Triple Negative Breast Cancers: An Obsolete Entity?

Triple negative breast cancer is defined on the basis of what it is not. It has served as a useful umbrella entity for management of patients with breast cancer for the last couple of decades. However, during this period a number of novel therapies have become available. These therapies have been documented to be useful in subsets of TNBCs that can be identified on the basis of distinct biologic alterations. Herein we revisit the categorization and usage of the TNBC as an entity to assess its utility in view of the currently available therapies.

Dual Prognostic Classification of Triple-Negative Breast Cancer by DNA Damage Immune Response and Homologous Recombination Deficiency.

PURPOSE: Triple-negative breast cancer (TNBC) is a heterogeneous disease. We previously showed that homologous recombination deficiency (HRD) and the DNA damage immune response (DDIR) signature are prognostic in TNBC. We hypothesized that these biomarkers reflect related but not completely interdependent biological processes, that their combined use would be prognostic, and that simultaneous assessment of the immunologic microenvironment and susceptibility to DNA damaging therapies might be able to identify subgroups with distinct therapeutic vulnerabilities. METHODS: We analyzed the dual DDIR/HRD classification in 341 patients with TNBC treated with adjuvant anthracycline-based chemotherapy on the SWOG S9313 trial and corroborated our findings in The Cancer Genome Atlas breast cancer data set. RESULTS: DDIR/HRD classification is highly prognostic in TNBC and identifies biologically and immunologically distinct subgroups. Immune-enriched DDIR+/HRD+ TNBCs have the most favorable prognosis, and DDIR+/HRD- and DDIR-/HRD+ TNBCs have favorable intermediate prognosis, despite the latter being immune-depleted. DDIR-/HRD- TNBCs have the worst prognosis and represent an internally heterogeneous group of immune-depleted chemoresistant tumors. CONCLUSION: Our findings propose DDIR/HRD classification as a potentially clinically relevant approach to categorize tumors on the basis of therapeutic vulnerabilities.

Targeting the Tumor-Tumor Microenvironment Crosstalk.

INTRODUCTION: Cancer development and progression is a complex process influenced by co-evolution of the cancer cells and their microenvironment. However, traditional anti-cancer therapy is mostly targeted toward cancer cells. To improve the efficacy of cancer drugs, the complex interactions between the tumor (T) and the tumor microenvironment (TME) should be considered while developing therapeutics. AREAS COVERED: The present review article will discuss the components of T-TME as well as the potential to co-target these two distinct elements. We document that these approaches have resulted in success in preventing tumor progression and metastasis, albeit in animal models in some cases. Lastly, it is important to consider the tissue context and tumor type as these could significantly modify the role of these molecules/pathways and hence the overall likelihood of response. Furthermore, we discuss the potential strategies to target the components of tumor microenvironment in anti-cancer therapy. PubMed and ClinicalTrials.gov was searched through May 2023. EXPERT OPINION: The tumor-tumor microenvironment cross talk and heterogeneity are major mechanisms conferring resistance to standard of care. Better understanding of the tissue specific T-TME interactions and dual targeting has the promise of improving cancer control and clinical outcomes.

Predicting Breast Cancer Events in Ductal Carcinoma In Situ (DCIS) Using Generative Adversarial Network Augmented Deep Learning Model.

Standard clinicopathological parameters (age, growth pattern, tumor size, margin status, and grade) have been shown to have limited value in predicting recurrence in ductal carcinoma in situ (DCIS) patients. Early and accurate recurrence prediction would facilitate a more aggressive treatment policy for high-risk patients (mastectomy or adjuvant radiation therapy), and simultaneously reduce over-treatment of low-risk patients. Generative adversarial networks (GAN) are a class of DL models in which two adversarial neural networks, generator and discriminator, compete with each other to generate high quality images. In this work, we have developed a deep learning (DL) classification network that predicts breast cancer events (BCEs) in DCIS patients using hematoxylin and eosin (H & E) images. The DL classification model was trained on 67 patients using image patches from the actual DCIS cores and GAN generated image patches to predict breast cancer events (BCEs). The hold-out validation dataset (n = 66) had an AUC of 0.82. Bayesian analysis further confirmed the independence of the model from classical clinicopathological parameters. DL models of H & E images may be used as a risk stratification strategy for DCIS patients to personalize therapy.

The Role of ESRP1 in the Regulation of PHGDH in Estrogen Receptor-Positive Breast Cancer.

Resistance to hormone therapy leads to a recurrence of estrogen receptor-positive breast cancer. We have demonstrated that the epithelial splicing regulatory protein 1 (ESRP1) significantly affects cell/tumor growth and metabolism and is associated with a poor prognosis in this breast cancer subtype. In this study, we aimed to investigate the ESRP1 protein-messenger RNA (mRNA) interaction in hormone therapy-resistant breast cancer. RNA-binding protein immunoprecipitation (RIP) followed by Clariom D (Applied Biosystems/Thermo Fisher Scientific) transcriptomics microarray (RIP-Chip) was performed to identify mRNA-binding partners of ESRP1. The integration of RIP-Chip and immunoprecipitation-mass spectrometry analyses identified phosphoglycerate dehydrogenase (PHGDH), a key metabolic enzyme, as a binding partner of ESRP1 in hormone-resistant breast cancer. Bioinformatic analysis showed ESRP1 binding to the 5' untranslated region of PHGDH. RNA electrophoresis mobility shift assay and RIP-quantitative reverse transcription-polymerase chain reaction further validated the ESRP1-PHGDH binding. In addition, knockdown of ESRP1 decreased PHGDH mRNA stability significantly, suggesting the posttranscriptional regulation of PHGDH by ESRP1. The presence or absence of ESRP1 levels significantly affected the stability in tamoxifen-resistant LCC2 and fulvestrant-resistant LCC9 cells. PHGDH knockdown in tamoxifen-resistant cells further reduced the oxygen consumption rate (ranging from P = .005 and P = .02), mimicking the effects of ESRP1 knockdown. Glycolytic parameters were also altered (ranging P = .001 and P = .005). ESRP1 levels did not affect the stability of PHGDH in T-47D cells, although knockdown of PHGDH affected the growth of these cells. In conclusion, to our knowledge, this study, for the first time, reports that ESRP1 binds to the 5' untranslated region of PHGDH, increasing its mRNA stability in hormone therapy-resistant estrogen receptor-positive breast cancer. These findings provide evidence for a novel mechanism of action of RNA-binding proteins such as ESRP1. These new insights could assist in developing novel strategies for the treatment of hormone therapy-resistant breast cancer.

Spatially variant immune infiltration scoring in human cancer tissues.

The Immunoscore is a method to quantify the immune cell infiltration within cancers to predict the disease prognosis. Previous immune profiling approaches relied on limited immune markers to establish patients' tumor immunity. However, immune cells exhibit a higher-level complexity that is typically not obtained by the conventional immunohistochemistry methods. Herein, we present a spatially variant immune infiltration score, termed as SpatialVizScore, to quantify immune cells infiltration within lung tumor samples using multiplex protein imaging data. Imaging mass cytometry (IMC) was used to target 26 markers in tumors to identify stromal, immune, and cancer cell states within 26 human tissues from lung cancer patients. Unsupervised clustering methods dissected the spatial infiltration of cells in tissue using the high-dimensional analysis of 16 immune markers and other cancer and stroma enriched labels to profile alterations in the tumors' immune infiltration patterns. Spatially resolved maps of distinct tumors determined the spatial proximity and neighborhoods of immune-cancer cell pairs. These SpatialVizScore maps provided a ranking of patients' tumors consisting of immune inflamed, immune suppressed, and immune cold states, demonstrating the tumor's immune continuum assigned to three distinct infiltration score ranges. Several inflammatory and suppressive immune markers were used to establish the cell-based scoring schemes at the single-cell and pixel-level, depicting the cellular spectra in diverse lung tissues. Thus, SpatialVizScore is an emerging quantitative method to deeply study tumor immunology in cancer tissues.

Tumor Infiltrating Lymphocytes in Multi-National Cohorts of Ductal Carcinoma In Situ (DCIS) of Breast.

Tumor-infiltrating lymphocytes (TILs) are prognostic in invasive breast cancer. However, their prognostic significance in ductal carcinoma in situ (DCIS) has been controversial. To investigate the prognostic role of TILs in DCIS outcome, we used different scoring methods for TILs in multi-national cohorts from Asian and European women. Self-described race was genetically confirmed using QC Infinium array combined with radmixture software. Stromal TILs, touching TILs, circumferential TILs, and hotspots were quantified on H&E-stained slides and correlated with the development of second breast cancer events (BCE) and other clinico-pathological variables. In univariate survival analysis, age older than 50 years, hormone receptor positivity and the presence of circumferential TILs were weakly associated with the absence of BCE at the 5-year follow-up in all cohorts (p < 0.03; p < 0.02; and p < 0.02, respectively, adjusted p = 0.11). In the multivariable analysis, circumferential TILs were an independent predictor of a better outcome (Wald test p = 0.01), whereas younger age was associated with BCE. Asian patients were younger with larger, higher grade, HR negative DCIS lesions, and higher TIL variables. The spatial arrangement of TILs may serve as a better prognostic indicator in DCIS cases than stromal TILs alone and may be added in guidelines for TILs evaluation in DCIS.

Protein Profiling of Breast Cancer for Treatment Decision-Making.

The increasing use of neoadjuvant therapy has resulted in therapeutic decisions being made on the basis of diagnostic needle core biopsy. For many patients, this method might yield the only fragment of tumor available for biomarker analysis, necessitating judicious use. Many multiplex protein analytic methods have been developed that employ fluorescence or other tags to overcome the limitations of immunohistochemistry while still retaining the spatial annotation. Interpretation of the data can be difficult because of the limitations of the human eye. Computational deconvolution of the signals may be necessary for some of these methods to enable identification of cell-specific localization and coexpression of biomarkers. Herein, we present the different methods that are coming of age and their application in cancer research, with a focus on breast cancer. We also discuss the limitations, which include high costs and long turnaround times. The methods are also based on the premise that preanalytical factors will have identical impact on all proteins analyzed. There is a need to establish standards to normalize the data and enable cross-sample comparisons. In spite of these limitations, the multiplex technologies are extremely valuable discovery tools and can provide novel insights into the biology of cancer and mechanisms of drug resistance.

Is conservative management of ductal carcinoma in situ risky?

Nonsurgical management of ductal carcinoma in situ is controversial and little is known about the long-term consequences of this approach. In this study, we aimed to determine the risk of (a) upstaging to invasive carcinoma at excision and (b) ipsilateral breast cancer events in patients who might have been eligible for nonsurgical management of DCIS trials. Data from women aged 20 years or older with a biopsy diagnosis of DCIS between January 1, 2010 to December 31, 2014 were collated. The women underwent biopsy and surgical resection (lumpectomy or mastectomy) and were treated with radiation or endocrine therapy as per treating physicians' choice. The development of ipsilateral breast cancer events (IBEs) was analyzed in patients with at least 5 years of follow-up after standard of care therapy for DCIS. Subset-analysis was undertaken to identify the incidence of IBEs in patients eligible for nonsurgical management trials. The study population consisted of 378 patients with matched cases of biopsy and surgical excision. The overall upstaging rate to IBC was 14.3 and 12.9% for COMET, 8.8% for LORIS, and 10.7% for LORD trial "eligible" patients. At 5 years of follow-up, ~11.5% of overall and trial eligible patients developed IBEs of which approximately half were invasive IBEs. In conclusion, women with DCIS who would have been eligible for nonsurgical management trials have a significantly high risk of developing ipsilateral breast events within 5 years of diagnosis. Better selection criteria are needed to identify DCIS patients who are at very low risk for the development of IBC.

Genomic clustering analysis identifies molecular subtypes of thymic epithelial tumors independent of World Health Organization histologic type.

Further characterization of thymic epithelial tumors (TETs) is needed. Genomic information from 102 evaluable TETs from The Cancer Genome Atlas (TCGA) dataset and from the IU-TAB-1 cell line (type AB thymoma) underwent clustering analysis to identify molecular subtypes of TETs. Six novel molecular subtypes (TH1-TH6) of TETs from the TCGA were identified, and there was no association with WHO histologic subtype. The IU-TAB-1 cell line clustered into the TH4 molecular subtype and in vitro testing of candidate therapeutics was performed. The IU-TAB-1 cell line was noted to be resistant to everolimus (mTORC1 inhibitor) and sensitive to nelfinavir (AKT1 inhibitor) across the endpoints measured. Sensitivity to nelfinavir was due to the IU-TAB-1 cell line's gain-of function (GOF) mutation in PIK3CA and amplification of genes observed from array comparative genomic hybridization (aCGH), including AURKA, ERBB2, KIT, PDGFRA and PDGFB, that are known upregulate AKT, while resistance to everolimus was primarily driven by upregulation of downstream signaling of KIT, PDGFRA and PDGFB in the presence of mTORC1 inhibition. We present a novel molecular classification of TETs independent of WHO histologic subtype, which may be used for preclinical validation studies of potential candidate therapeutics of interest for this rare disease.

Thymic Carcinomas and Second Malignancies: A Single-Center Review.

Thymic carcinomas account for less than 0.01% of new cancer diagnoses annually and are more aggressive than thymomas. Autoimmune disorders have been associated with thymomas and only recently with thymic carcinomas. Second malignancies are well described after thymomas. The aim of this study was to analyze the incidence of second malignancies in patients with thymic carcinomas. All cases of thymic carcinomas were identified from the pathology archives of Indiana University. Histological materials were reviewed and further correlated with clinical data to identify incidence of second cancers in patients with thymic carcinomas. Histological material was available for review in 92 cases of thymic carcinoma. Clinical data were available for 85 patients. Fourteen of these (16.5%) patients had a second malignancy; these included small cell lung carcinoma, "testicular cancer", embryonal carcinoma, seminoma, breast carcinoma (two cases), prostatic adenocarcinoma, Hodgkin's lymphoma, thyroid carcinoma, bladder carcinoma (two cases), renal cell carcinoma, and melanoma. The latter could precede, be concurrent with, or follow the diagnosis thymic carcinoma. The incidence of second cancers in patients with thymic carcinomas is similar to that reported for thymomas. Abnormalities in immunological surveillance may be responsible for this high incidence of second malignancies in thymic tumors.

CIBERSORT analysis of TCGA and METABRIC identifies subgroups with better outcomes in triple negative breast cancer.

Studies have shown that the presence of tumor infiltrating lymphocytes (TILs) in Triple Negative Breast Cancer (TNBC) is associated with better prognosis. However, the molecular mechanisms underlying these immune cell differences are not well delineated. In this study, analysis of hematoxylin and eosin images from The Cancer Genome Atlas (TCGA) breast cancer cohort failed to show a prognostic benefit of TILs in TNBC, whereas CIBERSORT analysis, which quantifies the proportion of each immune cell type, demonstrated improved overall survival in TCGA TNBC samples with increased CD8 T cells or CD8 plus CD4 memory activated T cells and in Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) TNBC samples with increased gamma delta T cells. Twenty-five genes showed mutational frequency differences between the TCGA high and low T cell groups, and many play important roles in inflammation or immune evasion (ATG2B, HIST1H2BC, PKD1, PIKFYVE, TLR3, NOTCH3, GOLGB1, CREBBP). Identification of these mutations suggests novel mechanisms by which the cancer cells attract immune cells and by which they evade or dampen the immune system during the cancer immunoediting process. This study suggests that integration of mutations with CIBERSORT analysis could provide better prediction of outcomes and novel therapeutic targets in TNBC cases.

Multi-protein spatial signatures in ductal carcinoma in situ (DCIS) of breast.

BACKGROUND: There is limited knowledge about DCIS cellular composition and relationship with breast cancer events (BCE). METHODS: Immunofluorescence multiplexing (MxIF) was used to image and quantify 32 cellular biomarkers in FFPE DCIS tissue microarrays. Over 75,000 DCIS cells from 51 patients (median 9 years follow-up for non-BCE cases) were analysed for profiles predictive of BCE. K-means clustering was used to evaluate cellular co-expression of epithelial markers with ER and HER2. RESULTS: Only ER, PR and HER2 significantly correlated with BCE. Cluster analysis identified 6 distinct cell groups with different levels of ER, Her2, cMET and SLC7A5. Clusters 1 and 3 were not significant. Clusters 2 and 4 (high ER/low HER2 and SLC7A5/mixed cMET) significantly correlated with low BCE risk (P = 0.001 and P = 0.034), while cluster 6 (high HER2/low ER, cMET and SLC7A5) correlated with increased risk (P = 0.018). Cluster 5 (similar to cluster 6, except high SLC7A5) trended towards significance (P = 0.072). A continuous expression score (Escore) based on these 4 clusters predicted likelihood of BCE (AUC = 0.79, log-rank test P = 5E-05; LOOCV AUC = 0.74, log-rank test P = 0.006). CONCLUSION: Multiplexed spatial analysis of limited tissue is a novel method for biomarker analysis and predicting BCEs. Further validation of Escore is needed in a larger cohort.

ColoType: a forty gene signature for consensus molecular subtyping of colorectal cancer tumors using whole-genome assay or targeted RNA-sequencing.

Colorectal cancer (CRC) tumors can be partitioned into four biologically distinct consensus molecular subtypes (CMS1-4) using gene expression. Evidence is accumulating that tumors in different subtypes are likely to respond differently to treatments. However, to date, there is no clinical diagnostic test for CMS subtyping. In this study, we used novel methodology in a multi-cohort training domain (n = 1,214) to develop the ColoType scores and classifier to predict CMS1-4 based on expression of 40 genes. In three validation cohorts (n = 1,744, in total) representing three distinct gene-expression measurement technologies, ColoType predicted gold-standard CMS subtypes with accuracies 0.90, 0.91, 0.88, respectively. To accommodate for potential intratumoral heterogeneity and tumors of mixed subtypes, ColoType was designed to report continuous scores measuring the prevalence of each of CMS1-4 in a tumor, in addition to specifying the most prevalent subtype. For analysis of clinical specimens, ColoType was also implemented with targeted RNA-sequencing (Illumina AmpliSeq). In a series of formalin-fixed, paraffin-embedded CRC samples (n = 49), ColoType by targeted RNA-sequencing agreed with subtypes predicted by two independent methods with accuracies 0.92, 0.82, respectively. With further validation, ColoType by targeted RNA-sequencing, may enable clinical application of CMS subtyping with widely-available and cost-effective technology.

Validation of the DNA Damage Immune Response Signature in Patients With Triple-Negative Breast Cancer From the SWOG 9313c Trial.

PURPOSE: To independently validate two biomarkers, a 44-gene DNA damage immune response (DDIR) signature and stromal tumor-infiltrating lymphocytes (sTILs), as prognostic markers in patients with triple-negative breast cancer (TNBC) treated with adjuvant doxorubicin (A) and cyclophosphamide (C) in SWOG 9313. METHODS: Four hundred twenty-five centrally determined patient cases with TNBC from S9313 were identified. DDIR signature was performed on RNA isolated from formalin-fixed paraffin-embedded tumor tissue, and samples were classified as DDIR negative or positive using predefined cutoffs. Evaluation of sTILs was performed as described previously. Markers were tested for prognostic value for disease-free survival (DFS) and overall survival (OS) using Cox regression models adjusted for treatment assignment, nodal status, and tumor size. RESULTS: Among 425 patients with TNBC, 33% were node positive. DDIR was tested successfully in 90% of patients (381 of 425), 62% of which were DDIR signature positive. DDIR signature positivity was associated with improved DFS (hazard ratio [HR], 0.67; 95% CI, 0.48 to 0.92; P = .015) and OS (HR, 0.61; 95% CI, 0.43 to 0.89; P = .010). sTILs density assessment was available in 99% of patients and was associated with improved DFS (HR, 0.70; 95% CI, 0.51 to 0.96; P = .026 for sTILs density ≥ 20% v < 20%) and OS (HR, 0.59; 95% CI, 0.41 to 0.85; P = .004 for sTILs density ≥ 20% v < 20%). DDIR signature score and sTILs density were moderately correlated (r = 0.60), which precluded statistical significance for DFS in a joint model. Three-year DFS and OS in a subgroup of patients with DDIR positivity and T1c/T2N0 disease were 88% and 94%, respectively. CONCLUSION: The prognostic role of sTILs and DDIR in early-stage TNBC was confirmed. DDIR signature conferred improved prognosis in two thirds of patients with TNBC treated with adjuvant AC. DDIR signature has the potential to stratify outcome and to identify patients with less projected benefit after AC chemotherapy.

Ductal carcinoma in situ of breast: update 2019.

Ductal carcinoma in situ is a non-invasive form of breast cancer. Its incidence is increasing due to widespread use of mammographic screening. It presents several diagnostic and management challenges in part due to its relatively indolent behaviour. Most series analysing biomarkers in these lesions are small (<100 patients) and large clinical trials have not been frequent. Herein, we review the recent progress made in understanding the biology of this entity and the tools available for prognostication.

Three-dimensional imaging and quantitative analysis in CLARITY processed breast cancer tissues.

The tumor microenvironment can be spatially heterogenous, which makes it challenging to fully characterize with standard 2D histology-based methods. In this study, we determined the feasibility of a CLARITY tissue-processing approach to analyze biopsies from breast cancer patients. Formalin-fixed human breast cancer core-needle biopsy specimens, were embedded, lipid-cleared, and multiplexed immunostained to identify key biomarkers (pan-cytokeratin, Ki67, CD3). Confocal microscopy was then used to image the specimens after refractive index matching. These data sets were then quantitatively compared to conventional slide-based FFPE histology. Using CLARITY, the gross and cellular morphology of the tissues were well preserved, and high optical transparency was achieved, with the exception of fibrotic regions. Specific staining of various cellular and nuclear markers was achieved using optimized antibody conditions. Manually determined composite Ki67 scores from the CLARITY datasets agreed with histology results. However, the CLARITY datasets (3D) revealed variation in the intra-tumoral Ki67 expression that was not evident in individual FFPE sections (2D). We further demonstrated that archived FFPE clinical specimens can be CLARITY-processed, immunostained, and imaged. In short, CLARITY-processed specimens may enable a more accurate, unbiased analysis of tumor samples in comparison to conventional slide-based histology, thus allowing for improved visualization of intra-tumoral heterogeneity.

Splicing factor ESRP1 controls ER-positive breast cancer by altering metabolic pathways.

The epithelial splicing regulatory proteins 1 and 2 (ESRP1 and ESRP2) control the epithelial-to-mesenchymal transition (EMT) splicing program in cancer. However, their role in breast cancer recurrence is unclear. In this study, we report that high levels of ESRP1, but not ESRP2, are associated with poor prognosis in estrogen receptor positive (ER+) breast tumors. Knockdown of ESRP1 in endocrine-resistant breast cancer models decreases growth significantly and alters the EMT splicing signature, which we confirm using TCGA SpliceSeq data of ER+ BRCA tumors. However, these changes are not accompanied by the development of a mesenchymal phenotype or a change in key EMT-transcription factors. In tamoxifen-resistant cells, knockdown of ESRP1 affects lipid metabolism and oxidoreductase processes, resulting in the decreased expression of fatty acid synthase (FASN), stearoyl-CoA desaturase 1 (SCD1), and phosphoglycerate dehydrogenase (PHGDH) at both the mRNA and protein levels. Furthermore, ESRP1 knockdown increases the basal respiration and spare respiration capacity. This study reports a novel role for ESRP1 that could form the basis for the prevention of tamoxifen resistance in ER+ breast cancer.