A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging

The immune system is critical in modulating cancer progression, but knowledge of immune composition, phenotype, and interactions with tumor is limited. We used multiplexed ion beam imaging by time-of-flight (MIBI-TOF) to simultaneously quantify in situ expression of 36 proteins covering identity, function, and immune regulation at sub-cellular resolution in 41 triple-negative breast cancer patients. Multi-step processing, including deep-learning-based segmentation, revealed variability in the composition of tumor-immune populations across individuals, reconciled by overall immune infiltration and enriched co-occurrence of immune subpopulations and checkpoint expression. Spatial enrichment analysis showed immune mixed and compartmentalized tumors, coinciding with expression of PD1, PD-L1, and IDO in a cell-type- and location-specific manner. Ordered immune structures along the tumor-immune border were associated with compartmentalization and linked to survival. These data demonstrate organization in the tumor-immune microenvironment that is structured in cellular composition, spatial arrangement, and regulatory-protein expression and provide a framework to apply multiplexed imaging to immune oncology

Pengembangan Modul Intervensi Untuk Meningkatkan Resiliensi Pada Individu Yang Mengalami Perubahan Fisik Menjadi Penyandang Disabilitas

Penelitian ini bertujuan untuk menindaklanjuti temuan sebelumnya dengan mengembangkan modul intervensi secara terperinci, yang selanjutnya dapat digunakan sebagai panduan dalam membantu meningkatkan resiliensi individu yang mengalami Perubahan kondisi fisik menjadi penyandang disabilitas. Penulis menyusun serta merinci rancangan implementasi awal yang direkomendasikan oleh penelitian sebelumnya kedalam langkah-langkah yang lebih sistematis dan operasional hingga memperoleh hasil akhir berupa modul. Metode yang digunakan berbasis tahapan riset aksi, meskipun proses yang dilakukan hanya sampai pada langkah ketiga, yaitu Perumusan solusi dari persoalan yang diangkat. Partisipan terdiri dari delapan individu yang mengalami Perubahan kondisi menjadi penyandang disabilitas. Selain partisipan, empat orang psikolog juga dilibatkan dalam penelitian ini sebagai penelaah modul. Hasil penelitian ini berupa sebuah paket modul intervensi untuk peningkatan resiliensi melalui penguatan faktor protektif serta pengembangan strategi koping dan adaptasi pada individu yang mengalami Perubahan kondisi fisik menjadi penyandang disabilitas. Paket modul tersebut terdiri dari 5 sub-modul yang telah disusun sedemikian rupa untuk memudahkan pelaksanaannya, terdiri dari modul: (1) memperkuat dukungan keluarga terhadap penyandang disabilitas; (2) pendampingan awal penyandang disabilitas; (3) intervensi lanjut 1 (penguatan faktor protektif internal); (4) intervensi lanjut 2 (pengembangan strategi koping); dan (5) intervensi lanjut 3 (langkah adaptasi positif)

A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging

The immune system is critical in modulating cancer progression, but knowledge of immune composition, phenotype, and interactions with tumor is limited. We used multiplexed ion beam imaging by time-of-flight (MIBI-TOF) to simultaneously quantify in situ expression of 36 proteins covering identity, function, and immune regulation at sub-cellular resolution in 41 triple-negative breast cancer patients. Multi-step processing, including deep-learning-based segmentation, revealed variability in the composition of tumor-immune populations across individuals, reconciled by overall immune infiltration and enriched co-occurrence of immune subpopulations and checkpoint expression. Spatial enrichment analysis showed immune mixed and compartmentalized tumors, coinciding with expression of PD1, PD-L1, and IDO in a cell-type- and location-specific manner. Ordered immune structures along the tumor-immune border were associated with compartmentalization and linked to survival. These data demonstrate organization in the tumor-immune microenvironment that is structured in cellular composition, spatial arrangement, and regulatory-protein expression and provide a framework to apply multiplexed imaging to immune oncology

A human lung tumor microenvironment interactome identifies clinically relevant cell-type cross-talk.

BackgroundTumors comprise a complex microenvironment of interacting malignant and stromal cell types. Much of our understanding of the tumor microenvironment comes from in vitro studies isolating the interactions between malignant cells and a single stromal cell type, often along a single pathway.ResultTo develop a deeper understanding of the interactions between cells within human lung tumors, we perform RNA-seq profiling of flow-sorted malignant cells, endothelial cells, immune cells, fibroblasts, and bulk cells from freshly resected human primary non-small-cell lung tumors. We map the cell-specific differential expression of prognostically associated secreted factors and cell surface genes, and computationally reconstruct cross-talk between these cell types to generate a novel resource called the Lung Tumor Microenvironment Interactome (LTMI). Using this resource, we identify and validate a prognostically unfavorable influence of Gremlin-1 production by fibroblasts on proliferation of malignant lung adenocarcinoma cells. We also find a prognostically favorable association between infiltration of mast cells and less aggressive tumor cell behavior.ConclusionThese results illustrate the utility of the LTMI as a resource for generating hypotheses concerning tumor-microenvironment interactions that may have prognostic and therapeutic relevance

Transcriptional profiling of long non-coding RNAs and novel transcribed regions across a diverse panel of archived human cancers

Background: Molecular characterization of tumors has been critical for identifying important genes in cancer biology and for improving tumor classification and diagnosis. Long non-coding RNAs, as a new, relatively unstudied class of transcripts, provide a rich opportunity to identify both functional drivers and cancer-type-specific biomarkers. However, despite the potential importance of long non-coding RNAs to the cancer field, no comprehensive survey of long non-coding RNA expression across various cancers has been reported. Results: We performed a sequencing-based transcriptional survey of both known long non-coding RNAs and novel intergenic transcripts across a panel of 64 archival tumor samples comprising 17 diagnostic subtypes of adenocarcinomas, squamous cell carcinomas and sarcomas. We identified hundreds of transcripts from among the known 1,065 long non-coding RNAs surveyed that showed variability in transcript levels between the tumor types and are therefore potential biomarker candidates. We discovered 1,071 novel intergenic transcribed regions and demonstrate that these show similar patterns of variability between tumor types. We found that many of these differentially expressed cancer transcripts are also expressed in normal tissues. One such novel transcript specifically expressed in breast tissue was further evaluated using RNA in situ hybridization on a panel of breast tumors. It was shown to correlate with low tumor grade and estrogen receptor expression, thereby representing a potentially important new breast cancer biomarker. Conclusions: This study provides the first large survey of long non-coding RNA expression within a panel of solid cancers and also identifies a number of novel transcribed regions differentially expressed across distinct cancer types that represent candidate biomarkers for future research

Automated Analysis and Classification of Histological Tissue Features by Multi-Dimensional Microscopic Molecular Profiling

<div><p>Characterization of the molecular attributes and spatial arrangements of cells and features within complex human tissues provides a critical basis for understanding processes involved in development and disease. Moreover, the ability to automate steps in the analysis and interpretation of histological images that currently require manual inspection by pathologists could revolutionize medical diagnostics. Toward this end, we developed a new imaging approach called multidimensional microscopic molecular profiling (MMMP) that can measure several independent molecular properties <i>in situ</i> at subcellular resolution for the same tissue specimen. MMMP involves repeated cycles of antibody or histochemical staining, imaging, and signal removal, which ultimately can generate information analogous to a multidimensional flow cytometry analysis on intact tissue sections. We performed a MMMP analysis on a tissue microarray containing a diverse set of 102 human tissues using a panel of 15 informative antibody and 5 histochemical stains plus DAPI. Large-scale unsupervised analysis of MMMP data, and visualization of the resulting classifications, identified molecular profiles that were associated with functional tissue features. We then directly annotated H&E images from this MMMP series such that canonical histological features of interest (e.g. blood vessels, epithelium, red blood cells) were individually labeled. By integrating image annotation data, we identified molecular signatures that were associated with specific histological annotations and we developed statistical models for automatically classifying these features. The classification accuracy for automated histology labeling was objectively evaluated using a cross-validation strategy, and significant accuracy (with a median per-pixel rate of 77% per feature from 15 annotated samples) for <i>de novo</i> feature prediction was obtained. These results suggest that high-dimensional profiling may advance the development of computer-based systems for automatically parsing relevant histological and cellular features from molecular imaging data of arbitrary human tissue samples, and can provide a framework and resource to spur the optimization of these technologies.</p></div

Overview of Multi-dimensional Microscopic Molecular Profiling (MMMP).

<p>The overall MMMP approach is depicted using an example tissue section from normal human duodenum (sample #1.9.7). (a) Slides were subjected to repeated cycles of staining and imaging with fluorescent primary antibodies and DAPI. At the end of each cycle, fluorescent signal was removed by a chemical bleaching process, and slides were again imaged, before proceeding to the next round of this iterative procedure. After the final antibody stain (#15 Sma), slides were analyzed with a series of histochemical stains. (b) A set of tiling images spanning each tissue section was initially generated by the microscope system. The tiling images were then computationally ‘stitched’ together to produce a single image per staining cycle for each sample. (c) Image registration was performed to align images from the same tissue section across cycles. Mean intensities of the DAPI signal from all immuno-fluorescence images are shown from before (Unregistered) and after (Registered) the image registration procedure was completed. (d) Following registration, signal intensities from the relevant channels for each image (columns) in the MMMP series were extracted for each pixel (rows) within the tissue section and compiled into a large data matrix of <i>in situ</i> molecular profiles.</p

Principal Components Analysis of MMMP Data.

<p>(a) The mean value of the cumulative percentage of the variance in the data explained is plotted as a function of the number of principal components for data representing 102 human tissue samples. Red error bars indicate +/–one standard deviation. (b) The cumulative percentage of variance explained by the first <i>X</i> principal components is shown as a function of <i>X</i> for an example tissue section from the terminal ileum of the small intestine (sample #1.10.7). (c) H&E (hematoxylin and eosin) stained image of sample #1.10.7. (d) Images depicting scaled intensity values for the first three principal components (PC-1, PC-2, and PC-3) of the MMMP data matrix for sample #1.10.7 are shown.</p