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Revitalizing antibiotics with macromolecular engineering: tackling gram-negative superbugs and mixed species bacterial biofilm infections in vivo
No full textThe escalating prevalence of multidrug-resistant Gram-negative pathogens, coupled with dwindling antibiotic development, has created a critical void in the clinical pipeline. This alarming issue is exacerbated by the formation of biofilms by these superbugs and their frequent coexistence in mixed-species biofilms, conferring extreme antibiotic tolerance. Herein, we present an amphiphilic cationic macromolecule, ACM-AHex, as an innovative antibiotic adjuvant to rejuvenate and repurpose resistant antibiotics, for instance, rifampicin, fusidic acid, erythromycin, and chloramphenicol. ACM-AHex mildly perturbs the bacterial membrane, enhancing antibiotic permeability, hampers efflux machinery, and produces reactive oxygen species, resulting in a remarkable 64–1024-fold potentiation in antibacterial activity. The macromolecule reduces bacterial virulence and macromolecule-drug cocktail significantly eradicate both mono- and multispecies bacterial biofilms, achieving >99.9% bacterial reduction in the murine biofilm infection model. Demonstrating potent biocompatibility across multiple administration routes, ACM-AHex offers a promising strategy to restore obsolete antibiotics and combat recalcitrant Gram-negative biofilm-associated infections, advocating for further clinical evaluation as a next-generation macromolecular antibiotic adjuvant
Nuances in the treatment of ewing sarcoma of the head and neck in a low–middle‐income country setting: a multi‐disciplinary approach
No full textBackground
Ewing sarcoma of the head and neck (ES-HN) is a rare subsite of ES, where therapeutic outcomes need to be explored further.
Methods
This retrospective study includes ES-HN patients registered at our center between 2003 and 2019. Demographic details and treatment outcomes were recorded from the hospital database. Prognostic factors for survival were identified by Cox regression.
Results
Eighty-five patients were included. Metastatic disease was seen in nine patients (10.59%). Local therapy included radiotherapy alone (n = 38; 44.7%), surgery plus radiotherapy (n = 15; 17.6%), or surgery alone (n = 8; 9.4%). The median overall survival (OS) was 37.4 months. On multivariable analysis, osseous primary (HR 0.40; p = 0.009) and male sex (HR 0.43; p = 0.023) were associated with superior OS. Leucocytosis (HR 3.46; p = 0.001) was associated with inferior OS.
Conclusions
ES-HN has favorable biology with metastases being rare at baseline. However, leucocytosis, extra-osseous disease, and female sex are poor prognostic factors. In resource-challenged settings, difficulties in administering local therapy may contribute to inferior outcomes
Unravelling the gut-tumor axis in neuroblastoma: promising leads and cautionary gaps
No full textWe read with great interest the article by Chu et al. investigating childhood neuroblastoma (NB) and gut microbiota (GM) using Mendelian randomization (MR) [1]. NB is the most common extracranial solid cancer among children [2], and novel insights into its etiology and causation are the need of the hour. The authors used genome-wide association study (GWAS) data from MiBioGen and IEU consortia to identify genus of GM which were associated with NB. They identified class Erysipelotrichia as potentially protective (IVW odds ratio ~ 0.37) and genus Oscillospira as a risk factor for NB (OR ~ 2.38). The authors also identified microbiome-linked host genes PELI2 and MUC4 which are associated with tumor pathways. It was interesting to find the proposition that short-chain fatty acids produced by Erysipelotrichia may promote neuronal differentiation in NB. This integration of clinical observations with mechanistic hypotheses represents an innovative direction in pediatric oncology research.
However, several limitations merit discussion. A foremost issue is related to limited population diversity as the GWAS database was predominantly European. GM varies considerably with geographical location, ethnicity and diet patterns [3], hence causal inferences drawn in a particular population may not be applicable across diverse global populations. Future studies should incorporate more diverse cohorts to enhance generalizability of findings.
An additional caveat is regarding the “snapshot” genomic data from the GWAS. There is no information regarding the tumor risk, stage, clinical characteristics and the modality of therapy received. It remains unclear as to whether GM plays a role in the initial tumorigenesis of NB or if the therapy received for the same alters the GM. NB patients are subjected to chemotherapy which is known to alter the GM [4]. In addition, use of antibiotics for management of complications arising out of chemotherapy can perturb the GM. It is difficult to draw inferences about chronology and causation without longitudinal genetic data.
Another limitation lies in the resolution of the microbial data. The researchers studied the gut microbiome using 16S rRNA sequencing aggregated at the genus or class level. Such coarse taxonomic resolution may confound heterogeneity at the strain-level. For instance, “Oscillospira” is a poorly characterized genus-level bacterial taxon, and class “Erysipelotrichia” encompasses a taxonomically broad category. Additionally, 16S rRNA sequencing lacks detailed functional information pertaining to gene activity, metabolite production and host interaction. This precludes elucidation of the precise mechanistic pathways responsible for the observed associations. A more robust approach would have been the use of shotgun metagenomic sequencing [5] which has the ability to identify microbes down to the species or strain level. In addition, it enables a more detailed pathway level analysis. Utilization of metatranscriptomics and/or stool metabolomics in the present study would have enhanced the interpretability of the findings. Future studies could also utilize gnotobiotic animal models or integrative multi-omics for uncovering the precise microbiome-gene-pathway-host crosstalk, which might prove critical in our understanding of this complex topic, enabling validation of these associations. It could potentially pave the way for microbiome-modulating therapeutic strategies in NB
PP475 Paired targeted genomic profiling of triple-negative breast cancer to identify novel mutations in residual disease
No full textBackground
Patients with residual disease after neoadjuvant chemotherapy (NACT) in triple-negative breast cancer (TNBC)have a significantly worse prognosis as compared to patients who have a complete pathological response. Residual disease is treated with capecitabine or olaparib (if germline BRCA mutation is present). We planned this study to identify new somatic alterations in the residual disease that were not present at the diagnosis that can potentially be targeted.
Methods
Patients diagnosed with TNBC and planned for treatment with NACT followed by surgery were eligible for this study. DNA was retrieved from archived formalin-fixed paraffin-embedded tissue (FFPE) blocks from the baseline biopsy tissue and the final surgical specimen. DNA was extracted from both FFPE blocks using QIAGEN kits. DNA sequencing was performed using Illumina Novaseq 6000 at a target depth of 300X to cover a custom-made panel of genes. A blood sample was also collected to perform germline testing. Inhouse bioinformatic pipelines were developed to analyze data from all three samples.
Results
We identified 36 patients with TNBC who received NACT from July 2023 to December 2023. Of these, 17 were excluded due to inadequate baseline or residual tissue DNA. Of the 19 patients, 10 had a pathological complete response, and 9 had residual disease. Of these, sequencing data from 5 pairs passed the quality check. In all 5 residual tumor tissues, a median of 1257 (range, 483-4757) new genetic alterations were observed that were absent in the germline or baseline biopsy samples. Across the 5 residual tissues, we identified 679 common missense variants. After eliminating variants with no deleterious effect using insilico tools, we found 2 deleterious mutations not present in the biopsy developed after NACT: OTC:A208T and RAG1:R759C.
Conclusions
We found two novel mutations in the residual disease that were absent before NACT administration. Further functional assays would be needed to validate these findings. However, these findings support the hypothesis that novel mutations develop after the administration of chemotherapy that can drive resistance to such therapy and may act as novel targets for new drug development
PXA-like tumors: prognostic implications
No full textPurpose
The classification of central nervous system (CNS) tumors has evolved significantly with the integration of molecular markers, particularly through DNA methylation profiling. We aimed to explore the disparity between epigenetic profiling, histomorphology, and CNS WHO grade by analyzing the therapeutic and survival duration of the patients.
Method
This retrospective study evaluated three ambiguous pediatric cases, aged 9 to 15 years, with a radiological diagnosis of high-grade glioma. A multidisciplinary approach assessed the discordance between histomorphology, epigenetic profiling, CNS WHO grade, and overall survival.
Results
Based on the 5th edition CNS WHO classification, two cases were diagnosed as diffuse pediatric-type high-grade gliomas (PHGG), H3 wild type, IDH wild type, NOS. One of the cases exhibited a BRAF V600E mutation and was classified as glioblastoma with BRAF V600E mutation. DNA methylation profiling using the Heidelberg/DKFZ Classifier classified all three cases as pleomorphic xanthoastrocytoma (PXA), despite a mean survival of only 13.7 months.
Conclusion
The study highlights that the methylation class PXA comprises tumors which can exhibit high-grade features and a poor prognosis
Pediatric high-grade gliomas, H3-wildtype, IDH-wildtype: Refining diagnostic criteria and exploring clinical associations
No full textBackground
Diffuse pediatric-type high-grade gliomas (pHGGs), H3-wildtype (H3 WT), and IDH-wildtype (IDH WT), represent a newly recognized, highly malignant brain tumor entity with unique molecular and epigenetic profiles. Despite their recent inclusion in the WHO 2021 classification, their histological and molecular diversity presents significant diagnostic and therapeutic challenges.
Methods
This study analyzed 12 cases of pHGGs, H3 WT and IDH WT, identified through whole-genome methylation profiling. Clinical, radiological, and histopathological evaluations were complemented by immunohistochemical profiling, employing an extended spectrum of antibodies, and molecular studies. Methylation profiling enabled precise tumor classification and correlation with known subtypes such as RTK-1, RTK-2, and MYCN.
Results
Among the 12 cases, 7 were classified under the RTK-1 subtype, 3 under RTK2 subtype, and a case each under MYCN, pHGGs, H3 WT, and IDH WT subtype B. Notably, 5/12 cases demonstrated a loss of H3K27me3 expression, contradicting the WHO 2021 recommendation for its retention as a diagnostic criterion. Furthermore, 6/7 RTK-1 subtype cases were linked to either mismatch-repair (MMR) deficiency or radiation-induced gliomas, highlighting an enrichment of these clinical scenarios within this subgroup. MGMT promoter methylation was observed in only 4/12 of these cases, consistent with its low prevalence in this tumor category.
Conclusions
This study provides a comprehensive characterization of pHGGs, H3 WT, IDH WT, and emphasizes the clinical and molecular complexity of this rare tumor entity. The findings challenge current WHO diagnostic criteria regarding H3K27me3 retention and demonstrate the critical role of molecular diagnostics, particularly methylation profiling, in refining classification and guiding clinical management. These results advocate for re-evaluation of existing diagnostic frameworks to better accommodate the observed variability and associations in this challenging tumor subtype
Integrating machine learning with dynamic multi-objective optimization for real-time decision-making
No full textReal-time decision-making in dynamic multi-objective optimization problems (DMOPs) is challenging due to constantly changing objectives and constraints. This paper integrates machine learning with Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve DMOPs and make real-time decisions. Learning-based methods have gained popularity for predicting solutions in new environments and capturing changing patterns in optimal solutions. However, existing approaches often struggle with training difficulty and reduced prediction accuracy due to irrelevant or redundant variables. Therefore, we introduce a new interdependent prediction (IDP) technique to identify correlations between variables and prediction targets and select significant variables for a predictive model. In this way, a better initial population is predicted. The IDP strategy is integrated within the dynamic NSGA-II, introducing a new algorithm called IDP-DNSGA-II. This integration facilitates rapid convergence, finding optimal or near-optimal solutions. The proposed method is evaluated against standard benchmarks, demonstrating superior performance in convergence speed and solution diversity with the changes in the problem environment. The IDP-DNSGA-II is validated through real-world optimization challenges in sustainable automobile production distribution in order-to-delivery systems to enhance environmental sustainability and operational efficiency. This study identifies the minimum frequency of change required in real-world problems to adequately track the optimal decision in real-time
Integration of prediction and optimization for smart stock portfolio selection
No full textMachine learning (ML) algorithms pose significant challenges in predicting unknown parameters for optimization models in decision-making scenarios. Conventionally, prediction models are optimized independently in decision-making processes, whereas ML algorithms primarily focus on minimizing prediction errors, neglecting the role of decision-making in downstream optimization tasks. The pursuit of high prediction accuracy may not always align with the goal of reducing decision errors. The idea of reducing decision errors has been proposed to address this limitation. This paper introduces an optimization process that integrates predictive regression models within a mean–variance optimization setting. This innovative technique introduces a general loss function to capture decision errors. Consequently, the predictive model not only focuses on forecasting unknown optimization parameters but also emphasizes the predicted values that minimize decision errors. This approach prioritizes decision accuracy over the potential accuracy of unknown parameter prediction. In contrast to traditional ML approaches that minimize standard loss functions such as mean squared error, our proposed model seeks to minimize the objective value derived directly from the decision-making problem. Furthermore, this strategy is validated by developing an optimization-based regression tree model for predicting stock returns and reducing decision errors. Empirical evaluations of our framework reveal its superiority over conventional regression tree methods, demonstrating enhanced decision quality. The computational experiments are conducted on a stock market dataset to compare the effectiveness of the proposed framework with the conventional regression tree-based approach. Remarkably, the results confirm the strengths inherent in this holistic approach
Global Precipitation Experiment—A New World Climate Research Programme Lighthouse Activity
No full textThe future state of the global water cycle and the prediction of freshwater availability for humans around the world remain among the challenges of climate research and are relevant to several United Nations Sustainable Development Goals. The Global Precipitation Experiment (GPEX) takes on the challenge of improving the prediction of precipitation quantity, phase, timing, and intensity, characteristics that are products of a complex integrated system. It will achieve this by leveraging existing World Climate Research Programme (WCRP) activities and community capabilities in satellite, surface-based, and airborne observations, modeling, and experimental research and by conducting new and focused activities. It was launched in October 2023 as a WCRP Lighthouse Activity. Here, we present an overview of the GPEX Science Plan that articulates the primary science questions related to precipitation measurements, process understanding, model performance and improvements, and plans for capacity development. The central phase of GPEX is the WCRP Years of Precipitation for 2–3 years with coordinated global field campaigns focusing on different storm types (atmospheric rivers, mesoscale convective systems, monsoons, and tropical cyclones, among others) over different regions and seasons. Activities are planned over the three phases (before, during, and after the Years of Precipitation) spanning a decade. These include gridded data evaluation and development, advanced modeling, enhanced understanding of processes critical to precipitation, multiscale prediction of precipitation events across scales, and capacity development. These activities will be further developed as part of the GPEX Implementation Plan
Mutation bias alters the distribution of fitness effects of mutations
No full textMutation bias is an important factor determining the diversity of genetic variants available for selection. As adaptation proceeds and some beneficial mutations are fixed, new beneficial mutations become rare, limiting further adaptation. The depletion of beneficial mutations is especially stark within the mutational class favored by the existing mutation bias. Recent theoretical work predicts that this problem may be alleviated by a change in the direction of mutation bias (i.e., a bias reversal). If populations sample previously underexplored types of mutations, the distribution of fitness effects (DFE) of mutations should shift towards more beneficial mutations. Here, we test this prediction using Escherichia coli, which has a transition mutation bias, with ∼ 54% single-nucleotide mutations being transitions compared to the unbiased expectation of ∼ 33% transitions. We generated mutant strains with a wide range of mutation biases, from 97% transitions to 98% transversions, either reinforcing or reversing the wild-type transition bias. Quantifying DFEs of ∼ 100 single mutations obtained from mutation accumulation experiments for each strain, we find strong support for the theoretical prediction. Strains that oppose the ancestral bias (i.e., with a strong transversion bias) have DFEs with the highest proportion of beneficial mutations, whereas strains that exacerbate the ancestral transition bias have up to 10-fold fewer beneficial mutations. Such dramatic differences in the DFE should drive large variation in the rate and outcome of adaptation, suggesting an important and generalized evolutionary role for mutation bias shifts