Merge pull request #122 from arenasg/cichowksi-santagata-sorger-2024

update methods and software text

_atlas-datasets/cichowski-santagata-sorger-2024.md

@@ -0,0 +1,55 @@
+---
+layout: secondary
+title: Data
+section_id: data
+
+data:
+  publication:
+    title: AKT and EZH2 inhibitors kill TNBCs by hijacking mechanisms of involution
+    journal: 'TBA'
+    authors: 'Schade A, Perurena N, Yang Y, Rodriguez CL, Krishnan A, Loi P, Mastellone GM, Pilla NF, Watanabe M, Xu Y, Nguyen V, Ota K, Davis RA, Mattioli K, Xiang D, Zoeller JL, Morganti S, Garrido-Castro AC, Tolaney S, Li Z, Barbie DA, Sorger PK, Helin K, Santagata S, Knott SRV, Cichowski K.'
+    description: 'Triple negative breast cancer (TNBC) is the most aggressive breast cancer subtype and has the  highest  rate  of  recurrence.  The  predominant  standard  of  care  for  advanced  TNBC  is  systemic chemotherapy  with  or  without  immunotherapy,  however  responses  are  typically  short-lived. Thus,  there  is  an  urgent  need  to  develop  more  effective  treatments.  PI3K  pathway  components represent  plausible  therapeutic  targets,  as  at  least  70%  of  TNBCs  have  PIK3CA/AKT1/PTEN alterations. However, unlike hormone receptor-positive tumors, it is still unclear if or how PI3K pathway inhibitors will be effective in triple-negative disease. Here we describe a promising AKT inhibitor-based  therapeutic  combination  for  TNBC. Specifically,  we  show  that  AKT  inhibitors potently synergize with agents that suppress the histone methyltransferase, EZH2, and promote robust tumor regression in multiple TNBC models in vivo. AKT and EZH2 inhibitors exert these effects by first cooperatively driving basal-like TNBC cells into a more differentiated, luminal-like state, which cannot be effectively induced by either agent alone. Once differentiated, these agents kill TNBCs by hijacking signals that normally drive mammary gland involution. Importantly, using a  machine  learning  approach  we  developed  a  classifier  that  can  be  used  to  predict  sensitivity. Together these findings identify a promising therapeutic strategy for this highly aggressive tumor type  and  illustrate  how  deregulated  epigenetic  enzymes  can  insulate  tumors  from  oncogenic vulnerabilities. These studies also reveal how developmental tissue-specific cell death pathways may be co-opted for therapeutic benefit.'
+links:
+  - Data Access: https://github.com/labsyspharm/cichowski-santagata-sorger-2024/
+---
+
+{% assign urlParts = page.url | split: '/' %}
+{% assign sectionId = urlParts[-1] %}
+
+{% include atlas-dataset-info.html
+    sectionId=sectionId
+    pubData=page.data
+    thumbnailDir=sectionId %}
+
+## Contents
+  * [Graphical Abstract](#graphical-abstract)
+  * [Data Access](#data-access)
+  * [Funding](#funding)
+
+### Graphical Abstract
+{% include enlarge-image.html src='publications/cichowski-santagata-sorger-2024.PNG' alt='EZH2 + AKT inhibitors in TNBC' %}
+
+### Data Explorations
+
+{%
+    assign stories = site.data-cards
+    | where_exp: "item", "item.url contains 'cichowski-santagata-sorger-2024'"
+    | where_exp: "item", "item.hide != true"
+%}
+
+{% assign dataCardArray = '' | split: '' %}
+{% for s in stories %}
+  {% unless s.url contains '-overview' %}
+    {% assign dataCardArray = dataCardArray | push: s %}
+  {% endunless %}
+{% endfor %}
+
+{% if dataCardArray.size > 0 %}
+  {% include cards.html cards=dataCardArray %}
+{% endif %}
+
+## Data Access
+Instructions to access data will be posted to the [GitHub repository](https://github.com/labsyspharm/cichowski-santagata-sorger-2024/) associated with this publication.
+
+### Funding  
+This work was supported by a grant from the Cancer Research UK Grand Challenge and the Mark Foundation for Cancer Research to the SPECIFICANCER team (KC) and a DOD BC201085P1 Transformative Breast Cancer Consortium Award (KC). A.S. was supported by an American Cancer Society Postdoctoral Fellowship (PF-22-040-01-ET). N.P. was supported by AACR-AstraZeneca Breast Cancer Research Fellowship (20-40-12-PERU). K.M. was supported by NHGRI F32 #1F32HG012318-01. S.S. was funded by National Cancer Institute grant U54-CA225088 and the BWH President’s Scholar Award. Z.L. was supported by NIH grant R01 CA222560.

_data-cards/cichowski-santagata-sorger-2024/combo-day-1.md

@@ -0,0 +1,9 @@
+---
+title: "HCI-004 PDX tumor: EZH2i/AKTi Combo Treatment Day 1"
+image: https://s3.amazonaws.com/www.cycif.org/cichowski-santagata-sorger-2024/Combo-day-1/panCK_0000ff-Ki67_ff6f00-cPARP_ffffff-CK14_00ff00-CK8_ff0000.jpg
+date: '2024-03-21'
+minerva_link: https://s3.amazonaws.com/www.cycif.org/cichowski-santagata-sorger-2024/Combo-day-1/index.html
+show_page_link: false
+info_link: https://s3.amazonaws.com/www.cycif.org/cichowski-santagata-sorger-2024
+featured: false
+---

_data-cards/cichowski-santagata-sorger-2024/combo-day-2.md

@@ -0,0 +1,9 @@
+---
+title: "HCI-004: PDX tumor EZH2i/AKTi Combo Treatment Day 2"
+image: https://s3.amazonaws.com/www.cycif.org/cichowski-santagata-sorger-2024/Combo-day-2/panCK_0000ff-Ki67_ff6f00-cPARP_ffffff-CK14_00ff00-CK8_ff0000.jpg
+date: '2024-03-21'
+minerva_link: https://s3.amazonaws.com/www.cycif.org/cichowski-santagata-sorger-2024/Combo-day-2/index.html
+show_page_link: false
+info_link: https://s3.amazonaws.com/www.cycif.org/cichowski-santagata-sorger-2024
+featured: false
+---

_data-cards/cichowski-santagata-sorger-2024/combo-day-30.md

@@ -0,0 +1,9 @@
+---
+title: "HCI-004 PDX tumor: EZH2i/AKTi Combo Treatment Day 30"
+image: https://s3.amazonaws.com/www.cycif.org/cichowski-santagata-sorger-2024/Combo-day-30/panCK_0000ff-Ki67_ff6f00-cPARP_ffffff-CK14_00ff00-CK8_ff0000.jpg
+date: '2024-03-21'
+minerva_link: https://s3.amazonaws.com/www.cycif.org/cichowski-santagata-sorger-2024/Combo-day-30/index.html
+show_page_link: false
+info_link: https://s3.amazonaws.com/www.cycif.org/cichowski-santagata-sorger-2024
+featured: false
+---

_data-cards/cichowski-santagata-sorger-2024/vehicle.md

@@ -0,0 +1,9 @@
+---
+title: "HCI-004 PDX tumor: Vehicle Treatment"
+image: https://s3.amazonaws.com/www.cycif.org/cichowski-santagata-sorger-2024/Vehicle/panCK_0000ff-Ki67_ff6f00-cPARP_ffffff-CK14_00ff00-CK8_ff0000.jpg
+date: '2024-03-21'
+minerva_link: https://s3.amazonaws.com/www.cycif.org/cichowski-santagata-sorger-2024/Vehicle/index.html
+show_page_link: false
+info_link: https://s3.amazonaws.com/www.cycif.org/cichowski-santagata-sorger-2024
+featured: false
+---

_pages/software.md

@@ -5,10 +5,9 @@ permalink: /software
 section_id: methods-software
 layout: software
 description: |
-    Analyzing spatially resolved data requires the development of new software. The LSP is actively engaged in a wide variety of open source software projects aimed at the analysis of highly multiplexed whole slide images. These include methods for stitching images together into seamless gigapixel mosaics, machine learning tools for interpreting imaging features, visualization tools, and rapid and intuitive interaction with image data.
+  At the [Laboratory of Systems Pharmacology](https://labsyspharm.org/), we pair innovative methods for data collection with cutting-edge computational methods and software. We are engaged in a variety of open-source software projects for the analysis, visualization, and quality control of highly multiplexed whole slide images. 
+
+  Our largest software projects are **MCMICRO**, an end-to-end pipeline for transforming images into quantitative single-cell feature data, and **Minerva**, a suite of lightweight tools for interactive viewing and fast sharing of multiplexed image data. Ongoing projects enable intuitive interaction with imaging data, facilitate multimodal data integration, and harness AI/ML for analyzing and interpreting image features.
 
-    The two largest software projects in the group are MCMICRO, an end-to-end pipeline for transforming images into quantitative cell x feature data and Minerva, a suite of tools for visualization of large and complex images in a browser.
     {: .mb-5 }
 ---
-
-

_publications/AKT-and-EZH2-inhibitors-kill-TNBCs-by-hijacking-mechanisms-of-involution.md

@@ -0,0 +1,19 @@
+---
+title: 'AKT and EZH2 inhibitors kill TNBCs by hijacking mechanisms of involution'
+contributors: 'Schade A, Perurena N, Yang Y, Rodriguez CL, Krishnan A, Loi P, Mastellone GM, Pilla NF, Watanabe M, Xu Y, Nguyen V, Ota K, Davis RA, Mattioli K, Xiang D, Zoeller JL, Morganti S, Garrido-Castro AC, Tolaney S, Li Z, Barbie DA, Sorger PK, Helin K, Santagata S, Knott SRV, Cichowski K. (2024).'
+publication: TBA
+publication_link:
+
+image: publications/cichowski-santagata-sorger-2024.PNG
+
+group: featured
+
+date: 2024-02-29
+
+minerva_link:
+pubmed_link:
+dataset_link: /atlas-datasets/cichowski-santagata-sorger-2024/
+preprint_link:
+pdf_link:
+show_page_link: false
+---

_software/ashlar.md

@@ -2,7 +2,8 @@
 # name of the software
 title: ASHLAR
 # summary of the tool
-description: ASHLAR (Alignment by Simultaneous Harmonization of Layer/Adjacency Registration) is Python tool for image registration and stitching that is more rapid and accurate than existing methods in assembling subcellular-resolution, multi-channel images up to several square centimeters in size. ASHLAR uses Bioformats software to read virtually any microscope image files and write the OME-TIFF format files.
+description: ASHLAR (Alignment by Simultaneous Harmonization of Layer/Adjacency Registration) is an open-source Python tool that combines multi-tile microscopy images into high-dimensional mosaic images. For multi-cycle imaging methods (like CyCIF), ASHLAR also aligns images from different cycles with a high level of accuracy. ASHLAR can be used with virtually any unstitched microscope image file and multiplexed imaging method.
+
 # thumbnail image, can be a logo too
 image: software/ashlar-logo_v2.png
 

_software/cycif.md

@@ -3,17 +3,16 @@
 title: CyCIF
 # summary of the tool
 description: |
-  CyCIF (cyclic immunofluorescence) is a robust and inexpensive method for highly multiplexed immunofluorescence imaging using standard instruments and reagents. The concept of repeatedly staining and imaging slides has been around for many years and most commonly involves antibody stripping using denaturants. [Gerdes et al (2013)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3718135/) described an approach in which fluorophores are chemically inactivated after each of several rounds of immunofluorescence. The [t-CyCIF (Tissue-based cyclic immunofluorescence) method](https://www.cycif.org/methods) by Lin et al (2018) builds on this and related approaches.
-
-  t-CyCIF uses formalin-fixed, paraffin-embedded (FFPE) tumor and tissue specimens mounted on glass slides. These are the most widely used specimens for histopathological diagnosis of cancer and other diseases. t-CyCIF generates multiplexed images of FFPE samples using an iterative process (a cycle) in which conventional low-plex fluorescence images are repeatedly collected from the same sample and then assembled into a high dimensional representation. Several variants are possible using direct and indirect immunofluorescence.
+  CyCIF (cyclic immunofluorescence) is a robust and inexpensive method for highly multiplexed immunofluorescence imaging using standard instruments and reagents. t-CyCIF generates multiplexed images of fixed, paraffin-embedded (FFPE) samples using an iterative process in which conventional low-plex fluorescence images are repeatedly collected from the same sample and then assembled into a high dimensional representation. Highly multiplexed images of intact tumor architecture can be used to quantify signal transduction cascades, measure the levels of tumor antigens, determine precise immune phenotypes, and more.
 # thumbnail image, can be a logo too
 image: software/cycif.png
 
 # link to the publication
-# maybe link to the entry on the publication page?
 publication: https://pubmed.ncbi.nlm.nih.gov/29993362/
-# link to micro-site
-documentation: https://www.cycif.org/
+# link to publication landing page
+dataset: https://www.tissue-atlas.org/atlas-datasets/lin-elife-2018/
+# link to protocol
+protocol: https://dx.doi.org/10.17504/protocols.io.5qpvorbndv4o/v2
 
 # for sorting purpose
 date: 2020-01-02

_software/cylinter.md

@@ -3,25 +3,21 @@
 title: Cylinter
 # summary of the tool
 description: |
-    CyLinter is quality control software for identifying and removing cell
-    segmentation instances corrupted by optical and/or image-processing
-    artifacts in multiplex microscopy images. The tool is user-guided and
-    comprises a set of modular and extensible QC modules instantiated in a
-    configurable Python Class object. Module results are cached to allow for
-    dynamic restarts.
+  CyLinter is quality control software for multiplexed microscopy images that identifies and removes single cell data that has been corrupted by artifacts. Artifacts can result from optical aberrations (e.g., from contaminating lint or hair on the sample) or image-processing errors (e.g., errors in single cell segmentation) and can confound downstream analyses. CyLinter guides users through the process of removing noisy data in an interactive, visual environment and improves the quality of the resulting single cell data.
+
 # thumbnail image, can be a logo too
 image: software/cylinter-logo_v2.png
 
 # link to the publication
 # maybe link to the entry on the publication page?
-publication:
+publication: https://doi.org/10.1101/2023.11.01.565120
 # link to github repo
 source code: https://github.com/labsyspharm/cylinter
 # link to micro-site
 documentation: https://labsyspharm.github.io/cylinter/
 
 # for sorting purpose
-date: 2016-01-01
+date: 2022-01-01
 # set the type for this item - will determine which page it appears on:
 # [ software | method ]
 type: software

_software/deep-dye-drop.md

@@ -3,25 +3,20 @@
 title: Deep Dye Drop
 # summary of the tool
 description: |
-  The Dye Drop Assay is a versatile, low-cost, highly reproducible multiplexed microscopy
-  method for obtaining detailed single-cell viability and cell cycle information from
-  perturbation experiments. Dye Drop is composed of two main parts: i) an experimental
-  method where cells in multi-well plates are perturbed in high throughput, stained, and
-  fixed using sequential density displacement, then imaged, and ii) a set of associated
-  computational tools that assign cell cycle state and calculate growth rate metrics. Dye
-  Drop can be combined with CyCIF to yield further molecular and spatial information.
+  The Dye Drop assay is a versatile, low-cost, multiplexed microscopy method for obtaining detailed single-cell viability and cell cycle data. The Dye Drop method uses a set of incrementally more dense solutions to prevent cell loss and improve the consistency of cell perturbation experiments. Dye Drop can also be combined with CyCIF to yield further molecular and spatial information. The method is paired with computational analysis tools that calculate cell state and growth rate metrics from the high throughput data.
 # thumbnail image, can be a logo too
 image: software/dyedrop-logo.png
 
 # link to the publication
-# maybe link to the entry on the publication page?
 publication: https://pubmed.ncbi.nlm.nih.gov/36376301/
 # link to github repo
 source code: https://github.com/datarail/DrugResponse
 # link to micro-site
 documentation: https://labsyspharm.github.io/dye-drop-microsite/
+# link to protocol
+protocol: https://www.protocols.io/view/deep-dye-drop-protocol-j8nlkeww5l5r/v1
 # link to associated data
-dataset: https://labsyspharm.shinyapps.io/HMSLINCS_BRCA_Browser/
+dataset_link: https://labsyspharm.shinyapps.io/HMSLINCS_BRCA_Browser/
 
 # for sorting purpose
 date: 2021-01-01

_software/mcmicro.md

@@ -2,7 +2,7 @@
 # name of the software
 title: Multiple-choice microscopy pipeline (MCMICRO)
 # summary of the tool
-description: MCMICRO is the end-to-end processing pipeline for multiplexed whole tissue imaging and tissue microarrays. It comprises stitching and registration, segmentation, and single-cell feature extraction. Each step of the pipeline is containerized to enable portable deployment across an array of compute environments, including local machines, job-scheduling clusters and cloud environments like AWS. The pipeline execution is implemented in Nextflow, a workflow language that facilitates caching of partial results, dynamic restarts, extensive logging and resource usage reports.
+description: MCMICRO is an open-source processing pipeline for multiplexed whole-tissue images and tissue microarrays. It consists of customizable modules that sequentially perform image processing and quantification steps, including stitching, registration, cell segmentation, single-cell quantification, and visualization. Each module is containerized with Docker, making it possible to deploy MCMICRO across various computing environments, including local machines, job-scheduling clusters, and cloud environments like AWS. MCMICRO is undergoing active development, and modules are regularly added and improved as part of the growing MCMICRO community.
 # thumbnail image, can be a logo too
 image: software/mcmicro-logo.svg
 
@@ -17,7 +17,7 @@ documentation: https://mcmicro.org
 video: https://vimeo.com/679368905
 
 # for sorting purpose
-date: 2022-01-01
+date: 2024-01-01
 # set the type for this item - will determine which page it appears on:
 # [ software | method ]
 type: software

_software/minerva.md

@@ -2,7 +2,7 @@
 # name of the software
 title: Minerva
 # summary of the tool
-description: Minerva is a suite of software tools for interpreting and interacting with complex images, organized around a guided analysis approach. The software enables fast sharing of large image data that is stored on Amazon S3 and viewed using a zoomable image viewer implemented using OpenSeadragon, making it ideal for integration into multi-omic browsers for data dissemination of tissue atlases. Check out the Minerva Wiki to learn more about the software and for news.
+description: Minerva is a suite of lightweight software tools that enables interactive viewing and fast sharing of large image data. With **Minerva Author**, users can import an image and generate a multi-waypoint, annotated **Minerva Story** that walks viewers through their data. Minerva Stories are hosted on the web and viewed through a web browser (no download required!), making them ideal for sharing large-scale image data as part of tissue atlases. 
 # thumbnail image, can be a logo too
 image: software/minerva_v2.png
 
@@ -17,7 +17,7 @@ documentation: https://www.minerva.im
 video: https://vimeo.com/439974633
 
 # for sorting purpose
-date: 2021-01-01
+date: 2023-01-01
 # set the type for this item - will determine which page it appears on:
 # [ software | method ]
 type: software

_software/orion.md

@@ -0,0 +1,21 @@
+---
+# name of the software
+title: Orion
+# summary of the tool
+description: |
+  Orion is a method for collecting one-shot 18-plex immunofluorescence images and diagnostic-grade H&E images from the same samples. The Orion method was developed in collaboration with RareCyte Inc. and uses a specialized microscope and fluorescent antibodies (known as ArgoFluors™), which can be imaged simultaneously and spectrally unmixed. We show that same-slide H&E and IF images provide complementary information that can be used to train ML models that effectively predict cancer progression.
+
+# thumbnail image, can be a logo too
+image: software/orion.png
+
+# link to the publication
+publication: http://www.ncbi.nlm.nih.gov/pmc/articles/pmc10368530/
+# link to associated data
+dataset_link: https://www.tissue-atlas.org/atlas-datasets/lin-chen-campton-2023/
+
+# for sorting purpose
+date: 2020-01-02
+# set the type for this item - will determine which page it appears on:
+# [ software | method ]
+type: method
+---