Investigating the Concordance in molecular subtypes of primary colorectal tumors and their matched synchronous liver metastasis

To date, no systematic analyses are available assessing concordance of molecular classifications between primary tumors (PT) and matched liver metastases (LM) of metastatic colorectal cancer (mCRC). We investigated concordance between PT and LM for four clinically relevant CRC gene signatures. Twenty-seven fresh and 55 formalin-fixed paraffin-embedded pairs of PT and synchronous LM of untreated mCRC patients were retrospectively collected and classified according to the MSI-like, BRAF-like, TGFB activated-like and the Consensus Molecular Subtypes (CMS) classification. We investigated classification concordance between PT and LM and association of TGFBa-like and CMS classification with overall survival. Fifty-one successfully profiled matched pairs were used for analyses. PT and matched LM were highly concordant in terms of BRAF-like and MSI-like signatures, (90.2% and 98% concordance, respectively). In contrast, 40% to 70% of PT that were classified as mesenchymal-like, based on the CMS and the TGFBa-like signature, respectively, lost this phenotype in their matched LM (60.8% and 76.5% concordance, respectively). This molecular switch was independent of the microenvironment composition. In addition, the significant change in subtypes was observed also by using methods developed to detect cancer cell-intrinsic subtypes. More importantly, the molecular switch did not influence the survival. PT classified as mesenchymal had worse survival as compared to nonmesenchymal PT (CMS4 vs CMS2, hazard ratio [HR] = 5.2, 95% CI = 1.5-18.5, P = .0048; TGFBa-like vs TGFBi-like, HR = 2.5, 95% CI = 1.1-5.6, P = .028). The same was not true for LM. Our study highlights that the origin of the tissue may have major consequences for precision medicine in mCRC

In Vitro Evaluation of Graft-versus-Graft Alloreactivity as a Tool to Identify the Predominant Cord Blood Unit before Double Cord Blood Transplantation

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Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods. MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions. ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models' prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR. ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods.MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions.ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models’ prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR.ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

Could Circulating Tumor Cells and ARV7 Detection Improve Clinical Decisions in Metastatic Castration-Resistant Prostate Cancer? The Istituto Nazionale dei Tumori (INT) Experience

Enzalutamide and abiraterone have been shown to improve progression-free survival (PFS) and overall survival (OS) in metastatic castration-resistant prostate cancer (mCRPC) patients. Moreover, some patients may not benefit from the inhibition of androgen receptor (AR) activity or, alternatively, may develop secondary resistance. Detection in patients’ circulating tumor cells (CTCs) of ARV7, a splicing variant of AR lacking the ligand-binding domain, showed a link with treatment failure. Independent confirmation of the predictive role of CTC status combined with ARV7 detection is, therefore, a priority for extending personalized biomarker-driven treatments to all patients. In this prospective observational study, CTC status and the expression of AR and ARV7 were measured in 37 mCRPC patients, before starting treatment with enzalutamide or abiraterone, by employing commercially available kits. CTC status was positive in 21/37 patients: 46% and 24% of CTC-positive patients were defined as AR- and ARV7-positive, respectively. Kaplan–Meier estimates showed that positivity for each variable was significantly associated with poorer radiological PFS, PSA-PFS, and OS. All considered treatment outcomes worsened when going from CTC-negative to CTC-positive/ARV7-negative to CTC-positive/ARV7-positive patients, both in the global case series and in patients stratified into three groups based on basal PSA levels. Presently, technical approaches appear to be mature for introducing CTC/ARV7 tests in clinical practice

Is a pharmacogenomic panel useful to estimate the risk of oxaliplatin-related neurotoxicity in colorectal cancer patients?

Oxaliplatin-induced peripheral neurotoxicity (OXPN) is a dose-limiting toxicity in colorectal cancer (CRC) patients. Single nucleotide polymorphisms (SNPs) in genes involved in drug transport may lead to higher intracellular oxaliplatin accumulation in the dorsal root ganglia and thus increased risk of OXPN. In this study, a panel of 5 SNPs, namely ABCC2 (-24C > T/rs717620 and c.4544 G > A/rs8187710), ABCG2 (c.421 C > A/rs2231142), ABCB1 (c.3435 C > T/rs1045642) and SLC31A1 (c.-36 + 2451 T > G/rs10981694), was evaluated to assess their association with grade 2-3 OXPN in metastatic CRC patients. SNPs were considered according to a dominant model (heterozygous + homozygous). Germline DNA was available from 120 patients who received oxaliplatin between 2010 and 2016. An external cohort of 80 patients was used to validate our results. At the univariable logistic analyses, there were no significant associations between SNPs and incidence of OXPN. Taking into account the strength of observed association between OXPN and the SNPs, a clinical risk score was developed as linear predictor from a multivariable logistic model including all the SNPs together. This score was significantly associated with grade 2-3 OXPN (p = 0.036), but the external calibration was not satisfactory due to relevant discrepancies between the two series. Our data suggest that the concomitant evaluation of multiple SNPs in oxaliplatin transporters is an exploratory strategy that may deserve further investigation for treatment customization in CRC patients