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Normal cells explore multiple states to survive stresses encountered during development and self-renewal as well as environmental stresses such as starvation, DNA damage, toxins or infection. Cancer cells co-opt normal stress mitigation pathways to survive stresses that accompany tumour initiation, progression, metastasis and immune evasion. Cancer therapies accentuate cancer cell stresses and invoke rapid non-genomic stress mitigation processes that maintain cell viability and thus represent key targetable resistance mechanisms. In this Review, we describe mechanisms by which tumour ecosystems, including cancer cells, immune cells and stroma, adapt to therapeutic stresses and describe three different approaches to exploit stress mitigation processes: (1) interdict stress mitigation to induce cell death; (2) increase stress to induce cellular catastrophe; and (3) exploit emergent vulnerabilities in cancer cells and cells of the tumour microenvironment. We review challenges associated with tumour heterogeneity, prioritizing actionable adaptive responses for optimal therapeutic outcomes, and development of an integrative framework to identify and target vulnerabilities that arise from adaptive responses and engagement of stress mitigation pathways. Finally, we discuss the need to monitor adaptive responses across multiple scales and translation of combination therapies designed to take advantage of adaptive responses and stress mitigation pathways to the clinic.

Targeting MAPK pathway using a combination of BRAF and MEK inhibitors is an efficient strategy to treat melanoma harboring BRAF-mutation. The development of acquired resistance is inevitable due to the signaling pathway rewiring. Combining western blotting, immunohistochemistry, and reverse phase protein array (RPPA), we aim to understanding the role of the mTORC1 signaling pathway, a center node of intracellular signaling network, in mediating drug resistance of BRAF-mutant melanoma to the combination of BRAF inhibitor (BRAFi) and MEK inhibitor (MEKi) therapy. The mTORC1 signaling pathway is initially suppressed by BRAFi and MEKi combination in melanoma but rebounds overtime after tumors acquire resistance to the combination therapy (CR) as assayed in cultured cells and PDX models. In vitro experiments showed that a subset of CR melanoma cells was sensitive to mTORC1 inhibition. The mTOR inhibitors, rapamycin and NVP-BEZ235, induced cell cycle arrest and apoptosis in CR cell lines. As a proof-of-principle, we demonstrated that rapamycin and NVP-BEZ235 treatment reduced tumor growth in CR xenograft models. Mechanistically, AKT or ERK contributes to the activation of mTORC1 in CR cells, depending on PTEN status of these cells. Our study reveals that mTOR activation is essential for drug resistance of melanoma to MAPK inhibitors, and provides insight into the rewiring of the signaling networks in CR melanoma.

Germline mutations inBRCA1orBRCA2exist in ~2–7% of breast cancer patients, which has led to the approval of PARP inhibitors in the advanced setting. We have previously reported a phase II neoadjuvant trial of single agent talazoparib for patients with germlineBRCApathogenic variants with a pathologic complete response (pCR) rate of 53%. As nearly half of the patients treated did not have pCR, better strategies are needed to overcome treatment resistance. To this end, we conducted multi-omic analysis of 13 treatment naïve breast cancer tumors from patients that went on to receive single-agent neoadjuvant talazoparib. We looked for biomarkers that were predictive of response (assessed by residual cancer burden) after 6 months of therapy. We found that all resistant tumors exhibited either the loss of SHLD2, expression of a hypoxia signature, or expression of a stem cell signature. These results indicate that the deep analysis of pre-treatment tumors can identify biomarkers that are predictive of response to talazoparib and potentially other PARP inhibitors, and provides a framework that will allow for better selection of patients for treatment, as well as a roadmap for the development of novel combination therapies to prevent emergence of resistance.

Image-based cell phenotyping relies on quantitative measurements as encoded representations of cells; however, defining suitable representations that capture complex imaging features is challenged by the lack of robust methods to segment cells, identify subcellular compartments, and extract relevant features. Variational autoencoder (VAE) approaches produce encouraging results by mapping an image to a representative descriptor, and outperform classical hand-crafted features for morphology, intensity, and texture at differentiating data. Although VAEs show promising results for capturing morphological and organizational features in tissue, single cell image analyses based on VAEs often fail to identify biologically informative features due to uninformative technical variation. Here we propose a multi-encoder VAE (ME-VAE) in single cell image analysis using transformed images as a self-supervised signal to extract transform-invariant biologically meaningful features, including emergent features not obvious from prior knowledge. We show that the proposed architecture improves analysis by making distinct cell populations more separable compared to traditional and recent extensions of VAE architectures and intensity measurements by enhancing phenotypic differences between cells and by improving correlations to other analytic modalities. Better feature extraction and image analysis methods enabled by the ME-VAE will advance our understanding of complex cell biology and enable discoveries previously hidden behind image complexity ultimately improving medical outcomes and drug discovery.

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

Apolipoprotein D (ApoD) gene expression is increased in several neurological disorders such as Alzheimer’s disease (AD) and multiple sclerosis. We previously showed that transgenic mice that overexpress human ApoD show a better resistance against paraquat or OC43 coronavirus-induced neurodegeneration. Here, we identified several nuclear factors from the cortex of control and OC43-infected mice which bind a fragment of the proximal ApoD promoter in vitro. Of interest, we detected apolipoprotein E (ApoE). Human ApoE consists of three isoforms (E2, E3, and E4) with the E4 and E2 alleles representing a greater and a lower risk for developping AD, respectively. Our results show that ApoE is located in the nucleus and on the ApoD promoter in human hepatic and glioblastoma cells lines. Furthermore, overexpression of ApoE3 and ApoE4 isoforms but not ApoE2 significantly inhibited the ApoD promoter activity in U87 cells (E3/E3 genotype) cultured under normal or different stress conditions while ApoE knock-down by siRNA had a converse effect. Consistent with these results, we also demonstrated by ChIP assay that E3 and E4 isoforms, but not E2, bind the ApoD promoter. Moreover, using the Allen Brain Atlas in situ hybridization database, we observed an inverse correlation between ApoD and ApoE mRNA expression during development and in several regions of the mouse brain, notably in the cortex, hippocampus, plexus choroid, and cerebellum. This negative correlation was also observed for cortex layers IV–VI based on a new Transcriptomic Atlas of the Mouse Neocortical Layers. These findings reveal a new function for ApoE by regulating ApoD gene expression. Electronic supplementary material The online version of this article (doi:10.1007/s12035-013-8456-0) contains supplementary material, which is available to authorized users.

Matrix metalloproteinase 9 (MMP-9) is one of the most studied enzymes in cancer. MMP-9 can cleave proteins of the extracellular matrix and a large number of receptors and growth factors. Accordingly, its expression must be tightly regulated to avoid excessive enzymatic activity, which is associated with disease progression. Although we know that epigenetic mechanisms play a central role in controlling mmp-9 gene expression, predicting how epigenetic drugs could be used to suppress mmp-9 gene expression is not trivial because epigenetic drugs also regulate the expression of key proteins that can tip the balance towards activation or suppression of MMP-9. Here, we review how our understanding of the biology and expression of MMP-9 could be exploited to augment clinical benefits, most notably in terms of the prevention and management of degenerative diseases and cancer.