Serial single-cell genomics reveals convergent subclonal evolution of resistance as early-stage breast cancer patients progress on endocrine plus CDK4/6 therapy

Mechanisms of sensitivity and resistance to CDK4/CDK6 inhibitors in hormone receptor-positive breast cancer treatment

Cell cycle dysregulation is a hallmark of cancer that promotes eccessive cell division. Cyclin-dependent kinase 4 (CDK4) and cyclin-dependent kinase 6 (CDK6) are key molecules in the G1-to-S phase cell cycle transition and are crucial for the onset, survival, and progression of breast cancer (BC). Small-molecule CDK4/CDK6 inhibitors (CDK4/6i) block phosphorylation of tumor suppressor Rb and thus restrain susceptible BC cells in G1 phase. Three CDK4/6i are approved for the first-line treatment of patients with advanced/metastatic hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) BC in combination with endocrine therapy (ET). Though this has improved the clinical outcomes for survival of BC patients, there is no established standard next-line treatment to tackle drug resistance. Recent studies suggest that CDK4/6i can modulate other distinct effects in both BC and breast stromal compartments, which may provide new insights into aspects of their clinical activity. This review describes the biochemistry of the CDK4/6-Rb-E2F pathway in HR+ BC, then discusses how CDK4/6i can trigger other effects in BC/breast stromal compartments, and finally outlines the mechanisms of CDK4/6i resistance that have emerged in recent preclinical studies and clinical cohorts, emphasizing the impact of these findings on novel therapeutic opportunities in BC.

A Cell Cycle-Aware Network for Data Integration and Label Transferring of Single-Cell RNA-Seq and ATAC-Seq

In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity, and confounding factors. As it is known, the cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it is not clear how it will work on the integrated single-cell multi-omics data. Here, a cell cycle-aware network (CCAN) is developed to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell type-specific variations. This is the first computational model to study the cell-cycle effects in the integration of single-cell multi-omics data. Validations on several benchmark datasets show the outstanding performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA-seq datasets from different protocols, integrating paired and unpaired scRNA-seq and scATAC-seq data, accurately transferring cell type labels from scRNA-seq to scATAC-seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data.

PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors

Tailoring optimal treatment for individual cancer patients remains a significant challenge. To address this issue, we developed PERCEPTION (PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology), a precision oncology computational pipeline. Our approach uses publicly available matched bulk and single-cell (sc) expression profiles from large-scale cell-line drug screens. These profiles help build treatment response models based on patients' sc-tumor transcriptomics. PERCEPTION demonstrates success in predicting responses to targeted therapies in cultured and patient-tumor-derived primary cells, as well as in two clinical trials for multiple myeloma and breast cancer. It also captures the resistance development in patients with lung cancer treated with tyrosine kinase inhibitors. PERCEPTION outperforms published state-of-the-art sc-based and bulk-based predictors in all clinical cohorts. PERCEPTION is accessible at https://github.com/ruppinlab/PERCEPTION . Our work, showcasing patient stratification using sc-expression profiles of their tumors, will encourage the adoption of sc-omics profiling in clinical settings, enhancing precision oncology tools based on sc-omics.

Gene-Expression Profiling to Decipher Breast Cancer Inter- and Intratumor Heterogeneity

Breast cancer is heterogeneous and differs substantially across different tumors (intertumor heterogeneity) and even within an individual tumor (intratumor heterogeneity). Gene-expression profiling has considerably impacted our understanding of breast cancer biology. Four main "intrinsic subtypes" of breast cancer (i.e., luminal A, luminal B, HER2-enriched, and basal-like) have been consistently identified by gene expression, showing significant prognostic and predictive value in multiple clinical scenarios. Thanks to the molecular profiling of breast tumors, breast cancer is a paradigm of treatment personalization. Several standardized prognostic gene-expression assays are presently being used in the clinic to guide treatment decisions. Moreover, the development of single-cell-level resolution molecular profiling has allowed us to appreciate that breast cancer is also heterogeneous within a single tumor. There is an evident functional heterogeneity within the neoplastic and tumor microenvironment cells. Finally, emerging insights from these studies suggest a substantial cellular organization of neoplastic and tumor microenvironment cells, thus defining breast cancer ecosystems and highlighting the importance of spatial localizations.

Personalized tumor combination therapy optimization using the single-cell transcriptome

BACKGROUND

The precise characterization of individual tumors and immune microenvironments using transcriptome sequencing has provided a great opportunity for successful personalized cancer treatment. However, the cancer treatment response is often characterized by in vitro assays or bulk transcriptomes that neglect the heterogeneity of malignant tumors in vivo and the immune microenvironment, motivating the need to use single-cell transcriptomes for personalized cancer treatment.

METHODS

Here, we present comboSC, a computational proof-of-concept study to explore the feasibility of personalized cancer combination therapy optimization using single-cell transcriptomes. ComboSC provides a workable solution to stratify individual patient samples based on quantitative evaluation of their personalized immune microenvironment with single-cell RNA sequencing and maximize the translational potential of in vitro cellular response to unify the identification of synergistic drug/small molecule combinations or small molecules that can be paired with immune checkpoint inhibitors to boost immunotherapy from a large collection of small molecules and drugs, and finally prioritize them for personalized clinical use based on bipartition graph optimization.

RESULTS

We apply comboSC to publicly available 119 single-cell transcriptome data from a comprehensive set of 119 tumor samples from 15 cancer types and validate the predicted drug combination with literature evidence, mining clinical trial data, perturbation of patient-derived cell line data, and finally in-vivo samples.

CONCLUSIONS

Overall, comboSC provides a feasible and one-stop computational prototype and a proof-of-concept study to predict potential drug combinations for further experimental validation and clinical usage using the single-cell transcriptome, which will facilitate and accelerate personalized tumor treatment by reducing screening time from a large drug combination space and saving valuable treatment time for individual patients. A user-friendly web server of comboSC for both clinical and research users is available at www.combosc.top . The source code is also available on GitHub at https://github.com/bm2-lab/comboSC .

The CDK4/6 inhibitors biomarker landscape: The most relevant biomarkers of response or resistance for further research and potential clinical utility

Cyclin-Dependent Kinase 4/6 inhibitors (CDK4/6is) in combination with Endocrine Therapy (ET) represent the standard frontline therapy for patients with Hormone Receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative metastatic Breast Cancer (mBC). Clinical activity and efficacy of CDK4/6is-based therapies have been proven both in the endocrine sensitive and resistant settings. Therapy resistance eventually underpins clinical progression to any CDK4/6is-based therapies, yet there is a lack of validated molecular biomarkers predictive of either intrinsic or acquired resistance to CDK4/6is in clinical practice. As the "post-CDK4/6is" landscape for the management of HR-positive/HER2-negative mBC is rapidly evolving with the introduction of novel therapies, there is an urgent need for the definition of clinically relevant molecular biomarkers of intrinsic/acquired resistance mechanisms to CDK4/6is. This narrative review outlines the role of currently approved CDK4/6is-based therapies, describes the most relevant molecular biomarkers of CDK4/6is-resistance, and ultimately provides a perspective on the clinical and research scenario.

Multi-omics analyses unravel DNA damage repair-related clusters in breast cancer with experimental validation

Background

As one of the most common malignancies worldwide, breast cancer (BC) exhibits high heterogeneity of molecular phenotypes. The evolving view regarding DNA damage repair (DDR) is that it is context-specific and heterogeneous, but its role in BC remains unclear.

Methods

Multi-dimensional data of transcriptomics, genomics, and single-cell transcriptome profiling were obtained to characterize the DDR-related features of BC. We collected 276 DDR-related genes based on the Molecular Signature Database (MSigDB) database and previous studies. We acquired public datasets included the SCAN-B dataset (GEO: GSE96058), METABRIC database, and TCGA-BRCA database. Corresponding repositories such as transcriptomics, genomics, and clinical information were also downloaded. We selected scRNA-seq data from GEO: GSE176078, GSE114727, GSE161529, and GSE158724. Bulk RNA-seq data from GEO: GSE176078, GSE18728, GSE5462, GSE20181, and GSE130788 were extracted for independent analyses.

Results

The DDR classification was constructed in the SCAN-B dataset (GEO: GSE96058) and METABRIC database, Among BC patients, there were two clusters with distinct clinical and molecular characteristics: the DDR-suppressed cluster and the DDR-active cluster. A superior survival rate is found for tumors in the DDR-suppressed cluster, while those with the DDR-activated cluster tend to have inferior prognoses and clinically aggressive behavior. The DDR classification was validated in the TCGA-BRCA cohort and shown similar results. We also found that two clusters have different pathway activities at the genomic level. Based on the intersection of the different expressed genes among these cohorts, we found that PRAME might play a vital role in DDR. The DDR classification was then enabled by establishing a DDR score, which was verified through multilayer cohort analysis. Furthermore, our results revealed that malignant cells contributed more to the DDR score at the single-cell level than nonmalignant cells. Particularly, immune cells with immunosuppressive properties (such as FOXP3+ CD4+ T cells) displayed higher DDR scores among those with distinguishable characteristics.

Conclusion

Collectively, this study performs general analyses of DDR heterogeneity in BC and provides insight into the understanding of individualized molecular and clinicopathological mechanisms underlying unique DDR profiles.

Single-cell dissection of tumor microenvironmental response and resistance to cancer therapy

Cancer treatment strategies have evolved significantly over the years, with chemotherapy, targeted therapy, and immunotherapy as major pillars. Each modality leads to unique treatment outcomes by interacting with the tumor microenvironment (TME), which imposes a fundamental selective pressure on cancer progression. The advent of single-cell profiling technologies has revolutionized our understanding of the intricate and heterogeneous nature of the TME at an unprecedented resolution. This review delves into the commonalities and differential manifestations of how cancer therapies reshape the microenvironment in diverse cancer types. We highlight how groundbreaking immune checkpoint blockade (ICB) strategies alone or in combination with tumor-targeting treatments are endowed with comprehensive mechanistic insights when decoded at the single-cell level, aiming to drive forward future research directions on personalized treatments.

Implications of tumour heterogeneity on cancer evolution and therapy resistance: lessons from breast cancer

Tumour heterogeneity is pervasive amongst many cancers and leads to disease progression, and therapy resistance. In this review, using breast cancer as an exemplar, we focus on the recent advances in understanding the interplay between tumour cells and their microenvironment using single cell sequencing and digital spatial profiling technologies. Further, we discuss the utility of lineage tracing methodologies in pre-clinical models of breast cancer, and how these are being used to unravel new therapeutic vulnerabilities and reveal biomarkers of breast cancer progression. © 2023 The Pathological Society of Great Britain and Ireland.

CDK4/6 Inhibitor Resistance in Hormone Receptor-Positive Metastatic Breast Cancer: Translational Research, Clinical Trials, and Future Directions

The emergence of CDK4/6 inhibitors, such as palbociclib, ribociclib, and abemaciclib, has revolutionized the treatment landscape for hormone receptor-positive breast cancer. These agents have demonstrated significant clinical benefits in terms of both progression-free survival and overall survival. However, resistance to CDK4/6 inhibitors remains a challenge, limiting their long-term efficacy. Understanding the complex mechanisms driving resistance is crucial for the development of novel therapeutic strategies and the improvement of patient outcomes. Translational research efforts, such as preclinical models and biomarker studies, offer valuable insight into resistance mechanisms and may guide the identification of novel combination therapies. This review paper aims to outline the reported mechanisms underlying CDK4/6 inhibitor resistance, drawing insights from both clinical data and translational research in order to help direct the future of treatment for hormone receptor-positive metastatic breast cancer.

Imagine beyond: recent breakthroughs and next challenges in mammary gland biology and breast cancer research

On 8 December 2022 the organizing committee of the European Network for Breast Development and Cancer labs (ENBDC) held its fifth annual Think Tank meeting in Amsterdam, the Netherlands. Here, we embraced the opportunity to look back to identify the most prominent breakthroughs of the past ten years and to reflect on the main challenges that lie ahead for our field in the years to come. The outcomes of these discussions are presented in this position paper, in the hope that it will serve as a summary of the current state of affairs in mammary gland biology and breast cancer research for early career researchers and other newcomers in the field, and as inspiration for scientists and clinicians to move the field forward.

Epigenetic reprogramming of cell cycle genes by ACK1 promotes breast cancer resistance to CDK4/6 inhibitor

Hormone receptor-positive, HER2-negative advanced breast cancers exhibit high sensitivity to CDK4/6 inhibitors such as palbociclib. However, most patients inevitably develop resistance, thus identification of new actionable therapeutic targets to overcome the recurrent disease is an urgent need. Immunohistochemical studies of tissue microarray revealed increased activation of non-receptor tyrosine kinase, ACK1 (also known as TNK2) in most of the breast cancer subtypes, independent of their hormone receptor status. Chromatin immunoprecipitation studies demonstrated that the nuclear target of activated ACK1, pY88-H4 epigenetic marks, were deposited at cell cycle genes, CCNB1, CCNB2 and CDC20, which in turn initiated their efficient transcription. Pharmacological inhibition of ACK1 using its inhibitor, (R)-9b dampened CCNB1, CCNB2 and CDC20 expression, caused G2/M arrest, culminating in regression of palbociclib-resistant breast tumor growth. Further, (R)-9b suppressed expression of CXCR4 receptor, which resulted in significant impairment of metastasis of breast cancer cells to lung. Overall, our pre-clinical data identifies activated ACK1 as an oncogene that epigenetically controls the cell cycle genes governing the G2/M transition in breast cancer cells. ACK1 inhibitor, (R)-9b could be a novel therapeutic option for the breast cancer patients that have developed resistance to CDK4/6 inhibitors.

Immunomodulatory effects of CDK4/6 inhibitors

The dysregulation of the cell cycle is one of the hallmarks of cancer. Cyclin-dependent kinase 4 (CDK4) and CDK6 play crucial roles in regulating cell cycle and other cellular functions. CDK4/6 inhibitors have achieved great success in treating breast cancers and are currently being tested extensively in other tumor types as well. Accumulating evidence suggests that CDK4/6 inhibitors exert antitumor effects through immunomodulation aside from cell cycle arrest. Here we outline the immunomodulatory activities of CDK4/6 inhibitors, discuss the immune mechanisms of drug resistance and explore avenues to harness their immunotherapeutic potential when combined with immune checkpoint inhibitors (ICIs) or chimeric antigen receptor (CAR) T-cell therapy to improve the clinical outcomes.

Cell facilitation promotes growth and survival under drug pressure in breast cancer

The interplay of positive and negative interactions between drug-sensitive and resistant cells influences the effectiveness of treatment in heterogeneous cancer cell populations. Here, we study interactions between estrogen receptor-positive breast cancer cell lineages that are sensitive and resistant to ribociclib-induced cyclin-dependent kinase 4 and 6 (CDK4/6) inhibition. In mono- and coculture, we find that sensitive cells grow and compete more effectively in the absence of treatment. During treatment with ribociclib, sensitive cells survive and proliferate better when grown together with resistant cells than when grown in monoculture, termed facilitation in ecology. Molecular, protein, and genomic analyses show that resistant cells increase metabolism and production of estradiol, a highly active estrogen metabolite, and increase estrogen signaling in sensitive cells to promote facilitation in coculture. Adding estradiol in monoculture provides sensitive cells with increased resistance to therapy and cancels facilitation in coculture. Under partial inhibition of estrogen signaling through low-dose endocrine therapy, estradiol supplied by resistant cells facilitates sensitive cell growth. However, a more complete blockade of estrogen signaling, through higher-dose endocrine therapy, diminished the facilitative growth of sensitive cells. Mathematical modeling quantifies the strength of competition and facilitation during CDK4/6 inhibition and predicts that blocking facilitation has the potential to control both resistant and sensitive cancer cell populations and inhibit the emergence of a refractory population during cell cycle therapy.

BAP18 facilitates CTCF-mediated chromatin accessible to regulate enhancer activity in breast cancer

The estrogen receptor alpha (ERα) signaling pathway is a crucial target for ERα-positive breast cancer therapeutic strategies. Co-regulators and other transcription factors cooperate for effective ERα-related enhancer activation. Recent studies demonstrate that the transcription factor CTCF is essential to participate in ERα/E2-induced enhancer transactivation. However, the mechanism of how CTCF is achieved remains unknown. Here, we provided evidence that BAP18 is required for CTCF recruitment on ERα-enriched enhancers, facilitating CTCF-mediated chromatin accessibility to promote enhancer RNAs transcription. Consistently, GRO-seq demonstrates that the enhancer activity is positively correlated with BAP18 enrichment. Furthermore, BAP18 interacts with SMARCA1/BPTF to accelerate the recruitment of CTCF to ERα-related enhancers. Interestingly, BAP18 is involved in chromatin accessibility within enhancer regions, thereby increasing enhancer transactivation and enhancer-promoter looping. BAP18 depletion increases the sensitivity of anti-estrogen and anti-enhancer treatment in MCF7 cells. Collectively, our study indicates that BAP18 coordinates with CTCF to enlarge the transactivation of ERα-related enhancers, providing a better understanding of BAP18/CTCF coupling chromatin remodeling and E-P looping in the regulation of enhancer transcription.

CDK4/6 inhibitor resistance in estrogen receptor positive breast cancer, a 2023 perspective

CDK4/6 inhibitors have become game-changers in the treatment of estrogen receptor-positive (ER+) breast cancer, and in combination with endocrine therapy are the standard of care first-line treatment for ER+/HER2-negative advanced breast cancer. Although CDK4/6 inhibitors prolong survival for these patients, resistance is inevitable and there is currently no clear standard next-line treatment. There is an urgent unmet need to dissect the mechanisms which drive intrinsic and acquired resistance to CDK4/6 inhibitors and endocrine therapy to guide the subsequent therapeutic decisions. We will review the insights gained from preclinical studies and clinical cohorts into the diverse mechanisms of CDK4/6 inhibitor action and resistance, and highlight potential therapeutic strategies in the context of CDK4/6 inhibitor resistance.

Deciphering breast cancer: from biology to the clinic

Breast cancer remains a leading cause of cancer-related mortality in women, reflecting profound disease heterogeneity, metastasis, and therapeutic resistance. Over the last decade, genomic and transcriptomic data have been integrated on an unprecedented scale and revealed distinct cancer subtypes, critical molecular drivers, clonal evolutionary trajectories, and prognostic signatures. Furthermore, multi-dimensional integration of high-resolution single-cell and spatial technologies has highlighted the importance of the entire breast cancer ecosystem and the presence of distinct cellular "neighborhoods." Clinically, a plethora of new targeted therapies has emerged, now being rapidly incorporated into routine care. Resistance to therapy, however, remains a crucial challenge for the field.

Working with Convex Responses: Antifragility from Finance to Oncology

We extend techniques and learnings about the stochastic properties of nonlinear responses from finance to medicine, particularly oncology, where it can inform dosing and intervention. We define antifragility. We propose uses of risk analysis for medical problems, through the properties of nonlinear responses (convex or concave). We (1) link the convexity/concavity of the dose-response function to the statistical properties of the results; (2) define "antifragility" as a mathematical property for local beneficial convex responses and the generalization of "fragility" as its opposite, locally concave in the tails of the statistical distribution; (3) propose mathematically tractable relations between dosage, severity of conditions, and iatrogenics. In short, we propose a framework to integrate the necessary consequences of nonlinearities in evidence-based oncology and more general clinical risk management.

The complex nature of heterogeneity and its roles in breast cancer biology and therapeutic responsiveness

Heterogeneity is a complex feature of cells and tissues with many interacting components. Depending on the nature of the research context, interacting features of cellular, drug response, genetic, molecular, spatial, temporal, and vascular heterogeneity may be present. We describe the various forms of heterogeneity with examples of their interactions and how they play a role in affecting cellular phenotype and drug responses in breast cancer. While cellular heterogeneity may be the most widely described and invoked, many forms of heterogeneity are evident within the tumor microenvironment and affect responses to the endocrine and cytotoxic drugs widely used in standard clinical care. Drug response heterogeneity is a critical determinant of clinical response and curative potential and also is multifaceted when encountered. The interactive nature of some forms of heterogeneity is readily apparent. For example, the process of metastasis has the properties of both temporal and spatial heterogeneity within the host, whereas each individual metastatic deposit may exhibit cellular, genetic, molecular, and vascular heterogeneity. This review describes the many forms of heterogeneity, their integrated activities, and offers some insights into how heterogeneity may be understood and studied in the future.

A machine learning framework for scRNA-seq UMI threshold optimization and accurate classification of cell types

Recent advances in single cell RNA sequencing (scRNA-seq) technologies have been invaluable in the study of the diversity of cancer cells and the tumor microenvironment. While scRNA-seq platforms allow processing of a high number of cells, uneven read quality and technical artifacts hinder the ability to identify and classify biologically relevant cells into correct subtypes. This obstructs the analysis of cancer and normal cell diversity, while rare and low expression cell populations may be lost by setting arbitrary high cutoffs for UMIs when filtering out low quality cells. To address these issues, we have developed a novel machine-learning framework that: 1. Trains cell lineage and subtype classifier using a gold standard dataset validated using marker genes 2. Systematically assess the lowest UMI threshold that can be used in a given dataset to accurately classify cells 3. Assign accurate cell lineage and subtype labels to the lower read depth cells recovered by setting the optimal threshold. We demonstrate the application of this framework in a well-curated scRNA-seq dataset of breast cancer patients and two external datasets. We show that the minimum UMI threshold for the breast cancer dataset could be lowered from the original 1500 to 450, thereby increasing the total number of recovered cells by 49%, while achieving a classification accuracy of >0.9. Our framework provides a roadmap for future scRNA-seq studies to determine optimal UMI threshold and accurately classify cells for downstream analyses.

Elacestrant (oral selective estrogen receptor degrader) Versus Standard Endocrine Therapy for Estrogen Receptor-Positive, Human Epidermal Growth Factor Receptor 2-Negative Advanced Breast Cancer: Results From the Randomized Phase III EMERALD Trial

PURPOSE

Patients with pretreated estrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative advanced breast cancer have poor prognosis. Elacestrant is a novel, oral selective ER degrader that demonstrated activity in early studies.

METHODS

This randomized, open-label, phase III trial enrolled patients with ER-positive/HER2-negative advanced breast cancer who had one-two lines of endocrine therapy, required pretreatment with a cyclin-dependent kinase 4/6 inhibitor, and ≤ 1 chemotherapy. Patients were randomly assigned to elacestrant 400 mg orally once daily or standard-of-care (SOC) endocrine monotherapy. Primary end points were progression-free survival (PFS) by blinded independent central review in all patients and patients with detectable ESR1 mutations.

RESULTS

Patients were randomly assigned to elacestrant (n = 239) or SOC (n = 238). ESR1 mutation was detected in 47.8% of patients, and 43.4% received two prior endocrine therapies. PFS was prolonged in all patients (hazard ratio = 0.70; 95% CI, 0.55 to 0.88; P = .002) and patients with ESR1 mutation (hazard ratio = 0.55; 95% CI, 0.39 to 0.77; P = .0005). Treatment-related grade 3/4 adverse events occurred in 7.2% receiving elacestrant and 3.1% receiving SOC. Treatment-related adverse events leading to treatment discontinuations were 3.4% in the elacestrant arm versus 0.9% in SOC. Nausea of any grade occurred in 35.0% receiving elacestrant and 18.8% receiving SOC (grade 3/4, 2.5% and 0.9%, respectively).

CONCLUSION

Elacestrant is the first oral selective ER degrader demonstrating a significant PFS improvement versus SOC both in the overall population and in patients with ESR1 mutations with manageable safety in a phase III trial for patients with ER-positive/HER2-negative advanced breast cancer.

Multianalyte liquid biopsy to aid the diagnostic workup of breast cancer

Breast cancer (BC) affects 1 in every 8 women in the United States and is currently the most prevalent cancer worldwide. Precise staging at diagnosis and prognosis are essential components for the clinical management of BC patients. In this study, we set out to evaluate the feasibility of the high-definition single cell (HDSCA) liquid biopsy (LBx) platform to stratify late-stage BC, early-stage BC, and normal donors using peripheral blood samples. Utilizing 5 biomarkers, we identified rare circulating events with epithelial, mesenchymal, endothelial and hematological origin. We detected a higher level of CTCs in late-stage patients, compared to the early-stage and normal donors. Additionally, we observed more tumor-associated large extracellular vesicles (LEVs) in the early-stage, compared to late-stage and the normal donor groups. Overall, we were able to detect reproducible patterns in the enumeration of rare cells and LEVs of cancer vs. normal donors and early-stage vs. late-stage BC with high accuracy, allowing for robust stratification. Our findings illustrate the feasibility of the LBx assay to provide robust detection of rare circulating events in peripheral blood draws and to stratify late-stage BC, early-stage BC, and normal donor samples.

Mechanical signatures of human colon cancers

Besides the standard parameters used for colorectal cancer (CRC) management, new features are needed in clinical practice to improve progression-free and overall survival. In some cancers, the microenvironment mechanical properties can contribute to cancer progression and metastasis formation, or constitute a physical barrier for drug penetration or immune cell infiltration. These mechanical properties remain poorly known for colon tissues. Using a multidisciplinary approach including clinical data, physics and geostatistics, we characterized the stiffness of healthy and malignant colon specimens. For this purpose, we analyzed a prospective cohort of 18 patients with untreated colon adenocarcinoma using atomic force microscopy to generate micrometer-scale mechanical maps. We characterized the stiffness of normal epithelium samples taken far away or close to the tumor area and selected tumor tissue areas. These data showed that normal epithelium was softer than tumors. In tumors, stroma areas were stiffer than malignant epithelial cell areas. Among the clinical parameters, tumor left location, higher stage, and RAS mutations were associated with increased tissue stiffness. Thus, in patients with CRC, measuring tumor tissue rigidity may have a translational value and an impact on patient care.

In Silico Investigations of Multi-Drug Adaptive Therapy Protocols

The standard of care for cancer patients aims to eradicate the tumor by killing the maximum number of cancer cells using the maximum tolerated dose (MTD) of a drug. MTD causes significant toxicity and selects for resistant cells, eventually making the tumor refractory to treatment. Adaptive therapy aims to maximize time to progression (TTP), by maintaining sensitive cells to compete with resistant cells. We explored both dose modulation (DM) protocols and fixed dose (FD) interspersed with drug holiday protocols. In contrast to previous single drug protocols, we explored the determinants of success of two-drug adaptive therapy protocols, using an agent-based model. In almost all cases, DM protocols (but not FD protocols) increased TTP relative to MTD. DM protocols worked well when there was more competition, with a higher cost of resistance, greater cell turnover, and when crowded proliferating cells could replace their neighbors. The amount that the drug dose was changed, mattered less. The more sensitive the protocol was to tumor burden changes, the better. In general, protocols that used as little drug as possible, worked best. Preclinical experiments should test these predictions, especially dose modulation protocols, with the goal of generating successful clinical trials for greater cancer control.

Ribociclib Induces Broad Chemotherapy Resistance and EGFR Dependency in ESR1 Wildtype and Mutant Breast Cancer

While endocrine therapy is highly effective for the treatment of oestrogen receptor-α (ERα)-positive breast cancer, a significant number of patients will eventually experience disease progression and develop treatment-resistant, metastatic cancer. The majority of resistant tumours remain dependent on ERα-action, with activating ESR1 gene mutations occurring in 15-40% of advanced cancers. Therefore, there is an urgent need to discover novel effective therapies that can eradicate cancer cells with aberrant ERα and to understand the cellular response underlying their action. Here, we evaluate the response of MCF7-derived, CRISPR-Cas9-generated cell lines expressing mutant ERα (Y537S) to a large number of drugs. We report sensitivity to numerous clinically approved inhibitors, including CDK4/6 inhibitor ribociclib, which is a standard-of-care therapy in the treatment of metastatic ERα-positive breast cancer and currently under evaluation in the neoadjuvant setting. Ribociclib treatment induces senescence in both wildtype and mutant ERα breast cancer models and leads to a broad-range drug tolerance. Strikingly, viability of cells undergoing ribociclib-induced cellular senescence is maintained via engagement of EGFR signalling, which may be therapeutically exploited in both wildtype and mutant ERα-positive breast cancer. Our study highlights a wide-spread reduction in sensitivity to anti-cancer drugs accompanied with an acquired vulnerability to EGFR inhibitors following CDK4/6 inhibitor treatment.

Therapeutic vaccines for breast cancer: Has the time finally come?

The ability to exploit the immune system as a weapon against cancer has revolutionised the treatment of cancer patients, especially through immune checkpoint inhibitors (ICIs). However, ICIs demonstrated a modest benefit in treating breast cancer (BC), with the exception of certain subsets of triple-negative BCs. An immune-suppressive tumour microenvironment (TME), typically present in BC, is an important factor in the poor response to immunotherapy.

After almost two decades of poor clinical trial results, cancer vaccines (CVs), an active immunotherapy, have come back in the spotlight because of some technological advancements, ultimately boosted by coronavirus disease 2019 pandemic. In particular, neoantigens are emerging as the preferred targets for CVs, with gene-based and viral vector–based platforms in development. Moreover, lipid nanoparticles proved to be immunogenic and efficient delivery vehicles.

Past clinical trials investigating CVs focused especially on the metastatic disease, where the TME is more likely compromised by inhibitory mechanisms. In this sense, favouring the use of CVs as monotherapy in premalignant or in the adjuvant setting and establishing combination treatments (i.e. CV plus ICI) in late-stage disease are promising strategies. This review provides a full overview of the past and current breast cancer vaccine landscape.