Validation Study of the American Joint Committee on Cancer Eighth Edition Prognostic Stage Compared With the Anatomic Stage in Breast Cancer

Validation of the AJCC 8th Edition Breast Cancer Prognostic Staging System in Legacy Alliance Trials (AFT-01)

BACKGROUND

The 8th edition American Joint Committee on Cancer staging system combined anatomic stage (AS) with receptor status and grade to create prognostic stage (PS). PS has been validated in single-institution and cancer registry studies; however, missing human epidermal growth factor receptor 2 (HER2) status and variable treatment and follow-up create limitations.

OBJECTIVE

Our objective was to compare the relative prognostic ability of PS versus AS to predict survival using breast cancer clinical trial data.

METHODS

Women with non-metastatic breast cancer enrolled in six Alliance for Clinical Trials in Oncology trials were included (enrollment years 1997-2010). AS and PS were constructed using pathological tumor size, nodal status, estrogen receptor (ER), progesterone receptor (PR), HER2 status, and grade. Unadjusted Cox proportional hazard models were estimated to predict overall survival within 5 years, with AS and PS as predictor variables. The relative predictive power of staging models was assessed by comparing Harrell concordance indices (C-indices). Kaplan-Meier-based mortality estimates were compared by stage.

RESULTS

Overall, 6924 women were included (median age 53 years); 45.2% were diagnosed with ER+/PR+/HER2- tumors, 26.2% with HER2+ tumors, and 17.1% with ER-/PR-/HER2- tumors. Median follow-up time was 5 years (interquartile range 2.95-5.00). PS significantly improved predictive performance (C-index 0.721) for overall survival compared with AS (0.700) (p = 0.020). Kaplan-Meier hazard estimates suggested PS did not distinguish mortality risk between patients with IIB and IIIA or IB and IIA disease.

CONCLUSIONS

PS has significantly improved predictive performance for OS compared with AS. As systemic therapies evolve, it will be important to re-evaluate the prognostic staging system, particularly for patients with intermediate-stage cancers.

CLINICALTRIALS

gov Identifier: NCT02171078.

Unveiling Bladder Cancer Prognostic Insights by Integrating Patient-Matched Sample and CpG Methylation Analysis

Bladder cancer prognosis remains a pressing clinical challenge, necessitating the identification of novel biomarkers for precise survival prediction and improved quality of life outcomes. This study proposes a comprehensive strategy to uncover key prognostic biomarkers in bladder cancer using DNA methylation analysis and extreme survival pattern observations in matched pairs of cancer and adjacent normal cells. Unlike traditional approaches that overlook cancer heterogeneity by analyzing entire samples, our methodology leverages patient-matched samples to account for this variability. Specifically, DNA methylation profiles from adjacent normal bladder tissue and bladder cancer tissue collected from the same individuals were analyzed to pinpoint critical methylation changes specific to cancer cells while mitigating confounding effects from individual genetic differences. Utilizing differential threshold settings for methylation levels within cancer-associated pathways enabled the identification of biomarkers that significantly impact patient survival. Our analysis identified distinct survival patterns associated with specific CpG sites, underscoring these sites' pivotal roles in bladder cancer outcomes. By hypothesizing and testing the influence of methylation levels on survival, we pinpointed CpG biomarkers that profoundly affect the prognosis. Notably, CpG markers, such as cg16269144 (PRKCZ), cg16624272 (PTK2), cg11304234, and cg26534425 (IL18), exhibited critical methylation thresholds that correlate with patient mortality. This study emphasizes the importance of tailored approaches to enhancing prognostic accuracy and refining therapeutic strategies for bladder cancer patients. The identified biomarkers pave the way for personalized prognostication and targeted interventions, promising advancements in bladder cancer management and patient care.

Exploring novel protein-based biomarkers for advancing breast cancer diagnosis: A review

This review provides a contemporary examination of the evolving landscape of breast cancer (BC) diagnosis, focusing on the pivotal role of novel protein-based biomarkers. The overview begins by elucidating the multifaceted nature of BC, exploring its prevalence, subtypes, and clinical complexities. A critical emphasis is placed on the transformative impact of proteomics, dissecting the proteome to unravel the molecular intricacies of BC. Navigating through various sources of samples crucial for biomarker investigations, the review underscores the significance of robust sample processing methods and their validation in ensuring reliable outcomes. The central theme of the review revolves around the identification and evaluation of novel protein-based biomarkers. Cutting-edge discoveries are summarised, shedding light on emerging biomarkers poised for clinical application. Nevertheless, the review candidly addresses the challenges inherent in biomarker discovery, including issues of standardisation, reproducibility, and the complex heterogeneity of BC. The future direction section envisions innovative strategies and technologies to overcome existing challenges. In conclusion, the review summarises the current state of BC biomarker research, offering insights into the intricacies of proteomic investigations. As precision medicine gains momentum, the integration of novel protein-based biomarkers emerges as a promising avenue for enhancing the accuracy and efficacy of BC diagnosis. This review serves as a compass for researchers and clinicians navigating the evolving landscape of BC biomarker discovery, guiding them toward transformative advancements in diagnostic precision and personalised patient care.

Implementation and Evaluation of a Breast Cancer Disease Model Using Real-World Claims Data in Germany from 2010 to 2020

Breast cancer is the leading cause of cancer-related mortality among women in Germany and worldwide. This retrospective claims data analysis utilizing data from AOK Baden-Wuerttemberg, a major statutory German health insurance provider, aimed to construct and assess a real-world data breast cancer disease model. The study included 27,869 female breast cancer patients and 55,738 age-matched controls, analyzing data from 2010 to 2020. Three distinct breast cancer stages were analyzed: Stage A (early breast cancer without lymph node involvement), Stage B (early breast cancer with lymph node involvement), and Stage C (primary distant metastatic breast cancer). Tumor subtypes were estimated based on the prescription of antihormonal or HER2-targeted therapy. The study established that 77.9% of patients had HR+ breast cancer and 9.8% HER2+; HR+/HER2- was the most common subtype (70.9%). Overall survival (OS) analysis demonstrated significantly lower survival rates for stages B and C than for controls, with 5-year OS rates ranging from 79.3% for stage B to 35.4% for stage C. OS rates were further stratified by tumor subtype and stage, revealing varying prognoses. Distant recurrence-free survival (DRFS) analysis showed higher recurrence rates in stage B than in stage A, with HR-/HER2- displaying the worst DRFS. This study, the first to model breast cancer subtypes, stages, and outcomes using German claims data, provides valuable insights into real-world breast cancer epidemiology and demonstrates that this breast cancer disease model has the potential to be representative of treatment outcomes.

Molecular Changes in Breast Cancer Induced by Radiation Therapy

PURPOSE

Breast cancer treatments are based on prognostic clinicopathologic features that form the basis for therapeutic guidelines. Although the utilization of these guidelines has decreased breast cancer-associated mortality rates over the past three decades, they are not adequate for individualized therapy. Radiation therapy (RT) is the backbone of breast cancer treatment. Although a highly successful therapeutic modality clinically, from a biological perspective, preclinical studies have shown RT to have the potential to alter tumor cell phenotype, immunogenicity, and the surrounding microenvironment, potentially changing the behavior of cancer cells and resulting in a significant variation in RT response. This review presents the recent advances in revealing the complex molecular changes induced by RT in the treatment of breast cancer and highlights the complexities of translating this information into clinically relevant tools for improved prognostic insights and the revelation of novel approaches for optimizing RT.

METHODS AND MATERIALS

Current literature was reviewed with a focus on recent advances made in the elucidation of tumor-associated radiation-induced molecular changes across molecular, genetic, and proteomic bases. This review was structured with the aim of providing an up-to-date overview over the very broad and complex subject matter of radiation-induced molecular changes and radioresistance, familiarizing the reader with the broader issue at hand.

RESULTS

The subject of radiation-induced molecular changes in breast cancer has been broached from various physiological focal points including that of the immune system, immunogenicity and the abscopal effect, tumor hypoxia, breast cancer classification and subtyping, molecular heterogeneity, and molecular plasticity. It is becoming increasingly apparent that breast cancer clinical subtyping alone does not adequately account for variation in RT response or radioresistance. Multiple components of the tumor microenvironment and immune system, delivered RT dose and fractionation schedules, radiation-induced bystander effects, and intrinsic tumor physiology and heterogeneity all contribute to the resultant RT outcome.

CONCLUSIONS

Despite recent advances and improvements in anticancer therapies, tumor resistance remains a significant challenge. As new analytical techniques and technologies continue to provide crucial insight into the complex molecular mechanisms of breast cancer and its treatment responses, it is becoming more evident that personalized anticancer treatment regimens may be vital in overcoming radioresistance.

Spatial and channel attention-based conditional Wasserstein GAN for direct and rapid image reconstruction in ultrasound computed tomography

Ultrasound computed tomography (USCT) is an emerging technology that offers a noninvasive and radiation-free imaging approach with high sensitivity, making it promising for the early detection and diagnosis of breast cancer. The speed-of-sound (SOS) parameter plays a crucial role in distinguishing between benign masses and breast cancer. However, traditional SOS reconstruction methods face challenges in achieving a balance between resolution and computational efficiency, which hinders their clinical applications due to high computational complexity and long reconstruction times. In this paper, we propose a novel and efficient approach for direct SOS image reconstruction based on an improved conditional generative adversarial network. The generator directly reconstructs SOS images from time-of-flight information, eliminating the need for intermediate steps. Residual spatial-channel attention blocks are integrated into the generator to adaptively determine the relevance of arrival time from the transducer pair corresponding to each pixel in the SOS image. An ablation study verified the effectiveness of this module. Qualitative and quantitative evaluation results on breast phantom datasets demonstrate that this method is capable of rapidly reconstructing high-quality SOS images, achieving better generation results and image quality. Therefore, we believe that the proposed algorithm represents a new direction in the research area of USCT SOS reconstruction.

Identifying tumor cell-released extracellular vesicles as biomarkers for breast cancer diagnosis by a three-dimensional hydrogel-based electrochemical immunosensor

Tumor cell-released LC3+ extracellular vesicles (LC3+ EVs) participate in immunosuppression during autophagy and contribute to the occurrence and development of breast cancer. In view of the strong association between the LC3+ EVs and breast cancer, developing an effective strategy for the quantitative detection of LC3+ EVs levels with high sensitivity to identify LC3+ EVs as new biomarkers for accurate diagnosis of breast cancer is crucial, but yet not been reported. Herein, an ultrasensitive electrochemical immunosensor is presented for the quantitative determination of LC3+ EVs using a three-dimensional graphene oxide hydrogel-methylene blue composite as a redox probe, showing a low detection limit and a wide linear range. With this immunosensor, the expression levels of LC3+ EVs in various practical sample groups including different cancer cell lines, the peripheral blood of tumor-bearing mice before and after immunotherapy, and the peripheral blood from breast cancer patients with different subtypes and stages were clearly distinguished. This study demonstrated that LC3+ EVs were superior as biomarkers for the accurate diagnosis of breast cancer compared to traditional biomarkers, particularly for cancer subtype discrimination. This work would provide a new noninvasive detection tool for the early diagnosis and prognosis assessment of breast cancer in clinics.

Comparative Analysis of Diagnostic Performance of Automatic Breast Ultrasound, Full-Field Digital Mammography and Contrast-Enhanced Mammography in Relation to Breast Composition

This single center study includes a comparative analysis of the diagnostic performance of full-field digital mammography (FFDM), contrast-enhanced mammography (CEM) and automatic breast ultrasound (ABUS) in the group of patients with breast American College of Radiology (ACR) categories C and D as well as A and B with FFDM. The study involved 297 patients who underwent ABUS and FFDM. Breast types C and D were determined in 40% of patients with FFDM and low- energy CEM. CEM was performed on 76 patients. Focal lesions were found in 131 patients, of which 115 were histopathologically verified. The number of lesions detected in patients with multiple lesions were 40 from 48 with ABUS, 13 with FFDM and 21 with CEM. Compliance in determining the number of foci was 82% for FFDM and 91% for both CEM and ABUS. In breast types C and D, 72% of all lesions were found with ABUS, 56% with CEM and 29% with FFDM (p = 0.008, p = 0.000); all invasive cancers were diagnosed with ABUS, 83% with CEM and 59% with FFDM (p = 0.000, p = 0.023); 100% DCIS were diagnosed with ABUS, 93% with CEM and 59% with FFDM. The size of lesions from histopathology in breast ACR categories A and B was 14-26 mm, while in breast categories C and D was 11-37 mm. In breast categories C and D, sensitivity of ABUS, FFDM and CEM was, respectively, 78.05, 85.37, 92.68; specificity: 40, 13.33, 8.33; PPV (positive predictive value): 78.05, 72.92, 77.55; NPV (negative predictive value): 40, 25, 25, accuracy: 67.86, 66.07, 73.58. In breast categories A and B, sensitivity of ABUS, FFDM and CEM was, respectively, 81.25, 93.75, 93.48; specificity: 18.18, 18.18, 16.67; PPV: 81.25, 83.33, 89.58; NPV: 18.18, 40, 25; accuracy: 69.49, 79.66, 84.62. The sensitivity of the combination of FFDM and ABUS was 100 for all types of breast categories; the accuracy was 75 in breast types C and D and 81.36 in breast types A and B. The study confirms the predominance of C and D breast anatomy types and the low diagnostic performance of FFDM within that group and indicates ABUS and CEM as potential additive methods in breast cancer diagnostics.

Nomogram for predicting survival in patients with mucinous breast cancer undergoing chemotherapy and surgery: a population-based study

BACKGROUND

The prognosis of patients with mucinous breast cancer (MuBC) is affected by several factors, but the low incidence of MuBC makes it difficult to conduct extensive and in-depth studies. This study was designed to establish a prognostic model and verify its accuracy in patients with MuBC after chemotherapy and surgery to help develop personalized treatment strategies.

MATERIALS AND METHODS

Patients with MuBC who underwent chemotherapy and surgery from 2004 to 2015 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. The prognostic factors of patients with MuBC were investigated using a Cox proportional hazards regression analysis. Based on the identified factors, a nomogram was constructed to forecast the overall survival (OS) of patients at 3, 5, and 10 years. Internal (from SEER) and external (from Yunnan Cancer Center, YNCC) verification queues were used to verify the nomogram and demonstrate the predictive capacity of this model.

RESULTS

The study comprised 1668 MuBC patients from the SEER database and 107 from the YNCC. The nomogram included four characteristics: age, anatomical stage, surgical method, and radiotherapy. The concordance indices in the training, internal verification, and external verification queues were 0.680, 0.768, and 0.864, respectively. The calibration curves for the nomogram showed excellent agreement between the predictions and observations. This nomogram has good clinical application value according to the decision curve analysis.

CONCLUSIONS

The prognosis of patients with MuBC who have undergone chemotherapy and surgery can be forecasted using this nomogram, which would be beneficial to help create individualized treatment plans for the affected patients.

Differential Cardiovascular Outcomes of Each Antihypertensive Drug Class in Patients With Hypertension and Breast Cancer Undergoing Doxorubicin-Containing Chemotherapy

PURPOSE

The preemptive use of renin-angiotensin system (RAS) inhibitors may reduce doxorubicin (DOX)-related cardiotoxicity. Using the national insurance claims data of Korea, this study compared cardiovascular (CV) outcomes following the use of four major antihypertensive drug classes in patients with hypertension and breast cancer who underwent DOX-containing chemotherapy.

METHODS

A total of 4,722 patients with hypertension and breast cancer who underwent DOX-containing chemotherapy were included. The outcomes were compared between patients who used RAS inhibitors, calcium channel blockers (CCBs), beta-blockers (BBs), and thiazide and thiazide-like diuretics (TDs). The primary outcome was a composite of incident heart failure and serious ventricular arrhythmias, including ventricular tachycardia and fibrillation, ischemic heart disease, and stroke.

RESULTS

In the propensity score-matched population, there were no significant differences in the primary outcome between RAS inhibitor and CCB users; however, patients with diabetes who used CCBs had a worse primary outcome than those who used RAS inhibitors (adjusted hazard ratio [aHR], 1.93; 95% confidence interval [CI], 1.06-3.51). BB and TD users had a worse primary outcome compared with RAS inhibitor (aHR, 1.88; 95% CI, 1.30-2.71 in BB users and aHR, 2.55; 95% CI, 1.37-4.75 in TD users) or CCB (aHR, 1.54; 95% CI, 1.09-2.16 in BB users and aHR, 2.08; 95% CI, 1.13-3.82 in TD users) users.

CONCLUSION

RAS inhibitors are preferred for the treating hypertension and improving CV outcomes in patients with hypertension and breast cancer undergoing DOX-containing chemotherapy, particularly in patients with comorbid diabetes. However, CCBs are equivalent to RAS inhibitors and are more favorable than BBs and TDs in terms of improving CV outcomes.

Effectiveness of post-mastectomy adjuvant chemotherapy for the treatment of patients with prognostic stage IB breast cancer: A SEER-based study

BACKGROUND

Adjuvant chemotherapy (AC) is an important, effective treatment for breast cancer. This study evaluates the effectiveness of post-mastectomy AC in treating patients with prognostic stage IB breast cancer.

METHOD

We conducted a retrospective cohort-based study using Surveillance, Epidemiology, and End-Results database. Overall survival (OS) and breast cancer-specific survival (BCSS) were calculated using the Kaplan-Meier method. Multivariate Cox risk models were used to identify the impact of AC. Stratified analysis was performed according to molecular subtypes, anatomic stages, and other risk factors to evaluate the effect of AC on survival.

RESULTS

28,825 women diagnosed with prognostic stage IB breast cancer were included. The 5-year OS was significantly higher in AC group than in non-adjuvant chemotherapy (NAC) group (P < 0.0001); however, the 5-year BCSS in AC group was significantly lower than in NAC group (P = 0.039). Multivariate analysis revealed that AC was a favorable prognostic factor for OS (P < 0.001), but not BCSS (P = 0.407). AC was not an independent prognostic factor for BCSS in patients with hormone receptor-positive, human epidermal growth factor receptor 2-negative (HR[+]/HER2[-]) subtype or pT1a-1b/N0-1 stage with HER2 overexpression (HER2[+]) subtype, regardless of whether HR was positive or negative (P > 0.05). Meanwhile, AC is not an independent prognostic factor for OS and BCSS in patients with lymph node micrometastases.

CONCLUSION

Our study demonstrates that patients with prognostic stage IB do not fully benefit from AC. Individualized treatment management is required for patients with pT1a-1b/N0-1 tumors, lymph node micrometastases, or HR(+)/HER2(-) subtypes.

PET/CT and SPECT/CT Imaging of HER2-Positive Breast Cancer

HER2 (Human Epidermal Growth Factor Receptor 2)-positive breast cancer is characterized by amplification of the HER2 gene and is associated with more aggressive tumor growth, increased risk of metastasis, and poorer prognosis when compared to other subtypes of breast cancer. HER2 expression is therefore a critical tumor feature that can be used to diagnose and treat breast cancer. Moving forward, advances in HER2 in vivo imaging, involving the use of techniques such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT), may allow for a greater role for HER2 status in guiding the management of breast cancer patients. This will apply both to patients who are HER2-positive and those who have limited-to-minimal immunohistochemical HER2 expression (HER2-low), with imaging ultimately helping clinicians determine the size and location of tumors. Additionally, PET and SPECT could help evaluate effectiveness of HER2-targeted therapies, such as trastuzumab or pertuzumab for HER2-positive cancers, and specially modified antibody drug conjugates (ADC), such as trastuzumab-deruxtecan, for HER2-low variants. This review will explore the current and future role of HER2 imaging in personalizing the care of patients diagnosed with breast cancer.

Proteasome-dependent senescent tumor cells mediate immunosuppression through CCL20 secretion and M2 polarization in pancreatic ductal adenocarcinoma

The outcome of pancreatic ductal adenocarcinoma (PDAC) remains poor due to few therapeutic options available and challenges with precision therapy to target each tumour's specific characteristics. In this study, a biologically meaningful patient stratification-prognostic model with therapeutic suggestion value based on tumor senescence was developed and validated in multiple independent cohorts. Further mechanistic investigation based on single-cell transcriptomic data and in vitro experiments revealed that complement derived from non-senescent tumor cells stimulates M1 differentiation and antigen presentation, while senescent tumor cells secrete CCL20 to favor immunosuppressive M2 polarization. Also, senescent phenotype depends on proteasome function, suggesting that high-risk, high-senescence patients may benefit from proteasome inhibitors, which reverse senescence-mediated resistance to conventional chemotherapy and improve outcome. In conclusion, the current study identified senescence as a tumor-specific, hazardous factor associated with immunosuppression in PDAC. Mechanistically, senescence abrogates complement-induced M1 activation and antigen presentation, and upregulates CCL20 to favor M2 polarization. The senescence-related risk model is prognostic and therapeutic-suggestive. In light of the reliance of senescent cells on proteasomal functions, proteasome inhibitors are promising agents for high-risk patients with senescent PDAC.

Live Cell Lineage Tracing of Dormant Cancer Cells

Breast cancer is a leading cause of global cancer-related deaths, and metastasis is the overwhelming culprit of poor patient prognosis. The most nefarious aspect of metastasis is dormancy, a prolonged period between primary tumor resection and relapse. Current therapies are insufficient at killing dormant cells; thus, they can remain quiescent in the body for decades until eventually undergoing a phenotypic switch, resulting in metastases that are more adaptable and drug resistant. Unfortunately, dormancy has few in vitro models, largely because lab-derived cell lines are highly proliferative. Existing models address tumor dormancy, not cellular dormancy, because tracking individual cells is technically challenging. To combat this problem, a live cell lineage approach to find and track individual dormant cells, distinguishing them from proliferative and dying cells over multiple days, is adapted. This approach is applied across a range of different in vitro microenvironments. This approach reveals that the proportion of cells that exhibit long-term quiescence is regulated by both cell intrinsic and extrinsic factors, with the most dormant cells found in 3D collagen gels. This paper envisions that this approach will prove useful to biologists and bioengineers in the dormancy community to identify, quantify, and study dormant tumor cells.

Novel Prognostic Staging System for Patients With De Novo Metastatic Breast Cancer

PURPOSE

Given the heterogeneity and improvement in outcomes for metastatic breast cancer (MBC), we developed a staging system that refines prognostic estimates for patients with metastatic cancer at the time of initial diagnosis, de novo MBC (dnMBC), on the basis of survival outcomes and disease-related variables.

METHODS

Patients with dnMBC (2010-2016) were selected from the National Cancer Database (NCDB). Recursive partitioning analysis (RPA) was used to group patients with similar overall survival (OS) on the basis of clinical T category, grade, estrogen receptor (ER), progesterone receptor, human epidermal growth factor receptor 2, histology, organ system site of metastases (bone-only, brain-only, visceral), and number of organ systems involved. Three-year OS rates were used to assign a final stage: IVA: >70%, IVB: 50%-70%, IVC: 25 to <50%, and IVD: <25%. Bootstrapping was applied with 1,000 iterations, and final stage assignments were made based on the most commonly occurring assignment. Unadjusted OS was estimated. Validation analyses were conducted using SEER and NCDB.

RESULTS

At a median follow-up of 52.9 months, the median OS of the original cohort (N = 42,467) was 35.4 months (95% CI, 34.8 to 35.9). RPA stratified patients into 53 groups with 3-year OS rates ranging from 73.5% to 5.7%; these groups were amalgamated into four stage groups: 3-year OS, A = 73.2%, B = 61.9%, C = 40.1%, and D = 17% (log-rank P < .001). After bootstrapping, the survival outcomes for the four stages remained significantly different (log-rank P < .001). This staging system was then validated using SEER data (N = 20,469) and a separate cohort from the NCDB (N = 7,645) (both log-rank P < .001).

CONCLUSION

Our findings regarding the heterogeneity in outcomes for patients with dnMBC could guide future revisions of the current American Joint Committee on Cancer staging guidelines for patients with newly diagnosed stage IV disease. Our findings should be independently confirmed.

The transcriptional risk scores for kidney renal clear cell carcinoma using XGBoost and multiple omics data

Most kidney cancers are kidney renal clear cell carcinoma (KIRC) that is a main cause of cancer-related deaths. Polygenic risk score (PRS) is a weighted linear combination of phenotypic related alleles on the genome that can be used to assess KIRC risk. However, standalone SNP data as input to the PRS model may not provide satisfactory result. Therefore, Transcriptional risk scores (TRS) based on multi-omics data and machine learning models were proposed to assess the risk of KIRC. First, we collected four types of multi-omics data (DNA methylation, miRNA, mRNA and lncRNA) of KIRC patients from the TCGA database. Subsequently, a novel TRS method utilizing multiple omics data and XGBoost model was developed. Finally, we performed prevalence analysis and prognosis prediction to evaluate the utility of the TRS generated by our method. Our TRS methods exhibited better predictive performance than the linear models and other machine learning models. Furthermore, the prediction accuracy of combined TRS model was higher than that of single-omics TRS model. The KM curves showed that TRS was a valid prognostic indicator for cancer staging. Our proposed method extended the current definition of TRS from standalone SNP data to multi-omics data and was superior to the linear models and other machine learning models, which may provide a useful implement for diagnostic and prognostic prediction of KIRC.

The influence of anatomic stage and receptor status on first recurrence for breast cancer within 5 years (AFT-01)

BACKGROUND

Risk-stratified follow-up guidelines that account for the absolute risk and timing of recurrence may improve the quality and efficiency of breast cancer follow-up. The objective of this study was to assess the relationship of anatomic stage and receptor status with timing of the first recurrence for patients with local-regional breast cancer and generate risk-stratified follow-up recommendations.

METHODS

The authors conducted a secondary analysis of 8007 patients with stage I-III breast cancer who enrolled in nine Alliance legacy clinical trials from 1997 to 2013 (ClinicalTrials.gov identifier NCT02171078). Patients who received standard-of-care therapy were included. Patients who were missing stage or receptor status were excluded. The primary outcome was days from the earliest treatment start date to the date of first recurrence. The primary explanatory variable was anatomic stage. The analysis was stratified by receptor type. Cox proportional-hazards regression models produced cumulative probabilities of recurrence. A dynamic programming algorithm approach was used to optimize the timing of follow-up intervals based on the timing of recurrence events.

RESULTS

The time to first recurrence varied significantly between receptor types (p < .0001). Within each receptor type, stage influenced the time to recurrence (p < .0001). The risk of recurrence was highest and occurred earliest for estrogen receptor (ER)-negative/progesterone receptor (PR)-negative/Her2neu-negative tumors (stage III; 5-year probability of recurrence, 45.5%). The risk of recurrence was lower for ER-positive/PR-positive/Her2neu-positive tumors (stage III; 5-year probability of recurrence, 15.3%), with recurrences distributed over time. Model-generated follow-up recommendations by stage and receptor type were created.

CONCLUSIONS

This study supports considering both anatomic stage and receptor status in follow-up recommendations. The implementation of risk-stratified guidelines based on these data has the potential to improve the quality and efficiency of follow-up.

Clinicopathologic Features of Breast Cancer in Egypt-Contemporary Profile and Future Needs: A Systematic Review and Meta-Analysis

PURPOSE

Breast cancer (BC) is the most common cancer among Egyptian females. No current national cancer database is available in Egypt to provide reliable data on the specific clinicopathologic features of BC in this population. Herein, we investigated the clinical profile of BC among Egyptian women.

METHODS

A systematic review of studies on BC published from inception until December 2021 was performed. We explored pooled estimated proportions of different stages of BC at presentation in Egypt and other clinicopathologic features including age, menopausal status, tumor (T) and lymph node (N) stages, and biological subtypes. Data analysis was performed using meta package (R).

RESULTS

Twenty-six studies were eligible for our systematic review and meta-analysis, including 31,172 BC cases. In 12 studies, including 15,067 patients with BC, the estimated mean age was 50.46 years (95% CI, 48.7 to 52.1; I2, 99%), with a pooled proportion of premenopausal/perimenopausal women of 57% (95% CI, 50 to 63; I2, 98%). Among 9,738 patients with BC, pooled proportions of stage I, II, III, and IV were 6% (95% CI, 4 to 8; I2, 90%), 37% (95% CI, 31 to 43; I2, 93%), 45% (95% CI, 42 to 49; I2, 78%), and 11% (95% CI, 9 to 15; I2, 87%), respectively. The pooled proportions of patients with T3 and T4 tumors were 21% (95% CI, 14 to 31; I2, 99%) and 8% (95% CI, 5 to 12; I2, 96%), respectively, while those with positive lymph nodes were 70% (95% CI, 59 to 79; I2, 99%).

CONCLUSION

Dominance of advanced stage and young age at diagnosis represented the two main features of BC among Egyptian women. Our data may serve to guide the policymakers in Egypt as well as other countries with lower resources to prioritize the diagnostic and therapeutic needs in this context.

Development and validation of nomograms to predict survival in patients with invasive micropapillary carcinoma of the breast

OBJECTIVES

The present study aimed to develop and validate nomograms to predict the survival of patients with breast invasive micropapillary carcinoma (IMPC) to aid objective decision-making.

DESIGN

Prognostic factors were identified using Cox proportional hazards regression analyses and used to construct nomograms to predict overall survival (OS) and breast cancer-specific survival (BCSS) at 3 and 5 years. Kaplan-Meier analysis, calibration curves, the area under the curve (AUC) and the concordance index (C-index) evaluated the nomograms' performance. Decision curve analysis (DCA), integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to compare the nomograms with the American Joint Committee on Cancer (AJCC) staging system.

SETTING

Patient data were collected from the Surveillance, Epidemiology, and End Results (SEER) database. This database holds data related to the incidence of cancer acquired from 18 population-based cancer registries in the US.

PARTICIPANTS

We ruled out 1893 patients and allowed the incorporation of 1340 patients into the present study.

RESULTS

The C-index of the AJCC8 stage was lower than that of the OS nomogram (0.670 vs 0.766) and the OS nomograms had higher AUCs than the AJCC8 stage (3 years: 0.839 vs 0.735, 5 years: 0.787 vs 0.658). On calibration plots, the predicted and actual outcomes agreed well, and DCA revealed that the nomograms had better clinical utility compared with the conventional prognosis tool. In the training cohort, the NRI for OS was 0.227, and for BCSS was 0.182, while the IDI for OS was 0.070, and for BCSS was 0.078 (both p<0.001), confirming its accuracy. The Kaplan-Meier curves for nomogram-based risk stratification showed significant differences (p<0.001).

CONCLUSIONS

The nomograms showed excellent discrimination and clinical utility to predict OS and BCSS at 3 and 5 years, and could identify high-risk patients, thus providing IMPC patients with personalised treatment strategies.

Value of pre-treatment 18F-FDG PET/CT radiomics in predicting the prognosis of stage III-IV colorectal cancer

Background and purpose

To investigate the value of radiomics features extracted from pre-treatment ¹⁸F-FDG PET/CT in predicting the outcomes of stage III-IV colorectal cancer (CRC), which may assist in clinical management strategies and precise treatment of stage III-IV CRC.

Materials and methods

124 patients with pathologically confirmed stage III-IV CRC who underwent pre-treatment ¹⁸F-FDG PET/CT scans were enrolled in this study. The least absolute shrinkage and selection operator Cox regression (LASSO-Cox) was used to select radiomics features, and the radiomics scores (Rad-scores) were calculated to build radiomics models. The performance of radiomics models was represented by the concordance index (C-index) and compared with clinical models and complex model. The bootstrap resampling method was used to create validation sets. Additionally, nomograms were developed based on complex models.

Results

The C-indices of the radiomics model for predicting PFS and OS were 0.712 (95%CI: 0.680-0.744) and 0.758 (0.728-0.789), respectively. In the clinical model, these values were 0.690 (0.664-0.0.717) and 0.738 (0.709-0.767), respectively. However, in the complex model were 0.734 (0.705-0.762) and 0.780 (0.754-0.807), respectively. The Kaplan-Meier curves demonstrated that the radiomics model could effectively separate patients with stage III-IV stage CRC into high- and low-risk groups (p < 0.001). Multivariate Cox regression analysis confirmed the independent prognostic value of Rad-scores.

Conclusion

Pre-treatment ¹⁸F-FDG PET/CT radiomics features can stratify the risk of patients with stage III-IV CRC and accurately predict their outcomes. These findings could be clinically valuable for precision treatment and management decisions in stage III-IV CRC.

Analysis of prognostic factors and construction of prognostic models for triple-positive breast cancer

Objective

By identifying the clinicopathological characteristics and prognostic influences of patients with triple-positive breast cancer (TPBC) at Xijing Hospital in China compared with those in the United States, this study aims to construct a nomogram model to forecast the overall survival rate (OS) of TPBC patients.

Method

The Surveillance, Epidemiology, and End Results (SEER) database was used to screen 5769 patients as the training cohort, and 191 patients from Xijing Hospital were used as the validation cohort. Cox risk-proportional model was applied to select variables and the nomogram model was constructed based on the training cohort. The performance of the model was evaluated by calculating the C-index and generating calibration plots in the training and validation cohorts.

Results

Cox multifactorial analysis showed that age, chemotherapy, radiotherapy, M-stage, T-stage, N-stage, and the mode of surgery were all independent risk factors for the prognosis of TPBC patients (all P<0.05). With this premise, the nomogram model was constructed and evaluated. The C-index value of the nomogram model was 0.830 in the training group and 0.914 in the validation group. Moreover, both the calibration and ROC curves for the proposed model exhibited reliable performance, and the clinical decision curve analysis showed that the proposed model can bring clinical benefits.

Conclusions

The constructed nomogram can accurately predict individual survival probabilities and may serve as a clinical decision support tool for clinicians to optimize treatment in individuals.

Prognostic impact of 18F-FDG PET/CT in pathologic stage II invasive ductal carcinoma of the breast: re-illuminating the value of PET/CT in intermediate-risk breast cancer

BACKGROUND

The aim of this study is to investigate the impact of ¹⁸F-FDG PET/CT on prognosis of stage II invasive ductal carcinoma (IDC) of the breast primarily treated with surgery.

METHODS

The clinical records of 297 consecutive IDC with preoperative PET/CT and pathologically staged II in surgery from 2013 to 2017 were retrospectively reviewed. The maximum standardized uptake value (SUVmax), peak standardized uptake value (SUVpeak), tumor-to-liver ratio (TLR), and metabolic tumor volume (MTV) were measured. Association of clinicopathologic factors (age, T stage, N stage, AJCC pathologic stage of IIA or IIB, pathologic prognostic stage, grade, hormonal receptor status, HER2 status, Ki-67, and adjuvant therapy) and PET parameters with DFS was assessed using the Cox proportional hazards model.

RESULTS

There were 35 recurrences and 10 deaths at a median follow-up of 49 months (range 0.8 ~ 87.3). All PET parameters were significantly associated with DFS in univariate analysis but in multivariate analysis, SUVpeak was the only factor significantly associated with DFS (hazard ratio 2.58, 95% confidence interval 1.29-5.15, P = 0.007). In cohorts with higher values of SUVpeak or TLR, patients who received adjuvant chemotherapy had significantly superior DFS.

CONCLUSION

Metabolic parameters derived from preoperative PET/CT was significantly associated with recurrence in stage II IDC primarily treated with surgery. PET/CT can be a powerful prognostic tool in conjunction with novel staging systems and current biomarkers for patients undergoing contemporary therapy. Our results urge to reconsider the currently underestimated value of PET/CT confined to diagnostic aspect and to newly recognize its prognostic impact in these intermediate-risk breast cancer.

The Role of Mesenchymal Stem Cells in Modulating the Breast Cancer Microenvironment

The role of mesenchymal stem cells (MSCs) in the breast tumor microenvironment (TME) is significant and multifaceted. MSCs are recruited to breast tumor sites through molecular signals released by tumor sites. Once in the TME, MSCs undergo polarization and interact with various cell populations, including immune cells, cancer-associated fibroblasts (CAFs), cancer stem cells (CSCs), and breast cancer cells. In most cases, MSCs play roles in breast cancer therapeutic resistance, but there is also evidence that indicates their abilities to sensitize cancer cells to chemotherapy and radiotherapy. MSCs possess inherent regenerative and homing properties, making them attractive candidates for cell-based therapies. Therefore, MSCs can be engineered to express therapeutic molecules or deliver anti-cancer agents directly to tumor sites. Unraveling the intricate relationship between MSCs and the breast TME has the potential to uncover novel therapeutic targets and advance our understanding of breast cancer biology.

Development and validation of an AI-enabled digital breast cancer assay to predict early-stage breast cancer recurrence within 6 years

BACKGROUND

Breast cancer (BC) grading plays a critical role in patient management despite the considerable inter- and intra-observer variability, highlighting the need for decision support tools to improve reproducibility and prognostic accuracy for use in clinical practice. The objective was to evaluate the ability of a digital artificial intelligence (AI) assay (PDxBr) to enrich BC grading and improve risk categorization for predicting recurrence.

METHODS

In our population-based longitudinal clinical development and validation study, we enrolled 2075 patients from Mount Sinai Hospital with infiltrating ductal carcinoma of the breast. With 3:1 balanced training and validation cohorts, patients were retrospectively followed for a median of 6 years. The main outcome was to validate an automated BC phenotyping system combined with clinical features to produce a binomial risk score predicting BC recurrence at diagnosis.

RESULTS

The PDxBr training model (n = 1559 patients) had a C-index of 0.78 (95% CI, 0.76-0.81) versus clinical 0.71 (95% CI, 0.67-0.74) and image feature models 0.72 (95% CI, 0.70-0.74). A risk score of 58 (scale 0-100) stratified patients as low or high risk, hazard ratio (HR) 5.5 (95% CI 4.19-7.2, p < 0.001), with a sensitivity 0.71, specificity 0.77, NPV 0.95, and PPV 0.32 for predicting BC recurrence within 6 years. In the validation cohort (n = 516), the C-index was 0.75 (95% CI, 0.72-0.79) versus clinical 0.71 (95% CI 0.66-0.75) versus image feature models 0.67 (95% CI, 0.63-071). The validation cohort had an HR of 4.4 (95% CI 2.7-7.1, p < 0.001), sensitivity of 0.60, specificity 0.77, NPV 0.94, and PPV 0.24 for predicting BC recurrence within 6 years. PDxBr also improved Oncotype Recurrence Score (RS) performance: RS 31 cutoff, C-index of 0.36 (95% CI 0.26-0.45), sensitivity 37%, specificity 48%, HR 0.48, p = 0.04 versus Oncotype RS plus AI-grade C-index 0.72 (95% CI 0.67-0.79), sensitivity 78%, specificity 49%, HR 4.6, p < 0.001 versus Oncotype RS plus PDxBr, C-index 0.76 (95% CI 0.70-0.82), sensitivity 67%, specificity 80%, HR 6.1, p < 0.001.

CONCLUSIONS

PDxBr is a digital BC test combining automated AI-BC prognostic grade with clinical-pathologic features to predict the risk of early-stage BC recurrence. With future validation studies, we anticipate the PDxBr model will enrich current gene expression assays and enhance treatment decision-making.

Role of multiple risk factors in mental disorders diagnosed in middle-aged women: A population-based follow-up study

The aims of the study were 1). to investigate the association between the potential risk factors including socio-demographic, lifestyle and DNA methylation and mental disorders in middle-aged women from a large population-based follow-up study, and 2). to estimate the risk score by combining the potential risk factors to examine the mental disorder's incidence. A total of 6461 women, aged 50-65 years, were included in the study. After a median follow-up of 17 years, 2026 (31%) women were diagnosed with mental disorders. The association between these factors and the risk of mental disorders was analyzed using Cox regression models. Harrell's concordance index (C-index) was used to quantify models' predictive performance for future mental disorders. Blood-based global DNA methylation was assessed by an enzyme-linked immunosorbent assay. We found that smoking (HR = 1.38, 95% CI: 1.24-1.54), less physical activity (HR = 1.33, 95% CI: 1.10-1.60), being single (HR = 1.16, 95% CI: 1.04-1.29) and unemployment (HR = 1.50, 95% CI: 1.33-1.70) were independently associated with an increased risk of overall mental disorders. Risk score models combining all these observed factors showed an increased risk, but the prediction ability was low, except for the risk of alcohol use disorders (AUD) and drug use disorders (DUD) (C-index = 0.8). Finally, women who developed MDD/anxiety during follow-up had significantly higher global DNA methylation at baseline than women who did not develop MDD/anxiety (p = 0.005). In conclusion, our results indicate that the studied risk factors were associated with mental disorders in a type-specific manner. The predictive model showed that smoking, alcohol consumption, education and physical activity may predict future AUD/DUD. Global DNA methylation may be a potential risk factor for MDD/anxiety incidence.

Stage-Specific Survival in Breast Cancer in Chinese and White Women: Comparative Data Analysis

BACKGROUND

Stage-specific survival, according to the eighth edition of the American Joint Committee on Cancer (AJCC) pathological prognostic staging (PPS) on breast cancer (BC), between Chinese and White American women remains unclear.

OBJECTIVE

This study aimed to assess stage-specific survival in BC between Chinese and White American women according to the eighth AJCC PPS.

METHODS

We included Chinese and White American women with BC diagnosed between 2010 and 2018 from the Surveillance, Epidemiology, and End Results database. A chi-square test, the Kaplan-Meier method, a receiver operating characteristic (ROC) curve, and multivariate Cox proportional hazards models were used for data analysis.

RESULTS

We included 376,818 individuals in this study: 369,522 White American and 7296 Chinese. Of them, 149,452 (39.7%) migrated from the seventh AJCC anatomic staging (AS) to the eighth AJCC PPS, 22,516 (6.0%) were upstaged, and 126,936 (33.7%) were downstaged. With a median follow-up duration of 44 months, the 5-year overall survival and cancer-specific survival (CSS) for the entire group were 87.4% and 95.9%, respectively. The seventh AJCC AS (P<.001) and the eighth AJCC PPS (P<.001) could significantly predict the survival outcomes of BC, and multivariate analysis revealed that both staging systems were significant prognostic indicators of CSS. The ROC curve revealed that the PPS had a better discriminating ability than the AS (area under the curve [AUC] 0.769 vs 0.753, P<.001). Similar trends were observed after stratification by the 2 ethnic groups. The eighth AJCC PPS had better discriminating ability than the seventh AJCC AS among both White American (AUC 0.769 vs 0.753, P<.001) and Chinese patients (AUC 0.790 vs 0.776, P<.001). In the seventh AJCC AS, Chinese women had better CSS in stage IA (P=.02), stage IIA (P=.005), and stage IIIB (P=.04) disease than White American women, but no significant CSS was observed in stage IB, IIB, IIIA, and IIIC disease between the 2 ethnic groups. Regarding the eighth AJCC PPS, Chinese women had better CSS in stage IA (P=.002) and IIIA (P=.046) disease than White American women, and CSS was similar in Chinese and White American women in other substages.

CONCLUSIONS

The eighth AJCC PPS has a similar discriminative ability between White American and Chinese individuals with BC compared with the seventh AJCC AS. Therefore, the eighth AJCC PPS is also applicable to Chinese individuals with BC.

Non-Invasive and Real-Time Monitoring of the Breast Cancer Metastasis Degree via Metabolomics

Breast cancer (BC) is a serious threat to women's health and metastasis is the major cause of BC-associated mortality. Various techniques are currently used to preoperatively describe the metastatic status of tumors, based on which a comprehensive treatment protocol was determined. However, accurately staging a tumor before surgery remains a challenge, which may lead to the miss of optimal treatment options. More severely, the failure to detect and remove occult micrometastases often causes tumor recurrences. There is an urgent need to develop a more precise and non-invasive strategy for the detection of the tumor metastasis in lymph nodes and distant organs. Based on the facts that tumor metastasis is closely related to the primary tumor microenvironment (TME) evolutions and that metabolomics profiling of the circulatory system can precisely reflect subtle changes within TME, we suppose whether metabolomic technology can be used to achieve non-invasive and real-time monitoring of BC metastatic status. In this study, the metastasis status of BC mouse models with different tumor-bearing times was firstly depicted to mimic clinical anatomic TNM staging system. Metabolomic profiling together with metastasis-related changes in TME among tumor-bearing mice with different metastatic status was conducted. A range of differential metabolites reflecting tumor metastatic states were screened and in vivo experiments proved that two main metastasis-driving factors in TME, TGF-β and hypoxia, were closely related to the regular changes of these metabolites. The differential metabolites level changes were also preliminarily confirmed in a limited number of clinical BC samples. Metabolite lysoPC (16:0) was found to be useful for clinical N stage diagnosis and the possible cause of its changes was analyzed by bioinformatics techniques.

The Value of the 8th Edition of American Joint Committee on Cancer Pathological Prognostic Staging on the Selection of Postmastectomy Radiotherapy for T1–2N1 Breast Cancer

Methods and Materials

Patients diagnosed with pT1-2N1M0 breast cancer between 2008 and 2018 in West China Hospital, Sichuan University were included. Locoregional-free survival (LRFS), distant metastasis-free survival (DMFS), disease-free survival (DFS), breast cancer-specific survival (BCSS), and overall survival (OS) were defined as endpoints. The propensity score matching (PSM), receiver operating characteristic (ROC) curve, the Kaplan-Meier analysis, and the Cox multivariable model were used for data analysis.

Results

We identified 1,615 patients with T1-2N1M0 breast cancer, and 44.9% (n = 744) of them were treated with PMRT. With a median follow-up of 76 months, 46 (2.8%) recurrences, 96 (5.9%) deaths, and 80 (5.0%) breast cancer-related deaths occurred. The 5-year LRFS, DMFS, DFS, BCSS, and OS were 98.6%, 95.3%, 93.7%, 96.5%, and 96.0%, respectively. PMRT could not improve 5-year LRFS, DMFS, DFS, BCSS, and OS compared with non-PMRT neither before nor after PSM in the era of contemporary systemic treatment. ROC curve showed that the 8th pathological prognostic staging had better discriminative ability compared with the 7th anatomical staging [the area under the curve (AUC) 0.653 vs. 0.546, P < 0.001]. In the anatomical staging system, PMRT had comparable 5-year BCSS in comparison with non-PMRT both in stages IIA (97.4% vs. 96.8%, P = 0.799) and IIB (95.3% vs. 97.0%, P = 0.071). When stratified according to the pathological staging, PMRT was associated with better 5-year BCSS in stage IIB (97.1% vs. 90.7%, P = 0.039), while not in stages IA, IB, IIA, and IIIA. Multivariate analysis demonstrated that PMRT was a significantly protective factor for BCSS in stage IIB (HR 0.331, 95% CI: 0.100-0.967, P = 0.044).

Conclusion

The new staging could better select high-risk patients with T1-2N1 breast cancer for radiotherapy compared with the 7th staging, and PMRT might be exempted except the 8th staging of IIB in the era of contemporary systemic therapy in this disease.

Comparison of 18F-fluorothymidine Positron Emission Tomography/Computed Tomography and 18F-fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Patients with Breast Cancer

The uptake of ¹⁸F-fluorothymidine (¹⁸F-FLT) depends on cells' proliferative rates. We compared the characteristics of ¹⁸F-FLT positron emission tomography/computed tomography (PET/CT) with those of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET/CT for breast cancer. We prospectively diagnosed patients with breast cancer who underwent ¹⁸F-FLT PET/CT and ¹⁸F-FDG PET/CT. Subsequently, significant differences and correlation coefficients of the maximum standardized uptake value (SUVmax) in primary breast cancer and axillary lymph nodes were statistically evaluated. We enrolled eight patients with breast cancer. In six treatment-naive patients, the SUVmax for primary lesions showed a significant difference (mean, 2.1 vs. 4.1, p = 0.031) and a strong correlation (r = 0.969) between ¹⁸F-FLT and ¹⁸F-FDG. Further, although the SUVmax for the axillary lymph nodes did not show a significant difference between ¹⁸F-FLT and ¹⁸F-FDG (P = 0.246), there was a strong correlation between the two (r = 0.999). In a patient-by-patient study, there were cases in which only ¹⁸F-FDG uptake was observed in lymph nodes and normal breasts. Bone metastases demonstrated lower accumulation than bone marrow on the ¹⁸F-FLT PET/CT. In conclusion, a strong correlation was observed between the ¹⁸F-FLT PET/CT and ¹⁸F-FDG PET/CT uptake. Differences in the biochemical characteristics of ¹⁸F-FLT and ¹⁸F-FDG were reflected in the accumulation differences for breast cancer, metastatic lesions, and normal organs.

Characteristics and prognosis of primary pulmonary osteosarcoma: a pooled analysis

BACKGROUND

Primary pulmonary osteosarcoma (PPOS) is an uncommon malignancy originating from the lung with low incidence, and its clinical characteristics and prognosis have not been systematically reported. Therefore, we aimed to recognize the prognostic factors and constructed a survival prediction model for PPOS.

METHODS

We collected the data from the Surveillance, Epidemiology, and End Results database and systematic review of previous studies. Demographical and clinical characteristics, radiographic manifestations, treatment modalities, and prognosis were analyzed. A prediction model via nomogram was constructed and then evaluated by the concordance index (C-index) and the receiver operating characteristic (ROC) curve.

RESULTS

A total of 49 cases were included for analysis with a median age of 67 years old (range 33-94 years), of which 32 (65.3%) were male. The median size was 6 cm (range 1.8-25 cm), and the median overall survival (OS) was eight months (interquartile range 4.5-12 months) with a 1-year OS rate of 30.8%. Tumor size over 7 cm (hazard ratio [HR] = 2.98; P = 0.018) and those without microscopic findings of osteoid found in the tumors (HR = 2.11; P = 0.048) were referred to a poor OS, while surgery was associated with an improved OS (HR = 0.20; P < 0.001). The C-index of the nomogram prediction model was 0.771, and the area under curve, sensitivity and specificity of the ROC curve were 0.818, 0.848 and 0.800, respectively.

CONCLUSIONS

Patients with PPOS had a poor prognosis, and tumor size was mostly prognostic. Surgery seemed to be an effective treatment, and the prediction model with a nomogram in our study could effectively predict the prognosis of patients with PPOS.

A vicious circle in breast cancer: The interplay between inflammation, reactive oxygen species, and microRNAs

Breast cancer (BC) is the most common cancer in women worldwide. This highly heterogeneous disease is molecularly stratified into luminal A, luminal B, HER2, triple-negative/basal-like, and normal-like subtypes. An important aspect in BC progression is the activation of inflammatory processes. The activation of CD8+/Th1, NK, and M1 tumor associated macrophages (TAMs), leads to tumor destruction. In contrast, an anti-inflammatory response mediated by CD4+/Th2 and M2 TAMs will favor tumor progression. Inflammation also stimulates the production of inflammatory mediators like reactive oxygen species (ROS). In chronic inflammation, ROS activates oxidative stress and endothelial dysfunction. In cancer, ROS plays a dual role with anti-tumorigenic and pro-tumorigenic effects in cell signaling pathways that control proliferation, survival, apoptosis, and inflammation. MicroRNAs (miRNAs), which are known to be involved in BC progression and inflammation, can be regulated by ROS. At the same time, miRNAs regulate the expression of genes modulating oxidative stress. In this review, we will discuss the interplay between inflammation, ROS, and miRNAs as anticancer and tumor promoter molecules in BC. A clear understanding of the role of miRNAs in the regulation of ROS production and inflammation, may lead to new opportunities for therapy in BC.

The construction of transcriptional risk scores for breast cancer based on lightGBM and multiple omics data

BACKGROUND

Polygenic risk score (PRS) can evaluate the individual-level genetic risk of breast cancer. However, standalone single nucleotide polymorphisms (SNP) data used for PRS may not provide satisfactory prediction accuracy. Additionally, current PRS models based on linear regression have insufficient power to leverage non-linear effects from thousands of associated SNPs. Here, we proposed a transcriptional risk score (TRS) based on multiple omics data to estimate the risk of breast cancer.

METHODS

The multiple omics data and clinical data of breast invasive carcinoma (BRCA) were collected from the cancer genome atlas (TCGA) and the gene expression omnibus (GEO). First, we developed a novel TRS model for BRCA utilizing single omic data and LightGBM algorithm. Subsequently, we built a combination model of TRS derived from each omic data to further improve the prediction accuracy. Finally, we performed association analysis and prognosis prediction to evaluate the utility of the TRS generated by our method.

RESULTS

The proposed TRS model achieved better predictive performance than the linear models and other ML methods in single omic dataset. An independent validation dataset also verified the effectiveness of our model. Moreover, the combination of the TRS can efficiently strengthen prediction accuracy. The analysis of prevalence and the associations of the TRS with phenotypes including case-control and cancer stage indicated that the risk of breast cancer increases with the increases of TRS. The survival analysis also suggested that TRS for the cancer stage is an effective prognostic metric of breast cancer patients.

CONCLUSIONS

Our proposed TRS model expanded the current definition of PRS from standalone SNP data to multiple omics data and outperformed the linear models, which may provide a powerful tool for diagnostic and prognostic prediction of breast cancer.

Expanding the Staging Criteria for T1-2N0 Hormone-Receptor Positive Breast Cancer Patients Enrolled in TAILORx

BACKGROUND

The American Joint Committee on Cancer (AJCC) 8th edition pathologic prognostic staging (PPS) incorporates anatomic and biologic factors. The OncotypeDX Breast Recurrence Score (RS) was included based on the initial report of the TAILORx trial, with T1-2N0 hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) breast cancer patients who had a RS < 11 staged as PPS 1A. This study examined whether the RS criteria for PPS 1A can be further expanded using patients enrolled in the TAILORx trial.

METHODS

The TAILORx trial enrolled 10,273 HR+HER2- T1-2N0 patients. Those with incomplete HR-status/grade and T3 disease were excluded for analysis. The recurrence-free interval (RFI) was compared between the patients who did and those who did not fall into the current PPS 1A category using the Kaplan-Meier method.

RESULTS

The study enrolled 9535 patients for analysis. The RS was < 11 in 16.1%, 11-17 in 35.9%, 18-25 in 32.4%, and > 25 in 15.6% of the patients. The majority (91.2%) of the patients (including all the T1N0 patients regardless of RS) were PPS 1A, and 8.8% were not-PPS 1A. The median follow-up time was 95 months. The PPS 1A patients had an 8-year RFI of 94.2%, which was similar to that of the patients with a RS of 11-17 who were not-PPS 1A (91.7%; p = 0.07) and better than that of the patients with a RS ≥ 18 who were not-PPS 1A (85.4% for a RS of 18-25, 76.0% for a RS > 25; both p < 0.01). Similar RFI trends were seen in patients who received endocrine therapy or chemotherapy followed by endocrine therapy.

CONCLUSIONS

Patients with T1-2N0 HR+HER2- breast cancer and a RS < 18 have an RFI similar to that of patients staged as PPS 1A by the current AJCC staging system, regardless of treatment, suggesting that the criteria for PPS 1A can be expanded to include a RS < 18.

Breast Cancer Epidemiology and Contemporary Breast Cancer Care: A Review of the Literature and Clinical Applications

Substantial progress has been made in contemporary breast cancer care, resulting in a consistently declining breast cancer mortality rate and an improvement in quality of life. Advancements include deescalation of therapy in low-risk populations and refining systemic therapy options. Research into molecular biomarkers continues to evolve and holds the promise of achieving the goal of precision medicine, while guidelines for supportive care and survivorship have been created to address the needs of an ever-increasing number of breast cancer survivors. A collaborative, multidisciplinary team approach is essential for patients and survivors to achieve optimal outcomes and enjoy productive high-quality lives. Gynecologists, in particular, play a key role in screening and survivorship care.

Breast lesions associated with mammographic architectural distortion: a study of 588 core needle biopsies

Architectural distortion (AD) on mammography is a localized alteration in the uniform texture of the breast characterized by lines radiating from a central point. Radiologic/pathologic correlation is challenging because the types of lesions associated with AD are not well defined and, thus, what signifies a discordant finding requiring excision is less clear. This retrospective case series was performed to elucidate the pathologic lesions associated with AD. Over a 6-year period, 588 core needle biopsies (CNBs) were performed for AD. Thirty-eight percent of the lesions were AD alone (single feature AD) and 62% had additional imaging features (multi-feature AD). Overall, 31% showed invasive carcinoma or ductal carcinoma in situ (DCIS), 37% showed benign lesions likely to correlate with AD, and 32% showed nonspecific benign findings. The invasive carcinomas tended to be low-grade (60%), ER-positive (98%), HER2-negative (98%), and often had lobular features (52%). Ninety-two percent were AJCC pathologic stage group I. Ninety-four cases of benign findings that correlated with AD without atypia underwent excision, and only one was found to have DCIS adjacent to the sclerosing lesion (1%). The remaining cases had benign findings without a clear correlate for AD. Sixty-eight cases without atypia underwent excision, and six multi-feature AD were upgraded to invasive carcinoma (9%). In conclusion, about one-third of CNBs for lesions associated with AD reveal carcinomas that are predominantly invasive, low-grade, ER-positive, HER2-negative, and low stage. Single-feature AD differed from multi-feature AD due to a lower number of carcinomas on CNB (18% vs 39%). For CNBs showing benign lesions on biopsy with a correlate for AD, the finding of malignancy on excision is low (1%). Radiologic/pathologic correlation and decisions to recommend excision will continue to be a challenge after CNB reveals nonspecific findings as some patients with multi-feature AD were found to have undetected invasive carcinomas.

Factors Associated with Screening Mammogram Uptake among Women Attending an Urban University Primary Care Clinic in Malaysia

Screening mammograms have resulted in a reduction in breast cancer mortality, yet the uptake in Malaysia was low. This study aimed to determine the prevalence and factors associated with screening mammogram uptake among women attending a Malaysian primary care clinic. A cross-sectional study was conducted among 200 women aged 40 to 74 attending the clinic. The data was collected using questionnaires assessing sociodemographic, clinical characteristics, knowledge and health beliefs. Multiple logistic regression was used to identify factors associated with mammogram uptake. The prevalence of screening mammograms was 46.0%. About 45.5% of women with high breast cancer risk had never undergone a mammogram. Older participants, aged 50 to 74 (OR = 2.57, 95% CI: 1.05, 6.29, p-value = 0.039) and those who received a physician’s recommendation (OR = 7.61, 95% CI: 3.81, 15.20, p-value < 0.001) were more likely to undergo screening mammography. Significant health beliefs associated with mammogram uptake were perceived barriers (OR = 0.81, 95% CI: 0.67, 0.97, p-value = 0.019) and cues to action (OR = 1.30, 95% CI: 1.06, 1.59, p-value = 0.012). Approximately half of the participants and those in the high-risk group had never undergone a mammogram. Older age, physician recommendation, perceived barriers and cues to action were significantly associated with mammogram uptake. Physicians need to play an active role in promoting breast cancer screening and addressing the barriers.

The Predictive Effect of the 8th AJCC Pathological Prognostic Staging on the Benefit of Postmastectomy Radiotherapy in N2/N3 Breast Cancer

Background

The role of the 8th American Joint Committee on Cancer (AJCC) pathological prognostic staging (PPS) on treatment-decision making of breast cancer (BC) remains unclear. This study aimed to investigate the predictive effect of the 8th AJCC PPS on the benefit of postmastectomy radiotherapy (PMRT) in N2/N3 BC.

Methods

We included women with stage N2/3 BC diagnosed between 2010 and 2018 from the Surveillance, Epidemiology, and End Results database. The effect of PMRT on breast cancer-specific survival (BCSS) was evaluated using the multivariate Cox proportional-hazards models.

Results

A total of 13,445 patients were identified, including 10,547 (78.4%) patients treated with PMRT. All patients had reassigned stages based on the 8th AJCC PPS. There were 7102 patients (52.8%) that had stage changed, including 1160 patients (8.6%) were upstaged and 5942 patients (44.2%) were downstaged from the 7th AJCC anatomical staging (AS) to the 8th AJCC PPS. Regarding the 7th AJCC AS, 7603 (56.5%), 948 (7.1%), and 4895 (36.4%) were stage IIIA, IIIB, and IIIC diseases, respectively. Using the 8th AJCC PPS, 3525 (26.2%), 460 (3.4%), 1335 (9.9%), 3457 (25.7%), 2169 (19.1%), and 2100 (15.6%) patients were restaged as IB, IIA, IIB, IIIA, IIIB, and IIIC diseases, respectively. The PPS displayed increased prognostic accuracy and improved model fit with respect to BCSS compared to the 7th AS (C-index, 0.731 vs 0.605, P < 0.001; Akaike Information Criterion, 42141 vs 43118). Regarding the AS, the receipt of PMRT was associated with a better BCSS in those with stage IIIA (P = 0.004), IIIB (P = 0.003), and IIIC (P < 0.001) diseases. Using the PPS, the receipt of PMRT was not associated with a better BCSS among patients with stage IB (P = 0.446), IIA (P = 0.140), and IIB (P = 0.248) disease, while the receipt of PMRT was associated with a better BCSS for those with stage IIIA (P = 0.009), IIIB (P < 0.001), and IIIC (P < 0.001) disease.

Conclusion

The 8th AJCC staging provides superior risk stratification and a better tool to predict the benefit of PMRT in N2/3 BC.

Cumulative Advanced Breast Cancer Risk Prediction Model Developed in a Screening Mammography Population

BACKGROUND

Estimating advanced breast cancer risk in women undergoing annual or biennial mammography could identify women who may benefit from less or more intensive screening. We developed an actionable model to predict cumulative 6-year advanced cancer (prognostic pathologic stage II or higher) risk according to screening interval.

METHODS

We included 931 186 women aged 40-74 years in the Breast Cancer Surveillance Consortium undergoing 2 542 382 annual (prior mammogram within 11-18 months) or 752 049 biennial (prior within 19-30 months) screening mammograms. The prediction model includes age, race and ethnicity, body mass index, breast density, family history of breast cancer, and prior breast biopsy subdivided by menopausal status and screening interval. We used fivefold cross-validation to internally validate model performance. We defined higher than 95th percentile as high risk (>0.658%), higher than 75th percentile to 95th or less percentile as intermediate risk (0.380%-0.658%), and 75th or less percentile as low to average risk (<0.380%).

RESULTS

Obesity, high breast density, and proliferative disease with atypia were strongly associated with advanced cancer. The model is well calibrated and has an area under the receiver operating characteristics curve of 0.682 (95% confidence interval = 0.670 to 0.694). Based on women's predicted advanced cancer risk under annual and biennial screening, 69.1% had low or average risk regardless of screening interval, 12.4% intermediate risk with biennial screening and average risk with annual screening, and 17.4% intermediate or high risk regardless of screening interval.

CONCLUSION

Most women have low or average advanced cancer risk and can undergo biennial screening. Intermediate-risk women may consider annual screening, and high-risk women may consider supplemental imaging in addition to annual screening.

Association Between Vascular Index Measured via Superb Microvascular Imaging and Molecular Subtype of Breast Cancer

Background

To determine whether vascular index (VI; defined as the ratio of Doppler signal pixels to pixels in the total lesion) measured via superb microvascular imaging in breast cancer correlates with immunohistochemically defined subtype and is able to predict molecular subtypes.

Methods

This prospective study involved 225 patients with 225 mass-type invasive breast cancers (mean size 2.6 ± 1.4 cm, range 0.4~5.9 cm) who underwent ultrasound and superb microvascular imaging (SMI) at Peking Union Medical College Hospital before breast surgery from December 2016 to June 2018. The correlations between primary tumor VI measured via SMI, clinicopathological findings, and molecular subtype were analyzed. The performance of VI for prediction of molecular subtypes in invasive breast cancer was investigated.

Results

The median VI of the 225 tumors was 7.3% (4.2%~11.8%) (range 0%~54.4%). Among the subtypes of the 225 tumors, 41 (18.2%) were luminal A, 91 (40.4%) were luminal B human epidermal growth factor receptor-2 (HER-2)-negative, 26 (11.6%) were luminal B HER-2-positive, 17 (7.6%) were HER-2-positive, and 50 (22.2%) were triple-negative, and the corresponding median VI values were 5.9% (2.6%~11.6%) (range 0%~47.1%), 7.3 (4.4%~10.5%) (range 0%~29.5%), 6.3% (3.9%~11.3%) (range 0.6%~22.2%), 8.2% (4.9%~15.6%) (range 0.9%~54.4%), and 9.2% (5.1%~15.3%) (range 0.7%~32.9%), respectively. Estrogen receptor (ER) negativity, higher tumor grade, and higher Ki-67 index (≥20%) were significantly associated with a higher VI value. Tumor size, ER status, and Ki-67 index were shown to independently influence VI. A cutoff value of 4.1% yielded 79.9% sensitivity and 41.5% specificity with an area under the receiver operating characteristic curve (AUC) of 0.58 for predicting that a tumor was of the luminal A subtype. A cutoff value of 16.4% yielded 30.0% sensitivity and 90.3% specificity with an AUC of 0.60 for predicting a triple-negative subtype.

Conclusions

VI, as a quantitative index obtained by SMI examination, could reflect histologic vascular changes in invasive breast cancer and was found to be higher in more biologically aggressive breast tumors. VI shows a certain degree of correlation with the molecular subtype of invasive breast cancer and plays a limited role in predicting the luminal A with high sensitivity and triple-negative subtype with high specificity.

Correlation between contrast-enhanced cone-beam breast computed tomography features and prognostic staging in breast cancer

OBJECTIVE

To evaluate whether contrast-enhanced cone-beam breast CT (CE-CBBCT) features can risk-stratify prognostic stage in breast cancer.

METHODS

Overall, 168 biopsy-proven breast cancer patients were analysed: 115 patients in the training set underwent scanning using v. 1.5 CE-CBBCT between August 2019 and December 2019, whereas 53 patients in the test set underwent scanning using v. 1.0 CE-CBBCT between May 2012 and August 2014. All patients were restaged according to the American Joint Committee on Cancer eighth edition prognostic staging system. Following the combination of CE-CBBCT imaging parameters and clinicopathological factors, predictors that were correlated with stratification of prognostic stage via logistic regression were analysed. Predictive performance was assessed according to the area under the receiver operating characteristic curve (AUC). Goodness-of-fit of the models was assessed using the Hosmer-Lemeshow test.

RESULTS

As regards differentiation between prognostic stage (PS) I and II/III, increased tumour-to-breast volume ratio (TBR), rim enhancement pattern, and the presence of penetrating vessels were significant predictors for PS II/III disease (p < 0.05). The AUCs in the training and test sets were 0.967 [95% confidence interval (CI) 0.938-0.996; p < 0.001] and 0.896 (95% CI, 0.809-0.983; p = 0.001), respectively. Two features were selected in the training set of PS II vs III, including tumour volume [odds ratio (OR)=1.817, p = 0.019] and calcification (OR = 4.600, p = 0.040), achieving an AUC of 0.790 (95% CI, 0.636-0.944, p = 0.001). However, there was no significant difference in the test set of PS II vs III (P＞0.05).

CONCLUSION

CE-CBBCT imaging biomarkers may provide a large amount of anatomical and radiobiological information for the pre-operative distinction of prognostic stage.

ADVANCES IN KNOWLEDGE

CE-CBBCT features have distinctive promise for stratification of prognostic stage in breast cancer.

Diagnosis and prognosis of breast cancer by high-performance serum metabolic fingerprints

Breast cancer (BrCa) is the most common cancer worldwide, and high-performance metabolic analysis is emerging in diagnosis and prognosis of BrCa. Here, we used nanoparticle-enhanced laser desorption/ionization mass spectrometry to record serum metabolic fingerprints of BrCa in seconds, achieving high reproducibility and low consumption of direct serum detection. Our analytical method, combined with the aid of machine learning algorithms, was demonstrated to provide high diagnostic efficiency with accuracy of 88.8% and desirable prognostic prediction (P < 0.005). Furthermore, seven metabolic biomarkers differentially enriched in BrCa serum and their related pathways were identified. Together, our findings provide a tool to characterize BrCa and highlight certain metabolic signatures as potential diagnostic and prognostic factors of diseases including but not limited to BrCa.

High-performance metabolic analysis is emerging in the diagnosis and prognosis of breast cancer (BrCa). Still, advanced tools are in demand to deliver the application potentials of metabolic analysis. Here, we used fast nanoparticle-enhanced laser desorption/ionization mass spectrometry (NPELDI-MS) to record serum metabolic fingerprints (SMFs) of BrCa in seconds, achieving high reproducibility and low consumption of direct serum detection without treatment. Subsequently, machine learning of SMFs generated by NPELDI-MS functioned as an efficient readout to distinguish BrCa from non-BrCa with an area under the curve of 0.948. Furthermore, a metabolic prognosis scoring system was constructed using SMFs with effective prediction performance toward BrCa (P < 0.005). Finally, we identified a biomarker panel of seven metabolites that were differentially enriched in BrCa serum and their related pathways. Together, our findings provide an efficient serum metabolic tool to characterize BrCa and highlight certain metabolic signatures as potential diagnostic and prognostic factors of diseases including but not limited to BrCa.

Feasibility of a tailored home-based exercise intervention during neoadjuvant chemotherapy in breast cancer patients

PURPOSE

To evaluate the feasibility of a home-based moderate-to-vigorous intensity, phased (introduction, intermediate, maintenance), exercise prescription in breast cancer patients receiving cardiotoxic neoadjuvant chemotherapy.

METHODS

Nineteen breast cancer patients were randomized to intervention or control for the duration of chemotherapy (16-24 weeks). The intervention was one aerobic exercise session at 80-90% VO_2max for 25 min/week and 65%-75% VO_2max for ≥ 50 min/week. Adherence to the tailored home-based program was assessed by heart rate monitors. Acceptability, tolerability, feasibility, efficacy, change in VO_2max, and patient reported outcomes, safety, and clinical events were assessed.

RESULTS

25.7% of eligible women consented (acceptability). Adherence was 87.6%. Women were not able to maintain exercise intensity as chemotherapy progressed (23.7% of exercise minutes were completed at prescribed heart rate during maintenance). Efficacy of the intervention was demonstrated by maintenance of VO_2max (-1.0 ± 13.2%) compared to (-27.5 ± 7.4%) the control group. Further, during and after therapy, patients in the intervention arm reported less fatigue (control-baseline: 14.4 ± 15.9; midpoint: 19.0 ± 11.4; follow-up: 29.4 ± 20.0; intervention-baseline: 29.2 ± 24.6; midpoint: 24.6 ± 14.4; follow-up: 23.6 ± 11.9), impairment in activities (control-baseline: 13.7 ± 16.0; midpoint: 32.8 ± 17.0; follow-up: 58.6 ± 27.9; intervention-baseline: 38.7 ± 31.8; midpoint: 47.1 ± 27.5; follow-up: 47.5 ± 31.0), and pain (control-baseline: 80.8 ± 17.1; midpoint: 73.9 ± 20.7; follow-up: 50.7 ± 25.7; intervention-baseline: 68.7 ± 28.4; midpoint: 61.4 ± 22.5; follow-up: 65.3 ± 22.4). There were no differences in adverse events, treatment delays, or pathological complete response.

CONCLUSIONS

Neoadjuvant breast cancer patients maintained approximately one hour/week of moderate-intensity exercise over the course of their treatment. Further, this volume of exercise was sufficient to maintain fitness capacity and quality of life compared to the control group.

TRIAL REGISTRY

ClinicalTrials.gov Identifier: NCT03280836, prospectively registered 9/13/2017, https://clinicaltrials.gov/ct2/show/NCT03280836 .

Validation of the Prognostic Stage from the American Joint Committee on Cancer 8th Staging Manual in Luminal B-Like Human Epidermal Growth Factor Receptor 2-Negative Breast Cancer

PURPOSE

The 8th edition American Joint Committee on Cancer (AJCC) prognostic staging system (PS) has been validated numerous times; however, the prognostic value of PS for breast cancer based on molecular subtype has rarely been explored. This study aimed to investigate the prognostic value of PS in Chinese patients with luminal B-like human epidermal growth factor receptor 2 (HER2)-negative breast cancer.

METHODS

A total of 407 eligible cases were included in the study. All of the cases were restaged using the 8th edition AJCC Anatomic Staging System (AS) and PS. The Kaplan-Meier method was used to calculate estimated survival and the Log rank test was used to compare the survival differences between groups.

RESULTS

The 5-year disease-specific survival (DSS) and overall survival (OS) rates were 90.3% and 93.5%, respectively, and there were statistically significant differences in the 5-year DSS and 5-year OS rates among the different anatomic and prognostic stage groups. The application of the PS resulted in the assignment of 215 (52.8%) patients to a different group. Different prognostic stage groups restaged from anatomic Stage III had significant differences in both DSS (χ 2 = 4.366, p = 0.037) and OS (χ 2 = 7.549, p = 0.006); additionally, different prognostic stage groups from the anatomic Stage II group had significant differences in DSS (χ 2 = 7.724, p = 0.021) but no significant differences in OS (χ 2 = 5.182, p = 0.075). However, different prognostic stage groups from anatomic Stage I had no significant differences in either DSS (χ 2= 0.159, p = 0.690) or OS (χ 2 = 0.099, p = 0.753).

CONCLUSION

The 8th edition AJCC PS refined the anatomic stage grouping in luminal B-like HER2-negative breast cancer and could lead to a more personalized approach to breast cancer treatment.

Utilization of neoadjuvant chemotherapy in high-risk, node-negative early breast cancer

BACKGROUND

Controversy exists regarding the optimal sequence of chemotherapy among women with operable node-negative breast cancers with high-risk tumor biology. We evaluated national patterns of neoadjuvant chemotherapy (NACT) use among women with early-stage HER2+, triple-negative (TNBC), and high-risk hormone receptor-positive (HR+) invasive breast cancers.

METHODS

Women ≥18 years with cT1-2/cN0 HER2+, TNBC, or high recurrence risk score (≥31) HR+ invasive breast cancers who received chemotherapy were identified in the National Cancer Database (2010-2016). Cochran-Armitage and logistic regression examined temporal trends and likelihood of undergoing NACT versus adjuvant chemotherapy based on patient age and molecular subtype.

RESULTS

Overall, 96,622 patients met study criteria; 25% received NACT and 75% underwent surgery first, with comparable 5-year estimates of overall survival (0.90, 95% CI 0.892-0.905 vs 0.91, 95% CI 0.907-0.913). During the study period, utilization of NACT increased from 14% to 36% and varied according to molecular subtype (year*molecular subtype p < 0.001, p-corrected < 0.001). Women with HER2+ (OR 4.17, 95% CI 3.70-4.60, p < 0.001, p-corrected < 0.001) and TNBC (OR 3.81, 95% CI 3.38-4.31, p < 0.001, p-corrected < 0.001) were more likely to receive NACT over time, without a change in use among those with HR+ disease (OR 1.58, 95% CI 0.88-2.87, p = 0.13, p-corrected = 0.17).

CONCLUSION

Among women with early-stage triple-negative and HER2+ breast cancers, utilization of NACT increased over time, a trend that correlates with previously reported improved rates of pCR and options post-neoadjuvant treatment with residual disease. Future research is needed to better understand multidisciplinary decisions for NACT and implications for breast cancer patients.

Incremental Spending Associated with Low-Value Treatments in Older Women with Breast Cancer

BACKGROUND

In most women ≥ 70 years old with hormone-receptor-positive breast cancer, axillary staging and adjuvant radiotherapy provide no survival advantage over surgery and hormone therapy alone. Despite recommendations for their omission, sentinel lymph node biopsy (SLNB) and adjuvant radiotherapy rates remain high. While treatment side effects are well documented, less is known about the incremental spending associated with SLNB and adjuvant radiotherapy.

METHODS

Using a statewide multipayer claims registry, we examined spending associated with breast cancer treatment in a retrospective cohort of women ≥ 70 years old undergoing surgery.

RESULTS

9074 women ≥70 years old underwent breast cancer resection between 2012 and 2019, with 78% (n = 7122) receiving SLNB and/or adjuvant radiotherapy within 90 days of surgery. Women undergoing SLNB were more likely to receive radiation (51% vs. 28%; p < 0.001 and OR = 2.68). Average 90-day spending varied substantially based upon treatment received, ranging from US$10,367 (breast-conserving surgery alone) to US$27,370 (mastectomy with SLNB and adjuvant radiotherapy). The relative increases in 90-day treatment spending in the breast-conserving surgery cohort was 65% for SLNB, 82% for adjuvant radiotherapy, and 120% for both treatments.

CONCLUSIONS

SLNB and adjuvant radiotherapy have significant spending implications in older women with breast cancer, even though they are unlikely to improve survival.

Re-wiring and gene expression changes of AC025034.1 and ATP2B1 play complex roles in early-to-late breast cancer progression

Background

Elucidating the dynamic topological changes across different stages of breast cancer, called stage re-wiring, could lead to identifying key latent regulatory signatures involved in cancer progression. Such dynamic regulators and their functions are mostly unknown. Here, we reconstructed differential co-expression networks for four stages of breast cancer to assess the dynamic patterns of cancer progression. A new computational approach was applied to identify stage-specific subnetworks for each stage. Next, prognostic traits of genes and the efficiency of stage-related groups were evaluated and validated, using the Log-Rank test, SVM classifier, and sample clustering. Furthermore, by conducting the stepwise VIF-feature selection method, a Cox-PH model was developed to predict patients’ risk. Finally, the re-wiring network for prognostic signatures was reconstructed and assessed across stages to detect gain/loss, positive/negative interactions as well as rewired-hub nodes contributing to dynamic cancer progression.

Results

After having implemented our new approach, we could identify four stage-specific core biological pathways. We could also detect an essential non-coding RNA, AC025034.1, which is not the only antisense to ATP2B1 (cell proliferation regulator), but also revealed a statistically significant stage-descending pattern; Moreover, AC025034.1 revealed both a dynamic topological pattern across stages and prognostic trait. We also identified a high-performance Overall-Survival-Risk model, including 12 re-wired genes to predict patients’ risk (c-index = 0.89). Finally, breast cancer-specific prognostic biomarkers of LINC01612, AC092142.1, and AC008969.1 were identified.

Conclusions

In summary new scoring method highlighted stage-specific core pathways for early-to-late progressions. Moreover, detecting the significant re-wired hub nodes indicated stage-associated traits, which reflects the importance of such regulators from different perspectives.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-021-01015-9.

Evaluation and Management of Liver Transplant Candidates With Prior Nonhepatic Cancer: Guidelines From the ILTS/SETH Consensus Conference

Liver transplant in patients with prior nonhepatic cancer is a matter of concern, needing further research, development, and consensus guidelines. This International Liver Transplantation Society/Sociedad Española De Trasplante Hepático consensus conference document focuses on the role of liver transplantation in patients with a prior history of nonhepatic cancer. This document addresses (1) the evaluation of transplant candidates with prior cancers based on the assessment of prognosis, the natural history of individual cancers, and the emerging role for circulating DNA and minimal residual disease in these patients; (2) the impact of prior treatments, including immunotherapy for prior malignancies; and (3) the surveillance of posttransplant cancer recurrence. The consensus statement is based on previously published guidelines, as well as a review of the current, relevant, published literature.

Predicting Cancer-Specific Survival Among Patients With Prostate Cancer After Radical Prostatectomy Based on the Competing Risk Model: Population-Based Study

Introduction: We aimed to develop an easy-to-use individual survival prognostication tool based on competing risk analyses to predict the risk of 5-year cancer-specific death after radical prostatectomy for patients with prostate cancer (PCa).

Methods: We obtained the data from the Surveillance, Epidemiology, and End Results (SEER) database (2004–2016). The main variables obtained included age at diagnosis, marital status, race, pathological extension, regional lymphonode status, prostate specific antigen level, pathological Gleason Score. In order to reveal the independent prognostic factors. The cumulative incidence function was used as the univariable competing risk analyses and The Fine and Gray's proportional subdistribution hazard approach was used as the multivariable competing risk analyses. With these factors, a nomogram and risk stratification based on the nomogram was established. Concordance index (C-index) and calibration curves were used for validation.

Results: A total of 95,812 patients were included and divided into training cohort (n = 67,072) and validation cohort (n = 28,740). Seven independent prognostic factors including age, race, marital status, pathological extension, regional lymphonode status, PSA level, and pathological GS were used to construct the nomogram. In the training cohort, the C-index was 0.828 (%95CI, 0.812–0.844), and the C-index was 0.838 (%95CI, 0.813–0.863) in the validation cohort. The results of the cumulative incidence function showed that the discrimination of risk stratification based on nomogram is better than that of the risk stratification system based on D'Amico risk stratification.

Conclusions: We successfully developed the first competing risk nomogram to predict the risk of cancer-specific death after surgery for patients with PCa. It has the potential to help clinicians improve post-operative management of patients.