Genetic Load and Potential Mutational Meltdown in Cancer Cell Populations

Deciphering the tumor immune microenvironment from a multidimensional omics perspective: insight into next-generation CAR-T cell immunotherapy and beyond

Tumor immune microenvironment (TIME) consists of intra-tumor immunological components and plays a significant role in tumor initiation, progression, metastasis, and response to therapy. Chimeric antigen receptor (CAR)-T cell immunotherapy has revolutionized the cancer treatment paradigm. Although CAR-T cell immunotherapy has emerged as a successful treatment for hematologic malignancies, it remains a conundrum for solid tumors. The heterogeneity of TIME is responsible for poor outcomes in CAR-T cell immunotherapy against solid tumors. The advancement of highly sophisticated technology enhances our exploration in TIME from a multi-omics perspective. In the era of machine learning, multi-omics studies could reveal the characteristics of TIME and its immune resistance mechanism. Therefore, the clinical efficacy of CAR-T cell immunotherapy in solid tumors could be further improved with strategies that target unfavorable conditions in TIME. Herein, this review seeks to investigate the factors influencing TIME formation and propose strategies for improving the effectiveness of CAR-T cell immunotherapy through a multi-omics perspective, with the ultimate goal of developing personalized therapeutic approaches.

Informing immunotherapy with multi-omics driven machine learning

Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.

Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges

The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.

Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis

The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer. Meanwhile, the human mind is limited in effectively handling and fully utilizing the accumulation of such enormous amounts of data. Machine learning-based approaches play a critical role in integrating and analyzing these large and complex datasets, which have extensively characterized lung cancer through the use of different perspectives from these accrued data. In this review, we provide an overview of machine learning-based approaches that strengthen the varying aspects of lung cancer diagnosis and therapy, including early detection, auxiliary diagnosis, prognosis prediction, and immunotherapy practice. Moreover, we highlight the challenges and opportunities for future applications of machine learning in lung cancer.

Oncogenic Viruses as Entropic Drivers of Cancer Evolution

Viral infection is an indisputable causal factor for nearly 17% of all human cancers. However, the diversity and complexity of oncogenic mechanisms raises new questions as to the mechanistic role of viruses in cancer. Classical viral oncogenes have been identified for all tumor-associated viruses. These oncogenes can have multiple oncogenic activities that may or may not be utilized in a particular tumor cell. In addition, stochastic events, like viral mutation and integration, as well as heritable host susceptibilities and immune deficiencies are also implicated in tumorigenesis. A more contemporary view of tumor biology highlights the importance of evolutionary forces that select for phenotypes better adapted to a complex and changing environment. Given the challenges of prioritizing singular mechanistic causes, it may be necessary to integrate concepts from evolutionary theory and systems biology to better understand viral cancer-driving forces. Here, we propose that viral infection provides a biological “entropy” that increases genetic variation and phenotypic plasticity, accelerating the main driving forces of cancer cell evolution. Viruses can also influence the evolutionary selection criteria by altering the tumor microenvironment and immune signaling. Utilizing concepts from cancer cell evolution, population genetics, thermodynamics, and systems biology may provide new perspectives on viral oncogenesis and identify novel therapeutic strategies for treating viruses and cancer.

Optimizing the evaluation of gene-targeted panels for tumor mutational burden estimation

Though whole exome sequencing (WES) is the gold-standard for measuring tumor mutational burden (TMB), the development of gene-targeted panels enables cost-effective TMB estimation. With the growing number of panels in clinical trials, developing a statistical method to effectively evaluate and compare the performance of different panels is necessary. The mainstream method uses R-squared value to measure the correlation between the panel-based TMB and WES-based TMB. However, the performance of a panel is usually overestimated via R-squared value based on the long-tailed TMB distribution of the dataset. Herein, we propose angular distance, a measurement used to compute the extent of the estimated bias. Our extensive in silico analysis indicates that the R-squared value reaches a plateau after the panel size reaches 0.5 Mb, which does not adequately characterize the performance of the panels. In contrast, the angular distance is still sensitive to the changes in panel sizes when the panel size reaches 6 Mb. In particular, R-squared values between the hypermutation-included dataset and the non-hypermutation dataset differ widely across many cancer types, whereas the angular distances are highly consistent. Therefore, the angular distance is more objective and logical than R-squared value for evaluating the accuracy of TMB estimation for gene-targeted panels.

Performance-weighted-voting model: An ensemble machine learning method for cancer type classification using whole-exome sequencing mutation

Background

With improvements in next-generation DNA sequencing technology, lower cost is needed to collect genetic data. More machine learning techniques can be used to help with cancer analysis and diagnosis.

Methods

We developed an ensemble machine learning system named performance-weighted-voting model for cancer type classification in 6,249 samples across 14 cancer types. Our ensemble system consists of five weak classifiers (logistic regression, SVM, random forest, XGBoost and neural networks). We first used cross-validation to get the predicted results for the five classifiers. The weights of the five weak classifiers can be obtained based on their predictive performance by solving linear regression functions. The final predicted probability of the performance-weighted-voting model for a cancer type can be determined by the summation of each classifier's weight multiplied by its predicted probability.

Results

Using the somatic mutation count of each gene as the input feature, the overall accuracy of the performance-weighted-voting model reached 71.46%, which was significantly higher than the five weak classifiers and two other ensemble models: the hard-voting model and the soft-voting model. In addition, by analyzing the predictive pattern of the performance-weighted-voting model, we found that in most cancer types, higher tumor mutational burden can improve overall accuracy.

Conclusion

This study has important clinical significance for identifying the origin of cancer, especially for those where the primary cannot be determined. In addition, our model presents a good strategy for using ensemble systems for cancer type classification.

Shape of population interfaces as an indicator of mutational instability in coexisting cell populations

Cellular populations such as avascular tumors and microbial biofilms may 'invade' or grow into surrounding populations. The invading population is often comprised of a heterogeneous mixture of cells with varying growth rates. The population may also exhibit mutational instabilities, such as a heavy deleterious mutation load in a cancerous growth. We study the dynamics of a heterogeneous, mutating population competing with a surrounding homogeneous population, as one might find in a cancerous invasion of healthy tissue. We find that the shape of the population interface serves as an indicator for the evolutionary dynamics within the heterogeneous population. In particular, invasion front undulations become enhanced when the invading population is near a mutational meltdown transition or when the surrounding 'bystander' population is barely able to reinvade the mutating population. We characterize these interface undulations and the effective fitness of the heterogeneous population in one- and two-dimensional systems.

Imposed mutational meltdown as an antiviral strategy

Following widespread infections of the most recent coronavirus known to infect humans, SARS‐CoV‐2, attention has turned to potential therapeutic options. With no drug or vaccine yet approved, one focal point of research is to evaluate the potential value of repurposing existing antiviral treatments, with the logical strategy being to identify at least a short‐term intervention to prevent within‐patient progression, while long‐term vaccine strategies unfold. Here, we offer an evolutionary/population‐genetic perspective on one approach that may overwhelm the capacity for pathogen defense (i.e., adaptation) – induced mutational meltdown – providing an overview of key concepts, review of previous theoretical and experimental work of relevance, and guidance for future research. Applied with appropriate care, including target specificity, induced mutational meltdown may provide a general, rapidly implemented approach for the within‐patient eradication of a wide range of pathogens or other undesirable microorganisms.

Pervasive lesion segregation shapes cancer genome evolution

Cancers arise through the acquisition of oncogenic mutations and grow by clonal expansion1,2. Here we reveal that most mutagenic DNA lesions are not resolved into a mutated DNA base pair within a single cell cycle. Instead, DNA lesions segregate, unrepaired, into daughter cells for multiple cell generations, resulting in the chromosome-scale phasing of subsequent mutations. We characterize this process in mutagen-induced mouse liver tumours and show that DNA replication across persisting lesions can produce multiple alternative alleles in successive cell divisions, thereby generating both multiallelic and combinatorial genetic diversity. The phasing of lesions enables accurate measurement of strand-biased repair processes, quantification of oncogenic selection and fine mapping of sister-chromatid-exchange events. Finally, we demonstrate that lesion segregation is a unifying property of exogenous mutagens, including UV light and chemotherapy agents in human cells and tumours, which has profound implications for the evolution and adaptation of cancer genomes.

Virus population dynamics examined with experimental model systems

Experimental evolution permits exploring the effect of controlled environmental variables in virus evolution. Several designs in cell culture and in vivo have established basic concepts that can assist in the interpretation of evolutionary events in the field. Important information has come from cytolytic and persistent infections in cell culture that have unveiled the power of virus-cell coevolution in virus and cell diversification. Equally informative are comparisons of the response of viral populations when subjected to different passage régimens. In particular, plaque-to-plaque transfers in cell culture have revealed unusual genotypes and phenotypes that populate minority layers of viral quasispecies. Some of these viruses display properties that contradict features established in virology textbooks. Several hypotheses and principles of population genetics have found experimental confirmation in experimental designs with viruses. The possibilities of using experimental evolution to understand virus behavior are still largely unexploited.