognostic implications. Our study aimed to identify EMT-related genes in LGG samples, explore their impact on the biological properties of lower grade gliomas (LGG) through the multi-omics analysis, and reveal the potential mechanism by which adjacent non-tumor tissue participated in the malignant progression of LGG. Based on the 121 differentially expressed EMT-related genes between normal samples from the GTEx database and LGG samples in the TCGA cohort, we identified two subtypes and constructed EMTsig. Because of the genetic, epigenetic, and transcriptomic heterogeneity, malignant features including clinical traits, molecular traits, metabolism, anti-tumor immunity, and stemness features were different between samples with C1 and C2. In addition, EMTsig could also quantify the EMT levels, variation in prognosis, and oncology treatment sensitivity of LGG patients. Therefore, EMTsig could assist us in developing objective diagnostic tools and in optimizing therapeutic strategies for LGG patients. Notably, with the GSVA, we found that adjacent non-tumor tissue might participate in the progression, metastasis, and formation of the tumor microenvironment in LGG. Therefore, the potential prognostic implications of adjacent non-tumor tissue should be considered when performing clinical interventions for LGG patients. Overall, our study investigated and validated the effects of EMT-related genes on the biological properties from multiple perspectives, and provided new insights into the function of adjacent non-tumor tissue in the malignant progression of LGG.

A novel qualitative signature based on lncRNA pairs for prognosis prediction in hepatocellular carcinoma

Background

Prognostic assessment is imperative for clinical management of patients with hepatocellular carcinoma (HCC). Most reported prognostic signatures are based on risk scores summarized from quantitative expression level of candidate genes, which are vulnerable against experimental batch effects and impractical for clinical application. We aimed to develop a robust qualitative signature to assess individual survival risk for HCC patients.

Methods

Long non-coding RNA (lncRNA) pairs correlated with overall survival (OS) were identified and an optimal combination of lncRNA pairs based on the majority voting rule was selected as a classification signature to predict the overall survival risk in the cancer genome atlas (TCGA). Then, the signature was further validated in two external datasets. Besides, biomolecular characteristics, immune infiltration status, and chemotherapeutics efficacy of different risk groups were further compared. Finally, we performed key lncRNA screening and validated it in vitro.

Results

A signature consisting of 50 lncRNA pairs (50-LPS) was identified in TCGA and successfully validated in external datasets. Patients in the high-risk group, when at least 25 of the 50-LPS voted for high risk, had significantly worse OS than the low-risk group. Multivariate Cox, receiver operating characteristic (ROC) curve and decision curve analyses (DCA) demonstrated that the 50-LPS was an independent prognostic factor and more powerful than other available clinical factors in OS prediction. Comparison analyses indicated that different risk groups had distinct biomolecular characteristics, immune infiltration status, and chemotherapeutics efficacy. TDRKH-AS1 was confirmed as a key lncRNA and associated with cell growth of HCC.

Conclusions

The 50-LPS could not only predict the prognosis of HCC patients robustly and individually, but also provide theoretical basis for therapy. Besides, TDRKH-AS1 was identified as a key lncRNA in the proliferation of HCC. The 50-LPS might guide personalized therapy for HCC patients in clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02507-z.

Global mapping of cancers: The Cancer Genome Atlas and beyond

Cancer genomes have been explored from the early 2000s through massive exome sequencing efforts, leading to the publication of The Cancer Genome Atlas in 2013. Sequencing techniques have been developed alongside this project and have allowed scientists to bypass the limitation of costs for whole-genome sequencing (WGS) of single specimens by developing more accurate and extensive cancer sequencing projects, such as deep sequencing of whole genomes and transcriptomic analysis. The Pan-Cancer Analysis of Whole Genomes recently published WGS data from more than 2600 human cancers together with almost 1200 related transcriptomes. The application of WGS on a large database allowed, for the first time in history, a global analysis of features such as molecular signatures, large structural variations and noncoding regions of the genome, as well as the evaluation of RNA alterations in the absence of underlying DNA mutations. The vast amount of data generated still needs to be thoroughly deciphered, and the advent of machine-learning approaches will be the next step towards the generation of personalized approaches for cancer medicine. The present manuscript wants to give a broad perspective on some of the biological evidence derived from the largest sequencing attempts on human cancers so far, discussing advantages and limitations of this approach and its power in the era of machine learning.

Decoding human cancer with whole genome sequencing: a review of PCAWG Project studies published in February 2020

Cancer is underlined by genetic changes. In an unprecedented international effort, the Pan-Cancer Analysis of Whole Genomes (PCAWG) of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) sequenced the tumors of over two thousand five hundred patients across 38 different cancer types, as well as the corresponding healthy tissue, with the aim of identifying genome-wide mutations exclusively found in cancer and uncovering new genetic changes that drive tumor formation. What set this project apart from earlier efforts is the use of whole genome sequencing (WGS) that enabled to explore alterations beyond the coding DNA, into cancer's non-coding genome. WGS of the entire cohort allowed to tease apart driving mutations that initiate and support carcinogenesis from passenger mutations that do not play an overt role in the disease. At least one causative mutation was found in 95% of all cancers, with many tumors showing an average of 5 driver mutations. The PCAWG Project also assessed the transcriptional output altered in cancer and rebuilt the evolutionary history of each tumor showing that initial driver mutations can occur years if not decades prior to a diagnosis. Here, I provide a concise review of the Pan-Cancer Project papers published on February 2020, along with key computational tools and the digital framework generated as part of the project. This represents an historic effort by hundreds of international collaborators, which provides a comprehensive understanding of cancer genetics, with publicly available data and resources representing a treasure trove of information to advance cancer research for years to come.

Comprehensive tumor molecular profile analysis in clinical practice

BACKGROUND

Tumor molecular profile analysis by Next Generation Sequencing technology is currently widely applied in clinical practice and has enabled the detection of predictive biomarkers of response to targeted treatment. In parallel with targeted therapies, immunotherapies are also evolving, revolutionizing cancer therapy, with Programmed Death-ligand 1 (PD-L1), Microsatellite instability (MSI), and Tumor Mutational Burden (TMB) analysis being the biomarkers employed most commonly.

METHODS

In the present study, tumor molecular profile analysis was performed using a 161 gene NGS panel, containing the majority of clinically significant genes for cancer treatment selection. A variety of tumor types have been analyzed, including aggressive and hard to treat cancers such as pancreatic cancer. Besides, the clinical utility of immunotherapy biomarkers (TMB, MSI, PD-L1), was also studied.

RESULTS

Molecular profile analysis was conducted in 610 cancer patients, while in 393 of them a at least one biomarker for immunotherapy response was requested. An actionable alteration was detected in 77.87% of the patients. 54.75% of them received information related to on-label or off-label treatment (Tiers 1A.1, 1A.2, 2B, and 2C.1) and 21.31% received a variant that could be used for clinical trial inclusion. The addition to immunotherapy biomarker to targeted biomarkers' analysis in 191 cases increased the number of patients with an on-label treatment recommendation by 22.92%, while an option for on-label or off-label treatment was provided in 71.35% of the cases.

CONCLUSIONS

Tumor molecular profile analysis using NGS is a first-tier method for a variety of tumor types and provides important information for decision making in the treatment of cancer patients. Importantly, simultaneous analysis for targeted therapy and immunotherapy biomarkers could lead to better tumor characterization and offer actionable information in the majority of patients. Furthermore, our data suggest that one in two patients may be eligible for on-label ICI treatment based on biomarker analysis. However, appropriate interpretation of results from such analysis is essential for implementation in clinical practice and accurate refinement of treatment strategy.

A novel prognostic mRNA/miRNA signature for esophageal cancer and its immune landscape in cancer progression

Mounting evidence shows that MicroRNAs (miRNAs) and their target genes are aberrantly expressed in many cancers and are linked to tumor occurrence and progression, especially in esophageal cancer (EC). This study purposed to explore new biomarkers related to the prognosis of EC and to uncover their potential mechanisms in promoting tumor progression. We identified 162 differentially expressed miRNAs and 4555 differentially expressed mRNAs in EC. Then, a risk model involving three miRNAs (miR‐4521, miR‐3682‐3p, and miR‐1269a) was designed to predict prognosis in EC patients. Furthermore, 7 target genes (Rho GTPase‐activating protein 24, Chromobox 3, Contactin‐associated protein 2, ELOVL fatty acid elongase 5, LIF receptor subunit alpha, transmembrane protein 44, and transmembrane protein 67) were selected for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses to reveal their potential mechanisms in promoting EC progression. After a series of correlation analyses, miRNA target genes were found to be significantly positively or negatively associated with immune infiltration, tumor microenvironment, cancer stemness properties, and tumor mutation burden at different degrees in EC. To further elucidate the role of miRNA signature in cancer progression, we performed a pan‐cancer analysis to determine whether these genes exert similar effects on other tumors. Interestingly, the miRNA target genes altered expression on tumor immunity; however, pan‐cancer progression was the same as that of EC. Thus, we explored the immune landscape of the miRNA signature and its target genes in EC and pan‐cancer. These findings demonstrated the versatility and effectiveness of our model in various cancers and provided a new direction for cancer management.

Single-cell profiling reveals the trajectories of natural killer cell differentiation in bone marrow and a stress signature induced by acute myeloid leukemia

Natural killer (NK) cells are innate cytotoxic lymphoid cells (ILCs) involved in the killing of infected and tumor cells. Among human and mouse NK cells from the spleen and blood, we previously identified by single-cell RNA sequencing (scRNAseq) two similar major subsets, NK1 and NK2. Using the same technology, we report here the identification, by single-cell RNA sequencing (scRNAseq), of three NK cell subpopulations in human bone marrow. Pseudotime analysis identified a subset of resident CD56^bright NK cells, NK0 cells, as the precursor of both circulating CD56^dim NK1-like NK cells and CD56^bright NK2-like NK cells in human bone marrow and spleen under physiological conditions. Transcriptomic profiles of bone marrow NK cells from patients with acute myeloid leukemia (AML) exhibited stress-induced repression of NK cell effector functions, highlighting the profound impact of this disease on NK cell heterogeneity. Bone marrow NK cells from AML patients exhibited reduced levels of CD160, but the CD160^high group had a significantly higher survival rate.

Commensal microbes and p53 in cancer progression

Aetiogenesis of cancer has not been fully determined. Recent advances have clearly defined a role for microenvironmental factors in cancer progression and initiation; in this context, microbiome has recently emerged with a number of reported correlative and causative links implicating alterations of commensal microbes in tumorigenesis. Bacteria appear to have the potential to directly alter physiological pathways of host cells and in specific circumstances, such as the mutation of the tumour suppressive factor p53, they can also directly switch the function of a gene from oncosuppressive to oncogenic. In this minireview, we report a number of examples on how commensal microbes alter the host cell biology, affecting the oncogenic process. We then discuss more in detail how interaction with the gut microbiome can affect the function of p53 mutant in the intestinal tumorigenesis.