Clinical Categorization Algorithm (CLICAL) and Machine Learning Approach (SRF-CLICAL) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-World Evidence from the Istituto Nazionale Tumori IRCCS Fondazione Pascale, Napoli, Italy

Immune biomarkers and predictive signatures in gastric cancer: Optimizing immunotherapy responses

Gastric cancer is a malignant disease with a poor prognosis and few therapeutic options once it has advanced. Immunotherapy using ICIs has emerged as a viable therapeutic method; nevertheless, reliable immunological biomarkers are required to identify who may benefit from these therapies. It focuses on key immune biomarkers and predictive signatures in gastric cancer, such as PD-L1 expression, microsatellite instability (MSI), tumor mutational burden (TMB), and Epstein-Barr virus (EBV) status, to optimize gastric cancer patients' immunotherapy responses. PD-L1 expression is a popular biomarker for ICI effectiveness. Tumors with high MSI-H and TMB are the most susceptible to ICIs because they are highly immunogenic. EBV-positive stomach tumors are highly immunogenic, and immunotherapy has a high response rate. Combining composite biomarker panels with multi-omics-based techniques improved patient selection accuracy. In recent years, machine learning models have been integrated into next-generation sequencing. Dynamic, real-time-monitorable biomarkers for real-time immune response monitoring are also being considered. Thus, enhancing biomarker-driven immunotherapy is critical for improving clinical outcomes with gastric cancer. There is still more work to be done in this field, and verifying developing biomarkers will be an important component in the future of customized cancer therapy.

Machine learning in the prediction of immunotherapy response and prognosis of melanoma: a systematic review and meta-analysis

Background

The emergence of immunotherapy has changed the treatment modality for melanoma and prolonged the survival of many patients. However, a handful of patients remain unresponsive to immunotherapy and effective tools for early identification of this patient population are still lacking. Researchers have developed machine learning algorithms for predicting immunotherapy response in melanoma, but their predictive accuracy has been inconsistent. Therefore, the present systematic review and meta-analysis was performed to comprehensively evaluate the predictive accuracy of machine learning in melanoma response to immunotherapy.

Methods

Relevant studies were searched in PubMed, Web of Sciences, Cochrane Library, and Embase from their inception to July 30, 2022. The risk of bias and applicability of the included studies were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed on R4.2.0.

Results

A total of 36 studies consisting of 30 cohort studies and 6 case-control studies were included. These studies were mainly published between 2019 and 2022 and encompassed 75 models. The outcome measures of this study were progression-free survival (PFS), overall survival (OS), and treatment response. The pooled c-index was 0.728 (95%CI: 0.629-0.828) for PFS in the training set, 0.760 (95%CI: 0.728-0.792) and 0.819 (95%CI: 0.757-0.880) for treatment response in the training and validation sets, respectively, and 0.746 (95%CI: 0.721-0.771) and 0.700 (95%CI: 0.677-0.724) for OS in the training and validation sets, respectively.

Conclusion

Machine learning has considerable predictive accuracy in melanoma immunotherapy response and prognosis, especially in the former. However, due to the lack of external validation and the scarcity of certain types of models, further studies are warranted.

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

BACKGROUND

The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology.

MATERIALS AND METHODS

We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data.

RESULTS

A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data.

CONCLUSION

AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice.

Exploring the State of Machine Learning and Deep Learning in Medicine: A Survey of the Italian Research Community

Artificial intelligence (AI) is becoming increasingly important, especially in the medical field. While AI has been used in medicine for some time, its growth in the last decade is remarkable. Specifically, machine learning (ML) and deep learning (DL) techniques in medicine have been increasingly adopted due to the growing abundance of health-related data, the improved suitability of such techniques for managing large datasets, and more computational power. ML and DL methodologies are fostering the development of new “intelligent” tools and expert systems to process data, to automatize human–machine interactions, and to deliver advanced predictive systems that are changing every aspect of the scientific research, industry, and society. The Italian scientific community was instrumental in advancing this research area. This article aims to conduct a comprehensive investigation of the ML and DL methodologies and applications used in medicine by the Italian research community in the last five years. To this end, we selected all the papers published in the last five years with at least one of the authors affiliated to an Italian institution that in the title, in the abstract, or in the keywords present the terms “machine learning” or “deep learning” and reference a medical area. We focused our research on journal papers under the hypothesis that Italian researchers prefer to present novel but well-established research in scientific journals. We then analyzed the selected papers considering different dimensions, including the medical topic, the type of data, the pre-processing methods, the learning methods, and the evaluation methods. As a final outcome, a comprehensive overview of the Italian research landscape is given, highlighting how the community has increasingly worked on a very heterogeneous range of medical problems.

Machine Learning Improves the Prediction of Responses to Immune Checkpoint Inhibitors in Metastatic Melanoma

Pretreatment LDH is a standard prognostic biomarker for advanced melanoma and is associated with response to ICI. We assessed the role of machine learning-based radiomics in predicting responses to ICI and in complementing LDH for prognostication of metastatic melanoma. From 2008-2022, 79 patients with 168 metastatic hepatic lesions were identified. All patients had arterial phase CT images 1-month prior to initiation of ICI. Response to ICI was assessed on follow-up CT at 3 months using RECIST criteria. A machine learning algorithm was developed using radiomics. Maximum relevance minimum redundancy (mRMR) was used to select features. ROC analysis and logistic regression analyses evaluated performance. Shapley additive explanations were used to identify the variables that are the most important in predicting a response. mRMR selection revealed 15 features that are associated with a response to ICI. The machine learning model combining both radiomics features and pretreatment LDH resulted in better performance for response prediction compared to models that included radiomics or LDH alone (AUC of 0.89 (95% CI: [0.76-0.99]) vs. 0.81 (95% CI: [0.65-0.94]) and 0.81 (95% CI: [0.72-0.91]), respectively). Using SHAP analysis, LDH and two GLSZM were the most predictive of the outcome. Pre-treatment CT radiomic features performed equally well to serum LDH in predicting treatment response.

Perspectives in Melanoma: meeting report from the Melanoma Bridge (December 2nd - 4th, 2021, Italy)

Advances in immune checkpoint and combination therapy have led to improvement in overall survival for patients with advanced melanoma. Improved understanding of the tumor, tumor microenvironment and tumor immune-evasion mechanisms has resulted in new approaches to targeting and harnessing the host immune response. Combination modalities with other immunotherapy agents, chemotherapy, radiotherapy, electrochemotherapy are also being explored to overcome resistance and to potentiate the immune response. In addition, novel approaches such as adoptive cell therapy, oncogenic viruses, vaccines and different strategies of drug administration including sequential, or combination treatment are being tested. Despite the progress in diagnosis of melanocytic lesions, correct classification of patients, selection of appropriate adjuvant and systemic theràapies, and prediction of response to therapy remain real challenges in melanoma. Improved understanding of the tumor microenvironment, tumor immunity and response to therapy has prompted extensive translational and clinical research in melanoma. There is a growing evidence that genomic and immune features of pre-treatment tumor biopsies may correlate with response in patients with melanoma and other cancers, but they have yet to be fully characterized and implemented clinically. Development of novel biomarker platforms may help to improve diagnostics and predictive accuracy for selection of patients for specific treatment. Overall, the future research efforts in melanoma therapeutics and translational research should focus on several aspects including: (a) developing robust biomarkers to predict efficacy of therapeutic modalities to guide clinical decision-making and optimize treatment regimens, (b) identifying mechanisms of therapeutic resistance to immune checkpoint inhibitors that are potentially actionable, (c) identifying biomarkers to predict therapy-induced adverse events, and (d) studying mechanism of actions of therapeutic agents and developing algorithms to optimize combination treatments. During the Melanoma Bridge meeting (December 2nd-4th, 2021, Naples, Italy) discussions focused on the currently approved systemic and local therapies for advanced melanoma and discussed novel biomarker strategies and advances in precision medicine as well as the impact of COVID-19 pandemic on management of melanoma patients.