Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer.

BACKGROUND: Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30-40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60-70% show residual disease (RD). The role of the tumor microenvironment in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response. METHODS: H&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) of the model development cohort and 79 patients (41 with pCR and 38 with RD) of the validation cohort were separated through a stratified eightfold cross-validation strategy for the first step and leave-one-out cross-validation strategy for the second step. A tile-level histology label prediction pipeline and four machine-learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction. RESULTS: The tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy of the model development cohort. The model was validated with an independent cohort with tile histology validation accuracy of 83.59% and NAC prediction accuracy of 81.01%. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD. CONCLUSION: Our machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.

A novel role for KIFC1-MYH9 interaction in triple-negative breast cancer aggressiveness and racial disparity.

African American (AA) women are twice as likely to develop triple-negative breast cancer (TNBC) as women of European descent. Additionally, AA women with TNBC present a much more aggressive disease course than their European American (EA) counterparts. Thus, there is an unmet clinical need to identify race-specific biomarkers and improve survival outcomes in AA patients with TNBC. The minus-end directed microtubule motor protein kinesin family member C1 (KIFC1) promotes centrosome clustering and chromosomal instability and is often overexpressed in TNBC. Previous findings suggest that KIFC1 plays a role in cell proliferation and migration in TNBC cells from AAs and that the levels of nuclear KIFC1 (nKIFC1) are particularly high in AA patients with TNBC. The nuclear localization of KIFC1 in interphase may underlie its previously unrecognized race-specific association. In this study, we found that in TNBC cells derived from AAs, nKIFC1 interacted with the tumor suppressor myosin heavy chain 9 (MYH9) over EA cells. Treatment of AA TNBC cells with commercial inhibitors of KIFC1 and MYH9 disrupted the interaction between KIFC1 and MYH9. To characterize the racial differences in the KIFC1-MYH9-MYC axis in TNBC, we established homozygous KIFC1 knockout (KO) TNBC cell lines. KIFC1 KO significantly inhibited proliferation, migration, and invasion in AA TNBC cells but not in EA TNBC cells. RNA sequencing analysis showed significant downregulation of genes involved in cell migration, invasion, and metastasis upon KIFC1 KO in TNBC cell lines from AAs compared to those from EAs. These data indicate that mechanistically, the role of nKIFC1 in driving TNBC progression and metastasis is stronger in AA patients than in EA patients, and that KIFC1 may be a critical therapeutic target for AA patients with TNBC.

Demographic disparities in the limited awareness of alcohol use as a breast cancer risk factor: empirical findings from a cross-sectional study of U.S. women.

BACKGROUND: Alcohol use is an established yet modifiable risk factor for breast cancer. However, recent research indicates that the vast majority of U.S. women are unaware that alcohol use is a risk factor for breast cancer. There is limited information about the sociodemographic characteristics and alcohol use correlates of awareness of the alcohol use and breast cancer link, and this is critically important for health promotion and intervention efforts. In this study, we assessed prevalence of the awareness of alcohol use as a risk factor for breast cancer among U.S. women and examined sociodemographic and alcohol use correlates of awareness of this link. METHODS: We conducted a 20-minute online cross-sectional survey, called the ABLE (Alcohol and Breast Cancer Link Awareness) survey, among U.S. women aged 18 years and older (N = 5,027) in the fall of 2021. Survey questions assessed awareness that alcohol use increases breast cancer risk (yes, no, don't know/unsure); past-year alcohol use and harmful drinking via the Alcohol Use Disorders Identification Test (AUDIT); and family, health, and sociodemographic characteristics. We conducted multivariate multinomial regression analysis to identify correlates of awareness that alcohol use increases breast cancer risk. RESULTS: Overall, 24.4% reported that alcohol use increased breast cancer risk, 40.2% reported they were unsure, and 35.4% reported that there was no link between alcohol use and breast cancer. In adjusted analysis, awareness of alcohol use as a breast cancer risk factor, compared to not being aware or unsure, was associated with being younger (18-25 years old), having a college degree, and having alcohol use disorder symptoms. Black women were less likely than white women to report awareness of the alcohol use and breast cancer link. CONCLUSIONS: Overall, only a quarter of U.S. women were aware that alcohol use increases breast cancer risk, although 40% expressed uncertainty. Differences in awareness by age, level of education, race and ethnicity and level of alcohol use offer opportunities for tailored prevention interventions, while the overall low level of awareness calls for widespread efforts to increase awareness of the breast cancer risk from alcohol use among U.S. women.

Polyploid giant cancer cell characterization: New frontiers in predicting response to chemotherapy in breast cancer.

Although polyploid cells were first described nearly two centuries ago, their ability to proliferate has only recently been demonstrated. It also becomes increasingly evident that a subset of tumor cells, polyploid giant cancer cells (PGCCs), play a critical role in the pathophysiology of breast cancer (BC), among other cancer types. In BC, PGCCs can arise in response to therapy-induced stress. Their progeny possess cancer stem cell (CSC) properties and can repopulate the tumor. By modulating the tumor microenvironment (TME), PGCCs promote BC progression, chemoresistance, metastasis, and relapse and ultimately impact the survival of BC patients. Given their pro- tumorigenic roles, PGCCs have been proposed to possess the ability to predict treatment response and patient prognosis in BC. Traditionally, DNA cytometry has been used to detect PGCCs.. The field will further derive benefit from the development of approaches to accurately detect PGCCs and their progeny using robust PGCC biomarkers. In this review, we present the current state of knowledge about the clinical relevance of PGCCs in BC. We also propose to use an artificial intelligence-assisted image analysis pipeline to identify PGCC and map their interactions with other TME components, thereby facilitating the clinical implementation of PGCCs as biomarkers to predict treatment response and survival outcomes in BC patients. Finally, we summarize efforts to therapeutically target PGCCs to prevent chemoresistance and improve clinical outcomes in patients with BC.

Computational benchmarking of putative KIFC1 inhibitors.

The centrosome in animal cells is instrumental in spindle pole formation, nucleation, proper alignment of microtubules during cell division, and distribution of chromosomes in each daughter cell. Centrosome amplification involving structural and numerical abnormalities in the centrosome can cause chromosomal instability and dysregulation of the cell cycle, leading to cancer development and metastasis. However, disturbances caused by centrosome amplification can also limit cancer cell survival by activating mitotic checkpoints and promoting mitotic catastrophe. As a smart escape, cancer cells cluster their surplus of centrosomes into pseudo-bipolar spindles and progress through the cell cycle. This phenomenon, known as centrosome clustering (CC), involves many proteins and has garnered considerable attention as a specific cancer cell-targeting weapon. The kinesin-14 motor protein KIFC1 is a minus end-directed motor protein that is involved in CC. Because KIFC1 is upregulated in various cancers and modulates oncogenic signaling cascades, it has emerged as a potential chemotherapeutic target. Many molecules have been identified as KIFC1 inhibitors because of their centrosome declustering activity in cancer cells. Despite the ever-increasing literature in this field, there have been few efforts to review the progress. The current review aims to collate and present an in-depth analysis of known KIFC1 inhibitors and their biological activities. Additionally, we present computational docking data of putative KIFC1 inhibitors with their binding sites and binding affinities. This first-of-kind comparative analysis involving experimental biology, chemistry, and computational docking of different KIFC1 inhibitors may help guide decision-making in the selection and design of potent inhibitors.

Inhibition of CPAP-tubulin interaction prevents proliferation of centrosome-amplified cancer cells.

Centrosome amplification is a hallmark of human cancers that can trigger cancer cell invasion. To survive, cancer cells cluster amplified extra centrosomes and achieve pseudobipolar division. Here, we set out to prevent clustering of extra centrosomes. Tubulin, by interacting with the centrosomal protein CPAP, negatively regulates CPAP-dependent peri-centriolar material recruitment, and concurrently microtubule nucleation. Screening for compounds that perturb CPAP-tubulin interaction led to the identification of CCB02, which selectively binds at the CPAP binding site of tubulin. Genetic and chemical perturbation of CPAP-tubulin interaction activates extra centrosomes to nucleate enhanced numbers of microtubules prior to mitosis. This causes cells to undergo centrosome de-clustering, prolonged multipolar mitosis, and cell death. 3D-organotypic invasion assays reveal that CCB02 has broad anti-invasive activity in various cancer models, including tyrosine kinase inhibitor (TKI)-resistant EGFR-mutant non-small-cell lung cancers. Thus, we have identified a vulnerability of cancer cells to activation of extra centrosomes, which may serve as a global approach to target various tumors, including drug-resistant cancers exhibiting high incidence of centrosome amplification.

A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images.

MOTIVATION: Predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) patients accurately is direly needed for clinical decision making. pCR is also regarded as a strong predictor of overall survival. In this work, we propose a deep learning system to predict pCR to NAC based on serial pathology images stained with hematoxylin and eosin and two immunohistochemical biomarkers (Ki67 and PHH3). To support human prior domain knowledge-based guidance and enhance interpretability of the deep learning system, we introduce a human knowledge-derived spatial attention mechanism to inform deep learning models of informative tissue areas of interest. For each patient, three serial breast tumor tissue sections from biopsy blocks were sectioned, stained in three different stains and integrated. The resulting comprehensive attention information from the image triplets is used to guide our prediction system for prognostic tissue regions. RESULTS: The experimental dataset consists of 26 419 pathology image patches of 1000×1000 pixels from 73 TNBC patients treated with NAC. Image patches from randomly selected 43 patients are used as a training dataset and images patches from the rest 30 are used as a testing dataset. By the maximum voting from patch-level results, our proposed model achieves a 93% patient-level accuracy, outperforming baselines and other state-of-the-art systems, suggesting its high potential for clinical decision making. AVAILABILITY AND IMPLEMENTATION: The codes, the documentation and example data are available on an open source at: https://github.com/jkonglab/PCR_Prediction_Serial_WSIs_biomarkers. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cancer as a prospective sequela of long COVID-19.

As the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to surge worldwide, our knowledge of coronavirus disease 2019 (COVID-19) is rapidly expanding. Although most COVID-19 patients recover within weeks of symptom onset, some experience lingering symptoms that last for months ("long COVID-19"). Early reports of COVID-19 sequelae, including cardiovascular, pulmonary, and neurological conditions, have raised concerns about the long-term effects of COVID-19, especially in hard-hit communities. It is becoming increasingly evident that cancer patients are more susceptible to SARS-CoV-2 infection and are at a higher risk of severe COVID-19 than the general population. Nevertheless, whether long COVID-19 increases the risk of cancer in those with no prior malignancies, remains unclear. Given, the disproportionate impact of the disease on the African American community, yet another unanswered question is whether racial disparities are to be expected in COVID-19 sequelae. Herein, we propose that long COVID-19 may predispose recovered patients to cancer development and accelerate cancer progression. This hypothesis is based on growing evidence of the ability of SARS-CoV-2 to modulate oncogenic pathways, promote chronic low-grade inflammation, and cause tissue damage. Comprehensive studies are urgently required to elucidate the effects of long COVID-19 on cancer susceptibility.

Lipidomic landscape in cancer: Actionable insights for membrane-based therapy and diagnoses.

Cancer cells display altered cellular lipid metabolism, including disruption in endogenous lipid synthesis, storage, and exogenous uptake for membrane biogenesis and functions. Altered lipid metabolism and, consequently, lipid composition impacts cellular function by affecting membrane structure and properties, such as fluidity, rigidity, membrane dynamics, and lateral organization. Herein, we provide an overview of lipid membranes and how their properties affect cellular functions. We also detail how the rewiring of lipid metabolism impacts the lipidomic landscape of cancer cell membranes and influences the characteristics of cancer cells. Furthermore, we discuss how the altered cancer lipidome provides cues for developing lipid-inspired innovative therapeutic and diagnostic strategies while improving our limited understanding of the role of lipids in cancer initiation and progression. We also present the arcade of membrane characterization techniques to cement their relevance in cancer diagnosis and monitoring of treatment response.

Centrosome amplification: a quantifiable cancer cell trait with prognostic value in solid malignancies.

Numerical and/or structural centrosome amplification (CA) is a hallmark of cancers that is often associated with the aberrant tumor karyotypes and poor clinical outcomes. Mechanistically, CA compromises mitotic fidelity and leads to chromosome instability (CIN), which underlies tumor initiation and progression. Recent technological advances in microscopy and image analysis platforms have enabled better-than-ever detection and quantification of centrosomal aberrancies in cancer. Numerous studies have thenceforth correlated the presence and the degree of CA with indicators of poor prognosis such as higher tumor grade and ability to recur and metastasize. We have pioneered a novel semi-automated pipeline that integrates immunofluorescence confocal microscopy with digital image analysis to yield a quantitative centrosome amplification score (CAS), which is a summation of the severity and frequency of structural and numerical centrosome aberrations in tumor samples. Recent studies in breast cancer show that CA increases across the disease progression continuum, while normal breast tissue exhibited the lowest CA, followed by cancer-adjacent apparently normal, ductal carcinoma in situ and invasive tumors, which showed the highest CA. This finding strengthens the notion that CA could be evolutionarily favored and can promote tumor progression and metastasis. In this review, we discuss the prevalence, extent, and severity of CA in various solid cancer types, the utility of quantifying amplified centrosomes as an independent prognostic marker. We also highlight the clinical feasibility of a CA-based risk score for predicting recurrence, metastasis, and overall prognosis in patients with solid cancers.

Barriers to breast cancer screening in Atlanta, GA: results from the Pink Panel survey at faith-based institutions.

PURPOSE: Our research sought to describe barriers to mammography screening among a sample of predominantly Black women in metropolitan Atlanta, Georgia. METHODS: The Pink Panel project convened community leaders from faith-based institutions to administer an offline survey to women via convenience sampling at fourteen churches in Atlanta in late 2019 and early 2020. With the COVID-19 pandemic, the research team switched to an online survey. The survey included seven questions about breast cancer awareness, barriers to breast cancer screening, and screening status. We used residence information to attain the 9-digit zip code to link to the Area Deprivation Index at the Census Block Group neighborhood level. We report results as descriptive statistics of the barriers to mammography screening. RESULTS: The 643 women represented 21 counties in Georgia, predominantly from metropolitan Atlanta, and 86% identified as Black. Among women aged 40 and older, 90% have ever had a mammogram. Among all women, 79% have ever had a mammogram, and 86% indicated that they would get a mammogram if offered in their neighborhood. The top barriers to mammography screening were lack of health insurance and high cost. Barriers to mammography screening did not differ substantially by Area Deprivation Index. CONCLUSION: Among metropolitan Atlanta women aged 40+ , nearly all reported ever having a mammogram. However, addressing the barriers, including lack of health insurance and high cost, that women reported may further improve mammography screening rates.

Exosomal Metabolic Signatures Are Associated with Differential Response to Neoadjuvant Chemotherapy in Patients with Breast Cancer.

Neoadjuvant chemotherapy (NAC) is commonly used in breast cancer (BC) patients to increase eligibility for breast-conserving surgery. Only 30% of patients with BC show pathologic complete response (pCR) after NAC, and residual disease (RD) is associated with poor long-term prognosis. A critical barrier to improving NAC outcomes in patients with BC is the limited understanding of the mechanisms underlying differential treatment outcomes. In this study, we evaluated the ability of exosomal metabolic profiles to predict NAC response in patients with BC. Exosomes isolated from the plasma of patients after NAC were used for metabolomic analyses to identify exosomal metabolic signatures associated with the NAC response. Among the 16 BC patients who received NAC, eight had a pCR, and eight had RD. Patients with RD had 2.52-fold higher exosome concentration in their plasma than those with pCR and showed significant enrichment of various metabolic pathways, including citrate cycle, urea cycle, porphyrin metabolism, glycolysis, and gluconeogenesis. Additionally, the relative exosomal levels of succinate and lactate were significantly higher in patients with RD than in those with pCR. These data suggest that plasma exosomal metabolic signatures could be associated with differential NAC outcomes in BC patients and provide insight into the metabolic determinants of NAC response in patients with BC.

A Case Series Exploration of Multi-Regional Expression Heterogeneity in Triple-Negative Breast Cancer Patients.

Extensive intratumoral heterogeneity (ITH) is believed to contribute to therapeutic failure and tumor recurrence, as treatment-resistant cell clones can survive and expand. However, little is known about ITH in triple-negative breast cancer (TNBC) because of the limited number of single-cell sequencing studies on TNBC. In this study, we explored ITH in TNBC by evaluating gene expression-derived and imaging-derived multi-region differences within the same tumor. We obtained tissue specimens from 10 TNBC patients and conducted RNA sequencing analysis of 2-4 regions per tumor. We developed a novel analysis framework to dissect and characterize different types of variability: between-patients (inter-tumoral heterogeneity), between-patients across regions (inter-tumoral and region heterogeneity), and within-patient, between-regions (regional intratumoral heterogeneity). We performed a Bayesian changepoint analysis to assess and classify regional variability as low (convergent) versus high (divergent) within each patient feature (TNBC and PAM50 subtypes, immune, stroma, tumor counts and tumor infiltrating lymphocytes). Gene expression signatures were categorized into three types of variability: between-patients (108 genes), between-patients across regions (183 genes), and within-patients, between-regions (778 genes). Based on the between-patient gene signature, we identified two distinct patient clusters that differed in menopausal status. Significant intratumoral divergence was observed for PAM50 classification, tumor cell counts, and tumor-infiltrating T cell abundance. Other features examined showed a representation of both divergent and convergent results. Lymph node stage was significantly associated with divergent tumors. Our results show extensive intertumoral heterogeneity and regional ITH in gene expression and image-derived features in TNBC. Our findings also raise concerns regarding gene expression based TNBC subtyping. Future studies are warranted to elucidate the role of regional heterogeneity in TNBC as a driver of treatment resistance.

Undercutting efforts of precision medicine: roadblocks to minority representation in breast cancer clinical trials.

Precision (or personalized) medicine holds great promise in the treatment of breast cancer. The success of personalized medicine is contingent upon inclusivity and representation for minority groups in clinical trials. In this article, we focus on the roadblocks for the African American demographic, including the barriers to access and enrollment in breast oncology trials, the prevailing classification of race and ethnicity, and the need to refine monolithic categorization by employing genetic ancestry mapping tools for a more accurate determination of race or ethnicity.

Donor acceptor fluorophores: synthesis, optical properties, TD-DFT and cytotoxicity studies.

Donor-π-acceptor (D-π-A) fluorophores consisting of a donor unit, a π linker, and an acceptor moiety have attracted attention in the last decade. In this study, we report the synthesis, characterization, optical properties, TD-DFT, and cytotoxicity studies of 17 near infrared (NIR) D-π-A analogs which have not been reported so far to the best of our knowledge. These fluorophores have chloroacrylic acid as the acceptor unit and various donor units such as indole, benzothiazole, benzo[e]indole, and quinoline. The fluorophores showed strong absorption in the NIR (700-970 nm) region due to their enhanced intramolecular charge transfer (ICT) between chloroacrylic acid and the donor moieties connected with the Vilsmeier-Haack linker. The emission wavelength maxima of the fluorophores were in between 798 and 870 nm. Compound 20 with a 4-quinoline donor moiety showed an emission wavelength above 1000 nm in the NIR II window. The synthesized fluorophores were characterized by 1H NMR and 13C NMR, and their optical properties were studied. Time dependent density functional theory (TD-DFT) calculations showed that the charge transfer occurs from the donor groups (indole, benzothiazole, benzo[e]indole, and quinoline) to the acceptor chloroacrylic acid moiety. Fluorophores with [HOMO] to [LUMO+1] transitions were shown to possess a charge separation character. The cytotoxicity of selected fluorophores, 4, 7, 10 and 12 was investigated against breast cancer cell lines and they showed better activity than the anti-cancer agent docetaxel.

QNBC Is Associated with High Genomic Instability Characterized by Copy Number Alterations and miRNA Deregulation.

Triple-negative breast cancer (TNBC) can be further classified into androgen receptor (AR)-positive TNBC and AR-negative TNBC or quadruple-negative breast cancer (QNBC). Here, we investigated genomic instability in 53 clinical cases by array-CGH and miRNA expression profiling. Immunohistochemical analysis revealed that 64% of TNBC samples lacked AR expression. This group of tumors exhibited a higher level of copy number alterations (CNAs) and a higher frequency of cases affected by CNAs than TNBCs. CNAs in genes of the chromosome instability 25 (CIN25) and centrosome amplification (CA) signatures were more frequent in the QNBCs and were similar between the groups, respectively. However, expression levels of CIN25 and CA20 genes were higher in QNBCs. miRNA profiling revealed 184 differentially expressed miRNAs between the groups. Fifteen of these miRNAs were mapped at cytobands with CNAs, of which eight (miR-1204, miR-1265, miR-1267, miR-23c, miR-548ai, miR-567, miR-613, and miR-943), and presented concordance of expression and copy number levels. Pathway enrichment analysis of these miRNAs/mRNAs pairings showed association with genomic instability, cell cycle, and DNA damage response. Furthermore, the combined expression of these eight miRNAs robustly discriminated TNBCs from QNBCs (AUC = 0.946). Altogether, our results suggest a significant loss of AR in TNBC and a profound impact in genomic instability characterized by CNAs and deregulation of miRNA expression.

Spotlighting the hypoxia-centrosome amplification axis.

The abysmal success rate of anticancer drugs in clinical trials, is in part, attributable to discordance between cultured cancer cells and patient tumors. While tumors in vivo, display a lower mitotic index, patient tumors portray much higher centrosomal aberrations, relative to in vitro cultured cells. The microenvironment too differs considerably between the in vitro and in vivo scenarios. Notably, another hallmark of cancer, hypoxia, is not recapitulated in cell lines cultured under normoxic conditions. These observations raise the possibility that hypoxia may be the missing link that explains the discordance between cell biological phenomena in vitro versus physiological conditions. Further, the interplay between hypoxia and centrosome amplification (CA) is relatively understudied. Recent research from our laboratory, geared toward examining the biological link between the two, has uncovered that hypoxia induces the expression of proteins (Plk4, Aurora A, Cyclin D) implicated in CA, in a hypoxia-inducible factor 1α (HIF-1α)-dependent context. Our studies evidence that hypoxia fuels CA that underlie intratumoral heterogeneity and metastatic potential of cancer cells. Given the advent of HIF-1α inhibitors, this research has ramifications in aiding patient risk stratification and designing new cancer drug therapies to facilitate clinical decision-making.

Quadruple-negative breast cancer: novel implications for a new disease.

Based on the androgen receptor (AR) expression, triple-negative breast cancer (TNBC) can be subdivided into AR-positive TNBC and AR-negative TNBC, also known as quadruple-negative breast cancer (QNBC). QNBC characterization and treatment is fraught with many challenges. In QNBC, there is a greater paucity of prognostic biomarkers and therapeutic targets than AR-positive TNBC. Although the prognostic role of AR in TNBC remains controversial, many studies revealed that a lack of AR expression confers a more aggressive disease course. Literature characterizing QNBC tumor biology and uncovering novel biomarkers for improved management of the disease remains scarce. In this comprehensive review, we summarize the current QNBC landscape and propose avenues for future research, suggesting potential biomarkers and therapeutic strategies that warrant investigation.

A novel prognostic two-gene signature for triple negative breast cancer.

The absence of a robust risk stratification tool for triple negative breast cancer (TNBC) underlies imprecise and nonselective treatment of these patients with cytotoxic chemotherapy. This study aimed to interrogate transcriptomes of TNBC resected samples using next generation sequencing to identify novel biomarkers associated with disease outcomes. A subset of cases (n = 112) from a large, well-characterized cohort of primary TNBC (n = 333) were subjected to RNA-sequencing. Reads were aligned to the human reference genome (GRCH38.83) using the STAR aligner and gene expression quantified using HTSEQ. We identified genes associated with distant metastasis-free survival and breast cancer-specific survival by applying supervised artificial neural network analysis with gene selection to the RNA-sequencing data. The prognostic ability of these genes was validated using the Breast Cancer Gene-Expression Miner v4. 0 and Genotype 2 outcome datasets. Multivariate Cox regression analysis identified a prognostic gene signature that was independently associated with poor prognosis. Finally, we corroborated our results from the two-gene prognostic signature by their protein expression using immunohistochemistry. Artificial neural network identified two gene panels that strongly predicted distant metastasis-free survival and breast cancer-specific survival. Univariate Cox regression analysis of 21 genes common to both panels revealed that the expression level of eight genes was independently associated with poor prognosis (p < 0.05). Adjusting for clinicopathological factors including patient's age, grade, nodal stage, tumor size, and lymphovascular invasion using multivariate Cox regression analysis yielded a two-gene prognostic signature (ACSM4 and SPDYC), which was associated with poor prognosis (p < 0.05) independent of other prognostic variables. We validated the protein expression of these two genes, and it was significantly associated with patient outcome in both independent and combined manner (p < 0.05). Our study identifies a prognostic gene signature that can predict prognosis in TNBC patients and could potentially be used to guide the clinical management of TNBC patients.

ROS- and HIF1α-dependent IGFBP3 upregulation blocks IGF1 survival signaling and thereby mediates high-glucose-induced cardiomyocyte apoptosis.

The prevalence of chronic hyperglycemia and its complications, imposing a critical burden on the worldwide economy and the global healthcare system, is a pressing issue. Mounting evidence indicates that oxidative stress and hypoxia, two noticeable features of hyperglycemia, play a joint crucial role in mediating cellular apoptosis. However, the underlying detailed molecular mechanism remains elusive. Triggered by the observation that insulin-like growth factor (IGF1)-binding protein 3 (IGFBP3) can mediate, in renal cells, high-glucose-induced apoptosis by elevating oxidative stress, we wish to, in this study, know whether or not the similar scenario holds in cardiac cells and, if so, to find its relevant molecular key players, thereby dissecting the underlying molecular pathway. Specifically, we used a combination of three different cellular sources (H9c2 cells, diabetic rats, and neonatal rat ventricular cardiomyocytes) as our model systems of study. We made use of Co-IP assay and western blot analysis in conjunction with loss-of-function reasoning, gain-of-function logic, and inhibitor treatment as our main analytical tools. As a result, briefly, our main findings are that hyperglycemia can induce cardiac IGFBP3 overexpression and secretion, that high levels of IGFBP3 can sequester IGF1 from IGF1 survival pathway, leading to apoptosis, and that IGFBP3 gene upregulation is hypoxia-inducible factor (HIF)1α-dependent and reactive oxygen species dependent. Piecing these findings together allows us to propose the improved molecular regulatory mechanism. In conclusion, we have established the molecular roles of IGFBP3, HIF1, and prolyl hydroxylase domain in connecting oxidative stress with hypoxia and in cellular apoptosis under hyperglycemia.