Proliferation is a fundamental trait of cancer cells but is poorly characterized in tumors by classical histologic methods. We use multiplexed tissue imaging to quantify the abundance of multiple cell cycle regulating proteins at single-cell level and develop robust multivariate proliferation metrics. Across cancers, the proliferative architecture is organized at two distinct spatial scales: large domains, and local niches enriched for specific immune lineages. A subset of tumor cells express cell cycle regulators in canonical patterns consistent with unrestrained proliferation, a phenomenon we refer to as “cell cycle coherence”. By contrast, the cell cycles of other tumor cell populations are skewed toward a specific phase or characterized by non-canonical (incoherent) marker combinations. Coherence varies across space, with changes in oncogene activity, and with therapeutic intervention, and is associated with aggressive behavior. Multivariate measures capture clinically significant features of cancer proliferation, a fundamental step in enabling more precise use of anti-cancer therapies.
Abnormal post-transcriptional regulation induced by alterations of mRNA-protein interactions is critical during tumorigenesis and cancer progression and is a hallmark of cancer cells. A more thorough understanding is needed to develop treatments and foresee outcomes. Cellular and mouse tumor models are insufficient for vigorous investigation as they lack consistency and translatability to humans. Moreover, to date, studies in human tumor tissue are predominately limited to expression analysis of proteins and mRNA, which do not necessarily provide information about the frequency of mRNA-protein interactions.Here, we demonstrate novel optimization of a method that is based on FISH and proximity ligation techniques to quantify mRNA interactions with RNA-binding proteins relevant for tumorigenesis and cancer progression in archival patientderived tumor tissue. This method was validated for multiple mRNA-protein pairs in several cellular models and in multiple types of archival human tumor samples. Furthermore, this approach allowed high-throughput analysis of mRNA-protein interactions across a wide range of tumor types and stages through tumor microarrays. This method is quantitative, specific, and sensitive for detecting interactions and their localization at both the individual cell and whole-tissue scales with single interaction sensitivity. This work presents an important tool in investigating post-transcriptional regulation in cancer on a highthroughput scale, with great potential for translatability into any applications where mRNA-protein interactions are of interest.Significance: This work presents an approach to sensitively, specifically, and quantitatively detect and localize native mRNA and protein interactions for analysis of abnormal post-transcriptional regulation in patient-derived archival tumor samples.
Proliferation is a fundamental trait of cancer cells but is poorly characterized in tumors by classical histologic methods. We use multiplexed tissue imaging to quantify the abundance of multiple cell cycle regulating proteins at single-cell level and develop robust multivariate proliferation metrics. Across cancers, the proliferative architecture is organized at two distinct spatial scales: large domains, and local niches enriched for specific immune lineages. A subset of tumor cells express cell cycle regulators in canonical patterns consistent with unrestrained proliferation, a phenomenon we refer to as "cell cycle coherence". By contrast, the cell cycles of other tumor cell populations are skewed toward a specific phase or characterized by non-canonical (incoherent) marker combinations. Coherence varies across space, with changes in oncogene activity, and with therapeutic intervention, and is associated with aggressive behavior. Multivariate measures capture clinically significant features of cancer proliferation, a fundamental step in enabling more precise use of anti-cancer therapies.
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