Deep reinforcement learning identifies personalized intermittent androgen deprivation therapy for prostate cancer.

The evolution of drug resistance leads to treatment failure and tumor progression. Intermittent androgen deprivation therapy (IADT) helps responsive cancer cells compete with resistant cancer cells in intratumoral competition. However, conventional IADT is population-based, ignoring the heterogeneity of patients and cancer. Additionally, existing IADT relies on pre-determined thresholds of prostate-specific antigen to pause and resume treatment, which is not optimized for individual patients. To address these challenges, we framed a data-driven method in two steps. First, we developed a time-varied, mixed-effect and generative Lotka-Volterra (tM-GLV) model to account for the heterogeneity of the evolution mechanism and the pharmacokinetics of two ADT drugs Cyproterone acetate and Leuprolide acetate for individual patients. Then, we proposed a reinforcement-learning-enabled individualized IADT framework, namely, I$^{2}$ADT, to learn the patient-specific tumor dynamics and derive the optimal drug administration policy. Experiments with clinical trial data demonstrated that the proposed I$^{2}$ADT can significantly prolong the time to progression of prostate cancer patients with reduced cumulative drug dosage. We further validated the efficacy of the proposed methods with a recent pilot clinical trial data. Moreover, the adaptability of I$^{2}$ADT makes it a promising tool for other cancers with the availability of clinical data, where treatment regimens might need to be individualized based on patient characteristics and disease dynamics. Our research elucidates the application of deep reinforcement learning to identify personalized adaptive cancer therapy.

Personalized Drug Therapy: Innovative Concept Guided With Proteoformics.

Personalized medicine can reduce adverse effects, enhance drug efficacy, and optimize treatment outcomes, which represents the essence of personalized medicine in the pharmacy field. Protein drugs are crucial in the field of personalized drug therapy and are currently the mainstay, which possess higher target specificity and biological activity than small-molecule chemical drugs, making them efficient in regulating disease-related biological processes, and have significant potential in the development of personalized drugs. Currently, protein drugs are designed and developed for specific protein targets based on patient-specific protein data. However, due to the rapid development of two-dimensional gel electrophoresis and mass spectrometry, it is now widely recognized that a canonical protein actually includes multiple proteoforms, and the differences between these proteoforms will result in varying responses to drugs. The variation in the effects of different proteoforms can be significant and the impact can even alter the intended benefit of a drug, potentially making it harmful instead of lifesaving. As a result, we propose that protein drugs should shift from being targeted through the lens of protein (proteomics) to being targeted through the lens of proteoform (proteoformics). This will enable the development of personalized protein drugs that are better equipped to meet patients' specific needs and disease characteristics. With further development in the field of proteoformics, individualized drug therapy, especially personalized protein drugs aimed at proteoforms as a drug target, will improve the understanding of disease mechanisms, discovery of new drug targets and signaling pathways, provide a theoretical basis for the development of new drugs, aid doctors in conducting health risk assessments and making more cost-effective targeted prevention strategies conducted by artificial intelligence/machine learning, promote technological innovation, and provide more convenient treatment tailored to individualized patient profile, which will benefit the affected individuals and society at large.

Making sense of breast cancer risk estimates.

Individual probabilistic assessments on the risk of cancer, primary or secondary, will not be understood by most patients. That is the essence of our arguments in this paper. Greater understanding can be achieved by extensive, intensive, and detailed counseling. But since probability itself is a concept that easily escapes our everyday intuition-consider the famous Monte Hall paradox-then it would also be wise to advise patients and potential patients, to not put undue weight on any probabilistic assessment. Such assessments can be of value to the epidemiologist in the investigation of different potential etiologies describing cancer evolution or to the clinical trialist as a way to maximize design efficiency. But to an ordinary individual we cannot anticipate that these assessments will be correctly interpreted.

Rheostats, toggles, and neutrals, Oh my! A new framework for understanding how amino acid changes modulate protein function.

Advances in personalized medicine and protein engineering require accurately predicting outcomes of amino acid substitutions. Many algorithms correctly predict that evolutionarily-conserved positions show "toggle" substitution phenotypes, which is defined when a few substitutions at that position retain function. In contrast, predictions often fail for substitutions at the less-studied "rheostat" positions, which are defined when different amino acid substitutions at a position sample at least half of the possible functional range. This review describes efforts to understand the impact and significance of rheostat positions: (1) They have been observed in globular soluble, integral membrane, and intrinsically disordered proteins; within single proteins, their prevalence can be up to 40%. (2) Substitutions at rheostat positions can have biological consequences and ∼10% of substitutions gain function. (3) Although both rheostat and "neutral" (defined when all substitutions exhibit wild-type function) positions are nonconserved, the two classes have different evolutionary signatures. (4) Some rheostat positions have pleiotropic effects on function, simultaneously modulating multiple parameters (e.g., altering both affinity and allosteric coupling). (5) In structural studies, substitutions at rheostat positions appear to cause only local perturbations; the overall conformations appear unchanged. (6) Measured functional changes show promising correlations with predicted changes in protein dynamics; the emergent properties of predicted, dynamically coupled amino acid networks might explain some of the complex functional outcomes observed when substituting rheostat positions. Overall, rheostat positions provide unique opportunities for using single substitutions to tune protein function. Future studies of these positions will yield important insights into the protein sequence/function relationship.

Multiomics approach towards characterization of tumor cell plasticity and its significance in precision and personalized medicine.

Cellular plasticity refers to the ability of cells to change their identity or behavior, which can be advantageous in some cases (e.g., tissue regeneration) but detrimental in others (e.g., cancer metastasis). With a better understanding of cellular plasticity, the complexity of cancer cells, their heterogeneity, and their role in metastasis is being unraveled. The plasticity of the cells could also prove as a nemesis to their characterization. In this review, we have attempted to highlight the possibilities and benefits of using multiomics approach in characterizing the plastic nature of cancer cells. There is a need to integrate fragmented evidence at different levels of cellular organization (DNA, RNA, protein, metabolite, epigenetics, etc.) to facilitate the characterization of different forms of plasticity and cell types. We have discussed the role of cellular plasticity in generating intra-tumor heterogeneity. Different omics level evidence is being provided to highlight the variety of molecular determinants discovered using different techniques. Attempts have been made to integrate some of this information to provide a quantitative assessment and scoring of the plastic nature of the cells. However, there is a huge gap in our understanding of mechanisms that lead to the observed heterogeneity. Understanding of these mechanism(s) is necessary for finding targets for early detection and effective therapeutic interventions in metastasis. Targeting cellular plasticity is akin to neutralizing a moving target. Along with the advancements in precision and personalized medicine, these efforts may translate into better clinical outcomes for cancer patients, especially in metastatic stages.

Organoid-based personalized medicine: from tumor outcome prediction to autologous transplantation.

Inter-individual variation largely influences disease susceptibility, as well as response to therapy. In a clinical context, the optimal treatment of a disease should consider inter-individual variation and formulate tailored decisions at an individual level. In recent years, emerging organoid technologies promise to capture part of an individual's phenotypic variability and prove helpful in providing clinically relevant molecular insights. Organoids are stem cell-derived 3-dimensional models that contain multiple cell types that can self-organize and give rise to complex structures mimicking the organization and functionality of the tissue of origin. Organoids therefore represent a more faithful recapitulation of the dynamics of the tissues of interest, compared to conventional monolayer cultures, thus supporting their use in evaluating disease prognosis, or as a tool to predict treatment outcomes. Additionally, the individualized nature of patient-derived organoids enables the use of autologous organoids as a source of transplantable material not limited by histocompatibility. An increasing amount of preclinical evidence has paved the way for clinical trials exploring the applications of organoid-based technologies, some of which are in phase I/II. This review focuses on the recent progress concerning the use of patient-derived organoids in personalized medicine, including (1) diagnostics and disease prognosis, (2) treatment outcome prediction to guide therapeutic advice, and (3) organoid transplantation or cell-based therapies. We discuss examples of these potential applications and the challenges associated with their future implementation.

Unleashing the potential of mRNA therapeutics for inherited neurological diseases.

Neurological monogenic loss-of-function diseases are hereditary disorders resulting from gene mutations that decrease or abolish the normal function of the encoded protein. These conditions pose significant therapeutic challenges, which may be resolved through the development of innovative therapeutic strategies. RNA-based technologies, such as mRNA replacement therapy, have emerged as promising and increasingly viable treatments. Notably, mRNA therapy exhibits significant potential as a mutation-agnostic approach that can address virtually any monogenic loss-of-function disease. Therapeutic mRNA carries the information for a healthy copy of the defective protein, bypassing the problem of targeting specific genetic variants. Moreover, unlike conventional gene therapy, mRNA-based drugs are delivered through a simplified process that requires only transfer to the cytoplasm, thereby reducing the mutagenic risks related to DNA integration. Additionally, mRNA therapy exerts a transient effect on target cells, minimizing the risk of long-term unintended consequences. The remarkable success of mRNA technology for developing COVID-19 vaccines has rekindled interest in mRNA as a cost-effective method for delivering therapeutic proteins. However, further optimization is required to enhance mRNA delivery, particularly to the central nervous system, while minimizing adverse drug reactions and toxicity. In this comprehensive review, we delve into past, present, and ongoing applications of mRNA therapy for neurological monogenic loss-of-function diseases. We also discuss the promises and potential challenges presented by mRNA therapeutics in this rapidly advancing field. Ultimately, we underscore the full potential of mRNA therapy as a game-changing therapeutic approach for neurological disorders.

Precision medicine for asthma treatment: Unlocking the potential of the epigenome and microbiome.

Asthma is a leading worldwide biomedical concern. Patients can experience life-threatening worsening episodes (exacerbations) usually controlled by anti-inflammatory and bronchodilator drugs. However, substantial heterogeneity in treatment response exists, and a subset of patients with unresolved asthma carry the major burden of this disease. The study of the epigenome and microbiome might bridge the gap between human genetics and environmental exposure to partially explain the heterogeneity in drug response. This review aims to provide a critical examination of the existing literature on the microbiome and epigenetic studies examining associations with asthma treatments and drug response, highlight convergent pathways, address current challenges, and offer future perspectives. Current epigenetic and microbiome studies have shown the bilateral relationship between asthma pharmacologic interventions and the human epigenome and microbiome. These studies, focusing on corticosteroids and to a lesser extent on bronchodilators, azithromycin, immunotherapy, and mepolizumab, have improved the understanding of the molecular basis of treatment response and identified promising biomarkers for drug response prediction. Immune and inflammatory pathways (eg, IL-2, TNF-α, NF-κB, and C/EBPs) underlie microbiome-epigenetic associations with asthma treatment, representing potential therapeutic pathways to be targeted. A comprehensive evaluation of these omics biomarkers could significantly contribute to precision medicine and new therapeutic target discovery.

Galectin-10 in serum extracellular vesicles reflects asthma pathophysiology.

BACKGROUND: Novel biomarkers (BMs) are urgently needed for bronchial asthma (BA) with various phenotypes and endotypes. OBJECTIVE: We sought to identify novel BMs reflecting tissue pathology from serum extracellular vesicles (EVs). METHODS: We performed data-independent acquisition of serum EVs from 4 healthy controls, 4 noneosinophilic asthma (NEA) patients, and 4 eosinophilic asthma (EA) patients to identify novel BMs for BA. We confirmed EA-specific BMs via data-independent acquisition validation in 61 BA patients and 23 controls. To further validate these findings, we performed data-independent acquisition for 6 patients with chronic rhinosinusitis without nasal polyps and 7 patients with chronic rhinosinusitis with nasal polyps. RESULTS: We identified 3032 proteins, 23 of which exhibited differential expression in EA. Ingenuity pathway analysis revealed that protein signatures from each phenotype reflected disease characteristics. Validation revealed 5 EA-specific BMs, including galectin-10 (Gal10), eosinophil peroxidase, major basic protein, eosinophil-derived neurotoxin, and arachidonate 15-lipoxygenase. The potential of Gal10 in EVs was superior to that of eosinophils in terms of diagnostic capability and detection of airway obstruction. In rhinosinusitis patients, 1752 and 8413 proteins were identified from EVs and tissues, respectively. Among 11 BMs identified in EVs and tissues from patients with chronic rhinosinusitis with nasal polyps, 5 (including Gal10 and eosinophil peroxidase) showed significant correlations between EVs and tissues. Gal10 release from EVs was implicated in eosinophil extracellular trapped cell death in vitro and in vivo. CONCLUSION: Novel BMs such as Gal10 from serum EVs reflect disease pathophysiology in BA and may represent a new target for liquid biopsy approaches.

Temporal trends and regional variability in BRAF and KRAS genetic testing in Denmark (2010-2022): Implications for precision medicine.

OBJECTIVE: This study aims to evaluate the developments in the testing of Kirsten Rat Sarcoma viral oncogene homolog (KRAS) and v-Raf murine sarcoma viral oncogene homolog B1 (BRAF) mutations across different cancer types and regions in Denmark from 2010 to 2022. STUDY DESIGN AND SETTING: Using comprehensive data from the Danish health registries, we linked molecular test results from the Danish Pathology Registry with cancer diagnoses from the Danish National Patient Registry between 2010 and 2022. We assessed the frequency and distribution of KRAS and BRAF mutations across all cancer types, years of testing, and the five Danish regions. RESULTS: The study included records of KRAS testing for 30 671 patients and BRAF testing for 30 860 patients. Most KRAS testing was performed in colorectal (78%) and lung cancer (18%), and BRAF testing in malignant melanoma (13%), colorectal cancer (67%), and lung cancer (12%). Testing rates and documentation mutational subtypes increased over time. Reporting of wildtype results varied between lung and colorectal cancer, with underreporting in lung cancer. Regional variations in testing and reporting were observed. CONCLUSION: Our study highlights substantial progress in KRAS and BRAF testing in Denmark from 2010 to 2022, evidenced by increased and more specific reporting of mutational test results, thereby improving the precision of cancer diagnosis and treatment. However, persistent regional variations and limited testing for cancer types beyond melanoma, colorectal, and lung cancer highlight the necessity for a nationwide assessment of the optimal testing approach.

Integration of proteomics in the molecular tumor board.

Cancer remains one of the most complex and challenging diseases in mankind. To address the need for a personalized treatment approach for particularly complex tumor cases, molecular tumor boards (MTBs) have been initiated. MTBs are interdisciplinary teams that perform in-depth molecular diagnostics to cooperatively and interdisciplinarily advise on the best therapeutic strategy. Current molecular diagnostics are routinely performed on the transcriptomic and genomic levels, aiming to identify tumor-driving mutations. However, these approaches can only partially capture the actual phenotype and the molecular key players of tumor growth and progression. Thus, direct investigation of the expressed proteins and activated signaling pathways provide valuable complementary information on the tumor-driving molecular characteristics of the tissue. Technological advancements in mass spectrometry-based proteomics enable the robust, rapid, and sensitive detection of thousands of proteins in minimal sample amounts, paving the way for clinical proteomics and the probing of oncogenic signaling activity. Therefore, proteomics is currently being integrated into molecular diagnostics within MTBs and holds promising potential in aiding tumor classification and identifying personalized treatment strategies. This review introduces MTBs and describes current clinical proteomics, its potential in precision oncology, and highlights the benefits of multi-omic data integration.

SNVstory: inferring genetic ancestry from genome sequencing data.

BACKGROUND: Genetic ancestry, inferred from genomic data, is a quantifiable biological parameter. While much of the human genome is identical across populations, it is estimated that as much as 0.4% of the genome can differ due to ancestry. This variation is primarily characterized by single nucleotide variants (SNVs), which are often unique to specific genetic populations. Knowledge of a patient's genetic ancestry can inform clinical decisions, from genetic testing and health screenings to medication dosages, based on ancestral disease predispositions. Nevertheless, the current reliance on self-reported ancestry can introduce subjectivity and exacerbate health disparities. While genomic sequencing data enables objective determination of a patient's genetic ancestry, existing approaches are limited to ancestry inference at the continental level. RESULTS: To address this challenge, and create an objective, measurable metric of genetic ancestry we present SNVstory, a method built upon three independent machine learning models for accurately inferring the sub-continental ancestry of individuals. We also introduce a novel method for simulating individual samples from aggregate allele frequencies from known populations. SNVstory includes a feature-importance scheme, unique among open-source ancestral tools, which allows the user to track the ancestral signal broadcast by a given gene or locus. We successfully evaluated SNVstory using a clinical exome sequencing dataset, comparing self-reported ethnicity and race to our inferred genetic ancestry, and demonstrate the capability of the algorithm to estimate ancestry from 36 different populations with high accuracy. CONCLUSIONS: SNVstory represents a significant advance in methods to assign genetic ancestry, opening the door to ancestry-informed care. SNVstory, an open-source model, is packaged as a Docker container for enhanced reliability and interoperability. It can be accessed from https://github.com/nch-igm/snvstory .

Sex, tissue, and mitochondrial interactions modify the transcriptional response to rapamycin in Drosophila.

BACKGROUND: Many common diseases exhibit uncontrolled mTOR signaling, prompting considerable interest in the therapeutic potential of mTOR inhibitors, such as rapamycin, to treat a range of conditions, including cancer, aging-related pathologies, and neurological disorders. Despite encouraging preclinical results, the success of mTOR interventions in the clinic has been limited by off-target side effects and dose-limiting toxicities. Improving clinical efficacy and mitigating side effects require a better understanding of the influence of key clinical factors, such as sex, tissue, and genomic background, on the outcomes of mTOR-targeting therapies. RESULTS: We assayed gene expression with and without rapamycin exposure across three distinct body parts (head, thorax, abdomen) of D. melanogaster flies, bearing either their native melanogaster mitochondrial genome or the mitochondrial genome from a related species, D. simulans. The fully factorial RNA-seq study design revealed a large number of genes that responded to the rapamycin treatment in a sex-dependent and tissue-dependent manner, and relatively few genes with the transcriptional response to rapamycin affected by the mitochondrial background. Reanalysis of an earlier study confirmed that mitochondria can have a temporal influence on rapamycin response. CONCLUSIONS: We found significant and wide-ranging effects of sex and body part, alongside a subtle, potentially time-dependent, influence of mitochondria on the transcriptional response to rapamycin. Our findings suggest a number of pathways that could be crucial for predicting potential side effects of mTOR inhibition in a particular sex or tissue. Further studies of the temporal response to rapamycin are necessary to elucidate the effects of the mitochondrial background on mTOR and its inhibition.

Stratifying esophago-gastric cancer treatment using a patient-derived organoid-based threshold.

BACKGROUND AND AIMS: This study sought to determine the value of patient-derived organoids (PDOs) from esophago-gastric adenocarcinoma (EGC) for response prediction to neoadjuvant chemotherapy (neoCTx). METHODS: Endoscopic biopsies of patients with locally advanced EGC (n = 120) were taken into culture and PDOs expanded. PDOs' response towards the single substances of the FLOT regimen and the combination treatment were correlated to patients' pathological response using tumor regression grading. A classifier based on FLOT response of PDOs was established in an exploratory cohort (n = 13) and subsequently confirmed in an independent validation cohort (n = 13). RESULTS: EGC PDOs reflected patients' diverse responses to single chemotherapeutics and the combination regimen FLOT. In the exploratory cohort, PDOs response to single 5-FU and FLOT combination treatment correlated with the patients' pathological response (5-FU: Kendall's τ = 0.411, P = 0.001; FLOT: Kendall's τ = 0.694, P = 2.541e-08). For FLOT testing, a high diagnostic precision in receiver operating characteristic (ROC) analysis was reached with an AUCROC of 0.994 (CI 0.980 to 1.000). The discriminative ability of PDO-based FLOT testing allowed the definition of a threshold, which classified in an independent validation cohort FLOT responders from non-responders with high sensitivity (90%), specificity (100%) and accuracy (92%). CONCLUSION: In vitro drug testing of EGC PDOs has a high predictive accuracy in classifying patients' histological response to neoadjuvant FLOT treatment. Taking into account the high rate of successful PDO expansion from biopsies, the definition of a threshold that allows treatment stratification paves the way for an interventional trial exploring PDO-guided treatment of EGC patients.