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.

Validation of the Meet-URO score in patients with metastatic renal cell carcinoma receiving first-line nivolumab and ipilimumab in the Italian Expanded Access Program.

BACKGROUND: The Meet-URO score allowed a more accurate prognostication than the International Metastatic RCC Database Consortium (IMDC) for patients with pre-treated metastatic renal cell carcinoma (mRCC) by adding the pre-treatment neutrophil-to-lymphocyte ratio and presence of bone metastases. MATERIALS AND METHODS: A post hoc analysis was carried out to validate the Meet-URO score on the overall survival (OS) of patients with IMDC intermediate-poor-risk mRCC treated with first-line nivolumab plus ipilimumab within the prospective Italian Expanded Access Programme (EAP). We additionally considered progression-free survival (PFS) and disease response rates. Harrell's c-index was calculated to compare the accuracy of survival prediction. RESULTS: Overall the EAP included 306 patients, with a median follow-up of 12.2 months, median OS was not reached, 1-year OS was 66.8% and median PFS was 7.9 months. By univariable analysis, both the IMDC score and the two additional variables of the Meet-URO score were associated with either OS or PFS (P < 0.001 for all comparisons). The four Meet-URO risk groups (G) had 1-year OS of 92%, 72%, 50% and 21% for G2 (29.1% of patients), G3 (28.8%), G4 (33.0%) and G5 (9.1%), respectively. OS was significantly shorter in each consecutive G (P = 0.001 for G3, P < 0.001 for both G4 and G5 compared to G2). Similarly, Meet-URO Gs 2-5 showed decreasing median PFS and response rates. The Meet-URO score showed the highest c-index for both OS (0.73) and PFS (0.67). Limitations include the post hoc nature of this analysis and the lack of a comparative arm to assess predictive value. CONCLUSION: The Meet-URO score appeared to show better prognostic classification than the IMDC alone in patients with mRCC at IMDC intermediate-poor risk treated with first-line nivolumab and ipilimumab.

Immune-inflammatory biomarkers as prognostic factors for immunotherapy in pretreated advanced urinary tract cancer patients: an analysis of the Italian SAUL cohort.

BACKGROUND: Reliable and affordable prognostic and predictive biomarkers for urothelial carcinoma treated with immunotherapy may allow patients' outcome stratification and drive therapeutic options. The SAUL trial investigated the safety and efficacy of atezolizumab in a real-world setting on 1004 patients with locally advanced or metastatic urothelial carcinoma who progressed to one to three prior systemic therapies. PATIENTS AND METHODS: Using the SAUL Italian cohort of 267 patients, we investigated the prognostic role of neutrophil-to-lymphocyte ratio (NLR) and systemic immune-inflammation index (SII) and the best performing one of these in combination with programmed death-ligand 1 (PD-L1) with or without lactate dehydrogenase (LDH). Previously reported cut-offs (NLR >3 and NLR >5; SII >1375) in addition to study-defined ones derived from receiver operating characteristic (ROC) analysis were used. RESULTS: The cut-off values for NLR and SII by the ROC analysis were 3.65 (sensitivity 60.4; specificity 63.0) and 884 (sensitivity 64.4; specificity 67.5), respectively. The median overall survival (OS) was 14.7 months for NLR <3.65 [95% confidence interval (CI) 9.9-not reached (NR)] versus 6.0 months for NLR ≥3.65 (95% CI 3.9-9.4); 14.7 months for SII <884 (95% CI 10.6-NR) versus 6.0 months for SII ≥884 (95% CI 3.7-8.6). The combination of SII, PD-L1, and LDH stratified OS better than SII plus PD-L1 through better identification of patients with intermediate prognosis (77% versus 48%, respectively). Multivariate analyses confirmed significant correlations with OS and progression-free survival for both the SII + PD-L1 + LDH and SII + PD-L1 combinations. CONCLUSION: The combination of immune-inflammatory biomarkers based on SII, PD-L1, with or without LDH is a potentially useful and easy-to-assess prognostic tool deserving validation to identify patients who may benefit from immunotherapy alone or alternative therapies.