Characterizing advanced breast cancer heterogeneity and treatment resistance through serial biopsies and comprehensive analytics

Molecular profiling of metastatic breast cancer and target-based therapeutic matching in an Asian tertiary phase I oncology unit

Introduction

Molecular profiling of metastatic breast cancer (MBC) through the widespread use of next-generation sequencing (NGS) has highlighted actionable mutations and driven trials of targeted therapy matched to tumour molecular profiles, with improved outcomes reported using such an approach. Here, we review NGS results and treatment outcomes for a cohort of Asian MBC patients in the phase I unit of a tertiary centre.

Methods

Patients with MBC referred to a phase I unit underwent NGS via Ion AmpliSeq Cancer Hotspot v2 (ACH v2, 2014-2017) prior to institutional change to FoundationOne CDx (FM1; 2017-2022). Patients were counselled on findings and enrolled on matched therapeutic trials, where available. Outcomes for all subsequent treatment events were recorded to data cut-off on January 31, 2022.

Results

A total of 215 patients were enrolled with successful NGS in 158 patients. The PI3K/AKT/PTEN pathway was the most altered with one or more of the pathway member genes PIK3/AKT/PTEN affected in 62% (98/158) patients and 43% of tumours harbouring a PIK3CA alteration. Tumour mutational burden (TMB) was reported in 96/109 FM1 sequenced patients, with a mean TMB of 5.04 mt/Mb and 13% (12/96) with TMB ≥ 10 mt/Mb. Treatment outcomes were evaluable in 105/158 patients, with a pooled total of 216 treatment events recorded. Matched treatment was administered in 47/216 (22%) events and associated with prolonged median progression-free survival (PFS) of 21.0 weeks [95% confidence interval (CI) 11.7, 26.0 weeks] versus 12.1 weeks (95% CI 10.0, 15.4 weeks) in unmatched, with hazard ratio (HR) for progression or death of 0.63 (95% CI 0.41, 0.97; p = 0.034). In the subgroup of PIK3/AKT/PTEN-altered MBC, the HR for progression or death was 0.57 (95% CI 0.35, 0.92; p = 0.02), favouring matched treatment. Per-patient overall survival (OS) analysis (n = 105) showed improved survival for patients receiving matched treatment versus unmatched, with median OS (mOS) of 30.1 versus 11.8 months, HR = 0.45 (95% CI 0.24, 0.84; p = 0.013). Objective response rate (ORR) in the overall population was similar in matched and unmatched treatment events (23.7% versus 17.2%, odds ratio of response 1.14 95% CI 0.50, 2.62; p = 0.75).

Conclusions

Broad-panel NGS in MBC is feasible, allowing therapeutic matching, which was associated with improvements in PFS and OS.

Research and experimental verification on the mechanisms of cellular senescence in triple-negative breast cancer

Background

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with high heterogeneity, poor prognosis, and a low 10-year survival rate of less than 50%. Although cellular senescence displays extensive effects on cancer, the comprehensions of cellular senescence-related characteristics in TNBC patients remains obscure.

Method

Single-cell RNA sequencing (scRNA-seq) data were analyzed by Seurat package. Scores for cellular senescence-related pathways were computed by single-sample gene set enrichment analysis (ssGSEA). Subsequently, unsupervised consensus clustering was performed for molecular cluster identification. Immune scores of patients in The Cancer Genome Atlas (TCGA) dataset and associated immune cell scores were calculated using Estimation of STromal and Immune cells in MAlignantTumours using Expression data (ESTIMATE) and Microenvironment Cell Populations-counter (MCP-counter), Tumor Immune Estimation Resource (TIMER) and Estimating the Proportion of Immune and Cancer cells (EPIC) methods, respectively. Immunotherapy scores were assessed using TIDE. Furthermore, feature genes were identified by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses; these were used to construct a risk model. Additionally, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and transwell assay were conducted for in vitro validation of hub genes.

Result

TNBC was classified into three subtypes based on cellular senescence-related pathways as clusters 1, 2, and 3. Specifically, cluster 1 showed the best prognosis, followed by cluster 2 and cluster 3. The levels of gene expression in cluster 2 were the lowest, whereas these were the highest in cluster 3. Moreover, clusters 1 and 3 showed a high degree of immune infiltration. TIDE scores were higher for cluster 3, suggesting that immune escape was more likely in patients with the cluster 3 subtype who were less likely to benefit from immunotherapy. Next, the TNBC risk model was constructed and validated. RT-qPCR revealed that prognostic risk genes (MMP28, ACP5 and KRT6A) were up-regulated while protective genes (CT83) were down-regulated in TNBC cell lines, validating the results of the bioinformatics analysis. Meanwhile, cellular experiments revealed that ACP5 could promote the migration and invasion abilities in two TNBC cell lines. Finally, we evaluated the validity of prognostic models for assessing TME characteristics and TNBC chemotherapy response.

Conclusion

In conclusion, these findings help to assess the efficacy of targeted therapies in patients with different molecular subtypes, have practical applications for subtype-specific treatment of TNBC patients, and provide information on prognostic factors, as well as guidance for the revelation of the molecular mechanisms by which senescence-associated genes influence TNBC progression.

Exploring the resistance mechanism of triple-negative breast cancer to paclitaxel through the scRNA-seq analysis

BACKGROUND

The triple negative breast cancer (TNBC) is the most malignant subtype of breast cancer with high aggressiveness. Although paclitaxel-based chemotherapy scenario present the mainstay in TNBC treatment, paclitaxel resistance is still a striking obstacle for cancer cure. So it is imperative to probe new therapeutic targets through illustrating the mechanisms underlying paclitaxel chemoresistance.

METHODS

The Single cell RNA sequencing (scRNA-seq) data of TNBC cells treated with paclitaxel at different points were downloaded from the Gene Expression Omnibus (GEO) database. The Seurat R package was used to filter and integrate the scRNA-seq expression matrix. Cells were further clustered by the FindClusters function, and the gene marker of each subset was defined by FindAllMarkers function. Then, the hallmark score of each cell was calculated by AUCell R package, the biological function of the highly expressed interest genes was analyzed by the DAVID database. Subsequently, we performed pseudotime analysis to explore the change patterns of drug resistance genes and SCENIC analysis to identify the key transcription factors (TFs). Finally, the inhibitors of which were also analyzed by the CTD database.

RESULTS

We finally obtained 6 cell subsets from 2798 cells, which were marked as AKR1C3+, WNT7A+, FAM72B+, RERG+, IDO1+ and HEY1+HCC1143 cell subsets, among which the AKR1C3+, IDO1+ and HEY1+ cell subsets proportions increased with increasing treatment time, and then were regarded as paclitaxel resistance subsets. Hallmark score and pseudotime analysis showed that these paclitaxel resistance subsets were associated with the inflammatory response, virus and interferon response activation. In addition, the gene regulatory networks (GRNs) indicated that 3 key TFs (STAT1, CEBPB and IRF7) played vital role in promoting resistance development, and five common inhibitors targeted these TFs as potential combination therapies of paclitaxel were identified.

CONCLUSION

In this study, we identified 3 paclitaxel resistance relevant IFs and their inhibitors, which offers essential molecular basis for paclitaxel resistance and beneficial guidance for the combination of paclitaxel in clinical TNBC therapy.

Advancements in Biotechnology and Stem Cell Therapies for Breast Cancer Patients

This comprehensive review article examines the integration of biotechnology and stem cell therapy in breast cancer diagnosis and treatment. It discusses the use of biotechnological tools such as liquid biopsies, genomic profiling, and imaging technologies for accurate diagnosis and monitoring of treatment response. Stem cell-based approaches, their role in modeling breast cancer progression, and their potential for breast reconstruction post-mastectomy are explored. The review highlights the importance of personalized treatment strategies that combine biotechnological tools and stem cell therapies. Ethical considerations, challenges in clinical translation, and regulatory frameworks are also addressed. The article concludes by emphasizing the potential of integrating biotechnology and stem cell therapy to improve breast cancer outcomes, highlighting the need for continued research and collaboration in this field.

Nanotechnology Applications in Breast Cancer Immunotherapy

Next-generation cancer treatments are expected not only to target cancer cells but also to simultaneously train immune cells to combat cancer while modulating the immune-suppressive environment of tumors and hosts to ensure a robust and lasting response. Achieving this requires carriers that can codeliver multiple therapeutics to the right cancer and/or immune cells while ensuring patient safety. Nanotechnology holds great potential for addressing these challenges. This article highlights the recent advances in nanoimmunotherapeutic development, with a focus on breast cancer. While immune checkpoint inhibitors (ICIs) have achieved remarkable success and lead to cures in some cancers, their response rate in breast cancer is low. The poor response rate in solid tumors is often associated with the low infiltration of anti-cancer T cells and an immunosuppressive tumor microenvironment (TME). To enhance anti-cancer T-cell responses, nanoparticles are employed to deliver ICIs, bispecific antibodies, cytokines, and agents that induce immunogenic cancer cell death (ICD). Additionally, nanoparticles are used to manipulate various components of the TME, such as immunosuppressive myeloid cells, macrophages, dendritic cells, and fibroblasts to improve T-cell activities. Finally, this article discusses the outlook, challenges, and future directions of nanoimmunotherapeutics.

The Role of Nodes and Nodal Assessment in Diagnosis, Treatment and Prediction in ER+, Node-Positive Breast Cancer

The majority of breast cancers are oestrogen receptor-positive (ER+). In ER+ cancers, oestrogen acts as a disease driver, so these tumours are likely to be susceptible to endocrine therapy (ET). ET works by blocking the hormone's synthesis or effect. A significant number of patients diagnosed with breast cancer will have the spread of tumour cells into regional lymph nodes either at the time of diagnosis, or as a recurrence some years later. Patients with node-positive disease have a poorer prognosis and can respond less well to ET. The nodal metastases may be genomically similar or, as is becoming more evident, may differ from the primary tumour. However, nodal metastatic disease is often not assessed, and treatment decisions are almost always based on biomarkers evaluated in the primary tumour. This review will summarise the evidence in the field on ER+, node-positive breast cancer, including diagnosis, treatment, prognosis and predictive tools.

The Breast Cancer Proteome and Precision Oncology

The goal of precision oncology is to translate the molecular features of cancer into predictive and prognostic tests that can be used to individualize treatment leading to improved outcomes and decreased toxicity. Success for this strategy in breast cancer is exemplified by efficacy of trastuzumab in tumors overexpressing ERBB2 and endocrine therapy for tumors that are estrogen receptor positive. However, other effective treatments, including chemotherapy, immune checkpoint inhibitors, and CDK4/6 inhibitors are not associated with strong predictive biomarkers. Proteomics promises another tier of information that, when added to genomic and transcriptomic features (proteogenomics), may create new opportunities to improve both treatment precision and therapeutic hypotheses. Here, we review both mass spectrometry-based and antibody-dependent proteomics as complementary approaches. We highlight how these methods have contributed toward a more complete understanding of breast cancer and describe the potential to guide diagnosis and treatment more accurately.

Machine Learning & Molecular Radiation Tumor Biomarkers

Developing radiation tumor biomarkers that can guide personalized radiotherapy clinical decision making is a critical goal in the effort towards precision cancer medicine. High-throughput molecular assays paired with modern computational techniques have the potential to identify individual tumor-specific signatures and create tools that can help understand heterogenous patient outcomes in response to radiotherapy, allowing clinicians to fully benefit from the technological advances in molecular profiling and computational biology including machine learning. However, the increasingly complex nature of the data generated from high-throughput and "omics" assays require careful selection of analytical strategies. Furthermore, the power of modern machine learning techniques to detect subtle data patterns comes with special considerations to ensure that the results are generalizable. Herein, we review the computational framework of tumor biomarker development and describe commonly used machine learning approaches and how they are applied for radiation biomarker development using molecular data, as well as challenges and emerging research trends.

Panobinostat Induced Spatial In Situ Biomarkers Predictive of Anti-PD-1 Efficacy in Mouse Mammary Carcinoma

Immunotherapies, including anti-PD-1 immune checkpoint blocking (ICB) antibodies, have revolutionized the treatment of many solid malignancies. However, their efficacy in breast cancer has been limited to a subset of patients with triple-negative breast cancer, where ICBs are routinely combined with a range of cytotoxic and targeted agents. Reliable biomarkers predictive of the therapeutic response to ICB in breast cancer are critically missing, though a combination response has been associated with immunogenic cell death (ICD). Here, we utilized a recently developed integrated analytical platform, the multiplex implantable microdevice assay (MIMA), to evaluate the presence and spatial cell relations of literature-based candidate markers predictive of ICB efficacy in luminal mouse mammary carcinoma. MIMA integrates (i) an implantable microdevice for the localized delivery of small amounts of drugs inside the tumor bed with (ii) sequential multiplex immunohistochemistry (mIHC) and spatial cell analysis pipelines to rapidly (within days) describe drug mechanisms of action and find predictive biomarkers in complex tumor tissue. We show that the expression of cleaved caspase-3, ICAM-1, neuropilin-1, myeloperoxidase, calreticulin, galectin-3, and PD-L1 were spatially associated with the efficacy of panobinostat, a pan-HDAC inhibitor that was previously shown to induce immunogenic cell death and synergize with anti-PD-1 in breast cancer. PD-L1 by itself, however, was not a reliable predictor. Instead, ICB efficacy was robustly identified through the in situ hotspot detection of galectin-3-positive non-proliferating tumor zones enriched in cell death and infiltrated by anti-tumor cytotoxic neutrophils positive for ICAM-1 and neuropilin-1. Such hotspots can be specifically detected using distance-based cluster analyses. Single-cell measurements of the functional states in the tumor microenvironment suggest that both qualitative and quantitative effects might drive effective therapy responses. Overall, the presented study provides (i) complementary biological knowledge about the earliest cell events of induced anti-tumor immunity in breast cancer, including the emergence of resistant cancer stem cells, and (ii) newly identified biomarkers in form of specific spatial cell associations. The approach used standard cell-type-, IHC-, and FFPE-based techniques, and therefore the identified spatial clustering of in situ biomarkers can be readily integrated into existing clinical or research workflows, including in luminal breast cancer. Since early drug responses were detected, the biomarkers could be especially applicable to window-of-opportunity clinical trials to rapidly discriminate between responding and resistant patients, thus limiting unnecessary treatment-associated toxicities.

A multiplex implantable microdevice assay identifies synergistic combinations of cancer immunotherapies and conventional drugs

Systematically identifying synergistic combinations of targeted agents and immunotherapies for cancer treatments remains difficult. In this study, we integrated high-throughput and high-content techniques-an implantable microdevice to administer multiple drugs into different sites in tumors at nanodoses and multiplexed imaging of tumor microenvironmental states-to investigate the tumor cell and immunological response signatures to different treatment regimens. Using a mouse model of breast cancer, we identified effective combinations from among numerous agents within days. In vivo studies in three immunocompetent mammary carcinoma models demonstrated that the predicted combinations synergistically increased therapeutic efficacy. We identified at least five promising treatment strategies, of which the panobinostat, venetoclax and anti-CD40 triple therapy was the most effective in inducing complete tumor remission across models. Successful drug combinations increased spatial association of cancer stem cells with dendritic cells during immunogenic cell death, suggesting this as an important mechanism of action in long-term breast cancer control.

Tackling heterogeneity in treatment-resistant breast cancer using a broad-spectrum therapeutic approach

Tumor heterogeneity can contribute to the development of therapeutic resistance in cancer, including advanced breast cancers. The object of the Halifax project was to identify new treatments that would address mechanisms of therapeutic resistance through tumor heterogeneity by uncovering combinations of therapeutics that could target the hallmarks of cancer rather than focusing on individual gene products. A taskforce of 180 cancer researchers, used molecular profiling to highlight key targets responsible for each of the hallmarks of cancer and then find existing therapeutic agents that could be used to reach those targets with limited toxicity. In many cases, natural health products and re-purposed pharmaceuticals were identified as potential agents. Hence, by combining the molecular profiling of tumors with therapeutics that target the hallmark features of cancer, the heterogeneity of advanced-stage breast cancers can be addressed.

Achillea fragrantissima (Forssk.) Sch.Bip Flower Dichloromethane Extract Exerts Anti-Proliferative and Pro-Apoptotic Properties in Human Triple-Negative Breast Cancer (MDA-MB-231) Cells: In Vitro and In Silico Studies

The aggressive triple-negative breast cancer (TNBC) is a challenging disease due to the absence of tailored therapy. The search for new therapies involves intensive research focusing on natural sources. Achillea fragrantissima (A. fragrantissima) is a traditional medicine from the Middle East region. Various solvent extracts from different A. fragrantissima plant parts, including flowers, leaves, and roots, were tested on TNBC MDA-MB-231 cells. Using liquid chromatography, the fingerprinting revealed rich and diverse compositions for A. fragrantissima plant parts using polar to non-polar solvent extracts indicating possible differences in bioactivities. Using the CellTiter-Glo™ viability assay, the half-maximal inhibitory concentration (IC₅₀) values were determined for each extract and ranged from 32.4 to 161.7 µg/mL. The A. fragrantissima flower dichloromethane extract had the lowest mean IC₅₀ value and was chosen for further investigation. Upon treatment with increasing A. fragrantissima flower dichloromethane extract concentrations, the MDA-MB-231 cells displayed, in a dose-dependent manner, enhanced morphological and biochemical hallmarks of apoptosis, including cell shrinkage, phosphatidylserine exposure, caspase activity, and mitochondrial outer membrane permeabilization, assessed using phase-contrast microscopy, fluorescence-activated single-cell sorting analysis, Image-iT™ live caspase, and mitochondrial transition pore opening activity, respectively. Anticancer target prediction and molecular docking studies revealed the inhibitory activity of a few A. fragrantissima flower dichloromethane extract-derived metabolites against carbonic anhydrase IX, an enzyme reported for its anti-apoptotic properties. In conclusion, these findings suggest promising therapeutic values of the A. fragrantissima flower dichloromethane extract against TNBC development.

siRNA Targeting Mcl-1 Potentiates the Anticancer Activity of Andrographolide Nanosuspensions via Apoptosis in Breast Cancer Cells

Breast cancer is the second leading cause of cancer-related death in the US. However, recurrence is frequently found despite adjuvant therapy being available. Combination therapy with cytotoxic drugs and gene therapy is being developed to be a new promising cancer treatment strategy. Introducing substituted dithiocarbamate moieties at the C12 position of andrographolide (3nAG) could improve its anticancer selectivity in the MCF-7 breast cancer cell line. However, its hydrophobicity is one of its main drawbacks. This work successfully prepared 3nAG nanosuspension stabilized with the chitosan derivative NSC (3nAGN-NSC) to increase solubility and pharmacological effectiveness. siRNAs have emerged as a promising therapeutic alternative for interfering with particular mRNA. The 3nAGN-NSC had also induced Mcl-1 mRNA expression in MCF-7 human breast cancer cells at 8, 12, and 24 h. This indicates that, in addition to Mcl-1 silencing by siRNA (siMcl-1) in MCF-7 with substantial Mcl-1 reliance, rationally devised combination treatment may cause the death of cancer cells in breast cancer. The Fa-CI analysis showed that the combination of 3nAGN-NSC and siMcl-1 had a synergistic effect with a combination index (CI) value of 0.75 (CI < 1 indicating synergistic effects) at the fractional inhibition of Fa 0.7. The synergistic effect was validated by flow cytometry, with the induction of apoptosis as the mechanism of reduced cell viability. Our findings suggested the rational use of 3nAGN-NSC in combination with siMcl-1 to kill breast cancer cells.

The Human Tumor Atlas Network's beginning steps toward the future of collaborative multi-omic discovery

The Human Tumor Atlas Network is a multi-institutional effort to generate genomic and histologic datasets spanning thousands of patients. Johnson et al., in this issue of Cell Reports Medicine, illustrate how disparate data types from a single case can be combined to discover novel therapeutic directions.

An omic and multidimensional spatial atlas from serial biopsies of an evolving metastatic breast cancer

Mechanisms of therapeutic resistance and vulnerability evolve in metastatic cancers as tumor cells and extrinsic microenvironmental influences change during treatment. To support the development of methods for identifying these mechanisms in individual people, here we present an omic and multidimensional spatial (OMS) atlas generated from four serial biopsies of an individual with metastatic breast cancer during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata that includes treatment times and doses, anatomic imaging, and blood-based response measurements to clinical and exploratory analyses, which includes comprehensive DNA, RNA, and protein profiles; images of multiplexed immunostaining; and 2- and 3-dimensional scanning electron micrographs. These data report aspects of heterogeneity and evolution of the cancer genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples of how integrative analyses of these data reveal potential mechanisms of response and resistance and suggest novel therapeutic vulnerabilities.

Did Everolimus Break the Rules?

A phase II study with everolimus (mTORC1 inhibitor) among advanced solid tumors patients with TSC1/TSC2 or MTOR alterations was recently published. Although efficacy was limited, the study provided the future groundwork to advance the targeted therapy approach.See related article by Adib et al., p. 3845.

Characterizing advanced breast cancer heterogeneity and treatment resistance through serial biopsies and comprehensive analytics

Molecular heterogeneity in metastatic breast cancer presents multiple clinical challenges in accurately characterizing and treating the disease. Current diagnostic approaches offer limited ability to assess heterogeneity that exists among multiple metastatic lesions throughout the treatment course. We developed a precision oncology platform that combines serial biopsies, multi-omic analysis, longitudinal patient monitoring, and molecular tumor boards, with the goal of improving cancer management through enhanced understanding of the entire cancer ecosystem within each patient. We describe this integrative approach using comprehensive analytics generated from serial-biopsied lesions in a metastatic breast cancer patient. The serial biopsies identified remarkable heterogeneity among metastatic lesions that presented clinically as discordance in receptor status and genomic alterations with mixed treatment response. Based on our study, we highlight clinical scenarios, such as rapid progression or mixed response, that indicate consideration for repeat biopsies to evaluate intermetastatic heterogeneity (IMH), with the objective of refining targeted therapy. We present a framework for understanding the clinical significance of heterogeneity in breast cancer between metastatic lesions utilizing multi-omic analyses of serial biopsies and its implication for effective personalized treatment.