Does Breast Cancer Subtype Impact Margin Status in Patients Undergoing Partial Mastectomy?

Background Molecular subtype in invasive breast cancer guides systemic therapy. It is unknown whether molecular subtype should also be considered to tailor surgical therapy. The present investigation was designed to evaluate whether breast cancer subtype impacted surgical margins in patients with invasive breast cancer stage I through III undergoing breast-conserving therapy. Methods Data from 2 randomized trials evaluating cavity shave margins (CSM) on margin status in patients undergoing partial mastectomy (PM) were used for this analysis. Patients were included if invasive carcinoma was present in the PM specimen and data for all 3 receptors (ER, PR, and HER2) were known. Patients were classified as luminal if they were ER and/or PR positive; HER2 enriched if they were ER and PR negative but HER2 positive; and TN if they were negative for all 3 receptors. The impact of subtype on the margin status was evaluated at completion of standard PM, prior to randomization to CSM versus no CSM. Non-parametric statistical analyses were performed using SPSS Version 26. Results Molecular subtype was significantly correlated with race ( P = .011), palpability ( P = .007), and grade ( P < .001). Subtype did not correlate with Hispanic ethnicity ( P = .760) or lymphovascular invasion ( P = .756). In this cohort, the overall positive margin rate was 33.7%. This did not vary based on molecular subtype (positive margin rate 33.7% for patients with luminal tumors vs 36.4% for those with TN tumors, P = .425). Discussion Molecular subtype does not predict margin status. Therefore, molecular subtype should not, independent of other factors, influence surgical decision-making.

Survival outcomes in elderly Taiwanese women according to breast cancer subtype and lymph node status: A single-center retrospective study

This study aimed to determine the rates of overall survival and recurrence-free survival among elderly Taiwanese women (>65 years old) according to breast cancer subtype and lymph node status. We identified 554 eligible patients who were >65 years old and had been treated based on international recommendations at our center between June 2005 and June 2015. Patients with the luminal A subtype had the highest rates of overall survival (90.6%) and recurrence-free survival (97.0%), while the lowest overall survival rate was observed in those with the triple-negative subtype (81.3%) and the lowest recurrence-free survival rate was observed in those with the luminal B subtype (84.0%). Multivariate Cox proportional hazard analysis, using the luminal A subtype as the reference, revealed significant differences in recurrence-free survival among luminal B patients according to lymph node status. Among elderly Taiwanese women with breast cancer, the breast cancer subtype might help predict survival outcomes. The luminal B subtype was associated with poor recurrence-free survival, and lymph node status was useful for predicting recurrence-free survival in this subset of patients.

Identifying Cancer Subtypes Using a Residual Graph Convolution Model on a Sample Similarity Network

Cancer subtype classification helps us to understand the pathogenesis of cancer and develop new cancer drugs, treatment from which patients would benefit most. Most previous studies detect cancer subtypes by extracting features from individual samples, ignoring their associations with others. We believe that the interactions of cancer samples can help identify cancer subtypes. This work proposes a cancer subtype classification method based on a residual graph convolutional network and a sample similarity network. First, we constructed a sample similarity network regarding cancer gene co-expression patterns. Then, the gene expression profiles of cancer samples as initial features and the sample similarity network were passed into a two-layer graph convolutional network (GCN) model. We introduced the initial features to the GCN model to avoid over-smoothing during the training process. Finally, the classification of cancer subtypes was obtained through a softmax activation function. Our model was applied to breast invasive carcinoma (BRCA), glioblastoma multiforme (GBM) and lung cancer (LUNG) datasets. The accuracy values of our model reached 82.58%, 85.13% and 79.18% for BRCA, GBM and LUNG, respectively, which outperformed the existing methods. The survival analysis of our results proves the significant clinical features of the cancer subtypes identified by our model. Moreover, we can leverage our model to detect the essential genes enriched in gene ontology (GO) terms and the biological pathways related to a cancer subtype.

MFmap: A semi-supervised generative model matching cell lines to tumours and cancer subtypes

Translating in vitro results from experiments with cancer cell lines to clinical applications requires the selection of appropriate cell line models. Here we present MFmap (model fidelity map), a machine learning model to simultaneously predict the cancer subtype of a cell line and its similarity to an individual tumour sample. The MFmap is a semi-supervised generative model, which compresses high dimensional gene expression, copy number variation and mutation data into cancer subtype informed low dimensional latent representations. The accuracy (test set F1 score >90%) of the MFmap subtype prediction is validated in ten different cancer datasets. We use breast cancer and glioblastoma cohorts as examples to show how subtype specific drug sensitivity can be translated to individual tumour samples. The low dimensional latent representations extracted by MFmap explain known and novel subtype specific features and enable the analysis of cell-state transformations between different subtypes. From a methodological perspective, we report that MFmap is a semi-supervised method which simultaneously achieves good generative and predictive performance and thus opens opportunities in other areas of computational biology.

Patient-Derived Xenografts of High-Grade Serous Ovarian Cancer Subtype as a Powerful Tool in Pre-Clinical Research

(1) Background. PDX models have become the preferred tool in research laboratories seeking to improve development and pre-clinical testing of new drugs. PDXs have been shown to capture the cellular and molecular characteristics of human tumors better than simpler cell line-based models. More recently, however, hints that PDXs may change their characteristics over time have begun to emerge, emphasizing the need for comprehensive analysis of PDX evolution. (2) Methods. We established a panel of high-grade serous ovarian carcinoma (HGSOC) PDXs and developed and validated a 300-SNP signature that can be successfully utilized to assess genetic drift across PDX passages and detect PDX contamination with lymphoproliferative tissues. In addition, we performed a detailed histological characterization and functional assessment of multiple PDX passages. (3) Results. Our data show that the PDXs remain largely stable throughout propagation, with marginal genetic drift at the time of PDX initiation and adaptation to mouse host. Importantly, our PDX lines retained the major histological characteristics of the original patients’ tumors even after multiple passages in mice, demonstrating a strong concordance with the clinical responses of their corresponding patients. (4) Conclusions. Our data underline the value of defined HGSOC PDXs as a pre-clinical tumor model.

The landscape of coding RNA editing events in pediatric cancer

Background RNA editing leads to post-transcriptional variation in protein sequences and has important biological implications. We sought to elucidate the landscape of RNA editing events across pediatric cancers. Methods Using RNA-Seq data mapped by a pipeline designed to minimize mapping ambiguity, we investigated RNA editing in 711 pediatric cancers from the St. Jude/Washington University Pediatric Cancer Genome Project focusing on coding variants which can potentially increase protein sequence diversity. We combined de novo detection using paired tumor DNA-RNA data with analysis of known RNA editing sites. Results We identified 722 unique RNA editing sites in coding regions across pediatric cancers, 70% of which were nonsynonymous recoding variants. Nearly all editing sites represented the canonical A-to-I (n = 706) or C-to-U sites (n = 14). RNA editing was enriched in brain tumors compared to other cancers, including editing of glutamate receptors and ion channels involved in neurotransmitter signaling. RNA editing profiles of each pediatric cancer subtype resembled those of the corresponding normal tissue profiled by the Genotype-Tissue Expression (GTEx) project. Conclusions In this first comprehensive analysis of RNA editing events in pediatric cancer, we found that the RNA editing profile of each cancer subtype is similar to its normal tissue of origin. Tumor-specific RNA editing events were not identified indicating that successful immunotherapeutic targeting of RNA-edited peptides in pediatric cancer should rely on increased antigen presentation on tumor cells compared to normal but not on tumor-specific RNA editing per se.