Chumsri, Saranya

Breast cancer is the most common cancer in women, causing more than 600,000 deaths every year. The human microbiome is increasingly recognized as a key regulator of cancer initiation, progression, and therapeutic response. Postbiotics—defined as non-viable microbial cells and/or their structural components and metabolites that confer health benefits—are emerging as promising and safer alternatives to live probiotics in oncology. This review provides a comprehensive mechanistic overview of the potential role of postbiotics against cancer, with a specific focus on breast cancer. Preclinical evidence demonstrates that selected postbiotics exert dose- and time-dependent anticancer effects against multiple breast cancer subtypes by modulating key oncogenic pathways (such as PI3K/AKT and NF-κB) and inducing epigenetic regulation through histone deacetylase inhibition. Beyond direct effects on tumor cell proliferation and apoptosis, postbiotics influence the breast cancer microenvironment by reshaping cytokine networks, suppressing pro-metastatic inflammation, and enhancing antitumor immune responses through the activation of NK cells and T cells. We also provide emerging links between microbiome composition, estrobolome activity, and breast cancer subtype-specific biology. Despite these encouraging findings, the clinical translation of postbiotics in oncology remains limited. Currently, only one registered clinical trial investigates postbiotics in the oncology setting (melanoma), and no clinical trials have specifically evaluated postbiotics in breast cancer patients. This highlights a substantial translational gap between preclinical evidence and clinical application. Accordingly, well-designed, tumor-specific clinical trials are urgently needed to validate the safety, efficacy, and therapeutic potential of postbiotics as novel strategies in personalized breast cancer management.

PURPOSE Incorporation of structured data elements within the electronic health record generates real-time data for many potential uses. However, this requires a change in documentation practices, which may increase documentation effort. The best strategies to support structured documentation interventions have not been thoroughly investigated. Herein, we report the results of extending a data capture tool across multiple disease sites and practice locations. METHODS A note-based data capture tool was developed to collect five data elements using minimum common oncology data elements language. Soft-stop close visit validation (CVV), offloading of data entry tasks to allied health staff, group education, individualized e-mails, and one-on-one assistance were used to support utilization. Baseline and follow-up surveys were collected. For each provider, we compared data capture in the 6 months before and after their CVV go-live. RESULTS Forty-five providers participated (31 physicians, 14 nurse pracationers and physician assistants [NPPAs]), resulting in 23,274 encounters pre-CVV go-live and 23,146 post-CVV go-live visits. Median percentage of all data elements captured increased from 69.5% to 82.6% across all providers. The effect of CVV was immediate and sustained. When surveyed, most respondents agreed with the value propositions of understanding practice population, outcomes, selecting trials to open, and identifying patients for trial enrollment. Most respondents (15/20, 75%) felt the soft stop was a helpful reminder. CONCLUSION Structured data can be successfully collected in provider notes with >80% data capture. Personalized assistance incorporating the structured data elements into users' preexisting templates and soft-stop CVV, along with monitoring the results of implementation and personalized follow-up, was used to support the structured data capture intervention.

Triple-negative breast cancer (TNBC) represents a biologically aggressive subtype with limited therapeutic targets; however, tumor-infiltrating lymphocytes (TILs) have emerged as the most robust immune biomarker with compelling prognostic and predictive significance. This comprehensive review synthesizes current evidence on TIL assessment methodology; the immunobiological landscape of TNBC, including regulatory T cell populations and PD-L1 correlations; gene expression underpinnings of the tumor immune microenvironment; and systematic evidence from meta-analyses and clinical trials. Meta-analytic estimates demonstrate that high TIL levels are associated with improved overall survival (HR 0.58, 95% CI 0.48–0.71) and disease-free survival (HR 0.66, 95% CI 0.57–0.76), with each 10% TIL increment conferring incremental benefit. High TILs predict superior pathologic complete response to neoadjuvant chemotherapy (OR 2.14, 95% CI 1.43–3.19), with lymphocyte-predominant breast cancer achieving pCR rates exceeding 80% with pembrolizumab-based chemoimmunotherapy. Future directions include prospective TIL-guided treatment trials, artificial intelligence-enabled standardization, and emerging adoptive TIL cellular therapies for metastatic disease.

Biological heterogeneity among different breast cancer (BC) subtypes results in markedly varying clinical outcomes. Identification and analysis of key gene biomarkers that are differentially regulated during radiation therapy (RT) may pose multiple clinical challenges for BC treatment. The purpose of the study is to identify and analyze the expression of key gene biomarkers and their networks that are differentially regulated after hypofractionated RT. Patients with BC (cT0-T2, N0, M0) were treated with hypofractionated whole breast RT 25 Gy in five fractions, 4 to 8 weeks before breast conservation surgery (BCS). Biopsy (pre-RT; n = 5) and surgical (post-RT; n = 14 or 15) BC tumor samples were used for NanoString targeted sequencing. We identified 165 and 244 differentially expressed genes (DEGs; p < 0.05) in BC tumor samples from BC patients post-RT using the nCounter BC360 and IO360 panels, respectively. Gene networks and pathway analysis revealed that RT increases the gene signature of tumor inflammation (TIS), cytotoxicity, and apoptosis, while downregulating the gene signatures of tumor cell proliferation, differentiation, and cell adhesion, and increases the claudin-low gene score. RT-induced mammary stemness and enhanced infiltration of stroma, mast, and macrophage cells in the BC tumor microenvironment (TME). Further, the nCounter IO360 (immuno-oncology) panel analysis validated the findings of BC360 and demonstrated that RT increased the myeloid inflammation signature and chemokine expression, modulated B, T, NK, and DC cell activities, and enhanced residual cancer burden (RCB) in BC tumors, thus creating an immunosuppressive TME. Collectively, RT sensitized BC tumors by increasing the gene signature of TIS, cytotoxicity, apoptosis, and mammary stemness. RT facilitated an immunosuppressive environment and increased RCB, suggesting that the therapeutic potential of RT is highly individualized for each patient based on their unique tumor biology, genetic makeup, and TME.

Abstract Purpose: We previously reported that postmenopausal women with estrogen receptor-α–positive breast cancer receiving adjuvant anastrozole 1 mg/day (ANA1) with estrone (E1) ≥1.3 pg/mL and estradiol (E2) ≥0.5 pg/mL [inadequate estrogen suppression (IES)] had a threefold increased risk of a breast cancer event. The objective of this study was to determine if increasing anastrozole to 10 mg/day (ANA10) could result in adequate estrogen suppression (AES: E1 &lt;1.3 pg/mL and/or E2 &lt;0.5 pg/mL) among those with IES on ANA1. Patients and Methods: Postmenopausal women with estrogen receptor-α–positive breast cancer planning to receive adjuvant ANA1 were eligible. E1 and E2 were assessed pre- and post-8 to 10 weeks of ANA1. Those with IES were switched to 8- to 10-week cycles of ANA10 followed by letrozole 2.5 mg/day. E1 and E2 were assessed after each cycle. Anastrozole concentrations were measured post-ANA1 and post-ANA10. Primary analyses included patients who documented taking at least 80% of the planned treatment (adherent cohort). Results: In total, 132 (84.6%) of 156 eligible patients were ANA1 adherent. IES occurred in 40 (30.3%) adherent patients. Twenty-five (78.1%) of 32 patients who began ANA10 were adherent, and AES was achieved in 19 (76.0%; 90% confidence interval, 58.1%–89.0%) patients. Anastrozole concentrations post-ANA1 and post-ANA10 did not differ by estrogen suppression status among adherent patients. AES was maintained/attained in 21 (91.3%) of 23 letrozole-adherent patients. Conclusions: Approximately 30% of ANA1-adherent patients had IES. Among those who switched to ANA10 and were adherent, 76% had AES. Further studies are required to validate emerging data that ANA1 results in IES for some patients and to determine the clinical benefit of switching to ANA10 or an alternative aromatase inhibitor.