A nomogram predicting the probability of primary breast cancer survival at 2- and 5-years using pathological and biological tumor parameters

Prognosis and Genomic Landscape of Liver Metastasis in Patients With Breast Cancer

Objective

The prognosis of breast cancer liver metastasis (BCLM) is poor, and its molecular mechanism is unclear. We aimed to determine the factors that affect the prognosis of patients with BCLM and investigate the genomic landscape of liver metastasis (LM).

Methods

We described the prognosis of patients with BCLM and focused on prognosis prediction for these patients based on clinicopathological factors. Nomogram models were constructed for progression-free survival (PFS) and overall survival (OS) by using a cohort of 231 patients with BCLM who underwent treatment at Shandong Cancer Hospital and Institute (SCHI). We explored the molecular mechanism of LM and constructed driver genes, mutation signatures by using a targeted sequencing dataset of 217 samples of LM and 479 unpaired samples of primary breast cancer (pBC) from Memorial Sloan Kettering Cancer Center (MSKCC).

Results

The median follow-up time for 231 patients with BCLM in the SCHI cohort was 46 months. The cumulative incidence of LM at 1, 2, and 5 years was 17.5%, 45.0%, and 86.8%, respectively. The median PFS and OS were 7 months (95% CI, 6-8) and 22 months (95% CI, 19-25), respectively. The independent factors that increased the progression risk of patients with LM were Karnofsky performance status (KPS) ≤ 80, TNBC subtype, grade III, increasing trend of CA153, and disease-free interval (DFS) ≤ 1 year. Simultaneously, the independent factors that increased the mortality risk of patients with LM were Ki-67 ≥ 30%, grade III, increasing trend of CA153, pain with initial LM, diabetes, and DFI ≤ 1 year. In the MSKCC dataset, the LM driver genes were ESR1, AKT1, ERBB2, and FGFR4, and LM matched three prominent mutation signatures: APOBEC cytidine deaminase, ultraviolet exposure, and defective DNA mismatch repair.

Conclusion

This study systematically describes the survival prognosis and characteristics of LM from the clinicopathological factors to the genetic level. These results not only enable clinicians to assess the risk of disease progression in patients with BCLM to optimize treatment options, but also help us better understand the underlying mechanisms of tumor metastasis and evolution and provide new therapeutic targets with potential benefits for drug-resistant patients.

Prognostic role of high cathepsin D expression in breast cancer: a systematic review and meta-analysis

Background:

High cathepsin D has been associated with poor prognosis in breast cancer; however, the results of many studies are controversial. Here, we assessed the association between high cathepsin D levels and worse breast cancer prognosis by conducting a meta-analysis.

Methods:

A comprehensive search strategy was used to search relevant literature in PUBMED and EMBASE by September 2018. The meta-analysis was performed in Review Manager 5.3 using hazard ratios (HRs) with 95% confidence intervals (CIs).

Results:

A total of 15,355 breast cancer patients from 26 eligible studies were included in this meta-analysis. Significant associations between elevated high cathepsin D and poor overall survival (OS) (HR = 1.61, 95% CI: 1.35–1.92, p < 0.0001) and disease-free survival (DFS) (HR = 1.52, 95% CI: 1.31–2.18, p < 0.001) were observed. In the subgroup analysis for DFS, high cathepsin D was significantly associated with poor prognosis in node-positive patients (HR = 1.38, 95% CI: 1.25–1.71, p < 0.00001), node-negative patients (HR = 1.78, 95% CI: 1.39–2.27, p < 0.0001), early stage patients (HR = 1.73, 95% CI: 1.34–2.23, p < 0.0001), and treated with chemotherapy patients (HR = 1.60, 95% CI: 1.21–2.12, p < 0.001). Interestingly, patients treated with tamoxifen had a low risk of relapse when their cathepsin D levels were high (HR = 0.71, 95% CI: 0.52–0.98, p = 0.04) and a high risk of relapse when their cathepsin D levels were low (HR = 1.50, 95% CI: 1.22–1.85, p = 0.0001).

Conclusions:

Our meta-analysis suggests that high expression levels of cathepsin D are associated with a poor prognosis in breast cancer. Based on our subgroup analysis, we believe that cathepsin D can act as a marker for poor breast cancer prognosis and also as a therapeutic target for breast cancer.

Prognostic models for breast cancer: a systematic review

BACKGROUND

Breast cancer is the most common cancer in women worldwide, with a great diversity in outcomes among individual patients. The ability to accurately predict a breast cancer outcome is important to patients, physicians, researchers, and policy makers. Many models have been developed and tested in different settings. We systematically reviewed the prognostic models developed and/or validated for patients with breast cancer.

METHODS

We conducted a systematic search in four electronic databases and some oncology websites, and a manual search in the bibliographies of the included studies. We identified original studies that were published prior to 1st January 2017, and presented the development and/or validation of models based mainly on clinico-pathological factors to predict mortality and/or recurrence in female breast cancer patients.

RESULTS

From the 96 articles selected from 4095 citations found, we identified 58 models, which predicted mortality (n = 28), recurrence (n = 23), or both (n = 7). The most frequently used predictors were nodal status (n = 49), tumour size (n = 42), tumour grade (n = 29), age at diagnosis (n = 24), and oestrogen receptor status (n = 21). Models were developed in Europe (n = 25), Asia (n = 13), North America (n = 12), and Australia (n = 1) between 1982 and 2016. Models were validated in the development cohorts (n = 43) and/or independent populations (n = 17), by comparing the predicted outcomes with the observed outcomes (n = 55) and/or with the outcomes estimated by other models (n = 32), or the outcomes estimated by individual prognostic factors (n = 8). The most commonly used methods were: Cox proportional hazards regression for model development (n = 32); the absolute differences between the predicted and observed outcomes (n = 30) for calibration; and C-index/AUC (n = 44) for discrimination. Overall, the models performed well in the development cohorts but less accurately in some independent populations, particularly in patients with high risk and young and elderly patients. An exception is the Nottingham Prognostic Index, which retains its predicting ability in most independent populations.

CONCLUSIONS

Many prognostic models have been developed for breast cancer, but only a few have been validated widely in different settings. Importantly, their performance was suboptimal in independent populations, particularly in patients with high risk and in young and elderly patients.

A conditional model predicting the 10-year annual extra mortality risk compared to the general population: a large population-based study in Dutch breast cancer patients

Objective

Many cancer survivors are facing difficulties in getting a life insurance; raised premiums and declinatures are common. We generated a prediction model estimating the conditional extra mortality risk of breast cancer patients in the Netherlands. This model can be used by life insurers to accurately estimate the additional risk of an individual patient, conditional on the years survived.

Methodology

All women diagnosed with stage I-III breast cancer in 2005–2006, treated with surgery, were selected from the Netherlands Cancer Registry. For all stages separately, multivariable logistic regression was used to estimate annual mortality risks, conditional on the years survived, until 10 years after diagnosis, resulting in 30 models. The conditional extra mortality risk was calculated by subtracting mortality rates of the general Dutch population from the patient mortality rates, matched by age, gender and year. The final model was internally and externally validated, and tested by life insurers.

Results

We included 23,234 patients: 10,101 stage I, 9,868 stage II and 3,265 stage III. The final models included age, tumor stage, nodal stage, lateralization, location within the breast, grade, multifocality, hormonal receptor status, HER2 status, type of surgery, axillary lymph node dissection, radiotherapy, (neo)adjuvant systemic therapy and targeted therapy. All models showed good calibration and discrimination. Testing of the model by life insurers showed that insurability using the newly-developed model increased with 13%, ranging from 0%-24% among subgroups.

Conclusion

The final model provides accurate conditional extra mortality risks of breast cancer patients, which can be used by life insurers to make more reliable calculations. The model is expected to increase breast cancer patients’ insurability and transparency among life insurers.

A Nomogram for Distinction and Potential Prediction of Liver Metastasis in Breast Cancer Patients

Liver metastasis from breast cancer has poor prognosis. We aimed at developing a reliable tool for making a distinction and prediction for liver metastasis in breast cancer patients, thus helping clinical diagnosis and treatment. In this study, totally 6238 patients from SEER database with known distant metastasis status and clinicopathologic variables were enrolled and divided randomly into training and validating groups. Logistic regression was used to screen variables and a nomogram was constructed. After multivariate logistic regression, sex, histology type, N stage, grade, age, ER, PR, HER2 status as significant variables for constructing the nomogram. The nomogram for distinguishing and predicting liver metastasis in breast cancer passed the calibration and validation steps and the areas under the receiver operating characteristic curve of the training set and the validation set were 0.6602 and 0.6511 respectively. Our nomogram is a reliable and robust tool for the distinction and prediction of liver metastasis in breast cancer patients, thus helping better choose medical examinations and optimize therapeutic regimen under the cooperation among medical oncologists and surgeons.

Clinical nomogram to predict bone-only metastasis in patients with early breast carcinoma

BACKGROUND

Bone is one of the most common sites of distant metastasis in breast cancer. The purpose of this study was to combine selected clinical and pathologic variables to develop a nomogram that can predict the likelihood of bone-only metastasis (BOM) as the first site of recurrence in patients with early breast cancer.

METHODS

Medical records of patients with non-metastatic breast cancer were retrospectively collected. On the basis of the analysis of patient and tumour characteristics using the Cox proportional hazards regression model, a nomogram to predict BOM was constructed for a 4175-patient-training cohort. The nomogram was validated in an independent cohort of 579 patients.

RESULTS

Among 4175 patients with non-metastatic breast cancer, 314 developed subsequent BOM. Age, T classification, lymph node status, lymphovascular space invasion, and hormone receptor status were significantly and independently associated with subsequent BOM. The nomogram had a concordance index of 0.69 in the training set and 0.73 in the validation set.

CONCLUSIONS

We have developed a clinical nomogram to predict subsequent BOM in patients with non-metastatic breast cancer. Selection of a patient population at high risk for BOM could facilitate research of more specific staging approaches or the selective use of bone-targeted therapy.

A preoperative nomogram to predict the risk of synchronous distant metastases at diagnosis of primary breast cancer

Background:

The detection of synchronous metastases at primary diagnosis of breast cancer (BC) affects its initial management. A risk calculator that incorporates many factors to evaluate an individual's risk of harbouring synchronous metastases would be useful to adapt cancer management.

Patients and Methods:

Patients with primary diagnosis of BC were identified from three institutional databases sharing homogeneous work-up recommendations. A risk score for synchronous metastases was estimated and a nomogram was constructed using the first database. Its performance was assessed by receiver characteristic (ROC) analysis. The nomogram was externally validated in the two independent cohorts.

Results:

A preoperative nomogram based on the clinical tumour size (P<0.001), clinical nodal status (P<0.001), oestrogen (P=0.17) and progesterone receptors (P=0.04) was developed. The nomogram accuracy was 87.3% (95% confidence interval (CI), 84.45–90.2%). Overall, the area under the ROC curve (AUC) was 86.1% for the validation set from the Institut Curie-René Huguenin, and 63.8% for the MD Anderson validation set. The negative predictive value (NPV) was high in the three cohorts (97–99%).

Conclusions:

We developed and validated a strong metastasis risk calculator that can evaluate with high accuracy an individual's risk of harbouring synchronous metastases at diagnosis of primary BC.

Condensed abstract:

A nomogram to predict synchronous metastases at diagnosis of breast cancer was developed and externally validated. This tool allows avoiding unnecessary expensive work-up.

New cathepsin D inhibitor library utilizing hydroxyethyl isosteres with cyclic tertiary amines

The design and synthesis of hydroxyethylamine isosteres as inhibitors of cathepsin D based on SAR data have been accomplished. A library of 96 of these hydroxyethylamine isosteres are described and many have proven to be very potent inhibitors of human cathepsin D activity as measured using a fluorometric assay technique, via peptide substrate Ac-Glu-Glu(Edans)-Lys-Pro-Ile-Cys-Phe-Phe-Arg-Leu-Gly-Lys(Methyl Red)-Glu-NH(2). Compounds showing strongest inhibition of cathepsin D activity were those that contain a hydroxyethyl-N'-2- or N'-(4-chlorophenyl)piperazine moiety (IC(50) values range from 0.55 to 8.5 nM), with N'-(2-pyrimidyl)piperizine (IC(50) values range from 0.5 to 21.6 nM), with N-N'- L-piperazinocolinamide (IC(50) values range from 0.001 - 0.25 nM), or N-N'-L-piperazinocolin-N-methylamide (IC(50) values range from 0.015 - 7.3 nM).

A nomogram to predict individual prognosis in node-negative breast carcinoma

BACKGROUND

Currently, the benefit of chemotherapy (CT) in node-negative breast carcinoma (NNBC) is discussed. The evaluation of classical clinical and histological factors is limited to assess individual outcome. A statistical model was developed to improve the prognostic accuracy of NNBC.

METHODS

A total of 305 node-negative breast carcinomas who underwent surgery (+/- radiotherapy) but no adjuvant treatment were selected. Putative prognosis factors including age, tumour size, oestrogen receptor (ER), progesterone receptor (PgR), Scarff-Bloom-Richardon (SBR) grading, urokinase plasminogen activator (uPA), plasminogen activator inhibitor 1 (PAI-1) and thymidine kinase (TK) were evaluated. The developed model was internally validated using Harrell's concordance index. A prognosis index (PI) was proposed and compared with Adjuvant! Online program.

RESULTS

Age (p < 0.001), pathological tumour size (pT) (p < 0.001), PgR (p = 0.02), and PAI-1 (p ≤ 0.001) were included in the Cox regression model predicting Breast cancer specific survival (BCSS) at 5-years. Internal validation revealed a concordance index of 0.71. A PI score was derived from our nomogram. The PI score was significantly associated with BCSS (hazard ratio (HR): 4.1 for intermediate, p=0.02, HR: 8.8, p < 0.001 for high group) as compared to Adjuvant! Online score (HR: 1.4, p=0.14).

CONCLUSION

A nomogram can be used to predict probability survival curves for individual breast cancer patients.

Predicting survival in women with breast cancer and brain metastasis: a nomogram outperforms current survival prediction models

BACKGROUND

Brain metastases (BMs) are a common occurrence in patients with breast cancer, and accurately predicting survival in these patients is critical to appropriate management. A survival nomogram for breast cancer patients with BM was constructed, and its performance is compared to current predictive models of survival.

METHODS

A Cox proportional hazards regression with a nomogram representation was used to model survival in a population of 261 women with breast cancer and BMs treated from 1999 to 2008. The model was validated internally by 10-fold cross-validation and bootstrapping, and concordance (c) indices were calculated. The predictive performance of the nomogram described here is compared to current prognostic models, including recursive partitioning analysis, graded prognostic assessment, and diagnosis-specific graded prognostic assessment.

RESULTS

The c-index for the model described here was 0.67. It outperformed recursive partitioning analysis, graded prognostic assessment, and diagnosis-specific graded prognostic assessment, based on c-index comparisons.

CONCLUSIONS

The nomogram described here outperformed current strategies for survival prediction in breast cancer patients with BMs. Two additional advantages of this nomogram are its ability to predict individualized, 1-, 3-, and 5-year survival for novel patients and its straightforward representations of the relative effects of each of 9 covariates on neurologic survival. This represents a potentially valuable alternative to current models of survival prediction in this patient population.