bims-metorg 2026-03-01 papers

Issue of 2026–03–01
four papers selected by
Bruna Martins Garcia, CABIMER

Ther Adv Med Oncol. 2026 ;18 17588359261424770

The relationship between genetic mutations and organ metastasis in non-small cell lung cancer.

Haiying Xue, Yuzhu Chen, Fei Qi, Kaixuan Chen, Xiaolin Liu, Muxin Zhang, Ziyuan Gao, Shanshan Cai, Tianbo Gao, Tongmei Zhang.

Non-small cell lung cancer is among the most prevalent cancers worldwide, with its high metastatic potential driving poor prognosis and mortality. Advances in molecular testing and high-throughput sequencing have highlighted the roles of driver genes (EGFR, ALK, KRAS) and key non-driver genes (TP53, STK11, KEAP1) in NSCLC metastasis. These mutations influence tumor invasiveness, drug resistance, and organ-specific metastatic patterns-EGFR and ALK mutations favor brain metastasis, KRAS mutations are linked to bone, liver, and multiple lung metastases, while TP53, STK11, and KEAP1 mutations increase multi-organ metastatic risk. This review summarizes the associations between genetic mutations and metastatic sites, explores underlying molecular mechanisms, and discusses mutation-based risk prediction and personalized therapeutic strategies. With multi-omics integration and further clinical research, genetic profiling may become a key tool for guiding metastasis prevention, early intervention, and treatment optimization in NSCLC.

Keywords: circulating tumor cells (CTCs); genetic mutations; non-small cell lung cancer; organ-specific metastasis; pre-metastatic niche (PMN)

DOI: https://doi.org/10.1177/17588359261424770

bioRxiv. 2026 Feb 18. pii: 2026.02.18.706515. [Epub ahead of print]

3D, multi-omic imaging reveals molecular biomarkers of the pre-metastatic niche in lung cancer.

The recurrence rate following complete surgical resection of primary non-small cell lung cancer is as high as 55%, yet no approach currently exists to evaluate the risk of local recurrence. The premetastatic paradigm is the recognition that metastasis is preceded by reprogramming naïve tissues to prime a microenvironment for tumor cell survival and subsequent reactivation. Identification of biomarkers of the pre-metastatic niche would allow us to evaluate a patient's risk of local relapse in the normal lung parenchyma surrounding the resected tumor. We designed a workflow incorporating in vivo modelling, radiology, and deep learning-guided three-dimensional (3D) imaging, spatial proteomics, and transcriptomics to identify previously unreported signals associated with the early transformation of the lung parenchyma announcing regional metastasis. We curated biorepository spanning timepoints before and after resection of primary Lewis Lung Carcinoma (LLC) tumors. Using radiology and cellular resolution 3D histology, we calculated the number and distribution of metastases in mouse lungs and developed an algorithm to guide placement of spatial proteomics and transcriptomics to regions containing early micro-metastases and the pre-metastatic microenvironment. Molecular and tissue features associated with presence, size, and location of metastases guided the identification of both myeloid (F4/80) and senescent (p16/p21) cell signatures in the premetastatic and metastatic environments. Finally, multiparametric flow cytometry of metastatic lungs in a senescence reporter GEMM (tdTomato-p16 INKA mice) resolved senescent cells including alveolar macrophages as the cellular phenotypes associated with these early premetastatic signatures. Altogether, this work highlights a novel AI-assisted approach for detection of biomarkers of tissue remodeling during lung cancer invasion.

DOI: https://doi.org/10.64898/2026.02.18.706515

Comput Biol Chem. 2026 Feb 20. pii: S1476-9271(26)00078-2. [Epub ahead of print]123 108953

A perturbed multilayer perceptron approach to predicting distant metastatic sites of cancer patients.

Shiyang Wang, Dostonjon Mamatkarimov, Kyungsook Han.

Cancer metastasis accounts for about 90% of cancer-related mortality, but is difficult to predict. In particular, distant metastasis is more difficult to predict by a learning method than lymph node metastasis due to the limited amount of data available for training a model and the inherent complexity of distant metastasis. Predicting distant metastatic sites from a primary cancer is even more difficult than predicting whether or not distant metastasis will occur. We developed a deep learning model called a perturbed multilayer perceptron (PMLP) to predict distant metastatic sites using expression levels of competing endogenous RNAs and their correlations at the primary site of cancer samples. In independent testing of PMLP on datasets which were not used in training, it showed high predictive performance (average AUC of 0.99, accuracy above 96%, and F1 scores above 0.91) in all metastatic sites. In comparison of the model with other state-of-the-art methods, our model showed a better performance. This model along with the explanation functionality of its prediction results can be used as useful aids to predict potential distant metastatic sites from gene expressions at the primary sites of cancer. To the best of our knowledge, this is the first study to employ PMLP combined with ceRNA correlation changes (ΔSCCs) for predicting specific distant metastatic sites, showing superior predictive performance with model interpretability.

Keywords: Cancer metastasis; Competitive endogenous RNA; Perturbed multilayer perceptron

DOI: https://doi.org/10.1016/j.compbiolchem.2026.108953

Cancers (Basel). 2026 Feb 09. pii: 566. [Epub ahead of print]18(4):

Disparities in Tumor Microenvironment Between Primary and Metastatic Colorectal Cancer: Impact on Immune Infiltration and Survival.

Ewelina Dziąg-Dudek, Aleksandra Semeniuk-Wojtaś, Magdalena Modzelewska, Arkadiusz Lubas, Letycja Róg, Barbara Górnicka, Rafał Stec.

BACKGROUND/OBJECTIVES: In recent years, growing evidence that the tumor microenvironment (TME) plays crucial roles in the progression and treatment responses of various cancers has emerged. Unfortunately, we still do not fully understand the mechanisms through which the TME influences cancer development. Therefore, the aim of this study is to assess the impact of the TME on the clinical course of the disease, comparing primary and metastatic tumors.
MATERIALS AND METHODS: This retrospective study included 30 colorectal cancer patients for which tissue samples from primary and metastatic tumors were available for immunohistochemistry. A multiple Cox proportional hazards regression analysis was performed to characterize differences between the microenvironments of primary and metastatic tumors, as well as between lesions diagnosed at different times after resection.
RESULTS: Immune cell infiltration was higher in metastatic than primary tumors. Statistically significant differences were observed only in the central part of the tumor, while cell infiltration at the periphery had no prognostic significance. In the multivariate analysis, a positive correlation was revealed between the expression of Programmed Death-Ligand 1 (PD-L1) on primary tumor cells (TCs) and survival (HR: 5.43; 95% CI: 1.89-15.61; p = 0.0017).
CONCLUSIONS: Primary and metastatic tumors differ regarding their tumor microenvironment. As such, the tumor immune status should be considered as a key factor when selecting a therapeutic strategy, as well as for post-treatment surveillance.

Keywords: CD68; CD8; PD-L1; colorectal cancer; immune cells; metastases; tumor microenvironment

DOI: https://doi.org/10.3390/cancers18040566