Alternative Tissue Fixation Protocols Dramatically Reduce the Impact of DNA Artifacts, Unraveling the Interpretation of Clinical Comprehensive Genomic Profiling.

Formalin-fixed paraffin-embedded (FFPE) samples represent the cornerstone of tissue-based analysis in precision medicine. Targeted next-generation sequencing panels are routinely used to analyze a limited number of genes to guide treatment decision-making for advanced-stage patients. The number and complexity of genetic alterations to be investigated are rapidly growing; in several instances, a comprehensive genomic profiling analysis is needed. The poor quality of genetic material extracted from FFPE samples may impact the feasibility/reliability of sequencing data. We sampled 9 colorectal cancers to allow 4 parallel fixations: (1) neutral buffered formalin (NBF), (2) acid-deprived formalin fixation (ADF), (3) precooled ADF (coldADF), and (4) glyoxal acid free (GAF). DNA extraction, fragmentation analysis, and sequencing by 2 large next-generation sequencing panels (OCAv3 and TSO500) followed. We comprehensively analyzed library and sequencing quality controls and the quality of sequencing results. Libraries from coldADF samples showed significantly longer reads than the others with both panels. ADF-derived and coldADF-derived libraries showed the lowest level of noise and the highest levels of uniformity with the OCAv3 panel, followed by GAF and NBF samples. The data uniformity was confirmed by the TSO500 results, which also highlighted the best performance in terms of the total region sequenced for the ADF and coldADF samples. NBF samples had a significantly smaller region sequenced and displayed a significantly lower number of evaluable microsatellite loci and a significant increase in single-nucleotide variations compared with other protocols. Mutational signature 1 (aging and FFPE artifact related) showed the highest (37%) and lowest (17%) values in the NBF and coldADF samples, respectively. Most of the identified genetic alterations were shared by all samples in each lesion. Five genes showed a different mutational status across samples and/or panels: 4 discordant results involved NBF samples. In conclusion, acid-deprived fixatives (GAF and ADF) guarantee the highest DNA preservation/sequencing performance, thus allowing more complex molecular profiling of tissue samples.

Comprehensive genomic profiling on metastatic Melanoma: results from a network screening from 7 Italian Cancer Centres.

BACKGROUND: The current therapeutic algorithm for Advanced Stage Melanoma comprises of alternating lines of Targeted and Immuno-therapy, mostly via Immune-Checkpoint blockade. While Comprehensive Genomic Profiling of solid tumours has been approved as a companion diagnostic, still no approved predictive biomarkers are available for Melanoma aside from BRAF mutations and the controversial Tumor Mutational Burden. This study presents the results of a Multi-Centre Observational Clinical Trial of Comprehensive Genomic Profiling on Target and Immuno-therapy treated advanced Melanoma. METHODS: 82 samples, collected from 7 Italian Cancer Centres of FFPE-archived Metastatic Melanoma and matched blood were sequenced via a custom-made 184-gene amplicon-based NGS panel. Sequencing and bioinformatics analysis was performed at a central hub. Primary analysis was carried out via the Ion Reporter framework. Secondary analysis and Machine Learning modelling comprising of uni and multivariate, COX/Lasso combination, and Random Forest, was implemented via custom R/Python scripting. RESULTS: The genomics landscape of the ACC-mela cohort is comparable at the somatic level for Single Nucleotide Variants and INDELs aside a few gene targets. All the clinically relevant targets such as BRAF and NRAS have a comparable distribution thus suggesting the value of larger scale sequencing in melanoma. No comparability is reached at the CNV level due to biotechnological biases and cohort numerosity. Tumour Mutational Burden is slightly higher in median for Complete Responders but fails to achieve statistical significance in Kaplan-Meier survival analysis via several thresholding strategies. Mutations on PDGFRB, NOTCH3 and RET were shown to have a positive effect on Immune-checkpoint treatment Overall and Disease-Free Survival, while variants in NOTCH4 were found to be detrimental for both endpoints. CONCLUSIONS: The results presented in this study show the value and the challenge of a genomics-driven network trial. The data can be also a valuable resource as a validation cohort for Immunotherapy and Target therapy genomic biomarker research.

Comprehensive Molecular Profiling of NPM1-Mutated Acute Myeloid Leukemia Using RNAseq Approach.

Acute myeloid leukemia (AML) is a complex hematologic malignancy with high morbidity and mortality. Nucleophosmin 1 (NPM1) mutations occur in approximately 30% of AML cases, and NPM1-mutated AML is classified as a distinct entity. NPM1-mutated AML patients without additional genetic abnormalities have a favorable prognosis. Despite this, 30-50% of them experience relapse. This study aimed to investigate the potential of total RNAseq in improving the characterization of NPM1-mutated AML patients. We explored genetic variations independently of myeloid stratification, revealing a complex molecular scenario. We showed that total RNAseq enables the uncovering of different genetic alterations and clonal subtypes, allowing for a comprehensive evaluation of the real expression of exome transcripts in leukemic clones and the identification of aberrant fusion transcripts. This characterization may enhance understanding and guide improved treatment strategies for NPM1mut AML patients, contributing to better outcomes. Our findings underscore the complexity of NPM1-mutated AML, supporting the incorporation of advanced technologies for precise risk stratification and personalized therapeutic strategies. The study provides a foundation for future investigations into the clinical implications of identified genetic variations and highlights the importance of evolving diagnostic approaches in leukemia management.

Think "HER2" different: integrative diagnostic approaches for HER2-low breast cancer.

This work explores the complex field of HER2 testing in the HER2-low breast cancer era, with a focus on methodological aspects. We aim to propose clear positions to scientific societies, institutions, pathologists, and oncologists to guide and shape the appropriate diagnostic strategies for HER2-low breast cancer. The fundamental question at hand is whether the necessary tools to effectively translate our knowledge about HER2 into practical diagnostic schemes for the lower spectrum of expression are available. Our investigation is centered on the significance of distinguishing between an immunohistochemistry (IHC) score 0 and score 1+ in light of the clinical implications now apparent, as patients with HER2-low breast cancer become eligible for trastuzumab-deruxtecan treatment. Furthermore, we discuss the definition of HER2-low beyond its conventional boundaries and assess the reliability of established diagnostic procedures designed at a time when therapeutic perspectives were non-existent for these cases. In this regard, we examine potential complementary technologies, such as gene expression analysis and liquid biopsy. Ultimately, we consider the potential role of artificial intelligence (AI) in the field of digital pathology and its integration into HER2 testing, with a particular emphasis on its application in the context of HER2-low breast cancer.

Enhancing breast cancer screening with urinary biomarkers and Random Forest supervised classification: A comprehensive investigation.

OBJECTIVES: Urinary sex hormones are investigated as potential biomarkers for the early detection of breast cancer, aiming to evaluate their relevance and applicability, in combination with supervised machine-learning data analysis, toward the ultimate goal of extensive screening. METHODS: Sex hormones were determined on urine samples collected from 250 post-menopausal women (65 healthy - 185 with breast cancer, recruited among the clinical patients of Candiolo Cancer Institute FPO-IRCCS (Torino, Italy). Two analytical procedures based on UHPLC-MS/HRMS were developed and comprehensively validated to quantify 20 free and conjugated sex hormones from urine samples. The quantitative data were processed by seven machine learning algorithms. The efficiency of the resulting models was compared. RESULTS: Among the tested models aimed to relate urinary estrogen and androgen levels and the occurrence of breast cancer, Random Forest (RF) proved to underscore all the other supervised classification approaches, including Partial Least Squares - Discriminant Analysis (PLS-DA), in terms of effectiveness and robustness. The final optimized model built on only five biomarkers (testosterone-sulphate, alpha-estradiol, 4-methoxyestradiol, DHEA-sulphate, and epitestosterone-sulphate) achieved an approximate 98% diagnostic accuracy on replicated validation sets. To balance the less-represented population of healthy women, a Synthetic Minority Oversampling TEchnique (SMOTE) data oversampling approach was applied. CONCLUSIONS: By means of tunable hyperparameters optimization, the RF algorithm showed great potential for early breast cancer detection, as it provides clear biomarkers ranking and their relative efficiency, allowing to ground the final diagnostic model on a restricted selection five steroid biomarkers only, as desirable for noninvasive tests with wide screening purposes.

scCircle-seq unveils the diversity and complexity of extrachromosomal circular DNAs in single cells.

Extrachromosomal circular DNAs (eccDNAs) have emerged as important intra-cellular mobile genetic elements that affect gene copy number and exert in trans regulatory roles within the cell nucleus. Here, we describe scCircle-seq, a method for profiling eccDNAs and unraveling their diversity and complexity in single cells. We implement and validate scCircle-seq in normal and cancer cell lines, demonstrating that most eccDNAs vary largely between cells and are stochastically inherited during cell division, although their genomic landscape is cell type-specific and can be used to accurately cluster cells of the same origin. eccDNAs are preferentially produced from chromatin regions enriched in H3K9me3 and H3K27me3 histone marks and are induced during replication stress conditions. Concomitant sequencing of eccDNAs and RNA from the same cell uncovers the absence of correlation between eccDNA copy number and gene expression levels, except for a few oncogenes, including MYC, contained within a large eccDNA in colorectal cancer cells. Lastly, we apply scCircle-seq to one prostate cancer and two breast cancer specimens, revealing cancer-specific eccDNA landscapes and a higher propensity of eccDNAs to form in amplified genomic regions. scCircle-seq is a scalable tool that can be used to dissect the complexity of eccDNAs across different cell and tissue types, and further expands the potential of eccDNAs for cancer diagnostics.

Emerging prognostic biomarkers in advanced cutaneous melanoma: a literature update.

INTRODUCTION: Over the past two years, the scientific community has witnessed an exponential growth in research focused on identifying prognostic biomarkers for melanoma, both in pre-clinical and clinical settings. This surge in studies reflects the need of developing effective prognostic indicators in the field of melanoma. AREAS COVERED: The aim of this work is to review the scientific literature on the most recent findings on the development or validation of prognostic biomarkers in melanoma, in the attempt of providing both clinicians and researchers with an updated broad synopsis of prognostic biomarkers in cutaneous melanoma. EXPERT OPINION: While the field of prognostic biomarkers in melanoma appears promising, there are several complexities and limitations to address. The interdependence of clinical, histological, and molecular features requires accurate classification of different biomarker families. Correlation does not imply causation, and adjustments for confounding factors are often overlooked. In this scenario, large-scale studies based on high-quality clinical trial data can provide more reliable evidence. It is essential to avoid oversimplification by focusing on a single biomarker, as the interactions among multiple factors contribute to define the disease course and patient's outcome. Furthermore, implementing well-supported evidence in real-life settings can help advance prognostic biomarker research in melanoma.

High clonal diversity and spatial genetic admixture in early prostate cancer and surrounding normal tissue.

Somatic copy number alterations (SCNAs) are pervasive in advanced human cancers, but their prevalence and spatial distribution in early-stage, localized tumors and their surrounding normal tissues are poorly characterized. Here, we perform multi-region, single-cell DNA sequencing to characterize the SCNA landscape across tumor-rich and normal tissue in two male patients with localized prostate cancer. We identify two distinct karyotypes: 'pseudo-diploid' cells harboring few SCNAs and highly aneuploid cells. Pseudo-diploid cells form numerous small-sized subclones ranging from highly spatially localized to broadly spread subclones. In contrast, aneuploid cells do not form subclones and are detected throughout the prostate, including normal tissue regions. Highly localized pseudo-diploid subclones are confined within tumor-rich regions and carry deletions in multiple tumor-suppressor genes. Our study reveals that SCNAs are widespread in normal and tumor regions across the prostate in localized prostate cancer patients and suggests that a subset of pseudo-diploid cells drive tumorigenesis in the aging prostate.