Differentiating between cancer and normal tissue samples using multi-hit combinations of genetic mutations

Neoantigen identification: Technological advances and challenges

Neoantigens have emerged as promising targets for cutting-edge immunotherapies, such as cancer vaccines and adoptive cell therapy. These neoantigens are unique to tumors and arise exclusively from somatic mutations or non-genomic aberrations in tumor proteins. They encompass a wide range of alterations, including genomic mutations, post-transcriptomic variants, and viral oncoproteins. With the advancements in technology, the identification of immunogenic neoantigens has seen rapid progress, raising new opportunities for enhancing their clinical significance. Prediction of neoantigens necessitates the acquisition of high-quality samples and sequencing data, followed by mutation calling. Subsequently, the pipeline involves integrating various tools that can predict the expression, processing, binding, and recognition potential of neoantigens. However, the continuous improvement of computational tools is constrained by the availability of datasets which contain validated immunogenic neoantigens. This review article aims to provide a comprehensive summary of the current knowledge as well as limitations in neoantigen prediction and validation. Additionally, it delves into the origin and biological role of neoantigens, offering a deeper understanding of their significance in the field of cancer immunotherapy. This article thus seeks to contribute to the ongoing efforts to harness neoantigens as powerful weapons in the fight against cancer.

Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications

Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, we survey the current leading computational methods to derive intricate mutational patterns in the context of clinical relevance. We begin with mutation signatures, explaining first how mutation signatures were developed and then examining the utility of studies using mutation signatures to correlate environmental effects on the cancer genome. Next, we examine current clinical research that employs mutation signatures and discuss the potential use cases and challenges of mutation signatures in clinical decision-making. We then examine computational studies developing tools to investigate complex patterns of mutations beyond the context of mutational signatures. We survey methods to identify cancer-driver genes, from single-driver studies to pathway and network analyses. In addition, we review methods inferring complex combinations of mutations for clinical tasks and using mutations integrated with multi-omics data to better predict cancer phenotypes. We examine the use of these tools for either discovery or prediction, including prediction of tumor origin, treatment outcomes, prognosis, and cancer typing. We further discuss the main limitations preventing widespread clinical integration of computational tools for the diagnosis and treatment of cancer. We end by proposing solutions to address these challenges using recent advances in machine learning.

The underexplored links between cancer and the internal body climate: Implications for cancer prevention and treatment

In order to effectively manage and cure cancer we should move beyond the general view of cancer as a random process of genetic alterations leading to uncontrolled cell proliferation or simply a predictable evolutionary process involving selection for traits that increase cell fitness. In our view, cancer is a systemic disease that involves multiple interactions not only among cells within tumors or between tumors and surrounding tissues but also with the entire organism and its internal "milieu". We define the internal body climate as an emergent property resulting from spatial and temporal interactions among internal components themselves and with the external environment. The body climate itself can either prevent, promote or support cancer initiation and progression (top-down effect; i.e., body climate-induced effects on cancer), as well as be perturbed by cancer (bottom-up effect; i.e., cancer-induced body climate changes) to further favor cancer progression and spread. This positive feedback loop can move the system towards a "cancerized" organism and ultimately results in its demise. In our view, cancer not only affects the entire system; it is a reflection of an imbalance of the entire system. This model provides an integrated framework to study all aspects of cancer as a systemic disease, and also highlights unexplored links that can be altered to both prevent body climate changes that favor cancer initiation, progression and dissemination as well as manipulate or restore the body internal climate to hinder the success of cancer inception, progression and metastasis or improve therapy outcomes. To do so, we need to (i) identify cancer-relevant factors that affect specific climate components, (ii) develop 'body climate biomarkers', (iii) define 'body climate scores', and (iv) develop strategies to prevent climate changes, stop or slow the changes, or even revert the changes (climate restoration).

The solid tumor microenvironment-Breaking the barrier for T cells: How the solid tumor microenvironment influences T cells: How the solid tumor microenvironment influences T cells

The tumor microenvironment (TME) plays a pivotal role in the behavior and development of solid tumors as well as shaping the immune response against them. As the tumor cells proliferate, the space they occupy and their physical interactions with the surrounding tissue increases. The growing tumor tissue becomes a complex dynamic structure, containing connective tissue, vascular structures, and extracellular matrix (ECM) that facilitates stimulation, oxygenation, and nutrition, necessary for its fast growth. Mechanical cues such as stiffness, solid stress, interstitial fluid pressure (IFP), matrix density, and microarchitecture influence cellular functions and ultimately tumor progression and metastasis. In this fight, our body is equipped with T cells as its spearhead against tumors. However, the altered biochemical and mechanical environment of the tumor niche affects T cell efficacy and leads to their exhaustion. Understanding the mechanobiological properties of the TME and their effects on T cells is key for developing novel adoptive tumor immunotherapies.

Definition of a novel breast tumor-specific classifier based on secretome analysis

BACKGROUND

During cancer development, the normal tissue microenvironment is shaped by tumorigenic events. Inflammatory mediators and immune cells play a key role during this process. However, which molecular features most specifically characterize the malignant tissue remains poorly explored.

METHODS

Within our institutional tumor microenvironment global analysis (T-MEGA) program, we set a prospective cohort of 422 untreated breast cancer patients. We established a dedicated pipeline to generate supernatants from tumor and juxta-tumor tissue explants and quantify 55 soluble molecules using Luminex or MSD. Those analytes belonged to five molecular families: chemokines, cytokines, growth factors, metalloproteinases, and adipokines.

RESULTS

When looking at tissue specificity, our dataset revealed some breast tumor-specific characteristics, as IL-16, as well as some juxta-tumor-specific secreted molecules, as IL-33. Unsupervised clustering analysis identified groups of molecules that were specific to the breast tumor tissue and displayed a similar secretion behavior. We identified a tumor-specific cluster composed of nine molecules that were secreted fourteen times more in the tumor supernatants than the corresponding juxta-tumor supernatants. This cluster contained, among others, CCL17, CCL22, and CXCL9 and TGF-β1, 2, and 3. The systematic comparison of tumor and juxta-tumor secretome data allowed us to mathematically formalize a novel breast cancer signature composed of 14 molecules that segregated tumors from juxta-tumors, with a sensitivity of 96.8% and a specificity of 96%.

CONCLUSIONS

Our study provides the first breast tumor-specific classifier computed on breast tissue-derived secretome data. Moreover, our T-MEGA cohort dataset is a freely accessible resource to the biomedical community to help advancing scientific knowledge on breast cancer.

A new method to accurately identify single nucleotide variants using small FFPE breast samples

Most tissue collections of neoplasms are composed of formalin-fixed and paraffin-embedded (FFPE) excised tumor samples used for routine diagnostics. DNA sequencing is becoming increasingly important in cancer research and clinical management; however it is difficult to accurately sequence DNA from FFPE samples. We developed and validated a new bioinformatic pipeline to use existing variant-calling strategies to robustly identify somatic single nucleotide variants (SNVs) from whole exome sequencing using small amounts of DNA extracted from archival FFPE samples of breast cancers. We optimized this strategy using 28 pairs of technical replicates. After optimization, the mean similarity between replicates increased 5-fold, reaching 88% (range 0-100%), with a mean of 21.4 SNVs (range 1-68) per sample, representing a markedly superior performance to existing tools. We found that the SNV-identification accuracy declined when there was less than 40 ng of DNA available and that insertion-deletion variant calls are less reliable than single base substitutions. As the first application of the new algorithm, we compared samples of ductal carcinoma in situ of the breast to their adjacent invasive ductal carcinoma samples. We observed an increased number of mutations (paired-samples sign test, P < 0.05), and a higher genetic divergence in the invasive samples (paired-samples sign test, P < 0.01). Our method provides a significant improvement in detecting SNVs in FFPE samples over previous approaches.

Identifying modules of cooperating cancer drivers

Identifying cooperating modules of driver alterations can provide insights into cancer etiology and advance the development of effective personalized treatments. We present Cancer Rule Set Optimization (CRSO) for inferring the combinations of alterations that cooperate to drive tumor formation in individual patients. Application to 19 TCGA cancer types revealed a mean of 11 core driver combinations per cancer, comprising 2-6 alterations per combination and accounting for a mean of 70% of samples per cancer type. CRSO is distinct from methods based on statistical co-occurrence, which we demonstrate is a suboptimal criterion for investigating driver cooperation. CRSO identified well-studied driver combinations that were not detected by other approaches and nominated novel combinations that correlate with clinical outcomes in multiple cancer types. Novel synergies were identified in NRAS-mutant melanomas that may be therapeutically relevant. Core driver combinations involving NFE2L2 mutations were identified in four cancer types, supporting the therapeutic potential of NRF2 pathway inhibition. CRSO is available at https://github.com/mikekleinsgit/CRSO/.

Precise base editing for the in vivo study of developmental signaling and human pathologies in zebrafish

While zebrafish is emerging as a new model system to study human diseases, an efficient methodology to generate precise point mutations at high efficiency is still lacking. Here we show that base editors can generate C-to-T point mutations with high efficiencies without other unwanted on-target mutations. In addition, we established a new editor variant recognizing an NAA protospacer adjacent motif, expanding the base editing possibilities in zebrafish. Using these approaches, we first generated a base change in the ctnnb1 gene, mimicking oncogenic an mutation of the human gene known to result in constitutive activation of endogenous Wnt signaling. Additionally, we precisely targeted several cancer-associated genes including cbl. With this last target, we created a new zebrafish dwarfism model. Together our findings expand the potential of zebrafish as a model system allowing new approaches for the endogenous modulation of cell signaling pathways and the generation of precise models of human genetic disease-associated mutations.

A novel workflow for cancer blood biomarker identification

Background

Over the last few years, great progress has been made in the development of key technologies to detect peripheral blood-based, tumor-specific biomarkers, such as circulating tumor cells (CTCs) and circulating cell free tumor DNA (ctDNA). Despite the considerable advances and their multiple clinical values, liquid biopsies are challenged by the very low concentrations of CTCs and ctDNA in blood samples. Additionally, blood biomarkers which were found using data-driven methods may only be effective in few datasets.

Methods

We firstly collected the genes which have expression correlations between blood and the other tissues/organs using Genotype-Tissue Expression (GTEx). Survival hazard genes and differential expression genes of each cancer type in The Cancer Genome Atlas (TCGA) were then selected by Cox regression model and Wilcoxon rank sum test, respectively. By combining the P values of two steps, several blood biomarkers can be inferred for each cancer type. After applying these potential blood biomarker sets to 13 datasets of blood samples from solid tumor patients using single sample gene set enrichment analyses (ssGSEA), we got an enrichment score (ES) for each sample.

Results

The inferred blood biomarker (BB infer) genes showed reliable predictive value in various malignancies. In all the blood samples that were analyzed, the ESs of positive BB Infer genes in cancer patients are higher than healthy people. Conversely, the ESs of negative BB Infer genes in cancer patients are lower than healthy people. Furthermore, lower ES of negative BB infer genes signify the dismal outcome of patients.

Conclusions

We developed a novel solid tumor blood biomarker inference workflow for cancer screening and diagnosis. Moreover, we demonstrated the utility of this inference method in a series of blood sample datasets of solid tumor patients. These results suggested the potential value of this method in the screening, diagnosis and prognosis of cancers.

Untangling a complex web: Computational analyses of tumor molecular profiles to decode driver mechanisms

Genome-scale studies focusing on molecular profiling of cancers across tissue types have revealed a plethora of aberrations across the genomic, transcriptomic, and epigenomic scales. The significant molecular heterogeneity across individual tumors even within the same tissue context complicates decoding the key etiologic mechanisms of this disease. Furthermore, it is increasingly likely that biologic mechanisms underlying the pathobiology of cancer involve multiple molecular entities interacting across functional scales. This has motivated the development of computational approaches that integrate molecular measurements with prior biological knowledge in increasingly intricate ways to enable the discovery of driver genomic aberrations across cancers. Here, we review diverse methodological approaches that have powered significant advances in our understanding of the genomic underpinnings of cancer at the cohort and at the individual tumor scales. We outline the key advances and challenges in the computational discovery of cancer mechanisms while motivating the development of systems biology approaches to comprehensively decode the biologic drivers of this complex disease.

Identifying multi-hit carcinogenic gene combinations: Scaling up a weighted set cover algorithm using compressed binary matrix representation on a GPU

Despite decades of research, effective treatments for most cancers remain elusive. One reason is that different instances of cancer result from different combinations of multiple genetic mutations (hits). Therefore, treatments that may be effective in some cases are not effective in others. We previously developed an algorithm for identifying combinations of carcinogenic genes with mutations (multi-hit combinations), which could suggest a likely cause for individual instances of cancer. Most cancers are estimated to require three or more hits. However, the computational complexity of the algorithm scales exponentially with the number of hits, making it impractical for identifying combinations of more than two hits. To identify combinations of greater than two hits, we used a compressed binary matrix representation, and optimized the algorithm for parallel execution on an NVIDIA V100 graphics processing unit (GPU). With these enhancements, the optimized GPU implementation was on average an estimated 12,144 times faster than the original integer matrix based CPU implementation, for the 3-hit algorithm, allowing us to identify 3-hit combinations. The 3-hit combinations identified using a training set were able to differentiate between tumor and normal samples in a separate test set with 90% overall sensitivity and 93% overall specificity. We illustrate how the distribution of mutations in tumor and normal samples in the multi-hit gene combinations can suggest potential driver mutations for further investigation. With experimental validation, these combinations may provide insight into the etiology of cancer and a rational basis for targeted combination therapy.

Estimating the number of genetic mutations (hits) required for carcinogenesis based on the distribution of somatic mutations

Individual instances of cancer are primarily a result of a combination of a small number of genetic mutations (hits). Knowing the number of such mutations is a prerequisite for identifying specific combinations of carcinogenic mutations and understanding the etiology of cancer. We present a mathematical model for estimating the number of hits based on the distribution of somatic mutations. The model is fundamentally different from previous approaches, which are based on cancer incidence by age. Our somatic mutation based model is likely to be more robust than age-based models since it does not require knowing or accounting for the highly variable mutation rate, which can vary by over three orders of magnitude. In fact, we find that the number of somatic mutations at diagnosis is weakly correlated with age at cancer diagnosis, most likely due to the extreme variability in mutation rates between individuals. Comparing the distribution of somatic mutations predicted by our model to the actual distribution from 6904 tumor samples we estimate the number of hits required for carcinogenesis for 17 cancer types. We find that different cancer types exhibit distinct somatic mutational profiles corresponding to different numbers of hits. Why might different cancer types require different numbers of hits for carcinogenesis? The answer may provide insight into the unique etiology of different cancer types.

Cancer is primarily a result of genetic mutations. Each individual instance of cancer is initiated by a specific combination of a small number of mutations (hits). In trying to identify these combinations of mutations, it is important to know how many hits to look for. However, there are conflicting estimates for the number of hits. We present a fundamentally different model for estimating the number of hits. We found that the number hits ranges from two-eight depending on cancer type. These findings may provide insight into the unique characteristics of different cancer types.