Development of a Probabilistic Boolean network (PBN) to model intraoperative blood pressure management.

BACKGROUND: Blood pressure is a vital sign for organ perfusion that anesthesiologists measure and modulate during surgery. However, current decision-making processes rely heavily on clinicians' experience, which can lead to variability in treatment across surgeries. With the advent of machine learning, we can now create models to predict the outcomes of interventions and guide perioperative decision-making. The first step in this process involves translating the clinical decision-making process into a framework understood by an algorithm. Probabilistic Boolean networks (PBNs) provide an information-rich approach to this problem. A PBN trends toward a steady state, and its decisions are easily understood via its Boolean predictor functions. We hypothesize that a PBN can be developed that corrects hemodynamic instability in patients by selecting clinical interventions to maintain blood pressure within a given range. METHODS: Data on patients over the age of 65 undergoing surgery with general anesthesia from 2018 to 2020 were drawn from the UF Health PRECEDE data set with IRB approval (IRB201700747). Parameters examined included heart rate, blood pressure, and frequency of medications given 15 min after anesthetic induction and 15 min before awakening. The medication frequency data were truncated into a 66/33 split for the training and validation set used in the PBN. The model was coded using Python 3 and evaluated by comparing the frequency of medications chosen by the program to the values in the testing set via linear regression analysis. RESULTS: The network developed successfully models a hemodynamically unstable patient and corrects the imbalance by administering medications. This is evidenced by the model achieving a stable, steady state matrix in all iterations. However, the model's ability to emulate clinical drug selection was variable. It was successful with its use of vasodilator selection but struggled with the appropriate selection of vasopressors. CONCLUSIONS: The PBN has demonstrated the ability to choose appropriate interventions based on a patient's current vitals. Additional work must be done to have the network emulate the frequency at which drugs are selected from in clinical practice. In its current state, the model provides an understanding of how a PBN behaves in the context of correcting hemodynamic instability and can aid in developing more robust models in the future.

Hematopoietic-specific heterozygous loss of Dnmt3a exacerbates colitis-associated colon cancer.

Clonal hematopoiesis (CH) is defined as clonal expansion of mutant hematopoietic stem cells absent diagnosis of a hematologic malignancy. Presence of CH in solid tumor patients, including colon cancer, correlates with shorter survival. We hypothesized that bone marrow-derived cells with heterozygous loss-of-function mutations of DNMT3A, the most common genetic alteration in CH, contribute to the pathogenesis of colon cancer. In a mouse model that combines colitis-associated colon cancer (CAC) with experimental CH driven by Dnmt3a+/Δ, we found higher tumor penetrance and increased tumor burden compared with controls. Histopathological analysis revealed accentuated colonic epithelium injury, dysplasia, and adenocarcinoma formation. Transcriptome profiling of colon tumors identified enrichment of gene signatures associated with carcinogenesis, including angiogenesis. Treatment with the angiogenesis inhibitor axitinib eliminated the colon tumor-promoting effect of experimental CH driven by Dnmt3a haploinsufficiency and rebalanced hematopoiesis. This study provides conceptually novel insights into non-tumor-cell-autonomous effects of hematopoietic alterations on colon carcinogenesis and identifies potential therapeutic strategies.

DNMT3A Harboring Leukemia-Associated Mutations Directs Sensitivity to DNA Damage at Replication Forks.

PURPOSE: In acute myeloid leukemia (AML), recurrent DNA methyltransferase 3A (DNMT3A) mutations are associated with chemoresistance and poor prognosis, especially in advanced-age patients. Gene-expression studies in DNMT3A-mutated cells identified signatures implicated in deregulated DNA damage response and replication fork integrity, suggesting sensitivity to replication stress. Here, we tested whether pharmacologically induced replication fork stalling, such as with cytarabine, creates a therapeutic vulnerability in cells with DNMT3A(R882) mutations. EXPERIMENTAL DESIGN: Leukemia cell lines, genetic mouse models, and isogenic cells with and without DNMT3A(mut) were used to evaluate sensitivity to nucleoside analogues such as cytarabine in vitro and in vivo, followed by analysis of DNA damage and signaling, replication restart, and cell-cycle progression on treatment and after drug removal. Transcriptome profiling identified pathways deregulated by DNMT3A(mut) expression. RESULTS: We found increased sensitivity to pharmacologically induced replication stress in cells expressing DNMT3A(R882)-mutant, with persistent intra-S-phase checkpoint activation, impaired PARP1 recruitment, and elevated DNA damage, which was incompletely resolved after drug removal and carried through mitosis. Pulse-chase double-labeling experiments with EdU and BrdU after cytarabine washout demonstrated a higher rate of fork collapse in DNMT3A(mut)-expressing cells. RNA-seq studies supported deregulated cell-cycle progression and p53 activation, along with splicing, ribosome biogenesis, and metabolism. CONCLUSIONS: Together, our studies show that DNMT3A mutations underlie a defect in recovery from replication fork arrest with subsequent accumulation of unresolved DNA damage, which may have therapeutic tractability. These results demonstrate that, in addition to its role in epigenetic control, DNMT3A contributes to preserving genome integrity during replication stress. See related commentary by Viny, p. 573.