Precision models in hepatocellular carcinoma
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Targeting of TAMs: can we be more clever than cancer cells?
With increasing incidence and geography, cancer is one of the leading causes of death, reduced quality of life and disability worldwide. Principal progress in the development of new anticancer therapies, in improving the efficiency of immunotherapeutic tools, and in the personification of conventional therapies needs to consider cancer-specific and patient-specific programming of innate immunity. Intratumoral TAMs and their precursors, resident macrophages and monocytes, are principal regulators of tumor progression and therapy resistance. Our review summarizes the accumulated evidence for the subpopulations of TAMs and their increasing number of biomarkers, indicating their predictive value for the clinical parameters of carcinogenesis and therapy resistance, with a focus on solid cancers of non-infectious etiology. We present the state-of-the-art knowledge about the tumor-supporting functions of TAMs at all stages of tumor progression and highlight biomarkers, recently identified by single-cell and spatial analytical methods, that discriminate between tumor-promoting and tumor-inhibiting TAMs, where both subtypes express a combination of prototype M1 and M2 genes. Our review focuses on novel mechanisms involved in the crosstalk among epigenetic, signaling, transcriptional and metabolic pathways in TAMs. Particular attention has been given to the recently identified link between cancer cell metabolism and the epigenetic programming of TAMs by histone lactylation, which can be responsible for the unlimited protumoral programming of TAMs. Finally, we explain how TAMs interfere with currently used anticancer therapeutics and summarize the most advanced data from clinical trials, which we divide into four categories: inhibition of TAM survival and differentiation, inhibition of monocyte/TAM recruitment into tumors, functional reprogramming of TAMs, and genetic enhancement of macrophages.
HER2 amplification and HER2 low expression in endometrial carcinoma: prevalence across molecular, histological and clinicopathological risk groups
Emerging HER2-targeted therapies provide new treatment options for patients with HER2-expressing tumors. This study investigates the prevalence of HER2 amplification and HER2 low expression across a well-characterized cohort of endometrial carcinoma.
Evolutionary optimization of model merging recipes
Large language models (LLMs) have become increasingly capable, but their development often requires substantial computational resources. Although model merging has emerged as a cost-effective promising approach for creating new models by combining existing ones, it currently relies on human intuition and domain knowledge, limiting its potential. Here we propose an evolutionary approach that overcomes this limitation by automatically discovering effective combinations of diverse open-source models, harnessing their collective intelligence without requiring extensive additional training data or compute. Our approach operates in both parameter space and data flow space, allowing optimization beyond just the weights of the individual models. This approach even facilitates cross-domain merging, generating models such as a Japanese LLM with math reasoning capabilities. Surprisingly, our Japanese math LLM achieved state-of-the-art performance on a variety of established Japanese LLM benchmarks, even surpassing models with substantially more parameters, despite not being explicitly trained for such tasks. Furthermore, a culturally aware Japanese vision–language model generated through our approach demonstrates its effectiveness in describing Japanese culture-specific content, outperforming previous Japanese vision–language models. This work not only contributes new state-of-the-art models back to the open-source community but also introduces a new paradigm for automated model composition, paving the way for exploring alternative, efficient approaches to foundation model development.
Efficient computation using spatial-photonic Ising machines with low-rank and circulant matrix constraints
Spatial-photonic Ising machines (SPIMs) have shown promise as an energy-efficient Ising machine, but currently can only solve a limited set of Ising problems. There is currently limited understanding on what experimental constraints may impact the performance of SPIM, and what computationally intensive problems can be efficiently solved by SPIM. Our results indicate that the performance of SPIMs is critically affected by the rank and precision of the coupling matrices. By developing and assessing advanced decomposition techniques, we expand the range of problems SPIMs can solve, overcoming the limitations of traditional Mattis-type matrices. Our approach accommodates a diverse array of coupling matrices, including those with inherently low ranks, applicable to complex NP-complete problems. We explore the practical benefits of the low-rank approximation in optimisation tasks, particularly in financial optimisation, to demonstrate the real-world applications of SPIMs. Finally, we evaluate the computational limitations imposed by SPIM hardware precision and suggest strategies to optimise the performance of these systems within these constraints.
A Consensus Statement on establishing causality, therapeutic applications and the use of preclinical models in microbiome research
The gut microbiome comprises trillions of microorganisms and profoundly influences human health by modulating metabolism, immune responses and neuronal functions. Disruption in gut microbiome composition is implicated in various inflammatory conditions, metabolic disorders and neurodegenerative diseases. However, determining the underlying mechanisms and establishing cause and effect is extremely difficult. Preclinical models offer crucial insights into the role of the gut microbiome in diseases and help identify potential therapeutic interventions. The Human Microbiome Action Consortium initiated a Delphi survey to assess the utility of preclinical models, including animal and cell-based models, in elucidating the causal role of the gut microbiome in these diseases. The Delphi survey aimed to address the complexity of selecting appropriate preclinical models to investigate disease causality and to study host–microbiome interactions effectively. We adopted a structured approach encompassing a literature review, expert workshops and the Delphi questionnaire to gather insights from a diverse range of stakeholders. Experts were requested to evaluate the strengths, limitations, and suitability of these models in addressing the causal relationship between the gut microbiome and disease pathogenesis. The resulting consensus statements and recommendations provide valuable insights for selecting preclinical models in future studies of gut microbiome-related diseases.
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