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

Integration of Organoids With CRISPR Screens: A Narrative Review

Organoids represent a significant advancement in disease modeling, demonstrated by their capacity to mimic the physiological/pathological structure and functional characteristics of the native tissue. Recently CRISPR/Cas9 technology has emerged as a powerful tool in combination with organoids for the development of novel therapies in preclinical settings. This review explores the current literature on applications of pooled CRISPR screening in organoids and the emerging role of these models in understanding cancer. We highlight the evolution of genome-wide CRISPR gRNA library screens in organoids, noting their increasing adoption in the field over the past decade. Noteworthy studies utilizing these screens to investigate oncogenic vulnerabilities and developmental pathways in various organoid systems are discussed. Despite the promise organoids hold, challenges such as standardization, reproducibility, and the complexity of data interpretation remain. The review also addresses the ideas of assessing tumor organoids (tumoroids) against established cancer hallmarks and the potential of studying intercellular cooperation within these models. Ultimately, we propose that organoids, particularly when personalized for patient-specific applications, could revolutionize drug screening and therapeutic approaches, minimizing the reliance on traditional animal models and enhancing the precision of clinical interventions.

Natural killer cells occupy unique spatial neighborhoods in HER2- and HER2+ human breast cancers

Tumor-infiltrating lymphocytes are considered clinically beneficial in breast cancer, but the significance of natural killer (NK) cells is less well characterized. As increasing evidence has demonstrated that the spatial organization of immune cells in tumor microenvironments is a significant parameter for impacting disease progression as well as therapeutic responses, an improved understanding of tumor-infiltrating NK cells and their location within tumor contextures is needed to improve the design of effective NK cell-based therapies. In this study, we developed a multiplex immunohistochemistry (mIHC) antibody panel designed to quantitatively interrogate leukocyte lineages, focusing on NK cells and their phenotypes, in two independent breast cancer patient cohorts (n = 26 and n = 30). Owing to the clinical evidence supporting a significant role for NK cells in HER2+ breast cancer in mediating responses to Trastuzumab, we further evaluated HER2- and HER2+ specimens separately. Consistent with literature, we found that CD3+ T cells were the dominant leukocyte subset across breast cancer specimens. In comparison, NK cells, identified by CD56 or NKp46 expression, were scarce in all specimens with low granzyme B expression indicating reduced cytotoxic functionality. Whereas NK cell density and phenotype did not appear to be influenced by HER2 status, spatial analysis revealed distinct NK cells phenotypes regarding their proximity to neoplastic tumor cells that associated with HER2 status. Spatial cellular neighborhood analysis revealed multiple unique neighborhood compositions surrounding NK cells, where NK cells from HER2- tumors were more frequently found proximal to neoplastic tumor cells, whereas NK cells from HER2+ tumors were instead more frequently found proximal to CD3+ T cells. This study establishes the utility of quantitative mIHC to evaluate NK cells at the single-cell spatial proteomics level and illustrates how spatial characteristics of NK cell neighborhoods vary within the context of HER2- and HER2+ breast cancers.

Real-world clinical multi-omics analyses reveal bifurcation of ER-independent and ER-dependent drug resistance to CDK4/6 inhibitors

To better understand drug resistance mechanisms to CDK4/6 inhibitors and inform precision medicine, we analyze real-world multi-omics data from 400 HR+/HER2- metastatic breast cancer patients treated with CDK4/6 inhibitors plus endocrine therapies, including 200 pre-treatment and 227 post-progression samples. The prevalences of ESR1 and RB1 alterations significantly increase in post-progression samples. Integrative clustering analysis identifies three subgroups harboring different resistance mechanisms: ER driven, ER co-driven and ER independent. The ER independent subgroup, growing from 5% pre-treatment to 21% post-progression, is characterized by down-regulated estrogen signaling and enrichment of resistance markers including TP53 mutations, CCNE1 over-expression and Her2/Basal subtypes. Trajectory inference analyses identify a pseudotime variable strongly correlated with ER independence and disease progression; and revealed bifurcated evolutionary trajectories for ER-independent vs. ER-dependent drug resistance mechanisms. Machine learning models predict therapeutic dependency on ESR1 and CDK4 among ER-dependent tumors and CDK2 dependency among ER-independent tumors, confirmed by experimental validation.

Entinostat in combination with nivolumab in metastatic pancreatic ductal adenocarcinoma: a phase 2 clinical trial

Pancreatic ductal adenocarcinoma (PDA) is characterized by low cytotoxic lymphocytes, abundant immune-suppressive cells, and resistance to immune checkpoint inhibitors (ICI). Preclinical PDA models showed the HDAC inhibitor entinostat reduced myeloid cell immunosuppression, sensitizing tumors to ICI therapy. This phase II study combined entinostat with nivolumab (PD1 inhibitor) in patients with advanced PDA (NCT03250273). Patients received entinostat 5 mg orally once weekly for 14-day lead-in, followed by entinostat and nivolumab. The primary endpoint was the objective response rate (ORR) by RECIST v1.1. Secondary endpoints included safety, duration of response, progression free-survival and overall survival. Between November 2017 and November 2020, 27 evaluable patients were enrolled. Three showed partial responses (11% ORR, 95% CI, 2.4%-29.2%) with a median response duration of 10.2 months. Median progression-free survival (PFS) and overall survival (OS) were, respectively, 1.89 (95% CI, 1.381-2.301) and 2.729 (95% CI, 1.841-5.622) months. Grade ≥3 treatment-related adverse events occurred in 19 patients (63%), including decreased lymphocyte count, anemia, hypoalbuminemia, and hyponatremia. As exploratory analysis, peripheral and tumor immune profiles changes were assessed using CyTOF, mIHC, and RNA-seq. Entinostat increased dendritic cell activation and maturation. Gene expression analysis revealed an enrichment in inflammatory response pathways with combination treatment. Although the primary endpoint was not met, entinostat and nivolumab showed durable responses in a small subset of PDA patients. Myeloid cell immunomodulation supported the preclinical hypothesis, providing a basis for future combinatorial therapies to enhance clinical benefits in PDA.

Multiplex imaging of localized prostate tumors reveals altered spatial organization of AR-positive cells in the microenvironment

Mapping the spatial interactions of cancer, immune, and stromal cell states presents novel opportunities for patient stratification and for advancing immunotherapy. While single-cell studies revealed significant molecular heterogeneity in prostate cancer cells, the impact of spatial stromal cell heterogeneity remains poorly understood. Here, we used cyclic immunofluorescent imaging on whole-tissue sections to uncover novel spatial associations between cancer and stromal cells in low- and high-grade prostate tumors and tumor-adjacent normal tissues. Our results provide a spatial map of single cells and recurrent cellular neighborhoods in the prostate tumor microenvironment of treatment-naive patients. We report unique populations of mast cells that show distinct spatial associations with M2 macrophages and regulatory T cells. Our results show disease-specific neighborhoods that are primarily driven by androgen receptor-positive (AR+) stromal cells and identify inflammatory gene networks active in AR+ prostate stroma.

Three-dimensional analysis of mitochondria in a patient-derived xenograft model of triple negative breast cancer reveals mitochondrial network remodeling following chemotherapy treatments

Mitochondria are hubs of metabolism and signaling and play an important role in tumorigenesis, therapeutic resistance, and metastasis in many cancer types. Various laboratory models of cancer demonstrate the extraordinary dynamics of mitochondrial structure, but little is known about the role of mitochondrial structure in resistance to anticancer therapy. We previously demonstrated the importance of mitochondrial structure and oxidative phosphorylation in the survival of chemotherapy-refractory triple negative breast cancer (TNBC) cells. As TNBC is a highly aggressive breast cancer subtype with few targeted therapy options, conventional chemotherapies remain the backbone of early TNBC treatment. Unfortunately, approximately 45% of TNBC patients retain substantial residual tumor burden following chemotherapy, associated with abysmal prognoses. Using an orthotopic patient-derived xenograft mouse model of human TNBC, we compared mitochondrial structures between treatment-naïve tumors and residual tumors after conventional chemotherapeutics were administered singly or in combination. We reconstructed 1,750 mitochondria in three dimensions from serial block-face scanning electron micrographs, providing unprecedented insights into the complexity and intra-tumoral heterogeneity of mitochondria in TNBC. Following exposure to carboplatin or docetaxel given individually, residual tumor mitochondria exhibited significant increases in mitochondrial complexity index, area, volume, perimeter, width, and length relative to treatment-naïve tumor mitochondria. In contrast, residual tumors exposed to those chemotherapies given in combination exhibited diminished mitochondrial structure changes. Further, we document extensive intra-tumoral heterogeneity of mitochondrial structure, especially prior to chemotherapeutic exposure. These results highlight the potential for structure-based monitoring of chemotherapeutic responses and reveal potential molecular mechanisms that underlie chemotherapeutic resistance in TNBC.

Spatial molecular profiling of mixed invasive ductal and lobular breast cancers reveals heterogeneity in intrinsic molecular subtypes, oncogenic signatures, and mutations

Mixed invasive ductal and lobular carcinoma (MDLC) is a rare histologic subtype of breast cancer displaying both E-cadherin positive ductal and E-cadherin negative lobular morphologies within the same tumor, posing challenges with regard to anticipated clinical management. It remains unclear whether these distinct morphologies also have distinct biology and risk of recurrence. Our spatially resolved transcriptomic, genomic, and single-cell profiling revealed clinically significant differences between ductal and lobular tumor regions including distinct intrinsic subtype heterogeneity - e.g., MDLC with triple-negative breast cancer (TNBC) or basal ductal and estrogen receptor positive (ER+) luminal lobular regions, distinct enrichment of cell cycle arrest/senescence and oncogenic (ER and MYC) signatures, genetic and epigenetic CDH1 inactivation in lobular but not ductal regions, and single-cell ductal and lobular subpopulations with unique oncogenic signatures further highlighting intraregional heterogeneity. Altogether, we demonstrated that the intratumoral morphological/histological heterogeneity within MDLC is underpinned by intrinsic subtype and oncogenic heterogeneity which may result in prognostic uncertainty and therapeutic dilemma.

Spatial molecular profiling of mixed invasive ductal-lobular breast cancers reveals heterogeneity in intrinsic molecular subtypes, oncogenic signatures, and mutations

Mixed invasive ductal and lobular carcinoma (MDLC) is a rare histologic subtype of breast cancer displaying both E-cadherin positive ductal and E-cadherin negative lobular morphologies within the same tumor, posing challenges with regard to anticipated clinical management. It remains unclear whether these distinct morphologies also have distinct biology and risk of recurrence. Our spatially-resolved transcriptomic, genomic, and single-cell profiling revealed clinically significant differences between ductal and lobular tumor regions including distinct intrinsic subtype heterogeneity (e.g., MDLC with TNBC/basal ductal and ER+/luminal lobular regions), distinct enrichment of senescence/dormancy and oncogenic (ER and MYC) signatures, genetic and epigenetic CDH1 inactivation in lobular, but not ductal regions, and single-cell ductal and lobular sub-populations with unique oncogenic signatures further highlighting intra-regional heterogeneity. Altogether, we demonstrated that the intra-tumoral morphological/histological heterogeneity within MDLC is underpinned by intrinsic subtype and oncogenic heterogeneity which may result in prognostic uncertainty and therapeutic dilemma.

Significance

MDLC displays both ductal and lobular tumor regions. Our multi-omic profiling approach revealed that these morphologically distinct tumor regions harbor distinct intrinsic subtypes and oncogenic features that may cause prognostic uncertainty and therapeutic dilemma. Thus histopathological/molecular profiling of individual tumor regions may guide clinical decision making and benefit patients with MDLC, particularly in the advanced setting where there is increased reliance on next generation sequencing.

Multiplex Single-Cell Bioprinting for Engineering of Heterogeneous Tissue Constructs with Subcellular Spatial Resolution

Tissue development, function, and disease are largely driven by the spatial organization of individual cells and their cell-cell interactions. Precision engineered tissues with single-cell spatial resolution, therefore, have tremendous potential for next generation disease models, drug discovery, and regenerative therapeutics. Despite significant advancements in biofabrication approaches to improve feature resolution, strategies to fabricate tissues with the exact same organization of individual cells in their native cellular microenvironment have remained virtually non-existent to date. Here we report a method to spatially pattern single cells with up to eight cell phenotypes and subcellular spatial precision. As proof-of-concept we first demonstrate the ability to systematically assess the influence of cellular microenvironments on cell behavior by controllably altering the spatial arrangement of cell types in bioprinted precision cell-cell interaction arrays. We then demonstrate, for the first time, the ability to produce high-fidelity replicas of a patient's annotated cancer biopsy with subcellular resolution. The ability to replicate native cellular microenvironments marks a significant advancement for precision biofabricated in-vitro models, where heterogenous tissues can be engineered with single-cell spatial precision to advance our understanding of complex biological systems in a controlled and systematic manner.

Some Guiding Principles for a "Simple" Correlative Light Electron Microscopy Experiment

In recent years, Correlative Multimodal Imaging (CMI) has become an "en vogue" technique and a bit of a buzzword. It entails combining information from different imaging modalities to extract more information from a sample that would otherwise not be possible from each individual technique. The best established CMI technology is correlative light and electron microscopy (CLEM), which applies light and electron microscopy on the exact same sample/structure. In general, it entails the detection of fluorescently tagged proteins or structures by light microscopy and subsequently their relative intracellular localization is determined with nanometer resolution using transmission electron microscopy (TEM). Here, we describe the different steps involved in a "simple" CLEM approach. We describe the overall workflow, instrumentation, and basic principles of sample preparation for a CLEM experiment exploiting stable expression of fluorescent proteins.

Segmentation of cellular ultrastructures on sparsely labeled 3D electron microscopy images using deep learning

Focused ion beam-scanning electron microscopy (FIB-SEM) images can provide a detailed view of the cellular ultrastructure of tumor cells. A deeper understanding of their organization and interactions can shed light on cancer mechanisms and progression. However, the bottleneck in the analysis is the delineation of the cellular structures to enable quantitative measurements and analysis. We mitigated this limitation using deep learning to segment cells and subcellular ultrastructure in 3D FIB-SEM images of tumor biopsies obtained from patients with metastatic breast and pancreatic cancers. The ultrastructures, such as nuclei, nucleoli, mitochondria, endosomes, and lysosomes, are relatively better defined than their surroundings and can be segmented with high accuracy using a neural network trained with sparse manual labels. Cell segmentation, on the other hand, is much more challenging due to the lack of clear boundaries separating cells in the tissue. We adopted a multi-pronged approach combining detection, boundary propagation, and tracking for cell segmentation. Specifically, a neural network was employed to detect the intracellular space; optical flow was used to propagate cell boundaries across the z-stack from the nearest ground truth image in order to facilitate the separation of individual cells; finally, the filopodium-like protrusions were tracked to the main cells by calculating the intersection over union measure for all regions detected in consecutive images along z-stack and connecting regions with maximum overlap. The proposed cell segmentation methodology resulted in an average Dice score of 0.93. For nuclei, nucleoli, and mitochondria, the segmentation achieved Dice scores of 0.99, 0.98, and 0.86, respectively. The segmentation of FIB-SEM images will enable interpretative rendering and provide quantitative image features to be associated with relevant clinical variables.

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior

Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells' characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework-a cell behavior hypothesis grammar-that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.

A framework for considering prior information in network-based approaches to omics data analysis

For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks (PKNs) in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of PKNs is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.

The Breast Cancer Proteome and Precision Oncology

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

Molecular Characterization and Landscape of Breast cancer Models from a multi-omics Perspective

Breast cancer is well-known to be a highly heterogenous disease. This facet of cancer makes finding a research model that mirrors the disparate intrinsic features challenging. With advances in multi-omics technologies, establishing parallels between the various models and human tumors is increasingly intricate. Here we review the various model systems and their relation to primary breast tumors using available omics data platforms. Among the research models reviewed here, breast cancer cell lines have the least resemblance to human tumors since they have accumulated many mutations and copy number alterations during their long use. Moreover, individual proteomic and metabolomic profiles do not overlap with the molecular landscape of breast cancer. Interestingly, omics analysis revealed that the initial subtype classification of some breast cancer cell lines was inappropriate. In cell lines the major subtypes are all well represented and share some features with primary tumors. In contrast, patient-derived xenografts (PDX) and patient-derived organoids (PDO) are superior in mirroring human breast cancers at many levels, making them suitable models for drug screening and molecular analysis. While patient derived organoids are spread across luminal, basal- and normal-like subtypes, the PDX samples were initially largely basal but other subtypes have been increasingly described. Murine models offer heterogenous tumor landscapes, inter and intra-model heterogeneity, and give rise to tumors of different phenotypes and histology. Murine models have a reduced mutational burden compared to human breast cancer but share some transcriptomic resemblance, and representation of many breast cancer subtypes can be found among the variety subtypes. To date, while mammospheres and three- dimensional cultures lack comprehensive omics data, these are excellent models for the study of stem cells, cell fate decision and differentiation, and have also been used for drug screening. Therefore, this review explores the molecular landscapes and characterization of breast cancer research models by comparing recent published multi-omics data and analysis.

Automated large volume sample preparation for vEM

New developments in electron microscopy technology, improved efficiency of detectors, and artificial intelligence applications for data analysis over the past decade have increased the use of volume electron microscopy (vEM) in the life sciences field. Moreover, sample preparation methods are continuously being modified by investigators to improve final sample quality, increase electron density, combine imaging technologies, and minimize the introduction of artifacts into specimens under study. There are a variety of conventional bench protocols that a researcher can utilize, though most of these protocols require several days. In this work, we describe the utilization of an automated specimen processor, the mPrep™ ASP-2000™, to prepare samples for vEM that are compatible with focused ion beam scanning electron microscopy (FIB-SEM), serial block face scanning electron microscopy (SBF-SEM), and array tomography (AT). The protocols described here aimed for methods that are completed in a much shorter period of time while minimizing the exposure of the operator to hazardous and toxic chemicals and improving the reproducibility of the specimens' heavy metal staining, all without compromising the quality of the data acquired using backscattered electrons during SEM imaging. As a control, we have included a widely used sample bench protocol and have utilized it as a comparator for image quality analysis, both qualitatively and using image quality analysis metrics.

N-acetylcysteine overcomes NF1 loss-driven resistance to PI3Kα inhibition in breast cancer

A genome-wide PiggyBac transposon-mediated screen and a resistance screen in a PIK3CA^H1047R-mutated murine tumor model reveal NF1 loss in mammary tumors resistant to the phosphatidylinositol 3-kinase α (PI3Kα)-selective inhibitor alpelisib. Depletion of NF1 in PIK3CA^H1047R breast cancer cell lines and a patient-derived organoid model shows that NF1 loss reduces sensitivity to PI3Kα inhibition and correlates with enhanced glycolysis and lower levels of reactive oxygen species (ROS). Unexpectedly, the antioxidant N-acetylcysteine (NAC) sensitizes NF1 knockout cells to PI3Kα inhibition and reverts their glycolytic phenotype. Global phospho-proteomics indicates that combination with NAC enhances the inhibitory effect of alpelisib on mTOR signaling. In public datasets of human breast cancer, we find that NF1 is frequently mutated and that such mutations are enriched in metastases, an indication for which use of PI3Kα inhibitors has been approved. Our results raise the attractive possibility of combining PI3Kα inhibition with NAC supplementation, especially in patients with drug-resistant metastases associated with NF1 loss.

Opportunities and challenges to engineer 3D models of tumor-adaptive immune interactions

Augmenting adaptive immunity is a critical goal for developing next-generation cancer therapies. T and B cells infiltrating the tumor dramatically influence cancer progression through complex interactions with the local microenvironment. Cancer cells evade and limit these immune responses by hijacking normal immunologic pathways. Current experimental models using conventional primary cells, cell lines, or animals have limitations for studying cancer-immune interactions directly relevant to human biology and clinical translation. Therefore, engineering methods to emulate such interplay at local and systemic levels are crucial to expedite the development of better therapies and diagnostic tools. In this review, we discuss the challenges, recent advances, and future directions toward engineering the tumor-immune microenvironment (TME), including key elements of adaptive immunity. We first offer an overview of the recent research that has advanced our understanding of the role of the adaptive immune system in the tumor microenvironment. Next, we discuss recent developments in 3D in-vitro models and engineering approaches that have been used to study the interaction of cancer and stromal cells with B and T lymphocytes. We summarize recent advancement in 3D bioengineering and discuss the need for 3D tumor models that better incorporate elements of the complex interplay of adaptive immunity and the tumor microenvironment. Finally, we provide a perspective on current challenges and future directions for modeling cancer-immune interactions aimed at identifying new biological targets for diagnostics and therapeutics.

Artificial intelligence assists precision medicine in cancer treatment

Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the same drugs or surgical methods in patients with the same tumor may have different curative effects, leading to the need for more accurate treatment methods for tumors and personalized treatments for patients. The precise treatment of tumors is essential, which renders obtaining an in-depth understanding of the changes that tumors undergo urgent, including changes in their genes, proteins and cancer cell phenotypes, in order to develop targeted treatment strategies for patients. Artificial intelligence (AI) based on big data can extract the hidden patterns, important information, and corresponding knowledge behind the enormous amount of data. For example, the ML and deep learning of subsets of AI can be used to mine the deep-level information in genomics, transcriptomics, proteomics, radiomics, digital pathological images, and other data, which can make clinicians synthetically and comprehensively understand tumors. In addition, AI can find new biomarkers from data to assist tumor screening, detection, diagnosis, treatment and prognosis prediction, so as to providing the best treatment for individual patients and improving their clinical outcomes.

Multiplexed In Situ Spatial Protein Profiling in the Pursuit of Precision Immuno-Oncology for Patients with Breast Cancer

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of many solid tumors. In breast cancer (BC), immunotherapy is currently approved in combination with chemotherapy, albeit only in triple-negative breast cancer. Unfortunately, most patients only derive limited benefit from ICIs, progressing either upfront or after an initial response. Therapeutics must engage with a heterogeneous network of complex stromal-cancer interactions that can fail at imposing cancer immune control in multiple domains, such as in the genomic, epigenomic, transcriptomic, proteomic, and metabolomic domains. To overcome these types of heterogeneous resistance phenotypes, several combinatorial strategies are underway. Still, they can be predicted to be effective only in the subgroups of patients in which those specific resistance mechanisms are effectively in place. As single biomarker predictive performances are necessarily suboptimal at capturing the complexity of this articulate network, precision immune-oncology calls for multi-omics tumor microenvironment profiling in order to identify unique predictive patterns and to proactively tailor combinatorial treatments. Multiplexed single-cell spatially resolved tissue analysis, through precise epitope colocalization, allows one to infer cellular functional states in view of their spatial organization. In this review, we discuss-through the lens of the cancer-immunity cycle-selected, established, and emerging markers that may be evaluated in multiplexed spatial protein panels to help identify prognostic and predictive patterns in BC.

Computational multiplex panel reduction to maximize information retention in breast cancer tissue microarrays

Recent state-of-the-art multiplex imaging techniques have expanded the depth of information that can be captured within a single tissue sample by allowing for panels with dozens of markers. Despite this increase in capacity, space on the panel is still limited due to technical artifacts, tissue loss, and long imaging acquisition time. As such, selecting which markers to include on a panel is important, since removing important markers will result in a loss of biologically relevant information, but identifying redundant markers will provide a room for other markers. To address this, we propose computational approaches to determine the amount of shared information between markers and select an optimally reduced panel that captures maximum amount of information with the fewest markers. Here we examine several panel selection approaches and evaluate them based on their ability to reconstruct the full panel images and information within breast cancer tissue microarray datasets using cyclic immunofluorescence as a proof of concept. We show that all methods perform adequately and can re-capture cell types using only 18 of 25 markers (72% of the original panel size). The correlation-based selection methods achieved the best single-cell marker mean intensity predictions with a Spearman correlation of 0.90 with the reduced panel. Using the proposed methods shown here, it is possible for researchers to design more efficient multiplex imaging panels that maximize the amount of information retained with the limited number of markers with respect to certain evaluation metrics and architecture biases.

Volume electron microscopy

Life exists in three dimensions, but until the turn of the century most electron microscopy methods provided only 2D image data. Recently, electron microscopy techniques capable of delving deep into the structure of cells and tissues have emerged, collectively called volume electron microscopy (vEM). Developments in vEM have been dubbed a quiet revolution as the field evolved from established transmission and scanning electron microscopy techniques, so early publications largely focused on the bioscience applications rather than the underlying technological breakthroughs. However, with an explosion in the uptake of vEM across the biosciences and fast-paced advances in volume, resolution, throughput and ease of use, it is timely to introduce the field to new audiences. In this Primer, we introduce the different vEM imaging modalities, the specialized sample processing and image analysis pipelines that accompany each modality and the types of information revealed in the data. We showcase key applications in the biosciences where vEM has helped make breakthrough discoveries and consider limitations and future directions. We aim to show new users how vEM can support discovery science in their own research fields and inspire broader uptake of the technology, finally allowing its full adoption into mainstream biological imaging.

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

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

Blood, Toil, and Taxoteres: Biological Determinates of Treatment-Induce ctDNA Dynamics for Interpreting Tumor Response

Collection and analysis of circulating tumor DNA (ctDNA) is one of the few methods of liquid biopsy that measures generalizable and tumor specific molecules, and is one of the most promising approaches in assessing the effectiveness of cancer care. Clinical assays that utilize ctDNA are commercially available for the identification of actionable mutations prior to treatment and to assess minimal residual disease after treatment. There is currently no clinical ctDNA assay specifically intended to monitor disease response during treatment, partially due to the complex challenge of understanding the biological sources of ctDNA and the underlying principles that govern its release. Although studies have shown pre- and post-treatment ctDNA levels can be prognostic, there is evidence that early, on-treatment changes in ctDNA levels are more accurate in predicting response. Yet, these results also vary widely among cohorts, cancer type, and treatment, likely due to the driving biology of tumor cell proliferation, cell death, and ctDNA clearance kinetics. To realize the full potential of ctDNA monitoring in cancer care, we may need to reorient our thinking toward the fundamental biological underpinnings of ctDNA release and dissemination from merely seeking convenient clinical correlates.