Background: Morphological evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer is gaining momentum as an immunological biomarker. This experiment evaluates the role of TILs in distant tumors as a measure of abscopal effect from cryoablation of breast cancer. Methods: BALB/c mice underwent bilateral orthotopic transplant with 4T1-12B (triple-negative) cells. At 2 weeks, left tumors were treated by either resection (standard of care group) or cryoablation (intervention group) followed by resection of the distant right tumors 1 week posttreatment. TIL scores were calculated from hematoxylin and eosin-stained sections and phenotyped for cytotoxic T-lymphocyte (CTL) markers by immunofluorescence. Primarily resected tumors served as baseline (T_baseline), whereas resected distant right-sided served as the readout for abscopal effect (Abs_Res or Abs_Cryo). Mice were monitored for tumor recurrence and metastasis. Results: The Abs_Cryo had a significant mean (SD) increase in stromal (2.8 [1.1]%; p = 0.015) and invasive margin TILs (50 [12]%; p = 0.02) compared with T_Baseline (1.0 [0]% and 31 [4.9]%, respectively). CTL phenotyping revealed a significant increase in mean (SD) CD8⁺ T cells (15.7 [12.1]; p = 0.02) and granzyme B (4.8 [3.6]; p = 0.048) for the Abs_Cryo compared with T_Baseline (5.2 [4.7] and 2.4 [0.9], respectively). Posttreatment, the cryoablation group had no recurrence or metastasis, whereas the resected group showed local recurrence and lung metastasis in 40% of the mice. Postprocedure increase in TIL score of distant tumors was associated with decrease in tumor relapse (p = 0.02). Conclusions: Cryoablation induced a robust tumor-specific TIL response compared with resection, suggesting an abscopal effect leading to the prevention of cancer recurrence and metastasis.

Background: Breast cancer is a complex and heterogeneous disease characterized by distinct molecular subtypes with varying prognoses and treatment responses. Multiple factors influence breast cancer outcomes including tumor biology, patient characteristics, and treatment modalities. Demographic factors such as age, race/ethnicity, menopausal status, and body mass index have been correlated with variations in incidence, mortality, and survival rates. Over the past decade, comprehensive genomic profiling has been widely used to identify molecular biomarkers and signatures to develop novel therapeutic strategies for patients. For instance, the FLEX registry (NCT03053193) enrolled stage I–III breast cancer patients across 90 institutions in the United States and stratified risk groups based on a 70-gene signature (MammaPrint^®-MP) and molecular subtype based on an 80-gene signature (BluePrint^®-BP). This study aimed to identify the gene expression patterns and biomarkers associated with breast cancer risk and progression by integrating transcriptomic and clinical data. Methods: Targeted 111 unique gene expression and clinical data points from 978 breast cancer samples, representing each BP subtype (26% Luminal A, 26% Luminal B, 25% Basal, 23% HER2), obtained from Agendia Inc. These genes were selected based on their involvement in the mercapturic acid pathway, white and brown adipose tissue markers, inflammation markers, tumor-associated genes, apoptosis, autophagy, and ER stress markers. All statistical analyses, including principal component analysis (PCA), were performed using R version [4.4.0]. Prognostic values and genetic alterations were investigated using various web-based programs as described in the Methods section. Results: PCA of gene expression data revealed distinct clustering patterns associated with risk categories and molecular subtypes, particularly with principal component 4 (PC4). Genes related to oxidative stress, autophagy, apoptosis, and histone modification showed altered expression across risk categories and molecular subtypes. Key differentially expressed genes included SOD2, KLK5, KLK7, IL8, GSTM1/2, GLI1, CBS, and IGF1. Pathway analysis highlighted the enrichment of processes related to autophagy, cellular stress response, apoptosis, glutathione metabolism, deacetylation, and oxidative stress in high-risk and basal-like tumors compared with Ultralow and Luminal A tumors, respectively. Conclusions: This study identified gene expression signatures associated with breast cancer risk and molecular subtypes. These findings provide insights into the biological processes that may drive breast cancer progression and could inform the development of prognostic biomarkers and personalized therapeutic strategies.

The prevalence and mortality associated with breast cancer have forced healthcare providers to leverage surgery, chemotherapy, radiation therapy, and immunotherapy to achieve a cure. Whereas mortality has significantly dropped over the decades, long-term toxicities and healthcare costs are prohibitive. Therefore, a better understanding of tumor biology through molecular profiling is being utilized for de-escalation of treatment where appropriate. As research evolves, there is growing evidence that less aggressive treatment regimens, when appropriately tailored, can be equally effective for certain patient populations. This approach not only enhances the quality of life for patients by reducing the financial, physical, and emotional burdens associated with more invasive therapies but also promotes a more personalized treatment strategy. By focusing on precision medicine and understanding the biological characteristics of tumors, healthcare providers and patients can make informed decisions that balance safety with efficacy. The field of molecular profiling is a promising avenue for precision-targeted de-escalation and escalation of therapy to minimize the risk–benefit ratio.