Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma.

Ductal carcinoma in situ (DCIS) is a pre-invasive lesion that is thought to be a precursor to invasive breast cancer (IBC). To understand the changes in the tumor microenvironment (TME) accompanying transition to IBC, we used multiplexed ion beam imaging by time of flight (MIBI-TOF) and a 37-plex antibody staining panel to interrogate 79 clinically annotated surgical resections using machine learning tools for cell segmentation, pixel-based clustering, and object morphometrics. Comparison of normal breast with patient-matched DCIS and IBC revealed coordinated transitions between four TME states that were delineated based on the location and function of myoepithelium, fibroblasts, and immune cells. Surprisingly, myoepithelial disruption was more advanced in DCIS patients that did not develop IBC, suggesting this process could be protective against recurrence. Taken together, this HTAN Breast PreCancer Atlas study offers insight into drivers of IBC relapse and emphasizes the importance of the TME in regulating these processes.

An immunofluorescence-guided segmentation model in H&E images is enabled by tissue artifact correction by CycleGAN.

Despite recent advances, the adoption of computer vision methods into clinical and commercial applications has been hampered by the limited availability of accurate ground truth tissue annotations required to train robust supervised models. Generating such ground truth can be accelerated by annotating tissue molecularly using immunofluorescence staining (IF) and mapping these annotations to a post-IF H&E (terminal H&E). Mapping the annotations between the IF and the terminal H&E increases both the scale and accuracy by which ground truth could be generated. However, discrepancies between terminal H&E and conventional H&E caused by IF tissue processing have limited this implementation. We sought to overcome this challenge and achieve compatibility between these parallel modalities using synthetic image generation, in which a cycle-consistent generative adversarial network (CycleGAN) was applied to transfer the appearance of conventional H&E such that it emulates the terminal H&E. These synthetic emulations allowed us to train a deep learning (DL) model for the segmentation of epithelium in the terminal H&E that could be validated against the IF staining of epithelial-based cytokeratins. The combination of this segmentation model with the CycleGAN stain transfer model enabled performative epithelium segmentation in conventional H&E images. The approach demonstrates that the training of accurate segmentation models for the breadth of conventional H&E data can be executed free of human-expert annotations by leveraging molecular annotation strategies such as IF, so long as the tissue impacts of the molecular annotation protocol are captured by generative models that can be deployed prior to the segmentation process.

Correction: Modeling differentiation-state transitions linked to therapeutic escape in triple-negative breast cancer.

[This corrects the article DOI: 10.1371/journal.pcbi.1006840.].

Modeling differentiation-state transitions linked to therapeutic escape in triple-negative breast cancer.

Drug resistance in breast cancer cell populations has been shown to arise through phenotypic transition of cancer cells to a drug-tolerant state, for example through epithelial-to-mesenchymal transition or transition to a cancer stem cell state. However, many breast tumors are a heterogeneous mixture of cell types with numerous epigenetic states in addition to stem-like and mesenchymal phenotypes, and the dynamic behavior of this heterogeneous mixture in response to drug treatment is not well-understood. Recently, we showed that plasticity between differentiation states, as identified with intracellular markers such as cytokeratins, is linked to resistance to specific targeted therapeutics. Understanding the dynamics of differentiation-state transitions in this context could facilitate the development of more effective treatments for cancers that exhibit phenotypic heterogeneity and plasticity. In this work, we develop computational models of a drug-treated, phenotypically heterogeneous triple-negative breast cancer (TNBC) cell line to elucidate the feasibility of differentiation-state transition as a mechanism for therapeutic escape in this tumor subtype. Specifically, we use modeling to predict the changes in differentiation-state transitions that underlie specific therapy-induced changes in differentiation-state marker expression that we recently observed in the HCC1143 cell line. We report several statistically significant therapy-induced changes in transition rates between basal, luminal, mesenchymal, and non-basal/non-luminal/non-mesenchymal differentiation states in HCC1143 cell populations. Moreover, we validate model predictions on cell division and cell death empirically, and we test our models on an independent data set. Overall, we demonstrate that changes in differentiation-state transition rates induced by targeted therapy can provoke distinct differentiation-state aggregations of drug-resistant cells, which may be fundamental to the design of improved therapeutic regimens for cancers with phenotypic heterogeneity.

Mass Spectrometry-Based Tissue Imaging of the Tumor Microenvironment.

Multiplex ion beam imaging (MIBI) and imaging mass cytometry (IMC) enable highly multiplexed antibody (40+) staining of frozen or formalin fixed, paraffin-embedded (FFPE) human or murine tissues through detection of metal ions liberated from primary antibodies by time-of-flight mass spectrometry (TOF). These methods make detection of more than 50 targets theoretically possible while maintaining spatial orientation. As such, they are ideal tools to identify the multiple immune, epithelial, and stromal cell subsets in the tumor microenvironment and to characterize spatial relationships and tumor-immune status in either murine models or human samples. This chapter summarizes methods for antibody conjugation and validation, staining, and preliminary data collection using IMC or MIBI in both human and mouse pancreatic adenocarcinoma samples. These protocols are intended to facilitate use of these complex platforms in not only tissue-based tumor immunology studies but also tissue-based oncology or immunology studies more broadly.

Pyrazolopyridine inhibitors of B-Raf(V600E). Part 4: rational design and kinase selectivity profile of cell potent type II inhibitors.

Cell potent inhibitors of B-Raf(V600E) that bind to the kinase in the DFG-out conformation are reported. These compounds utilize the hinge-binding group and lipophilic linker from a previously disclosed series of B-Raf(V600E) inhibitors that bind to the kinase in an atypical DFG-in, αC-helix-out conformation. This new series demonstrates that DFG-out kinase inhibitors can be rationally designed from related inhibitors which utilize an unconventional binding mode. Kinase selectivity profiles are compared. The pattern of kinase selectivity was found to be determined by the feature of the inhibitor which extends into the back pocket of the kinase and leads to the kinase conformation, rather than by the hinge-binding group or other minor modifications.

The discovery of potent and selective pyridopyrimidin-7-one based inhibitors of B-RafV600E kinase.

Herein we describe the discovery of a novel series of ATP competitive B-Raf inhibitors via structure based drug design (SBDD). These pyridopyrimidin-7-one based inhibitors exhibit both excellent cellular potency and striking B-Raf selectivity. Optimization led to the identification of compound 17, a potent, selective and orally available agent with excellent pharmacokinetic properties and robust tumor growth inhibition in xenograft studies.

Multiplexed Ion Beam Imaging: Insights into Pathobiology.

Next-generation tools for multiplexed imaging have driven a new wave of innovation in understanding how single-cell function and tissue structure are interrelated. In previous work, we developed multiplexed ion beam imaging by time of flight, a highly multiplexed platform that uses secondary ion mass spectrometry to image dozens of antibodies tagged with metal reporters. As instrument throughput has increased, the breadth and depth of imaging data have increased as well. To extract meaningful information from these data, we have developed tools for cell identification, cell classification, and spatial analysis. In this review, we discuss these tools and provide examples of their application in various contexts, including ductal carcinoma in situ, tuberculosis, and Alzheimer's disease. We hope the synergy between multiplexed imaging and automated image analysis will drive a new era in anatomic pathology and personalized medicine wherein quantitative spatial signatures are used routinely for more accurate diagnosis, prognosis, and therapeutic selection.

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning.

A principal challenge in the analysis of tissue imaging data is cell segmentation-the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1 million manually labeled cells, an order of magnitude more than all previously published segmentation training datasets. We used TissueNet to train Mesmer, a deep-learning-enabled segmentation algorithm. We demonstrated that Mesmer is more accurate than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We then adapted Mesmer to harness cell lineage information in highly multiplexed datasets and used this enhanced version to quantify cell morphology changes during human gestation. All code, data and models are released as a community resource.

Molecular classification and biomarkers of clinical outcome in breast ductal carcinoma in situ: Analysis of TBCRC 038 and RAHBT cohorts.

Ductal carcinoma in situ (DCIS) is the most common precursor of invasive breast cancer (IBC), with variable propensity for progression. We perform multiscale, integrated molecular profiling of DCIS with clinical outcomes by analyzing 774 DCIS samples from 542 patients with 7.3 years median follow-up from the Translational Breast Cancer Research Consortium 038 study and the Resource of Archival Breast Tissue cohorts. We identify 812 genes associated with ipsilateral recurrence within 5 years from treatment and develop a classifier that predicts DCIS or IBC recurrence in both cohorts. Pathways associated with recurrence include proliferation, immune response, and metabolism. Distinct stromal expression patterns and immune cell compositions are identified. Our multiscale approach employed in situ methods to generate a spatially resolved atlas of breast precancers, where complementary modalities can be directly compared and correlated with conventional pathology findings, disease states, and clinical outcome.

Discovery and SAR of spirochromane Akt inhibitors.

A novel series of spirochromane pan-Akt inhibitors is reported. SAR optimization furnished compounds with improved enzyme potencies and excellent selectivity over the related AGC kinase PKA. Attempted replacement of the phenol hinge binder provided compounds with excellent Akt enzyme and cell activities but greatly diminished selectivity over PKA.

Pyrazolopyridine inhibitors of B-RafV600E. Part 2: structure-activity relationships.

Structure-activity relationships around a novel series of B-Raf(V600E) inhibitors are reported. The enzymatic and cellular potencies of inhibitors derived from two related hinge-binding groups were compared and3-methoxypyrazolopyridine proved to be superior. The 3-alkoxy group of lead B-Raf(V600E) inhibitor 1 was extended and minimally affected potency. The propyl sulfonamide tail of compound 1, which occupies the small lipophilic pocket formed by an outward shift of the αC-helix, was expanded to a series of arylsulfonamides. X-ray crystallography revealed that this lipophilic pocket unexpectedly enlarges to accommodate the bulkier aryl group.

Discovery of spirocyclic sulfonamides as potent Akt inhibitors with exquisite selectivity against PKA.

We describe the design and synthesis of novel bicyclic spiro sulfonamides as potent Akt inhibitors. Through structure-based rational design, we have successfully improved PKA selectivity of previously disclosed spirochromanes. Representative compounds showed favorable Akt potency while exhibiting up to 1000-fold selectivity against PKA.

Multiplexed imaging analysis of the tumor-immune microenvironment reveals predictors of outcome in triple-negative breast cancer.

Triple-negative breast cancer, the poorest-prognosis breast cancer subtype, lacks clinically approved biomarkers for patient risk stratification and treatment management. Prior literature has shown that interrogation of the tumor-immune microenvironment may be a promising approach to fill these gaps. Recently developed high-dimensional tissue imaging technology, such as multiplexed ion beam imaging, provide spatial context to protein expression in the microenvironment, allowing in-depth characterization of cellular processes. We demonstrate that profiling the functional proteins involved in cell-to-cell interactions in the microenvironment can predict recurrence and overall survival. We highlight the immunological relevance of the immunoregulatory proteins PD-1, PD-L1, IDO, and Lag3 by tying interactions involving them to recurrence and survival. Multivariate analysis reveals that our methods provide additional prognostic information compared to clinical variables. In this work, we present a computational pipeline for the examination of the tumor-immune microenvironment using multiplexed ion beam imaging that produces interpretable results, and is generalizable to other cancer types.

Discovery of pyrrolopyrimidine inhibitors of Akt.

The discovery and optimization of a series of pyrrolopyrimidine based protein kinase B (Pkb/Akt) inhibitors discovered via HTS and structure based drug design is reported. The compounds demonstrate potent inhibition of all three Akt isoforms and knockdown of phospho-PRAS40 levels in LNCaP cells and tumor xenografts.

Discovery of dihydrothieno- and dihydrofuropyrimidines as potent pan Akt inhibitors.

Herein we report the discovery and synthesis of a novel series of dihydrothieno- and dihydrofuropyrimidines (2 and 3) as potent pan Akt inhibitors. Utilizing previous SAR and analysis of the amino acid sequences in the binding site we have designed inhibitors displaying increased PKA and general kinase selectivity with improved tolerability compared to the progenitor pyrrolopyrimidine (1). A representative dihydrothieno compound (34) was advanced into a PC3-NCI prostate mouse tumor model in which it demonstrated a dose-dependent reduction in tumor growth and stasis when dosed orally daily at 200 mg/kg.

Deregulating MYC in a model of HER2+ breast cancer mimics human intertumoral heterogeneity.

The c-MYC (MYC) oncoprotein is often overexpressed in human breast cancer; however, its role in driving disease phenotypes is poorly understood. Here, we investigate the role of MYC in HER2+ disease, examining the relationship between HER2 expression and MYC phosphorylation in HER2+ patient tumors and characterizing the functional effects of deregulating MYC expression in the murine NeuNT model of amplified-HER2 breast cancer. Deregulated MYC alone was not tumorigenic, but coexpression with NeuNT resulted in increased MYC Ser62 phosphorylation and accelerated tumorigenesis. The resulting tumors were metastatic and associated with decreased survival compared with NeuNT alone. MYC;NeuNT tumors had increased intertumoral heterogeneity including a subtype of tumors not observed in NeuNT tumors, which showed distinct metaplastic histology and worse survival. The distinct subtypes of MYC;NeuNT tumors match existing subtypes of amplified-HER2, estrogen receptor-negative human tumors by molecular expression, identifying the preclinical utility of this murine model to interrogate subtype-specific differences in amplified-HER2 breast cancer. We show that these subtypes have differential sensitivity to clinical HER2/EGFR-targeted therapeutics, but small-molecule activators of PP2A, the phosphatase that regulates MYC Ser62 phosphorylation, circumvents these subtype-specific differences and ubiquitously suppresses tumor growth, demonstrating the therapeutic utility of this approach in targeting deregulated MYC breast cancers.

Transcriptional signatures in histologic structures within glioblastoma tumors may predict personalized drug sensitivity and survival.

BACKGROUND: Glioblastoma is a rapidly fatal brain cancer that exhibits extensive intra- and intertumoral heterogeneity. Improving survival will require the development of personalized treatment strategies that can stratify tumors into subtypes that differ in therapeutic vulnerability and outcomes. Glioblastoma stratification has been hampered by intratumoral heterogeneity, limiting our ability to compare tumors in a consistent manner. Here, we develop methods that mitigate the impact of intratumoral heterogeneity on transcriptomic-based patient stratification. METHODS: We accessed open-source transcriptional profiles of histological structures from 34 human glioblastomas from the Ivy Glioblastoma Atlas Project. Principal component and correlation network analyses were performed to assess sample inter-relationships. Gene set enrichment analysis was used to identify enriched biological processes and classify glioblastoma subtype. For survival models, Cox proportional hazards regression was utilized. Transcriptional profiles from 156 human glioblastomas were accessed from The Cancer Genome Atlas to externally validate the survival model. RESULTS: We showed that intratumoral histologic architecture influences tumor classification when assessing established subtyping and prognostic gene signatures, and that indiscriminate sampling can produce misleading results. We identified the cellular tumor as a glioblastoma structure that can be targeted for transcriptional analysis to more accurately stratify patients by subtype and prognosis. Based on expression from cellular tumor, we created an improved risk stratification gene signature. CONCLUSIONS: Our results highlight that biomarker performance for diagnostics, prognostics, and prediction of therapeutic response can be improved by analyzing transcriptional profiles in pure cellular tumor, which is a critical step toward developing personalized treatment for glioblastoma.

Activation of PP2A and Inhibition of mTOR Synergistically Reduce MYC Signaling and Decrease Tumor Growth in Pancreatic Ductal Adenocarcinoma.

In cancer, kinases are often activated and phosphatases suppressed, leading to aberrant activation of signaling pathways driving cellular proliferation, survival, and therapeutic resistance. Although pancreatic ductal adenocarcinoma (PDA) has historically been refractory to kinase inhibition, therapeutic activation of phosphatases is emerging as a promising strategy to restore balance to these hyperactive signaling cascades. In this study, we hypothesized that phosphatase activation combined with kinase inhibition could deplete oncogenic survival signals to reduce tumor growth. We screened PDA cell lines for kinase inhibitors that could synergize with activation of protein phosphatase 2A (PP2A), a tumor suppressor phosphatase, and determined that activation of PP2A and inhibition of mTOR synergistically increase apoptosis and reduce oncogenic phenotypes in vitro and in vivo. This combination treatment resulted in suppression of AKT/mTOR signaling coupled with reduced expression of c-MYC, an oncoprotein implicated in tumor progression and therapeutic resistance. Forced expression of c-MYC or loss of PP2A B56α, the specific PP2A subunit shown to negatively regulate c-MYC, increased resistance to mTOR inhibition. Conversely, decreased c-MYC expression increased the sensitivity of PDA cells to mTOR inhibition. Together, these studies demonstrate that combined targeting of PP2A and mTOR suppresses proliferative signaling and induces cell death and implicates this combination as a promising therapeutic strategy for patients with PDA. SIGNIFICANCE: These findings present a combinatorial strategy targeting serine/threonine protein phosphatase PP2A and mTOR in PDA, a cancer for which there are currently no targeted therapeutic options.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/79/1/209/F1.large.jpg.

MIBI-TOF: A multiplexed imaging platform relates cellular phenotypes and tissue structure.

Understanding tissue structure and function requires tools that quantify the expression of multiple proteins while preserving spatial information. Here, we describe MIBI-TOF (multiplexed ion beam imaging by time of flight), an instrument that uses bright ion sources and orthogonal time-of-flight mass spectrometry to image metal-tagged antibodies at subcellular resolution in clinical tissue sections. We demonstrate quantitative, full periodic table coverage across a five-log dynamic range, imaging 36 labeled antibodies simultaneously with histochemical stains and endogenous elements. We image fields of view up to 800 µm × 800 µm at resolutions down to 260 nm with sensitivities approaching single-molecule detection. We leverage these properties to interrogate intrapatient heterogeneity in tumor organization in triple-negative breast cancer, revealing regional variability in tumor cell phenotypes in contrast to a structured immune response. Given its versatility and sample back-compatibility, MIBI-TOF is positioned to leverage existing annotated, archival tissue cohorts to explore emerging questions in cancer, immunology, and neurobiology.