Triple Negative Breast Cancer Dataset Research Articles

Sideroflexin-1 promotes progression and sensitivity to lapatinib in triple-negative breast cancer by inhibiting TOLLIP-mediated autophagic degradation of CIP2A

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer and it lacks specific therapeutic targets and effective treatment protocols. By analyzing a proteomic TNBC dataset, we found significant upregulation of sideroflexin 1 (SFXN1) in tumor tissues. However, the precise function of SFXN1 in TNBC remains unclear. Immunoblotting was performed to determine SFXN1 expression levels. Label-free quantitative proteomics and liquid chromatography-tandem mass spectrometry were used to identify the downstream targets of SFXN1. Mechanistic studies of SFXN1 and cellular inhibitor of PP2A (CIP2A) were performed using immunoblotting, immunofluorescence staining, and reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Functional experiments were used to investigate the role of SFXN1 in TNBC cells. SFXN1 was significantly overexpressed in TNBC tumor tissues and was associated with unfavorable outcomes in patients with TNBC. Functional experiments demonstrated that SFXN1 promoted TNBC growth and metastasis in vitro and in vivo. Mechanistic studies revealed that SFXN1 promoted TNBC progression by inhibiting the autophagy receptor TOLLIP (toll interacting protein)-mediated autophagic degradation of CIP2A. The pro-tumorigenic effect of SFXN1 overexpression was partially prevented by lapatinib-mediated inhibition of the CIP2A/PP2A/p-AKT pathway. These findings may provide a new targeted therapy for patients with TNBC.

Abstract 573: SLC22A17 as a promising target for triple-negative breast cancer

Abstract Introduction: Triple-negative breast cancer (TNBC) represents a highly aggressive subtype of breast cancer, accounting for approximately 15% of all cases of invasive breast cancer globally. The absence of a well-defined cell membrane receptor has significantly limited the therapeutic options available for treating this malignancy. Consequently, there is a compelling need to explore novel receptors that could potentially revolutionize the treatment strategies employed for TNBC. Recent scientific research has unveiled SLC22A17, a cell surface receptor known for its involvement in iron transport, as a substantial contributor to the development of various cancer types. Thus, the primary objective of this study is to comprehensively evaluate the significance of SLC22A17 and its potential as a therapeutic target in TNBC. Experimental Procedures: In this study, we meticulously examined the expression patterns of SLC22A17 across distinct subtypes of breast cancer utilizing immunohistochemistry. Computational tools were employed to delve into the role of SLC22A17 within TNBC datasets. To assess the impact of SLC22A17 on TNBC, we employed various methods, including siRNA-mediated silencing, to investigate its role in cell proliferation, apoptosis, epithelial-mesenchymal transition (EMT), and migration. Additionally, we conducted western blot analyses to assess the expression levels of SLC22A17 and cellular signaling molecules, such as Akt/mTOR and JAK/STAT, within the context of TNBC cells. Results: Our analysis revealed a significant upregulation of SLC22A17 in both TNBC tissues and cell lines, establishing a direct correlation with patient survival outcomes. Silencing the expression of SLC22A17 resulted in reduced cell proliferation, migration, EMT, increased apoptosis, and enhanced autophagy. These effects were accompanied by the inhibition of Akt/mTOR and STAT3 signaling pathways. Moreover, we observed the involvement of the inflammatory cytokine TNF-α, which induced the expression of SLC22A17 in TNBC cell lines and governed various hallmark features of TNBC. Notably, the suppression of SLC22A17 mitigated these processes. Conclusion: In conclusion, this investigation suggests the pivotal role played by SLC22A17 in TNBC and posits its potential as a novel and promising therapeutic target for the management of this formidable disease. Citation Format: Ajaikumar B. Kunnumakkara, Krishan Kumar Thakur. SLC22A17 as a promising target for triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 573.

Abstract 5645: HuR regulates aerobic glycolysis in triple negative breast cancer (TNBC)

Abstract Introduction: The RNA-binding protein Human Antigen R (HuR/ELAVL1) is ubiquitously expressed and exerts post-transcriptional regulation on mRNAs characterized by U/AU-rich sequences in their 3' UTR. Elevated cytoplasmic expression of HuR has been associated with various malignancies, influencing key cellular processes such as proliferation, apoptosis, angiogenesis, and metastasis. The involvement of HuR influences prognostic outcomes and hence is a potential biomarker and therapeutic target in cancer research. The present study investigates the oncogenic role of HuR in TNBCs, an aggressive subtype of breast cancer. Methods: Immunohistochemistry was performed on TNBC patient tumor tissue (n=40) to asses the expressions of HuR and MMP9. Human TNBC cell lines MDA-MB-231 and MDA-MB-468 were used for in vitro studies after confirming cytoplasmic HuR accumulation in these cells via Immunofluorescence. The RNA binding activity of HuR was disrupted using CMLD2, a specific inhibitor of HuR, and subsequent physiological changes were assessed through functional assays. An exploratory investigation of HuR-moderated metabolic changes was performed employing NANOSTRING nCounter XT gene expression assay using metabolic pathways panel. Investigation and/or validation of molecular changes were done via immunoblotting. Results: Meta-analysis using Liu-2014 TNBC dataset correlated high HuR protein expression with reduced patient survival (p=0.028). HuR (77%) and MMP9(55%) were found to be overexpressed in TNBC tissues. Inhibiting HuR with CMLD2 resulted in the downregulation of the protein and its downstream targets CDK2, MMP9, and β catenin. HuR inhibition compromised proliferation, migration, Matrigel invasion and EMT (downregulated E-Cadherin, N-Cadherin, and Vimentin) in TNBC cell lines. Further, HuR inhibition resulted in decreased clonogenicity in 2D and 3D TNBC cultures. Transcriptomics studies showed that HuR inhibition in MDA-MB-231 altered the profile of glycolytic genes in addition to other pathways such as cell cycle and DNA damage repair. In accordance, the expression of glycolytic proteins was reduced upon CMLD2 treatment. Similar results were obtained on HuR knockdown by siRNA suggesting that HuR-mediated effects on TNBC progression might be in part through its modulation of aerobic glycolysis. Conclusion: Overall, our results demonstrate HuR as a positive regulator of aerobic glycolysis in TNBCs and the ability of CMLD2 to inhibit HuR provides a rationale to consider it as a potential anticancer agent for TNBCs. Citation Format: Arundhathi Dev J R, Ajay Gogia, Sandeep R. Mathur, S.V.S. Deo, Chandra Prakash Prasad. HuR regulates aerobic glycolysis in triple negative breast cancer (TNBC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5645.

Image Segmentation of Triple-Negative Breast Cancer by Incorporating Multiscale and Parallel Attention Mechanisms

Breast cancer is a highly prevalent cancer. Triple-negative breast cancer (TNBC) is more likely to recur and metastasize than other subtypes of breast cancer. Research on the treatment of TNBC is of great importance, and accurate segmentation of the breast lesion area is an important step in the treatment of TNBC. Currently, the gold standard for tumor segmentation is still sketched manually by doctors, which requires expertise in the field of medical imaging and consumes a great deal of doctors’ time and energy. Automatic segmentation of breast cancer not only reduces the burden of doctors but also improves work efficiency. Therefore, it is of great significance to study the automatic segmentation technique for breast cancer lesion regions. In this paper, a deep-learning-based automatic segmentation algorithm for TNBC images is proposed. The experimental data were dynamic contrast-enhanced magnetic resonance imaging TNBC dataset provided by the Cancer Hospital of Zhengzhou University. The experiments were analyzed by comparing several models with UNet, Attention-UNet, ResUNet, and SegNet and using evaluation indexes such as Dice score and Iou. Compared to UNet, Attention-UNet, ResUNet, and SegNet, the proposed method improved the Dice score by 2.1%, 1.54%, 0.88%, and 9.65%, respectively. The experimental results show that the proposed deep-learning-based TNBC image segmentation model can effectively improve the segmentation performance of TNBC tumors.

Identification of TFR2 as a novel ferroptosis‑related gene that serves an important role in prognosis and progression of triple‑negative breast cancer.

Effective targeted therapeutic strategies for triple-negative breast cancer (TNBC), the most malignant subtype of breast cancer, are currently lacking. Ferroptosis has been reported to be associated with the onset and advancement of various cancer types, including TNBC. However, there are limited studies on the correlation between TNBC and ferroptosis-related genes. In addition, the potential biomarkers of ferroptosis in TNBC need further investigation. The present study aimed to assess the prognostic role of a novel ferroptosis-related gene signature in the context of TNBC. The signature was established utilizing The Cancer Genome Atlas dataset. This three-gene model [transferrin receptor 2 (TFR2), regulator of G protein signaling 4 and zinc finger protein 36] was developed utilizing least absolute shrinkage and selection operator regression analysis and demonstrated satisfactory predictive performance in TNBC. The area under the curve values of the receiver operating characteristic curves in this model concerning the 1-, 2- and 3-year survival prediction were 0.721, 0.840 and 0.856, respectively. The predictive performance of the model was verified using the TNBC dataset GSE25307. Gene set enrichment analysis (GSEA) demonstrated the enrichment of genes in the low-risk group in a number of important metabolic pathways. Single-sample GSEA demonstrated a variation in the expression levels of immune checkpoint molecules between the high- and low-risk groups. The inhibitory impact of TFR2 knockdown on the proliferative capacity of TNBC cells was verified through in vitro experiments. The data also demonstrated that TFR2 knockdown facilitated the ferroptosis of TNBC cells. Additional assessments indicated that the effects of TFR2 knockdown were partially reversed upon treatment with the ferroptosis inhibitor ferrostatin-1. In conclusion, in the present study, a novel and accurate ferroptosis-related predictive signature was established for TNBC with potential future clinical applications. To the best of our knowledge, the present study is the first to report that TFR2 regulated ferroptosis in TNBC cells in vitro.

C9orf142 transcriptionally activates MTBP to drive progression and resistance to CDK4/6 inhibitor in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) presents the most challenging subtype of all breast cancers because of its aggressive clinical phenotypes and absence of viable therapy targets. In order to identify effective molecular targets for treating patients with TNBC, we conducted an integration analysis of our recently published TNBC dataset of quantitative proteomics and RNA-Sequencing, and found the abnormal upregulation of chromosome 9 open reading frame 142 (C9orf142) in TNBC. However, the functional roles of C9orf142 in TNBC are unclear. In vitro and in vivo functional experiments were performed to assess potential roles of C9orf142 in TNBC. Immunoblotting, real-time quantitative polymerase chain reaction (RT-qPCR),and immunofluorescent staining were used to investigate the expression levels of C9orf142 and its downstream molecules. The molecular mechanisms underlying C9orf142-regulated mouse double minute 2 (MDM2)-binding protein (MTBP) were determined by chromatin immunoprecipitation (ChIP) and dual-luciferase reporter assays. In TNBC tissues and metastatic lymph nodes, we observed that C9orf142 exhibited abnormal up-regulation, and its elevated expression was indicative of unfavorable prognosis for TNBC patients. Both in vitro and in vivo functional experiments demonstrated that C9orf142 accelerated TNBC growth and metastasis. Further mechanism exploration revealed that C9orf142 transcriptionally activated MTBP, thereby regulating its downstream MDM2/p53/p21 signaling axis and the transition of cell cycle from G1 to S phase. Functional rescue experiment demonstrated that knockdown of MTBP attenuated C9orf142-mediated tumour growth and metastasis. Furthermore, depletion of C9orf142 remarkably increased the responsiveness of TNBC cells to CDK4/6 inhibitor abemaciclib. Together, these findings unveil a previously unrecognized effect of C9orf142 in TNBC progression and responsiveness to CDK4/6 inhibitor, and emphasize C9orf142 as a promising intervention target for TNBC treatment.

FunSpace: A functional and spatial analytic approach to cell imaging data using entropy measures.

Spatial heterogeneity in the tumor microenvironment (TME) plays a critical role in gaining insights into tumor development and progression. Conventional metrics typically capture the spatial differential between TME cellular patterns by either exploring the cell distributions in a pairwise fashion or aggregating the heterogeneity across multiple cell distributions without considering the spatial contribution. As such, none of the existing approaches has fully accounted for the simultaneous heterogeneity caused by both cellular diversity and spatial configurations of multiple cell categories. In this article, we propose an approach to leverage spatial entropy measures at multiple distance ranges to account for the spatial heterogeneity across different cellular organizations. Functional principal component analysis (FPCA) is applied to estimate FPC scores which are then served as predictors in a Cox regression model to investigate the impact of spatial heterogeneity in the TME on survival outcome, potentially adjusting for other confounders. Using a non-small cell lung cancer dataset (n = 153) as a case study, we found that the spatial heterogeneity in the TME cellular composition of CD14+ cells, CD19+ B cells, CD4+ and CD8+ T cells, and CK+ tumor cells, had a significant non-zero effect on the overall survival (p = 0.027). Furthermore, using a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer dataset (n = 33), our proposed method identified a significant impact of cellular interactions between tumor and immune cells on the overall survival (p = 0.046). In simulation studies under different spatial configurations, the proposed method demonstrated a high predictive power by accounting for both clinical effect and the impact of spatial heterogeneity.

C-X-C Motif Chemokine Ligand 9 Correlates with Favorable Prognosis in Triple-Negative Breast Cancer by Promoting Immune Cell Infiltration.

C-X-C motif chemokine ligand 9 (CXCL9) plays an important role in antitumor immunity through the recruitment, proliferation, and activation of immune cells (IC). Here, we evaluated the expression patterns of CXCL9 and programmed death-ligand 1 (PD-L1) in a cohort of 268 patients with triple-negative breast cancer (TNBC) by tissue microarray (TMA). The correlations between CXCL9 expression in ICs or tumor cells (TC) and clinicopathologic parameters, PD-L1 expression, tumor-infiltrating lymphocytes (TIL) and survival were analyzed in this cohort (n = 268). In addition, we analyzed a TNBC dataset (n = 138) from The Cancer Genome Atlas (TCGA) to identify correlation between CXCL9 expression and other immune gene expression, immune infiltration, and prognosis. The results of the TMA cohort (n = 268) showed that CXCL9 was expressed in 80.6% cases, with elevated expression levels in ICs relative to in TCs (median: 1% vs. 0%). CXCL9 expressed in ≥1% of ICs was categorized as the CXCL9-IC-positive group. CXCL9-IC expression was strongly and positively correlated with the PD-L1 expression, CD3+ TILs, CD4+ TILs, CD8+ TILs, and CD19+ TILs (all P < 0.0001). Survival analyses showed that the CXCL9-IC-positive group demonstrated prolonged disease-free survival (P = 0.038) and overall survival (P = 0.023) compared with the negative group. The analyses from TCGA cohort (n = 138) showed that elevated CXCL9 expression correlated with increased infiltration of B cells, macrophages, natural killer cells, monocytes and increased expression of immune checkpoint molecules and other CXCL family members, including CXCL10 and CXCL11. These findings confirm the regulatory role of CXCL9 in antitumor immunity and suggest a potential role in treatments involving immune checkpoint blockade.

A weighted distance-based approach for deriving consensus tumor evolutionary trees.

The acquisition of somatic mutations by a tumor can be modeled by a type of evolutionary tree. However, it is impossible to observe this tree directly. Instead, numerous algorithms have been developed to infer such a tree from different types of sequencing data. But such methods can produce conflicting trees for the same patient, making it desirable to have approaches that can combine several such tumor trees into a consensus or summary tree. We introduce The Weighted m-Tumor Tree Consensus Problem (W-m-TTCP) to find a consensus tree among multiple plausible tumor evolutionary histories, each assigned a confidence weight, given a specific distance measure between tumor trees. We present an algorithm called TuELiP that is based on integer linear programming which solves the W-m-TTCP, and unlike other existing consensus methods, allows the input trees to be weighted differently. On simulated data we show that TuELiP outperforms two existing methods at correctly identifying the true underlying tree used to create the simulations. We also show that the incorporation of weights can lead to more accurate tree inference. On a Triple-Negative Breast Cancer dataset, we show that including confidence weights can have important impacts on the consensus tree identified. An implementation of TuELiP and simulated datasets are available at https://bitbucket.org/oesperlab/consensus-ilp/src/main/.

Altered Expression of CYSLTR1 is Associated With Adverse Clinical Outcome in Triple Negative Breast Tumors: An In Silico Approach.

Triple negative breast cancer (TNBC) has high relapse rates due to dysregulated inflammatory signaling pathways and significant changes in the tumor microenvironment, probably influencing the failure of several therapies. The Cysteinyl Leukotriene Receptor 1 (CYSLTR1), a leukotriene modulator of inflammation, has been shown to play an important role in cancer pathogenesis and survival but few studies have been reported on its role in breast cancer. The present work was conducted using publicly available platforms that have omics data to assess the clinical potential of CYSLTR1 expression and its prognostic validation in large cohorts of samples from breast cancer patients. Web platforms containing clinical information, RNA-seq and protein data were selected to perform in silico analyses of the potential marker CYLSTR1. Added together, the platforms included modules for correlation, expression, prognosis, drug interactions, and construction of gene networks. Kaplan-Meier curves revealed that reduced levels of CYSLTR1 corresponded to an unfavorable outcome for overall survival (p<0.005) as well as relapse-free survival (p<0.001) in the basal subtype. Additionally, CYSLTR1 was downregulated in breast tumor samples compared to adjacent healthy tissue (p<0.01) and the basal subtype exhibited the lowest expression of CYSLTR1 relative to the other subtypes (p<0.0001). Furthermore, gene networking analysis showed strong associations of CYSLTR1 with two protein-coding genes (P2RY10 and XCR1) when tested on a TNBC dataset. Our data highlighted the relevance of CYSLTR1 since it may play an important role in TNBC therapy. However, further in vitro and in vivo studies should be directed towards validating our findings in an effort to improve our understanding of TNBC pathology.

Abstract P2-23-01: Patient-derived xenografts allow deconvolution of single agent and combination chemotherapy responses in triple-negative breast cancer

Abstract Background: Triple-negative breast cancer (TNBC) patients frequently receive combination chemotherapy treatment, but a direct comparison of response to carboplatin, docetaxel, and their combination in 50 TNBC patient-derived xenografts (PDXs) showed that combination treatment was largely ineffective at generating enhanced responses over the best single agent. This suggests de-escalation of chemotherapy may be possible if molecular mechanisms and biomarkers underlying response to individual treatments can be identified. To this end, we performed multi-omics profiling for the 50 TNBC PDXs. Methods: Orthotopic TNBC PDXs were treated with four weekly cycles of docetaxel, carboplatin, or the combination. Changes in tumor volume after 4 weeks of treatment were assessed quantitatively and by modified RECIST criteria. Genomic, transcriptomic, and mass-spectrometry-based proteomic profiling were performed on baseline tumors prior to treatments to identify associations with chemotherapy response at the gene and pathway level. ProMS was used to integrate both RNA and protein data to select a 5 RNA feature combination for optimized prediction of carboplatin response in a logistic regression model. Publicly available neoadjuvant chemotherapy clinical datasets with transcriptomic data and response information used for validation/testing included TNBC samples from: GSE18864, I-SPY2 (GSE194040), and BrighTNess (GSE164458). Results: Proteogenomic profiles revealed distinct genes associated with response to each agent and their combination, respectively, suggesting distinct molecular mechanisms underlying response to each treatment. A substantial number of genes associated with single agent and combination treatment were validated in multiple independent patient cohorts receiving platinum and taxane containing neoadjuvant therapy, confirming clinical relevance of our PDX panel. For the same treatment, different types of molecular data identified distinct sets of associated genes, providing highly complementary information. At the pathway level, RNA and protein data converged to metabolic and E2F/G2M related pathways which were upregulated in PDXs resistant or responsive to all treatment types, respectively, while variable levels of MYC-related proliferation pathways were observed across all treatments suggesting pathways that are common across and unique to different treatments. Several individual genes found to be higher in PDXs with better response to either single-agent had discriminatory power in external clinical TNBC datasets treated with similar neoadjuvant chemotherapy regimens. In addition, a logistic regression-based carboplatin response prediction model trained to select a group of 5 RNA markers (TKT, MAGI2, ATF6B, MCM7, LRP6) using both RNA and protein data performed the best in predicting response to cisplatin in a clinical TNBC dataset vs predicting response to other datasets with taxane and platinum + taxane combination containing chemotherapy regimens, demonstrating specificity of the prediction model. These results suggest potential individual biomarkers or biomarker combinations to select TNBC tumors that may respond to either single agent carboplatin, docetaxel, or their combination. PDXs refractory to all treatment arms had higher levels of proteostasis-related pathways including proteasome degradation and the unfolded protein response (UPR) related to endoplasmic reticulum stress and altered levels of chromatin regulation. Subsequent pharmacological targeting of the UPR pathway and targeting HDACs enhanced chemotherapy response. Conclusion: Proteogenomic characterization identifies molecular mechanisms and putative biomarkers for stratifying TNBC tumors for single or combination chemotherapy treatments, suggests targeted therapies to augment chemotherapy response, and provides a valuable resource for researchers and clinicians. Citation Format: Jonathan T. Lei, Chen Huang, Ramakrishnan R. Srinivasan, Suhas Vasaikar, Lacey E. Dobrolecki, Alaina N. Lewis, Na Zhao, Jin Cao, Susan G. Hilsenbeck, C. Kent Osborne, Mothaffar Rimawi, Matthew J. Ellis, Varduhi Petrosyan, Alexander B. Saltzman, Anna Malovannaya, John D. Landua, Bo Wen, Antrix Jain, Gerburg M. Wulf, Shunqiang Li, Daniel C. Kraushaar, Tao Wang, Xi Chen, Gloria V. Echeverria, Meenakshi Anurag, Bing Zhang, Michael T. Lewis. Patient-derived xenografts allow deconvolution of single agent and combination chemotherapy responses in triple-negative breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-23-01.

Sialyl LewisX/A and Cytokeratin Crosstalk in Triple Negative Breast Cancer.

Triple-negative breast cancer (TNBC) encompasses multiple entities and is generally highly aggressive and metastatic. We aimed to determine the clinical and biological relevance of Sialyl-Lewis X and A (sLeX/A)-a fucosylated glycan involved in metastasis-in TNBC. Here, we studied tissues from 50 TNBC patients, transcripts from a TNBC dataset from The Cancer Genome Atlas (TCGA) database, and a primary breast cancer cell line. All 50 TNBC tissue samples analysed expressed sLeX/A. Patients with high expression of sLeX/A had 3 years less disease-free survival than patients with lower expression. In tissue, sLeX/A negatively correlated with cytokeratins 5/6 (CK5/6, which was corroborated by the inverse correlation between fucosyltransferases and CK5/6 genes. Our observations were confirmed in vitro when inhibition of sLeX/A remarkably increased expression of CK5/6, followed by a decreased proliferation and invasion capacity. Among the reported glycoproteins bearing sLeX/A and based on the STRING tool, α6 integrin showed the highest interaction score with CK5/6. This is the first report on the sLeX/A expression in TNBC, highlighting its association with lower disease-free survival and its inverse crosstalk with CK5/6 with α6 integrin as a mediator. All in all, sLeX/A is critical for TNBC malignancy and a potential prognosis biomarker and therapeutic target.

Immune-Related Gene Signatures to Predict the Effectiveness of Chemoimmunotherapy in Triple-Negative Breast Cancer Using Exploratory Subgroup Discovery

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited therapeutic options. Although immunotherapy has shown potential in TNBC patients, clinical studies have only demonstrated a modest response. Therefore, the exploration of immunotherapy in combination with chemotherapy is warranted. In this project we identified immune-related gene signatures for TNBC patients that may explain differences in patients' outcomes after anti-PD-L1+chemotherapy treatment. First, we ran the exploratory subgroup discovery algorithm on the TNBC dataset comprised of 422 patients across 24 studies. Secondly, we narrowed down the search to twelve homogenous subgroups based on tumor mutational burden (TMB, low or high), relapse status (disease-free or recurred), tumor cellularity (high, low and moderate), menopausal status (pre- or post) and tumor stage (I, II and III). For each subgroup we identified a union of the top 10% of genotypic patterns. Furthermore, we employed a multinomial regression model to predict significant genotypic patterns that would be linked to partial remission after anti-PD-L1+chemotherapy treatment. Finally, we uncovered distinct immune cell populations (T-cells, B-cells, Myeloid, NK-cells) for TNBC patients with various treatment outcomes. CD4-Tn-LEF1 and CD4-CXCL13 T-cells were linked to partial remission on anti-PD-L1+chemotherapy treatment. Our informatics pipeline may help to select better responders to chemoimmunotherapy, as well as pinpoint the underlying mechanisms of drug resistance in TNBC patients at single-cell resolution.

MIAMI: mutual information-based analysis of multiplex imaging data.

MotivationStudying the interaction or co-expression of the proteins or markers in the tumor microenvironment of cancer subjects can be crucial in the assessment of risks, such as death or recurrence. In the conventional approach, the cells need to be declared positive or negative for a marker based on its intensity. For multiple markers, manual thresholds are required for all the markers, which can become cumbersome. The performance of the subsequent analysis relies heavily on this step and thus suffers from subjectivity and lacks robustness.ResultsWe present a new method where different marker intensities are viewed as dependent random variables, and the mutual information (MI) between them is considered to be a metric of co-expression. Estimation of the joint density, as required in the traditional form of MI, becomes increasingly challenging as the number of markers increases. We consider an alternative formulation of MI which is conceptually similar but has an efficient estimation technique for which we develop a new generalization. With the proposed method, we analyzed a lung cancer dataset finding the co-expression of the markers, HLA-DR and CK to be associated with survival. We also analyzed a triple negative breast cancer dataset finding the co-expression of the immuno-regulatory proteins, PD1, PD-L1, Lag3 and IDO, to be associated with disease recurrence. We demonstrated the robustness of our method through different simulation studies.Availability and implementationThe associated R package can be found here, https://github.com/sealx017/MIAMI.Supplementary information Supplementary data are available at Bioinformatics online.

SPF: A spatial and functional data analytic approach to cell imaging data.

The tumor microenvironment (TME), which characterizes the tumor and its surroundings, plays a critical role in understanding cancer development and progression. Recent advances in imaging techniques enable researchers to study spatial structure of the TME at a single-cell level. Investigating spatial patterns and interactions of cell subtypes within the TME provides useful insights into how cells with different biological purposes behave, which may consequentially impact a subject's clinical outcomes. We utilize a class of well-known spatial summary statistics, the K-function and its variants, to explore inter-cell dependence as a function of distances between cells. Using techniques from functional data analysis, we introduce an approach to model the association between these summary spatial functions and subject-level outcomes, while controlling for other clinical scalar predictors such as age and disease stage. In particular, we leverage the additive functional Cox regression model (AFCM) to study the nonlinear impact of spatial interaction between tumor and stromal cells on overall survival in patients with non-small cell lung cancer, using multiplex immunohistochemistry (mIHC) data. The applicability of our approach is further validated using a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer dataset.

Abstract 1010: LIG1 deletion predicts chemotherapy resistance, chromosomal instability, and poor prognosis in triple negative breast cancer

Abstract Introduction: Cytotoxic chemotherapy for sporadic Triple Negative Breast Cancer (TNBC) remains the standard of care and the recent approval for adjuvant PD1 therapy is not biomarker guided. Pathological complete response (pCR) is often not achieved and portends poor survival. Predictive markers for individual drugs have proven elusive. Approach: Microscaled proteogenomics (MPG) was applied to snap-frozen TNBC clinical trial core needle biopsies obtained before treatment with carboplatin and docetaxel (WashU: NCT201404107 and BCM: NCT02544987). Clinical endpoints for discovery analysis were pathological complete response (pCR) and residual cancer burden (RCB). Standard non-parametric statistical tests were employed to identify proteogenomic features associated with these endpoints. Results: Copy number aberrations (CNA) are a recurrent feature of TNBC and a potential driver of chemotherapy sensitivity. We therefore sought CNA with concordant changes at the mRNA and protein levels that also associate with pCR status. Genes located within a recurrent interstitial deletion at chromosomal location 19q13.3 were the most significantly down-regulated at mRNA and protein level in chemotherapy resistant cases. 19q13.3 encodes multiple DNA damage response (DDR) genes; however, only LIG1, a DNA ligase required for lagging strand synthesis and DNA repair, showed concordant changes at both the mRNA and protein level. In multiple independent TNBC data sets, LIG1 deletion was associated lack of pCR and poor metastasis-free survival. Additionally in the BrighTNess TNBC trial lower LIG1 mRNA levels were associated with increased chemotherapy resistance in the carboplatin containing arms (no pCR and residual cancer burden I-III; p=0.0008 and 0.003 respectively). In PDX-derived short-term cultures and PDXs treated with docetaxel or carboplatin, a specific association of carboplatin resistance with LIG1 deletion was observed. LIG1 depleted-tumors did not harbor elevated scores for homologous recombination defect signature, suggesting LIG1 loss is an orthogonal pathway for TNBC pathogenesis The high chromosomal instability index in LIG1 deletion tumors in our TNBC study was robustly reproduced in multiple datasets (including TCGA-BRCA ; Metastatic breast cancer project). LIG1 copy number deletion was also associated with poor progression free survival, and high chromosomal instability in multiple other cancers (including TCGA-​UCEC HR=2.23, TCGA-HNSC HR=1.46, TCGA-PRAD HR=2.07, TCGA- COAD HR=1.75 and TCGA-KIRP HR=4.00). Conclusion: Deletion of LIG1 is associated with chromosomal instability in TNBC and occurs in tumors without genomic evidence for defects in homologous recombination. Other clinical features of LIG1 deleted TNBC and how LIG1 loss may cause chromosomal instability and tumorigenesis will be discussed. Citation Format: Meenakshi Anurag, Eric Jaehnig, Jonathan Lei, Beom-Jun Kim, Anh Minh Tran Huynh, Yongchao Dou, Tanmayi Vashist, Erik Bergstrom, Xuxu Gou, Viktoriya Korchina, Donna Marie Muzny, Kristen Otte, Harshavardhan Doddapaneni, Lacey Dobrolecki, Gloria Vittone Echeverria, Bora Lim, Mothaffar Rimawi, Karsten Krug, Ian Hageman, Henry Rodriguez, Ana I. Robles, Tara Hiltke, Kent Osborne, Michael Gillette, George Miles, Steven Carr, Michael T Lewis, Bing Zhang, Foluso Ademuyiwa, Shankha Satpathy, Matthew J. Ellis. LIG1 deletion predicts chemotherapy resistance, chromosomal instability, and poor prognosis in triple negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1010.

Immunogenomic landscape analyses of immune molecule signature-based risk panel for patients with triple-negative breast cancer

Triple-negative breast cancer (TNBC) presented as high heterogeneous immunogenicity that lacks useful clinical signatures to risk-stratify immune-benefit subtypes. We hypothesized that molecular-based phenotypic characterization of TNBC tumors and their immunity may overcome these challenges. We enrolled 1,145 patients with TNBC for analysis. Through combining algorithm integration analysis and TNBC datasets, a tumor immune risk score (TIRS) panel consisting of 8 potential biomarkers was identified. The TIRS panel represented excellent effectiveness as an independent predictor. High- and low risk stratification of patients was further achieved by TIRS, and significant survival and immune-infiltration pattern differences were found in each cohort, both at the transcriptome and protein levels. Non-negative matrix factorization clustering further identified four different tumor immune microenvironment types (TIMTs), among which TIMT-II was associated with the best prognosis and immune status, whereas TIMT-IV had the opposite effect, TIMT-III was associated with highly unstable genomes, and TIMT-I displayed stem-cell-related characteristics along with high stromal scores and may have extensive enrichment of tumor-associated fibroblasts and vascular cells. In conclusion, our TIRS panel could serve as a robust prognostic signature and provide therapeutic benefits for immunotherapy. Additionally, coordinating four TIMTs may be helpful for clinical decision-making in TNBC patients.

Development of an immune-related prognostic biomarker for triple-negative breast cancer

Purpose: Oncology studies employing digital dissection methodologies have provided some insight on the biological features of tumor microenvironment of Triple-negative breast cancer (TNBC), but molecular diagnostics rarely have therapeutic impact. We aimed to identify a novel prognostic biomarker to investigate immune characteristics of TNBC using transcriptomic features. Patients and Methods: We extracted whole transcriptome from breast cancer tissue of 30 TNBC patients and then used bioinformatics approaches to characterize the different immune cell contents in tumor tissue and para-cancerous tissue. We extract 2 indicators to describe the major differences in immune infiltration in the microenvironment between tumor tissue and para-cancerous tissue. We then combined the 2 indicators that represent the levels of increased and decreased infiltration in each sample to obtain the Immune Infiltration Score (IIS). Then we compared the tumor-infiltrating immune cell contents and immune infiltrating status in TNBC samples with CIBERSORT and ESTIMATE score to validate the IIS. Finally, 132 TNBC patients from the Cancer Genome Atlas program (TCGA) dataset was used to validate the predictive power of IIS. Results: 4 types of upregulated and 4 types of downregulated immune cells were identified in the tumor tissue samples of the TNBC patients. Then we developed a novel biomarker, IIS. Results showed that IIS score can clearly separate cancer and para-cancerous tissue. Using the same cutoff value of 0 in the TNBC-TCGA cohort, we show that those patients with a higher IIS had significantly higher PD-L1 expression and shorter progression-free survival time than those with a lower IIS value, indicating IIS score can be generalized to other TNBC datasets. Conclusion: we explored the immune infiltration landscape in 30 TNBC patients and provided IIS as a novel and reliable biomarker to evaluate the progression-free survival and prognosis of the TNBC patients.

BCL6 and the Notch pathway: a signaling axis leading to a novel druggable biotarget in triple negative breast cancer.

The transcriptional repressor B-cell lymphoma 6 (BCL6) is dysregulated in several neoplasms, but its role in triple negative breast cancer (TNBC), a highly aggressive subtype which lacks effective treatment, is unclear. The presence of intratumoral cancer stem cells (CSCs) is a main cause of tumor relapse. The Notch signaling pathway is crucial for regulating CSC self-renewal and promoting breast cancer (BC) development and resistance to anticancer therapies. Here, we investigated signaling cascades of BCL6 in the CSC compartment of TNBCs, and the mechanisms that govern its activity, mainly through Notch signaling. Gene expression, somatic copy number alterations and clinical data from the Cancer Genome Atlas and METABRIC were accessed through theXena and cbioportal browsers. Public transcriptome profiles from TNBC datasets were retrieved from theGene Expression Omnibus. Mammosphere formation efficiency was calculated after BCL6 knockdown via transient siRNAtransfection, stable silencing or pharmacological inhibition. The effects exhibited via BCL6 inhibition in putative TNBC stem-like cells were evaluated by immunofluorescence and qRT-PCR analyses. Chromatin immunoprecipitation experiments were performed to validate a putative BCL6 responsive element located in the first intron of the Numb gene and to define the circuit of corepressors engaged by BCL6 following its inhibition. Immunoprecipitation assays were carried out to investigate a novel interaction at the basis of BCL6 control of CSC activity in TNBC. In silico analyses of benchmarked public datasets revealed a significant enrichment of BCL6 in cancer stemness related pathways, particularly of Notch signaling in TNBC. In vitro stable inhibition of BCL6 significantly reduced tumor cell growth and, accordingly, we found thatthe mammosphere formation efficiency of BCL6 silencedcells was significantly impaired by pharmacological inhibition of Notch signaling. BCL6 was found to be expressed at significantly higher levels in TNBC mammospheres than in their adherent counterparts, and loss of BCL6 function significantly decreased mammosphere formation with preferential targeting of CD44-positive versus ALDH-positive stem-like cells. Functional interplay between BCL6 and the chromatin remodeling factor EZH2 triggered the BCL6/Notch stemness signaling axis via inhibition of Numb transcription. Our results may be instrumental for the prospective design of combination treatment strategies that selectively target novel TNBC-associated biomarker(s) whose activity is implicated in the regulation of cancer stemness (such as BCL6) and molecules in developmentally conserved signaling pathways (such as Notch) to achieve long-lasting tumor control and improve patient outcomes.

Integrated multi-cohorts for characterizing the immunogenomic landscape and predicting drug response in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is the breast cancer subtype with the highest fatality rate, and it seriously threatens women's health. Recent studies found that the level of immune cell infiltration in TNBC was associated with tumor progression and prognosis. However, due to practical constraints, most of these TNBC immune infiltration studies only used a small number of patient samples and a few immune cell types. Therefore, it is necessary to integrate more TNBC patient samples and immune cell types to comprehensively study immune infiltration in TNBC to contribute to the prognosis and treatment of patients. In this study, 12 TNBC datasets were integrated and an extensive collection of 182 gene sets with immune-related signatures were included to comprehensively investigate tumor immune microenvironment of TNBC. A single sample gene set enrichment analysis was performed to calculate the infiltration score of each immune-related signature in each patient, and an immune-related risk scoring model for TNBC was constructed to accurately assess patient prognosis. Significant differences were found in immunogenomic landscape between different immune risk subtypes. In addition, the immunotherapy response and chemotherapy drug sensitivity of patients with different immune risk subtypes were also analyzed. The results showed that there were significant differences in these characteristics. Finally, a prediction model for immune risk subtypes of TNBC patients was constructed to accurately predict patients with unknown subtypes. Based on the aforementioned findings, we believed that the immune-related risk score constructed in this study can assist in providing personalized medicine to TNBC patients.