Radiomics in Triple Negative Breast Cancer: New Horizons in an Aggressive Subtype of the Disease

A Review of AI-Based Radiomics and Computational Pathology Approaches in Triple-Negative Breast Cancer: Current Applications and Perspectives

Breast cancer is one of the most common and deadly cancers worldwide. Approximately, 20% of all breast cancers are characterized as triple negative (TNBC). TNBC typically is associated with a poorer prognosis relative to other breast cancer subtypes. Due to its aggressiveness and lack of response to hormonal therapy, conventional cytotoxic chemotherapy is the usual treatment; however, this treatment is not always effective, and an important percentage of patients develop recurrence. More recently, immunotherapy has started to be used on some populations with TNBC showing promising results. Unfortunately, immunotherapy is only applicable to a minority of patients and responses in metastatic TNBC have overall been modest in comparison to other cancer types. This situation evidences the need for developing effective biomarkers that help to stratify and personalize patient management. Thanks to recent advances in artificial intelligence (AI), there has been an increasing interest in its use for medical applications aiming at supporting clinical decision making. Several works have used AI in combination with diagnostic medical imaging, more specifically radiology and digitized histopathological tissue samples, aiming to extract disease-specific information that is difficult to quantify by the human eye. These works have demonstrated that analysis of such images in the context of TNBC has great potential for (1) risk-stratifying patients to identify those patients who are more likely to experience disease recurrence or die from the disease and (2) predicting pathologic complete response. In this manuscript, we present an overview on AI and its integration with radiology and histopathological images for developing prognostic and predictive approaches for TNBC. We present state of the art approaches in the literature and discuss the opportunities and challenges with developing AI algorithms regarding further development and clinical deployment, including identifying those patients who may benefit from certain treatments (e.g., adjuvant chemotherapy) from those who may not and thereby should be directed toward other therapies, discovering potential differences between populations, and identifying disease subtypes.

Adaptive radiotherapy for breast cancer

Research in the field of local and locoregional breast cancer radiotherapy aims to maintain excellent oncological outcomes while reducing treatment-related toxicity. Adaptive radiotherapy (ART) considers variations in target and organs at risk (OARs) anatomy occurring during the treatment course and integrates these in re-optimized treatment plans. Exploiting ART routinely in clinic may result in smaller target volumes and better OAR sparing, which may lead to reduction of acute as well as late toxicities. In this review MR-guided and CT-guided ART for breast cancer patients according to different clinical scenarios (neoadjuvant and adjuvant partial breast irradiation, whole breast, chest wall and regional nodal irradiation) are reviewed and their advantages as well as challenging aspects discussed.

Understanding Breast Cancer Aggressiveness and Its Implications in Diagnosis and Treatment

Breast cancer (BC) is the most common form of cancer in women worldwide [...].

Dimethyl Fumarate Induces Apoptosis via Inhibition of NF-κB and Enhances the Effect of Paclitaxel and Adriamycin in Human TNBC Cells

Triple-negative breast cancer (TNBC) has the poorest prognosis of all breast cancer subtypes. Recently, the activation of NF-κB, which is involved in the growth and survival of malignant tumors, has been demonstrated in TNBC, suggesting that NF-κB may serve as a new therapeutic target. In the present study, we examined whether dimethyl fumarate (DMF), an NF-κB inhibitor, induces apoptosis in TNBC cells and enhances the apoptosis-inducing effect of paclitaxel and adriamycin. Cell survival was analyzed by the trypan blue assay and apoptosis assay. Protein detection was examined by immunoblotting. The activation of NF-κB p65 was correlated with poor prognosis in patients with TNBC. DMF induced apoptosis in MDA-MB-231 and BT-549 cells at concentrations that were non-cytotoxic to the normal mammary cell line MCF-10A. Furthermore, DMF inhibited NF-κB nuclear translocation and Survivin, XIAP, Bcl-xL, and Bcl-2 expression in MDA-MB-231 and BT-549 cells. Moreover, DMF enhanced the apoptosis-inducing effect of paclitaxel and adriamycin in MDA-MB-231 cells. These findings suggest that DMF may be an effective therapeutic agent for the treatment of TNBC, in which NF-κB is constitutively active. DMF may also be useful as an adjuvant therapy to conventional anticancer drugs.

Integration of radiogenomic features for early prediction of pathological complete response in patients with triple-negative breast cancer and identification of potential therapeutic targets

Background

We established a radiogenomic model to predict pathological complete response (pCR) in triple-negative breast cancer (TNBC) and explored the association between high-frequency mutations and drug resistance.

Methods

From April 2018 to September 2019, 112 patients who had received neoadjuvant chemotherapy were included. We randomly split the study population into training and validation sets (2:1 ratio). Contrast-enhanced magnetic resonance imaging scans were obtained at baseline and after two cycles of treatment and were used to extract quantitative radiomic features and to construct two radiomics-only models using a light gradient boosting machine. By incorporating the variant allele frequency features obtained from baseline core tissues, a radiogenomic model was constructed to predict pCR. Additionally, we explored the association between recurrent mutations and drug resistance.

Results

The two radiomics-only models showed similar performance with AUCs of 0.71 and 0.73 (p = 0.55). The radiogenomic model had a higher predictive ability than the radiomics-only model in the validation set (p = 0.04), with a corresponding AUC of 0.87 (0.73–0.91).

Two highly frequent mutations were selected after comparing the mutation sites of pCR and non-pCR populations. The MED23 mutation p.P394H caused epirubicin resistance in vitro (p < 0.01). The expression levels of γ-H2A.X, p-ATM and p-CHK2 in MED23 p.P394H cells were significantly lower than those in wild type cells (p < 0.01). In the HR repair system, the GFP positivity rate of MED23 p.P394H cells was higher than that in wild-type cells (p < 0.01).

Conclusions

The proposed radiogenomic model has the potential to accurately predict pCR in TNBC patients. Epirubicin resistance after MED23 p.P394H mutation might be affected by HR repair through regulation of the p-ATM-γ-H2A.X-p-CHK2 pathway.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03452-1.