Genomic dissection and mutation-specific target discovery for breast cancer PIK3CA hotspot mutations.

BACKGROUND: Recent advancements in high-throughput genomics and targeted therapies have provided tremendous potential to identify and therapeutically target distinct mutations associated with cancers. However, to date the majority of targeted therapies are used to treat all functional mutations within the same gene, regardless of affected codon or phenotype. RESULTS: In this study, we developed a functional genomic analysis workflow with a unique isogenic cell line panel bearing two distinct hotspot PIK3CA mutations, E545K and H1047R, to accurately identify targetable differences between mutations within the same gene. We performed RNA-seq and ATAC-seq and identified distinct transcriptomic and epigenomic differences associated with each PIK3CA hotspot mutation. We used this data to curate a select CRISPR knock out screen to identify mutation-specific gene pathway vulnerabilities. These data revealed AREG as a E545K-preferential target that was further validated through in vitro analysis and publicly available patient databases. CONCLUSIONS: Using our multi-modal genomics framework, we discover distinct differences in genomic regulation between PIK3CA hotspot mutations, suggesting the PIK3CA mutations have different regulatory effects on the function and downstream signaling of the PI3K complex. Our results demonstrate the potential to rapidly uncover mutation specific molecular targets, specifically AREG and a proximal gene regulatory region, that may provide clinically relevant therapeutic targets. The methods outlined provide investigators with an integrative strategy to identify mutation-specific targets for the treatment of other oncogenic mutations in an isogenic system.

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.

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.

Quantifying mRNA in Highly Degraded Fixed Tissues by Nanostring Technology: A Comparative Study.

Archive tissues are the most available source of human tissues useful for molecular analysis in translational research. The main issues for those specimens are the modification and degradation of biomolecules, namely proteins, DNA, and RNA. In the last decade, several high-throughput analytical methods have been applied to archive tissues. Although histological tissues are fixed in neutral-buffered formalin nowadays, in the recent past, Bouin's solution was also used in tissue processing. The present study aims to investigate the feasibility of nCounter Nanostring hybridization in quantifying mRNA in highly degraded samples, such as Bouin's fixed and paraffin-embedded (BFPE) tissues, in comparison to the standard formalin-fixed and paraffin-embedded (FFPE) tissues as a source of RNA. A total of 16 paraffin-embedded tissue blocks from eight patients were analyzed (8 were FFPE and 8 were BEPE). Nanostring technology was applied to 300 ng of each RNA sample, whereas 360 ng of the same templates were retrotranscribed and submitted to qPCR and ddPCR. Our results show that the Nanostring technology outperforms the reference methods (ddPCR and qPCR) in detecting target mRNA in FFPE and BFPE samples. However, even Nanostring technology does not escape the limitation imposed by the degradation of the RNA templates, which could lead to misleading conclusions on the gene expression level.

Genomic dissection and mutation-specific target discovery for breast cancer PIK3CA hotspot mutations.

Background: Recent advancements in high-throughput genomics and targeted therapies have provided tremendous potential to identify and therapeutically target distinct mutations associated with cancers. However, to date the majority of targeted therapies are used to treat all functional mutations within the same gene, regardless of affected codon or phenotype. Results: In this study, we developed a functional genomic analysis workflow with a unique isogenic cell line panel bearing two distinct hotspot PIK3CA mutations, E545K and H1047R, to accurately identify targetable differences between mutations within the same gene. We performed RNA-seq and ATAC-seq and identified distinct transcriptomic and epigenomic differences associated with each PIK3CA hotspot mutation. We used this data to curate a select CRISPR knock out screen to identify mutation-specific gene pathway vulnerabilities. These data revealed AREG as a E545K-preferential target that was further validated through in vitro analysis and publicly available patient databases. Conclusions: Using our multi-modal genomics framework, we discover distinct differences in genomic regulation between PIK3CA hotspot mutations, suggesting the PIK3CA mutations have different regulatory effects on the function and downstream signaling of the PI3K complex. Our results demonstrate the potential to rapidly uncover mutation specific molecular targets, specifically AREG and a proximal gene regulatory region, that may provide clinically relevant therapeutic targets. The methods outlined provide investigators with an integrative strategy to identify mutation-specific targets for the treatment of other oncogenic mutations in an isogenic system.

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.

Navigating practical challenges in immunotherapy for metastatic triple negative breast cancer.

Immunotherapy has revolutionized cancer therapy and now represents a standard of care for many tumor types, including triple-negative breast cancer. Despite the positive results that have led to the approval of immunotherapy in both early- and advanced-stage triple-negative breast cancer, pivotal clinical trials cannot address the myriad questions arising in everyday clinical practice, often falling short in delivering all the information that clinicians require. In this manuscript, we aim to address some of these practical questions, with the purpose of providing clinicians with a guide for optimizing the use of immune checkpoint inhibitors in the management of breast cancer patients and identifying opportunities for future research to clarify unresolved questions.

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.