Mitochondrial dynamics orchestrate many essential cellular functions, including metabolism, which is instrumental in promoting cancer growth and metastatic progression. However, how mitochondrial dynamics influences metastatic progression remains poorly understood. Here, we show that breast cancer cells with low metastatic potential exhibit a more fused mitochondrial network compared to highly metastatic cells. To study the impact of mitochondrial dynamics on metastasis, we promoted mitochondrial elongation in metastatic breast cancer cells by individual genetic deletion of three key regulators of mitochondrial fission (Drp1, Fis1, Mff) or by pharmacological intervention with leflunomide. Omics analyses revealed that mitochondrial elongation causes substantial alterations in metabolic pathways and processes related to cell adhesion. In vivo, enhanced mitochondrial elongation by loss of mitochondrial fission mediators or treatment with leflunomide notably reduced metastasis formation. Furthermore, the transcriptomic signature associated with elongated mitochondria correlated with improved clinical outcome in patients with breast cancer. Overall, our findings highlight mitochondrial dynamics as a potential therapeutic target in breast cancer.

Compared to minimally invasive brain metastases (MI BrM), highly invasive (HI) lesions form abundant contacts with cells in the peritumoral brain parenchyma and are associated with poor prognosis. Reactive astrocytes (RAs) labeled by phosphorylated STAT3 (pSTAT3) have recently emerged as a promising therapeutic target for BrM. Here, we explore whether BrM invasion pattern is influenced by pSTAT3+ RAs and may serve as a predictive biomarker for STAT3 inhibition. We used immunohistochemistry to identify pSTAT3⁺ RAs in HI and MI human and patient-derived xenograft (PDX) BrM. Using PDX, syngeneic, and transgenic mouse models of HI and MI BrM, we assessed how pharmacological STAT3 inhibition or RA-specific STAT3 genetic ablation affected BrM growth in vivo. Cancer cell invasion was modeled in vitro using a brain slice-tumor co-culture assay. We performed single-cell RNA sequencing of human BrM and adjacent brain tissue. RAs expressing pSTAT3 are situated at the brain-tumor interface and drive BrM invasive growth. HI BrM invasion pattern was associated with delayed growth in the context of STAT3 inhibition or genetic ablation. We demonstrate that pSTAT3⁺ RAs secrete Chitinase 3-like-1 (CHI3L1), which is a known STAT3 transcriptional target. Furthermore, single-cell RNA sequencing identified CHI3L1-expressing RAs in human HI BrM. STAT3 activation, or recombinant CHI3L1 alone, induced cancer cell invasion into the brain parenchyma using a brain slice-tumor plug co-culture assay. Together, these data reveal that pSTAT3⁺ RA-derived CHI3L1 is associated with BrM invasion, implicating STAT3 and CHI3L1 as clinically relevant therapeutic targets for the treatment of HI BrM.

Brain metastases (BM) are associated with significant morbidity and mortality in patients with advanced cancer. Despite significant advances in surgical, radiation, and systemic therapy in recent years, the median overall survival of patients with BM is less than 1 year. The acquisition of medical images, such as computed tomography (CT) and magnetic resonance imaging (MRI), is critical for the diagnosis and stratification of patients to appropriate treatments. Radiomic analyses have the potential to improve the standard of care for patients with BM by applying artificial intelligence (AI) with already acquired medical images to predict clinical outcomes and direct the personalized care of BM patients. Herein, we outline the existing literature applying radiomics for the clinical management of BM. This includes predicting patient response to radiotherapy and identifying radiation necrosis, performing virtual biopsies to predict tumor mutation status, and determining the cancer of origin in brain tumors identified via imaging. With further development, radiomics has the potential to aid in BM patient stratification while circumventing the need for invasive tissue sampling, particularly for patients not eligible for surgical resection.

Background: Brain metastasis invasion pattern (BMIP) is an emerging biomarker associated with recurrence-free and overall survival in patients, and differential response to therapy in preclinical models. Currently, BMIP can only be determined from the histopathological examination of surgical specimens, precluding its use as a biomarker prior to therapy initiation. The aim of this study was to investigate the potential of machine learning (ML) approaches to develop a noninvasive magnetic resonance imaging (MRI)-based biomarker for BMIP determination. Methods: From an initial cohort of 329 patients, a subset of 132 patients met the inclusion criteria for this retrospective study. We evaluated the ability of an expert neuroradiologist to reliably predict BMIP. Thereafter, the dataset was randomly divided into training/validation (80% of cases) and test subsets (20% of cases). The ground truth for BMIP was the histopathologic evaluation of resected specimens. Following MRI sequence co-registration, advanced feature extraction techniques deriving hand-crafted radiomic features with traditional ML classifiers and convolution-based deep learning (CDL) models were trained and evaluated. Different ML approaches were used individually or using ensembling techniques to determine the model with the best performance for BMIP prediction. Results: Expert evaluation of brain MRI scans could not reliably predict BMIP, with an accuracy of 44%–59% depending on the semantic feature used. Among the different ML and CDL models evaluated, the best-performing model achieved an accuracy of 85% and an F1 score of 90%. Conclusions: ML approaches can effectively predict BMIP, representing a noninvasive MRI-based approach to guide the management of patients with brain metastases.

PURPOSE: MAP2K1/MEK1 mutations are potentially actionable drivers in cancer. MAP2K1 mutations have been functionally classified into three groups according to their dependency on upstream RAS/RAF signaling. However, the clinical efficacy of mitogen-activated protein kinase (MAPK) pathway inhibitors (MAPKi) for MAP2K1-mutant tumors is not well defined. We sought to characterize the genomic and clinical landscape of MAP2K1 mutant tumors to evaluate the relationship between MAP2K1 mutation class and clinical activity of MAPKi. METHODS: We interrogated American Association for Cancer Research (AACR) GENIE (v13) to analyze solid tumors with MAP2K1 mutations. We performed a systematic review and meta-analysis of published reports of patients with MAP2K1-mutant cancers treated with MAPKi according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The primary end point was progression-free survival (PFS), and secondary end points were overall treatment response rate (ORR), duration of response (DOR), and overall survival. RESULTS: In the AACR GENIE data set, class 2 MAP2K1 mutations (63%) were more prevalent than class 1 (24%) and class 3 (13%) mutations ( P < .0001). Co-occurring MAPK pathway-activating mutations were more likely to occur in class 1 versus class 2 or 3 MAP2K1-mutant tumors ( P < .0001). Our systematic meta-analysis of the literature identified 46 patients with MAP2K1-mutant tumors who received MAPKi. In these patients, ORR was 28% and median PFS was 3.9 months. ORR did not differ according to MAP2K1 mutation class or cancer type. However, patients with class 2 mutations experienced longer PFS (5.0 months) and DOR (23.8 months) compared with patients with class 1, 3, or unclassified MAP2K1 mutations (PFS 3.5 months, P = .04; DOR 4.2 months, P = .02). CONCLUSION: Patients with class 2 MAP2K1 mutations represent a novel subgroup that may derive benefit from MAPKi. Prospective clinical studies with novel MAPKi regimens are warranted in these patients.

Background: BRAF mutations are found in 10% of colon cancers (CCs) and are associated with poor prognosis in metastatic disease. BRAF V600E predicts sensitivity to cetuximab + encorafenib in the metastatic setting. With new trials testing encorafenib-containing regimens for early-stage CC, we sought to characterize the clinical outcomes of early-stage BRAF V600E CC. Methods: We performed a systematic review and meta-analysis. Key inclusion criteria were a diagnosis of stage 2/3 BRAF V600E CC. Co-primary endpoints were overall survival (OS) and recurrence/disease-free survival (DFS). Meta-analysis was performed with a random-effects model incorporating sample size, hazard ratio (HR), and 95% confidence intervals (CIs). Results: A total of 206 studies underwent full-text review. Of these, six randomized controlled trials were included, comprising 6836 and 843 patients with wild-type (WT) and BRAF V600E, respectively. BRAF V600E was associated with inferior OS (HR 1.49, CI 1.21–1.75) and DFS (HR 1.17, CI 1.03–1.33). This finding remains in patients with microsatellite instability—low/stable or proficient mismatch repair (OS: HR 1.66, CI 1.36–2.02, DFS: HR 1.45, CI 1.22–1.72). Conclusions: BRAF V600E is associated with inferior prognoses compared to BRAF WT in early-stage CC. This finding will help optimize trial design for this population.