Biological processes associated with breast cancer clinical outcome depend on the molecular subtypes

Risk of locoregional recurrence after breast cancer surgery by molecular subtype-a systematic review and network meta-analysis

BACKGROUND

The prevention of locoregional recurrence (LRR) is crucial in breast cancer, as it translates directly into reduced breast cancer-related death. Breast cancer is subclassified into distinct intrinsic biological subtypes with varying clinical outcomes.

AIMS

To perform a systematic review and network meta-analysis (NMA) to determine the rate of LRR by breast cancer molecular subtype.

METHODS

A NMA was performed as per PRISMA-NMA guidelines. Molecular subtypes were classified by St Gallen expert consensus statement (2013). Analysis was performed using R and Shiny.

RESULTS

Five studies were included including 6731 patients whose molecular subtypes were available. Overall, 47.3% (3182/6731) were Luminal A (LABC: estrogen receptor (ER) + /human epidermal growth factor receptor-2 (HER2) - /progesterone receptor (PR) + or Ki-67 < 20%), 25.5% (1719/6731) were Luminal B (LBBC: ER + /HER2 - /PR - or Ki-67 ≥ 20%), 11.2% (753/6731) were Luminal B-HER2 + (LBBC-HER2: ER + /HER2 +), 6.9% (466/6731) were HER2 + (HER2 ER - /HER2 +), and finally 9.1% (611/6731) were triple-negative breast cancer (TNBC: ER - /HER2 -). The median follow-up was 74.0 months and the overall LRR rate was 4.0% (271/6731). The LRR was 1.7% for LABC (55/3182), 5.1% for LBBC (88/1719), 6.0% for LBBC-HER2 (45/753), 6.0% for HER2 (28/466), and 7.9% for TNBC (48/611). At NMA, patients with TNBC (odds ratio (OR) 3.73, 95% confidence interval (CI) 1.80-7.74), HER2 (OR 3.24, 95% CI 1.50-6.99), LBBC-HER2 (OR 2.38, 95% CI 1.09-5.20), and LBBC (OR 2.20, 95% CI 1.07-4.50) were significantly more likely to develop LRR compared to LABC.

CONCLUSION

TNBC and HER2 subtypes are associated with the highest risk of LRR. Multidisciplinary team discussions should consider these findings to optimize locoregional control following breast cancer surgery.

Tumour immune characterisation of primary triple-negative breast cancer using automated image quantification of immunohistochemistry-stained immune cells

The tumour immune microenvironment (TIME) in breast cancer is acknowledged with an increasing role in treatment response and prognosis. With a growing number of immune markers analysed, digital image analysis may facilitate broader TIME understanding, even in single-plex IHC data. To facilitate analyses of the latter an open-source image analysis pipeline, Tissue microarray MArker Quantification (TMArQ), was developed and applied to single-plex stainings for p53, CD3, CD4, CD8, CD20, CD68, FOXP3, and PD-L1 (SP142 antibody) in a 218-patient triple negative breast cancer (TNBC) cohort with complementary pathology scorings, clinicopathological, whole genome sequencing, and RNA-sequencing data. TMArQ's cell counts for analysed immune markers were on par with results from alternative methods and consistent with both estimates from human pathology review, different quantifications and classifications derived from RNA-sequencing as well as known prognostic patterns of immune response in TNBC. The digital cell counts demonstrated how immune markers are coexpressed in the TIME when considering TNBC molecular subtypes and DNA repair deficiency, and how combination of immune status with DNA repair deficiency status can improve the prognostic stratification in chemotherapy treated patients. These results underscore the value and potential of integrating TIME and specific tumour intrinsic alterations/phenotypes for the molecular understanding of TNBC.

Nomograms for predicting recurrence of HER2-positive breast cancer with different HR status based on ultrasound and clinicopathological characteristics

PURPOSE

This study aimed to identify ultrasound and clinicopathological characteristics related to recurrence in HER2-positive (HER2+) breast cancer, and to develop nomograms for predicting recurrence.

METHODS

In this dual-center study, we retrospectively enrolled 570 patients with HER2+ breast cancer. The ultrasound and clinicopathological characteristics of hormone receptor (HR)-/HER2+ patients and HR+/HER2+ patients were analyzed separately according to HR status. Eighty percent of the original samples from HR-/HER2+ and HR+/HER2+ patients were extracted by bootstrap sampling as the training cohorts, while the remaining 20% were used as the external validation cohorts. Informative characteristics were screened through univariate and multivariable Cox regression in the training cohorts and used to develop nomograms for predicting recurrence. The predictive accuracy was calculated using Harrell's C-index and calibration curves.

RESULTS

Three informative characteristics (axillary nodal status, calcification, and Adler degree) were identified in HR-/HER2+ patients, and another three (histological grade, axillary nodal status, and echogenic halo) in HR+/HER2+ patients. Based on these, two separate nomograms were constructed to assess recurrence risk. In the training cohorts, the C-index was 0.740 (95% CI: 0.667-0.811) for HR-/HER2+ nomogram, and 0.749 (95% CI: 0.679-0.820) for HR+/HER2+ nomogram. In the validation cohorts, the C-index was 0.708 (95% CI: 0.540-0.877) for HR-/HER2+ group, and 0.705 (95% CI: 0.557-0.853) for HR+/HER2+ group. The calibration curves also indicated the excellent accuracy of the nomograms.

CONCLUSIONS

Ultrasound performance of HER2+ breast cancers with different HR status was significantly different. Nomograms integrating ultrasound and clinicopathological characteristics exhibited favorable performance and have the potential to serve as a reliable method for predicting recurrence in heterogeneous breast cancer.

Alginate-Chitosan Biodegradable and Biocompatible Based Hydrogel for Breast Cancer Immunotherapy and Diagnosis: A Comprehensive Review

Breast cancer is the most common type of cancer and the second leading cause of cancer-related mortality in females. There are many side effects due to chemotherapy and traditional surgery, like fatigue, loss of appetite, skin irritation, and drug resistance to cancer cells. Immunotherapy has become a hopeful approach toward cancer treatment, generating long-lasting immune responses in malignant tumor patients. Recently, hydrogel has received more attention toward cancer therapy due to its specific characteristics, such as decreased toxicity, fewer side effects, and better biocompatibility drug delivery to the particular tumor location. Researchers globally reported various investigations on hydrogel research for tumor diagnosis. The hydrogel-based multilayer platform with controlled nanostructure has received more attention for its antitumor effect. Chitosan and alginate play a leading role in the formation of the cross-link in a hydrogel. Also, they help in the stability of the hydrogel. This review discusses the properties, preparation, biocompatibility, and bioavailability of various research and clinical approaches of the multipolymer hydrogel made of alginate and chitosan for breast cancer treatment. With a focus on cases of breast cancer and the recovery rate, there is a need to find out the role of hydrogel in drug delivery for breast cancer treatment.

Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images

BACKGROUND

Nottingham histological grade (NHG) is a well established prognostic factor in breast cancer histopathology but has a high inter-assessor variability with many tumours being classified as intermediate grade, NHG2. Here, we evaluate if DeepGrade, a previously developed model for risk stratification of resected tumour specimens, could be applied to risk-stratify tumour biopsy specimens.

METHODS

A total of 11,955,755 tiles from 1169 whole slide images of preoperative biopsies from 896 patients diagnosed with breast cancer in Stockholm, Sweden, were included. DeepGrade, a deep convolutional neural network model, was applied for the prediction of low- and high-risk tumours. It was evaluated against clinically assigned grades NHG1 and NHG3 on the biopsy specimen but also against the grades assigned to the corresponding resection specimen using area under the operating curve (AUC). The prognostic value of the DeepGrade model in the biopsy setting was evaluated using time-to-event analysis.

RESULTS

Based on preoperative biopsy images, the DeepGrade model predicted resected tumour cases of clinical grades NHG1 and NHG3 with an AUC of 0.908 (95% CI: 0.88; 0.93). Furthermore, out of the 432 resected clinically-assigned NHG2 tumours, 281 (65%) were classified as DeepGrade-low and 151 (35%) as DeepGrade-high. Using a multivariable Cox proportional hazards model the hazard ratio between DeepGrade low- and high-risk groups was estimated as 2.01 (95% CI: 1.06; 3.79).

CONCLUSIONS

DeepGrade provided prediction of tumour grades NHG1 and NHG3 on the resection specimen using only the biopsy specimen. The results demonstrate that the DeepGrade model can provide decision support to identify high-risk tumours based on preoperative biopsies, thus improving early treatment decisions.

Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data

The inherent heterogeneity of cancer contributes to highly variable responses to any anticancer treatments. This underscores the need to first identify precise biomarkers through complex multi-omics datasets that are now available. Although much research has focused on this aspect, identifying biomarkers associated with distinct drug responders still remains a major challenge. Here, we develop MOMLIN, a multi-modal and -omics machine learning integration framework, to enhance drug-response prediction. MOMLIN jointly utilizes sparse correlation algorithms and class-specific feature selection algorithms, which identifies multi-modal and -omics-associated interpretable components. MOMLIN was applied to 147 patients' breast cancer datasets (clinical, mutation, gene expression, tumor microenvironment cells and molecular pathways) to analyze drug-response class predictions for non-responders and variable responders. Notably, MOMLIN achieves an average AUC of 0.989, which is at least 10% greater when compared with current state-of-the-art (data integration analysis for biomarker discovery using latent components, multi-omics factor analysis, sparse canonical correlation analysis). Moreover, MOMLIN not only detects known individual biomarkers such as genes at mutation/expression level, most importantly, it correlates multi-modal and -omics network biomarkers for each response class. For example, an interaction between ER-negative-HMCN1-COL5A1 mutations-FBXO2-CSF3R expression-CD8 emerge as a multimodal biomarker for responders, potentially affecting antimicrobial peptides and FLT3 signaling pathways. In contrast, for resistance cases, a distinct combination of lymph node-TP53 mutation-PON3-ENSG00000261116 lncRNA expression-HLA-E-T-cell exclusions emerged as multimodal biomarkers, possibly impacting neurotransmitter release cycle pathway. MOMLIN, therefore, is expected advance precision medicine, such as to detect context-specific multi-omics network biomarkers and better predict drug-response classifications.

Single-cell RNA sequencing to explore cancer-associated fibroblasts heterogeneity: "Single" vision for "heterogeneous" environment

Cancer-associated fibroblasts (CAFs), a phenotypically and functionally heterogeneous stromal cell, are one of the most important components of the tumour microenvironment. Previous studies have consolidated it as a promising target against cancer. However, variable therapeutic efficacy-both protumor and antitumor effects have been observed not least owing to the strong heterogeneity of CAFs. Over the past 10 years, advances in single-cell RNA sequencing (scRNA-seq) technologies had a dramatic effect on biomedical research, enabling the analysis of single cell transcriptomes with unprecedented resolution and throughput. Specifically, scRNA-seq facilitates our understanding of the complexity and heterogeneity of diverse CAF subtypes. In this review, we discuss the up-to-date knowledge about CAF heterogeneity with a focus on scRNA-seq perspective to investigate the emerging strategies for integrating multimodal single-cell platforms. Furthermore, we summarized the clinical application of scRNA-seq on CAF research. We believe that the comprehensive understanding of the heterogeneity of CAFs form different visions will generate innovative solutions to cancer therapy and achieve clinical applications.

The Relationship Between Body Composition and Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer Patients

Background The pathological response rate in operable breast cancer (BC) patients receiving neoadjuvant chemotherapy (NAC) is postulated to be related to body composition. The success of complete pathological response (pCR) is a known prognostic factor in BC patients treated with NAC. We aimed to accurately measure body composition through BMI and skeletal muscle mass and observe their effects on pCR. Materials and methods Patients diagnosed with operable BC who had a positron emission tomography-computed tomography (PET-CT) or chest/abdominal CT taken at the time of diagnosis were retrospectively screened and enrolled in this study. Muscle mass was defined by third lumbar vertebra (L3) level transverse CT images, and data, including weight and height, were collected from the chemotherapy records. All these data were evaluated together with the postoperative pathological results. Results Sixty-nine operable BC patients with a median age of 46 (range: 29-72) years were included in the study. In all patients, regardless of sarcopenia, 23% (n = 16) achieved pCR to NAC. The pCR rate was 37.5% (n=6) in sarcopenic patients and 62.5% (n=10) in non-sarcopenic patients (p = 0.530). Overweight (n=4; 25%) and obese (n=2; 12.5%) patients also had a lower pathological response than normal-weight (n=10; 62.5%) BC patients (p=0.261). Conclusion Both sarcopenia and obesity independently and synergistically contribute to poorer pathological responses after NAC. Addressing these conditions through tailored interventions, such as nutritional support, exercise programs, and careful monitoring of body composition, could improve treatment outcomes. Further research with larger patient populations and comprehensive body measurements is essential to fully understand these relationships and develop effective strategies to mitigate their impact.

Predictive and Prognostic Relevance of Tumor-Infiltrating Immune Cells: Tailoring Personalized Treatments against Different Cancer Types

TIICs are critical components of the TME and are used to estimate prognostic and treatment responses in many malignancies. TIICs in the tumor microenvironment are assessed and quantified by categorizing immune cells into three subtypes: CD66b+ tumor-associated neutrophils (TANs), FoxP3+ regulatory T cells (Tregs), and CD163+ tumor-associated macrophages (TAMs). In addition, many cancers have tumor-infiltrating M1 and M2 macrophages, neutrophils (Neu), CD4+ T cells (T-helper), CD8+ T cells (T-cytotoxic), eosinophils, and mast cells. A variety of clinical treatments have linked tumor immune cell infiltration (ICI) to immunotherapy receptivity and prognosis. To improve the therapeutic effectiveness of immune-modulating drugs in a wider cancer patient population, immune cells and their interactions in the TME must be better understood. This study examines the clinicopathological effects of TIICs in overcoming tumor-mediated immunosuppression to boost antitumor immune responses and improve cancer prognosis. We successfully analyzed the predictive and prognostic usefulness of TIICs alongside TMB and ICI scores to identify cancer's varied immune landscapes. Traditionally, immune cell infiltration was quantified using flow cytometry, immunohistochemistry, gene set enrichment analysis (GSEA), CIBERSORT, ESTIMATE, and other platforms that use integrated immune gene sets from previously published studies. We have also thoroughly examined traditional limitations and newly created unsupervised clustering and deconvolution techniques (SpatialVizScore and ProTICS). These methods predict patient outcomes and treatment responses better. These models may also identify individuals who may benefit more from adjuvant or neoadjuvant treatment. Overall, we think that the significant contribution of TIICs in cancer will greatly benefit postoperative follow-up, therapy, interventions, and informed choices on customized cancer medicines.

Tumor-infiltrating lymphocytes in HER2-positive breast cancer treated with neoadjuvant chemotherapy and dual HER2-blockade

Tumor-infiltrating lymphocytes (TILs) have been associated with outcomes in HER2-positive breast cancer patients treated with neoadjuvant chemotherapy and trastuzumab. However, it remains unclear if TILs could be a prognostic and/or predictive biomarker in the context of dual HER2-targeting treatment. In this study, we evaluated the association between TILs and pathological response (pCR) and invasive-disease free survival (IDFS) in 389 patients with stage II-III HER2 positive breast cancer who received neoadjuvant anthracycline-containing or anthracycline-free chemotherapy combined with trastuzumab and pertuzumab in the TRAIN-2 trial. Although no significant association was seen between TILs and pCR, patients with TIL scores ≥60% demonstrated an excellent 3-year IDFS of 100% (95% CI 100-100), regardless of hormone receptor status, nodal stage and attainment of pCR. Additionally, in patients with hormone receptor positive disease, TILs as a continuous variable showed a trend to a positive association with pCR (adjusted Odds Ratio per 10% increase in TILs 1.15, 95% CI 0.99-1.34, p = 0.070) and IDFS (adjusted Hazard Ratio per 10% increase in TILs 0.71, 95% CI 0.50-1.01, p = 0.058). We found no interactions between TILs and anthracycline treatment. Our results suggest that high TIL scores might be able to identify stage II-III HER2-positive breast cancer patients with a favorable prognosis.

The molecular subtyping and precision medicine in triple-negative breast cancer---based on Fudan TNBC classification

Triple-negative breast cancer (TNBC) is widely recognized as the most aggressive form of breast cancer, occurring more frequently in younger patients and characterized by high heterogeneity, early distant metastases and poor prognosis. Multiple treatment options have failed to achieve the expected therapeutic effects due to the lack of clear molecular targets. Based on genomics, transcriptomics and metabolomics, the multi-omics analysis further clarifies TNBC subtyping, which provides a greater understanding of tumour heterogeneity and targeted therapy sensitivity. For instance, the luminal androgen receptor subtype (LAR) exhibits responsiveness to anti-AR therapy, and the basal-like immune-suppressed subtype (BLIS) tends to benefit from poly (ADP-ribose) polymerase inhibitors (PARPis) and anti-angiogenic therapy. The efficacy of multi-dimensional combination therapy holds immense importance in guiding personalized and precision medicine for TNBC. This review offers a systematic overview of recent FuDan TNBC molecular subtyping and its role in the instruction of clinical precision therapy.

Exploration of the relationship between tumor-infiltrating lymphocyte score and histological grade in breast cancer

BACKGROUND

The histological grade is an important factor in the prognosis of invasive breast cancer and is vital to accurately identify the histological grade and reclassify of Grade2 status in breast cancer patients.

METHODS

In this study, data were collected from 556 invasive breast cancer patients, and then randomly divided into training cohort (n = 335) and validation cohort (n = 221). All patients were divided into actual low risk group (Grade1) and high risk group (Grade2/3) based on traditional histological grade, and tumor-infiltrating lymphocyte score (TILs-score) obtained from multiphoton images, and the TILs assessment method proposed by International Immuno-Oncology Biomarker Working Group (TILs-WG) were also used to differentiate between high risk group and low risk group of histological grade in patients with invasive breast cancer. Furthermore, TILs-score was used to reclassify Grade2 (G2) into G2 /Low risk and G2/High risk. The coefficients for each TILs in the training cohort were retrieved using ridge regression and TILs-score was created based on the coefficients of the three kinds of TILs.

RESULTS

Statistical analysis shows that TILs-score is significantly correlated with histological grade, and is an independent predictor of histological grade (odds ratio [OR], 2.548; 95%CI, 1.648-3.941; P < 0.0001), but TILs-WG is not an independent predictive factor for grade (P > 0.05 in the univariate analysis). Moreover, the risk of G2/High risk group is higher than that of G2/Low risk group, and the survival rate of patients with G2/Low risk is similar to that of Grade1, while the survival rate of patients with G2/High risk is even worse than that of patients with G3.

CONCLUSION

Our results suggest that TILs-score can be used to predict the histological grade of breast cancer and potentially to guide the therapeutic management of breast cancer patients.

TBACkp: HER2 expression status classification network focusing on intrinsic subenvironmental characteristics of breast cancer liver metastases

The HER2 expression status in breast cancer liver metastases is a crucial indicator for the diagnosis, treatment, and prognosis assessment of patients. And typical diagnosis involves assessing the HER2 expression status through invasive procedures like biopsy. However, this method has certain drawbacks, such as being difficult in obtaining tissue samples and requiring long examination periods. To address these limitations, we propose an AI-aided diagnostic model. This model enables rapid diagnosis. It diagnoses a patient's HER2 expression status on the basis of preprocessed images, which is the region of the lesion extracted from a CT image rather than from an actual tissue sample. The algorithm of the model adopts a parallel structure, including a Branch Block and a Trunk Block. The Branch Block is responsible for extracting the gradient characteristics between the tumor sub-environments, and the Trunk Block is for fusing the characteristics extracted by the Branch Block. The Branch Block contains CNN with self-attention, which combines the advantages of CNN and self-attention to extract more meticulous and comprehensive image features. And the Trunk Block is so designed that it fuses the extracted image feature information without affecting the transmission of the original image features. The Conv-Attention is used to calculate the attention in the Trunk Block, which uses kernel dot product and is responsible for providing the weight for the self-attention in the process of using convolution induced deviation calculation. Combined with the structure of the model and the method used, we refer to this model as TBACkp. The dataset comprises the enhanced abdominal CT images of 151 patients with liver metastases from breast cancer, together with the corresponding HER2 expression levels for each patient. The experimental results are as follows: (AUC: 0.915, ACC: 0.854, specificity: 0.809, precision: 0.863, recall: 0.881, F1-score: 0.872). The results demonstrate that this method can accurately assess the HER2 expression status in patients when compared with other advanced deep learning model.

CMC: Cancer miRNA Census - a list of cancer-related miRNA genes

A growing body of evidence indicates an important role of miRNAs in cancer; however, there is no definitive, convenient-to-use list of cancer-related miRNAs or miRNA genes that may serve as a reference for analyses of miRNAs in cancer. To this end, we created a list of 165 cancer-related miRNA genes called the Cancer miRNA Census (CMC). The list is based on a score, built on various types of functional and genetic evidence for the role of particular miRNAs in cancer, e.g. miRNA-cancer associations reported in databases, associations of miRNAs with cancer hallmarks, or signals of positive selection of genetic alterations in cancer. The presence of well-recognized cancer-related miRNA genes, such as MIR21, MIR155, MIR15A, MIR17 or MIRLET7s, at the top of the CMC ranking directly confirms the accuracy and robustness of the list. Additionally, to verify and indicate the reliability of CMC, we performed a validation of criteria used to build CMC, comparison of CMC with various cancer data (publications and databases), and enrichment analyses of biological pathways and processes such as Gene Ontology or DisGeNET. All validation steps showed a strong association of CMC with cancer/cancer-related processes confirming its usefulness as a reference list of miRNA genes associated with cancer.

A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization

Genetic screens in cancer cell lines inform gene function and drug discovery. More comprehensive screen datasets with multi-omics data are needed to enhance opportunities to functionally map genetic vulnerabilities. Here, we construct a second-generation map of cancer dependencies by annotating 930 cancer cell lines with multi-omic data and analyze relationships between molecular markers and cancer dependencies derived from CRISPR-Cas9 screens. We identify dependency-associated gene expression markers beyond driver genes, and observe many gene addiction relationships driven by gain of function rather than synthetic lethal effects. By combining clinically informed dependency-marker associations with protein-protein interaction networks, we identify 370 anti-cancer priority targets for 27 cancer types, many of which have network-based evidence of a functional link with a marker in a cancer type. Mapping these targets to sequenced tumor cohorts identifies tractable targets in different cancer types. This target prioritization map enhances understanding of gene dependencies and identifies candidate anti-cancer targets for drug development.

Machine learning prediction of pathological complete response and overall survival of breast cancer patients in an underserved inner-city population

BACKGROUND

Generalizability of predictive models for pathological complete response (pCR) and overall survival (OS) in breast cancer patients requires diverse datasets. This study employed four machine learning models to predict pCR and OS up to 7.5 years using data from a diverse and underserved inner-city population.

METHODS

Demographics, staging, tumor subtypes, income, insurance status, and data from radiology reports were obtained from 475 breast cancer patients on neoadjuvant chemotherapy in an inner-city health system (01/01/2012 to 12/31/2021). Logistic regression, Neural Network, Random Forest, and Gradient Boosted Regression models were used to predict outcomes (pCR and OS) with fivefold cross validation.

RESULTS

pCR was not associated with age, race, ethnicity, tumor staging, Nottingham grade, income, and insurance status (p > 0.05). ER-/HER2+ showed the highest pCR rate, followed by triple negative, ER+/HER2+, and ER+/HER2- (all p < 0.05), tumor size (p < 0.003) and background parenchymal enhancement (BPE) (p < 0.01). Machine learning models ranked ER+/HER2-, ER-/HER2+, tumor size, and BPE as top predictors of pCR (AUC = 0.74-0.76). OS was associated with race, pCR status, tumor subtype, and insurance status (p < 0.05), but not ethnicity and incomes (p > 0.05). Machine learning models ranked tumor stage, pCR, nodal stage, and triple-negative subtype as top predictors of OS (AUC = 0.83-0.85). When grouping race and ethnicity by tumor subtypes, neither OS nor pCR were different due to race and ethnicity for each tumor subtype (p > 0.05).

CONCLUSION

Tumor subtypes and imaging characteristics were top predictors of pCR in our inner-city population. Insurance status, race, tumor subtypes and pCR were associated with OS. Machine learning models accurately predicted pCR and OS.

Initial interactions with the FDA on developing a validation dataset as a medical device development tool

Quantifying tumor-infiltrating lymphocytes (TILs) in breast cancer tumors is a challenging task for pathologists. With the advent of whole slide imaging that digitizes glass slides, it is possible to apply computational models to quantify TILs for pathologists. Development of computational models requires significant time, expertise, consensus, and investment. To reduce this burden, we are preparing a dataset for developers to validate their models and a proposal to the Medical Device Development Tool (MDDT) program in the Center for Devices and Radiological Health of the U.S. Food and Drug Administration (FDA). If the FDA qualifies the dataset for its submitted context of use, model developers can use it in a regulatory submission within the qualified context of use without additional documentation. Our dataset aims at reducing the regulatory burden placed on developers of models that estimate the density of TILs and will allow head-to-head comparison of multiple computational models on the same data. In this paper, we discuss the MDDT preparation and submission process, including the feedback we received from our initial interactions with the FDA and propose how a qualified MDDT validation dataset could be a mechanism for open, fair, and consistent measures of computational model performance. Our experiences will help the community understand what the FDA considers relevant and appropriate (from the perspective of the submitter), at the early stages of the MDDT submission process, for validating stromal TIL density estimation models and other potential computational models. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Canonical Kaiso target genes define a functional signature that associates with breast cancer survival and the invasive lobular carcinoma histological type

Invasive lobular carcinoma (ILC) is a low- to intermediate-grade histological breast cancer type caused by mutational inactivation of E-cadherin function, resulting in the acquisition of anchorage independence (anoikis resistance). Most ILC cases express estrogen receptors, but options are limited in relapsed endocrine-refractory disease as ILC tends to be less responsive to standard chemotherapy. Moreover, ILC can relapse after >15 years, an event that currently cannot be predicted. E-cadherin inactivation leads to p120-catenin-dependent relief of the transcriptional repressor Kaiso (ZBTB33) and activation of canonical Kaiso target genes. Here, we examined whether an anchorage-independent and ILC-specific transcriptional program correlated with clinical parameters in breast cancer. Based on the presence of a canonical Kaiso-binding consensus sequence (cKBS) in the promoters of genes that are upregulated under anchorage-independent conditions, we defined an ILC-specific anoikis resistance transcriptome (ART). Converting the ART genes into human orthologs and adding published Kaiso target genes resulted in the Kaiso-specific ART (KART) 33-gene signature, used subsequently to study correlations with histological and clinical variables in primary breast cancer. Using publicly available data for ERPOS Her2NEG breast cancer, we found that expression of KART was positively associated with the histological ILC breast cancer type (p < 2.7E-07). KART expression associated with younger patients in all invasive breast cancers and smaller tumors in invasive ductal carcinoma of no special type (IDC-NST) (<2 cm, p < 6.3E-10). We observed associations with favorable long-term prognosis in both ILC (hazard ratio [HR] = 0.51, 95% CI = 0.29-0.91, p < 3.4E-02) and IDC-NST (HR = 0.79, 95% CI = 0.66-0.93, p < 1.2E-04). Our analysis thus defines a new mRNA expression signature for human breast cancer based on canonical Kaiso target genes that are upregulated in E-cadherin deficient ILC. The KART signature may enable a deeper understanding of ILC biology and etiology. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Immunological and clinicopathological features predict HER2-positive breast cancer prognosis in the neoadjuvant NeoALTTO and CALGB 40601 randomized trials

The identification of prognostic markers in patients receiving neoadjuvant therapy is crucial for treatment optimization in HER2-positive breast cancer, with the immune microenvironment being a key factor. Here, we investigate the complexity of B and T cell receptor (BCR and TCR) repertoires in the context of two phase III trials, NeoALTTO and CALGB 40601, evaluating neoadjuvant paclitaxel with trastuzumab and/or lapatinib in women with HER2-positive breast cancer. BCR features, particularly the number of reads and clones, evenness and Gini index, are heterogeneous according to hormone receptor status and PAM50 subtypes. Moreover, BCR measures describing clonal expansion, namely evenness and Gini index, are independent prognostic factors. We present a model developed in NeoALTTO and validated in CALGB 40601 that can predict event-free survival (EFS) by integrating hormone receptor and clinical nodal status, breast pathological complete response (pCR), stromal tumor-infiltrating lymphocyte levels (%) and BCR repertoire evenness. A prognostic score derived from the model and including those variables, HER2-EveNT, allows the identification of patients with 5-year EFS > 90%, and, in those not achieving pCR, of a subgroup of immune-enriched tumors with an excellent outcome despite residual disease.

Concordance of RT-qPCR with immunohistochemistry and its beneficial role in breast cancer subtyping

This study was to compare the concordance of transcription-quantitative polymerase chain reaction (RT-qPCR) with immunohistochemistry (IHC) in determining estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and tumor proliferation index (Ki67) status in breast cancer, and to assess the prognosis based on different subtypes. Totally 323 breast cancer patients were selected, including 216 in the training set and 107 in the validation set. Logistic regression models were constructed using 5-fold cross-validation with the mRNA expression of each biomarker as the predictor and the corresponding IHC expression level as the binary response variable. Receiver operating characteristic curve was used to determine the cutoff value. When the thresholds of ER, PR, HER2, and Ki67 were 0.764, 0.709, 0.161, and 0.554, there existed high concordance rates between IHC and RT-qPCR in ER (94.4%), PR (88.0%) and HER2 (89.4%) and a medium concordance rate in Ki67 (67.8%), which were further confirmed in the validation set (ER: 81.3%, PR: 78.3%, HER2: 80.4%, and Ki67: 69.1%). Based on the subtyping stratified by RT-qPCR, the 5-year recurrence-free interval rates of patients with luminal, HER2-enriched, and triple-negative subtypes were 88% (95% CI: 0.84-0.93), 82% (95% CI: 0.73-0.92) and 58% (95% CI: 0.42-0.80), respectively, which were similar to those assessed by IHC (88%, 78% and 47%). RT-qPCR may be a complementary method to IHC, which can not only provide additional useful information in clinic, but also show more advantages over IHC in determining certain subtypes of breast cancer.

Genomic Alterations Associated with Estrogen Receptor Pathway Activity in Metastatic Breast Cancer Have a Differential Impact on Downstream ER Signaling

Mutations in the estrogen receptor gene (ESR1), its transcriptional regulators, and the mitogen-activated protein kinase (MAPK) pathway are enriched in patients with endocrine-resistant metastatic breast cancer (MBC). Here, we integrated whole genome sequencing with RNA sequencing data from the same samples of 101 ER-positive/HER2-negative MBC patients who underwent a tumor biopsy prior to the start of a new line of treatment for MBC (CPCT-02 study, NCT01855477) to analyze the downstream effects of DNA alterations previously linked to endocrine resistance, thereby gaining a better understanding of the associated mechanisms. Hierarchical clustering was performed using expression of ESR1 target genes. Genomic alterations at the DNA level, gene expression levels, and last administered therapy were compared between the identified clusters. Hierarchical clustering revealed two distinct clusters, one of which was characterized by increased expression of ESR1 and its target genes. Samples in this cluster were significantly enriched for mutations in ESR1 and amplifications in FGFR1 and TSPYL. Patients in the other cluster showed relatively lower expression levels of ESR1 and its target genes, comparable to ER-negative samples, and more often received endocrine therapy as their last treatment before biopsy. Genes in the MAPK-pathway, including NF1, and ESR1 transcriptional regulators were evenly distributed. In conclusion, RNA sequencing identified a subgroup of patients with clear expression of ESR1 and its downstream targets, probably still benefiting from ER-targeting agents. The lower ER expression in the other subgroup might be partially explained by ER activity still being blocked by recently administered endocrine treatment, indicating that biopsy timing relative to endocrine treatment needs to be considered when interpreting transcriptomic data.

MAGI1 Prevents Senescence and Promotes the DNA Damage Response in ER+ Breast Cancer

MAGI1 acts as a tumor suppressor in estrogen receptor-positive (ER+) breast cancer (BC), and its loss correlates with a more aggressive phenotype. To identify the pathways and events affected by MAGI1 loss, we deleted the MAGI1 gene in the ER+ MCF7 BC cell line and performed RNA sequencing and functional experiments in vitro. Transcriptome analyses revealed gene sets and biological processes related to estrogen signaling, the cell cycle, and DNA damage responses affected by MAGI1 loss. Upon exposure to TNF-α/IFN-γ, MCF7 MAGI1 KO cells entered a deeper level of quiescence/senescence compared with MCF7 control cells and activated the AKT and MAPK signaling pathways. MCF7 MAGI1 KO cells exposed to ionizing radiations or cisplatin had reduced expression of DNA repair proteins and showed increased sensitivity towards PARP1 inhibition using olaparib. Treatment with PI3K and AKT inhibitors (alpelisib and MK-2206) restored the expression of DNA repair proteins and sensitized cells to fulvestrant. An analysis of human BC patients' transcriptomic data revealed that patients with low MAGI1 levels had a higher tumor mutational burden and homologous recombination deficiency. Moreover, MAGI1 expression levels negatively correlated with PI3K/AKT and MAPK signaling, which confirmed our in vitro observations. Pharmacological and genomic evidence indicate HDACs as regulators of MAGI1 expression. Our findings provide a new view on MAGI1 function in cancer and identify potential treatment options to improve the management of ER+ BC patients with low MAGI1 levels.

Facts and Hopes in Immunotherapy for Early-Stage Triple-Negative Breast Cancer

A substantial fraction of early-stage triple-negative breast cancer (eTNBC) is characterized by high levels of stromal tumor-infiltrating lymphocytes (sTIL) and has a good prognosis even without systemic treatment, highlighting the importance of an endogenous anticancer immune response. Still, a considerable proportion of patients with eTNBC need some "therapeutical push" to kick-start this immune response. Exploiting this immune response with immune-checkpoint inhibition (ICI), in combination with chemotherapy, has made its way into standard of care in eTNBC. Major challenges in the near future include finding those patients with eTNBC who can be treated with ICI alone or with a reduced chemotherapy backbone. Exploring the optimal duration of ICI and finding biomarkers to predict response will be key to enable personalized implementation of ICI in patients with eTNBC. For patients who currently do not respond effectively to ICI plus chemotherapy, challenges lie in finding new immunomodulatory therapies and developing response-guided neoadjuvant approaches.

Characterization of the immune environment in pregnancy-associated breast cancer

Pregnancy-associated breast cancer (PrBC) is a rare and clinically challenging condition. Specific immune mechanisms and pathways are involved in maternal-fetal tolerance and tumor-host immunoediting. The comprehension of the molecular processes underpinning this immune synergy in PrBC is needed to improve patients' clinical management. Only a few studies focused on the immune biology of PrBC and attempted to identify bona fide biomarkers. Therefore, clinically actionable information remains extremely puzzling for these patients. In this review article, we discuss the current knowledge on the immune environment of PrBC, in comparison with pregnancy-unrelated breast cancer and in the context of maternal immune changes during pregnancy. A particular emphasis is given to the actual role of potential immune-related biomarkers for PrBC clinical management.

PET/CT in Patients with Breast Cancer Treated with Immunotherapy

Significant advances in breast cancer (BC) treatment have been made in the last decade, including the use of immunotherapy and, in particular, immune checkpoint inhibitors that have been shown to improve the survival of patients with triple negative BC. This narrative review summarizes the studies supporting the use of immunotherapy in BC. Furthermore, the usefulness of 2-deoxy-2-[¹⁸F]fluoro-D-glucose (2-[¹⁸F]FDG) positron emission/computerized tomography (PET/CT) to image the tumor heterogeneity and to assess treatment response is explored, including the different criteria to interpret 2-[¹⁸F]FDG PET/CT imaging. The concept of immuno-PET is also described, by explaining the advantages of mapping treatment targets with a non-invasive and whole-body tool. Several radiopharmaceuticals in the preclinical phase are referred too, and, considering their promising results, translation to human studies is needed to support their use in clinical practice. Overall, this is an evolving field in BC treatment, despite PET imaging developments, the future trends also include expanding immunotherapy to early-stage BC and using other biomarkers.

Immunotherapy in breast cancer: an overview of current strategies and perspectives

Recent progress in immunobiology has led the way to successful host immunity enhancement against breast cancer. In triple-negative breast cancer, the combination of cancer immunotherapy based on PD-1/PD-L1 immune checkpoint inhibitors with chemotherapy was effective both in advanced and early setting phase 3 clinical trials. These encouraging results lead to the first approvals of immune checkpoint inhibitors in triple-negative breast cancer and thus offer new therapeutic possibilities in aggressive tumors and hard-to-treat populations. Furthermore, several ongoing trials are investigating combining immunotherapies involving immune checkpoint inhibitors with conventional therapies and as well as with other immunotherapeutic strategies such as cancer vaccines, CAR-T cells, bispecific antibodies, and oncolytic viruses in all breast cancer subtypes. This review provides an overview of immunotherapies currently under clinical development and updated key results from clinical trials. Finally, we discuss the challenges to the successful implementation of immune treatment in managing breast cancer and their implications for the design of future clinical trials.

Predictive Biomarkers for Response to Immunotherapy in Triple Negative Breast Cancer: Promises and Challenges

Triple negative breast cancer (TNBC) is a highly heterogeneous disease with a poor prognosis and a paucity of therapeutic options. In recent years, immunotherapy has emerged as a new treatment option for patients with TNBC. However, this therapeutic evolution is paralleled by a growing need for biomarkers which allow for a better selection of patients who are most likely to benefit from this immune checkpoint inhibitor (ICI)-based regimen. These biomarkers will not only facilitate a better optimization of treatment strategies, but they will also avoid unnecessary side effects in non-responders, and limit the increasing financial toxicity linked to the use of these agents. Huge efforts have been deployed to identify predictive biomarkers for the ICI, but until now, the fruits of this labor remained largely unsatisfactory. Among clinically validated biomarkers, only programmed death-ligand 1 protein (PD-L1) expression has been prospectively assessed in TNBC trials. In addition to this, microsatellite instability and a high tumor mutational burden are approved as tumor agnostic biomarkers, but only a small percentage of TNBC fits this category. Furthermore, TNBC should no longer be approached as a single biological entity, but rather as a complex disease with different molecular, clinicopathological, and tumor microenvironment subgroups. This review provides an overview of the validated and evolving predictive biomarkers for a response to ICI in TNBC.

'Why is survival with triple negative breast cancer so low? insights and talking points from preclinical and clinical research'

INTRODUCTION

Triple negative breast cancer is typically related to poor prognosis, early metastasis, and high recurrence rate. Intrinsic and extrinsic biological features of TNBC and resistance mechanisms to conventional therapies can support its aggressive behavior, characterizing TNBC how extremely heterogeneous. Novel combination strategies are under investigation, including immunotherapeutic agents, anti-drug conjugates, PARP inhibitors, and various targeting agents, exploring, in the meanwhile, possible predictive biomarkers to correctly select patients for the optimal treatment for their specific subtype.

AREAS COVERED

This article examines the main malignity characteristics across different subtype, both histological and molecular, and the resistance mechanisms, both primary and acquired, to different drugs explored in the landscape of TNBC treatment, that lead TNBC to still has high mortality rate.

EXPERT OPINION

The complexity of TNBC is not only the main reason of its aggressivity, but its heterogeneity should be exploited in terms of therapeutics opportunities, combining agents with different mechanism of action, after a correct selection by biologic or molecular biomarkers. The main goal is to understand what TNBC really is and to act selectively on its characteristics, with a personalized anticancer treatment.

Association of body mass index and inflammatory dietary pattern with breast cancer pathologic and genomic immunophenotype in the nurses' health study

BACKGROUND

Breast tumor immune infiltration is clearly associated with improved treatment response and outcomes in breast cancer. However, modifiable patient factors associated with breast cancer immune infiltrates are poorly understood. The Nurses' Health Study (NHS) offers a unique cohort to study immune gene expression in tumor and adjacent normal breast tissue, immune cell-specific immunohistochemistry (IHC), and patient exposures. We evaluated the association of body mass index (BMI) change since age 18, physical activity, and the empirical dietary inflammatory pattern (EDIP) score, all implicated in systemic inflammation, with immune cell-specific expression scores.

METHODS

This population-based, prospective observational study evaluated 882 NHS and NHSII participants diagnosed with invasive breast cancer with detailed exposure and gene expression data. Of these, 262 women (training cohort) had breast tumor IHC for four classic immune cell markers (CD8, CD4, CD20, and CD163). Four immune cell-specific scores were derived via lasso regression using 105 published immune expression signatures' association with IHC. In the remaining 620 patient evaluation cohort, we evaluated association of each immune cell-specific score as outcomes, with BMI change since age 18, physical activity, and EDIP score as predictors, using multivariable-adjusted linear regression.

RESULTS

Among women with paired expression/IHC data from breast tumor tissue, we identified robust correlation between novel immune cell-specific expression scores and IHC. BMI change since age 18 was positively associated with CD4+ (β = 0.16; p = 0.009), and CD163 novel immune scores (β = 0.14; p = 0.04) in multivariable analyses. In other words, for each 10 unit (kg/m2) increase in BMI, the percentage of cells positive for CD4 and CD163 increased 1.6% and 1.4%, respectively. Neither physical activity nor EDIP was significantly associated with any immune cell-specific expression score in multivariable analyses.

CONCLUSIONS

BMI change since age 18 was positively associated with novel CD4+ and CD163+ cell scores in breast cancer, supporting further study of the effect of modifiable factors like weight gain on the immune microenvironment.

K-RAS Associated Gene-Mutation-Based Algorithm for Prediction of Treatment Response of Patients with Subtypes of Breast Cancer and Especially Triple-Negative Cancer

Purpose: There is an urgent need for developing new biomarker tools to accurately predict treatment response of breast cancer, especially the deadly triple-negative breast cancer. We aimed to develop gene-mutation-based machine learning (ML) algorithms as biomarker classifiers to predict treatment response of first-line chemotherapy with high precision. Methods: Random Forest ML was applied to screen the algorithms of various combinations of gene mutation profiles of primary tumors at diagnosis using a TCGA Cohort (n = 399) with up to 150 months follow-up as a training set and validated in a MSK Cohort (n = 807) with up to 220 months follow-up. Subtypes of breast cancer including triple-negative and luminal A (ER+, PR+ and HER2−) were also assessed. The predictive performance of the candidate algorithms as classifiers was further assessed using logistic regression, Kaplan−Meier progression-free survival (PFS) plot, and univariate/multivariate Cox proportional hazard regression analyses. Results: A novel algorithm termed the 12-Gene Algorithm based on mutation profiles of KRAS, PIK3CA, MAP3K1, MAP2K4, PTEN, TP53, CDH1, GATA3, KMT2C, ARID1A, RunX1, and ESR1, was identified. The performance of this algorithm to distinguish non-progressed (responder) vs. progressed (non-responder) to treatment in the TCGA Cohort as determined using AUC was 0.96 (95% CI 0.94−0.98). It predicted progression-free survival (PFS) with hazard ratio (HR) of 21.6 (95% CI 11.3−41.5) (p < 0.0001) in all patients. The algorithm predicted PFS in the triple-negative subgroup with HR of 19.3 (95% CI 3.7−101.3) (n = 42, p = 0.000). The 12-Gene Algorithm was validated in the MSK Cohort with a similar AUC of 0.97 (95% CI 0.96−0.98) to distinguish responder vs. non-responder patients, and had a HR of 18.6 (95% CI 4.4−79.2) to predict PFS in the triple-negative subgroup (n = 75, p < 0.0001). Conclusions: The novel 12-Gene algorithm based on multitude gene-mutation profiles identified through ML has a potential to predict breast cancer treatment response to therapies, especially in triple-negative subgroups patients, which may assist personalized therapies and reduce mortality.

Deletion of mdig enhances H3K36me3 and metastatic potential of the triple negative breast cancer cells

In this report, we provide evidence showing diminished expression of the mineral dust-induced gene (mdig), a previously identified oncogenic gene, in human triple negative breast cancer (TNBC). Using a mouse model of orthotopic xenograft of the TNBC MDA-MB-231 cells, we demonstrate that mdig promotes the growth of primary tumors but inhibits metastasis of these cells in vivo. Knockout of mdig resulted in an enhancement of H3K36me3 in the genome and upregulation of some X chromosome-linked genes for cell motility, invasion, and metastasis. Silencing MAGED2, one of the most upregulated and H3K36me3-enriched genes resulted from mdig depletion, can partially reverse the invasive migration of the mdig knockout cells. The anti-metastatic and inhibitory role of mdig on H3K36me3 was cross-validated in another cell line, A549 lung cancer cells. Together, our data suggest that mdig is antagonist against H3K36me3 that enforces expression of genes, such as MAGED2, for cell invasion and metastasis.

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Loss of mdig expression in TNBC and metastatic breast cancer

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Knockout of mdig enforces metastasis of the TNBC cells

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Mdig antagonizes H3K36me3 that promotes expression of X-linked metastatic genes

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Silencing MAGED2 reduces invasive migration of the mdig knockout cells

Intercellular Communication Reveals Therapeutic Potential of Epithelial-Mesenchymal Transition in Triple-Negative Breast Cancer

(1) Background: Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with high intra-tumoral heterogeneity. The epithelial-mesenchymal transition (EMT) is one of the inducers of cancer metastasis and migration. However, the description of the EMT process in TNBC using single-cell RNA sequencing (scRNA-seq) remains unclear. (2) Methods: In this study, we analyzed 8938 cellular gene expression profiles from five TNBC patients. We first scored each malignant cell based on functional pathways to determine its EMT characteristics. Then, a pseudo-time trajectory analysis was employed to characterize the cell trajectories. Furthermore, CellChat was used to identify the cellular communications. (3) Results: We identified 888 epithelium-like and 846 mesenchyme-like malignant cells, respectively. A further pseudo-time trajectory analysis indicated the transition trends from epithelium-like to mesenchyme-like in malignant cells. To characterize the potential regulators of the EMT process, we identified 10 dysregulated transcription factors (TFs) between epithelium-like and mesenchyme-like malignant cells, in which overexpressed forkhead box protein A1 (FOXA1) was recognized as a poor prognosis marker of TNBC. Furthermore, we dissected the cell-cell communications via ligand-receptor (L-R) interactions. We observed that tumor-associated macrophages (TAMs) may support the invasion of malignant epithelial cells, based on CXCL-CXCR2 signaling. The tumor necrosis factor (TNF) signaling pathway secreted by TAMs was identified as an outgoing communication pattern, mediating the communications between monocytes/TAMs and malignant epithelial cells. Alternatively, the TNF-related ligand-receptor (L-R) pairs showed promising clinical implications. Some immunotherapy and anti-neoplastic drugs could interact with the L-R pairs as a potential strategy for the treatment of TNBC. In summary, this study enhances the understanding of the EMT process in the TNBC microenvironment, and dissections of EMT-related cell communications also provided us with potential treatment targets.

Prognostic and Predictive Significance of Stromal Tumor-Infiltrating Lymphocytes (sTILs) in ER-Positive/HER2-Negative Postmenopausal Breast Cancer Patients

The clinical impact of tumor-infiltrating lymphocytes (TILs) is less known for breast cancer patients with the estrogen receptor-positive (ER+)/human epidermal growth factor receptor-negative (HER−) subtype. Here, we explored the prognostic and predictive value of TILs regarding distant recurrence-free interval (DRFI) and breast cancer-specific survival (BCSS) in 763 postmenopausal patients randomized to receive tamoxifen vs. no systemic treatment. TILs were assessed in whole section tumor samples stained with H&E and divided into low (<10%), intermediate (10−39%), or high (≥40%). High TILs were associated with poor prognostic variables and good prognoses for all patients, but not within the ER+/HER2− group. Within the ER+/HER2− group, high gene expression of CD19 and PD-L1 and high IMMUNE1 score indicated good prognosis in multivariable analysis while high CD8 and CD19 gene expression and high IMMUNE1 score were associated with less tamoxifen benefit. These results indicate that within the ER+/HER2− subtype there could be subsets of patients where expression of specific TIL markers might be used to reveal candidates for immune therapy interventions upon failure of the endocrine therapy.

S-phase fraction, lymph node status and disease staging as the main prognostic factors to differentiate between young and older patients with invasive breast carcinoma

The influence of age on the outcome of patients with invasive breast carcinoma (IBC) has not yet been fully established. The present study investigated two subgroups of patients either side of the age spectrum, and evaluated cytometric, histopathological and molecular characteristics. The series involved 219 patients with IBC that had long-term follow-up, which were divided into two subgroups: Young (≤45 years; n=103) and old patients (≥75 years; n=116). Immunohistochemical evaluation of hormonal receptors, Ki67 index and HER2 status (plus HER2 silver in situ hybridization in equivocal cases) were used as the basis for surrogate molecular subtyping. Ploidy and S-phase fraction (SPF) were analysed by DNA flow cytometry. Differences between the subgroups' characteristics were assessed by the two proportion Z test. Kaplan-Meier estimation and log-rank test were applied for survival analyses. The median age in the subgroups were 40 years (range, 19-45 years) in the young group and 78 years (range, 75-91 years) in the older subgroup. Young patients exhibited higher lymph node involvement, more advanced disease staging, higher SPF tumour proliferative activity, and a trend of lower incidence of Luminal A and higher incidence of Luminal B tumours. The median SPF value was significantly higher in young patients [7.1% (range, 1.5-23.7%) vs. 4.5% (range, 0.7-26.4%)], whereas the ploidy pattern showed no significant difference. In the whole series, as within IBC of no special type, young patients had a higher rate of recurrence (46.6 vs. 22.4%; P<0.001) and deaths from disease (35.9 vs. 20.7%; P=0.030), with a statistically significant difference for disease-free survival. In conclusion, the present study indicated that young patients with IBC exhibited more aggressive disease, with an increased risk of recurrence and shorter disease-free survival. SPF, lymph node status and staging appeared to be the main prognostic factors to differentiate young from older patients with IBC.

Prostate Cancer Tumor Stroma: Responsibility in Tumor Biology, Diagnosis and Treatment

Simple Summary

The crosstalk between prostate stroma and its epithelium is essential to tissue homeostasis. Likewise, reciprocal signaling between tumor cells and the stromal compartment is required in tumor progression to facilitate or stimulate key processes such as cell proliferation and invasion. The aim of the present work was to review the current state of knowledge on the significance of tumor stroma in the genesis, progression and therapeutic response of prostate carcinoma. Additionally, we addressed the future therapeutic opportunities.

Abstract

Prostate cancer (PCa) is a common cancer among males globally, and its occurrence is growing worldwide. Clinical decisions about the combination of therapies are becoming highly relevant. However, this is a heterogeneous disease, ranging widely in prognosis. Therefore, new approaches are needed based on tumor biology, from which further prognostic assessments can be established and complementary strategies can be identified. The knowledge of both the morphological structure and functional biology of the PCa stroma compartment can provide new diagnostic, prognostic or therapeutic possibilities. In the present review, we analyzed the aspects related to the tumor stromal component (both acellular and cellular) in PCa, their influence on tumor behavior and the therapeutic response and their consideration as a new therapeutic target.

Immunotherapy in triple-negative breast cancer: Insights into tumor immune landscape and therapeutic opportunities

Triple-negative breast cancer (TNBC) is a clinically aggressive subtype of breast cancer that represents 15-20% of breast tumors and is more prevalent in young pre-menopausal women. It is the subtype of breast cancers with the highest metastatic potential and recurrence at the first 5 years after diagnosis. In addition, mortality increases when a complete pathological response is not achieved. As TNBC cells lack estrogen, progesterone, and HER2 receptors, patients do not respond well to hormone and anti-HER2 therapies, and conventional chemotherapy remains the standard treatment. Despite efforts to develop targeted therapies, this disease continues to have a high unmet medical need, and there is an urgent demand for customized diagnosis and therapeutics. As immunotherapy is changing the paradigm of anticancer treatment, it arises as an alternative treatment for TNBC patients. TNBC is classified as an immunogenic subtype of breast cancer due to its high levels of tumor mutational burden and presence of immune cell infiltrates. This review addresses the implications of these characteristics for the diagnosis, treatment, and prognosis of the disease. Herein, the role of immune gene signatures and tumor-infiltrating lymphocytes as biomarkers in TNBC is reviewed, identifying their application in patient diagnosis and stratification, as well as predictors of efficacy. The expression of PD-L1 expression is already considered to be predictive of response to checkpoint inhibitor therapy, but the challenges regarding its value as biomarker are described. Moreover, the rationales for different formats of immunotherapy against TNBC currently under clinical research are discussed, and major clinical trials are highlighted. Immune checkpoint inhibitors have demonstrated clinical benefit, particularly in early-stage tumors and when administered in combination with chemotherapy, with several regimens approved by the regulatory authorities. The success of antibody-drug conjugates and research on other emerging approaches, such as vaccines and cell therapies, will also be addressed. These advances give hope on the development of personalized, more effective, and safe treatments, which will improve the survival and quality of life of patients with TNBC.

Triple negative breast cancer: approved treatment options and their mechanisms of action

PURPOSE

Breast cancer, the most prevalent cancer worldwide, consists of 4 main subtypes, namely, Luminal A, Luminal B, HER2-positive, and Triple-negative breast cancer (TNBC). Triple-negative breast tumors, which do not express estrogen, progesterone, and HER2 receptors, account for approximately 15-20% of breast cancer cases. The lack of traditional receptor targets contributes to the heterogenous, aggressive, and refractory nature of these tumors, resulting in limited therapeutic strategies.

METHODS

Chemotherapeutics such as taxanes and anthracyclines have been the traditional go to treatment regimens for TNBC patients. Paclitaxel, docetaxel, doxorubicin, and epirubicin have been longstanding, Food and Drug Administration (FDA)-approved therapies against TNBC. Additionally, the FDA approved PARP inhibitors such as olaparib and atezolizumab to be used in combination with chemotherapies, primarily to improve their efficiency and reduce adverse patient outcomes. The immunotherapeutic Keytruda was the latest addition to the FDA-approved list of drugs used to treat TNBC.

RESULTS

The following review aims to elucidate current FDA-approved therapeutics and their mechanisms of action, shedding a light on the various strategies currently used to circumvent the treatment-resistant nature of TNBC cases.

CONCLUSION

The recent approval and use of therapies such as Trodelvy, olaparib and Keytruda has its roots in the development of an understanding of signaling pathways that drive tumour growth. In the future, the emergence of novel drug delivery methods may help increase the efficiency of these therapies whiel also reducing adverse side effects.

RNA sequencing-based single sample predictors of molecular subtype and risk of recurrence for clinical assessment of early-stage breast cancer

Multigene assays for molecular subtypes and biomarkers can aid management of early invasive breast cancer. Using RNA-sequencing we aimed to develop single-sample predictor (SSP) models for clinical markers, subtypes, and risk of recurrence (ROR). A cohort of 7743 patients was divided into training and test set. We trained SSPs for subtypes and ROR assigned by nearest-centroid (NC) methods and SSPs for biomarkers from histopathology. Classifications were compared with Prosigna in two external cohorts (ABiM, n = 100 and OSLO2-EMIT0, n = 103). Prognostic value was assessed using distant recurrence-free interval. Agreement between SSP and NC for PAM50 (five subtypes) was high (85%, Kappa = 0.78) for Subtype (four subtypes) very high (90%, Kappa = 0.84) and for ROR risk category high (84%, Kappa = 0.75, weighted Kappa = 0.90). Prognostic value was assessed as equivalent and clinically relevant. Agreement with histopathology was very high or high for receptor status, while moderate for Ki67 status and poor for Nottingham histological grade. SSP and Prosigna concordance was high for subtype (OSLO-EMIT0 83%, Kappa = 0.73 and ABiM 80%, Kappa = 0.72) and moderate and high for ROR risk category (68 and 84%, Kappa = 0.50 and 0.70, weighted Kappa = 0.70 and 0.78). Pooled concordance for emulated treatment recommendation dichotomized for chemotherapy was high (85%, Kappa = 0.66). Retrospective evaluation suggested that SSP application could change chemotherapy recommendations for up to 17% of postmenopausal ER+/HER2-/N0 patients with balanced escalation and de-escalation. Results suggest that NC and SSP models are interchangeable on a group-level and nearly so on a patient level and that SSP models can be derived to closely match clinical tests.

Clinicopathological Characteristics and Prognosis of HER2-Low Early-Stage Breast Cancer: A Single-Institution Experience

Background

Human epidermal growth factor 2 (HER2)-low breast cancer, which is defined as HER2 1+ or 2+ in immunohistochemistry without gene amplification, accounts for a considerable part of all breast cancers. However, it remains controversial whether HER2-low breast cancer is a distinct entity. Our aim was to compare the clinicopathological features and survival outcomes between HER2-zero and HER2-low early breast cancer.

Methods

The study was a retrospective analysis that enrolled 1,039 patients with available HER2 expression data in a single institute from 2013 to 2014, of whom 262 HER2-positive patients were excluded from the subsequent analysis. The remaining patients were divided into HER2-zero and HER2-low groups. Each group was further categorized into a hormone receptor (HR)-positive and an HR-negative subgroup. Clinicopathological characteristics were collected and compared between HER2-zero and HER2-low groups. The primary endpoint was disease-free survival (DFS) and overall survival (OS), which were analyzed using the Kaplan–Meier method with log-rank test, landmark analysis, and Cox proportional hazards model.

Results

A total of 777 non-HER2-positive patients were included in this analysis, of whom 126, 552, 53, and 46 patients were HR-positive/HER2-zero, HR-positive/HER2-low, HR-negative/HER2-zero, and HR-negative/HER2-low, respectively. No significant difference in DFS and OS was detected between the HER2-zero group and the HER2-low group when paired by HR status. Landmark analysis with a time point set at 5 years indicated that HR-positive/HER2-low patients had a better DFS compared with HR-positive/HER2-zero patients after 5 years (p = 0.0047). HER2-low status was an independent prognostic factor for DFS after 5 years [hazard ratio (HR) 0.31, 95% confidence interval (CI) 0.13–0.75, p = 0.01].

Conclusion

The clinicopathological characteristics and prognosis of HER2-zero and HER2-low breast cancer were similar regardless of HR status. Patients with HR-positive/HER2-low tumors tended to have a better DFS than their HR-positive/HER2-zero counterparts after 5 years.

Luminal androgen receptor breast cancer subtype and investigation of the microenvironment and neoadjuvant chemotherapy response

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype with low overall survival rates and high molecular heterogeneity; therefore, few targeted therapies are available. The luminal androgen receptor (LAR) is the most consistently identified TNBC subtype, but the clinical utility has yet to be established. Here, we constructed a novel genomic classifier, LAR-Sig, that distinguishes the LAR subtype from other TNBC subtypes and provide evidence that it is a clinically distinct disease. A meta-analysis of seven TNBC datasets (n = 1086 samples) from neoadjuvant clinical trials demonstrated that LAR patients have significantly reduced response (pCR) rates than non-LAR TNBC patients (odds ratio = 2.11, 95% CI: 1.33, 2.89). Moreover, deconvolution of the tumor microenvironment confirmed an enrichment of luminal epithelium corresponding with a decrease in basal and myoepithelium in LAR TNBC tumors. Increased immunosuppression in LAR patients may lead to a decreased presence of cycling T-cells and plasma cells. While, an increased presence of myofibroblast-like cancer-associated cells may impede drug delivery and treatment. In summary, the lower levels of tumor infiltrating lymphocytes (TILs), reduced immune activity in the micro-environment, and lower pCR rates after NAC, suggest that new therapeutic strategies for the LAR TNBC subtype need to be developed.

Protein expression of the gp78 E3-ligase predicts poor breast cancer outcome based on race

Women of African ancestry suffer higher rates of breast cancer mortality compared to all other groups in the United States. Though the precise reasons for these disparities remain unclear, many recent studies have implicated a role for differences in tumor biology. Using an epitope-validated antibody against the endoplasmic reticulum-associated degradation (ERAD) E3 ubiquitin ligase, gp78, we show that elevated levels of gp78 in patient breast cancer cells predict poor survival. Moreover, high levels of gp78 are associated with poor outcomes in both ER-positive and ER-negative tumors, and breast cancers expressing elevated amounts of gp78 protein are enriched in gene expression pathways that influence cell cycle, metabolism, receptor-mediated signaling, and cell stress response pathways. In multivariate analysis adjusted for subtype and grade, gp78 protein is an independent predictor of poor outcomes in women of African ancestry. Furthermore, gene expression signatures, derived from patients stratified by gp78 protein expression, are strong predictors of recurrence and pathological complete response in retrospective clinical trial data and share many common features with gene sets previously identified to be overrepresented in breast cancers based on race. These findings implicate a prominent role for gp78 in tumor progression and offer new insights into our understanding of racial differences in breast cancer outcomes.

Association between tumor 18F-fluorodeoxyglucose metabolism and survival in women with estrogen receptor-positive, HER2-negative breast cancer

We examined whether ¹⁸F-fluorodeoxyglucose metabolism is associated with distant relapse-free survival (DRFS) and overall survival (OS) in women with estrogen receptor (ER)-positive, HER2-negative breast cancer. This was a cohort study examining the risk factors for survival that had occurred at the start of the study. A cohort from Asan Medical Center, Korea, recruited between November 2007 and December 2014, was included. Patients received anthracycline-based neoadjuvant chemotherapy. The maximum standardized uptake value (SUV) of ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) was measured. The analysis included 466 women. The median (interquartile range) follow-up period without distant metastasis or death was 6.2 (5.3-7.6) years. Multivariable analysis of hazard ratio (95% confidence interval [CI]) showed that the middle and high tertiles of SUV were prognostic for DRFS (2.93, 95% CI 1.62-5.30; P < 0.001) and OS (4.87, 95% CI 1.94-12.26; P < 0.001). The 8-year DRFS rates were 90.7% (95% CI 85.5-96.1%) for those in the low tertile of maximum SUV vs. 73.7% (95% CI 68.0-79.8%) for those in the middle and high tertiles of maximum SUV. ¹⁸F-fluorodeoxyglucose PET/CT may assess the risk of distant metastasis and death in ER-positive, HER2-negative patients.

Emergence of Nanotechnology as a Powerful Cavalry against Triple-Negative Breast Cancer (TNBC)

Triple-negative breast cancer (TNBC) is considered one of the un-manageable types of breast cancer, involving devoid of estrogen, progesterone, and human epidermal growth factor receptor 2 (HER 2) receptors. Due to their ability of recurrence and metastasis, the management of TNBC remains a mainstay challenge, despite the advancements in cancer therapies. Conventional chemotherapy remains the only treatment regimen against TNBC and suffers several limitations such as low bioavailability, systemic toxicity, less targetability, and multi-drug resistance. Although various targeted therapies have been introduced to manage the hardship of TNBC, they still experience certain limitations associated with the survival benefits. The current research thus aimed at developing and improving the strategies for effective therapy against TNBC. Such strategies involved the emergence of nanoparticles. Nanoparticles are designated as nanocavalries, loaded with various agents (drugs, genes, etc.) to battle the progression and metastasis of TNBC along with overcoming the limitations experienced by conventional chemotherapy and targeted therapy. This article documents the treatment regimens of TNBC along with their efficacy towards different subtypes of TNBC, and the various nanotechnologies employed to increase the therapeutic outcome of FDA-approved drug regimens.

The Transcriptomic Portrait of Locally Advanced Breast Cancer and Its Prognostic Value in a Multi-Country Cohort of Latin American Patients

Purposes

Most molecular-based published studies on breast cancer do not adequately represent the unique and diverse genetic admixture of the Latin American population. Searching for similarities and differences in molecular pathways associated with these tumors and evaluating its impact on prognosis may help to select better therapeutic approaches.

Patients and Methods

We collected clinical, pathological, and transcriptomic data of a multi-country Latin American cohort of 1,071 stage II-III breast cancer patients of the Molecular Profile of Breast Cancer Study (MPBCS) cohort. The 5-year prognostic ability of intrinsic (transcriptomic-based) PAM50 and immunohistochemical classifications, both at the cancer-specific (OSC) and disease-free survival (DFS) stages, was compared. Pathway analyses (GSEA, GSVA and MetaCore) were performed to explore differences among intrinsic subtypes.

Results

PAM50 classification of the MPBCS cohort defined 42·6% of tumors as LumA, 21·3% as LumB, 13·3% as HER2E and 16·6% as Basal. Both OSC and DFS for LumA tumors were significantly better than for other subtypes, while Basal tumors had the worst prognosis. While the prognostic power of traditional subtypes calculated with hormone receptors (HR), HER2 and Ki67 determinations showed an acceptable performance, PAM50-derived risk of recurrence best discriminated low, intermediate and high-risk groups. Transcriptomic pathway analysis showed high proliferation (i.e. cell cycle control and DNA damage repair) associated with LumB, HER2E and Basal tumors, and a strong dependency on the estrogen pathway for LumA. Terms related to both innate and adaptive immune responses were seen predominantly upregulated in Basal tumors, and, to a lesser extent, in HER2E, with respect to LumA and B tumors.

Conclusions

This is the first study that assesses molecular features at the transcriptomic level in a multicountry Latin American breast cancer patient cohort. Hormone-related and proliferation pathways that predominate in PAM50 and other breast cancer molecular classifications are also the main tumor-driving mechanisms in this cohort and have prognostic power. The immune-related features seen in the most aggressive subtypes may pave the way for therapeutic approaches not yet disseminated in Latin America.

Clinical Trial Registration

ClinicalTrials.gov (Identifier: NCT02326857).

Decision Theory versus Conventional Statistics for Personalized Therapy of Breast Cancer

Estrogen and progesterone receptors being present or not represents one of the most important biomarkers for therapy selection in breast cancer patients. Conventional measurement by immunohistochemistry (IHC) involves errors, and numerous attempts have been made to increase precision by additional information from gene expression. This raises the question of how to fuse information, in particular, if there is disagreement. It is the primary domain of Dempster–Shafer decision theory (DST) to deal with contradicting evidence on the same item (here: receptor status), obtained through different techniques. DST is widely used in technical settings, such as self-driving cars and aviation, and is also promising to deliver significant advantages in medicine. Using data from breast cancer patients already presented in previous work, we focus on comparing DST with classical statistics in this work, to pave the way for its application in medicine. First, we explain how DST not only considers probabilities (a single number per sample), but also incorporates uncertainty in a concept of ‘evidence’ (two numbers per sample). This allows for very powerful displays of patient data in so-called ternary plots, a novel and crucial advantage for medical interpretation. Results are obtained according to conventional statistics (ODDS) and, in parallel, according to DST. Agreement and differences are evaluated, and the particular merits of DST discussed. The presented application demonstrates how decision theory introduces new levels of confidence in diagnoses derived from medical data.

Clinical and Molecular Characteristics of ER-Positive Ultralow Risk Breast Cancer Tumors Identified by the 70-Gene Signature

The metastatic potential of estrogen receptor (ER)-positive breast cancers is heterogenous and distant recurrences occur months to decades after primary diagnosis. We have previously shown that patients with tumors classified as ultralow risk by the 70-gene signature have a minimal long-term risk of fatal breast cancer. Here, we evaluate the previously unexplored underlying clinical and molecular characteristics of ultralow risk tumors in 538 ER-positive patients from the Stockholm tamoxifen randomized trial (STO-3). Out of the 98 ultralow risk tumors, 89% were luminal A molecular subtype, whereas 26% of luminal A tumors were of ultralow risk. Compared with other ER-positive tumors, ultralow risk tumors were significantly (Fisher's test, P<0.05) more likely to be of smaller tumor size, lower grade, progesterone receptor (PR)-positive, human epidermal growth factor 2 (HER2)-negative and have low Ki-67 levels (proliferation-marker). Moreover, ultralow risk tumors showed significantly lower expression scores of multi-gene modules associated with the AKT/mTOR-pathway, proliferation (AURKA), HER2/ERBB2-signaling, IGF1-pathway, PTEN-loss, and immune response (IMMUNE1 and IMMUNE2), and higher expression scores of the PIK3CA-mutation-associated module. Furthermore, 706 genes were significantly (FDR<0.001) differentially expressed in ultralow risk tumors, including lower expression of genes involved in immune response, PI3K/Akt/mTOR-pathway, histones, cell cycle, DNA repair, apoptosis, and higher expression of genes coding for epithelial-to-mesenchymal transition, and homeobox proteins, among others. In conclusion, ultralow risk tumors, associated with minimal long-term risk of fatal disease, differ from other ER-positive tumors, including luminal A molecular subtype tumors. Identification of these characteristics is important to improve our prediction of non-fatal versus fatal breast cancer. This article is protected by copyright. All rights reserved.

A Novel IGLC2 Gene Linked With Prognosis of Triple-Negative Breast Cancer

BACKGROUND

Immunoglobulin-related genes are associated with the favorable prognosis of triple-negative breast cancer (TNBC) patients. We aimed to analyze the function and prognostic value of immunoglobulin lambda constant 2 (IGLC2) in TNBC patients.

METHODS

We knocked down the gene expression of IGLC2 (IGLC2-KD) in MDA-MB-231 cells to evaluate the proliferation, migration, and invasion of tumors via 3-(4,5-Dimethythiazol-2-yl)-2,5-diphenyl tetrazolium bromide assay, wound healing, and transwell cell migration assay respectively. Relapse-free survival (RFS) and distant metastasis-free survival (DMFS) analyses were conducted using the KM plotter online tool. The GSE76275 data set was used to analyze the association of IGLC2 and clinical characteristics. A pathway enrichment analysis was conducted using the next-generation sequencing data of wild-type and IGLC2-KD MDA-MB-231 cells.

RESULTS

The low gene expression of IGLC2 was related to unfavorable RFS, DMFS. The high expression of IGLC2 was exhibited in the basal-like immune-activated (BLIA) TNBC molecular subtype, which was immune-activated and showed excellent response to immune therapy. IGLC2 was positively correlated with programmed death-ligand 1 (PD-L1) as shown by Spearman correlation (r = 0.25, p < 0.0001). IGLC2 had a strong prognostic effect on lymph node-negative TNBC (RFS range: 0.31, q value= 8.2e-05; DMFS = 0.16, q value = 8.2e-05) but had no significance on lymph node-positive ones. The shRNA-mediated silencing of IGLC2 increased the proliferation, migration, and invasion of MDA-MB-231 cells. The results of pathway enrichment analysis showed that IGLC2 is related to the PI3K-Akt signaling pathway, MAPK signaling pathway, and extracellular matrix-receptor interaction. We confirmed that MDA-MB-231 tumor cells expressed IGLC2, subverting the traditional finding of generation by immune cells.

CONCLUSIONS

IGLC2 linked with the proliferation, migration, and invasion of MDA-MB-231 cells. A high expression of IGLC2 was related to favorable prognosis for TNBC patients. IGLC2 may serve as a biomarker for the identification of TNBC patients who can benefit the most from immune checkpoint blockade treatment.