High frequency of PIK3CA and TERT promoter mutations in fibromatosis-like spindle cell carcinomas

Fibromatosis-Like Metaplastic Carcinoma of the Breast: A Challenging Diagnosis With Potential Pitfalls

Fibromatosis-like metaplastic carcinoma (FLMC) is a rare subtype of metaplastic carcinoma of the breast. Diagnosing this entity poses significant challenges, particularly in core biopsies due to limited sampling and overlap with benign spindle cell lesions such as nodular fasciitis and fibromatosis. We present an example of FLMC in an asymptomatic middle-aged woman. As her breast imaging revealed an irregular lobulated mass, a core biopsy was performed which showed benign breast tissue with fibrosis. However, this was discordant with her imaging findings, hence a subsequent vacuum-assisted biopsy was done which revealed low-grade spindle cell proliferation, consistent with FLMC. This report emphasizes the necessity of a triple assessment and highlights the potential pitfalls in diagnosing FLMC, particularly the risk of misinterpretation due to its histological similarities with benign entities. Understanding these challenges will be crucial to avoid diagnostic delays of this rare breast cancer subtype.

Breast Cancer Histopathology in the Age of Molecular Oncology

For more than a century, microscopic histology has been the cornerstone for cancer diagnosis, and breast carcinoma is no exception. In recent years, clinical biomarkers, gene expression profiles, and other molecular tests have shown increasing utility for identifying the key biological features that guide prognosis and treatment of breast cancer. Indeed, the most common histologic pattern-invasive ductal carcinoma of no special type-provides relatively little guidance to management beyond triggering grading, biomarker testing, and clinical staging. However, many less common histologic patterns can be recognized by trained pathologists, which in many cases can be linked to characteristic biomarker and gene expression patterns, underlying mutations, prognosis, and therapy. Herein we describe more than a dozen such histomorphologic subtypes (including lobular, metaplastic, salivary analog, and several good prognosis special types of breast cancer) in the context of their molecular and clinical features.

Accurate Detection of Multiple Tumor Mutations in Formalin-Fixed Paraffin-Embedded Tissues by Coupling Sequence Artifacts Elimination and Mutation Enrichment With MeltArray

Formalin-fixed paraffin-embedded (FFPE) tissues are the primary source of DNA for companion diagnostics (CDx) of cancers. Degradation of FFPE tissue DNA and inherent tumor heterogeneity constitute serious challenges in current CDx assays. To address these limitations, we introduced sequence artifact elimination and mutation enrichment to MeltArray, a highly multiplexed PCR approach, to establish an integrated protocol that provides accuracy, ease of use, and rapidness. Using PIK3CA mutations as a model, we established a MeltArray protocol that could eliminate sequence artifacts completely and enrich mutations from 23.5- to 59.4-fold via a single-reaction pretreatment step comprising uracil-DNA-glycosylase excision and PCR clamping. The entire protocol could identify 13 PIK3CA hotspot mutations of 0.05% to 0.5% mutant allele fractions within 5 hours. Evaluation of 106 breast cancer and 40 matched normal FFPE tissue samples showed that all 47 PIK3CA mutant samples were from the cancer tissue, and no false-positive results were detected in the normal samples. Further evaluation of 105 colorectal and 40 matched normal FFPE tissue samples revealed that 11 PIK3CA mutants were solely from the cancer sample. The detection results of our protocol were consistent with those of the droplet digital PCR assays that underwent sequence artifact elimination. Of the 60 colorectal samples with next-generation sequencing results, the MeltArray protocol detected 2 additional mutant samples with low mutant allele fractions. We conclude that the new protocol provides an improved alternative to current CDx assays for detecting tumor mutations in FFPE tissue DNA.

Integrating Protein-Protein Interaction Networks and Somatic Mutation Data to Detect Driver Modules in Pan-Cancer

With the constant update of large-scale sequencing data and the continuous improvement of cancer genomics data, such as International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), it gains increasing importance to detect the functional high-frequency mutation gene set in cells that causes cancer in the field of medicine. In this study, we propose a new recognition method of driver modules, named ECSWalk to solve the issue of mutated gene heterogeneity and improve the accuracy of driver modules detection, based on human protein-protein interaction networks and pan-cancer somatic mutation data. This study first utilizes high mutual exclusivity and high coverage between mutation genes and topological structure similarity of the nodes in complex networks to calculate interaction weights between genes. Second, the method of random walk with restart is utilized to construct a weighted directed network, and the strong connectivity principle of the directed graph is utilized to create the initial candidate modules with a certain number of genes. Finally, the large modules in the candidate modules are split using induced subgraph method, and the small modules are expanded using a greedy strategy to obtain the optimal driver modules. This method is applied to TCGA pan-cancer data and the experimental results show that ECSWalk can detect driver modules more effectively and accurately, and can identify new candidate gene sets with higher biological relevance and statistical significance than MEXCOWalk and HotNet2. Thus, ECSWalk is of theoretical implication and practical value for cancer diagnosis, treatment and drug targets.