Structure-independent machine-learning predictions of the CDK12 interactome.

Cyclin-dependent kinase 12 (CDK12) is a critical regulatory protein involved in transcription and DNA repair processes. Dysregulation of CDK12 has been implicated in various diseases, including cancer. Understanding the CDK12 interactome is pivotal for elucidating its functional roles and potential therapeutic targets. Traditional methods for interactome prediction often rely on protein structure information, limiting applicability to CDK12 characterized by partly disordered terminal C region. In this study, we present a structure-independent machine-learning model that utilizes proteins' sequence and functional data to predict the CDK12 interactome. This approach is motivated by the disordered character of the CDK12 C-terminal region mitigating a structure-driven search for binding partners. Our approach incorporates multiple data sources, including protein-protein interaction networks, functional annotations, and sequence-based features, to construct a comprehensive CDK12 interactome prediction model. The ability to predict CDK12 interactions without relying on structural information is a significant advancement, as many potential interaction partners may lack crystallographic data. In conclusion, our structure-independent machine-learning model presents a powerful tool for predicting the CDK12 interactome and holds promise in advancing our understanding of CDK12 biology, identifying potential therapeutic targets, and facilitating precision-medicine approaches for CDK12-associated diseases.

Morphophenotypic classification of tumor organoids as an indicator of drug exposure and penetration potential.

The dynamics of tumor progression is driven by multiple factors, which can be exogenous to the tumor (microenvironment) or intrinsic (genetic, epigenetic or due to intercellular interactions). While tumor heterogeneity has been extensively studied on the level of cell genetic profiles or cellular composition, tumor morphological diversity has not been given as much attention. The limited analysis of tumor morphophenotypes may be attributed to the lack of accurate models, both experimental and computational, capable of capturing changes in tumor morphology with fine levels of spatial detail. Using a three-dimensional, agent-based, lattice-free computational model, we generated a library of multicellular tumor organoids, the experimental analogues of in vivo tumors. By varying three biologically relevant parameters-cell radius, cell division age and cell sensitivity to contact inhibition, we showed that tumor organoids with similar growth dynamics can express distinct morphologies and possess diverse cellular compositions. Taking advantage of the high-resolution of computational modeling, we applied the quantitative measures of compactness and accessible surface area, concepts that originated from the structural biology of proteins. Based on these analyses, we demonstrated that tumor organoids with similar sizes may differ in features associated with drug effectiveness, such as potential exposure to the drug or the extent of drug penetration. Both these characteristics might lead to major differences in tumor organoid's response to therapy. This indicates that therapeutic protocols should not be based solely on tumor size, but take into account additional tumor features, such as their morphology or cellular packing density.

Micropharmacology: An In Silico Approach for Assessing Drug Efficacy Within a Tumor Tissue.

Systemic chemotherapy is one of the main anticancer treatments used for most kinds of clinically diagnosed tumors. However, the efficacy of these drugs can be hampered by the physical attributes of the tumor tissue, such as tortuous vasculature, dense and fibrous extracellular matrix, irregular cellular architecture, tumor metabolic gradients, and non-uniform expression of the cell membrane receptors. This can impede the transport of therapeutic agents to tumor cells in sufficient quantities. In addition, tumor microenvironments undergo dynamic spatio-temporal changes during tumor progression and treatment, which can also obstruct drug efficacy. To examine ways to improve drug delivery on a cell-to-tissue scale (single-cell pharmacology), we developed the microscale pharmacokinetics/pharmacodynamics (microPKPD) modeling framework. Our model is modular and can be adjusted to include only the mathematical equations that are crucial for a biological problem under consideration. This modularity makes the model applicable to a broad range of pharmacological cases. As an illustration, we present two specific applications of the microPKPD methodology that help to identify optimal drug properties. The hypoxia-activated drugs example uses continuous drug concentrations, diffusive-advective transport through the tumor interstitium, and passive transmembrane drug uptake. The targeted therapy example represents drug molecules as discrete particles that move by diffusion and actively bind to cell receptors. The proposed modeling approach takes into account the explicit tumor tissue morphology, its metabolic landscape and/or specific receptor distribution. All these tumor attributes can be assessed from patients' diagnostic biopsies; thus, the proposed methodology can be developed into a tool suitable for personalized medicine, such as neoadjuvant chemotherapy.

Molecular Dynamics Simulations of Membrane-Bound STIM1 to Investigate Conformational Changes during STIM1 Activation upon Calcium Release.

Calcium is involved in important intracellular processes, such as intracellular signaling from cell membrane receptors to the nucleus. Typically, calcium levels are kept at less than 100 nM in the nucleus and cytosol, but some calcium is stored in the endoplasmic reticulum (ER) lumen for rapid release to activate intracellular calcium-dependent functions. Stromal interacting molecule 1 (STIM1) plays a critical role in early sensing of changes in the ER's calcium level, especially when there is a sudden release of stored calcium from the ER. Inactive STIM1, which has a bound calcium ion, is activated upon ion release. Following activation of STIM1, there is STIM1-assisted initiation of extracellular calcium entry through channels in the cell membrane. This extracellular calcium entering the cell then amplifies intracellular calcium-dependent actions. At the end of the process, ER levels of stored calcium are reestablished. The main focus of this work was to study the conformational changes accompanying homo- or heterodimerization of STIM1. For this purpose, the ER luminal portion of STIM1 (residues 58-236), which includes the sterile alpha motif (SAM) domain plus the calcium-binding EF-hand domains 1 and 2 attached to the STIM1 transmembrane region (TM), was modeled and embedded in a virtual membrane. Next, molecular dynamics simulations were performed to study the conformational changes that take place during STIM1 activation and subsequent protein-protein interactions. Indeed, the simulations revealed exposure of residues in the EF-hand domains, which may be important for dimerization steps. Altogether, understanding conformational changes in STIM1 can help in drug discovery when targeting this key protein in intracellular calcium functions.

Microenvironmental Niches and Sanctuaries: A Route to Acquired Resistance.

A tumor vasculature that is functionally abnormal results in irregular gradients of metabolites and drugs within the tumor tissue. Recently, significant efforts have been committed to experimentally examine how cellular response to anti-cancer treatments varies based on the environment in which the cells are grown. In vitro studies point to specific conditions in which tumor cells can remain dormant and survive the treatment. In vivo results suggest that cells can escape the effects of drug therapy in tissue regions that are poorly penetrated by the drugs. Better understanding how the tumor microenvironments influence the emergence of drug resistance in both primary and metastatic tumors may improve drug development and the design of more effective therapeutic protocols. This chapter presents a hybrid agent-based model of the growth of tumor micrometastases and explores how microenvironmental factors can contribute to the development of acquired resistance in response to a DNA damaging drug. The specific microenvironments of interest in this work are tumor hypoxic niches and tumor normoxic sanctuaries with poor drug penetration. We aim to quantify how spatial constraints of limited drug transport and quiescent cell survival contribute to the development of drug resistant tumors.

Enhanced sampling simulations of DNA step parameters.

A novel approach for the selection of step parameters as reaction coordinates in enhanced sampling simulations of DNA is presented. The method uses three atoms per base and does not require coordinate overlays or idealized base pairs. This allowed for a highly efficient implementation of the calculation of all step parameters and their Cartesian derivatives in molecular dynamics simulations. Good correlation between the calculated and actual twist, roll, tilt, shift, and slide parameters is obtained, while the correlation with rise is modest. The method is illustrated by its application to the methylated and unmethylated 5'-CATGTGACGTCACATG-3' double stranded DNA sequence. One-dimensional umbrella simulations indicate that the flexibility of the central CG step is only marginally affected by methylation.

Emerging therapeutic approaches for peritoneal metastases from gastrointestinal cancers.

Peritoneal metastases from gastrointestinal malignancies present difficult management decisions, with options consisting primarily of systemic chemotherapy or major surgery with or without hyperthermic intraperitoneal chemotherapy. Current research is investigating expanding therapeutic modalities, and the aim of this review is to provide an overview of the existing and emerging therapies for the peritoneal metastases from gastrointestinal cancers, primarily through the recent literature (2015 and newer). These include the current data with systemic therapy and cytoreduction with hyperthermic intraperitoneal or pressurized intraperitoneal aerosol chemotherapy, as well as novel promising modalities under investigation, including dominating oncolytic viral therapy and adoptive cellular, biologic, and bacteria therapy, or nanotechnology. The novel diverse strategies, although preliminary and preclinical in murine models, individually and collectively contribute to the treatment of peritoneal metastases, offering hope for improved outcomes and quality of life. We foresee that these evolving treatment approaches will facilitate the transfer of knowledge and data among studies and advance discovery of new drugs and optimized treatments for patients with peritoneal metastases.

Pancreatic Ductal Adenocarcinoma (PDAC): A Review of Recent Advancements Enabled by Artificial Intelligence.

Pancreatic Ductal Adenocarcinoma (PDAC) remains one of the most formidable challenges in oncology, characterized by its late detection and poor prognosis. Artificial intelligence (AI) and machine learning (ML) are emerging as pivotal tools in revolutionizing PDAC care across various dimensions. Consequently, many studies have focused on using AI to improve the standard of PDAC care. This review article attempts to consolidate the literature from the past five years to identify high-impact, novel, and meaningful studies focusing on their transformative potential in PDAC management. Our analysis spans a broad spectrum of applications, including but not limited to patient risk stratification, early detection, and prediction of treatment outcomes, thereby highlighting AI's potential role in enhancing the quality and precision of PDAC care. By categorizing the literature into discrete sections reflective of a patient's journey from screening and diagnosis through treatment and survivorship, this review offers a comprehensive examination of AI-driven methodologies in addressing the multifaceted challenges of PDAC. Each study is summarized by explaining the dataset, ML model, evaluation metrics, and impact the study has on improving PDAC-related outcomes. We also discuss prevailing obstacles and limitations inherent in the application of AI within the PDAC context, offering insightful perspectives on potential future directions and innovations.

Challenges with sirolimus experimental data to inform QSP model of post-transplantation cyclophosphamide regimens.

Dose optimization of sirolimus may further improve outcomes in allogeneic hematopoietic cell transplant (HCT) patients receiving post-transplantation cyclophosphamide (PTCy) to prevent graft-versus-host disease (GVHD). Sirolimus exposure-response association studies in HCT patients (i.e., the association of trough concentration with clinical outcomes) have been conflicting. Sirolimus has important effects on T-cells, including conventional (Tcons) and regulatory T-cells (Tregs), both of which have been implicated in the mechanisms by which PTCy prevents GVHD, but there is an absence of validated biomarkers of sirolimus effects on these cell subsets. Considering the paucity of existing biomarkers and the complexities of the immune system, we conducted a literature review to inform a quantitative systems pharmacology (QSP) model of GVHD. The published literature presented multiple challenges. The sirolimus pharmacokinetic models insufficiently describe sirolimus distribution to relevant physiological compartments. Despite multiple publications describing sirolimus effects on Tcons and Tregs in preclinical and human ex vivo models, consistent parameters relating sirolimus concentrations to circulating Tcons and Tregs could not be found. Each aspect presents a challenge in building a QSP model of sirolimus and its temporal effects on T-cell subsets and GVHD prevention. To optimize GVHD prevention regimens, phase I studies and systematic studies of immunosuppressant concentration-effect association are needed for QSP modeling.

Translation of AI into oncology clinical practice.

Artificial intelligence (AI) is a transformative technology that is capturing popular imagination and can revolutionize biomedicine. AI and machine learning (ML) algorithms have the potential to break through existing barriers in oncology research and practice such as automating workflow processes, personalizing care, and reducing healthcare disparities. Emerging applications of AI/ML in the literature include screening and early detection of cancer, disease diagnosis, response prediction, prognosis, and accelerated drug discovery. Despite this excitement, only few AI/ML models have been properly validated and fewer have become regulated products for routine clinical use. In this review, we highlight the main challenges impeding AI/ML clinical translation. We present different clinical use cases from the domains of radiology, radiation oncology, immunotherapy, and drug discovery in oncology. We dissect the unique challenges and opportunities associated with each of these cases. Finally, we summarize the general requirements for successful AI/ML implementation in the clinic, highlighting specific examples and points of emphasis including the importance of multidisciplinary collaboration of stakeholders, role of domain experts in AI augmentation, transparency of AI/ML models, and the establishment of a comprehensive quality assurance program to mitigate risks of training bias and data drifts, all culminating toward safer and more beneficial AI/ML applications in oncology labs and clinics.

A framework for mutational signature analysis based on DNA shape parameters.

The mutation risk of a DNA locus depends on its oligonucleotide context. In turn, mutability of oligonucleotides varies across individuals, due to exposure to mutagenic agents or due to variable efficiency and/or accuracy of DNA repair. Such variability is captured by mutational signatures, a mathematical construct obtained by a deconvolution of mutation frequency spectra across individuals. There is a need to enhance methods for inferring mutational signatures to make better use of sparse mutation data (e.g., resulting from exome sequencing of cancers), to facilitate insight into underlying biological mechanisms, and to provide more accurate mutation rate baselines for inferring positive and negative selection. We propose a conceptualization of mutational signatures that represents oligonucleotides via descriptors of DNA conformation: base pair, base pair step, and minor groove width parameters. We demonstrate how such DNA structural parameters can accurately predict mutation occurrence due to DNA repair failures or due to exposure to diverse mutagens such as radiation, chemical exposure, and the APOBEC cytosine deaminase enzymes. Furthermore, the mutation frequency of DNA oligomers classed by structural features can accurately capture systematic variability in mutagenesis of >1,000 tumors originating from diverse human tissues. A nonnegative matrix factorization was applied to mutation spectra stratified by DNA structural features, thereby extracting novel mutational signatures. Moreover, many of the known trinucleotide signatures were associated with an additional spectrum in the DNA structural descriptor space, which may aid interpretation and provide mechanistic insight. Overall, we suggest that the power of DNA sequence motif-based mutational signature analysis can be enhanced by drawing on DNA shape features.

Pharmacometabonomic association of cyclophosphamide 4-hydroxylation in hematopoietic cell transplant recipients.

The widely used alkylating agent cyclophosphamide (CY) has substantive interpatient variability in the area under the curve (AUC) of it and its metabolites. Numerous factors may influence the drug-metabolizing enzymes that metabolize CY to 4-hydroxycyclophosphamide (4HCY), the principal precursor to CY's cytotoxic metabolite. We sought to identify endogenous metabolomics compounds (EMCs) associated with 4HCY formation clearance (ratio of 4HCY/CY AUC) using global metabolomics. Patients who undergo hematopoietic cell transplantation receiving post-transplant CY (PT-CY) were enrolled, cohort 1 (n = 26) and cohort 2 (n = 25) donating longitudinal blood samples before they started HCT (pre-HCT), before infusion of the donor allograft (pre-graft), before the first dose of PT-CY (pre-CY), and 24 h after the first dose of PT-CY (24-h post-CY), which is also immediately before the second dose of CY. A total of 512 and 498 EMCs were quantitated in two cohorts, respectively. Both univariate linear regression with false discovery rate (FDR), and pathway enrichment analyses using a global association test were performed. At the pre-CY time point, no EMCs were associated at FDR less than 0.1. At pre-HCT, cohort 1 had one EMC (levoglucosan) survive the FDR threshold. At pre-graft, cohort 1 and cohort 2 had 20 and 13 EMCs, respectively, exhibiting unadjusted p values less than 0.05, with the only EMCs having an FDR less than 0.1 being two unknown EMCs. At 24-h post-CY, there were three EMCs, two ketones, and threitol, at FDR less than 0.1 in cohort 2. These results demonstrate the potential of pharmacometabonomics, but future studies in larger samples are needed to optimize CY.

Multi-omics approach to identifying isoform variants as therapeutic targets in cancer patients.

Cancer-specific alternatively spliced events (ASE) play a role in cancer pathogenesis and can be targeted by immunotherapy, oligonucleotide therapy, and small molecule inhibition. However, identifying actionable ASE targets remains challenging due to the uncertainty of its protein product, structure impact, and proteoform (protein isoform) function. Here we argue that an integrated multi-omics profiling strategy can overcome these challenges, allowing us to mine this untapped source of targets for therapeutic development. In this review, we will provide an overview of current multi-omics strategies in characterizing ASEs by utilizing the transcriptome, proteome, and state-of-art algorithms for protein structure prediction. We will discuss limitations and knowledge gaps associated with each technology and informatics analytics. Finally, we will discuss future directions that will enable the full integration of multi-omics data for ASE target discovery.

Concepts and Applications of Information Theory to Immuno-Oncology.

Recent successes of immune-modulating therapies for cancer have stimulated research on information flow within the immune system and, in turn, clinical applications of concepts from information theory. Through information theory, one can describe and formalize, in a mathematically rigorous fashion, the function of interconnected components of the immune system in health and disease. Specifically, using concepts including entropy, mutual information, and channel capacity, one can quantify the storage, transmission, encoding, and flow of information within and between cellular components of the immune system on multiple temporal and spatial scales. To understand, at the quantitative level, immune signaling function and dysfunction in cancer, we present a methodology-oriented review of information-theoretic treatment of biochemical signal transduction and transmission coupled with mathematical modeling.

Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues.

A main goal of mathematical and computational oncology is to develop quantitative tools to determine the most effective therapies for each individual patient. This involves predicting the right drug to be administered at the right time and at the right dose. Such an approach is known as precision medicine. Mathematical modelling can play an invaluable role in the development of such therapeutic strategies, since it allows for relatively fast, efficient and inexpensive simulations of a large number of treatment schedules in order to find the most effective. This review is a survey of mathematical models that explicitly take into account the spatial architecture of three-dimensional tumours and address tumour development, progression and response to treatments. In particular, we discuss models of epithelial acini, multicellular spheroids, normal and tumour spheroids and organoids, and multi-component tissues. Our intent is to showcase how these in silico models can be applied to patient-specific data to assess which therapeutic strategies will be the most efficient. We also present the concept of virtual clinical trials that integrate standard-of-care patient data, medical imaging, organ-on-chip experiments and computational models to determine personalized medical treatment strategies.

Structure and properties of DOTA-chelated radiopharmaceuticals within the 225Ac decay pathway.

The successful delivery of toxic cargo directly to tumor cells is of primary importance in targeted (α) particle therapy. Complexes of radioactive atoms with the 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) chelating agent are considered as effective materials for such delivery processes. The DOTA chelator displays high affinity to radioactive metal isotopes and retains this capability after conjugation to tumor targeting moieties. Although the α-decay chains are well defined for many isotopes, the stability of chelations during the decay process and the impact of released energy on their structures remain unknown. The radioactive isotope 225Ac is an α-particle emitter that can be easily chelated by DOTA. However, 225Ac has a complex decay chain with four α-particle emissions during decay of each radionuclide. To advance our fundamental understanding of the consequences of α-decay on the stability of tumor-targeted 225Ac-DOTA conjugate radiopharmaceuticals, we performed first principles calculations of the structure, stability, and electronic properties of the DOTA chelator to the 225Ac radioactive isotope, and the initial daughters in the decay chain, 225Ac, 221Fr, 217At and 213Bi. Our calculations show that the atomic positions, binding energies, and electron localization functions are affected by the interplay between spin-orbit coupling, weak dispersive interactions, and environmental factors. Future empirical measurements may be guided and interpreted in light of these results.

Targeting Ligand Specificity Linked to Tumor Tissue Topological Heterogeneity via Single-Cell Micro-Pharmacological Modeling.

Targeted therapy has held promise to be a successful anticancer treatment due to its specificity towards tumor cells that express the target receptors. However, not all targeting drugs used in the clinic are equally effective in tumor eradication. To examine which biochemical and biophysical properties of targeted agents are pivotal for their effective distribution inside the tumor and their efficient cellular uptake, we combine mathematical micro-pharmacological modeling with in vivo imaging of targeted human xenograft tumors in SCID mice. The mathematical model calibrated to experimental data was used to explore properties of the targeting ligand (diffusion and affinity) and ligand release schemes (rates and concentrations) with a goal to identify the properties of cells and ligands that enable high receptor saturation. By accounting for heterogeneities typical of in vivo tumors, our model was able to identify cell- and tissue-level barriers to efficient drug uptake. This work provides a base for utilizing experimentally measurable properties of a ligand-targeted agent and patient-specific attributes of the tumor tissue to support the development of novel targeted imaging agents and for improvement in their delivery to individual tumor cells.

Molecular Dynamics Simulations of 5-Hydroxycytosine Damaged DNA.

Oxidation of cytosine is a leading cause of mutations and can lead to cancer. Here we report molecular dynamics simulations that characterized the structure and flexibility of 5-hydroxycytosine damaged DNA. A total of four systems were studied: undamaged DNA, damaged DNA base paired to a matching guanine, damaged DNA base paired to a mismatching adenine, and the corresponding undamaged mismatched strand. The simulations showed high spatial similarity between undamaged and damaged DNA; however, the matched damaged strand had greater overtwisting flexibility, and for both the matched and unmatched strands sugar puckering was much more flexible at the damaged site. The mismatch introduced larger changes, notably a loss in hydrogen bonding and a gain in stacking interactions, as well as effects on base pair and step geometry and solvation. Implications for damage recognition are discussed.

BII stability and base step flexibility of N6-adenine methylated GATC motifs.

The effect of N6-adenine methylation on the flexibility and shape of palindromic GATC sequences has been investigated by molecular dynamics simulations. Variations in DNA backbone geometry were observed, which were dependent on the degree of methylation and the identity of the bases. While the effect was small, more frequent BI to BII conversions were observed in the GA step of hemimethylated DNA. The increased BII population of the hemimethylated system positively correlated with increased stacking interactions between methylated adenine and guanine, while stacking interactions decreased at the TC step for the fully methylated strand. The flexibility of the AT and TC steps was marginally affected by methylation, in a fashion that was correlated with stacking interactions. The facilitated BI to BII conversion in hemimethylated strands might be of importance for SeqA selectivity and binding.