PIK3CA testing in hormone receptor-positive/HER2-negative metastatic breast cancer: real-world data from Italian molecular pathology laboratories

Introduction: PIK3CA gene mutations occur in approximately 40% of hormone receptor-positive/HER2-negative (HR+/HER2-) metastatic breast cancers (MBCs), electing them to targeted therapy. Testing PIK3CA status is complex due to selection of biological specimen and testing method. Materials & methods: This work investigates real-life experience on PIK3CA testing in HR+/HER2- MBC. Clinical, technical and molecular data on PIK3CA testing were collected from two referral laboratories. Additionally, the results of a nationwide PIK3CA survey involving 116 institutions were assessed. Results: Overall, n = 35 MBCs were PIK3CA-mutated, with mutations mostly occurring in exons 9 (n = 19; 51.4%) and 20 (n = 15; 40.5%). The nationwide survey revealed significant variability across laboratories in terms of sampling methodology, technical assessment and clinical report signing healthcare figures for PIK3CA molecular testing in diagnostic routine practice. Conclusion: This study provides insights into the real-world routine of PIK3CA testing in HR+/HER2- MBC and highlights the need for standardization and networking in predictive pathology.

ESR1 mutations in HR+/HER2-metastatic breast cancer: Enhancing the accuracy of ctDNA testing

Activating mutations of the estrogen receptor alpha gene (ESR1) are common mechanisms of endocrine therapy (ET) resistance in hormone receptor-positive (HR + )/Human Epidermal Growth Factor Receptor 2 (HER2)-negative metastatic breast cancer (MBC). Recent clinical findings emphasize that both old and new generations of selective ER degraders (SERDs) demonstrate enhanced clinical effectiveness in patients with MBC who have detectable ESR1 mutations via liquid biopsy. This stands in contrast to individuals with MBC carrying these mutations and undergoing conventional endocrine monotherapies like aromatase inhibitors (AIs). Liquid biopsy, particularly the analysis of circulating tumor DNA (ctDNA), has emerged as a promising, minimally invasive alternative to conventional tissue-based testing for identifying ESR1 mutations. Within the context of the PADA-1 and EMERALD trials, distinct molecular methodologies and assays, specifically digital droplet PCR (ddPCR) and next-generation sequencing (NGS), have been employed to evaluate the mutational status of ESR1 within ctDNA. This manuscript critically examines the advantages and indications of various ctDNA testing methods on liquid biopsy for HR+/HER2-negative MBC. Specifically, we delve into the capabilities of ddPCR and NGS in identifying ESR1 mutations. Each methodology boasts unique strengths and limitations: ddPCR excels in its analytical sensitivity for pinpointing hotspot mutations, while NGS offers comprehensive coverage of the spectrum of ESR1 mutations. The significance of meticulous sample handling and timely analysis is emphasized, acknowledging the transient nature of cfDNA. Furthermore, we underscore the importance of detecting sub-clonal ESR1 mutations, as these variants can exert a pivotal influence on predicting both endocrine therapy resistance and responsiveness to SERDs. In essence, this work discusses the role of ctDNA analysis for detecting ESR1 mutations and their implications in tailoring effective therapeutic strategies for HR+/HER2- MBC.

Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking

BACKGROUND

Biobanks are vital research infrastructures aiming to collect, process, store, and distribute biological specimens along with associated data in an organized and governed manner. Exploiting diverse datasets produced by the biobanks and the downstream research from various sources and integrating bioinformatics and "omics" data has proven instrumental in advancing research such as cancer research. Biobanks offer different types of biological samples matched with rich datasets comprising clinicopathologic information. As digital pathology and artificial intelligence (AI) have entered the precision medicine arena, biobanks are progressively transitioning from mere biorepositories to integrated computational databanks. Consequently, the application of AI and machine learning on these biobank datasets holds huge potential to profoundly impact cancer research.

METHODS

In this paper, we explore how AI and machine learning can respond to the digital evolution of biobanks with flexibility, solutions, and effective services. We look at the different data that ranges from specimen-related data, including digital images, patient health records and downstream genetic/genomic data and resulting "Big Data" and the analytic approaches used for analysis.

RESULTS

These cutting-edge technologies can address the challenges faced by translational and clinical research, enhancing their capabilities in data management, analysis, and interpretation. By leveraging AI, biobanks can unlock valuable insights from their vast repositories, enabling the identification of novel biomarkers, prediction of treatment responses, and ultimately facilitating the development of personalized cancer therapies.

CONCLUSIONS

The integration of biobanking with AI has the potential not only to expand the current understanding of cancer biology but also to pave the way for more precise, patient-centric healthcare strategies.

An Indeterminate for Malignancy FNA Report Does Not Increase the Surgical Risk of Incidental Thyroid Carcinoma

Incidental thyroid carcinomas (ITCs) are a fairly frequent finding in daily routine practice, with papillary thyroid microcarcinoma being the most frequent entity. In our work, we isolated incidental cases arising in thyroids removed for other cytologically indeterminate and histologically benign nodules. We retrospectively retrieved cases with available thyroid Fine Needle Aspiration (FNA, 3270 cases), selecting those with an indeterminate cytological diagnosis (Bethesda classes III−IV, 652 cases). Subsequently, we restricted the analysis to surgically treated patients (163 cases) finding an incidental thyroid carcinoma in 22 of them. We found a 13.5% ITC rate, with ITCs representing 46.8% of all cancer histologically diagnosed in this indeterminate setting. Patients received a cytological diagnosis of Bethesda class III and IV in 41% and 59% of cases, respectively. All ITC cases turned out to be papillary thyroid microcarcinomas; 36% of cases were multifocal, with foci bilaterally detected in 50% of cases. We found an overall ITC rate concordant with the literature and with our previous findings. The assignment of an indeterminate category to FNA did not increase the risk of ITCs in our cohort. Rather, a strong statistical significance (p < 0.01) was found comparing the larger size of nodules that underwent FNA and the smaller size of their corresponding ITC nodule.