NPJ Precision Oncology最新文献

Chemotherapy remains indispensable in the treatment of malignant tumors but is often limited by the prevailing "one size fits all" approach, which neglects inter-patient variablity in pharmacokinetics and treatment response, often resulting in suboptimal outcomes. In this study, we explored individualized chemotherapy protocols in a clinically relevant mouse model of breast cancer using a novel algorithm-assisted therapy design (AATD). Two strategies were applied: a two-stage computational therapy protocol designed to stabilize blood concentrations of pegylated liposomal doxorubicin (PLD); and a model-predictive approach that optimizes dosing based on individual tumor characteristics. Compared to the standard maximum tolerated dose protocol, AATD-based personalized chemotherapy, guided by real-time monitoring of treatment response, tumor growth, and drug concentrations, significantly improved overall survival. Our findings in a mouse model of triple-negative breast cancer provide compelling evidence that chemotherapy can be personalized and optimized through algorithm-assisted therapy design.

Epithelial ovarian cancer (EOC) remains the most lethal gynaecological malignancy in developed countries, with recurrence and drug resistance posing significant clinical challenges. Brain metastases (BM) from epithelial ovarian cancer, once rare, are an increasing phenomenon and are characterised by a dismal prognosis. To explore the molecular underpinnings of BM in EOC, we conducted a multimodal genomics and transcriptomics analysis of matched primary tumour and brain metastases samples from a retrospective cohort. Our findings revealed high genomic concordance between primary tumour (PT) and BM, with alterations in key pathways such as MYC (MYC Proto-Oncogene, bHLH Transcription Factor) targets, extracellular matrix remodelling, and inflammatory signalling characterizing the BM. AFP (Alpha-fetoprotein) and GFAP (Glial Fibrillary Acidic Protein) emerged as potential biomarkers from the primary lesion for BM onset, while network analysis identified MET (MET Proto-Oncogene, Receptor Tyrosine Kinase), GDF15 (Growth Differentiation Factor 15), and S100A9 (S100 Calcium Binding Protein A9) as candidate mediators of tumour-brain crosstalk. These results offer new insights into EOC brain tropism, highlighting potential targets for therapeutic intervention and personalized patient management in the precision oncology era.

The survival benefits of neoadjuvant chemotherapy combined with immunotherapy in locally advanced esophageal squamous cell carcinoma (ESCC) remain unclear, with treatment responses varying due to tumor microenvironment (TME) heterogeneity. In this phase II study, 24 patients received neoadjuvant sintilimab, albumin-bound paclitaxel, and carboplatin, with major pathological response (MPR), disease-free survival (DFS), and overall survival (OS) as primary endpoints. Results showed a 41.7% MPR rate, with 3 year DFS and OS rates of 75.0% and 79.2%, respectively. The PD-L1 expression of all patients increased during treatment; and its post-treatment levels were more strongly associated with the response to PD-1 inhibitors than pre-treatment levels. Besides, the mass spectrometry-based proteomic quantification identified 9 downregulated proteins in responders, including immune regulation-related proteins and peptidyl-serine phosphorylation proteins, revealing the TME changes linked to therapy efficacy. Additionally, 14 differentially expressed proteins were found at baseline between responders and non-responders, with high CD44 expression correlating with a favorable anti-PD-1 response. Post-treatment analysis revealed 27 differential proteins, 10 of which negatively correlated with efficacy. In conclusion, neoadjuvant sintilimab plus chemotherapy demonstrates promising efficacy and safety. Proteomic profiling of the TME further elucidates the heterogeneity of immunotherapy responses, offering insights for precision strategies in ESCC neoadjuvant therapy. Trial registration This study was prospectively registered in the Chinese Clinical Trial Registry (ChiCTR2000041081).

Small cell lung cancer (SCLC) is a highly aggressive malignancy with strong associations to smoking, characterized by initial platinum sensitivity followed by rapid recurrence and poor long-term survival. The evolutionary processes driving this high plasticity and intratumoral heterogeneity remain inadequately understood, hampering the development of effective therapies. In this study, we established a comprehensive spatial transcriptomic (ST) landscape of SCLC. Our approach integrated two key methodological innovations: the Edgeindex metric for the quantitative assessment of tumor spatial architecture, and a specialized artificial neural network (ANN) model for precise tumor annotation. Utilizing this analytical framework, we systematically resolved SCLC heterogeneity across clinical, spatial, functional, and temporal dimensions. Furthermore, pathway enrichment analysis was performed to explore the underlying molecular mechanisms. This work provides a multi-dimensional resource for deciphering the complexity of SCLC.

Ovarian cancer (OC) is the leading cause of death among gynecological cancers, with high mortality due to late diagnosis. Immunotherapy and nanomedicine show potential in targeting cancer cells and inducing immunogenic cell death. However, challenges like poor biodegradability and untargeted immune activation persist. Emerging solutions, including enhanced nanoparticles and personalized medicine, aim to improve efficacy while minimizing side effects by tailoring treatments to individual profiles and disease stages.

Identifying potential molecular targets for GC liver metastasis (GCLM) may provide new treatment avenues. Initially, using label-free proteomics to screen clinical samples from GCLM patients suggested ASF1B as a possible promoter of GCLM. We further validated this finding with in vitro experiments and spleen injection liver metastasis model, subsequent transcriptome sequencing after ASF1B knockdown revealed SLC7A11-mediated ferroptosis is critical for GCLM progression. Mechanistically, ASF1B recruits and binds to the transcription factor HOXB3, thereby promoting ZDHHC9's transcriptional level. Additionally, ZDHHC9 regulates SLC7A11-mediated ferroptosis in GC cells. Further tumor metastasis assays showed ZDHHC9 promotes peritoneal, pulmonary, and hepatic metastases in GC. Subsequently, immunoprecipitation and LC-MS analyses revealed the molecular interaction between ZDHHC9 and PCBP1. ZDHHC9, a palmitoyltransferase, inhibits ferroptosis by palmitoylating PCBP1. Mechanistically, ZDHHC9 palmitoylates PCBP1 at residue C109, inhibiting PCBP1 ubiquitination and thereby suppressing SLC7A11-mediated ferroptosis. In line with this, further experiments showed PCBP1 regulates ferroptosis by modulating SLC7A11 RNA stability. Finally, IHC and immunofluorescence revealed significant clinical correlations among ASF1B, ZDHHC9, PCBP1, and SLC7A11. Additionally, this signaling axis is strongly associated with PD-L1 expression. In conclusion, this study demonstrates ASF1B promotes GC liver metastasis by inhibiting ferroptosis via the ZDHHC9/PCBP1/SLC7A11 axis, providing a potential immunotherapeutic target for GCLM.

Longitudinal data analysis of the patient's treatment course is critical to uncovering variables that influence outcomes. However, existing tools have significant limitations in integrating multilayered time-series data, particularly in linking treatment events with survival outcomes. Here, we developed ShinyEvents, a web-based framework for complex longitudinal data analysis. ShinyEvents allows users to upload data and generate interactive timelines of clinical events, enabling cohort-level analyses such as treatment clustering and endpoint assignment. It also provides informative cohort visualizations, such as a Sankey diagram of the treatment line and a Swimmer diagram of the clinical course. Finally, our tool can infer real-world progression-free survival (rwPFS) based on user-defined endpoints and perform Kaplan-Meier and Cox proportional hazards regression analysis. With these features, the tool can then associate treatment lines with clinical outcomes. As a case study, we analyzed Moffitt patients with muscle-invasive bladder cancer treated with neoadjuvant chemotherapy followed by surgery. Patients treated with cisplatin and gemcitabine exhibited more favorable rwPFS and overall survival, which is consistent with prior reports. Altogether, ShinyEvents provides a unified framework for integrating longitudinal real-world data with survival analytics, fostering transparent and reproducible collaboration between clinicians and data scientists. A live demo is available at https://shawlab-moffitt.shinyapps.io/shinyevents/ .

Brain MRI is the primary imaging modality for pediatric brain tumors, yet incomplete acquisitions are common, hindering the clinical utility of existing deep learning models for tumor segmentation and prognosis. These models are typically trained on complete MRI sequences and exhibit performance degradation when MRI sequences are missing at test time. In this retrospective study of 715 patients from the Children's Brain Tumor Network and BraTS-PEDs, and 43 patients with 157 longitudinal MRIs from PNOC003/007 clinical trials, we developed strategies for handling missing sequences. Methods included a dropout-trained segmentation model that randomly omitted FLAIR and/or T1w inputs during training, a generative model for image synthesis, copy-substitution heuristics, and zeroed inputs. The dropout model achieved robust segmentation under missing MRI, with ≤0.04 Dice drop relative to complete-input and stable prognostic accuracy in survival analysis using model-derived tumor volumes and clinical covariates. Generative synthesis achieved high image quality (SSIM > 0.90) and removed artifacts, benefiting visual interpretability. Together, these approaches can facilitate broader deployment of AI tools in real-world pediatric neuro-oncology settings.

Cervical adenocarcinoma, a significant subtype of cervical cancer, exhibits variable responses to immune checkpoint inhibitors, with some patients experiencing hyperprogressive disease (HPD) characterised by accelerated tumour progression. The molecular and cellular mechanisms underlying HPD remain poorly understood. In this study, we employed single-cell RNA sequencing, T cell receptor profiling, and spatial transcriptomics to longitudinally analyse the tumour and its microenvironment of a single case of cervical adenocarcinoma who developed HPD following anti-PD-1 immunotherapy. Our integrated analyses revealed two key processes associated with HPD: Immunosuppression involving an IFNγ-STAT1-LAG3 axis in T cells, and fibroblast-supported epithelial proliferation and survival via IGF1-IGF1R signalling. Additionally, spatial transcriptomics identified the formation of a distinct pro-metastatic niche post-immunotherapy. These findings nominate LAG3 and IGF1R as candidate therapeutic targets in this patient and demonstrate the power of real-time, multi-modal profiling to identify adaptive hyperprogression mechanisms. Our work illustrates the potential value of longitudinal monitoring in precision oncology and provides a conceptual framework that could be tested for developing personalised interventions for minimal residual disease, non-responders, and HPD.

We evaluated clinical outcomes and safety profiles in patients with non-small cell lung cancer (NSCLC) treated in early-phase trials. A retrospective review of 546 NSCLC cases treated from January 2016 to December 2024 at The University of Texas MD Anderson Cancer Center was performed using the MD Anderson CHIMERA database. Patients were categorized into seven groups based on treatment regimen. The overall objective response rate (ORR) was 19.9%, and the highest ORRs were in the targeted combination (30.8%) and targeted monotherapy (29.7%) groups. Gene alteration-matched therapy, serum albumin level, metastatic burden, treatment group, and liver metastasis were independently associated with progression-free survival. For overall survival, albumin level, metastatic burden, liver metastasis, and treatment regimen group showed independent significant associations. While overall safety profiles were tolerable, combination regimens were associated with increased proportions of grade ≥3 adverse events and dose modifications.