Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

Prelaj, A.; Miskovic, V.; Zanitti, M.; Trovo, F.; Genova, C.; Viscardi, G.; Rebuzzi, S.E.; Mazzeo, L.; Provenzano, L.; Kosta, S.; Favali, M.; Spagnoletti, A.; Castelo-Branco, L.; Dolezal, J.; Pearson, A.T.; Lo Russo, G.; Proto, C.; Ganzinelli, M.; Giani, C.; Ambrosini, E.; Turajlic, S.; Au, L.; Koopman, M.; Delaloge, S.; Kather, J.N.; de Braud, F.; Garassino, M.C.; Pentheroudakis, G.; Spencer, C.; Pedrocchi, A.L.G.

doi:10.1016/j.annonc.2023.10.125

Cited by 59 publications

(7 citation statements)

References 136 publications

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“…cfDNA 5hmC has several advantages over other potential markers for ICI treatment response prediction. Currently emerging ICI biomarkers span genomic, epigenetic, transcriptomic, and protein levels [7,46]. DNA methylation markers in tumor tissue have been reported for ICI treatment response prediction in lung cancer [47,48].…”

Section: Discussionmentioning

confidence: 99%

5-Hydroxymethylcytosine in Cell-Free DNA Predicts Immunotherapy Response in Lung Cancer

Shao,

Xu,

Olsen

et al. 2024

Cells

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Immune checkpoint inhibitors (ICIs) drastically improve therapeutic outcomes for lung cancer, but accurately predicting individual patient responses to ICIs remains a challenge. We performed the genome-wide profiling of 5-hydroxymethylcytosine (5hmC) in 85 plasma cell-free DNA (cfDNA) samples from lung cancer patients and developed a 5hmC signature that was significantly associated with progression-free survival (PFS). We built a 5hmC predictive model to quantify the 5hmC level and validated the model in the validation, test, and control sets. Low weighted predictive scores (wp-scores) were significantly associated with a longer PFS compared to high wp-scores in the validation [median 7.6 versus 1.8 months; p = 0.0012; hazard ratio (HR) 0.12; 95% confidence interval (CI), 0.03–0.54] and test (median 14.9 versus 3.3 months; p = 0.00074; HR 0.10; 95% CI, 0.02–0.50) sets. Objective response rates in patients with a low or high wp-score were 75.0% (95% CI, 42.8–94.5%) versus 0.0% (95% CI, 0.0–60.2%) in the validation set (p = 0.019) and 80.0% (95% CI, 44.4–97.5%) versus 0.0% (95% CI, 0.0–36.9%) in the test set (p = 0.0011). The wp-scores were also significantly associated with PFS in patients receiving single-agent ICI treatment (p < 0.05). In addition, the 5hmC predictive signature demonstrated superior predictive capability to tumor programmed death-ligand 1 and specificity to ICI treatment response prediction. Moreover, we identified novel 5hmC-associated genes and signaling pathways integral to ICI treatment response in lung cancer. This study provides proof-of-concept evidence that the cfDNA 5hmC signature is a robust biomarker for predicting ICI treatment response in lung cancer.

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Section: Discussionmentioning

confidence: 99%

5-Hydroxymethylcytosine in Cell-Free DNA Predicts Immunotherapy Response in Lung Cancer

Shao,

Xu,

Olsen

et al. 2024

Cells

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“…In the context of personalized medicine, this is evident as the possibility to predict several prognostic markers allows us to identify the best treatment for a specific patient [41][42][43][44][45][46]. Radiomics analysis could be a promising tool to evaluate a lesion "virtually", with the possibility to analyze the whole tumor during the disease history to obtain those markers which can affect the treatment choice [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65]. In addition, this approach is safe and inexpensive since radiomics data are obtained from radiological studies which a patient should be subjected during staging and follow-up [66][67][68][69][70][71][72][73][74][75][76][77][78][79][80][81][82][83][84].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment

Granata,

Fusco,

Brunese

et al. 2024

Diagnostics

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Purpose: We aimed to assess the efficacy of machine learning and radiomics analysis using magnetic resonance imaging (MRI) with a hepatospecific contrast agent, in a pre-surgical setting, to predict tumor budding in liver metastases. Methods: Patients with MRI in a pre-surgical setting were retrospectively enrolled. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. Balancing was performed and inter- and intraclass correlation coefficients were calculated to assess the between observer and within observer reproducibility of all radiomics extracted features. A Wilcoxon–Mann–Whitney nonparametric test and receiver operating characteristics (ROC) analysis were carried out. Balancing and feature selection procedures were performed. Linear and non-logistic regression models (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered. Results: The internal training set included 49 patients and 119 liver metastases. The validation cohort consisted of a total of 28 single lesion patients. The best single predictor to classify tumor budding was original_glcm_Idn obtained in the T1-W VIBE sequence arterial phase with an accuracy of 84%; wavelet_LLH_firstorder_10Percentile was obtained in the T1-W VIBE sequence portal phase with an accuracy of 92%; wavelet_HHL_glcm_MaximumProbability was obtained in the T1-W VIBE sequence hepatobiliary excretion phase with an accuracy of 88%; and wavelet_LLH_glcm_Imc1 was obtained in T2-W SPACE sequences with an accuracy of 88%. Considering the linear regression analysis, a statistically significant increase in accuracy to 96% was obtained using a linear weighted combination of 13 radiomic features extracted from the T1-W VIBE sequence arterial phase. Moreover, the best classifier was a KNN trained with the 13 radiomic features extracted from the arterial phase of the T1-W VIBE sequence, obtaining an accuracy of 95% and an AUC of 0.96. The validation set reached an accuracy of 94%, a sensitivity of 86% and a specificity of 95%. Conclusions: Machine learning and radiomics analysis are promising tools in predicting tumor budding. Considering the linear regression analysis, there was a statistically significant increase in accuracy to 96% using a weighted linear combination of 13 radiomics features extracted from the arterial phase compared to a single radiomics feature.

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“…Prelaj et al analyzing real-world data to predict outcomes in patients that respond or do not to treatment using deep learning techniques. They even reviewed several studies using AI tools to confirm classical and new biomarkers like human leukocyte antigen loss of heterozygosity (HLA LOH) and genomic ITH for IO response [ 82 ].…”

Section: Ai: the New Frontiermentioning

confidence: 99%

“…The studies reviewed lacked endpoints and pipelines. Therefore, future studies should be well designed as prospective studies, choosing the most appropriate AI approach—an example is represented by the I3LUNG study (NCT05537922)—or as observational studies guided by data, especially for biomarker-driven detection, like in the NCT0555096 study and the so-called APOLLO 11 trial [ 82 ].…”

Section: Ai: the New Frontiermentioning

confidence: 99%

Immunotherapy and Cancer: The Multi-Omics Perspective

Donisi,

Pretta,

Pusceddu

et al. 2024

IJMS

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Immunotherapies have revolutionized cancer treatment approaches. Because not all patients respond positively to immune therapeutic agents, it represents a challenge for scientists who strive to understand the mechanisms behind such resistance. In-depth exploration of tumor biology, using novel technologies such as omics science, can help decode the role of the tumor immune microenvironment (TIME) in producing a response to the immune blockade strategies. It can also help to identify biomarkers for patient stratification and personalized treatment. This review aims to explore these new models and highlight their possible pivotal role in changing clinical practice.

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Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

Cited by 59 publications

References 136 publications

5-Hydroxymethylcytosine in Cell-Free DNA Predicts Immunotherapy Response in Lung Cancer

5-Hydroxymethylcytosine in Cell-Free DNA Predicts Immunotherapy Response in Lung Cancer

Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment

Immunotherapy and Cancer: The Multi-Omics Perspective

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