Single-cell and spatial profiling identify three response trajectories to pembrolizumab and radiation therapy in triple negative breast cancer

Chaperonin containing TCP1 subunit 5 as a novel pan-cancer prognostic biomarker for tumor stemness and immunotherapy response: insights from multi-omics data, integrated machine learning, and experimental validation

BACKGROUND

Chaperonin containing TCP1 subunit 5 (CCT5), a vital component of the molecular chaperonin complex, has been implicated in tumorigenesis, cancer stemness maintenance, and therapeutic resistance. Nevertheless, its comprehensive roles in pan-cancer progression, underlying biological functions, and potential as a predictor of immunotherapy response remains poorly understood.

METHODS

We performed a comprehensive multi-omics pan-cancer analysis of CCT5 across 33 cancer types, integrating bulk RNA-seq, single-cell RNA-seq (scRNA-seq), and spatial transcriptomics data. CCT5 expression patterns, prognostic relevance, stemness association, and immune microenvironment relationships were evaluated. A novel CCT5-based signature (CCT5.Sig) was developed using machine learning on 23 immune checkpoint blockade (ICB) cohorts (n = 1394) spanning eight cancer types. Model performance was assessed using AUC metrics and survival analyses.

RESULTS

CCT5 was significantly overexpressed in tumor tissues and primarily localized to malignant and cycling cells. High CCT5 expression correlated with poor prognosis in multiple cancers and was enriched in oncogenic, cell cycle, and DNA damage repair pathways. CCT5 expression was positively associated with mRNAsi, mDNAsi, and CytoTRACE scores, indicating a role in stemness maintenance. Furthermore, CCT5-high tumors exhibited immune-cold phenotypes, with reduced TILs and CD8⁺ T cell activity. The CCT5.Sig model, based on genes co-expressed with CCT5, achieved superior predictive accuracy for ICB response (AUC = 0.82 in validation and 0.76 in independent testing), outperforming existing pan-cancer signatures.

CONCLUSION

This study reveals the multifaceted oncogenic roles of CCT5 and highlights its potential as a pan-cancer biomarker for prognosis and immunotherapy response. The machine learning-derived CCT5.Sig model provides a robust tool for patient stratification and may inform personalized immunotherapy strategies.

Efficacy, safety and exploratory analysis of neoadjuvant tislelizumab (a PD-1 inhibitor) plus nab-paclitaxel followed by epirubicin/cyclophosphamide for triple-negative breast cancer: a phase 2 TREND trial

The optimal chemotherapy backbone and specific population of triple-negative breast cancer (TNBC) patients that benefit from neoadjuvant immunotherapy are not well established. This prospective, single-arm, phase II TREND trial assessed the efficacy and safety of tislelizumab plus nab-paclitaxel and epirubicin/cyclophosphamide-based chemotherapy as a neoadjuvant treatment for TNBC (ChiCTR2000035262). The primary endpoint was pathological complete response (pCR), with the secondary endpoints including safety assessment and objective response rate (ORR). ScRNA-seq, bulk RNA-seq, TCR-seq, cyTOF and WES were performed on pre-treatment and post-treatment samples. Among 53 total enrolled patients, 44 completed the combined neoadjuvant therapy, and 30 of 44 patients (68.18%) achieved pCR. Additionally, 14 out of 44 patients had a complete response (31.82%), with an ORR of 93.18%. The most commonly observed treatment-related adverse events (TRAEs) were alopecia, nausea and liver injury with 6 cases classified as grade 3 or higher adverse events. Immune response-related pathways, including TNF signaling pathway, T cell receptor signaling pathway, were enriched in pCR group. Pre-treatment model was identified and construct to predict response to immunotherapy. CDKN1A+ CD8 T lymphocytes were enriched in pCR group after neoadjuvant immunotherapy. Dynamic change of immune-related pathways at an early stage during the neoadjuvant immunotherapy may be associated with the treatment efficacy. In conclusion, neoadjuvant treatment of tislelizumab with nab-paclitaxel and anthracycline-based chemotherapy showed promising clinical activity and was well-tolerated among TNBC patients, without high incidence of TRAEs. These findings provide evidence supporting neoadjuvant tislelizumab with chemotherapy as an effective rational approach for treating TNBC.

Machine learning and single-cell analysis uncover distinctive characteristics of CD300LG within the TNBC immune microenvironment: experimental validation

Investigating the essential function of CD300LG within the tumor microenvironment in triple-negative breast cancer (TNBC). Transcriptomic and single-cell data from TNBC were systematically collected and integrated. Four machine learning algorithms were employed to identify distinct target genes in TNBC patients. Specifically, CIBERSORT and ssGSEA algorithms were utilized to elucidate immune infiltration patterns, whereas TIDE and TCGA algorithms predicted immune-related outcomes. Moreover, single-cell sequencing data were analyzed to investigate the function of CD300LG-positive cells within the tumor microenvironment. Finally, immunofluorescence staining confirmed the significance of CD300LG in tumor phenotyping. After machine learning screening and independent dataset validation, CD300LG was identified as a unique prognostic biomarker for triple-negative breast cancer. Enrichment analysis revealed that CD300LG expression is strongly linked to immune infiltration and inflammation-related pathways, especially those associated with the cell cycle. The presence of CD8+ T cells and M1-type macrophages was elevated in the CD300LG higher group, whereas the abundance of M2-type macrophage infiltration showed a significant decrease. Immunotherapy prediction models indicated that individuals with low CD300LG expression exhibited better responses to PD-1 therapy. Additionally, single-cell RNA sequencing and immunofluorescence analyses uncovered a robust association between CD300LG and genes involved in tumor invasion. CD300LG plays a pivotal role in the tumor microenvironment of TNBC and represents a promising therapeutic target.

Single-cell meta-analysis of T cells reveals clonal dynamics of response to checkpoint immunotherapy

Despite the crucial role of T cell clones in anti-tumor activity, their characterization and association with clinical outcomes following immune checkpoint inhibitors are lacking. Here, we analyzed paired single-cell RNA sequencing/T cell receptor sequencing of 767,606 T cells across 460 samples spanning 6 cancer types. We found a robust signature of response based on expanded CD8+ clones that differentiates responders from non-responders. Analysis of persistent clones showed transcriptional changes that are differentially induced by therapy in the different response groups, suggesting an improved reinvigoration capacity in responding patients. Moreover, a gene trajectory analysis revealed changes in the pseudo-temporal state of de novo clones that are associated with clinical outcomes. Lastly, we found that clones shared between tumor and blood are more abundant in non-responders and execute distinct transcriptional programs. Overall, our results highlight differences in clonal transcriptional states that are linked to patient response, offering valuable insights into the mechanisms driving effective anti-tumor immunity.

Targets and promising adjuvants for improving breast tumor response to radiotherapy

Breast cancer ranks among the most common cancers globally, with significant mortality rates in advanced stages. Despite progress in treatment, therapy resistance, particularly to radiotherapy, remains a major challenge. Radiosensitization offers a promising solution to enhance radiotherapy effectiveness. This approach specifically increases tumor cells' vulnerability to IR. Recent research has explored molecular targets and strategies to improve radiosensitivity in breast cancer. Examples include inhibiting DNA repair pathways, altering the TME, targeting signaling pathways, and using immunomodulators. These strategies not only amplify destructive effects of IR but may also reduce required radiation doses, thereby minimizing normal tissue injury. This review examines promising molecular targets and combination therapies to boost radiosensitivity in breast cancer. It also highlights recent advances in immune modulation, TME remodeling, targeted molecular therapy, and metabolic pathway targeting. These advancements offer insights into the future of radiosensitization research. By systematically analyzing these strategies, the article aims to provide a comprehensive understanding of radiosensitization's current state and future potential in breast cancer treatment.

A macrophage-neutrophil program drives mammary carcinogenesis

The molecular and cellular pathways through which breast cancer evades immunosurveillance remain poorly understood. Recent data from Camargo et al. demonstrate that - on recruitment to the tumor microenvironment by ductal macrophages - a heterogeneous population of neutrophils can establish physical contacts with malignant cells within spatial niches that sustain mammary oncogenesis.

Emerging clinical applications of single-cell RNA sequencing in oncology

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of complex tissues both in health and in disease. Over the past decade, scRNA-seq has been applied to tumour samples obtained from patients with cancer in hundreds of studies, thereby advancing the view that each tumour is a complex ecosystem and uncovering the diverse states of both cancer cells and the tumour microenvironment. Such studies have primarily investigated and provided insights into the basic biology of cancer, although considerable research interest exists in leveraging these findings towards clinical applications. In this Review, we summarize the available data from scRNA-seq studies investigating samples from patients with cancer with a particular focus on findings that are of potential clinical relevance. We highlight four main research objectives of scRNA-seq studies and describe some of the most relevant findings towards such goals. We also describe the limitations of scRNA-seq, as well as future approaches in this field that are anticipated to further advance clinical applicability.

High-Throughput Proteoform Imaging for Revealing Spatial-Resolved Changes in Brain Tissues Associated with Alzheimer's Disease

Spatially resolved characterization of proteoforms has substantial potential to significantly advance the understanding of physiological and disease mechanisms. However, challenges remain regarding throughput and coverage. A robust method is developed for high-throughput proteoform imaging (HTPi) by combining matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) and region-specific top-down proteomic analysis. MALDI MSI enables the imaging of proteoforms on tissue sections at a rate of 7 h cm-2 (100-µm spatial resolution), and the identification sensitivity of the proteoforms is improved by narrow-bore monolithic columns with low adsorption, yielding 366 annotated proteoform images from the mouse brain. The obtained proteoform images reveals differential expression of individual proteoforms across the brain regions, and distinct spatial distribution patterns of various proteoforms generated from a single gene. Given its ability to visualize proteoform, HTPi is further applied to explore spatial pathological changes associated with Alzheimer's disease (AD) in 5 × FAD mice. 158 annotated proteoform images are obtained in hippocampal regions at 50-µm spatial resolution, illuminating 14 differential proteoforms in the subiculum region and highlighting their significant associations with amyloid-β pathology in AD. The results highlight the power of HTPi in unraveling the intricate molecular landscape of brain tissues and its potential in elucidating disease mechanisms.

Exosomal circRNAs: key modulators in breast cancer progression

Breast cancer (BC) poses significant challenges globally, necessitating a deeper understanding of its complexities. Exosomes are cell-specific secreted extracellular vesicles of interest, characterized by a lipid bilayer structure. Exosomes can carry a variety of bioactive components, including nucleic acids, lipids, amino acids, and small molecules, to mediate intercellular signaling. CircRNAs are a novel class of single-stranded RNA molecules, characterized by a closed-loop structure. CircRNAs mainly exert ceRNA functions to intricately modulate gene expression and signaling pathways in breast cancer, influencing tumor progression and therapeutic responses. The unique packaging of circRNAs within exosomes serves as novel genetic information transmitters, facilitating communication between BC cells and microenvironmental cells, thereby regulating critical aspects of BC progression, immune evasion, and drug resistance. Besides, exosomal circRNAs possess the capabilities of serving as diagnostic and therapeutic biomarkers of BC, due to their stability, specificity, and regulatory roles in tumorigenesis and metastasis. Therefore, this review aims to elucidate the novel roles and mechanisms of exosomal circRNAs in BC progression, as well as their potential for diagnosis and therapeutics. The ongoing investigations of exosomal circRNAs will potentially revolutionize treatment paradigms and improve patient outcomes of BC.

Determinants of Response to Sequential Pembrolizumab with Trastuzumab plus Platinum/5-FU in HER2-Positive Gastric Cancer: A Phase II Chemoimmunotherapy Trial

PURPOSE

Adding pembrolizumab to first-line fluoropyrimidine (5-FU)/platinum chemotherapy plus trastuzumab improves outcomes in advanced HER2+ gastroesophageal adenocarcinomas, but the benefit is largely confined to dual HER2+ and PD-L1+ patients. To assess the contributions of components, we conducted a phase II trial evaluating 5-FU/platinum/trastuzumab and added pembrolizumab in cycle 2 in patients with metastatic HER2+ disease.

PATIENTS AND METHODS

Treatment-naïve patients with advanced HER2+ gastroesophageal cancer underwent a baseline biopsy and received a single dose of 5-FU/platinum with trastuzumab followed by repeat biopsy. Pembrolizumab was added, and a third biopsy was performed after six cycles. The primary endpoint was the objective response rate. Secondary endpoints included progression-free and overall survival. Exploratory biomarker analysis and dynamic changes in HER2 and PD-L1 were prespecified.

RESULTS

Sixteen patients were enrolled. The objective response rate was 69%, and the median progression-free survival was 11.9 months. Serial whole-exome, single-cell RNA, T-cell receptor sequencing, and spatial transcriptomics from pretreatment and on-treatment samples revealed early trastuzumab-induced NK cell infiltration in HER2+ tumor beds and an increase in Fc receptor gamma III expression in macrophages, suggesting that trastuzumab directs Fc receptor-mediated antibody-dependent cytotoxicity. This favorable remodeling was enhanced by the addition of pembrolizumab, primarily in PD-L1+ samples. We observed TGF-β signaling in HER2-negative tumor regions, which was associated with nonresponder status.

CONCLUSIONS

These data highlight the biology of intratumoral heterogeneity and the impact of tumor and immune cell features on clinical outcomes and may partly explain the lesser magnitude of pembrolizumab benefit in HER2+ and PD-L1-negative subgroups.

Urchin-like magnetic nanoparticles loaded with type X collagen siRNA and Stattic to treat triple negative breast cancer under rotating magnetic field like an "enchanted micro-scalpel"

Magnetic nanoparticles effectively target drug delivery, contrast agents, biosensors, and more. Urchin-like magnetic nanoparticles (UMN) with abundant spike-like structures exhibit superior magneto-mechanical force to destroy tumor cells compared with other shapes of magnetic nanoparticles. However, when cell contents are released from tumor cells induced by magneto-mechanical force, they can act on surrounding tumor cells to facilitate tumor development. Therefore, multifunctional UMN is necessary. Interleukin-6 (IL-6) is an important inflammatory factor which is released after cell rupture, it can activate the STAT3 signaling pathway to promote tumor progression. Type X collagen (COL10A1) is a significant component of the extracellular matrix, ranking second among all aberrant genes in triple negative breast cancer (TNBC), and its knockdown can suppress tumorigenesis and metastasis. Here, we built a rotating magnetic field (RMF) platform, and a novel UMN using a straightforward solvothermal method was synthesized, which was much simpler than existing method. Stattic (STAT3 inhibitor) and COL10A1 siRNA were loaded onto the UMN@PEI to form UMNP/St/si. The RMF drove UMNP/St/si disrupted the cell membrane, promoted cell death. The inhibitory effects of UMNP/St/si under RMF on TNBC were verified both in vitro and in vivo. Furthermore, despite the increase in IL-6 due to cell rupture, IL-6/STAT3 signaling pathway was inhibited by Stattic, compensating for the deficiency of magneto-mechanical force. Moreover, the underlying mechanical mechanism of UMNP/St/si after exposure to RMF was also analyzed. It suggests that UMNP/St/si is a promising and effective strategy for TNBC treatment and provides valuable insights for treating other diseases as well.

Mapping molecular landscapes in triple-negative breast cancer: insights from spatial transcriptomics

The tumor microenvironment (TME) of triple-negative breast cancer (TNBC) is a highly heterogeneous and very aggressive form of the disease that has few suitable treatment options; however, spatial transcriptomics (ST) is a powerful tool for elucidation of the TME in TNBC. Because of its spatial context preservation, ST has a unique capability to map tumor-stroma interactions, immune infiltration, and therapy resistance mechanisms (which are key to understanding TNBC progression), compared with conventional transcriptomics. This review shows the use of ST in TNBC, its utilization in spatial biomarker identification, intratumoral heterogeneity definition, molecular subtyping refinement, and prediction of immunotherapy responses. Recent insight from ST-driven insights has explained the key spatial patterns on immune evasion, chemotherapy resistance, racial disparities of TNBC, and aspects for patient stratification and therapeutic decision. With the integration of ST with the subjects of proteomics and imaging mass cytometry, this approach has been enlarged and is now applied in precision medicine and biomarker discovery. Recently, advancements in AI-based spatial analysis for tumor classification, identification of biomarkers, and creation of therapy prediction models have occurred. However, continued developments in ST technologies, computational tools, and partnerships amongst multiple centers to facilitate the integration of ST into clinical routine practice are needed to unlock novel therapeutic targets.

In Silico Validation of OncoOrigin: An Integrative AI Tool for Primary Cancer Site Prediction with Graphical User Interface to Facilitate Clinical Application

Cancers of unknown primary (CUPs) represent a significant diagnostic and therapeutic challenge in the field of oncology. Due to the limitations of current diagnostic tools in these cases, novel approaches must be brought forward to improve treatment outcomes for these patients. The objective of this study was to develop a machine-learning-based software for primary cancer site prediction (OncoOrigin), based on genetic data acquired from tumor DNA sequencing. By design, this was an in silico diagnostic study, conducted using data from the cBioPortal database (accessed on 21 September 2024) and several data processing and machine learning Python libraries. The study involved over 20,000 tumor samples with information on patient age, sex, and the presence of genetic variants in over 600 genes. The main outcome of interest was machine-learning-based discrimination between cancer site classes. Model quality was assessed by training set cross-validation and evaluation on a segregated test set. Finally, the optimal model was incorporated with a graphical user interface into the OncoOrigin software. Feature importance for class discrimination was also determined on the optimal model. Out of the four tested machine learning estimators, the XGBoostClassifier-based model proved superior in test set evaluation, with a top-2 accuracy of 0.91 and ROC-AUC of 0.97. Unlike other machine learning models published in the literature, OncoOrigin stands out as the only one integrated with a graphical user interface, which is crucial for facilitating its use by oncology specialists in everyday clinical practice, where its application and implementation will have the greatest value in the future.

MXene-based nanosheet for enhanced glioma therapy via photonic hyperthermia to boost the abscopal effect of radioimmunotherapy

Radiotherapy (RT) effectiveness is limited by low DNA damage in tumor cells, surrounding tissue harm, and tumor radioresistance with active DNA repair. Herein, we have engineered a two-dimensional nanomaterial consisting of MXene nanosheets at its core, coated with gold nanorods and a cisplatin shell, and further modified with polyvinyl alcohol, referred to as APMP. The APMP exploits its distinctive electronic properties and photothermal effects to augment radiosensitivity and impede DNA damage repair mechanisms. In vitro experiments demonstrate that APMP elevates reactive oxygen species (ROS) production to approximately 2.6 times higher than that achieved with radiotherapy alone, thereby significantly enhancing the sensitivity to radiotherapy. Combining APMP with photothermal therapy (PTT) and RT is a promising glioblastoma treatment strategy, achieving tumor destruction via localized hyperthermia and overcoming radioresistance. This approach achieves precise tumor targeting, reducing side effects and enhancing therapeutic response in preclinical models. The novel core-shell design enables potent radiotherapy-specific radiosensitizers that drive immunogenic cell death, enhancing glioblastoma combination immunotherapy. This universal strategy heralds a new era in integrating radiotherapy sensitizers with immunotherapy.

Single-cell and transcriptome analyses revealed CTHRC1 a potential therapeutic target mediating invasion and tumor microenvironment in TNBC: experimental validation

Background

Investigating the pivotal role of CTHRC1 in the tumor microenvironment of triple-negative breast cancer (TNBC).

Method

The RNA transcriptomic data obtained from the Cancer Genome Atlas and single-cell sequencing data from TNBC in Gene Expression Omnibus (GEO) were acquired and subjected to analysis. A comprehensive investigation was conducted with a specific focus on characterizing CTHRC1 in TNBC and its correlation with invasive genes. Furthermore, additional analyses were performed to explore the relationship between CTHRC1, tumor immune cell infiltration, and immunotherapy in TNBC. The expression of CTHRC1 in the tumor microenvironment, cellular differentiation, and cellular communication was systematically analyzed using single-cell data from TNBC.

Result

The expression of CTHRC1 in patients with TNBC gradually increases concomitantly with the progression of tumor T-stage and N-stage. Simultaneously, there is a concurrent increase in the expression of most invasive gene sets. Furthermore, there is a significant augmentation in both infiltration abundance and activity of M2-type macrophages associated with elevated levels of CTHRC1 expression. Single-cell data reveal an upregulated expression of the invasive gene set in CTHRC1-positive cancer associated fibroblasts (CAFs), thereby modulating their interaction with M2-type macrophages. Multiple immunofluorescence analyses confirmed that CTHRC1 modulates immune cell infiltration and tumor cell invasion through the mediation of CAFs.

Conclusion

CTHRC1 was a molecule that exhibits characteristic expression in TNBC. CTHRC1 positive CAFs exert regulatory effects within the immunosuppressive microenvironment of TNBC by modulating M2-type macrophages.

Quantifying and interpreting biologically meaningful spatial signatures within tumor microenvironments

The tumor microenvironment (TME) plays a crucial role in orchestrating tumor cell behavior and cancer progression. Recent advances in spatial profiling technologies have uncovered novel spatial signatures, including univariate distribution patterns, bivariate spatial relationships, and higher-order structures. These signatures have the potential to revolutionize tumor mechanism and treatment. In this review, we summarize the current state of spatial signature research, highlighting computational methods to uncover spatially relevant biological significance. We discuss the impact of these advances on fundamental cancer biology and translational research, address current challenges and future research directions.

Profiling immune cell tissue niches in the spatial -omics era

Immune responses require complex, spatially coordinated interactions between immune cells and their tissue environment. For decades, we have imaged tissue sections to visualize a limited number of immune-related macromolecules in situ, functioning as surrogates for cell types or processes of interest. However, this inevitably provides a limited snapshot of the tissue's immune landscape. Recent developments in high-throughput spatial -omics technologies, particularly spatial transcriptomics, and its application to human samples has facilitated a more comprehensive understanding of tissue immunity by mapping fine-grained immune cell states to their precise tissue location while providing contextual information about their immediate cellular and tissue environment. These data provide opportunities to investigate mechanisms underlying the spatial distribution of immune cells and its functional implications, including the identification of immune niches, although the criteria used to define this term have been inconsistent. Here, we review recent technological and analytic advances in multiparameter spatial profiling, focusing on how these methods have generated new insights in translational immunology. We propose a 3-step framework for the definition and characterization of immune niches, which is powerfully facilitated by new spatial profiling methodologies. Finally, we summarize current approaches to analyze adaptive immune repertoires and lymphocyte clonal expansion in a spatially resolved manner.

Analysis of single-cell and spatial transcriptomics in TNBC cell-cell interactions

Triple-negative breast cancer (TNBC) is a highly malignant tumor in women, characterized by high morbidity, mortality, and recurrence rates. Although surgical treatment, radiotherapy, and chemotherapy are the mainstays of current treatment methods, the high heterogeneity of TNBC results in unsatisfactory outcomes with low 5-year survival rates. Rapid advancements in omics technology have propelled the understanding of TNBC molecular biology. The emergence of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) has significantly enhanced knowledge of tumor heterogeneity and the distribution, functionality, and intercellular interactions of various cell types within the tumor microenvironment, including tumor cells, T cells, B cells, macrophages, and fibroblasts. The present study provides an overview of the technical characteristics of scRNA-seq and ST, highlighting their applications in exploring TNBC heterogeneity, cell spatial distribution patterns, and intercellular interactions. This review aims to enhance the comprehension of TNBC at the cellular level for the development of effective therapeutic targets.

Heterogeneity of tertiary lymphoid structures predicts the response to neoadjuvant therapy and immune microenvironment characteristics in triple-negative breast cancer

BACKGROUND

Tertiary lymphoid structures (TLSs) impact cancer outcomes, including in triple-negative breast cancer (TNBC), where their role in immune modulation during neoadjuvant therapy (NAT) is underexplored.

METHODS

This study employed single-cell RNA sequencing (scRNA-seq), multiplex immunofluorescence (mIF) staining, and radiomic techniques to evaluate TLSs and the tumour microenvironment (TME) in TNBC patient samples before and after NAT.

RESULTS

The presence of TLSs in TNBC was associated with B-cell maturation and T-cell activation. Compared with TLS-low TNBC, TLS-high TNBC showed significantly greater expression of immunoglobulin family genes (IGHM and IGHG1) in B cells and greater cytotoxicity of neoantigen-specific CD8 + T cells (neoTCR8). Additionally, mIF revealed notable differences between TLSs and the TME in TNBC. Although CD8 + T-cell levels do not predict the NAT response effectively, TLS maturity strongly correlated with better NAT outcomes and prognosis (P < 0.05). An imaging biomarker scoring system was also developed to predict TLS status and NAT efficacy.

CONCLUSION

Our results demonstrated changes in TLSs and the TME in TNBC patients post-NAT. These findings confirm the predictive value of mature TLSs (mTLSs) and support the use of personalised immunotherapy based on post-NAT immune characteristics, thereby improving clinical outcomes.

Temporal and spatial composition of the tumor microenvironment predicts response to immune checkpoint inhibition

Immune checkpoint inhibition (ICI) has fundamentally changed cancer treatment. However, only a minority of patients with metastatic triple negative breast cancer (TNBC) benefit from ICI, and the determinants of response remain largely unknown. To better understand the factors influencing patient outcome, we assembled a longitudinal cohort with tissue from multiple timepoints, including primary tumor, pre-treatment metastatic tumor, and on-treatment metastatic tumor from 117 patients treated with ICI (nivolumab) in the phase II TONIC trial. We used highly multiplexed imaging to quantify the subcellular localization of 37 proteins in each tumor. To extract meaningful information from the imaging data, we developed SpaceCat, a computational pipeline that quantifies features from imaging data such as cell density, cell diversity, spatial structure, and functional marker expression. We applied SpaceCat to 678 images from 294 tumors, generating more than 800 distinct features per tumor. Spatial features were more predictive of patient outcome, including features like the degree of mixing between cancer and immune cells, the diversity of the neighboring immune cells surrounding cancer cells, and the degree of T cell infiltration at the tumor border. Non-spatial features, including the ratio between T cell subsets and cancer cells and PD-L1 levels on myeloid cells, were also associated with patient outcome. Surprisingly, we did not identify robust predictors of response in the primary tumors. In contrast, the metastatic tumors had numerous features which predicted response. Some of these features, such as the cellular diversity at the tumor border, were shared across timepoints, but many of the features, such as T cell infiltration at the tumor border, were predictive of response at only a single timepoint. We trained multivariate models on all of the features in the dataset, finding that we could accurately predict patient outcome from the pre-treatment metastatic tumors, with improved performance using the on-treatment tumors. We validated our findings in matched bulk RNA-seq data, finding the most informative features from the on-treatment samples. Our study highlights the importance of profiling sequential tumor biopsies to understand the evolution of the tumor microenvironment, elucidating the temporal and spatial dynamics underlying patient responses and underscoring the need for further research on the prognostic role of metastatic tissue and its utility in stratifying patients for ICI.

PEARL: A Phase Ib/II Biomarker Study of Adding Radiation Therapy to Pembrolizumab Before Neoadjuvant Chemotherapy in Human Epidermal Growth Factor Receptor 2-Negative Breast Cancer

PURPOSE

To assess safety and immune biomarkers after preoperative radiation therapy (RT) and anti-PD1 therapy in breast cancer.

MATERIALS AND METHODS

A phase I/IIb trial of pembrolizumab with RT was conducted in patients with triple-negative breast cancer (TNBC) and hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2-) breast cancer. All received pembrolizumab followed by a second cycle + RT (anti-PD1/RT) of 24 Gy/three daily fractions delivered to the breast tumor and then neoadjuvant chemotherapy (NAC). Blood and tumor biopsies were obtained at baseline, after anti-PD1, and after anti-PD-RT. Coprimary end points were safety and change in tumor-infiltrating lymphocytes (TILs). Secondary end points were pathologic complete response (pCR), residual cancer burden (RCB) rates, and event-free survival (EFS).

RESULTS

Sixty-six patients with stage I-III breast cancer (54 TNBC, 12 HR+/HER2-) were enrolled. The median follow-up was 32 months. Safety end point was met. Incidence of grade ≥3 toxicities was 41%. The pCR rate was 59.2%, 33.3%, and 54.5% for the TNBC, HR+/HER2-, and entire cohort, respectively. A total of 77.8% of TNBC and 41.6% of HR+/HER2- had a near pCR (RCB 0-1). The 3-year EFS was 80%. In the entire cohort, PD-L1 expression increased after anti-PD1 (median Combined Positive Score [CPS], 7.49-23.20; 95% CI, -41.88 to -6.30; P = .044) and anti-PD1/RT (median CPS, 7.49-23.41; 95% CI, -41.88 to -6.30; P = .009), compared with baseline. In TNBC, adding RT to anti-PD1 significantly decreased TILs (28.9%-17.1%; 95% CI, 2.46 to 21.09; P = .014). Baseline TILs correlated with PD-L1 expression and TNF-a.

CONCLUSION

Preoperative RT with pembrolizumab is safe and results in high pCR rates and 3-year EFS, despite the lack of pembrolizumab during NAC. PD-L1 and TILs may be predictive biomarkers for preoperative anti-PD1/RT response. Reduction in TILs after adding RT to anti-PD1 highlights the importance of treatment sequencing.

Spatial transcriptomics in breast cancer: providing insight into tumor heterogeneity and promoting individualized therapy

A comprehensive understanding of tumor heterogeneity, tumor microenvironment and the mechanisms of drug resistance is fundamental to advancing breast cancer research. While single-cell RNA sequencing has resolved the issue of "temporal dynamic expression" of genes at the single-cell level, the lack of spatial information still prevents us from gaining a comprehensive understanding of breast cancer. The introduction and application of spatial transcriptomics addresses this limitation. As the annual technical method of 2020, spatial transcriptomics preserves the spatial location of tissues and resolves RNA-seq data to help localize and differentiate the active expression of functional genes within a specific tissue region, enabling the study of spatial location attributes of gene locations and cellular tissue environments. In the context of breast cancer, spatial transcriptomics can assist in the identification of novel breast cancer subtypes and spatially discriminative features that show promise for individualized precise treatment. This article summarized the key technical approaches, recent advances in spatial transcriptomics and its applications in breast cancer, and discusses the limitations of current spatial transcriptomics methods and the prospects for future development, with a view to advancing the application of this technology in clinical practice.

Discovering explainable biomarkers for breast cancer anti-PD1 response via network Shapley value analysis

BACKGROUND AND OBJECTIVE

Immunotherapy holds promise in enhancing pathological complete response rates in breast cancer, albeit confined to a select cohort of patients. Consequently, pinpointing factors predictive of treatment responsiveness is of paramount importance. Gene expression and regulation, inherently operating within intricate networks, constitute fundamental molecular machinery for cellular processes and often serve as robust biomarkers. Nevertheless, contemporary feature selection approaches grapple with two key challenges: opacity in modeling and scarcity in accounting for gene-gene interactions METHODS: To address these limitations, we devise a novel feature selection methodology grounded in cooperative game theory, harmoniously integrating with sophisticated machine learning models. This approach identifies interconnected gene regulatory network biomarker modules with priori genetic linkage architecture. Specifically, we leverage Shapley values on network to quantify feature importance, while strategically constraining their integration based on network expansion principles and nodal adjacency, thereby fostering enhanced interpretability in feature selection. We apply our methods to a publicly available single-cell RNA sequencing dataset of breast cancer immunotherapy responses, using the identified feature gene set as biomarkers. Functional enrichment analysis with independent validations further illustrates their effective predictive performance RESULTS: We demonstrate the sophistication and excellence of the proposed method in data with network structure. It unveiled a cohesive biomarker module encompassing 27 genes for immunotherapy response. Notably, this module proves adept at precisely predicting anti-PD1 therapeutic outcomes in breast cancer patients with classification accuracy of 0.905 and AUC value of 0.971, underscoring its unique capacity to illuminate gene functionalities CONCLUSION: The proposed method is effective for identifying network module biomarkers, and the detected anti-PD1 response biomarkers can enrich our understanding of the underlying physiological mechanisms of immunotherapy, which have a promising application for realizing precision medicine.

Research trends, hotspots and future directions of tertiary lymphoid structures in cancer: a comprehensive informatics analysis and visualization study

Many studies have reported the presence of tertiary lymphoid structures (TLSs) in cancer, but the research progress of TLSs in cancer has not been systematically analyzed. Therefore, we analyzed the global scientific knowledge in the field using informatics methods. The results showed that TLSs in cancer have received increasing attention since the 21st century, with an annual publication growth rate of 27.86%. Unsupervised hierarchical clustering based on machine learning further categorized the research features into four clusters, with the cluster related to immunotherapy being considered an emerging cluster. TLSs and immunotherapy were identified as the top two hotspots with the highest occurrence frequency and total link strength. The Walktrap algorithm indicated that "TLSs, carcinoma, prognostic value" and "high endothelial venules, germinal-centers, node-like structures" are important to TLSs but remain underexplored, representing promising research directions. These findings suggest that cancer-related TLSs have brought new insights into antitumor immunity, and targeting TLSs has the potential to transform the landscape of antitumor immunotherapy.

Spatial oncology: Translating contextual biology to the clinic

Microscopic examination of cells in their tissue context has been the driving force behind diagnostic histopathology over the past two centuries. Recently, the rise of advanced molecular biomarkers identified through single cell profiling has increased our understanding of cellular heterogeneity in cancer but have yet to significantly impact clinical care. Spatial technologies integrating molecular profiling with microenvironmental features are poised to bridge this translational gap by providing critical in situ context for understanding cellular interactions and organization. Here, we review how spatial tools have been used to study tumor ecosystems and their clinical applications. We detail findings in cell-cell interactions, microenvironment composition, and tissue remodeling for immune evasion and therapeutic resistance. Additionally, we highlight the emerging role of multi-omic spatial profiling for characterizing clinically relevant features including perineural invasion, tertiary lymphoid structures, and the tumor-stroma interface. Finally, we explore strategies for clinical integration and their augmentation of therapeutic and diagnostic approaches.

Spatial transcriptomics: a new frontier in accurate localization of breast cancer diagnosis and treatment

Breast cancer is one of the most prevalent cancers in women globally. Its treatment and prognosis are significantly influenced by the tumor microenvironment and tumor heterogeneity. Precision therapy enhances treatment efficacy, reduces unwanted side effects, and maximizes patients' survival duration while improving their quality of life. Spatial transcriptomics is of significant importance for the precise treatment of breast cancer, playing a critical role in revealing the internal structural differences of tumors and the composition of the tumor microenvironment. It offers a novel perspective in studying the spatial structure and cell interactions within tumors, facilitating more effective personalized treatments for breast cancer. This article will summarize the latest findings in the diagnosis and treatment of breast cancer from the perspective of spatial transcriptomics, focusing on the revelation of the tumor microenvironment, identification of new therapeutic targets, enhancement of disease diagnostic accuracy, comprehension of tumor progression and metastasis, assessment of drug responses, creation of high-resolution maps of tumor cells, representation of tumor heterogeneity, and support for clinical decision-making, particularly in elucidating the tumor microenvironment, tumor heterogeneity, immunotherapy and their correlation with clinical outcomes.

Infiltration of Common Myeloid Progenitor (CMP) Cells is Associated With Less Aggressive Tumor Biology, Lower Risk of Brain Metastasis, Better Response to Immunotherapy, and Higher Patient Survival in Breast Cancer

OBJECTIVE

To investigate the clinical relevance of common myeloid progenitor (CMP) cells in breast tumor microenvironment (TME).

BACKGROUND

The role of rare cells in TME is less studied. In Silico transcriptomic analyses of real-world data enable us to detect and quantify rare cells, including CMP cells.

METHODS

A total of 5176 breast cancer (BC) patients from SCAN-B, METABRIC, and 5 single-cell sequence cohorts were analyzed using the xCell algorithm. The high group was defined as more than two-thirds of the CMP scores in each cohort.

RESULTS

CMP cells consist of 0.07% to 0.25% of bulk breast tumor cells, more in estrogen receptor-positive (ER+) compared with triple-negative (TN) subtype (0.1% to 0.75%, 0.18% to 0.33% of immune cells, respectively). CMP cells did not correlate with any of the myeloid lineages or stem cells in TME. CMP infiltration was higher in smaller tumors, with lower Nottingham grade, and in ER+/HER2- than in TNBC consistently in both SCAN-B and METABRIC cohorts. High CMP was significantly associated with a lower risk of brain metastasis and with better survival, particularly in ER+/HER2-. High CMP enriched epithelial-to-mesenchymal transition and angiogenesis pathways, and less cell proliferation and DNA repair gene sets. High CMP ER+/HER2- was associated with less immune cell infiltration and cytolytic activity ( P <0.001). CMP infiltration correlated with neoadjuvant chemoimmunotherapy response for both ER+/HER2- and TNBC in the ISPY-2 cohort (AUC=0.69 and 0.74, respectively).

CONCLUSIONS

CMP in BC is inversely associated with cell proliferation and brain metastasis, better response to immunotherapy, and survival. This is the first to report the clinical relevance of CMP infiltration in BC.

Next-generation spatial transcriptomics: unleashing the power to gear up translational oncology

The growing advances in spatial transcriptomics (ST) stand as the new frontier bringing unprecedented influences in the realm of translational oncology. This has triggered systemic experimental design, analytical scope, and depth alongside with thorough bioinformatics approaches being constantly developed in the last few years. However, harnessing the power of spatial biology and streamlining an array of ST tools to achieve designated research goals are fundamental and require real-world experiences. We present a systemic review by updating the technical scope of ST across different principal basis in a timeline manner hinting on the generally adopted ST techniques used within the community. We also review the current progress of bioinformatic tools and propose in a pipelined workflow with a toolbox available for ST data exploration. With particular interests in tumor microenvironment where ST is being broadly utilized, we summarize the up-to-date progress made via ST-based technologies by narrating studies categorized into either mechanistic elucidation or biomarker profiling (translational oncology) across multiple cancer types and their ways of deploying the research through ST. This updated review offers as a guidance with forward-looking viewpoints endorsed by many high-resolution ST tools being utilized to disentangle biological questions that may lead to clinical significance in the future.

Deciphering breast cancer dynamics: insights from single-cell and spatial profiling in the multi-omics era

As one of the most common tumors in women, the pathogenesis and tumor heterogeneity of breast cancer have long been the focal point of research, with the emergence of tumor metastasis and drug resistance posing persistent clinical challenges. The emergence of single-cell sequencing (SCS) technology has introduced novel approaches for gaining comprehensive insights into the biological behavior of malignant tumors. SCS is a high-throughput technology that has rapidly developed in the past decade, providing high-throughput molecular insights at the individual cell level. Furthermore, the advent of multitemporal point sampling and spatial omics also greatly enhances our understanding of cellular dynamics at both temporal and spatial levels. The paper provides a comprehensive overview of the historical development of SCS, and highlights the most recent advancements in utilizing SCS and spatial omics for breast cancer research. The findings from these studies will serve as valuable references for future advancements in basic research, clinical diagnosis, and treatment of breast cancer.

Spatial landscapes of cancers: insights and opportunities

Solid tumours comprise many different cell types organized in spatially structured arrangements, with substantial intratumour and intertumour heterogeneity. Advances in spatial profiling technologies over the past decade hold promise to capture the complexity of these cellular architectures to build a holistic view of the intricate molecular mechanisms that shape the tumour ecosystem. Some of these mechanisms act at the cellular scale and are controlled by cell-autonomous programmes or communication between nearby cells, whereas other mechanisms result from coordinated efforts between large networks of cells and extracellular molecules organized into tissues and organs. In this Review we provide insights into the application of single-cell and spatial profiling tools, with a focus on spatially resolved transcriptomic tools developed to understand the cellular architecture of the tumour microenvironment and identify opportunities to use them to improve clinical management of cancers.

Mismatch repair-proficient tumor footprints in the sands of immune desert: mechanistic constraints and precision platforms

Mismatch repair proficient (MMRp) tumors of colorectal origin are one of the prevalent yet unpredictable clinical challenges. Despite earnest efforts, optimal treatment modalities have yet to emerge for this class. The poor prognosis and limited actionability of MMRp are ascribed to a low neoantigen burden and a desert-like microenvironment. This review focuses on the critical roadblocks orchestrated by an immune evasive mechanistic milieu in the context of MMRp. The low density of effector immune cells, their weak spatiotemporal underpinnings, and the high-handedness of the IL-17-TGF-β signaling are intertwined and present formidable challenges for the existing therapies. Microbiome niche decorated by Fusobacterium nucleatum alters the metabolic program to maintain an immunosuppressive state. We also highlight the evolving strategies to repolarize and reinvigorate this microenvironment. Reconstruction of anti-tumor chemokine signaling, rational drug combinations eliciting T cell activation, and reprograming the maladapted microbiome are exciting developments in this direction. Alternative vulnerability of other DNA damage repair pathways is gaining momentum. Integration of liquid biopsy and ex vivo functional platforms provide precision oncology insights. We illustrated the perspectives and changing landscape of MMRp-CRC. The emerging opportunities discussed in this review can turn the tide in favor of fighting the treatment dilemma for this elusive cancer.

From virtual patients to digital twins in immuno-oncology: lessons learned from mechanistic quantitative systems pharmacology modeling

Virtual patients and digital patients/twins are two similar concepts gaining increasing attention in health care with goals to accelerate drug development and improve patients' survival, but with their own limitations. Although methods have been proposed to generate virtual patient populations using mechanistic models, there are limited number of applications in immuno-oncology research. Furthermore, due to the stricter requirements of digital twins, they are often generated in a study-specific manner with models customized to particular clinical settings (e.g., treatment, cancer, and data types). Here, we discuss the challenges for virtual patient generation in immuno-oncology with our most recent experiences, initiatives to develop digital twins, and how research on these two concepts can inform each other.

Exploiting temporal aspects of cancer immunotherapy

Many mechanisms underlying an effective immunotherapy-induced antitumour response are transient and critically time dependent. This is equally true for several immunological events in the tumour microenvironment induced by other cancer treatments. Immune checkpoint therapy (ICT) has proven to be very effective in the treatment of some cancers, but unfortunately, with many cancer types, most patients do not experience a benefit. To improve outcomes, a multitude of clinical trials are testing combinations of ICT with various other treatment modalities. Ideally, those combination treatments should take time-dependent immunological events into account. Recent studies have started to map the dynamic cellular and molecular changes that occur during treatment with ICT, in the tumour and systemically. Here, we overlay the dynamic ICT response with the therapeutic response following surgery, radiotherapy, chemotherapy and targeted therapies. We propose that by combining treatments in a time-conscious manner, we may optimally exploit the interactions between the individual therapies.

Quantitative systems pharmacology modeling of macrophage-targeted therapy combined with PD-L1 inhibition in advanced NSCLC

Immune checkpoint inhibitors remained the standard-of-care treatment for advanced non-small cell lung cancer (NSCLC) for the past decade. In unselected patients, anti-PD-(L)1 monotherapy achieved an overall response rate of about 20%. In this analysis, we developed a pharmacokinetic and pharmacodynamic module for our previously calibrated quantitative systems pharmacology model (QSP) to simulate the effectiveness of macrophage-targeted therapies in combination with PD-L1 inhibition in advanced NSCLC. By conducting in silico clinical trials, the model confirmed that anti-CD47 treatment is not an optimal option of second- and later-line treatment for advanced NSCLC resistant to PD-(L)1 blockade. Furthermore, the model predicted that inhibition of macrophage recruitment, such as using CCR2 inhibitors, can potentially improve tumor size reduction when combined with anti-PD-(L)1 therapy, especially in patients who are likely to respond to anti-PD-(L)1 monotherapy and those with a high level of tumor-associated macrophages. Here, we demonstrate the application of the QSP platform on predicting the effectiveness of novel drug combinations involving immune checkpoint inhibitors based on preclinical or early-stage clinical trial data.

Comprehensive characterization of stemness-related lncRNAs in triple-negative breast cancer identified a novel prognostic signature related to treatment outcomes, immune landscape analysis and therapeutic guidance: a silico analysis with in vivo experiments

BACKGROUND

Cancer stem cells (CSCs) and long non-coding RNAs (lncRNAs) are known to play a crucial role in the growth, migration, recurrence, and drug resistance of tumor cells, particularly in triple-negative breast cancer (TNBC). This study aims to investigate stemness-related lncRNAs (SRlncRNAs) as potential prognostic indicators for TNBC patients.

METHODS

Utilizing RNA sequencing data and corresponding clinical information from the TCGA database, and employing Weighted Gene Co-expression Network Analysis (WGCNA) on TNBC mRNAsi sourced from an online database, stemness-related genes (SRGs) and SRlncRNAs were identified. A prognostic model was developed using univariate Cox and LASSO-Cox analysis based on SRlncRNAs. The performance of the model was evaluated using Kaplan-Meier analysis, ROC curves, and ROC-AUC. Additionally, the study delved into the underlying signaling pathways and immune status associated with the divergent prognoses of TNBC patients.

RESULTS

The research identified a signature of six SRlncRNAs (AC245100.6, LINC02511, AC092431.1, FRGCA, EMSLR, and MIR193BHG) for TNBC. Risk scores derived from this signature were found to correlate with the abundance of plasma cells. Furthermore, the nominated chemotherapy drugs for TNBC exhibited considerable variability between different risk score groups. RT-qPCR validation confirmed abnormal expression patterns of these SRlncRNAs in TNBC stem cells, affirming the potential of the SRlncRNAs signature as a prognostic biomarker.

CONCLUSION

The identified signature not only demonstrates predictive power in terms of patient outcomes but also provides insights into the underlying biology, signaling pathways, and immune status associated with TNBC prognosis. The findings suggest the possibility of guiding personalized treatments, including immune checkpoint gene therapy and chemotherapy strategies, based on the risk scores derived from the SRlncRNA signature. Overall, this research contributes valuable knowledge towards advancing precision medicine in the context of TNBC.

Coordinated Immune Cell Networks in the Bone Marrow Microenvironment Define the Graft versus Leukemia Response with Adoptive Cellular Therapy

Understanding how intra-tumoral immune populations coordinate to generate anti-tumor responses following therapy can guide precise treatment prioritization. We performed systematic dissection of an established adoptive cellular therapy, donor lymphocyte infusion (DLI), by analyzing 348,905 single-cell transcriptomes from 74 longitudinal bone-marrow samples of 25 patients with relapsed myeloid leukemia; a subset was evaluated by protein-based spatial analysis. In acute myelogenous leukemia (AML) responders, diverse immune cell types within the bone-marrow microenvironment (BME) were predicted to interact with a clonally expanded population of ZNF683 + GZMB + CD8+ cytotoxic T lymphocytes (CTLs) which demonstrated in vitro specificity for autologous leukemia. This population, originating predominantly from the DLI product, expanded concurrently with NK and B cells. AML nonresponder BME revealed a paucity of crosstalk and elevated TIGIT expression in CD8+ CTLs. Our study highlights recipient BME differences as a key determinant of effective anti-leukemia response and opens new opportunities to modulate cell-based leukemia-directed therapy.

Neoadjuvant radioimmunotherapy synergy in triple-negative breast cancer: Is microenvironment-guided patient selection on the horizon?

Neoadjuvant chemotherapy plus immunotherapy for triple-negative breast cancer (TNBC) is associated with improved but incomplete response. In this issue of Cancer Cell, Shiao et al. characterize longitudinal biopsies from a window of opportunity study with single-cell RNA sequencing (scRNA-seq) and spatial proteomic profiling and elucidate synergy between radiotherapy (RT) and pembrolizumab.