Control-FREEC viewer: a tool for the visualization and exploration of copy number variation data.

BACKGROUND: Copy number alterations (CNAs) are genetic changes commonly found in cancer that involve different regions of the genome and impact cancer progression by affecting gene expression and genomic stability. Computational techniques can analyze copy number data obtained from high-throughput sequencing platforms, and various tools visualize and analyze CNAs in cancer genomes, providing insights into genetic mechanisms driving cancer development and progression. However, tools for visualizing copy number data in cancer research have some limitations. In fact, they can be complex to use and require expertise in bioinformatics or computational biology. While copy number data analysis and visualization provide insights into cancer biology, interpreting results can be challenging, and there may be multiple explanations for observed patterns of copy number alterations. RESULTS: We created Control-FREEC Viewer, a tool that facilitates effective visualization and exploration of copy number data. With Control-FREEC Viewer, experimental data can be easily loaded by the user. After choosing the reference genome, copy number data are displayed in whole genome or single chromosome view. Gain or loss on a specific gene can be found and visualized on each chromosome. Analysis parameters for subsequent sessions can be stored and images can be exported in raster and vector formats. CONCLUSIONS: Control-FREEC Viewer enables users to import and visualize data analyzed by the Control-FREEC tool, as well as by other tools sharing a similar tabular output, providing a comprehensive and intuitive graphical user interface for data visualization.

Differential Expression of NOTCH-1 and Its Molecular Targets in Response to Metronomic Followed by Conventional Therapy in a Patient with Advanced Triple-Negative Breast Cancer.

A group of 27 patients diagnosed with metastatic triple-negative breast cancer (mTNBC) was randomly distributed into two groups and underwent different lines of metronomic treatment (mCHT). The former group (N 14) received first-line mCHT and showed a higher overall survival rate than the second group (N 13), which underwent second-line mCHT. Analysis of one patient still alive from the first group, diagnosed with mTNBC in 2019, showed a complete metabolic response (CMR) after a composite approach implicating first-line mCHT followed by second-line epirubicin and third-line nab-paclitaxel, and was chosen for subsequent molecular characterization. We found altered expression in the cancer stemness-associated gene NOTCH-1 and its corresponding protein. Additionally, we found changes in the expression of oncogenes, such as MYC and AKT, along with their respective proteins. Overall, our data suggest that a first-line treatment with mCHT followed by MTD might be effective by negatively regulating stemness traits usually associated with the emergence of drug resistance.

First-hit SETBP1 mutations cause a myeloproliferative disorder with bone marrow fibrosis.

ABSTRACT: SETBP1 mutations are found in various clonal myeloid disorders. However, it is unclear whether they can initiate leukemia, because SETBP1 mutations typically appear as later events during oncogenesis. To answer this question, we generated a mouse model expressing mutated SETBP1 in hematopoietic tissue: this model showed profound alterations in the differentiation program of hematopoietic progenitors and developed a myeloid neoplasm with megakaryocytic dysplasia, splenomegaly, and bone marrow fibrosis, prompting us to investigate SETBP1 mutations in a cohort of 36 triple-negative primary myelofibrosis (TN-PMF) cases. We identified 2 distinct subgroups, one carrying SETBP1 mutations and the other completely devoid of somatic variants. Clinically, a striking difference in disease aggressiveness was noted, with patients with SETBP1 mutation showing a much worse clinical course. In contrast to myelodysplastic/myeloproliferative neoplasms, in which SETBP1 mutations are mostly found as a late clonal event, single-cell clonal hierarchy reconstruction in 3 patients with TN-PMF from our cohort revealed SETBP1 to be a very early event, suggesting that the phenotype of the different SETBP1+ disorders may be shaped by the opposite hierarchy of the same clonal SETBP1 variants.

Evaluating the performance of large language models in haematopoietic stem cell transplantation decision-making.

In a first-of-its-kind study, we assessed the capabilities of large language models (LLMs) in making complex decisions in haematopoietic stem cell transplantation. The evaluation was conducted not only for Generative Pre-trained Transformer 4 (GPT-4) but also conducted on other artificial intelligence models: PaLm 2 and Llama-2. Using detailed haematological histories that include both clinical, molecular and donor data, we conducted a triple-blind survey to compare LLMs to haematology residents. We found that residents significantly outperformed LLMs (p = 0.02), particularly in transplant eligibility assessment (p = 0.01). Our triple-blind methodology aimed to mitigate potential biases in evaluating LLMs and revealed both their promise and limitations in deciphering complex haematological clinical scenarios.

Contribution of pks+ E. coli mutations to colorectal carcinogenesis.

The dominant mutational signature in colorectal cancer genomes is C > T deamination (COSMIC Signature 1) and, in a small subgroup, mismatch repair signature (COSMIC signatures 6 and 44). Mutations in common colorectal cancer driver genes are often not consistent with those signatures. Here we perform whole-genome sequencing of normal colon crypts from cancer patients, matched to a previous multi-omic tumour dataset. We analyse normal crypts that were distant vs adjacent to the cancer. In contrast to healthy individuals, normal crypts of colon cancer patients have a high incidence of pks + (polyketide synthases) E.coli (Escherichia coli) mutational and indel signatures, and this is confirmed by metagenomics. These signatures are compatible with many clonal driver mutations detected in the corresponding cancer samples, including in chromatin modifier genes, supporting their role in early tumourigenesis. These results provide evidence that pks + E.coli is a potential driver of carcinogenesis in the human gut.

Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients.

Recurring sequences of genomic alterations occurring across patients can highlight repeated evolutionary processes with significant implications for predicting cancer progression. Leveraging the ever-increasing availability of cancer omics data, here we unveil cancer's evolutionary signatures tied to distinct disease outcomes, representing "favored trajectories" of acquisition of driver mutations detected in patients with similar prognosis. We present a framework named ASCETIC (Agony-baSed Cancer EvoluTion InferenCe) to extract such signatures from sequencing experiments generated by different technologies such as bulk and single-cell sequencing data. We apply ASCETIC to (i) single-cell data from 146 myeloid malignancy patients and bulk sequencing from 366 acute myeloid leukemia patients, (ii) multi-region sequencing from 100 early-stage lung cancer patients, (iii) exome/genome data from 10,000+ Pan-Cancer Atlas samples, and (iv) targeted sequencing from 25,000+ MSK-MET metastatic patients, revealing subtype-specific single-nucleotide variant signatures associated with distinct prognostic clusters. Validations on several datasets underscore the robustness and generalizability of the extracted signatures.

Effects of blocking CD24 and CD47 'don't eat me' signals in combination with rituximab in mantle-cell lymphoma and chronic lymphocytic leukaemia.

Mantle-cell lymphoma (MCL) is a B-cell non-Hodgkin Lymphoma (NHL) with a poor prognosis, at high risk of relapse after conventional treatment. MCL-associated tumour microenvironment (TME) is characterized by M2-like tumour-associated macrophages (TAMs), able to interact with cancer cells, providing tumour survival and resistance to immuno-chemotherapy. Likewise, monocyte-derived nurse-like cells (NLCs) present M2-like profile and provide proliferation signals to chronic lymphocytic leukaemia (CLL), a B-cell malignancy sharing with MCL some biological and phenotypic features. Antibodies against TAMs targeted CD47, a 'don't eat me' signal (DEMs) able to quench phagocytosis by TAMs within TME, with clinical effectiveness when combined with Rituximab in pretreated NHL. Recently, CD24 was found as valid DEMs in solid cancer. Since CD24 is expressed during B-cell differentiation, we investigated and identified consistent CD24 in MCL, CLL and primary human samples. Phagocytosis increased when M2-like macrophages were co-cultured with cancer cells, particularly in the case of paired DEMs blockade (i.e. anti-CD24 + anti-CD47) combined with Rituximab. Similarly, unstimulated CLL patients-derived NLCs provided increased phagocytosis when DEMs blockade occurred. Since high levels of CD24 were associated with worse survival in both MCL and CLL, anti-CD24-induced phagocytosis could be considered for future clinical use, particularly in association with other agents such as Rituximab.

Characterization of cancer subtypes associated with clinical outcomes by multi-omics integrative clustering.

Cancer patients show heterogeneous phenotypes and very different outcomes and responses even to common treatments, such as standard chemotherapy. This state-of-affairs has motivated the need for the comprehensive characterization of cancer phenotypes and fueled the generation of large omics datasets, comprising multiple omics data reported for the same patients, which might now allow us to start deciphering cancer heterogeneity and implement personalized therapeutic strategies. In this work, we performed the analysis of four cancer types obtained from the latest efforts by The Cancer Genome Atlas, for which seven distinct omics data were available for each patient, in addition to curated clinical outcomes. We performed a uniform pipeline for raw data preprocessing and adopted the Cancer Integration via MultIkernel LeaRning (CIMLR) integrative clustering method to extract cancer subtypes. We then systematically review the discovered clusters for the considered cancer types, highlighting novel associations between the different omics and prognosis.

LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution.

BACKGROUND: Longitudinal single-cell sequencing experiments of patient-derived models are increasingly employed to investigate cancer evolution. In this context, robust computational methods are needed to properly exploit the mutational profiles of single cells generated via variant calling, in order to reconstruct the evolutionary history of a tumor and characterize the impact of therapeutic strategies, such as the administration of drugs. To this end, we have recently developed the LACE framework for the Longitudinal Analysis of Cancer Evolution. RESULTS: The LACE 2.0 release aimed at inferring longitudinal clonal trees enhances the original framework with new key functionalities: an improved data management for preprocessing of standard variant calling data, a reworked inference engine, and direct connection to public databases. CONCLUSIONS: All of this is accessible through a new and interactive Shiny R graphical interface offering the possibility to apply filters helpful in discriminating relevant or potential driver mutations, set up inferential parameters, and visualize the results. The software is available at: github.com/BIMIB-DISCo/LACE.

Targeting the immune microenvironment in Waldenström macroglobulinemia via halting the CD40/CD40-ligand axis.

Recent investigations have improved our understanding of the molecular aberrations supporting Waldenström macroglobulinemia (WM) biology; however, whether the immune microenvironment contributes to WM pathogenesis remains unanswered. First, we showed how a transgenic murine model of human-like lymphoplasmacytic lymphoma/WM exhibits an increased number of regulatory T cells (Tregs) relative to control mice. These findings were translated into the WM clinical setting, in which the transcriptomic profiling of Tregs derived from patients with WM unveiled a peculiar WM-devoted messenger RNA signature, with significant enrichment for genes related to nuclear factor κB-mediated tumor necrosis factor α signaling, MAPK, and PI3K/AKT, which was paralleled by a different Treg functional phenotype. We demonstrated significantly higher Treg induction, expansion, and proliferation triggered by WM cells, compared with their normal cellular counterpart; with a more profound effect within the context of CXCR4C1013G-mutated WM cells. By investigating the B-cell-to-T-cell cross talk at single-cell level, we identified the CD40/CD40-ligand as a potentially relevant axis that supports WM cell-Tregs interaction. Our findings demonstrate the existence of a Treg-mediated immunosuppressive phenotype in WM, which can be therapeutically reversed by blocking the CD40L/CD40 axis to inhibit WM cell growth.

Pan-cancer landscape of AID-related mutations, composite mutations, and their potential role in the ICI response.

Activation-induced cytidine deaminase, AICDA or AID, is a driver of somatic hypermutation and class-switch recombination in immunoglobulins. In addition, this deaminase belonging to the APOBEC family may have off-target effects genome-wide, but its effects at pan-cancer level are not well elucidated. Here, we used different pan-cancer datasets, totaling more than 50,000 samples analyzed by whole-genome, whole-exome, or targeted sequencing. AID mutations are present at pan-cancer level with higher frequency in hematological cancers and higher presence at transcriptionally active TAD domains. AID synergizes initial hotspot mutations by a second composite mutation. AID mutational load was found to be independently associated with a favorable outcome in immune-checkpoint inhibitors (ICI) treated patients across cancers after analyzing 2000 samples. Finally, we found that AID-related neoepitopes, resulting from mutations at more frequent hotspots if compared to other mutational signatures, enhance CXCL13/CCR5 expression, immunogenicity, and T-cell exhaustion, which may increase ICI sensitivity.

The co-evolution of the genome and epigenome in colorectal cancer.

Colorectal malignancies are a leading cause of cancer-related death1 and have undergone extensive genomic study2,3. However, DNA mutations alone do not fully explain malignant transformation4-7. Here we investigate the co-evolution of the genome and epigenome of colorectal tumours at single-clone resolution using spatial multi-omic profiling of individual glands. We collected 1,370 samples from 30 primary cancers and 8 concomitant adenomas and generated 1,207 chromatin accessibility profiles, 527 whole genomes and 297 whole transcriptomes. We found positive selection for DNA mutations in chromatin modifier genes and recurrent somatic chromatin accessibility alterations, including in regulatory regions of cancer driver genes that were otherwise devoid of genetic mutations. Genome-wide alterations in accessibility for transcription factor binding involved CTCF, downregulation of interferon and increased accessibility for SOX and HOX transcription factor families, suggesting the involvement of developmental genes during tumourigenesis. Somatic chromatin accessibility alterations were heritable and distinguished adenomas from cancers. Mutational signature analysis showed that the epigenome in turn influences the accumulation of DNA mutations. This study provides a map of genetic and epigenetic tumour heterogeneity, with fundamental implications for understanding colorectal cancer biology.

Phenotypic plasticity and genetic control in colorectal cancer evolution.

Genetic and epigenetic variation, together with transcriptional plasticity, contribute to intratumour heterogeneity1. The interplay of these biological processes and their respective contributions to tumour evolution remain unknown. Here we show that intratumour genetic ancestry only infrequently affects gene expression traits and subclonal evolution in colorectal cancer (CRC). Using spatially resolved paired whole-genome and transcriptome sequencing, we find that the majority of intratumour variation in gene expression is not strongly heritable but rather 'plastic'. Somatic expression quantitative trait loci analysis identified a number of putative genetic controls of expression by cis-acting coding and non-coding mutations, the majority of which were clonal within a tumour, alongside frequent structural alterations. Consistently, computational inference on the spatial patterning of tumour phylogenies finds that a considerable proportion of CRCs did not show evidence of subclonal selection, with only a subset of putative genetic drivers associated with subclone expansions. Spatial intermixing of clones is common, with some tumours growing exponentially and others only at the periphery. Together, our data suggest that most genetic intratumour variation in CRC has no major phenotypic consequence and that transcriptional plasticity is, instead, widespread within a tumour.

SparseSignatures: An R package using LASSO-regularized non-negative matrix factorization to identify mutational signatures from human tumor samples.

We outline the features of the R package SparseSignatures and its application to determine the signatures contributing to mutation profiles of tumor samples. We describe installation details and illustrate a step-by-step approach to (1) prepare the data for signature analysis, (2) determine the optimal parameters, and (3) employ them to determine the signatures and related exposure levels in the point mutation dataset. For complete details on the use and execution of this protocol, please refer to Lal et al. (2021).

PMCE: efficient inference of expressive models of cancer evolution with high prognostic power.

MOTIVATION: Driver (epi)genomic alterations underlie the positive selection of cancer subpopulations, which promotes drug resistance and relapse. Even though substantial heterogeneity is witnessed in most cancer types, mutation accumulation patterns can be regularly found and can be exploited to reconstruct predictive models of cancer evolution. Yet, available methods can not infer logical formulas connecting events to represent alternative evolutionary routes or convergent evolution. RESULTS: We introduce PMCE, an expressive framework that leverages mutational profiles from cross-sectional sequencing data to infer probabilistic graphical models of cancer evolution including arbitrary logical formulas, and which outperforms the state-of-the-art in terms of accuracy and robustness to noise, on simulations. The application of PMCE to 7866 samples from the TCGA database allows us to identify a highly significant correlation between the predicted evolutionary paths and the overall survival in 7 tumor types, proving that our approach can effectively stratify cancer patients in reliable risk groups. AVAILABILITY AND IMPLEMENTATION: PMCE is freely available at https://github.com/BIMIB-DISCo/PMCE, in addition to the code to replicate all the analyses presented in the manuscript. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Characterization of SARS-CoV-2 Mutational Signatures from 1.5+ Million Raw Sequencing Samples.

We present a large-scale analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) substitutions, considering 1,585,456 high-quality raw sequencing samples, aimed at investigating the existence and quantifying the effect of mutational processes causing mutations in SARS-CoV-2 genomes when interacting with the human host. As a result, we confirmed the presence of three well-differentiated mutational processes likely ruled by reactive oxygen species (ROS), apolipoprotein B editing complex (APOBEC), and adenosine deaminase acting on RNA (ADAR). We then evaluated the activity of these mutational processes in different continental groups, showing that some samples from Africa present a significantly higher number of substitutions, most likely due to higher APOBEC activity. We finally analyzed the activity of mutational processes across different SARS-CoV-2 variants, and we found a significantly lower number of mutations attributable to APOBEC activity in samples assigned to the Omicron variant.

De novo mutational signature discovery in tumor genomes using SparseSignatures.

Cancer is the result of mutagenic processes that can be inferred from tumor genomes by analyzing rate spectra of point mutations, or "mutational signatures". Here we present SparseSignatures, a novel framework to extract signatures from somatic point mutation data. Our approach incorporates a user-specified background signature, employs regularization to reduce noise in non-background signatures, uses cross-validation to identify the number of signatures, and is scalable to large datasets. We show that SparseSignatures outperforms current state-of-the-art methods on simulated data using a variety of standard metrics. We then apply SparseSignatures to whole genome sequences of pancreatic and breast tumors, discovering well-differentiated signatures that are linked to known mutagenic mechanisms and are strongly associated with patient clinical features.

Mutational signatures and heterogeneous host response revealed via large-scale characterization of SARS-CoV-2 genomic diversity.

To dissect the mechanisms underlying the inflation of variants in the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) genome, we present a large-scale analysis of intra-host genomic diversity, which reveals that most samples exhibit heterogeneous genomic architectures, due to the interplay between host-related mutational processes and transmission dynamics. The decomposition of minor variants profiles unveils three non-overlapping mutational signatures related to nucleotide substitutions and likely ruled by APOlipoprotein B Editing Complex (APOBEC), Reactive Oxygen Species (ROS), and Adenosine Deaminase Acting on RNA (ADAR), highlighting heterogeneous host responses to SARS-CoV-2 infections. A corrected-for-signatures dN/dS analysis demonstrates that such mutational processes are affected by purifying selection, with important exceptions. In fact, several mutations appear to transit toward clonality, defining new clonal genotypes that increase the overall genomic diversity. Furthermore, the phylogenomic analysis shows the presence of homoplasies and supports the hypothesis of transmission of minor variants. This study paves the way for the integrated analysis of intra-host genomic diversity and clinical outcomes of SARS-CoV-2 infections.

Integrated Genomic, Functional, and Prognostic Characterization of Atypical Chronic Myeloid Leukemia.

Atypical chronic myeloid leukemia (aCML) is a BCR-ABL1-negative clonal disorder, which belongs to the myelodysplastic/myeloproliferative group. This disease is characterized by recurrent somatic mutations in SETBP1, ASXL1 and ETNK1 genes, as well as high genetic heterogeneity, thus posing a great therapeutic challenge. To provide a comprehensive genomic characterization of aCML we applied a high-throughput sequencing strategy to 43 aCML samples, including both whole-exome and RNA-sequencing data. Our dataset identifies ASXL1, SETBP1, and ETNK1 as the most frequently mutated genes with a total of 43.2%, 29.7 and 16.2%, respectively. We characterized the clonal architecture of 7 aCML patients by means of colony assays and targeted resequencing. The results indicate that ETNK1 variants occur early in the clonal evolution history of aCML, while SETBP1 mutations often represent a late event. The presence of actionable mutations conferred both ex vivo and in vivo sensitivity to specific inhibitors with evidence of strong in vitro synergism in case of multiple targeting. In one patient, a clinical response was obtained. Stratification based on RNA-sequencing identified two different populations in terms of overall survival, and differential gene expression analysis identified 38 significantly overexpressed genes in the worse outcome group. Three genes correctly classified patients for overall survival.