The artificial intelligence and machine learning in lung cancer immunotherapy

Gao, Qing; Yang, Lei; Lu, Mengji; Jin, Renjing; Ye, Huan; Ma, Teng

doi:10.1186/s13045-023-01456-y

Cited by 43 publications

(18 citation statements)

References 81 publications

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“…Lung cancer is one of the most frequently diagnosed cancers and the leading cause of cancer-related death worldwide [ 1 – 3 ], with lung adenocarcinoma (LUAD) representing the most common subtype of all diagnoses [ 4 , 5 ]. Early-stage LUAD can be cured by radical resection, but there is currently no curative treatment for patients with late-stage LUAD, despite the recent progression in molecularly targeted therapy, immunotherapy, and combinatorial therapy [ 6 – 8 ].…”

Section: Introductionmentioning

confidence: 99%

LINC01559 promotes lung adenocarcinoma metastasis by disrupting the ubiquitination of vimentin

Feng,

Xu,

Jiang

et al. 2024

Biomark Res

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Background Distant metastasis is the major cause of lung adenocarcinoma (LUAD)-associated mortality. However, molecular mechanisms involved in LUAD metastasis remain to be fully understood. While the role of long non-coding RNAs (lncRNAs) in cancer development, progression, and treatment resistance is being increasingly appreciated, the list of dysregulated lncRNAs that contribute to LUAD pathogenesis is also rapidly expanding. Methods Bioinformatics analysis was conducted to interrogate publicly available LUAD datasets. In situ hybridization and qRT-PCR assays were used to test lncRNA expression in human LUAD tissues and cell lines, respectively. Wound healing as well as transwell migration and invasion assays were employed to examine LUAD cell migration and invasion in vitro. LUAD metastasis was examined using mouse models in vivo. RNA pulldown and RNA immunoprecipitation were carried out to test RNA–protein associations. Cycloheximide-chase assays were performed to monitor protein turnover rates and Western blotting was employed to test protein expression. Results The expression of the lncRNA LINC01559 was commonly upregulated in LUADs, in particular, in those with distant metastasis. High LINC01559 expression was associated with poor outcome of LUAD patients and was potentially an independent prognostic factor. Knockdown of LINC01559 diminished the potential of LUAD cell migration and invasion in vitro and reduced the formation of LUAD metastatic lesions in vivo. Mechanistically, LINC01559 binds to vimentin and prevents its ubiquitination and proteasomal degradation, leading to promotion of LUAD cell migration, invasion, and metastasis. Conclusion LINC01559 plays an important role in LUAD metastasis through stabilizing vimentin. The expression of LINC01559 is potentially an independent prognostic factor of LUAD patients, and LINC01559 targeting may represent a novel avenue for the treatment of late-stage LUAD.

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Section: Introductionmentioning

confidence: 99%

LINC01559 promotes lung adenocarcinoma metastasis by disrupting the ubiquitination of vimentin

Feng,

Xu,

Jiang

et al. 2024

Biomark Res

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“…This includes research progress in areas such as predicting responses to targeted therapy in breast cancer and prognosis prediction of glioma [ 16 , 17 ]. Meanwhile, a growing number of studies have combined genomics, radiology, proteomics data, and pathology to estimate PD-L1 expression levels, TMB, and tumor microenvironment (TME) or predict the response to immunotherapy and side effects in patients with cancer [ 18 – 20 ]. Recent studies reported that a machine learning analysis of circulating immune cell characteristics or CT images in patients with NSCLC could be used to predict immunotherapy benefits [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

A novel sub-regional radiomics model to predict immunotherapy response in non-small cell lung carcinoma

Peng,

Zou,

Zhang

et al. 2024

J Transl Med

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Background Identifying precise biomarkers of immunotherapy response for non-small cell lung carcinoma (NSCLC) before treatment is challenging. This study aimed to construct and investigate the potential performance of a sub-regional radiomics model (SRRM) as a novel tumor biomarker in predicting the response of patients with NSCLC treated with immune checkpoint inhibitors, and test whether its predictive performance is superior to that of conventional radiomics, tumor mutational burden (TMB) score and programmed death ligand-1 (PD-L1) expression. Methods We categorized 264 patients from retrospective databases of two centers into training (n = 159) and validation (n = 105) cohorts. Radiomic features were extracted from three sub-regions of the tumor region of interest using the K-means method. We extracted 1,896 features from each sub-region, resulting in 5688 features per sample. The least absolute shrinkage and selection operator regression method was used to select sub-regional radiomic features. The SRRM was constructed and validated using the support vector machine algorithm. We used next-generation sequencing to classify patients from the two cohorts into high TMB (≥ 10 muts/Mb) and low TMB (< 10 muts/Mb) groups; immunohistochemistry was performed to assess PD-L1 expression in formalin-fixed, paraffin-embedded tumor sections, with high expression defined as ≥ 50% of tumor cells being positive. Associations between the SRRM and progression-free survival (PFS) and variant genes were assessed. Results Eleven sub-regional radiomic features were employed to develop the SRRM. The areas under the receiver operating characteristic curve (AUCs) of the proposed SRRM were 0.90 (95% confidence interval [CI] 0.84−0.96) and 0.86 (95% CI 0.76−0.95) in the training and validation cohorts, respectively. The SRRM (low vs. high; cutoff value = 0.936) was significantly associated with PFS in the training (hazard ratio [HR] = 0.35 [0.24−0.50], P < 0.001) and validation (HR = 0.42 [0.26−0.67], P = 0.001) cohorts. A significant correlation between the SRRM and three variant genes (H3C4, PAX5, and EGFR) was observed. In the validation cohort, the SRRM demonstrated a higher AUC (0.86, P < 0.001) than that for PD-L1 expression (0.66, P = 0.034) and TMB score (0.54, P = 0.552). Conclusions The SRRM had better predictive performance and was superior to conventional radiomics, PD-L1 expression, and TMB score. The SRRM effectively stratified the progression-free survival (PFS) risk among patients with NSCLC receiving immunotherapy.

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“…Despite these challenges, there is great hope for expanding treatment strategies targeting TME, including depletion or "reprogramming" of cancer promoting host cells in TME; Intervention measures to modify extracellular matrix (ECM), matrix components, and extracellular vesicles (EVs); Cell based therapies and vaccines; And immune checkpoint inhibitors. Moreover, integrating multiple cancer model data and advanced computational analysis, including artificial intelligence, has the potential to adopt a comprehensive system level approach about analyzing and integrating all the complexities of TME to identify key nodes [ 145 ]. In addition, significant advances in bioengineering will provide a platform for large-scale testing, such as in ex vivo organoids and tissue slices that accurately recurrent organ specific TMEs [ 146 – 148 ].…”

Section: Introductionmentioning

confidence: 99%

The expanding Pandora’s toolbox of CD8+T cell: from transcriptional control to metabolic firing

Wu,

Lu,

Zhao

et al. 2023

J Transl Med

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CD8+ T cells are the executor in adaptive immune response, especially in anti-tumor immunity. They are the subset immune cells that are of high plasticity and multifunction. Their development, differentiation, activation and metabolism are delicately regulated by multiple factors. Stimuli from the internal and external environment could remodel CD8+ T cells, and correspondingly they will also make adjustments to the microenvironmental changes. Here we describe the most updated progresses in CD8+ T biology from transcriptional regulation to metabolism mechanisms, and also their interactions with the microenvironment, especially in cancer and immunotherapy. The expanding landscape of CD8+ T cell biology and discovery of potential targets to regulate CD8+ T cells will provide new viewpoints for clinical immunotherapy.

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The artificial intelligence and machine learning in lung cancer immunotherapy

Cited by 43 publications

References 81 publications

LINC01559 promotes lung adenocarcinoma metastasis by disrupting the ubiquitination of vimentin

LINC01559 promotes lung adenocarcinoma metastasis by disrupting the ubiquitination of vimentin

A novel sub-regional radiomics model to predict immunotherapy response in non-small cell lung carcinoma

The expanding Pandora’s toolbox of CD8+T cell: from transcriptional control to metabolic firing

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