Spatial intra-tumor heterogeneity is associated with survival of lung adenocarcinoma patients

Hua-Jun Wu, Daniel Temko, Zoltan Maliga, Andre Moreira, Emi Sei, Darlan Conterno Minussi, Jamie Dean, Charlotte Lee, Qiong Xu, Guillaume Hochart, Connor Jacobson, Clarence Yapp, Denis Schapiro, Peter Sorger, Erin H. Seeley, Nicholas Navin, Robert J. Downey, and Franziska Michor

Cell Genomics. 2022 Aug; 2(8): 100165. PMID: 35404441

Intratumor heterogeneity (ITH) of human tumors is important for tumor progression, treatment response, and drug resistance. However, the spatial distribution of ITH remains incompletely understood. Here, we present spatial analysis of ITH in lung adenocarcinomas from 147 patients using multi-region mass spectrometry of >5000 regions, single cell copy number sequencing of ~2000 single cells, and cyclic immunofluorescence of >10 million cells. We identified two distinct spatial patterns among tumors, termed clustered and random geographic diversification (GD). These patterns were observed in the same samples using both proteomic and genomic data. The random proteomic GD pattern, which is characterized by decreased cell adhesion and lower levels of tumor-interacting endothelial cells, was significantly associated with increased risk of recurrence or death in two independent patient cohorts. Our study presents comprehensive spatial mapping of ITH in lung adenocarcinoma and provides insights into the mechanisms and clinical consequences of geographic diversification of intratumor heterogeneity.

Publication

Contents

Data overviews

Data Overviews provide access to minimally processed Level 2 images with no annotation or quality control. Click any of the following thumbnail images for an interactive view of the full-resolution images.

Data image
P132115
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P132630
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P132666
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P133537
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P136690
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P137591
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P137753
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P137757
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P137905
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P137941
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P137974
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P138007

Data Access

About the Data Files

This dataset can be used to reproduce the analysis of 12 lung tumors, as described (Wu HJ, Temko D, Maliga Z, et. al. Cell Genomics, 2022), and to develop new computational tools for processing and analysis of tumors stained for standard histopathology (hematoxylin and eosin) or highly multiplexed (20+) fluorescent tissue imaging.

The dataset includes raw brightfield and immunofluorescence images of surgical resections of lung adenocarcinoma from twelve patients. The stitched image pyramids for highly multiplexed fluorescence image data, segmentation masks and single-cell intensity files for each sample generated by MCMICRO is included.

The position of histologically defined regions (20-60 per tumor) in the H&E stained sections and the corresponding region in the adjacent section stained by multiplexed fluorescence microscopy (CyCIF) are indicated.

Download the primary data

Download the primary data

File Types

Each folder corresponds to a patient sample (N). The following files are available for each patient and are located on Synapse (synID syn32529019) or on Amazon Web Services (AWS).

Free account registration is required to download files from Synapse. Files available through AWS S3 are made available using a “requester pays” model. The person downloading the data must have an AWS account, and AWS will charge the user’s account $0.10/GB for downloading all or part of the data. Images and metadata are available in the bucket at the following location: s3://wu-temko-maliga-2022

File Type Description Location
N.ome.tif Stitched multiplex CyCIF image pyramid in ome.tif format AWS
N_HE.vsi Hematoxylin and Eosin stained image of adjacent FFPE tissue section in .vsi format AWS
_N_HE_/ folder: raw image data accompanying .vsi file AWS
markers.csv list of all markers in ome.tif image Synapse
N.csv single-cell feature table, including intensity data for all channels Synapse
N_ROI.csv X and Y coordinates for histologically annotated regions in CyCIF and H&E images Synapse
raw/ folder of raw IF image data in .rcpnl format AWS
segmentation/ folder of segmentation maps for tissue image in .tif format AWS


N.ome.tif

Patient File Name Location File size
P132115 P132115.ome.tif AWS 90.6 GB
P132630 P132630.ome.tif AWS 99.9 GB
P132666 P132666.ome.tif AWS 123.1 GB
P133537 P133537.ome.tif AWS 87.8 GB
P136690 P136690.ome.tif AWS 56.5 GB
P137591 P137591.ome.tif AWS 91.8 GB
P137753 P137753.ome.tif AWS 144.4 GB
P137757 P137757.ome.tif AWS 100.8 GB
P137905 P137905.ome.tif AWS 132.4 GB
P137941 P137941.ome.tif AWS 121.0 GB
P137974 P137974.ome.tif AWS 134.6 GB
P138007 P138007.ome.tif AWS 128.6 GB


N_HE.vsi

Patient File Name Location File size
P132115 P132115_HE.vsi AWS 531.2 KB
P132630 P132630_HE.vsi AWS 845.8 KB
P132666 P132666_HE.vsi AWS 592.7 KB
P133537 P133537_HE.vsi AWS 948.9 KB
P136690 P136690_HE.vsi AWS 866.2 KB
P137591 P137591_HE.vsi AWS 625.6 KB
P137753 P137753_HE.vsi AWS 540.9 KB
P137757 P137757_HE.vsi AWS 742.2 KB
P137905 P137905_HE.vsi AWS 685.5 KB
P137941 P137941_HE.vsi AWS 683.5 KB
P137974 P137974_HE.vsi AWS 636.0 KB
P138007 P138007_HE.vsi AWS 678.0 KB


_N_HE_/

Patient File Name Location File size
P132115 frame_t.ets AWS 928.7 MB
P132630 frame_t.ets AWS 1.3 GB
P132666 frame_t.ets AWS 1.5 GB
P133537 frame_t.ets AWS 980.1 MB
P136690 frame_t.ets AWS 792.1 MB
P137591 frame_t.ets AWS 1.1 GB
P137753 frame_t.ets AWS 1.5 GB
P137757 frame_t.ets AWS 1.6 GB
P137905 frame_t.ets AWS 1.4 GB
P137941 frame_t.ets AWS 1.8 GB
P137974 frame_t.ets AWS 1.6 GB
P138007 frame_t.ets AWS 2.0 GB


markers.csv

Patient File Name Synapse ID File size
P132115 markers.csv syn32563757 530 bytes
P132630 markers.csv syn32563890 579 bytes
P132666 markers.csv syn32564039 530 bytes
P133537 markers.csv syn32564369 579 bytes
P136690 markers.csv syn32564613 579 bytes
P137591 markers.csv syn32564701 530 bytes
P137753 markers.csv syn32565266 530 bytes
P137757 markers.csv syn32565460 437 bytes
P137905 markers.csv syn32565554 530 bytes
P137941 markers.csv syn32565759 530 bytes
P137974 markers.csv syn32566194 623 bytes
P138007 markers.csv syn32566617 623 bytes


N.csv

Patient File Name Synapse ID File size
P132115 P132115.csv syn32563869 624.7 MB
P132630 P132630.csv syn32564017 960.1 MB
P132666 P132666.csv syn32564305 1.107 GB
P133537 P133537.csv syn32564460 622.8 MB
P136690 P136690.csv syn32564683 503.3 MB
P137591 P137591.csv syn32564818 574.3 MB
P137753 P137753.csv syn32565424 1.064 GB
P137905 P137905.csv syn32565737 896.9 MB
P137757 P137757.csv syn32565509 529.4 MB
P137941 P137941.csv syn32566154 1.338 GB
P137974 P137974.csv syn32566607 1.073 GB
P138007 P138007.csv syn32566788 1.339 GB


N_ROI.csv

Patient File Name Synapse ID File size
P132115 P132115_ROI.csv syn32563870 2.2 KB
P132630 P132630_ROI.csv syn32564019 4.5 KB
P132666 P132666_ROI.csv syn32564307 2.6 KB
P133537 P133537_ROI.csv syn32564462 3.2 KB
P136690 P136690_ROI.csv syn32564685 3.7 KB
P137591 P137591_ROI.csv syn32564821 3.5 KB
P137753 P137753_ROI.csv syn32565425 2.9 KB
P137757 P137757_ROI.csv syn32565511 1.5 KB
P137905 P137905_ROI.csv syn32565738 2.3 KB
P137941 P137941_ROI.csv syn32566155 1.5 KB
P137974 P137974_ROI.csv syn32566609 2.5 KB
P138007 P138007_ROI.csv syn32566789 2.1 KB


raw/

Patient Number of Files Folder size Location
P132115 13 78.9 GB AWS
P132630 13 86.8 GB AWS
P132666 13 109.3 GB AWS
P133537 13 77.1 GB AWS
P136690 13 48.2 GB AWS
P137591 13 81.5 GB AWS
P137753 13 125.3 GB AWS
P137757 9 75.6 GB AWS
P137905 13 115.7 GB AWS
P137941 13 106.1 GB AWS
P137974 13 118.9 GB AWS
P138007 13 112.5 GB AWS


segmentation/

Patient Number of Files Folder size Location
P132115 12 35.3 GB AWS
P132630 12 38.8 GB AWS
P132666 12 48.9 GB AWS
P133537 12 34.5 GB AWS
P136690 12 21.6 GB AWS
P137591 12 36.5 GB AWS
P137753 12 56.1 GB AWS
P137757 12 48.9 GB AWS
P137905 12 51.8 GB AWS
P137941 12 47.5 GB AWS
P137974 12 53.2 GB AWS
P138007 12 50.3 GB AWS

About Accessing AWS Data

To browse and download the data use either a graphical file transfer application that supports S3 such as CyberDuck, or the AWS CLI tools. A graphical tool may be more convenient but the CLI tools will likely offer higher download speeds. Please refer to the documentation for your chosen tool on how to sign in and enable access to requester-pays buckets. There is unfortunately no web-browser-based mechanism for accessing a requester-pays bucket. Keep in mind the download costs, which will run over $200 for downloading one copy of the entire dataset. For users who wish to perform processing within AWS to avoid transfer charges, note that the bucket is located in the us-east-1 region so any other resources must be instantiated in this same region.