High Intratumoral Stromal Content Defines Reactive Breast Cancer as a Low-risk Breast Cancer Subtype

Phytochemicals targeting epidermal growth factor receptor (EGFR) for the prevention and treatment of HNSCC: A review

Head and neck squamous cell carcinoma (HNSCC) develops from the mucosal epithelium of the oral cavity, pharynx, and larynx, and is the most common malignancy of the head and neck, the incidence of which continues to rise. The epidermal growth factor receptor is thought to play a key role in the pathogenesis of HNSCC. Inhibition of epidermal growth factor receptor has been identified as an effective target for the treatment of HNSCC. Many phytochemicals have emerged as potential new drugs for the treatment of HNSCC. A systematic search was conducted for research articles published in PubMed, and Medline on relevant aspects. This review provides an overview of the available literature and reports highlighting the in vitro effects of phytochemicals on epidermal growth factor in various HNSCC cell models and in vivo in animal models and emphasizes the importance of epidermal growth factor as a current therapeutic target for HNSCC. Based on our review, we conclude that phytochemicals targeting the epidermal growth factor receptor are potentially effective candidates for the development of new drugs for the treatment of HNSCC. It provides an idea for further development and application of herbal medicines for cancer treatment.

Impact of Cuproptosis-related markers on clinical status, tumor immune microenvironment and immunotherapy in colorectal cancer: A multi-omic analysis

Background

Cuproptosis, a novel identified cell death form induced by copper, is characterized by aggregation of lipoylated mitochondrial enzymes and the destabilization of Fe-S cluster proteins. However, the function and potential clinical value of cuproptosis and cuproptosis-related biomarkers in colorectal cancer (CRC) remain largely unknown.

Methods

A comprehensive multi-omics (transcriptomics, genomics, and single-cell transcriptome) analysis was performed for identifying the influence of 16 cuproptosis-related markers on clinical status, molecular functions and tumor microenvironment (TME) in CRC. A novel cuproptosis-related scoring system (CuproScore) based on cuproptosis-related markers was also constructed to predict the prognosis of CRC individuals, TME and the response to immunotherapy. In addition, our transcriptome cohort of 15 paired CRC tissue, tissue-array, and various assays in 4 kinds of CRC cell lines in vitro were applied for verification.

Results

Cuproptosis-related markers were closely associated with both clinical prognosis and molecular functions. And the cuproptosis-related molecular phenotypes and scoring system (CuproScore) could distinguish and predict the prognosis of CRC patients, TME, and the response to immunotherapy in both public and our transcriptome cohorts. Besides, the expression, function and clinical significance of these markers were also checked and analyzed in CRC cell lines and CRC tissues in our own cohorts.

Conclusions

In conclusion, we indicated that cuproptosis and CPRMs played a significant role in CRC progression and in modeling the TME. Inducing cuproptosis may be a useful tool for tumor therapy in the future.

The Variety of 3D Breast Cancer Models for the Study of Tumor Physiology and Drug Screening

Breast cancer is the most common cancer in women and responsible for multiple deaths worldwide. 3D cancer models enable a better representation of tumor physiology than the conventional 2D cultures. This review summarizes the important components of physiologically relevant 3D models and describes the spectrum of 3D breast cancer models, e.g., spheroids, organoids, breast cancer on a chip and bioprinted tissues. The generation of spheroids is relatively standardized and easy to perform. Microfluidic systems allow control over the environment and the inclusion of sensors and can be combined with spheroids or bioprinted models. The strength of bioprinting relies on the spatial control of the cells and the modulation of the extracellular matrix. Except for the predominant use of breast cancer cell lines, the models differ in stromal cell composition, matrices and fluid flow. Organoids are most appropriate for personalized treatment, but all technologies can mimic most aspects of breast cancer physiology. Fetal bovine serum as a culture supplement and Matrigel as a scaffold limit the reproducibility and standardization of the listed 3D models. The integration of adipocytes is needed because they possess an important role in breast cancer.

The Great Immune Escape: Understanding the Divergent Immune Response in Breast Cancer Subtypes

Breast cancer, the most common type of cancer affecting women, encompasses a collection of histologic (mainly ductal and lobular) and molecular subtypes exhibiting diverse clinical presentation, disease trajectories, treatment options, and outcomes. Immunotherapy has revolutionized treatment for some solid tumors but has shown limited promise for breast cancers. In this review, we summarize recent advances in our understanding of the complex interactions between tumor and immune cells in subtypes of breast cancer at the cellular and microenvironmental levels. We aim to provide a perspective on opportunities for future immunotherapy agents tailored to specific features of each subtype of breast cancer.

SIGNIFICANCE

Although there are currently over 200 ongoing clinical trials testing immunotherapeutics, such as immune-checkpoint blockade agents, these are largely restricted to the triple-negative and HER2+ subtypes and primarily focus on T cells. With the rapid expansion of new in vitro, in vivo, and clinical data, it is critical to identify and highlight the challenges and opportunities unique for each breast cancer subtype to drive the next generation of treatments that harness the immune system.

Predictive value of collagen in cancer

Cancer is a complex disease and a significant cause of mortality worldwide. Over the course of nearly all cancer types, collagen within the tumor microenvironment influences emergence, progression, and metastasis. This review discusses collagen regulation within the tumor microenvironment, pathological involvement of collagen, and predictive values of collagen and related extracellular matrix components in main cancer types. A survey of predictive tests leveraging collagen assays using clinical cohorts is presented. A conclusion is that collagen has high predictive value in monitoring cancer processes and stratifying by outcomes. New approaches should be considered that continue to define molecular facets of collagen related to cancer.

Proteomic analysis of archival breast cancer clinical specimens identifies biological subtypes with distinct survival outcomes

Despite advances in genomic classification of breast cancer, current clinical tests and treatment decisions are commonly based on protein level information. Formalin-fixed paraffin-embedded (FFPE) tissue specimens with extended clinical outcomes are widely available. Here, we perform comprehensive proteomic profiling of 300 FFPE breast cancer surgical specimens, 75 of each PAM50 subtype, from patients diagnosed in 2008-2013 (n = 178) and 1986-1992 (n = 122) with linked clinical outcomes. These two cohorts are analyzed separately, and we quantify 4214 proteins across all 300 samples. Within the aggressive PAM50-classified basal-like cases, proteomic profiling reveals two groups with one having characteristic immune hot expression features and highly favorable survival. Her2-Enriched cases separate into heterogeneous groups differing by extracellular matrix, lipid metabolism, and immune-response features. Within 88 triple-negative breast cancers, four proteomic clusters display features of basal-immune hot, basal-immune cold, mesenchymal, and luminal with disparate survival outcomes. Our proteomic analysis characterizes the heterogeneity of breast cancer in a clinically-applicable manner, identifies potential biomarkers and therapeutic targets, and provides a resource for clinical breast cancer classification.

The Prognostic Role of Intratumoral Stromal Content in Lobular Breast Cancer

Simple Summary

High intratumoral stromal content is related to worse outcomes in several types of cancer. However, its prognostic role in breast cancer seems to differ between different subtypes. High intratumoral stromal content is a negative prognostic marker in triple-negative breast cancer, while the opposite is the case for estrogen-receptor-positive breast cancer, in which higher stromal content is indicative of a better prognosis. Most lobular breast cancers are estrogen-receptor-positive, and the tumor tissue has a clearly defined histological appearance, often with a high intratumoral stromal content. To date, the prognostic role of intratumoral stromal content in lobular breast cancer remains unclear. In this study, we aimed to investigate the prognostic importance of intratumoral stromal content in estrogen-receptor-positive lobular breast cancer. Our results show that high intratumoral stromal content is an easily assessed and clinically useful indicator of a good prognosis in lobular breast cancer.

Abstract

Previous studies have shown that high intratumoral stromal content is associated with a worse prognosis in breast cancer, especially in the triple-negative subtype. However, contradictory results have been reported for estrogen-receptor-positive (ER+) breast cancer, indicating that the prognostic role of intratumoral stromal content may be subtype-dependent. In this study, we investigated the importance of intratumoral stromal content for breast cancer-specific mortality (BCM) in a well-defined subgroup (n = 182) of ER+/human-epidermal growth-factor-receptor-2 negative (HER2−) invasive lobular breast cancer (ILC). The intratumoral stromal content was assessed on hematoxylin–eosin-stained whole sections and graded into high stroma (>50%) or low stroma (≤50%). A total of 82 (45%) patients had high-stroma tumors, and 100 (55%) had low-stroma tumors. High-stroma tumors were associated with a lower Nottingham histological grade, low Ki67, and a luminal A-like subtype. After a 10-year follow-up, the patients with high-stroma tumors had a lower BCM (HR: 0.43, 95% CI: 0.21–0.89, p = 0.023) in univariable analysis. Essentially the same effect was found in both the multivariable analysis (10-year follow-up) and univariable analysis (25-year follow-up), but these findings were not strictly significant. In ER+/HER2− ILC, high intratumoral stromal content is an easily assessable histological indicator of a good prognosis.

Roles of the Immune/Methylation/Autophagy Landscape on Single-Cell Genotypes and Stroke Risk in Breast Cancer Microenvironment

This study sought to perform integrative analysis of the immune/methylation/autophagy landscape on breast cancer prognosis and single-cell genotypes. Breast Cancer Recurrence Risk Score (BCRRS) and Breast Cancer Prognostic Risk Score (BCPRS) were determined based on 6 prognostic IMAAGs obtained from the TCGA-BRCA cohort. BCRRS and BCPRS, respectively, were used to construct a risk prediction model of overall survival and progression-free survival. Predictive capacity of the model was evaluated using clinical data. Analysis showed that BCRRS is associated with a high risk of stroke. In addition, PPI and drug-ceRNA networks based on differences in BCPRS were constructed. Single cells were genotyped through integrated scRNA-seq of the TNBC samples based on clustering results of BCPRS-related genes. The findings of this study show the potential regulatory effects of IMAAGs on breast cancer tumor microenvironment. High AUCs of 0.856 and 0.842 were obtained for the OS and PFS prognostic models, respectively. scRNA-seq analysis showed high expression levels of adipocytes and adipose tissue macrophages (ATMs) in high BCPRS clusters. Moreover, analysis of ligand-receptor interactions and potential regulatory mechanisms were performed. The LINC00276&MALAT1/miR-206/FZD4-Wnt7b pathway was also identified which may be useful in future research on targets against breast cancer metastasis and recurrence. Neural network-based deep learning models using BCPRS-related genes showed that these genes can be used to map the tumor microenvironment. In summary, analysis of IMAAGs, BCPRS, and BCRRS provides information on the breast cancer microenvironment at both the macro- and microlevels and provides a basis for development of personalized treatment therapy.

Prognostic and predictive impact of stroma cells defined by PDGFRb expression in early breast cancer: results from the randomized SweBCG91RT trial

Purpose

Predictive biomarkers are needed to aid the individualization of radiotherapy (RT) in breast cancer. Cancer-associated fibroblasts have been implicated in tumor radioresistance and can be identified by platelet-derived growth factor receptor-beta (PDGFRb). This study aims to analyze how PDGFRb expression affects RT benefit in a large randomized RT trial.

Methods

PDGFRb was assessed by immunohistochemistry on tissue microarrays from 989 tumors of the SweBCG91RT trial, which enrolled lymph node-negative, stage I/IIA breast cancer patients randomized to RT after breast-conserving surgery. Outcomes were analyzed at 10 years for ipsilateral breast tumor recurrence (IBTR) and any recurrence and 15 years for breast cancer specific death (BCSD).

Results

PDGFRb expression correlated with estrogen receptor negativity and younger age. An increased risk for any recurrence was noted in univariable analysis for the medium (HR 1.58, CI 95% 1.11–2.23, p = 0.011) or PDGFRb high group (1.49, 1.06–2.10, p = 0.021) compared to the low group. No differences in IBTR or BCSD risk were detected. RT benefit regarding IBTR risk was significant in the PDGFRb low (0.29, 0.12–0.67, p = 0.004) and medium (0.31, 0.16–0.59, p < 0.001) groups but not the PDGFRb high group (0.64, 0.36–1.11, p = 0.110) in multivariable analysis. Likewise, risk reduction for any recurrence was less pronounced in the PDGFRb high group. No significant interaction between RT and PDGFRb-score could be detected.

Conclusion

A higher PDGFRb-score conferred an increased risk of any recurrence, which partly can be explained by its association with estrogen receptor negativity and young age. Reduced RT benefit was noted among patients with high PDGFRb, however without significant interaction.

Profiling of conditionally reprogrammed cell lines for in vitro chemotherapy response prediction of pancreatic cancer

BACKGROUND

The establishment of patient-derived models for pancreatic ductal adenocarcinoma (PDAC) using conventional methods has been fraught with low success rate, mainly because of the small number of tumour cells and dense fibrotic stroma. Here, we sought to establish patient-derived model of PDAC and perform genetic analysis with responses to anticancer drug by using the conditionally reprogrammed cell (CRC) methodology.

METHODS

We performed in vitro and in vivo tumourigenicity assays and analysed histological characteristics by immunostaining. We investigated genetic profiles including mutation patterns and copy number variations using targeted deep sequencing and copy-number analyses. We assessed the responses of cultured CRCs to the available clinical anticancer drugs based on patient responsiveness.

FINDINGS

We established a total of 28 CRCs from patients. Of the 28 samples, 27 showed KRAS mutations in codon 12/13 or codon 61. We found that somatic mutations were shared in the primary-CRC pairs and shared mutations included key oncogenic mutations such as KRAS (9 pairs), TP53 (8 pairs), and SMAD4 (3 pairs). Overall, CRCs preserved the genetic characteristics of primary tumours with high concordance, with additional confirmation of low-AF NPM1 mutation in CRC (35 shared mutations out of 36 total, concordance rate=97.2%). CRCs of the responder group were more sensitive to anticancer agents than those of the non-responder group (P < 0.001).

INTERPRETATION

These results show that a pancreatic cancer cell line model can be efficiently established using the CRC methodology, to better support a personalized therapeutic approach for pancreatic cancer patients.

FUNDING

2014R1A1A1006272, HI19C0642-060019, 2019R1A2C2008050, 2020R1A2C209958611, and 2020M3E5E204028211.

Tumour Stroma Ratio Assessment Using Digital Image Analysis Predicts Survival in Triple Negative and Luminal Breast Cancer

We aimed to determine the clinical significance of tumour stroma ratio (TSR) in luminal and triple negative breast cancer (TNBC) using digital image analysis and machine learning algorithms. Automated image analysis using QuPath software was applied to a cohort of 647 breast cancer patients (403 luminal and 244 TNBC) using digital H&E images of tissue microarrays (TMAs). Kaplan-Meier and Cox proportional hazards were used to ascertain relationships with overall survival (OS) and breast cancer specific survival (BCSS). For TNBC, low TSR (high stroma) was associated with poor prognosis for both OS (HR 1.9, CI 1.1-3.3, p = 0.021) and BCSS (HR 2.6, HR 1.3-5.4, p = 0.007) in multivariate models, independent of age, size, grade, sTILs, lymph nodal status and chemotherapy. However, for luminal tumours, low TSR (high stroma) was associated with a favourable prognosis in MVA for OS (HR 0.6, CI 0.4-0.8, p = 0.001) but not for BCSS. TSR is a prognostic factor of most significance in TNBC, but also in luminal breast cancer, and can be reliably assessed using quantitative image analysis of TMAs. Further investigation into the contribution of tumour subtype stromal phenotype may further refine these findings.

Development of Radiotracers for Breast Cancer-The Tumor Microenvironment as an Emerging Target

Molecular imaging plays an increasingly important role in the diagnosis and treatment of different malignancies. Radiolabeled probes enable the visualization of the primary tumor as well as the metastases and have been also employed in targeted therapy and theranostic approaches. With breast cancer being the most common malignancy in women worldwide it is of special interest to develop novel targeted treatments. However, tumor microenvironment and escape mechanisms often limit their therapeutic potential. Addressing tumor stroma associated targets provides a promising option to inhibit tumor growth and angiogenesis and to disrupt tumor tissue architecture. This review describes recent developments on radiolabeled probes used in diagnosis and treatment of breast cancer especially in triple negative type with the focus on potential targets offered by the tumor microenvironment, like tumor associated macrophages, cancer associated fibroblasts, and endothelial cells.

Epigenetic Reprogramming of Cancer-Associated Fibroblasts Deregulates Glucose Metabolism and Facilitates Progression of Breast Cancer

The mechanistic contributions of cancer-associated fibroblasts (CAFs) in breast cancer progression remain to be fully understood. While altered glucose metabolism in CAFs could fuel cancer cells, how such metabolic reprogramming emerges and is sustained needs further investigation. Studying fibroblasts isolated from patients with benign breast tissues and breast cancer, in conjunction with multiple animal models, we demonstrate that CAFs exhibit a metabolic shift toward lactate and pyruvate production and fuel biosynthetic pathways of cancer cells. The depletion or suppression of the lactate production of CAFs alter the tumor metabolic profile and impede tumor growth. The glycolytic phenotype of the CAFs is in part sustained through epigenetic reprogramming of HIF-1α and glycolytic enzymes. Hypoxia induces epigenetic reprogramming of normal fibroblasts, resulting in a pro-glycolytic, CAF-like transcriptome. Our findings suggest that the glucose metabolism of CAFs evolves during tumor progression, and their breast cancer-promoting phenotype is partly mediated by oxygen-dependent epigenetic modifications.

Alterations in Immune-Related Genes as Potential Marker of Prognosis in Breast Cancer

The tumor microenvironment (TME) is a heterogeneous system that contributes to breast cancer progression. The Cancer Genome Atlas (TCGA) database provides global gene expression profiling data for further analysis of various malignancies, including breast cancer. Based on the ESTIMATE algorithm, immune and stromal scores were calculated according to immune or stromal components in the TME. We divided breast cancer cases into high- and low-score groups and identified differentially expressed genes (DEGs) that were significantly associated with overall survival. We performed enrichment analysis and constructed a protein-protein interaction network and found that the DEGs were mainly involved in primary immunodeficiency, T cell receptor signaling pathway and cytokine-cytokine receptor reaction. Furthermore, we explored the effect of aging on immune and stromal scores, which was validated by lower immune/stromal scores, lower infiltration of T cells and lower expression of immune checkpoints in the elder group. In conclusion, certain differentially expressed immune-related genes contribute to longer overall survival, and aging influences the immune microenvironment and immunotherapy efficacy by changing the tumor-infiltrating lymphocyte (TIL) abundance and checkpoint expression in breast cancer.

Immune and Stroma Related Genes in Breast Cancer: A Comprehensive Analysis of Tumor Microenvironment Based on the Cancer Genome Atlas (TCGA) Database

Background: Tumor microenvironment is essential for breast cancer progression and metastasis. Our study sets out to examine the genes affecting stromal and immune infiltration in breast cancer progression and prognosis.

Materials and Methods: This work provides an approach for quantifying stromal and immune scores by using ESTIMATE algorithm based on gene expression matrix of breast cancer patients in TCGA database. We found differentially expressed genes (DEGs) through limma R package. Functional enrichments were accessed through Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Besides, we constructed a protein-protein network, identified several hub genes in Cytoscape, and discovered functionally similar genes in GeneMANIA. Hub genes were validated with prognostic data by Kaplan-Meier analysis both in The Cancer Genome Atlas (TCGA) database and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database and a meta-analysis of hub genes prognosis data was utilized in multiple databases. Furthermore, their relationship with infiltrating immune cells was evaluated by Tumor IMmune Estimation Resource (TIMER) web tool. Cox regression was utilized for overall survival (OS) and recurrence-free survival (RFS) in TCGA database and OS in METABRIC database in order to evaluate the impact of stromal and immune scores on patients prognosis.

Results: One thousand and eighty-five breast cancer patients were investigated and 480 differentiated expressed genes (DEGs) were found based on the analysis of mRNA expression profiles. Functional analysis of DEGs revealed their potential functions in immune response and extracellular interaction. Protein-protein interaction network gave evidence of 10 hub genes. Some of the hub genes could be used as predictive markers for patients prognosis. In this study, we found that tumor purity and specific immune cells infiltration varied in response to hub genes expression. The multivariate cox regression highlighted the fact that immune score played a detrimental role in overall survival (HR = 0.45, 95% CI: 0.27–0.74, p = 0.002) and recurrence-free survival (HR = 0.41, 95% CI: 0.22–0.77, p = 0.006) in TCGA database. These result was confirmed in METABRIC database that immune score was a protector of OS (HR = 0.88, 95% CI: 0.77–0.99, p = 0.039).

Conclusions: Our findings promote a better understanding of the potential genes behind the regulation of tumor microenvironment and cells infiltration. Immune score should be considered as a prognostic factor for patients' survival.

Reactive stroma and trastuzumab resistance in HER2-positive early breast cancer

We investigated the value of reactive stroma as a predictor for trastuzumab resistance in patients with early HER2-positive breast cancer receiving adjuvant therapy. The pathological reactive stroma and the mRNA gene signatures that reflect reactive stroma in 209 HER2-positive breast cancer samples from the FinHer adjuvant trial were evaluated. Levels of stromal gene signatures were determined as a continuous parameter, and pathological reactive stromal findings were defined as stromal predominant breast cancer (SPBC; ≥50% stromal) and correlated with distant disease-free survival. Gene signatures associated with reactive stroma in HER2-positive early breast cancer (N = 209) were significantly associated with trastuzumab resistance in estrogen receptor (ER)-negative tumors (hazard ratio [HR] = 1.27 p interaction = 0.014 [DCN], HR = 1.58, p interaction = 0.027 [PLAU], HR = 1.71, p interaction = 0.019 [HER2STROMA, novel HER2 stromal signature]), but not in ER-positive tumors (HR = 0.73 p interaction = 0.47 [DCN], HR = 0.71, p interaction = 0.73 [PLAU], HR = 0.84; p interaction = 0.36 [HER2STROMA]). Pathological evaluation of HER2-positive/ER-negative tumors suggested an association between SPBC and trastuzumab resistance. Reactive stroma did not correlate with tumor-infiltrating lymphocytes (TILs), and the expected benefit from trastuzumab in patients with high levels of TILs was pronounced only in tumors with low stromal reactivity (SPBC <50%). In conclusion, reactive stroma in HER2-positive/ER-negative early breast cancer tumors may predict resistance to adjuvant trastuzumab therapy.

p62-DNA-encoding plasmid reverts tumor grade, changes tumor stroma, and enhances anticancer immunity

Previously, we reported that the administration of a p62/SQSTM1-encoding plasmid demonstrates high safety and signs of clinical benefits for human cancer patients. The treatment also suppressed tumor growth and metastasis in dogs and mouse models. Here we investigated some mechanistic aspects of these effects. In mammary tumors bearing-dogs, i.m. injections of p62 plasmid reduced tumor sizes and their aggressive potential in 5 out of 6 animals, with one carcinoma switching to adenoma. The treatment increased levels of smooth muscle actin in stroma cells and type III collagen in the extracellular matrix, which correlate with a good clinical prognosis. The p62 treatment also increased the abundance of intratumoral T-cells. Because of the role of adaptive immunity cannot be tested in dogs, we compared the protective effects of the p62 plasmid against B16 melanoma in wild type C57BL/6J mice versus their SCID counterpart lacking lymphocytes. The plasmid was only protective in the wild type strain. Also, p62 plasmid amplified the anti-tumor effect of T-cell transfer from tumor-bearing animals to animals challenged with the same tumors. We conclude that the plasmid acts via re-modeling of the tumor microenvironment, making it more favorable for increased anti-cancer immunity. Thus, the p62-encoding plasmid might be a new adjuvant for cancer treatments.

Breast cancer quantitative proteome and proteogenomic landscape

In the preceding decades, molecular characterization has revolutionized breast cancer (BC) research and therapeutic approaches. Presented herein, an unbiased analysis of breast tumor proteomes, inclusive of 9995 proteins quantified across all tumors, for the first time recapitulates BC subtypes. Additionally, poor-prognosis basal-like and luminal B tumors are further subdivided by immune component infiltration, suggesting the current classification is incomplete. Proteome-based networks distinguish functional protein modules for breast tumor groups, with co-expression of EGFR and MET marking ductal carcinoma in situ regions of normal-like tumors and lending to a more accurate classification of this poorly defined subtype. Genes included within prognostic mRNA panels have significantly higher than average mRNA-protein correlations, and gene copy number alterations are dampened at the protein-level; underscoring the value of proteome quantification for prognostication and phenotypic classification. Furthermore, protein products mapping to non-coding genomic regions are identified; highlighting a potential new class of tumor-specific immunotherapeutic targets.

Gene expression profiles can classify breast cancer into five clinically relevant subtypes. Here, the authors perform an in-depth quantitative profiling of the proteome of 45 breast tumors, and show they can recapitulate the transcriptome-based classifications and identify many potentially antigenic tumour-specific peptides.

ColXα1 is a stromal component that colocalizes with elastin in the breast tumor extracellular matrix

The tumor microenvironment regulates tissue development and homeostasis, and its dysregulation contributes to neoplastic progression. Increased expression of type X collagen α‐1 (ColXα1) in tumor‐associated stroma correlates with poor pathologic response to neoadjuvant chemotherapy in estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2)‐positive breast cancers. Evaluation of ColXα1 expression patterns suggests a potential connection with elastin fibers. To investigate the possible interaction between ColXα1 and elastin, we evaluated the expression of ColXα1 in relation to elastin fibers in normal breast tissue, ductal carcinoma in situ, and invasive breast carcinomas at cellular and subcellular levels. Our findings demonstrate that ColXα1 colocalizes with elastin in invasive breast cancer‐associated stroma by immunohistochemistry, immunofluorescence, and electron microscopy. In 212 invasive breast carcinomas, this complex was aberrantly and selectively expressed in tumor extracellular matrix in 79% of ER+/HER2−, 80% of ER+/HER2+, 76% of ER−/HER2+, and 58% of triple negative breast cancers. In contrast, ColXα1 was generally absent, while elastin was present perivascularly in normal breast tissue. ColXα1 and elastin were coexpressed in 58% of ductal carcinoma in situ (DCIS) in periductal areas. In mass‐forming DCIS with desmoplastic stroma, the complex was intensely expressed in periductal areas as well as within the tumor‐associated stroma in all cases. Our data suggest that the breast carcinoma neoplastic process may involve aberrant expression of ColXα1 and elastin in the tumor microenvironment emerging early at the DCIS stage. Enrichment of these complexes in tumor‐associated stroma may represent a stromal signature indicative of intrinsic differences between breast cancers. These findings shed light on investigation into the role of aberrant collagen complex expression in tumorigenesis and tumor progression which may be leveraged in therapeutic and theranostic applications.

In Situ Drug Delivery to Breast Cancer-Associated Extracellular Matrix

The extracellular matrix (ECM) contributes to tumor progression through changes induced by tumor and stromal cell signals that promote increased ECM density and stiffness. The increase in ECM stiffness is known to promote tumor cell invasion into surrounding tissues and metastasis. In addition, this scar-like ECM creates a protective barrier around the tumor that reduces the effectiveness of innate and synthetic antitumor agents. Herein, clinically approved breast cancer therapies as well as novel experimental approaches that target the ECM are discussed, including in situ hydrogel drug delivery systems, an emerging technology the delivers toxic chemotherapeutics, gene-silencing microRNAs, and tumor suppressing immune cells directly inside the tumor. Intratumor delivery of therapeutic agents has the potential to drastically reduce systemic side effects experienced by the patient and increase the efficacy of these agents. This review also describes the opposing effects of ECM degradation on tumor progression, where some studies report improved drug delivery and delayed cancer progression and others report enhanced metastasis and decreased patient survival. Given the recent increase in ECM-targeting drugs entering preclinical and clinical trials, understanding and addressing the factors that impact the effect of the ECM on tumor progression is imperative for the sake of patient safety and survival outcome.

Cdh1 and Pik3ca Mutations Cooperate to Induce Immune-Related Invasive Lobular Carcinoma of the Breast

CDH1 and PIK3CA are the two most frequently mutated genes in invasive lobular carcinoma (ILC) of the breast. Transcription profiling has identified molecular subtypes for ILC, one of which, immune-related (IR), is associated with gene expression linked to lymphocyte and macrophage infiltration. Here, we report that deletion of Cdh1, together with activation of Pik3ca in mammary epithelium of genetically modified mice, leads to formation of IR-ILC-like tumors with immune cell infiltration, as well as gene expression linked to T-regulatory (Treg) cell signaling and activation of targetable immune checkpoint pathways. Interestingly, these tumors show enhanced Rac1- and Yap-dependent transcription and signaling, as well as sensitivity to PI3K, Rac1, and Yap inhibitors in culture. Finally, high-dimensional immunophenotyping in control mouse mammary gland and IR-ILC tumors by mass cytometry shows dramatic alterations in myeloid and lymphoid populations associated with immune suppression and exhaustion, highlighting the potential for therapeutic intervention via immune checkpoint regulators.

Identification of a five-lncRNA signature for predicting the risk of tumor recurrence in patients with breast cancer

Long non-coding RNAs (lncRNAs) are a major class of non-coding RNAs, and the functional deregulations of lncRNAs have been shown to be associated with the development and progression of BC. In this work, we conduct an integrative analysis on five re-annotated lncRNA expression datasets from the Gene Expression Omnibus (GEO) which included a total of 891 BC samples. We identified a five-lncRNA signature that was significantly associated with DFS in the training cohort of 327 patients. We found the five-lncRNA signature could effectively stratify patients in the training dataset into high- and low-risk groups with significantly different DFS (p = 3.29 × 10^-5 , log-rank test). The five-lncRNA signature was effectively validated in four independent cohorts, and prognostic analysis results showed that the five-lncRNA signature was independent of clinical prognostic factors, such as BC subtypes and adjuvant treatments. Furthermore, GSEA suggested that the five-lncRNA signature was involved in BC metastasis-related pathways. Our findings indicate that these five lncRNAs may be implicated in BC pathogenesis, and further, these lncRNAs may potentially serve as novel candidate biomarkers for the identification of BC patients at high risk for tumor recurrence.

Personalized regression enables sample-specific pan-cancer analysis

Motivation

In many applications, inter-sample heterogeneity is crucial to understanding the complex biological processes under study. For example, in genomic analysis of cancers, each patient in a cohort may have a different driver mutation, making it difficult or impossible to identify causal mutations from an averaged view of the entire cohort. Unfortunately, many traditional methods for genomic analysis seek to estimate a single model which is shared by all samples in a population, ignoring this inter-sample heterogeneity entirely. In order to better understand patient heterogeneity, it is necessary to develop practical, personalized statistical models.

Results

To uncover this inter-sample heterogeneity, we propose a novel regularizer for achieving patient-specific personalized estimation. This regularizer operates by learning two latent distance metrics-one between personalized parameters and one between clinical covariates-and attempting to match the induced distances as closely as possible. Crucially, we do not assume these distance metrics are already known. Instead, we allow the data to dictate the structure of these latent distance metrics. Finally, we apply our method to learn patient-specific, interpretable models for a pan-cancer gene expression dataset containing samples from more than 30 distinct cancer types and find strong evidence of personalization effects between cancer types as well as between individuals. Our analysis uncovers sample-specific aberrations that are overlooked by population-level methods, suggesting a promising new path for precision analysis of complex diseases such as cancer.

Availability and implementation

Software for personalized linear and personalized logistic regression, along with code to reproduce experimental results, is freely available at github.com/blengerich/personalized_regression.

Proteomic Features of Colorectal Cancer Identify Tumor Subtypes Independent of Oncogenic Mutations and Independently Predict Relapse-Free Survival

BACKGROUND

The directed study of the functional proteome in colorectal cancer (CRC) has identified critical protein markers and signaling pathways; however, the prognostic relevance of many of these proteins remains unclear.

METHODS

We determined the prognostic implications of the functional proteome in 263 CRC tumor samples from patients treated at MD Anderson Cancer Center (MDACC) and 462 patients from The Cancer Genome Atlas (TCGA) to identify patterns of protein expression that drive tumorigenesis. A total of 163 validated proteins were analyzed by reverse phase protein array (RPPA). Unsupervised hierarchical clustering of the tumor proteins from the MDACC cohort was performed, and clustering was validated using RPPA data from TCGA CRC. Cox regression was used to identify predictors of tumor recurrence.

RESULTS

Clustering revealed dichotomization, with subtype A notable for a high epithelial-mesenchymal transition (EMT) protein signature, while subtype B was notable for high Akt/TSC/mTOR pathway components. Survival data were only available for the MDACC cohort and were used to evaluate prognostic relevance of these protein signatures. Group B demonstrated worse relapse-free survival (hazard ratio 2.11, 95% confidence interval 1.04-4.27, p = 0.039), although there was no difference in known genomic drivers between the two proteomic groups. Proteomic grouping and stage were significant predictors of recurrence on multivariate analysis. Eight proteins were found to be significant predictors of tumor recurrence on multivariate analysis: Collagen VI, FOXO3a, INPP4B, LcK, phospho-PEA15, phospho-PRAS40, Rad51, phospho-S6.

CONCLUSION

CRC can be classified into distinct subtypes by proteomic features independent of common oncogenic driver mutations. Proteomic analysis has identified key biomarkers with prognostic importance, however these findings require further validation in an independent cohort.

Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer

We report a comprehensive analysis of 412 muscle-invasive bladder cancers characterized by multiple TCGA analytical platforms. Fifty-eight genes were significantly mutated, and the overall mutational load was associated with APOBEC-signature mutagenesis. Clustering by mutation signature identified a high-mutation subset with 75% 5-year survival. mRNA expression clustering refined prior clustering analyses and identified a poor-survival "neuronal" subtype in which the majority of tumors lacked small cell or neuroendocrine histology. Clustering by mRNA, long non-coding RNA (lncRNA), and miRNA expression converged to identify subsets with differential epithelial-mesenchymal transition status, carcinoma in situ scores, histologic features, and survival. Our analyses identified 5 expression subtypes that may stratify response to different treatments.

Stromal Clusterin Expression Predicts Therapeutic Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer

BACKGROUND

Expression of clusterin correlates with tumor progression and therapeutic response in several human malignancies, including breast cancer. However, its predictive value in the neoadjuvant setting in breast cancer remains unexplored. The objective of this explorative study was to determine whether clusterin expression in breast cancer correlated with clinical pathologic characteristics and whether its expression was predictive of response to neoadjuvant chemotherapy (NAC).

MATERIALS AND METHODS

We determined the clusterin expression pattern in 72 triple negative breast cancers (TNBC) treated with NAC before surgery. Clusterin expression was evaluated by immunohistochemistry and was correlated with pathologic characteristics and response to NAC using residual cancer burden score.

RESULTS

Immunohistochemistry analysis revealed a differential pattern of expression between tumor and stroma. Clusterin expression in the tumor associated stroma as opposed to expression by the neoplastic epithelium was significantly associated with neoadjuvant-treated TNBC. Low stromal clusterin, low stromal content, and high tumor-infiltrating lymphocytes were associated with a significantly greater likelihood of achieving a good pathologic response as reflected by lower residual cancer burden scores (P = .002, P = .003, and P = .001, respectively). Tumor and/or stromal clusterin expression were not associated with patient age, tumor histologic grade, stage, and lymph node status.

CONCULSION

This study suggests a potential role for the assessment of stromal clusterin as a predictive biomarker for response of TNBC to neoadjuvant therapy. Further validation of this biomarker in a large study is needed.

Prognostic and functional role of subtype-specific tumor-stroma interaction in breast cancer

None of the clinically relevant gene expression signatures available for breast cancer were specifically developed to capture the influence of the microenvironment on tumor cells. Here, we attempted to build subtype‐specific signatures derived from an in vitro model reproducing tumor cell modifications after interaction with activated or normal stromal cells. Gene expression signatures derived from HER2+, luminal, and basal breast cancer cell lines (treated by normal fibroblasts or cancer‐associated fibroblasts conditioned media) were evaluated in clinical tumors by in silico analysis on published gene expression profiles (GEPs). Patients were classified as microenvironment‐positive (μENV+ve), that is, with tumors showing molecular profiles suggesting activation by the stroma, or microenvironment‐negative (μENV−ve) based on correlation of their tumors' GEP with the respective subtype‐specific signature. Patients with estrogen receptor alpha (ER)+/HER2−/μENV+ve tumors were characterized by 2.5‐fold higher risk of developing distant metastases (HR = 2.546; 95% CI: 1.751–3.701, P = 9.84E‐07), while μENV status did not affect, or only suggested the risk of distant metastases, in women with HER2+ (HR = 1.541; 95% CI: 0.788–3.012, P = 0.206) or ER‐/HER2− tumors (HR = 1.894; 95% CI: 0.938–3.824; P = 0.0747), respectively. In ER+/HER2− tumors, the μENV status remained significantly associated with metastatic progression (HR = 2.098; CI: 1.214–3.624; P = 0.00791) in multivariable analysis including size, age, and Genomic Grade Index. Validity of our in vitro model was also supported by in vitro biological endpoints such as cell growth (MTT assay) and migration/invasion (Transwell assay). In vitro‐derived gene signatures tracing the bidirectional interaction with cancer activated fibroblasts are subtype‐specific and add independent prognostic information to classical prognostic variables in women with ER+/HER2− tumors.

Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome

BACKGROUND

Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes.

METHODS

Tumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a "cluster-of-clusters" approach with consensus clustering.

RESULTS

Based on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed.

CONCLUSIONS

The six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer.