Mobilizing health equity through Computable Biomedical Knowledge (CBK): a call to action to the library, information sciences, and health informatics communities.

The twin pandemics of COVID-19 and structural racism brought into focus health disparities and disproportionate impacts of disease on communities of color. Health equity has subsequently emerged as a priority. Recognizing that the future of health care will be informed by advanced information technologies including artificial intelligence (AI), machine learning, and algorithmic applications, the authors argue that to advance towards states of improved health equity, health information professionals need to engage in and encourage the conduct of research at the intersections of health equity, health disparities, and computational biomedical knowledge (CBK) applications. Recommendations are provided with a means to engage in this mobilization effort.

Epigenetic developmental mechanisms underlying sex differences in cancer.

Cancer risk is modulated by hereditary and somatic mutations, exposures, age, sex, and gender. The mechanisms by which sex and gender work alone and in combination with other cancer risk factors remain underexplored. In general, cancers that occur in both the male and female sexes occur more commonly in XY compared with XX individuals, regardless of genetic ancestry, geographic location, and age. Moreover, XY individuals are less frequently cured of their cancers, highlighting the need for a greater understanding of sex and gender effects in oncology. This will be necessary for optimal laboratory and clinical cancer investigations. To that end, we review the epigenetics of sexual differentiation and its effect on cancer hallmark pathways throughout life. Specifically, we will touch on how sex differences in metabolism, immunity, pluripotency, and tumor suppressor functions are patterned through the epigenetic effects of imprinting, sex chromosome complement, X inactivation, genes escaping X inactivation, sex hormones, and life history.

Daily glucocorticoids promote glioblastoma growth and circadian synchrony to the host.

Glioblastoma (GBM) is the most common primary brain tumor in adults with a poor prognosis despite aggressive therapy. A recent, retrospective clinical study found that administering Temozolomide in the morning increased patient overall survival by 6 months compared to evening. Here, we tested the hypothesis that daily host signaling regulates tumor growth and synchronizes circadian rhythms in GBM. We found daily Dexamethasone promoted or suppressed GBM growth depending on time of day of administration and on the clock gene, Bmal1. Blocking circadian signals, like VIP or glucocorticoids, dramatically slowed GBM growth and disease progression. Finally, mouse and human GBM models have intrinsic circadian rhythms in clock gene expression in vitro and in vivo that entrain to the host through glucocorticoid signaling, regardless of tumor type or host immune status. We conclude that GBM entrains to the circadian circuit of the brain, which modulates its growth through clockcontrolled cues, like glucocorticoids.

A Phase 1/2 Study of Disulfiram and Copper With Concurrent Radiation Therapy and Temozolomide for Patients With Newly Diagnosed Glioblastoma.

PURPOSE: This phase 1/2 study aimed to evaluate the safety and preliminary efficacy of combining disulfiram and copper (DSF/Cu) with radiation therapy (RT) and temozolomide (TMZ) in patients with newly diagnosed glioblastoma (GBM). METHODS AND MATERIALS: Patients received standard RT and TMZ with DSF (250-375 mg/d) and Cu, followed by adjuvant TMZ plus DSF (500 mg/d) and Cu. Pharmacokinetic analyses determined drug concentrations in plasma and tumors using high-performance liquid chromatography-mass spectrometry. RESULTS: Thirty-three patients, with a median follow-up of 26.0 months, were treated, including 12 IDH-mutant, 9 NF1-mutant, 3 BRAF-mutant, and 9 other IDH-wild-type cases. In the phase 1 arm, 18 patients were treated; dose-limiting toxicity probabilities were 10% (95% CI, 3%-29%) at 250 mg/d and 21% (95% CI, 7%-42%) at 375 mg/d. The phase 2 arm treated 15 additional patients at 250 mg/d. No significant difference in overall survival or progression-free survival was noted between IDH- and NF1-mutant cohorts compared with institutional counterparts treated without DSF/Cu. However, extended remission occurred in 3 BRAF-mutant patients. Diethyl-dithiocarbamate-copper, the proposed active metabolite of DSF/Cu, was detected in plasma but not in tumors. CONCLUSIONS: The maximum tolerated dose of DSF with RT and TMZ is 375 mg/d. DSF/Cu showed limited clinical efficacy for most patients. However, promising efficacy was observed in BRAF-mutant GBM, warranting further investigation.

Treating sex and gender differences as a continuous variable can improve precision cancer treatments.

BACKGROUND: The significant sex and gender differences that exist in cancer mechanisms, incidence, and survival, have yet to impact clinical practice. One barrier to translation is that cancer phenotypes cannot be segregated into distinct male versus female categories. Instead, within this convenient but contrived dichotomy, male and female cancer phenotypes are highly overlapping and vary between female- and male- skewed extremes. Thus, sex and gender-specific treatments are unrealistic, and our translational goal should be adaptation of treatment to the variable effects of sex and gender on targetable pathways. METHODS: To overcome this obstacle, we profiled the similarities in 8370 transcriptomes of 26 different adult and 4 different pediatric cancer types. We calculated the posterior probabilities of predicting patient sex and gender based on the observed sexes of similar samples in this map of transcriptome similarity. RESULTS: Transcriptomic index (TI) values were derived from posterior probabilities and allowed us to identify poles with local enrichments for male or female transcriptomes. TI supported deconvolution of transcriptomes into measures of patient-specific activity in sex and gender-biased, targetable pathways. It identified sex and gender-skewed extremes in mechanistic phenotypes like cell cycle signaling and immunity, and precisely positioned each patient's whole transcriptome on an axis of continuously varying sex and gender phenotypes. CONCLUSIONS: Cancer type, patient sex and gender, and TI value provides a novel and patient- specific mechanistic identifier that can be used for realistic sex and gender-adaptations of precision cancer treatment planning.

Some efforts to improve cancer therapy involve the idea of personalizing treatments to who a patient is and how their cancer operates. Personalizing treatment can involve straighforward features like a patient's age, family cancer history, personal disease and surgical histories, as well as more complex features like analysis of their specific cancer's mechanisms of growth and spread throughout the body. One glaring omission in common personalization schemes is the sex and gender of the patient. While patient sex and gender is known to substantially affect cancer rates and response to treatment, we do not yet use this information in treatment planning. There are multiple reasons for this but among them is that we tend to think about sex and gender as an either/or categorization. You are either a male/man or a female/woman. This is not accurate as there are many variables that contribute to who an individual is as a male/man or female/woman. This variability is a challenge to incorporating these features into personalized treatment planning. Here, we have developed a method to address this challenge. It is our great hope that this will enable the use of this critically important element of personalization in cancer treatment planning and improve survival rates for all patients.

Circadian regulation of MGMT expression and promoter methylation underlies daily rhythms in TMZ sensitivity in glioblastoma.

BACKGROUND: Glioblastoma (GBM) is the most common primary brain tumor in adults. Despite extensive research and clinical trials, median survival post-treatment remains at 15 months. Thus, all opportunities to optimize current treatments and improve patient outcomes should be considered. A recent retrospective clinical study found that taking TMZ in the morning compared to the evening was associated with a 6-month increase in median survival in patients with MGMT-methylated GBM. Here, we hypothesized that TMZ efficacy depends on time-of-day and O6-Methylguanine-DNA Methyltransferase (MGMT) activity in murine and human models of GBM. METHODS AND RESULTS: In vitro recordings using real-time bioluminescence reporters revealed that GBM cells have intrinsic circadian rhythms in the expression of the core circadian clock genes Bmal1 and Per2, as well as in the DNA repair enzyme, MGMT. Independent measures of MGMT transcript levels and promoter methylation also showed daily rhythms intrinsic to GBM cells. These cells were more susceptible to TMZ when delivered at the daily peak of Bmal1 transcription. We found that in vivo morning administration of TMZ also decreased tumor size and increased body weight compared to evening drug delivery in mice bearing GBM xenografts. Finally, inhibition of MGMT activity with O6-Benzylguanine abrogated the daily rhythm in sensitivity to TMZ in vitro by increasing sensitivity at both the peak and trough of Bmal1 expression. CONCLUSION: We conclude that chemotherapy with TMZ can be dramatically enhanced by delivering at the daily maximum of tumor Bmal1 expression and minimum of MGMT activity and that scoring MGMT methylation status requires controlling for time of day of biopsy.

Functional network disorganization and cognitive decline following fractionated whole-brain radiation in mice.

Cognitive dysfunction following radiotherapy (RT) is one of the most common complications associated with RT delivered to the brain, but the precise mechanisms behind this dysfunction are not well understood, and to date, there are no preventative measures or effective treatments. To improve patient outcomes, a better understanding of the effects of radiation on the brain's functional systems is required. Functional magnetic resonance imaging (fMRI) has shown promise in this regard, however, compared to neural activity, hemodynamic measures of brain function are slow and indirect. Understanding how RT acutely and chronically affects functional brain organization requires more direct examination of temporally evolving neural dynamics as they relate to cerebral hemodynamics for bridging with human studies. In order to adequately study the underlying mechanisms of RT-induced cognitive dysfunction, the development of clinically mimetic RT protocols in animal models is needed. To address these challenges, we developed a fractionated whole-brain RT protocol (3Gy/day for 10 days) and applied longitudinal wide field optical imaging (WFOI) of neural and hemodynamic brain activity at 1, 2, and 3 months post RT. At each time point, mice were subject to repeated behavioral testing across a variety of sensorimotor and cognitive domains. Disruptions in cortical neuronal and hemodynamic activity observed 1 month post RT were significantly worsened by 3 months. While broad changes were observed in functional brain organization post RT, brain regions most impacted by RT occurred within those overlapping with the mouse default mode network and other association areas similar to prior reports in human subjects. Further, significant cognitive deficits were observed following tests of novel object investigation and responses to auditory and contextual cues after fear conditioning. Our results fill a much-needed gap in understanding the effects of whole-brain RT on systems level brain organization and how RT affects neuronal versus hemodynamic signaling in the cortex. Having established a clinically-relevant injury model, future studies can examine therapeutic interventions designed to reduce neuroinflammation-based injury following RT. Given the overlap of sequelae that occur following RT with and without chemotherapy, these tools can also be easily incorporated to examine chemotherapy-related cognitive impairment.

An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients.

Imaging is central to the clinical surveillance of brain tumors yet it provides limited insight into a tumor's underlying biology. Machine learning and other mathematical modeling approaches can leverage paired magnetic resonance images and image-localized tissue samples to predict almost any characteristic of a tumor. Image-based modeling takes advantage of the spatial resolution of routine clinical scans and can be applied to measure biological differences within a tumor, changes over time, as well as the variance between patients. This approach is non-invasive and circumvents the intrinsic challenges of inter- and intratumoral heterogeneity that have historically hindered the complete assessment of tumor biology and treatment responsiveness. It can also reveal tumor characteristics that may guide both surgical and medical decision-making in real-time. Here we describe a general framework for the acquisition of image-localized biopsies and the construction of spatiotemporal radiomics models, as well as case examples of how this approach may be used to address clinically relevant questions.

Cognition and Brain System Segregation in Pediatric Brain Tumor Patients Treated with Proton Therapy.

Purpose: Pediatric brain tumor patients often experience significant cognitive sequelae. Resting-state functional MRI (rsfMRI) provides a measure of brain network organization, and we hypothesize that pediatric brain tumor patients treated with proton therapy will demonstrate abnormal brain network architecture related to cognitive outcome and radiation dosimetry. Participants and Methods: Pediatric brain tumor patients treated with proton therapy were enrolled on a prospective study of cognitive assessment using the NIH Toolbox Cognitive Domain. rsfMRI was obtained in participants able to complete unsedated MRI. Brain system segregation (BSS), a measure of brain network architecture, was calculated for the whole brain, the high-level cognition association systems, and the sensory-motor systems. Results: Twenty-six participants were enrolled in the study for cognitive assessment, and 18 completed rsfMRI. There were baseline cognitive deficits in attention and inhibition and processing speed prior to radiation with worsening performance over time in multiple domains. Average BSS across the whole brain was significantly decreased in participants compared with healthy controls (1.089 and 1.101, respectively; P = 0.001). Average segregation of association systems was significantly lower in participants than in controls (P < 0.001) while there was no difference in the sensory motor networks (P = 0.70). Right hippocampus dose was associated with worse attention and inhibition (P < 0.05) and decreased segregation in the dorsal attention network (P < 0.05). Conclusion: Higher mean dose to the right hippocampus correlated with worse dorsal attention network segregation and worse attention and inhibition cognitive performance. Patients demonstrated alterations in brain network organization of association systems measured with rsfMRI; however, somatosensory system segregation was no different from healthy children. Further work with preradiation rsfMRI is needed to assess the effects of surgery and presence of a tumor on brain network architecture.

Cognitive deficits and altered functional brain network organization in pediatric brain tumor patients.

Survivors of pediatric brain tumors experience significant cognitive deficits from their diagnosis and treatment. The exact mechanisms of cognitive injury are poorly understood, and validated predictors of long-term cognitive outcome are lacking. Resting state functional magnetic resonance imaging allows for the study of the spontaneous fluctuations in bulk neural activity, providing insight into brain organization and function. Here, we evaluated cognitive performance and functional network architecture in pediatric brain tumor patients. Forty-nine patients (7-18 years old) with a primary brain tumor diagnosis underwent resting state imaging during regularly scheduled clinical visits. All patients were tested with a battery of cognitive assessments. Extant data from 139 typically developing children were used as controls. We found that obtaining high-quality imaging data during routine clinical scanning was feasible. Functional network organization was significantly altered in patients, with the largest disruptions observed in patients who received propofol sedation. Awake patients demonstrated significant decreases in association network segregation compared to controls. Interestingly, there was no difference in the segregation of sensorimotor networks. With a median follow-up of 3.1 years, patients demonstrated cognitive deficits in multiple domains of executive function. Finally, there was a weak correlation between decreased default mode network segregation and poor picture vocabulary score. Future work with longer follow-up, longitudinal analyses, and a larger cohort will provide further insight into this potential predictor.

Accounting for sex differences as a continuous variable in cancer treatments.

The significant sex differences that exist in cancer mechanisms, incidence, and survival, have yet to impact clinical practice. We propose that one barrier to translation is that sex differences in cancer phenotypes resemble sex differences in height: highly overlapping, but distinct, male and female population distributions that vary continuously between female- and male- skewed extremes. A consequence of this variance is that sex-specific treatments are rendered unrealistic, and our translational goal should be adaptation of treatment to the variable sex-effect on targetable pathways. To develop a tool that could advance this goal, we applied a Bayesian Nearest Neighbor (BNN) analysis to 8370 cancer transcriptomes from 26 different adult and 4 different pediatric cancer types to establish patient-specific Transcriptomic Indices (TI). TI precisely positions a patient's whole transcriptome on axes of mechanistic phenotypes like cell cycle signaling and immunity that exhibit skewing in the cancer population relative to sex-identified extremes (poles). Importantly, the TI approach reveals that even when TI values are identical, underlying mechanisms in male and female individuals can differ in identifiable ways. Thus, cancer type, patient sex, and TI value provides a novel and patient- specific mechanistic identifier that can be used for precision cancer treatment planning.

Image-based models of T-cell distribution identify a clinically meaningful response to a dendritic cell vaccine in patients with glioblastoma.

BACKGROUND: Glioblastoma is an extraordinarily heterogeneous tumor, yet the current treatment paradigm is a "one size fits all" approach. Hundreds of glioblastoma clinical trials have been deemed failures because they did not extend median survival, but these cohorts are comprised of patients with diverse tumors. Current methods of assessing treatment efficacy fail to fully account for this heterogeneity. METHODS: Using an image-based modeling approach, we predicted T-cell abundance from serial MRIs of patients enrolled in the dendritic cell (DC) vaccine clinical trial. T-cell predictions were quantified in both the contrast-enhancing and non-enhancing regions of the imageable tumor, and changes over time were assessed. RESULTS: A subset of patients in a DC vaccine clinical trial, who had previously gone undetected, were identified as treatment responsive and benefited from prolonged survival. A mere two months after initial vaccine administration, responsive patients had a decrease in model-predicted T-cells within the contrast-enhancing region, with a simultaneous increase in the T2/FLAIR region. CONCLUSIONS: In a field that has yet to see breakthrough therapies, these results highlight the value of machine learning in enhancing clinical trial assessment, improving our ability to prospectively prognosticate patient outcomes, and advancing the pursuit towards individualized medicine.

PyInteraph2 and PyInKnife2 to Analyze Networks in Protein Structural Ensembles.

Due to the complex nature of noncovalent interactions and their long-range effects, analyzing protein conformations using network theory can be enlightening. Protein Structure Networks (PSNs) provide a convenient formalism to study protein structures in relation to essential properties such as key residues for structural stability, allosteric communication, and the effects of modifications of the protein. PSNs can be defined according to very different principles, and the available tools have limitations in input formats, supported models, and version control. Other outstanding problems are related to the definition of network cutoffs and the assessment of the stability of the network properties. The protein science community could benefit from a common framework to carry out these analyses and make them easier to reproduce, reuse, and evaluate. We here provide two open-source software packages, PyInteraph2 and PyInKnife2, to implement and analyze PSNs in a reproducible and documented manner. PyInteraph2 interfaces with multiple formats for protein ensembles and incorporates different network models with the possibility of integrating them into a macronetwork and performing various downstream analyses, including hubs, connected components, and several other centrality measures, and visualizes the networks or further analyzes them thanks to compatibility with Cytoscape.PyInKnife2 that supports the network models implemented in PyInteraph2. It employs a jackknife resampling approach to estimate the convergence of network properties and streamline the selection of distance cutoffs. We foresee that the modular structure of the code and the supported version control system will promote the transition to a community-driven effort, boost reproducibility, and establish common protocols in the PSN field. As developers, we will guarantee the introduction of new functionalities and maintenance, assistance, and training of new contributors.

HaploCart: Human mtDNA haplogroup classification using a pangenomic reference graph.

Current mitochondrial DNA (mtDNA) haplogroup classification tools map reads to a single reference genome and perform inference based on the detected mutations to this reference. This approach biases haplogroup assignments towards the reference and prohibits accurate calculations of the uncertainty in assignment. We present HaploCart, a probabilistic mtDNA haplogroup classifier which uses a pangenomic reference graph framework together with principles of Bayesian inference. We demonstrate that our approach significantly outperforms available tools by being more robust to lower coverage or incomplete consensus sequences and producing phylogenetically-aware confidence scores that are unbiased towards any haplogroup. HaploCart is available both as a command-line tool and through a user-friendly web interface. The C++ program accepts as input consensus FASTA, FASTQ, or GAM files, and outputs a text file with the haplogroup assignments of the samples along with the level of confidence in the assignments. Our work considerably reduces the amount of data required to obtain a confident mitochondrial haplogroup assignment.

Accounting for sex differences variability in the design of sex-adapted cancer treatments.

The significant sex differences that exist in cancer mechanisms, incidence, and survival, have yet to impact clinical practice. We hypothesized that one barrier to translation is that sex differences in cancer phenotypes resemble sex differences in height: highly overlapping, but distinct, male and female population distributions that vary continuously between female- and male- biased extremes. A consequence of this variance is that sex-specific treatments are rendered unrealistic, and our translational goal should be adaptation of treatment to the unique mix of sex-biased mechanisms that are present in each patient. To develop a tool that could advance this goal, we applied a Bayesian Nearest Neighbor (BNN) analysis to 8370 cancer transcriptomes from 26 different adult and 4 different pediatric cancer types to establish patient-specific Transcriptomic Sex Indices (TSI). TSI precisely partitions an individual patient's whole transcriptome into female- and male- biased components such that cancer type, patient sex, and transcriptomics, provide a novel and patient-specific mechanistic identifier that can be used for sex-adapted, precision cancer treatment planning.

Summary of fifth annual public MCBK meeting: Mobilizing computable biomedical knowledge (CBK) around the world.

The massive growth of biomedical knowledge in computable formats poses a challenge for organizations as they consider mobilizing artifacts to be findable, accessible, interoperable, reusable, and trustable. Formed in 2016, the Mobilizing Computable Biomedical Knowledge (MCBK) community is taking action to ensure that health organizations have the infrastructure in place to access and apply computable knowledge; to develop national policies and standards that require all data to be discoverable and available for safe and fair use; and to promote the widespread adoption and implementation of health knowledge in support of healthcare, biomedical research, public health, and education. This report summarizes the main outcomes of the Fifth Annual MCBK meeting, also considered the first manifestly global MCBK meeting, which was held virtually July 12 to 13, 2022. Over 200 participants from diverse domains around the world joined this meeting to frame and address important dimensions for mobilizing CBK.

Diagnostic thresholds and optimal collection protocol of salivary pepsin for gastroesophageal reflux disease.

Gastroesophageal reflux disease (GERD) is primarily diagnosed based on symptoms and response to a proton-pump inhibitor (PPI) trial. Gold standard testing requires an invasive endoscopic procedure, often with ambulatory pH monitoring. Salivary pepsin is a potential noninvasive modality for GERD diagnosis. This study aimed to assess diagnostic performance of salivary pepsin thresholds for GERD and determine optimal collection protocol of saliva in an external validation cohort. Over 10 months, adults with symptoms of GERD undergoing esophagogastroduodenoscopy with wireless pH-monitoring off PPI were enrolled. Saliva was self-collected by participants over 4 days across three different time points: fasting ante meridiem (AM), post-prandial, and bedtime (PM). Pepsin levels were calculated via Peptest. Pepsin variability and agreement were determined using linear mixed effects models and intraclass correlation. Validation of diagnostic threshold and performance characteristics were evaluated by receiver-operator curve analysis. Twenty participants enrolled in the study; 50% with physiologic acid exposure (acid exposure time < 4% no GERD) and 50% with elevated acid exposure (GERD). Mean pepsin concentrations were significantly lower in the AM (22.6 ± 25.2 ng/mL) compared to post-prandial (44.5 ± 36.7 ng/mL) and PM (55.4 ± 47.0 ng/mL). Agreement between pepsin concentrations across 3 days was substantial for AM samples (kappa 0.61), with lower agreement for post-prandial and PM samples. A single AM pepsin concentration of 25 ng/mL was 67% accurate for GERD with 56% sensitivity and 78% specificity. This validation study highlights fair accuracy and performance characteristics of a single fasting AM salivary pepsin concentration for the diagnosis of GERD.

Voicing the need to consider sex-specific differences in research.

Researchers are exploring sex differences in experimental models of both development and disease-but are we doing enough? In this collection of Voices, we celebrate researchers who are asking this question and starting to offer mechanistic clues on sexually dimorphic differences seen in interorgan communication, metabolic disease, neurological disorders, and more.

Single-cell multiomics identifies clinically relevant mesenchymal stem-like cells and key regulators for MPNST malignancy.

Malignant peripheral nerve sheath tumor (MPNST), a highly aggressive Schwann cell (SC)-derived soft tissue sarcoma, arises from benign neurofibroma (NF); however, the identity, heterogeneity and origins of tumor populations remain elusive. Nestin+ cells have been implicated as tumor stem cells in MPNST; unexpectedly, single-cell profiling of human NF and MPNST and their animal models reveal a broad range of nestin-expressing SC lineage cells and dynamic acquisition of discrete cancer states during malignant transformation. We uncover a nestin-negative mesenchymal neural crest-like subpopulation as a previously unknown malignant stem-like state common to murine and human MPNSTs, which correlates with clinical severity. Integrative multiomics profiling further identifies unique regulatory networks and druggable targets against the malignant subpopulations in MPNST. Targeting key epithelial-mesenchymal transition and stemness regulators including ZEB1 and ALDH1A1 impedes MPNST growth. Together, our studies reveal the underlying principles of tumor cell-state evolution and their regulatory circuitries during NF-to-MPNST transformation, highlighting a hitherto unrecognized mesenchymal stem-like subpopulation in MPNST disease progression.