Evaluation of the Geographical Accessibility of Genome-Matched Clinical Trials on a National Experience.

BACKGROUND: Molecular-driven oncology allows oncologists to identify treatments that match a cancer's genomic profile. Clinical trials are promoted as an effective modality to deliver a molecularly matched treatment. We explore the role of geographical accessibility in Italy, and its impact on patient access to clinical trials. MATERIAL AND METHODS: We retrospectively reviewed molecular data from a single-institutional case series of patients receiving next-generation sequencing testing between March 2019 and July 2020. Actionable alterations were defined as the ones with at least one matched treatment on Clinicaltrials.gov at the time of genomic report signature. We then calculated the hypothetical distance to travel to reach the nearest assigned clinical trial. RESULTS: We identified 159 patients eligible for analysis. One hundred and one could be potentially assigned to a clinical trial in Italy, and the median distance that patients needed to travel to reach the closest location with a suitable clinical trial was 76 km (interquartile range = 127.46 km). Geographical distribution of clinical trials in Italy found to be heterogeneous, with Milan and Naples being the areas with a higher concentration. We then found that the probability of having a clinical trial close to a patient's hometown increased over time, according to registered studies between 2015 and 2020. CONCLUSIONS: The median distance to be travelled to the nearest trial was generally acceptable for patients, and trials availability is increasing. Nevertheless, many areas are still lacking trials, so efforts are required to increase and homogenize the possibilities to be enrolled in clinical trials for Italian patients with cancer.

High-Fat Diet Promotes Acute Promyelocytic Leukemia through PPARδ-Enhanced Self-renewal of Preleukemic Progenitors.

Risk and outcome of acute promyelocytic leukemia (APL) are particularly worsened in obese-overweight individuals, but the underlying molecular mechanism is unknown. In established mouse APL models (Ctsg-PML::RARA), we confirmed that obesity induced by high-fat diet (HFD) enhances leukemogenesis by increasing penetrance and shortening latency, providing an ideal model to investigate obesity-induced molecular events in the preleukemic phase. Surprisingly, despite increasing DNA damage in hematopoietic stem cells (HSC), HFD only minimally increased mutational load, with no relevant impact on known cancer-driving genes. HFD expanded and enhanced self-renewal of hematopoietic progenitor cells (HPC), with concomitant reduction in long-term HSCs. Importantly, linoleic acid, abundant in HFD, fully recapitulates the effect of HFD on the self-renewal of PML::RARA HPCs through activation of peroxisome proliferator-activated receptor delta, a central regulator of fatty acid metabolism. Our findings inform dietary/pharmacologic interventions to counteract obesity-associated cancers and suggest that nongenetic factors play a key role. PREVENTION RELEVANCE: Our work informs interventions aimed at counteracting the cancer-promoting effect of obesity. On the basis of our study, individuals with a history of chronic obesity may still significantly reduce their risk by switching to a healthier lifestyle, a concept supported by evidence in solid tumors but not yet in hematologic malignancies. See related Spotlight, p. 47.

Immune-Related Adverse Event Likelihood Score Identifies "Pure" IRAEs Strongly Associated With Outcome in a Phase I-II Trial Population.

BACKGROUND: Immune-related adverse events (IRAE) pose a significant diagnostic and therapeutic challenge in patients treated with immune-oncology (IO) drugs. IRAEs have been suggested to correlate with better outcome, but studies are conflicting. Estimating the true incidence of IRAEs is particularly difficult in the early phase I/II trial setting. A key issue is the lack of IRAE diagnostic criteria, necessary to discriminate "pure" IRAEs from other treatment-related adverse events not sustained by an autoimmune process. METHODS: In patients treated with immune-oncology (IO) drugs in phases I-II trials at our institute, we identified high confidence (HC) or low confidence (LC) IRAEs by clinical consensus. We empirically developed an IRAE likelihood score (ILS) based on commonly available clinical data. Correlation with outcome was explored by multivariate Cox analysis. To mitigate immortal time-bias, analyses were conducted (1) at 2-month landmark and (2) modeling IRAEs as time-dependent covariate. RESULTS: Among 202 IO-treated patients, 29.2% developed >1 treatment-related adverse events (TRAE). Based on ILS >5, we classified patients in no IRAE (n = 143), HC IRAE (n = 24), or LC IRAE (n = 35). hazard ratios (HR) for HC were significantly lower than LC patients (HR for PFS ranging 0.24-0.44, for OS 0.18-0.23, all P < .01). CONCLUSION: ILS provides a simple system to identify bona fide IRAEs, pruning for other treatment-related events likely due to different pathophysiology. Applying stringent criteria leads to lower and more reliable estimates of IRAE incidence and identifies events with significant impact on survival.

Benchmarking and improving the performance of variant-calling pipelines with RecallME.

MOTIVATION: The steady increment of Whole Genome/Exome sequencing and the development of novel Next Generation Sequencing-based gene panels requires continuous testing and validation of variant calling (VC) pipelines and the detection of sequencing-related issues to be maintained up-to-date and feasible for the clinical settings. State of the art tools are reliable when used to compute standard performance metrics. However, the need for an automated software to discriminate between bioinformatic and sequencing issues and to optimize VC parameters remains unmet. RESULTS: The aim of the current work is to present RecallME, a bioinformatic suite that tracks down difficult-to-detect variants as insertions and deletions in highly repetitive regions, thus providing the maximum reachable recall for both single nucleotide variants and small insertion and deletions and to precisely guide the user in the pipeline optimization process. AVAILABILITY AND IMPLEMENTATION: Source code is freely available under MIT license at https://github.com/mazzalab-ieo/recallme. RecallME web application is available at https://translational-oncology-lab.shinyapps.io/recallme/. To use RecallME, users must obtain a license for ANNOVAR by themselves.

The Challenge of Choosing the Best Classification Method in Radiomic Analyses: Recommendations and Applications to Lung Cancer CT Images.

Radiomics uses high-dimensional sets of imaging features to predict biological characteristics of tumors and clinical outcomes. The choice of the algorithm used to analyze radiomic features and perform predictions has a high impact on the results, thus the identification of adequate machine learning methods for radiomic applications is crucial. In this study we aim to identify suitable approaches of analysis for radiomic-based binary predictions, according to sample size, outcome balancing and the features-outcome association strength. Simulated data were obtained reproducing the correlation structure among 168 radiomic features extracted from Computed Tomography images of 270 Non-Small-Cell Lung Cancer (NSCLC) patients and the associated to lymph node status. Performances of six classifiers combined with six feature selection (FS) methods were assessed on the simulated data using AUC (Area Under the Receiver Operating Characteristics Curves), sensitivity, and specificity. For all the FS methods and regardless of the association strength, the tree-based classifiers Random Forest and Extreme Gradient Boosting obtained good performances (AUC ≥ 0.73), showing the best trade-off between sensitivity and specificity. On small samples, performances were generally lower than in large-medium samples and with larger variations. FS methods generally did not improve performances. Thus, in radiomic studies, we suggest evaluating the choice of FS and classifiers, considering specific sample size, balancing, and association strength.

Machine learning-based reclassification of germline variants of unknown significance: The RENOVO algorithm.

The increasing scope of genetic testing allowed by next-generation sequencing (NGS) dramatically increased the number of genetic variants to be interpreted as pathogenic or benign for adequate patient management. Still, the interpretation process often fails to deliver a clear classification, resulting in either variants of unknown significance (VUSs) or variants with conflicting interpretation of pathogenicity (CIP); these represent a major clinical problem because they do not provide useful information for decision-making, causing a large fraction of genetically determined disease to remain undertreated. We developed a machine learning (random forest)-based tool, RENOVO, that classifies variants as pathogenic or benign on the basis of publicly available information and provides a pathogenicity likelihood score (PLS). Using the same feature classes recommended by guidelines, we trained RENOVO on established pathogenic/benign variants in ClinVar (training set accuracy = 99%) and tested its performance on variants whose interpretation has changed over time (test set accuracy = 95%). We further validated the algorithm on additional datasets including unreported variants validated either through expert consensus (ENIGMA) or laboratory-based functional techniques (on BRCA1/2 and SCN5A). On all datasets, RENOVO outperformed existing automated interpretation tools. On the basis of the above validation metrics, we assigned a defined PLS to all existing ClinVar VUSs, proposing a reclassification for 67% with >90% estimated precision. RENOVO provides a validated tool to reduce the fraction of uninterpreted or misinterpreted variants, tackling an area of unmet need in modern clinical genetics.

Master protocols in immuno-oncology: do novel drugs deserve novel designs?

The rapid rise to fame of immuno-oncology (IO) drugs has generated unprecedented interest in the industry, patients and doctors, and has had a major impact in the treatment of most cancers. An interesting aspect in the clinical development of many IO agents is the increasing reliance on nonconventional trial design, including the so-called 'master protocols' that incorporate various adaptive features and often heavily rely on biomarkers to select patient populations most likely to benefit. These novel designs promise to maximize the clinical benefit that can be reaped from clinical research, but are not without costs. Their acceptance as solid evidence basis for use outside of the research context requires profound cultural changes by multiple stakeholders, including regulatory bodies, decision-makers, statisticians, researchers, doctors and, most importantly, patients. Here we review characteristics of recent and ongoing trials testing IO drugs with unconventional design, and we highlight trends and critical aspects.