Altona Prognostic Index: A New Prognostic Index for ER-Positive and Her2-Negative Breast Cancer of No Special Type

CD301 and LSECtin glycan-binding receptors of innate immune cells serve as prognostic markers and potential predictors of immune response in breast cancer subtypes

Glycosylation is a prominent posttranslational modification, and alterations in glycosylation are a hallmark of cancer. Glycan-binding receptors, primarily expressed on immune cells, play a central role in glycan recognition and immune response. Here, we used the recombinant C-type glycan-binding receptors CD301, Langerin, SRCL, LSECtin, and DC-SIGNR to recognize their ligands on tissue microarrays (TMA) of a large cohort (n = 1859) of invasive breast cancer of different histopathological types to systematically determine the relevance of altered glycosylation in breast cancer. Staining frequencies of cancer cells were quantified in an unbiased manner by a computer-based algorithm. CD301 showed the highest overall staining frequency (40%), followed by LSECtin (16%), Langerin (4%) and DC-SIGNR (0.5%). By Kaplan-Meier analyses, we identified LSECtin and CD301 as prognostic markers in different breast cancer subtypes. Positivity for LSECtin was associated with inferior disease-free survival in all cases, particularly in estrogen receptor positive (ER+) breast cancer of higher histological grade. In triple negative breast cancer, positivity for CD301 correlated with a worse prognosis. Based on public RNA single-cell sequencing data of human breast cancer infiltrating immune cells, we found CLEC10A (CD301) and CLEC4G (LSECtin) exclusively expressed in distinct subpopulations, particularly in dendritic cells and macrophages, indicating that specific changes in glycosylation may play a significant role in breast cancer immune response and progression.

Re-evaluation of publicly available gene-expression databases using machine-learning yields a maximum prognostic power in breast cancer

Gene expression signatures refer to patterns of gene activities and are used to classify different types of cancer, determine prognosis, and guide treatment decisions. Advancements in high-throughput technology and machine learning have led to improvements to predict a patient's prognosis for different cancer phenotypes. However, computational methods for analyzing signatures have not been used to evaluate their prognostic power. Contention remains on the utility of gene expression signatures for prognosis. The prevalent approaches include random signatures, expert knowledge, and machine learning to construct an improved signature. We unify these approaches to evaluate their prognostic power. Re-evaluation of publicly available gene-expression data from 8 databases with 9 machine-learning models revealed previously unreported results. Gene-expression signatures are confirmed to be useful in predicting a patient's prognosis. Convergent evidence from [Formula: see text] 10,000 signatures implicates a maximum prognostic power. By calculating the concordance index, which measures how well patients with different prognoses can be discriminated, we show that a signature can correctly discriminate patients' prognoses no more than 80% of the time. Additionally, we show that more than 50% of the potentially available information is still missing at this value. We surmise that an accurate prognosis must incorporate molecular, clinical, histological, and other complementary factors.

Development and validation of an extended Cox prognostic model for patients with ER/PR+ and HER2- breast cancer: a retrospective cohort study

Background

The purpose of this study was to explore a new estrogen receptor (ER) and/or progesterone receptor (PR)+ and human epidermal growth factor receptor 2 (HER2)− breast cancer prognostic model, called the extended Cox prognostic model, for determining the cutoff values for multiple continuous prognostic factors and their interaction via the new model concept and variable selection method.

Methods

A total of 335 patients with ER/PR+ and HER2− breast cancer were enrolled for the final analysis. The primary endpoint was breast cancer-specific mortality (BCSM). Prognostic factors (histological grade, histological type, stage, T, N, lymphovascular invasion (LVI), P53, Ki67, ER, PR, and age) were included in this study. The four continuous variables (Ki67, ER, PR, and age) were partitioned into a series of binary variables that were fitted in the multivariate Cox analysis. A smoothly clipped absolute deviation (SCAD) variable selection method was used. Model performance was expressed in discrimination and calibration.

Results

We developed an extended Cox model with a time threshold of 164-week (more than 3 years) postoperation and developed a user-friendly nomogram based on our extended Cox model to facilitate clinical application. We found that the cutoff values for PR, Ki67, and age were 20%, 60%, and 41–55 years, respectively. There was an interaction between age and PR for patients aged ≥ 41 years and PR ≥ 20% at 164-week postoperation: the older the patients with ER/PR+, HER2−, and PR ≥ 20% were, the lower the survival and more likely to recur and metastasize exceeding 164 weeks (more than 3 years) after surgery.

Conclusions

Our study offers guidance on the prognosis of patients with ER/PR+ and HER2− breast cancer in China. The new concept can inform modeling and the determination of cutoff values of prognostic factors in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02790-0.