Prognostic and predictive biomarkers in breast cancer: Past, present and future

IL21R hypomethylation as a biomarker for distinguishing benign and malignant breast tumours

Some benign and malignant breast tumours are similar in pathological morphology, which are difficult to be distinguished in clinical diagnosis. In this study, we intended to explore novel biomarkers for differential diagnosis of benign and malignant breast tumours. Methylation EPIC 850K beadchip and RNA-sequencing were used to analyse 29 tissue samples from patients with early-stage breast cancer (BC) and benign breast tumours for differently methylated and expressed genes. The altered methylation of IL21R was semi-quantitatively validated in an independent study with 566 tissue samples (279 BC vs. 287 benign breast tumours) using mass spectrometry. Binary logistic regression analysis was performed to evaluate the association between IL21R methylation and BC. BC-associated IL21R hypomethylation and overexpression were identified in the discovery round. In the validation round, BC patients presented significant IL21R hypomethylation compared to women with benign breast tumours (ORs ≥1.29 per-10% methylation, p-values ≤ 5.69E-14), and this hypomethylation was even enhanced in BC patients with ER-negative and PR-negative tumours as well as with triple-negative tumours. The methylation of IL21R showed efficient discriminatory power to distinguish benign breast tumours from BC (area under curve (AUC) = 0.88), and especially from ER-negative BC (AUC = 0.95), PR-negative BC (AUC = 0.93) and triple-negative BC (AUC = 0.96). We disclosed significant IL21R hypomethylation in patients with BC compared to women with benign breast tumours, and revealed the somatic change of DNA methylation could be a potential biomarker for molecular pathology of BC.

Nottingham prognostic x (NPx): a risk stratification tool in ER-positive HER2-negative breast cancer: a validation study

AIMS

In this study, we validate the use of Nottingham Prognostic x (NPx), consisting of tumour size, tumour grade, progesterone receptor (PR) and Ki67 in luminal BC.

MATERIALS AND METHODS

Two large cohorts of luminal early-stage BC (n = 2864) were included. PR and Ki67 expression were assessed using full-face resection samples using immunohistochemistry. NPx was calculated and correlated with clinical variables and outcome, together with Oncotype DX recurrence score (RS), that is frequently used as a risk stratifier in luminal BC.

RESULTS

In the whole cohort, 38% of patients were classified as high risk using NPx which showed significant association with parameters characteristics of aggressive tumour behaviour and shorter survival (P < 0.0001). NPx classified the moderate Nottingham Prognostic Index (NPI) risk group (n = 1812) into two distinct prognostic subgroups. Of the 82% low-risk group, only 3.8% developed events. Contrasting this, 14% of the high-risk patients developed events during follow-up. A strong association was observed between NPx and Oncotype Dx RS (P < 0.0001), where 66% of patients with intermediate risk RS who had subsequent distant metastases also had a high-risk NPx.

CONCLUSION

NPx is a reliable prognostic index in patients with luminal early-stage BC, and in selected patients may be used to guide adjuvant chemotherapy recommendations.

Personalized treatment using predictive biomarkers in solid organ malignancies: A review

In recent years, the influence of specific biomarkers in the diagnosis and prognosis of solid organ malignancies has been increasingly prominent. The relevance of the use of predictive biomarkers, which predict cancer response to specific forms of treatment provided, is playing a more significant role than ever before, as it affects diagnosis and initiation of treatment, monitoring for efficacy and side effects of treatment, and adjustment in treatment regimen in the long term. In the current review, we explored the use of predictive biomarkers in the treatment of solid organ malignancies, including common cancers such as colorectal cancer, breast cancer, lung cancer, prostate cancer, and cancers associated with high mortalities, such as pancreatic cancer, liver cancer, kidney cancer and cancers of the central nervous system. We additionally analyzed the goals and types of personalized treatment using predictive biomarkers, and the management of various types of solid organ malignancies using predictive biomarkers and their relative efficacies so far in the clinical settings.

Quantitative comparison of immunohistochemical HER2-low detection in an interlaboratory study

AIMS

Recently, human epidermal growth factor 2 (HER2)-low (i.e. HER2 score 1+ or 2+ without amplification) breast cancer patients became eligible for trastuzumab-deruxtecan treatment. To improve assay standardisation and detection of HER2-low in a quantitative manner, we conducted an external quality assessment-like study in the Netherlands. Dynamic range cell lines and immunohistochemistry (IHC) calibrators were used to quantify HER2 expression and to assess interlaboratory variability.

METHODS AND RESULTS

Three blank slides with a dynamic range cell line and an IHC calibrator were stained with routine HER2 assays by 35 laboratories. Four different antibody clones were used: 19 (54.3%) 4B5, six (17.1%) A0485, five (14.3%) DG44 (HercepTest) and five (14.3%) SP3. Laboratories used two different detection kits for 4B5 assays: 14 (73.7%) ultraView and five (26.3%) OptiView. Variability of HER2 expression in cell lines, measured with artificial intelligence software, was median (min-max) = negative core 0.5% (0.0-57.0), 1+ core 4.3% (1.6-71.3), 2+ core 42.8% (30.4-92.6) and 3+ core 96.2% (91.8-98.8). The calibrators DG44 and 4B5 OptiView had the highest analytical sensitivity, closely followed by 4B5 ultraView. SP3 was the least sensitive. Calibrators of A0485 assays were not analysable due to background staining.

CONCLUSIONS

As assays were validated for detecting HER2-amplified tumours, not all assays and antibodies proved suitable for HER2-low detection. Some tests showed distinct expression in the negative cell line. Dynamic range cell line controls and quantitative analysis using calibrators demonstrated more interlaboratory variability than commonly appreciated. Revalidation of HER2 tests by laboratories is needed to ensure clinical applicable HER2-low assays.

DNA Damage Response in Early Breast Cancer: A Phase III Cohort in the Phobos Study

We assessed the impact of DNA damage response and repair (DDR) biomarker expressions in 222 node-positive early breast cancer (BC) patients from a previous Phase III GOIM 9902 trial of adjuvant taxanes. At a median follow-up of 64 months, the original study showed no disease-free survival (DFS) or overall survival (OS) differences with the addition of docetaxel (D) to epirubicine-cyclophosphamide (EC). Immunohistochemistry was employed to assess the expression of DDR phosphoproteins (pATM, pATR, pCHK1, γH2AX, pRPA32, and pWEE1) in tumor tissue, and their association with clinical outcomes was evaluated through the Cox elastic net model. Over an extended follow-up of 234 months, we confirmed no significant differences in DFS or OS between patients treated with EC and those receiving D → EC. A DDR risk score, inversely driven by ATM and ATR expression, emerged as an independent prognostic factor for both DFS (HR = 0.41, p < 0.0001) and OS (HR = 0.61, p = 0.046). Further validation in a public adjuvant BC cohort was possible only for ATM, confirming its protective role. Overall, our findings confirm the potential role of the DDR pathway in BC prognostication and in shaping treatment strategies advocating for an integrated approach, combining molecular markers with clinical-pathological factors.

Patient characteristics associated with delayed time to adjuvant chemotherapy among women treated for stage I-IIIA breast cancer

For patients with breast cancer, delays in chemotherapy initiation have been adversely associated with recurrence and survival. We evaluated patient-level factors associated with delayed chemotherapy initiation, from both diagnosis and surgery, in a community-based cohort of women with early-stage breast cancer. For the Optimal Breast Cancer Chemotherapy Dosing study, we identified a cohort of 34,109 women diagnosed with stage I-IIIA breast cancer at two U.S. integrated healthcare delivery systems between 2004 and 2019. We used logistic regression to calculate odds ratios (OR) and 95% confidence intervals (CI) to identify patient factors associated with delays in chemotherapy initiation after diagnosis (≥90 days) and surgery (≥60 days). Among 10,968 women receiving adjuvant chemotherapy, 21.1% experienced delays in chemotherapy initiation after diagnosis and 21.3% after surgery. Older age, non-Hispanic Black and Hispanic race and ethnicity, and ER+ and/or PR+ disease were associated with increased likelihood of delays to chemotherapy initiation after diagnosis and surgery. People diagnosed in 2012-2019 (vs. 2005-2011), with a higher grade and larger tumor size were less likely to experience delays. Other factors were associated with a higher likelihood of delays specifically from diagnosis (earlier stage, mastectomy vs. breast-conserving surgery), or surgery (higher comorbidity, increased nodal number). Women diagnosed with breast cancer who were at highest risk of progression and recurrence were less likely to experience delays in chemotherapy initiation after diagnosis and surgery. Understanding reasons for chemotherapy delays beyond patient factors may be potentially important to reduce risk of breast cancer recurrence and progression.

Discovery of Alkyl Triphenylphosphonium Pinostrobin Derivatives as Potent Anti-Breast Cancer Agents

Pinostrobin demonstrated anticancer properties, but its hydrophobic feature led to a reduction in bioavailability. The mitochondria-targeted approach successfully synthesized eight new alkyl triphenylphosphonium pinostrobin derivatives (1-8) with good yield in this study. Seven compounds (1-3, 5-8) showed greater cytotoxic potency against the human MCF-7 breast cancer cell line than pinostrobin. Molecular docking studies were performed with two important targets in hormone-dependent anticancer strategies, estrogen receptor α (ERα) ligand binding domains, 3ERT (antagonist recognition and antiproliferative function), and 1GWR (agonist recognition and pro-proliferative function). In addition, the MD simulation study of the two most potent compounds (2 and 3) complexed with both ERα forms suggested that compounds 2 and 3 could serve as favourable antagonists. Furthermore, the in silico ADMET prediction indicated that compounds 2 and 3 could be potential drug candidates.

Low progesterone receptor levels in high-grade DCIS correlate with HER2 upregulation and the presence of invasive components

Objective

In this study, we investigated pivotal molecular markers in human high-grade breast ductal carcinoma in situ (DCIS). Expression status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth receptor 2 (HER2) was measured among various subtypes (Luminal (Lum) A, LumB HER2-, LumB HER2+, HER2-enriched and triple-negative).

Methods

In total, 357 DCIS cases were classified into respective subtypes, according to the 2013 St. Gallen guidelines. Each subtype was categorized into three subcategories: "Pure" (those without an invasive component), "W/invasive" (those with an invasive component), and "All" (the entire group of the given subtype). ER and PR expression were registered as intervals. Equivocal HER2 immunohistochemistry (IHC) cases (2+) were further investigated using dual-color in situ hybridization.

Results

The majority of patients (71%) were over the age of 50. We discovered no significant differences in the proportion of age between the "Pure" and "W/invasive" groups. There was no significant difference in ER/PR expression between "Pure" luminal subtypes of DCIS and "W/invasive" cases. We compared the HER2 IHC scores of "0", "1+", and "2+" among LumA and LumB HER2 subtypes and identified no statistically significant differences between "Pure" and "W/invasive" (p = 0.603). ER and PR expression ≥ 50% cutoff value was present in > 90% of all LumA cases. The incidences of cases with ER expression at cutoff values of < 10% and ≥ 50% in LumA were significantly different compared to other luminal subtypes (p < 0.0001). The proportion of cases with PR expression < 20% showed significant differences in the various luminal subtypes. In luminal B subtypes, low PR expression (< 20%) was significantly associated with both strong HER2 expression (3+) and the presence of an invasive component (p = 0.0001 and p = 0.0365, respectively).

Conclusions

ER and PR expression at ≥ 50% cutoff values were found in more than 90% of LumA cases. Samples with ER < 10% and ≥ 50% in LumA were significantly different compared to other luminal subtypes (p < 0.0001). Low PR expression in high-grade DCIS was strongly associated with HER2 overexpression (3+) and an invasive component (p = 0.0001 and p = 0.0365, respectively).

Searching for the "Holy Grail" of breast cancer recurrence risk: a narrative review of the hunt for a better biomarker and the promise of circulating tumor DNA (ctDNA)

BACKGROUND

This paper is a narrative review of a major clinical challenge at the heart of breast cancer care: determining which patients are at risk of recurrence, which require systemic therapy, and which remain at risk in the survivorship phase of care despite initial therapy.

METHODS

We review the literature on prognostic and predictive biomarkers in breast cancer with a focus on detection of minimal residual disease.

RESULTS

While we have many tools to estimate and refine risk that are used to individualize local and systemic therapy, we know that we continue to over treat many patients and undertreat others. Many patients also experience what is, at least in hindsight, needless fear of recurrence. In this review, we frame this dilemma for the practicing breast oncologist and discuss the search for what we term the "holy grail" of breast cancer evaluation: the ideal biomarker of residual distant disease. We review the history of attempts to address this problem and the up-to-date science on biomarkers, circulating tumor cells and circulating tumor DNA (ctDNA).

CONCLUSION

This review suggests that the emerging promise of ctDNA may help resolve a crticical dilemma at the heart of breast cancer care, and improve prognostication, treatment selection, and outcomes for patients with breast cancer.

Revealing prognostic insights of programmed cell death (PCD)-associated genes in advanced non-small cell lung cancer

The management of patients with advanced non-small cell lung cancer (NSCLC) presents significant challenges due to cancer cells' intricate and heterogeneous nature. Programmed cell death (PCD) pathways are crucial in diverse biological processes. Nevertheless, the prognostic significance of cell death in NSCLC remains incompletely understood. Our study aims to investigate the prognostic importance of PCD genes and their ability to precisely stratify and evaluate the survival outcomes of patients with advanced NSCLC. We employed Weighted Gene Co-expression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO), univariate and multivariate Cox regression analyses for prognostic gene screening. Ultimately, we identified seven PCD-related genes to establish the PCD-related risk score for the advanced NSCLC model (PRAN), effectively stratifying overall survival (OS) in patients with advanced NSCLC. Multivariate Cox regression analysis revealed that the PRAN was the independent prognostic factor than clinical baseline factors. It was positively related to specific metabolic pathways, including hexosamine biosynthesis pathways, which play crucial roles in reprogramming cancer cell metabolism. Furthermore, drug prediction for different PRAN risk groups identified several sensitive drugs explicitly targeting the cell death pathway. Molecular docking analysis suggested the potential therapeutic efficacy of navitoclax in NSCLC, as it demonstrated strong binding with the amino acid residues of C-C motif chemokine ligand 14 (CCL14), carboxypeptidase A3 (CPA3), and C-X3-C motif chemokine receptor 1 (CX3CR1) proteins. The PRAN provides a robust personalized treatment and survival assessment tool in advanced NSCLC patients. Furthermore, identifying sensitive drugs for distinct PRAN risk groups holds promise for advancing targeted therapies in NSCLC.

Red Wine as an Aromatase Inhibitor: A Narrative Review

As estrogen-dependent breast cancer is more affected by the local production of estrogen via aromatase than serum estrogen, aromatase inhibitors for treating breast carcinomas in postmenopausal women have been developed. As the aromatase enzyme converts endogenous androgen to estrogenic compounds, its blockade lowers the in situ production of estrogen, demonstrated to encourage tumor proliferation. Red wine, but not white wine, may have aromatase-inhibiting properties that are being elucidated, although the exact mechanisms of action are not known. Polyphenols, tannins, and resveratrol have all been implicated as aromatase blockers, and there may also be synergistic interplay among selected constituents. The role of red wine would be in chemoprevention, the use of natural or synthetic substances to retard, block, or reverse cancer. One gene encodes aromatase, so aromatase inhibition would stop endogenous estrogen production. The role of aromatase inhibition in breast cancer in premenopausal women is not clear. While animal studies have demonstrated that red wine contains constituents that could block aromatase in vivo, the benefits also exist with nonalcoholic grape seed extract. Further investigation is needed but there are challenges in designing appropriate clinical trials for a substance as variable as red wine. While there is insufficient evidence to advocate for red wine as an aromatase inhibitor, there is sufficient evidence to warrant further investigation.

Circulating tumor DNA: from discovery to clinical application in breast cancer

Breast cancer (BC) stands out as the cancer with the highest incidence of morbidity and mortality among women worldwide, and its incidence rate is currently trending upwards. Improving the efficiency of breast cancer diagnosis and treatment is crucial, as it can effectively reduce the disease burden. Circulating tumor DNA (ctDNA) originates from the release of tumor cells and plays a pivotal role in the occurrence, development, and metastasis of breast cancer. In recent years, the widespread application of high-throughput analytical technology has made ctDNA a promising biomarker for early cancer detection, monitoring minimal residual disease, early recurrence monitoring, and predicting treatment outcomes. ctDNA-based approaches can effectively compensate for the shortcomings of traditional screening and monitoring methods, which fail to provide real-time information and prospective guidance for breast cancer diagnosis and treatment. This review summarizes the applications of ctDNA in various aspects of breast cancer, including screening, diagnosis, prognosis, treatment, and follow-up. It highlights the current research status in this field and emphasizes the potential for future large-scale clinical applications of ctDNA-based approaches.

Rapid differentiation of estrogen receptor status in patient biopsy breast cancer aspirates with an optical nanosensor

Breast cancer is a substantial source of morbidity and mortality worldwide. It is particularly more difficult to treat at later stages, and treatment regimens depend heavily on both staging and the molecular subtype of the tumor. However, both detection and molecular analyses rely on standard imaging and histological method, which are costly, time-consuming, and lack necessary sensitivity/specificity. The estrogen receptor (ER) is, along with the progesterone receptor (PR) and human epidermal growth factor (HER-2), among the primary molecular markers which inform treatment. Patients who are negative for all three markers (triple negative breast cancer, TNBC), have fewer treatment options and a poorer prognosis. Therapeutics for ER+ patients are effective at preventing disease progression, though it is necessary to improve the speed of subtyping and distribution of rapid detection methods. In this work, we designed a near-infrared optical nanosensor using single-walled carbon nanotubes (SWCNT) as the transducer and an anti-ERα antibody as the recognition element. The nanosensor was evaluated for its response to recombinant ERα in buffer and serum prior to evaluation with ER- and ER+ immortal cell lines. We then used a minimal volume of just 10 μL from 26 breast cancer biopsy samples which were aspirated to mimic fine needle aspirates. 20 samples were ER+, while 6 were ER-, representing 13 unique patients. We evaluated the potential of the nanosensor by investigating several SWCNT chiralities through direct incubation or fractionation deployment methods. We found that the nanosensor can differentiate ER- from ER+ patient biopsies through a shift in its center wavelength upon sample addition. This was true regardless of which of the three SWCNT chiralities we observed. Receiver operating characteristic area under the curve analyses determined that the strongest classifier with an AUC of 0.94 was the (7,5) chirality after direct incubation and measurement, and without further processing. We anticipate that further testing and development of this nanosensor may push its utility toward field-deployable, rapid ER subtyping with potential for additional molecular marker profiling.

SM, Jacob L, Babu G. Study of Biomarker Discordance between Primary and Recurrent Sites and its Clinical Implications in Metastatic Breast Cancer : A Single Institutional Study from India

S. Shanthala Immunophenotypic discordance of receptors between primary and metastatic sites significantly impacts treatment outcomes. Current international guidelines recommend rebiopsy of accessible metastatic lesions to reassess tissue biomarkers. While existing literature on biomarker changes is conflicting and heterogeneous, similar studies on the Indian cohort of breast cancer patients are lacking. In this context, we aimed to evaluate the frequencies of biomarker changes between biopsies from primary and recurrent sites, and their association with various clinicopathological characteristics, including the type of metastasis and treatment in metastatic breast cancer (MBC) patients. This is an ambispective study performed at a single center. Immunohistochemical (IHC) expression of paired primary and recurrence samples of MBC patients was reviewed for the expression of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor 2 (HER2), and Ki-67. Concordance, loss, and gain of receptors were assessed based on the Allred scores for ER, PR, and HER2. Ki-67 was assessed based on a 14% cutoff. Further, receptor changes were studied in relation to age, menopausal status, morphology, grade, stage, metastatic sites, interval between biopsies, and treatment. At progression, biopsies were obtained from 41.18% of locoregional recurrence and 58.82% of metastatic sites. Despite high discordance of 47% for ER and 68.6% for PR, true receptor conversion was observed in 9.8%, 21.56%, and 5.88% for ER, PR, and HER2, respectively. There was a significant correlation between age and ER discordance ( p  = 0.029). Loss in PR significantly correlated with a gain in Ki-67. Of all the metastatic sites, the lung was significantly associated with PR and Ki-67 concordance ( p  = 0.008 and p  = 0.0425, respectively). Discordance of receptors was neither related to the sites of biopsy (local recurrence or metastatic site) nor to the time interval between biopsies, prior chemotherapy, or hormone therapy. In conclusion, metastatic progression of the disease is accompanied by age-dependent discordance of ER. Unparalleled changes in PR in relation to ER suggest that ER-independent pathways may influence PR expression in MBC. Furthermore, the concurrence of PR loss with Ki-67 gain indicates an aggressive phenotype with disease progression. Hence, follow-up testing of samples for receptor expression is beneficial in determining prognosis and guiding therapeutic decisions.

Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma

BACKGROUND

 Imaging biomarkers are quantitative parameters from imaging modalities, which are collected noninvasively, allow conclusions about physiological and pathophysiological processes, and may consist of single (monoparametric) or multiple parameters (bi- or multiparametric).

METHOD

 This review aims to present the state of the art for the quantification of multimodal and multiparametric imaging biomarkers. Here, the use of biomarkers using artificial intelligence will be addressed and the clinical application of imaging biomarkers in breast and prostate cancers will be explained. For the preparation of the review article, an extensive literature search was performed based on Pubmed, Web of Science and Google Scholar. The results were evaluated and discussed for consistency and generality.

RESULTS AND CONCLUSION

 Different imaging biomarkers (multiparametric) are quantified based on the use of complementary imaging modalities (multimodal) from radiology, nuclear medicine, or hybrid imaging. From these techniques, parameters are determined at the morphological (e. g., size), functional (e. g., vascularization or diffusion), metabolic (e. g., glucose metabolism), or molecular (e. g., expression of prostate specific membrane antigen, PSMA) level. The integration and weighting of imaging biomarkers are increasingly being performed with artificial intelligence, using machine learning algorithms. In this way, the clinical application of imaging biomarkers is increasing, as illustrated by the diagnosis of breast and prostate cancers.

KEY POINTS

· Imaging biomarkers are quantitative parameters to detect physiological and pathophysiological processes.. · Imaging biomarkers from multimodality and multiparametric imaging are integrated using artificial intelligence algorithms.. · Quantitative imaging parameters are a fundamental component of diagnostics for all tumor entities, such as for mammary and prostate carcinomas..

CITATION FORMAT

· Bäuerle T, Dietzel M, Pinker K et al. Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma. Fortschr Röntgenstr 2024; 196: 354 - 362.

Traditional Clinicopathological Biomarkers Still Determine Disease-Free and Overall Survival in Invasive Breast Cancer Patients: A Pilot Study

Background: Molecular classification, tumor diameter, Ki67 expression, and brachytherapy administration still act as the most potent potential predictors of breast cancer recurrence and overall survival. Methods: Over the period of 23 months, we included in the study 92 invasive breast cancer (IBrC) patients initially diagnosed at the Clinical Ward of Breast Cancer and Reconstructive Surgery, Oncology Center in Bydgoszcz, Poland. The probability of disease-free survival (DFS) and overall survival (OS) in relation to potential prognostic factors for the patients were determined using a Kaplan-Meier analysis, and univariate and multivariate Cox regression analyses evaluated the predictive factors of IBrC patients. The investigation of the potential prognostic model's accuracy was analyzed using the ROC curve. Results: Patients with tumor size < 2 cm, Ki67 expression < 20%, luminal-A molecular subtype, and extra-dose brachytherapy boost administration displayed the most favorable prognosis according to breast cancer disease-free survival and overall survival. The estimated 5 year probability of DFS and OS rates in women with tumor diameter < 2 cm were 89% and 90%, respectively. In tumor diameter > 2 cm, the estimated 5 year probability of DFS was 73% and OS was 76%. Interestingly, the tumor diameter of 1.6 cm with a specificity of 60.5% and a sensitivity of 75% occurred as the best threshold point to differentiate patients with cancer recurrence from those without cancer progression. Conclusions: Our study provides essential information on the clinicopathological profile and future outcomes of early stage IBrC patients. Furthermore, the tumor diameter cut-off value of 1.6 cm discriminating between disease recurrence and those without disease progression patients represents an innovative direction for further research.

PIK3CA mutation-driven immune signature as a prognostic marker for evaluating the tumor immune microenvironment and therapeutic response in breast cancer

PURPOSE

Gene mutations drive tumor immune microenvironment (TIME) heterogeneity, in turn affecting prognosis and immunotherapy efficacy. PIK3CA is the most frequently mutated gene in breast cancer (BC), yet its relevance to BC prognosis remains controversial. Herein, we sought to determine the impact of PIK3CA mutation-driven immune genes (PDIGs) on BC prognosis in relation to TIME heterogeneity.

METHODS

PIK3CA mutation characteristics were compared and verified between the TCGA-BRCA dataset and a patient cohort from our hospital. PIK3CA mutation-driven differentially expressed genes were identified for consensus clustering and weighted gene co-expression network analysis to select the modules most relevant to the immune subtype. Thereafter, the two were intersected to obtain PDIGs. Univariate Cox, LASSO, and multivariate Cox regression analyses were sequentially performed on PDIGs to obtain a PIK3CA mutation-driven immune signature (PDIS), which was then validated using the Gene Expression Omnibus (GEO) database. Differences in functional enrichment, mutation landscape, immune infiltration, checkpoint gene expression, and drug response were compared between different risk groups.

RESULTS

PIK3CA mutation frequencies in the TCGA and validation cohorts were 34.49% and 40.83%, respectively. PIK3CA mutants were significantly associated with ER, PR, and molecular BC subtypes in our hospital cohort. The PDIS allowed for effective risk stratification and exhibited prognostic power in TCGA and GEO sets. The low-risk patients exhibited greater immune infiltration, higher expression of common immune checkpoint factors, and lower scores for tumor immune dysfunction and exclusion.

CONCLUSION

The PDIS can be used as an effective prognostic model for predicting immunotherapy response to guide clinical decision-making.

Research progress of LSD1-based dual-target agents for cancer therapy

Lysine-specific demethylase 1 (LSD1) is a histone lysine demethylase that is significantly overexpressed or dysregulated in different cancers and plays important roles in cell growth, invasion, migration, immune escape, angiogenesis, gene regulation, and transcription. Therefore, it is a superb target for the discovery of novel antitumor agents. However, because of their innate and acquired resistance and low selectivity, LSD1 inhibitors are associated with limited therapeutic efficacy and high toxicity. Furthermore, LSD1 inhibitors synergistically improve the efficacy of additional antitumor drugs, which encourages numerous medicinal chemists to innovate and develop new-generation LSD1-based dual-target agents. This review discusses the theoretical foundation of the design of LSD1-based dual-target agents and summarizes their possible applications in treating cancers.

Deep Learning-Based Multiparametric MRI Model for Preoperative T-Stage in Rectal Cancer

BACKGROUND

Conventional MRI staging can be challenging in the preoperative assessment of rectal cancer. Deep learning methods based on MRI have shown promise in cancer diagnosis and prognostication. However, the value of deep learning in rectal cancer T-staging is unclear.

PURPOSE

To develop a deep learning model based on preoperative multiparametric MRI for evaluation of rectal cancer and to investigate its potential to improve T-staging accuracy.

STUDY TYPE

Retrospective.

POPULATION

After cross-validation, 260 patients (123 with T-stage T1-2 and 134 with T-stage T3-4) with histopathologically confirmed rectal cancer were randomly divided to the training (N = 208) and test sets (N = 52).

FIELD STRENGTH/SEQUENCE

3.0 T/Dynamic contrast enhanced (DCE), T2-weighted imaging (T2W), and diffusion-weighted imaging (DWI).

ASSESSMENT

The deep learning (DL) model of multiparametric (DCE, T2W, and DWI) convolutional neural network were constructed for evaluating preoperative diagnosis. The pathological findings served as the reference standard for T-stage. For comparison, the single parameter DL-model, a logistic regression model composed of clinical features and subjective assessment of radiologists were used.

STATISTICAL TESTS

The receiver operating characteristic curve (ROC) was used to evaluate the models, the Fleiss' kappa for the intercorrelation coefficients, and DeLong test for compare the diagnostic performance of ROCs. P-values less than 0.05 were considered statistically significant.

RESULTS

The Area Under Curve (AUC) of the multiparametric DL-model was 0.854, which was significantly higher than the radiologist's assessment (AUC = 0.678), clinical model (AUC = 0.747), and the single parameter DL-models including T2W-model (AUC = 0.735), DWI-model (AUC = 0.759), and DCE-model (AUC = 0.789).

DATA CONCLUSION

In the evaluation of rectal cancer patients, the proposed multiparametric DL-model outperformed the radiologist's assessment, the clinical model as well as the single parameter models. The multiparametric DL-model has the potential to assist clinicians by providing more reliable and precise preoperative T staging diagnosis.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Neutrophil extracellular trap-associated risk index for predicting outcomes and response to Wnt signaling inhibitors in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a type of breast cancer with poor prognosis, which is prone to distant metastasis and therapy resistance. The presence of neutrophil extracellular traps (NETs) contributes to the progression of breast cancer and is an efficient predictor of TNBC. We obtained the bulk and single-cell RNA sequencing data from public databases. Firstly, we identified five NET-related genes and constructed NET-related subgroups. Then, we constructed a risk index with three pivotal genes based on the differentially expressed genes between subgroups. Patients in the high-risk group had worse prognosis, clinicopathological features, and therapy response than low-risk group. Functional enrichment analysis revealed that the low-risk group was enriched in Wnt signaling pathway, and surprisingly, the drug sensitivity prediction showed that Wnt signaling pathway inhibitors had higher drug sensitivity in the low-risk group. Finally, verification experiments in vitro based on MDA-MB-231 and BT-549 cells showed that tumor cells with low-risk scores had less migration, invasion, and proliferative abilities and high drug sensitivity to Wnt signaling pathway inhibitors. In this study, multi-omics analysis revealed that genes associated with NETs may influence the occurrence, progression, and treatment of TNBC. Moreover, the bioinformatics analysis and cell experiments demonstrated that the risk index could predict the population of TNBC likely to benefit from treatment with Wnt signaling pathway inhibitors.

Advances in the study of marketed antibody-drug Conjugates (ADCs) for the treatment of breast cancer

Breast cancer continues to have a high incidence rate among female malignancies. Despite significant advancements in treatment modalities, the heterogeneous nature of breast cancer and its resistance to various therapeutic approaches pose considerable challenges. Antibody-drug conjugates (ADCs) effectively merge the specificity of antibodies with the cytotoxicity of chemotherapeutic agents, offering a novel strategy for precision treatment of breast cancer. Notably, trastuzumab emtansine (T-DM1) has provided a new therapeutic option for HER2-positive breast cancer patients globally, especially those resistant to conventional treatments. The development of trastuzumab deruxtecan (T-DXd) and sacituzumab govitecan (SG) has further broadened the applicability of ADCs in breast cancer therapy, presenting new hopes for patients with low HER2 expression and triple-negative breast cancer. However, the application of ADCs presents certain challenges. For instance, their treatment may lead to adverse reactions such as interstitial lung disease, thrombocytopenia, and diarrhea. Moreover, prolonged treatment could result in ADCs resistance, complicating the therapeutic process. Economically, the high costs of ADCs might hinder their accessibility in low-income regions. This article reviews the structure, mechanism of action, and clinical trials of commercially available ADCs for breast cancer treatment, with a focus on the clinical trials of the three drugs, aiming to provide insights for clinical applications and future research.

Routine molecular applications and recent advances in breast cancer diagnostics

Cancer stands as one of the most common and lethal diseases, imposing a substantial burden on global mortality rates. Breast cancer is distinct from other forms of cancer in which it is the primary cause of death for women. Early detection of breast cancer can significantly lower the risk of mortality, improving the prognosis for those who are affected. The death rate of breast cancer has been steadily rising, according to epidemiological data, especially since the COVID-19 pandemic. This emphasizes the necessity of sensitive and precise technologies that can be utilized in early breast cancer diagnosis. In this process, biomarkers play a pivotal role by facilitating the early detection and diagnosis of breast cancer. Currently, a wide variety of cancer biomarkers have been identified, improving the accuracy of cancer diagnosis. These biomarkers can be applied in liquid biopsies as well as on solid tissues. In the context of breast cancer, biomarkers are particularly valuable for determining who is predisposed to the disease, predicting prognosis at the time of diagnosis, and selecting the best course of therapy. This review comprehensively explores the recently developed gene-based biomarkers from biofluids that are used in the context of breast cancer, as well as the conventional and cutting-edge techniques that have been employed for breast cancer diagnosis.

Copper Chaperone for Superoxide Dismutase Subtypes as a Prognostic Marker in Luminal B Breast Cancer

Background

Copper chaperone for superoxide dismutase (CCS) is an essential component of the oxidation-reduction system. In breast cancer cells, CCS expression is highly up-regulated, which contributes to cellular proliferation and migration. Breast cancer is a multifaceted disease with different tumor prognoses and responses to clinical treatments, which may be associated with multiple molecular subtypes of CCS.

Methods

The CCS expression patterns in breast cancer were investigated by TNMplot, cBioPortal, and HPA network database. The correlation of CCS expression with clinicopathological parameters was analyzed using the UALCAN database. The Cancer Genome Atlas (TCGA) data set was used to analyze the Clinical characteristics of CCS in luminal B patients. The bc-GenExMiner database was used to analyze the effects of BReast-CAncer susceptibility gene (BRCA)1/2, TP53 mutation status, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (HER) expression on CCS expression. The survival curves and prognostic value of CCS in luminal B breast cancer were performed through Kaplan-Meier curves, univariate and multivariate Cox regression using the PrognoScan, bc-GenExMiner, and Clinical bioinformatics analysis platform.

Results

We found that CCS expression was associated with patient age, race, ER, and PR status. We also discovered that BRCA1/2 mutations had an effect on CCS expression. The luminal B subtype had the highest CCS expression, which was linked to poor survival compared with other subtypes. In addition, Kaplan-Meier curve analysis showed that luminal B patients with high CCS mRNA expression showed a poor survival and the CCS gene is an independent predictor of outcome in patients with luminal B breast cancer by univariate and multivariate Cox regression.

Conclusions

Our findings emphasize the significant expression of CCS in luminal B breast cancer and its potential as an autonomous prognostic determinant for this specific molecular subtype. These findings suggest that CCS holds promise as a prospective marker for the treatment of luminal B breast cancer.

Clinico-Pathological and Prognostic Significance of a Combination of Tumor Biomarkers in Iranian Patients With Breast Cancer

PURPOSE

Breast cancer is one of the most common cancers in the world. It is a multifaceted malignancy with different histopathological and biological features. Molecular biomarkers play an essential role in accurate diagnosis, classification, prognosis, prediction of treatment response, and cancer surveillance. This study investigated the clinico-pathological and prognostic significance of HER3 and ROR1 in breast cancer samples.

METHODS

Tissue microarrays (TMA) were constructed using tissue blocks of 444 Iranian breast cancer patients diagnosed with breast cancer. Overall survival (OS) and disease-free survival (DFS) were assessed after 5 years follow-up. TMA slides were stained with monoclonal antibodies against ROR1, HER3, ER, PR, Ki67, P53, HER2 and CK5/6 using IHC and correlation between the investigated tumor markers and the clinico-pathological parameters of patients were analyzed.

RESULTS

Our results showed a significant correlation between ROR1 and ER, PR, HER3, and CK5/6 expression. Additionally, there was a significant correlation between HER3 and ER, PR, HER2, and Ki67 expression. Ki67 was also correlated with HER2 and P53 expression. HER3 expression was significantly correlated with tumor stage, lymph node metastasis, perineural invasion, and multifocal tumors. Furthermore, ROR1 expression was significantly associated with tumor metastasis, lympho-vascular invasion, and perineural invasion. While HER2-HER3 coexpression was significantly associated with poor OS, HER3-ROR1 coexpression was associated with lymph node invasion, lymph node metastasis, and distant metastasis.

CONCLUSION

ROR1 and HER3 displayed significant association with different clinic-pathological features and in addition to the other tumor biomarkers could be considered as diagnostic and prognostic biomarkers in breast cancer patients.

Proteomic-based electrochemical non-invasive biosensor for early breast cancer diagnosis

Breast cancer is the second highest cause of cancer-related mortality in women worldwide and in the United States, accounting for around 571,000 deaths per year. Early detection of breast cancer increases treatment results and the possibility of a cure. While existing diagnostic modalities such as mammography, ultrasound, and biopsy exist, some are prohibitively expensive, uncomfortable, time-consuming, and have limited sensitivity, necessitating the development of a cost-effective, rapid, and highly sensitive approach such as an electrochemical biosensor. Our research focuses on detecting breast cancer patients using the ECM1 biomarker, which has higher expression in synthetic urine. Our study has two primary objectives: (i) Diverse ECM1 protein concentrations are measured using electrochemical impedance spectroscopy and ELISA. Establishing a standard curve for the electrochemical biosensor by calibrating ECM-1 protein levels using electrochemical impedance spectroscopy. (ii) Validation of the effectiveness of the electrochemical biosensor. This aim entails testing the unknown concentration of ECM1 in the synthetic urine to ensure the efficiency of the biosensor to detect the biomarker in the early stages. The results show that the synthetic urine solution's ECM-1 detection range ranges from 1 pg/ml to 500 ng/ml. This shows that by detecting changes in ECM-1 protein levels in patient urine, the electrochemical biosensor can consistently diagnose breast cancer in its early stages or during increasing recurrence. Our findings highlight the electrochemical biosensor's efficacy in detecting early-stage breast cancer biomarkers (ECM-1) in patient urine. Further studies will be conducted with patient samples and develop handheld hardware for patient usage.

Integrated single-cell and bulk RNA sequencing analysis identifies a neoadjuvant chemotherapy-related gene signature for predicting survival and therapy in breast cancer

Neoadjuvant chemotherapy (NAC) is a well-established treatment modality for locally advanced breast cancer (BC). However, it can also result in severe toxicities while controlling tumors. Therefore, reliable predictive biomarkers are urgently needed to objectively and accurately predict NAC response. In this study, we integrated single-cell and bulk RNA-seq data to identify nine genes associated with the prognostic response to NAC: NDRG1, CXCL14, HOXB2, NAT1, EVL, FBP1, MAGED2, AR and CIRBP. Furthermore, we constructed a prognostic risk model specifically linked to NAC. The clinical independence and generalizability of this model were effectively demonstrated. Additionally, we explore the underlying cancer hallmarks and microenvironment features of this NAC response-related risk score, and further assess the potential impact of risk score on drug response. In summary, our study constructed and validated a nine-gene signature associated with NAC prognosis, which was accomplished through the integration of single-cell and bulk RNA data. The results of our study are of crucial significance in the prediction of the efficacy of NAC in BC, and may have implications for the clinical management of this disease.

Genetic Testing Enhances the Precision Diagnosis and Treatment of Breast Cancer

The contemporary comprehension of breast cancer has progressed to the molecular level. As a heterogeneous malignancy, conventional pathological diagnosis and histological classification could no longer meet the needs of precisely managing breast cancer. Genetic testing based on gene expression profiles and gene mutations has emerged and substantially contributed to the precise diagnosis and treatment of breast cancer. Multigene assays (MGAs) are explored for early-stage breast cancer patients, aiding the selection of adjuvant therapy and predicting prognosis. For metastatic breast cancer patients, testing specific genes indicates potentially effective antitumor agents. In this review, genetic testing in early-stage and metastatic breast cancer is summarized, as well as the advantages and challenges of genetic testing in breast cancer.

Unraveling the Potential of miRNAs from CSCs as an Emerging Clinical Tool for Breast Cancer Diagnosis and Prognosis

Breast cancer (BC) is the most diagnosed cancer in women and the second most common cancer globally. Significant advances in BC research have led to improved early detection and effective therapies. One of the key challenges in BC is the presence of BC stem cells (BCSCs). This small subpopulation within the tumor possesses unique characteristics, including tumor-initiating capabilities, contributes to treatment resistance, and plays a role in cancer recurrence and metastasis. In recent years, microRNAs (miRNAs) have emerged as potential regulators of BCSCs, which can modulate gene expression and influence cellular processes like BCSCs' self-renewal, differentiation, and tumor-promoting pathways. Understanding the miRNA signatures of BCSCs holds great promise for improving BC diagnosis and prognosis. By targeting BCSCs and their associated miRNAs, researchers aim to develop more effective and personalized treatment strategies that may offer better outcomes for BC patients, minimizing tumor recurrence and metastasis. In conclusion, the investigation of miRNAs as regulators of BCSCs opens new directions for advancing BC research through the use of bioinformatics and the development of innovative therapeutic approaches. This review summarizes the most recent and innovative studies and clinical trials on the role of BCSCs miRNAs as potential tools for early diagnosis, prognosis, and resistance.

Current Status of Cancer Genomics and Imaging Phenotypes: What Radiologists Need to Know

Ongoing discoveries in cancer genomics and epigenomics have revolutionized clinical oncology and precision health care. This knowledge provides unprecedented insights into tumor biology and heterogeneity within a single tumor, among primary and metastatic lesions, and among patients with the same histologic type of cancer. Large-scale genomic sequencing studies also sparked the development of new tumor classifications, biomarkers, and targeted therapies. Because of the central role of imaging in cancer diagnosis and therapy, radiologists need to be familiar with the basic concepts of genomics, which are now becoming the new norm in oncologic clinical practice. By incorporating these concepts into clinical practice, radiologists can make their imaging interpretations more meaningful and specific, facilitate multidisciplinary clinical dialogue and interventions, and provide better patient-centric care. This review article highlights basic concepts of genomics and epigenomics, reviews the most common genetic alterations in cancer, and discusses the implications of these concepts on imaging by organ system in a case-based manner. This information will help stimulate new innovations in imaging research, accelerate the development and validation of new imaging biomarkers, and motivate efforts to bring new molecular and functional imaging methods to clinical radiology. Keywords: Oncology, Cancer Genomics, Epignomics, Radiogenomics, Imaging Markers Supplemental material is available for this article. © RSNA, 2023.

The Role of Nodes and Nodal Assessment in Diagnosis, Treatment and Prediction in ER+, Node-Positive Breast Cancer

The majority of breast cancers are oestrogen receptor-positive (ER+). In ER+ cancers, oestrogen acts as a disease driver, so these tumours are likely to be susceptible to endocrine therapy (ET). ET works by blocking the hormone's synthesis or effect. A significant number of patients diagnosed with breast cancer will have the spread of tumour cells into regional lymph nodes either at the time of diagnosis, or as a recurrence some years later. Patients with node-positive disease have a poorer prognosis and can respond less well to ET. The nodal metastases may be genomically similar or, as is becoming more evident, may differ from the primary tumour. However, nodal metastatic disease is often not assessed, and treatment decisions are almost always based on biomarkers evaluated in the primary tumour. This review will summarise the evidence in the field on ER+, node-positive breast cancer, including diagnosis, treatment, prognosis and predictive tools.

A Novel Albumin-Related Nutrition Biomarker Predicts Breast Cancer Prognosis in Neoadjuvant Chemotherapy: A Two-Center Cohort Study

BACKGROUND

Recently, there has been a growing focus on the prognostic significance of nutrition-related biomarkers. We attempted to explore the association between a novel albumin-related nutrition marker called "lymphocyte × albumin (LA)" and disease-free survival (DFS) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC).

METHODS

In total, 711 non-metastatic breast cancer patients who underwent NAC at two medical centers were retrospectively analyzed. We performed least absolute shrinkage and selection operator (LASSO) Cox regression analysis as well as multivariate Cox regression analyses to identify the variables associated with DFS and to establish a predictive nomogram.

RESULTS

The nomogram incorporated four variables based on the multivariate analysis of DFS in the training cohort: LA, ypN stage, ypT stage, and hormone receptor status. In comparison with the traditional TNM staging system, the nomogram demonstrated superior discrimination, calibration ability, and clinical usefulness in both the training set and internal and external validation sets. Furthermore, patients stratified into different risk groups resulted in significant differences in DFS.

CONCLUSIONS

LA is an independent prognostic biomarker, and LA-based prognostic nomogram offers a more precise assessment of DFS for breast cancer patients treated with NAC, potentially serving as a valuable tool for personalized prognostic predictions.

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.

Integrative analyses of RNA-seq and ChIP-seq Reveal MITF as a Target Gene of TFPI-2 in MDA231 Cells

Breast cancer is the most prevalent cancer in female patients worldwide. Tissue factor pathway inhibitor 2 (TFPI-2) is identified as an important tumor suppressor in various cancers. Recent studies have shown that TFPI-2 translocates into the nucleus, where it modulates the transcription of the matrix metalloproteinase-2 (MMP-2) gene. However, its biological role and molecular mechanisms in the progression of breast cancer remain unclear. In this study, we identified 5125 differentially expressed genes (DEGs) from RNA sequencing (RNA-seq) in TFPI-2-overexpressing MDA231 cells compared with control cells. Gene ontology and Kyoto encyclopedia of genes and genomes (KEGG) analysis shown that cell cycle, cell differentiation, proteoglycans in cancer, and pathways associated with cancer were highly enriched in downregulated DEGs. Integration of the RNA-seq and ChIP-sequencing (ChIP-seq) data identified 73 genes directly controlled by TFPI-2 in MDA231 cells. Among them, melanocyte inducing transcription factor (MITF) gene expression was repressed by TFPI-2, which was further verified by a luciferase reporter assay and ChIP-quantitative PCR. Our study provides evidence of a novel role of TFPI-2 in human breast cancer involving targeting of the MITF.

Radiogenomics: a key component of precision cancer medicine

Radiogenomics, focusing on the relationship between genomics and imaging phenotypes, has been widely applied to address tumour heterogeneity and predict immune responsiveness and progression. It is an inevitable consequence of current trends in precision medicine, as radiogenomics costs less than traditional genetic sequencing and provides access to whole-tumour information rather than limited biopsy specimens. By providing voxel-by-voxel genetic information, radiogenomics can allow tailored therapy targeting a complete, heterogeneous tumour or set of tumours. In addition to quantifying lesion characteristics, radiogenomics can also be used to distinguish benign from malignant entities, as well as patient characteristics, to better stratify patients according to disease risk, thereby enabling more precise imaging and screening. Here, we have characterised the radiogenomic application in precision medicine using a multi-omic approach. we outline the main applications of radiogenomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalised medicine. Finally, we discuss the challenges in the field of radiogenomics and the scope and clinical applicability of these methods.

Prognostic Factors for Cancer-Specific Survival and Disease-Free Interval of Dogs with Mammary Carcinomas

Canine mammary tumors (CMTs) are the most diagnosed neoplasms in dogs; however, there are few studies analyzing the influence of epidemiological, clinicopathological, and histopathological data on cancer-specific survival (CSS), disease-free interval (DFI), and overall survival (OS) in a large cohort. To contribute to the understanding of the biological behavior of this neoplasm, 385 cases were analyzed, 89% malignant, 4% benign, and 7% non-neoplastic lesions. Among the dogs diagnosed with malignant neoplasms, 86% had early clinical stages (I-III), while 14% had regional or distant metastasis at the time of diagnosis. Carcinoma in a mixed tumor was the most frequent histological type with 44% of the cases and had the best prognosis. Analyzed factors such as the presence of pseudocyesis, previous history of the disease, advanced clinical stage (IV-V), and presence of ulceration obtained significant results for CSS, DFI, and OS through univariate analysis and had a negative impact on the survival of the patients. Multivariate analysis showed that histological grading and age proved to be the best independent parameters for the prognostic evaluation of CSS and DFI in this study. These factors were also significant in the overall survival analysis. Therefore, these parameters should be considered valuable risk and prognostic factors for CMTs.

Extensive review on breast cancer its etiology, progression, prognostic markers, and treatment

As the most frequent and vulnerable malignancy among women, breast cancer universally manifests a formidable healthcare challenge. From a biological and molecular perspective, it is a heterogenous disease and is stratified based on the etiological factors driving breast carcinogenesis. Notably, genetic predispositions and epigenetic impacts often constitute the heterogeneity of this disease. Typically, breast cancer is classified intrinsically into histological subtypes in clinical landscapes. These stratifications empower physicians to tailor precise treatments among the spectrum of breast cancer therapeutics. In this pursuit, numerous prognostic algorithms are extensively characterized, drastically changing how breast cancer is portrayed. Therefore, it is a basic requisite to comprehend the multidisciplinary rationales of breast cancer to assist the evolution of novel therapeutic strategies. This review aims at highlighting the molecular and genetic grounds of cancer additionally with therapeutic and phytotherapeutic context. Substantially, it also renders researchers with an insight into the breast cancer cell lines as a model paradigm for breast cancer research interventions.

Pharmacogenomics: Driving Personalized Medicine

Personalized medicine tailors therapies, disease prevention, and health maintenance to the individual, with pharmacogenomics serving as a key tool to improve outcomes and prevent adverse effects. Advances in genomics have transformed pharmacogenetics, traditionally focused on single gene-drug pairs, into pharmacogenomics, encompassing all "-omics" fields (e.g., proteomics, transcriptomics, metabolomics, and metagenomics). This review summarizes basic genomics principles relevant to translation into therapies, assessing pharmacogenomics' central role in converging diverse elements of personalized medicine. We discuss genetic variations in pharmacogenes (drug-metabolizing enzymes, drug transporters, and receptors), their clinical relevance as biomarkers, and the legacy of decades of research in pharmacogenetics. All types of therapies, including proteins, nucleic acids, viruses, cells, genes, and irradiation, can benefit from genomics, expanding the role of pharmacogenomics across medicine. Food and Drug Administration approvals of personalized therapeutics involving biomarkers increase rapidly, demonstrating the growing impact of pharmacogenomics. A beacon for all therapeutic approaches, molecularly targeted cancer therapies highlight trends in drug discovery and clinical applications. To account for human complexity, multicomponent biomarker panels encompassing genetic, personal, and environmental factors can guide diagnosis and therapies, increasingly involving artificial intelligence to cope with extreme data complexities. However, clinical application encounters substantial hurdles, such as unknown validity across ethnic groups, underlying bias in health care, and real-world validation. This review address the underlying science and technologies germane to pharmacogenomics and personalized medicine, integrated with economic, ethical, and regulatory issues, providing insights into the current status and future direction of health care. SIGNIFICANCE STATEMENT: Personalized medicine aims to optimize health care for the individual patients with use of predictive biomarkers to improve outcomes and prevent adverse effects. Pharmacogenomics drives biomarker discovery and guides the development of targeted therapeutics. This review addresses basic principles and current trends in pharmacogenomics, with large-scale data repositories accelerating medical advances. The impact of pharmacogenomics is discussed, along with hurdles impeding broad clinical implementation, in the context of clinical care, ethics, economics, and regulatory affairs.

Biomarkers in Breast Cancer: An Old Story with a New End

Breast cancer is the second most frequent cancer in the world. It is a heterogeneous disease and the leading cause of cancer mortality in women. Advances in molecular technologies allowed for the identification of new and more specifics biomarkers for breast cancer diagnosis, prognosis, and risk prediction, enabling personalized treatments, improving therapy, and preventing overtreatment, undertreatment, and incorrect treatment. Several breast cancer biomarkers have been identified and, along with traditional biomarkers, they can assist physicians throughout treatment plan and increase therapy success. Despite the need of more data to improve specificity and determine the real clinical utility of some biomarkers, others are already established and can be used as a guide to make treatment decisions. In this review, we summarize the available traditional, novel, and potential biomarkers while also including gene expression profiles, breast cancer single-cell and polyploid giant cancer cells. We hope to help physicians understand tumor specific characteristics and support decision-making in patient-personalized clinical management, consequently improving treatment outcome.

Integrating Proteomics and Lipidomics for Evaluating the Risk of Breast Cancer Progression: A Pilot Study

Metastasis is a serious and often life-threatening condition, representing the leading cause of death among women with breast cancer (BC). Although the current clinical classification of BC is well-established, the addition of minimally invasive laboratory tests based on peripheral blood biomarkers that reflect pathological changes in the body is of utmost importance. In the current study, the serum proteome and lipidome profiles for 50 BC patients with (25) and without (25) metastasis were studied. Targeted proteomic analysis for concertation measurements of 125 proteins in the serum was performed via liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM MS) using the BAK 125 kit (MRM Proteomics Inc., Victoria, BC, Canada). Untargeted label-free lipidomic analysis was performed using liquid chromatography coupled to tandem mass-spectrometry (LC-MS/MS), in both positive and negative ion modes. Finally, 87 serum proteins and 295 lipids were quantified and showed a moderate correlation with tumor grade, histological and biological subtypes, and the number of lymph node metastases. Two highly accurate classifiers that enabled distinguishing between metastatic and non-metastatic BC were developed based on proteomic (accuracy 90%) and lipidomic (accuracy 80%) features. The best classifier (91% sensitivity, 89% specificity, AUC = 0.92) for BC metastasis diagnostics was based on logistic regression and the serum levels of 11 proteins: alpha-2-macroglobulin, coagulation factor XII, adiponectin, leucine-rich alpha-2-glycoprotein, alpha-2-HS-glycoprotein, Ig mu chain C region, apolipoprotein C-IV, carbonic anhydrase 1, apolipoprotein A-II, apolipoprotein C-II and alpha-1-acid glycoprotein 1.

Identification of Novel Diagnostic and Prognostic Gene Signature Biomarkers for Breast Cancer Using Artificial Intelligence and Machine Learning Assisted Transcriptomics Analysis

BACKGROUND

Breast cancer (BC) is one of the most common female cancers. Clinical and histopathological information is collectively used for diagnosis, but is often not precise. We applied machine learning (ML) methods to identify the valuable gene signature model based on differentially expressed genes (DEGs) for BC diagnosis and prognosis.

METHODS

A cohort of 701 samples from 11 GEO BC microarray datasets was used for the identification of significant DEGs. Seven ML methods, including RFECV-LR, RFECV-SVM, LR-L1, SVC-L1, RF, and Extra-Trees were applied for gene reduction and the construction of a diagnostic model for cancer classification. Kaplan-Meier survival analysis was performed for prognostic signature construction. The potential biomarkers were confirmed via qRT-PCR and validated by another set of ML methods including GBDT, XGBoost, AdaBoost, KNN, and MLP.

RESULTS

We identified 355 DEGs and predicted BC-associated pathways, including kinetochore metaphase signaling, PTEN, senescence, and phagosome-formation pathways. A hub of 28 DEGs and a novel diagnostic nine-gene signature (COL10A, S100P, ADAMTS5, WISP1, COMP, CXCL10, LYVE1, COL11A1, and INHBA) were identified using stringent filter conditions. Similarly, a novel prognostic model consisting of eight-gene signatures (CCNE2, NUSAP1, TPX2, S100P, ITM2A, LIFR, TNXA, and ZBTB16) was also identified using disease-free survival and overall survival analysis. Gene signatures were validated by another set of ML methods. Finally, qRT-PCR results confirmed the expression of the identified gene signatures in BC.

CONCLUSION

The ML approach helped construct novel diagnostic and prognostic models based on the expression profiling of BC. The identified nine-gene signature and eight-gene signatures showed excellent potential in BC diagnosis and prognosis, respectively.

Calcium Homeostasis in the Development of Resistant Breast Tumors

Cancer is one of the main health problems worldwide. Only in 2020, this disease caused more than 19 million new cases and almost 10 million deaths, with breast cancer being the most diagnosed worldwide. Today, despite recent advances in breast cancer treatment, a significant percentage of patients will either not respond to therapy or will eventually experience lethal progressive disease. Recent studies highlighted the involvement of calcium in the proliferation or evasion of apoptosis in breast carcinoma cells. In this review, we provide an overview of intracellular calcium signaling and breast cancer biology. We also discuss the existing knowledge on how altered calcium homeostasis is implicated in breast cancer development, highlighting the potential utility of Ca²⁺ as a predictive and prognostic biomarker, as well as its potential for the development of new pharmacological treatments to treat the disease.

Spatial Proteomics for the Molecular Characterization of Breast Cancer

Breast cancer (BC) is a major global health issue, affecting a significant proportion of the female population and contributing to high rates of mortality. One of the primary challenges in the treatment of BC is the disease's heterogeneity, which can lead to ineffective therapies and poor patient outcomes. Spatial proteomics, which involves the study of protein localization within cells, offers a promising approach for understanding the biological processes that contribute to cellular heterogeneity within BC tissue. To fully leverage the potential of spatial proteomics, it is critical to identify early diagnostic biomarkers and therapeutic targets, and to understand protein expression levels and modifications. The subcellular localization of proteins is a key factor in their physiological function, making the study of subcellular localization a major challenge in cell biology. Achieving high resolution at the cellular and subcellular level is essential for obtaining an accurate spatial distribution of proteins, which in turn can enable the application of proteomics in clinical research. In this review, we present a comparison of current methods of spatial proteomics in BC, including untargeted and targeted strategies. Untargeted strategies enable the detection and analysis of proteins and peptides without a predetermined molecular focus, whereas targeted strategies allow the investigation of a predefined set of proteins or peptides of interest, overcoming the limitations associated with the stochastic nature of untargeted proteomics. By directly comparing these methods, we aim to provide insights into their strengths and limitations and their potential applications in BC research.

Protective Effect of HER2 Gene Polymorphism rs24537331 in the Outcome of Canine Mammary Tumors

The role of HER2 in canine mammary tumors is not completely elucidated, and the contradictory results published so far may be, in part, explained by the genetic variability recognized in the canine HER2 gene. Single nucleotide polymorphism (SNPs) in HER2 were recently associated with less aggressive canine mammary tumor histotypes. This study assesses the relationship between SNPs rs24537329 and rs24537331 in canine HER2 gene and clinicopathological characteristics and outcome of mammary tumors in a group of 206 female dogs. Allelic variants were observed in 69.8% and 52.7% of the dogs for SNP rs24537329 and rs24537331, respectively. Our results demonstrated that SNP rs24537331 was associated with decreased tumoral necrosis (HR: 3.09; p = 0.012) and with longer disease-specific overall survival (HR: 2.59; p = 0.013). However, no statistically significant associations were found between SNP rs24537329 and the tumors' clinicopathological characteristics or survival. Our data suggest that SNP rs24537331 may have a protective effect in canine mammary tumors, allowing the identification of a subgroup of animals prone to develop less aggressive forms of the disease. This study emphasizes the importance of the genetic tests associated with clinical images and histological examinations when assessing CMT outcomes.

Nontoxic Fluorescent Nanoprobes for Multiplexed Detection and 3D Imaging of Tumor Markers in Breast Cancer

Multiplexed fluorescent immunohistochemical analysis of breast cancer (BC) markers and high-resolution 3D immunofluorescence imaging of the tumor and its microenvironment not only facilitate making the disease prognosis and selecting effective anticancer therapy (including photodynamic therapy), but also provides information on signaling and metabolic mechanisms of carcinogenesis and helps in the search for new therapeutic targets and drugs. The characteristics of imaging nanoprobe efficiency, such as sensitivity, target affinity, depth of tissue penetration, and photostability, are determined by the properties of their components, fluorophores and capture molecules, and by the method of their conjugation. Regarding individual nanoprobe components, fluorescent nanocrystals (NCs) are widely used for optical imaging in vitro and in vivo, and single-domain antibodies (sdAbs) are well established as highly specific capture molecules in diagnostic and therapeutic applications. Moreover, the technologies of obtaining functionally active sdAb–NC conjugates with the highest possible avidity, with all sdAb molecules bound to the NC in a strictly oriented manner, provide 3D-imaging nanoprobes with strong comparative advantages. This review is aimed at highlighting the importance of an integrated approach to BC diagnosis, including the detection of biomarkers of the tumor and its microenvironment, as well as the need for their quantitative profiling and imaging of their mutual location, using advanced approaches to 3D detection in thick tissue sections. The existing approaches to 3D imaging of tumors and their microenvironment using fluorescent NCs are described, and the main comparative advantages and disadvantages of nontoxic fluorescent sdAb–NC conjugates as nanoprobes for multiplexed detection and 3D imaging of BC markers are discussed.

The effect of attendance in the Dutch breast cancer screening program on breast tumor characteristics among migrant women

BACKGROUND

In general, migrant women have a lower breast cancer (BC) incidence rate and higher BC mortality than autochthonous women. Further, migrant women show lower participation in the national BC screening program. To further investigate those aspects, we aimed to determine differences in incidence and tumor characteristics between autochthonous and migrant BC patients in Rotterdam, the Netherlands.

METHODS

We selected women diagnosed with BC in Rotterdam during 2012-2015 from the Netherlands Cancer Registry. Incidence rates were calculated by migrant status (i.e., women with or without migration background). Multivariable analyses revealed adjusted odds ratios (OR) and 95% confidence intervals (CI) on the association between migration status and patient and tumor characteristics, additionally stratified by screening attendance (yes/no).

RESULTS

In total 1372 autochthonous and 450 migrant BC patients were included for analysis. BC incidence was lower among migrants than among autochthonous women. Overall, migrant women were younger at BC diagnosis (53 vs. 64 years, p < 0.001), and had higher risks of positive lymph nodes (OR 1.76, 95% CI 1.33-2.33) and high grade tumors (OR 1.35, 95% CI 1.04-1.75). Especially non-screened migrant women had higher risk of positive nodes (OR 2.73, 95% CI 1.43-5.21). Among the subgroup of screened women, we observed no significant differences between migrant and autochthonous patients.

CONCLUSION

Migrant women have lower BC incidence than autochthonous women, but diagnosis was more often at younger age and with unfavorable tumor characteristics. Attending the screening program strongly reduces the latter. Therefore, promotion of participation in the screening program is recommended.

Overexpression of tripartite motif-containing 47 (TRIM47) confers sensitivity to PARP inhibition via ubiquitylation of BRCA1 in triple negative breast cancer cells

Triple-negative breast cancers (TNBC) frequently harbor defects in DNA double-strand break repair through homologous recombination (HR), such as BRCA1 dysfunction. However, less than 15% of TNBC patients were found to carry BRCA1 mutation, indicating that there are other mechanisms regulating BRCA1-deficient in TNBC. In the current study, we shown that overexpression of TRIM47 correlates with progression and poor prognosis in triple-negative breast cancer. Moreover, we demonstrated that TRIM47 directly interacts with BRCA1 and induces ubiquitin-ligase-mediated proteasome turnover of BRCA1, subsequently leads to a decrease of BRCA1 protein levels in TNBC. Moreover, the downstream gene expression of BRCA1, such as p53, p27, p21 was significantly reduced in the overexpression of TRIM47 cell lines but increased in TRIM47-deleted cells. Functionally, we found that overexpression of TRIM47 in TNBC cells confers an exquisite sensitivity to olaparib, an inhibitor of poly-(ADP-ribose)-polymerase (PARP), but TRIM47 inhibition significantly confers TNBC cells resistance to olaparib both in vitro and in vivo. Furthermore, we showed that overexpression of BRCA1 significant increase the olaparib resistance in TRIM47-overexpression-induced PARP inhibitions sensitivity. Taken together, our results uncover a novel mechanism for BRCA1-deficient in TNBC and targeting TRIM47/BRCA1 axis may be a promising prognostic factor and a valuable therapeutic target for TNBC.

DNA Methylation and Prospects for Predicting the Therapeutic Effect of Neoadjuvant Chemotherapy for Triple-Negative and Luminal B Breast Cancer

Despite advances in the diagnosis and treatment of breast cancer (BC), the main cause of deaths is resistance to existing therapies. An approach to improve the effectiveness of therapy in patients with aggressive BC subtypes is neoadjuvant chemotherapy (NACT). Yet, the response to NACT for aggressive subtypes is less than 65% according to large clinical trials. An obvious fact is the lack of biomarkers predicting the therapeutic effect of NACT. In a search for epigenetic markers, we performed genome-wide differential methylation screening by XmaI-RRBS in cohorts of NACT responders and nonresponders, for triple-negative (TN) and luminal B tumors. The predictive potential of the most discriminative loci was further assessed in independent cohorts by methylation-sensitive restriction enzyme quantitative PCR (MSRE-qPCR), a promising method for the implementation of DNA methylation markers in diagnostic laboratories. The selected most informative individual markers were combined into panels demonstrating cvAUC = 0.83 (TMEM132D and MYO15B markers panel) for TN tumors and cvAUC = 0.76 (TTC34, LTBR and CLEC14A) for luminal B tumors. The combination of methylation markers with clinical features that correlate with NACT effect (clinical stage for TN and lymph node status for luminal B tumors) produces better classifiers, with cvAUC = 0.87 for TN tumors and cvAUC = 0.83 for luminal B tumors. Thus, clinical characteristics predictive of NACT response are independently additive to the epigenetic classifier and in combination improve prediction.

Association of Enolase-1 with Prognosis and Immune Infiltration in Breast Cancer by Clinical Stage

Purpose

Enolase-1 (ENO1) plays a key role in malignancies. Previous studies on the association between ENO1 expression and breast cancer prognosis had yielded inconsistent results. In the present study, we assessed the prognostic effect of ENO1 in breast cancer using Guangzhou Breast Cancer Study (GZBCS) cohort with full consideration of the potential confounders and the modification effects. The results were further validated in the TCGA-BRCA cohort and explained by tumor immunity.

Methods

ENO1 protein expressions were evaluated by immunohistochemistry in tissue microarrays from 961 patients with primary invasive breast cancer. Chi-square tests were used to assess the association of ENO1 levels with the patient's characteristics. Cox regression models were applied to assess the prognostic effects. The TCGA-BRCA cohort was utilized to validate the results and explore the potential mechanisms. The immune infiltration was determined using the CIBERSORT and ssGSEA algorithms; the correlation between ENO1 expression and the abundance of tumor-infiltrating immune cells (TIICs) and scores of immune-related functions was evaluated by Wilcoxon signed-rank tests and Spearman's rank test.

Results

ENO1 protein expression exerted a protective effect on OS in stage I/II patients (HR=0.58, 95% CI: 0.35-0.96) but not in stage III patients (HR=1.42, 95% CI: 0.81-2.49, P _interaction=0.04) in GZBCS; consistent results were obtained at mRNA levels in TCGA cohort. Immune infiltration analyses revealed that ENO1 was positively correlated with multiple antitumor TIICs (including M1 macrophages, B cells, CD8 T cells, T helper 2 cells, and NK cells) only in stage I/II but not stage III patients.

Conclusion

A higher expression of ENO1 was associated with a better prognosis only in early-stage breast cancer, which may be related to the different effects of ENO1 on immune infiltration, suggesting that ENO1 may be a promising target for precision immunotherapy in breast cancer.

Artificial intelligence aids in development of nanomedicines for cancer management

Over the last decade, the nanomedicine has experienced unprecedented development in diagnosis and management of diseases. A number of nanomedicines have been approved in clinical use, which has demonstrated the potential value of clinical transition of nanotechnology-modified medicines from bench to bedside. The application of artificial intelligence (AI) in development of nanotechnology-based products could transform the healthcare sector by realizing acquisition and analysis of large datasets, and tailoring precision nanomedicines for cancer management. AI-enabled nanotechnology could improve the accuracy of molecular profiling and early diagnosis of patients, and optimize the design pipeline of nanomedicines by tuning the properties of nanomedicines, achieving effective drug synergy, and decreasing the nanotoxicity, thereby, enhancing the targetability, personalized dosing and treatment potency of nanomedicines. Herein, the advances in AI-enabled nanomedicines in cancer management are elaborated and their application in diagnosis, monitoring and therapy as well in precision medicine development is discussed.

A review of prognostic and predictive biomarkers in breast cancer

Breast cancer (BC) is a common cancer all over the world that affects women. BC is one of the leading causes of cancer mortality in women, which today has decreased with the advancement of technology and new diagnostic and therapeutic methods. BCs are histologically divided into in situ and invasive carcinoma, and both of them can be divided into ductal and lobular. The main function after the diagnosis of invasive breast cancer is which patient should use chemotherapy, which patient should receive adjuvant therapy, and which should not. If the decision is for adjuvant therapy, the next challenge is to identify the most appropriate treatment or combination of treatments for a particular patient. Addressing the first challenge can be helped by prognostic biomarkers, while addressing the second challenge can be done by predictive biomarkers. Among the molecular markers related to BC, ER, PR, HER2, and the Mib1/Ki-67 proliferation index are the most significant ones and are tightly confirmed in the standard care of all primary, recurrent, and metastatic BC patients. CEA and CA-15-3 antigens are the most valuable markers of serum tumors in BC patients. Determining the series of these markers helps monitor response to the treatment and early detection of recurrence or metastasis. miRNAs have been demonstrated to be intricate in mammary gland growth, proliferation, and formation of BC known to be incriminated in BC biology. By combining established prognostic factors with valid prognostic/predicted biomarkers, we can start the journey to personalized treatment for every recently diagnosed BC patient.