Tumor-Associated Stromal Cellular Density as a Predictor of Recurrence and Mortality in Breast Cancer: Results from Ethnically Diverse Study Populations

Prognostic role of pre-diagnostic circulating inflammatory biomarkers in breast cancer survival: evidence from the EPIC cohort study

BACKGROUND

Inflammation influences tumour progression and cancer prognosis, but its role preceding breast cancer (BC) and its prognostic implications remain inconclusive.

METHODS

We studied pre-diagnostic plasma inflammatory biomarkers in 1538 women with BC from the EPIC study. Cox proportional hazards models assessed their relationship with all-cause and BC-specific mortality, adjusting for tumour characteristics and lifestyle factors.

RESULTS

Over a 7-year follow-up after diagnosis, 229 women died, 163 from BC. Elevated IL-6 levels were associated with increased all-cause mortality risk (HR1-SD 1.25, 95% CI 1.07-1.47). Among postmenopausal, IL-6 was associated with higher all-cause (HR1-SD 1.41, 95% CI 1.18-1.69) and BC-specific mortality (HR1-SD 1.31, 95% CI 1.03-1.66), (PHeterogeneity (pre/postmenopausal) < 0.05 for both), while IL-10 and TNFα were associated with all-cause mortality only (HR1-SD 1.19, 95% CI 1.02-1.40 and HR1-SD 1.28, 95% CI 1.06-1.56). Among ER+PR+, IL-10 was associated with all-cause and BC-specific mortality (HR1-SD 1.35, 95% CI 1.10-1.65 and HR1-SD 1.42 95% CI 1.08-1.86), while TNF-α was associated with all-cause mortality in HER2- (HR1-SD 1.31, 95% CI 1.07-1.61). An inflammatory score predicted higher all-cause mortality, especially in postmenopausal women (HR1-SD 1.30, 95% CI 1.07-1.58).

CONCLUSIONS

Higher pre-diagnosis IL-6 levels suggest poorer long-term survival among BC survivors. In postmenopausal survivors, elevated IL-6, IL-10, and TNFα and inflammatory scores seem to predict all-cause mortality.

Developing the next-generation cancer research workforce in the National Institutes of Health Intramural Research Program

Although the National Institutes of Health is renowned for being the largest funder of biomedical research in the world, the research and associated career development programs on its own campuses are relatively unknown. These intramural programs provide many outstanding and programmatically unique opportunities for research-intensive careers and training in cancer biology, prevention, diagnosis, and therapeutics. Their complementary foci, structures, and review mechanisms make the extramural and intramural cancer research contributions of the National Institutes of Health the perfect partners in the quest to rid the world of cancer as we know it.

Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer

INTRODUCTION

Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes.

AREAS COVERED

In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings.

EXPERT OPINION

The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays.

A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer

Breast cancer is a heterogeneous disease with variable survival outcomes. Pathologists grade the microscopic appearance of breast tissue using the Nottingham criteria, which are qualitative and do not account for noncancerous elements within the tumor microenvironment. Here we present the Histomic Prognostic Signature (HiPS), a comprehensive, interpretable scoring of the survival risk incurred by breast tumor microenvironment morphology. HiPS uses deep learning to accurately map cellular and tissue structures to measure epithelial, stromal, immune, and spatial interaction features. It was developed using a population-level cohort from the Cancer Prevention Study-II and validated using data from three independent cohorts, including the Prostate, Lung, Colorectal, and Ovarian Cancer trial, Cancer Prevention Study-3, and The Cancer Genome Atlas. HiPS consistently outperformed pathologists in predicting survival outcomes, independent of tumor-node-metastasis stage and pertinent variables. This was largely driven by stromal and immune features. In conclusion, HiPS is a robustly validated biomarker to support pathologists and improve patient prognosis.

Pretreatment dual-energy CT for predicting early response to induction chemotherapy and survival in nasopharyngeal carcinoma

OBJECTIVES

To evaluate the predictive performance of pretreatment dual-energy CT (DECT) for early response to induction chemotherapy and survival in nasopharyngeal carcinoma (NPC).

METHODS

In this retrospective study, 56 NPC patients who underwent pretreatment DECT scans with posttreatment follow-up were enrolled. The DECT-derived normalised iodine concentration (nIC), effective atomic number (Zeff), 40-180 keV (20 keV interval), and Mix-0.3 value of the tumour lesions were measured to predict the early response to induction chemotherapy and survival in nasopharyngeal carcinoma. The Mann‒Whitney U test, ROC analysis, Kaplan‒Meier method with log-rank test, and Cox proportional hazards model were performed to evaluate the predictive performance of DECT parameters, respectively.

RESULTS

Among all DECT-derived parameters, ROC analysis showed the predictive performances of nIC and Zeff values for early objective response to induction chemotherapy (AUCs of 0.803 and 0.826), locoregional failure-free survival (AUCs of 0.786 and 0.767), progression-free survival (AUCs of 0.856 and 0.731) and overall survival (AUCs of 0.765 and 0.799) in NPC patients, respectively (all p < 0.05). Moreover, multivariate analysis showed that a high nIC value was an independent predictor of poor survival in NPC. In addition, survival analysis indicated that NPC patients with higher nIC values in primary tumours tend to have lower 5-year locoregional failure-free survival, progression-free survival and overall survival rates than those with lower nIC values.

CONCLUSIONS

DECT-derived nIC and Zeff values can predict early response to induction chemotherapy and survival in NPC; in particular, a high nIC value is an independent predictive factor of poor survival in NPC.

CLINICAL RELEVANCE STATEMENT

Preoperative dual-energy computed tomography may provide predictive value for early response and survival outcomes in patients with nasopharyngeal carcinoma, and facilitate their clinical management.

KEY POINTS

• Pretreatment dual-energy computed tomography helps to predict early response to therapy and survival in NPC. • NIC and Zeff values derived from dual-energy computed tomography can predict early objective response to induction chemotherapy and survival in NPC. • A high nIC value is an independent predictive factor of poor survival in NPC.

Mammographic Density Decline, Tamoxifen Response, and Prognosis by Molecular Characteristics of Estrogen Receptor-Positive Breast Cancer

BACKGROUND

Mammographic breast density (MBD) decline post-tamoxifen initiation is a favorable prognostic factor in estrogen receptor (ER)-positive breast cancer (BC) and has potential utility as a biomarker of tamoxifen response. However, the prognostic value of MBD decline may vary by molecular characteristics among ER-positive patients.

METHODS

We investigated associations between MBD decline (≥10% vs <10%) and breast cancer-specific mortality (BCSM) among ER-positive breast cancer patients aged 36-87 years at diagnosis treated with tamoxifen at Kaiser Permanente Northwest (1990-2008). Patients who died of BC (case patients; n = 62) were compared with those who did not (control patients; n = 215) overall and by tumor molecular characteristics (immunohistochemistry [IHC]-based subtype [luminal A-like: ER-positive/progesterone receptor [PR]-positive/HER2-negative/low Ki67; luminal B-like: ER-positive and 1 or more of PR-negative, HER2-positive, high Ki67] and modified IHC [mIHC]-based recurrence score of ER/PR/Ki67). Percent MBD was measured in the unaffected breast at baseline mammogram (mean = 6 months before tamoxifen initiation) and follow-up (mean = 12 months post-tamoxifen initiation). Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were computed from logistic regression models. All statistical tests were 2-sided.

RESULTS

MBD decline was statistically significantly associated with reduced risk of BCSM overall (OR = 0.38, 95% CI = 0.15 to 0.92). This association was, however, stronger among women with aggressive tumor characteristics including luminal B-like (OR = 0.17, 95% CI = 0.04 to 0.73) vs A-like (OR = 0.74, 95% CI = 0.19 to 2.92); large (OR = 0.26, 95% CI = 0.08 to 0.78) vs small (OR = 0.41, 95% CI = 0.04 to 3.79) tumors; PR-negative (OR = 0.02, 95% CI = 0.001 to 0.37) vs PR-positive (OR = 0.50, 95% CI = 0.18 to 1.40) disease; and high (OR = 0.25, 95% CI = 0.07 to 0.93) vs low (OR = 0.44, 95% CI = 0.10 to 2.09) mIHC3 score.

CONCLUSION

The findings support MBD decline as a prognostic marker of tamoxifen response among patients with aggressive ER-positive BC phenotypes, for whom understanding treatment effectiveness is critical.

Mechanotransduction at the Plasma Membrane-Cytoskeleton Interface

Mechanical cues are crucial for survival, adaptation, and normal homeostasis in virtually every cell type. The transduction of mechanical messages into intracellular biochemical messages is termed mechanotransduction. While significant advances in biochemical signaling have been made in the last few decades, the role of mechanotransduction in physiological and pathological processes has been largely overlooked until recently. In this review, the role of interactions between the cytoskeleton and cell-cell/cell-matrix adhesions in transducing mechanical signals is discussed. In addition, mechanosensors that reside in the cell membrane and the transduction of mechanical signals to the nucleus are discussed. Finally, we describe two examples in which mechanotransduction plays a significant role in normal physiology and disease development. The first example is the role of mechanotransduction in the proliferation and metastasis of cancerous cells. In this system, the role of mechanotransduction in cellular processes, including proliferation, differentiation, and motility, is described. In the second example, the role of mechanotransduction in a mechanically active organ, the gastrointestinal tract, is described. In the gut, mechanotransduction contributes to normal physiology and the development of motility disorders.

The Role of the BH4 Cofactor in Nitric Oxide Synthase Activity and Cancer Progression: Two Sides of the Same Coin

Cancer development is associated with abnormal proliferation, genetic instability, cell death resistance, metabolic reprogramming, immunity evasion, and metastasis. These alterations are triggered by genetic and epigenetic alterations in genes that control cell homeostasis. Increased reactive oxygen and nitrogen species (ROS, RNS) induced by different enzymes and reactions with distinct molecules contribute to malignant transformation and tumor progression by modifying DNA, proteins, and lipids, altering their activities. Nitric oxide synthase plays a central role in oncogenic signaling modulation and redox landscape. Overexpression of the three NOS isoforms has been found in innumerous types of cancer contributing to tumor growth and development. Although the main function of NOS is the production of nitric oxide (NO), it can be a source of ROS in some pathological conditions. Decreased tetrahydrobiopterin (BH4) cofactor availability is involved in NOS dysfunction, leading to ROS production and reduced levels of NO. The regulation of NOSs by BH4 in cancer is controversial since BH4 has been reported as a pro-tumoral or an antitumoral molecule. Therefore, in this review, the role of BH4 in the control of NOS activity and its involvement in the capabilities acquired along tumor progression of different cancers was described.