Proposed Analysis: Spatial and/or temporal intra-tumor heterogeneity

[AntoniaChroni]

What are the scientific goals of the analysis?

After talking to @jharenza about the Nautilus harmonization of tissue sites in the histologies file, she suggested looking into algorithms/computational frameworks that enable the investigation of spatial and/or temporal intra-tumor heterogeneity (ITH) by using multi-regional tumor samples.

We will investigate spatial and temporal ITH in different brain tumors. We utilize the spatial information of tumor regions for the longitudinal tumor samples in the PBTA cohort.

What methods do you plan to use to accomplish the scientific goals?

We propose a multi-regional profiling of genomic, transcriptomic, and immune features to characterize tumor evolution and its associated microenvironment. The analysis will be divided in the following parts:

1. Estimate intra-tumor heterogeneity in terms of

• Number of mutations, VAFs
• Number of clones and evolutionary trajectory: Fishplot, CloneFinder
• Fusion data
• Mutational signatures

Other methods to consider using:

• http://viq854.github.io/lichee/
• https://github.com/IARCbioinfo/ITH_pipeline/tree/master
• If sc-DNA-seq available: https://github.com/qasimyu/scclone ; https://github.com/bioinformatics-polito/PhyliCS
  https://github.com/sahandk/HINTRA
• Estimation of ITH: https://github.com/comicsfct/OncoHIT; https://github.com/Sun-lab/basic

2. Describe tumor microenvironment

• Delineate cell types within the primary site
• Separate healthy vs malignant microenvironment
• Cell clustering analysis
• Map clones in the cell types
• DE and GO analysis
• Network graph-based spatial statistical models on spatially annotated molecular data to quantitatively examine modularity and spatial organization in the TME: Biswas et al. 2022

3. Spatial omics
   Assign spatial information by leveraging spatial location within the anatomical site https://www.science.org/doi/10.1126/science.abq4964

• Spatial statistics: Heindl et al. 2015 https://www.sciencedirect.com/science/article/pii/S0023683722022838?ref=pdf_download&fr=RR-2&rr=7f38a1d1d9e541e0
• Physical coordinates (via microscopy)
• Measuring Spatial Distances, e.g., Balsat et al. 2014; Feichtenbeiner et al. 2014; Heindl et al. 2015
• Collaboration with Translational Imaging Research Unit
• Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology: https://www.sciencedirect.com/science/article/pii/S0023683722022838
• If CT images available: https://github.com/LiJiaqi96/ITHscore

4. Immune profiling

• Single-cell and spatial transcriptome framework: https://www.science.org/doi/10.1126/sciadv.adf5464#sec-4
• Spatial Heterogeneity in the Tumor Microenvironment: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4968167/
• Single-cell differentiation trajectory inference: Monocle3 https://cole-trapnell-lab.github.io/monocle3/
• Estimation of STromal and Immune cells in MAlignant Tumours using Expression data' (ESTIMATE)-a method that uses gene expression signatures to infer the fraction of stromal and immune cells in tumour samples. Sharma et al. 2019 used this framework for lung cancer data analysis
• WXS and T-cell receptor (TCR) repertoire sequencing on multi-regional tumors: Yan et al. 2019
• Immuno profiling and TCR, Ligand-receptor interactions (CellPhoneDB)
• Spatial metabolomics https://www.science.org/doi/10.1126/sciadv.add0455

What input data are required for this analysis?

• Genomic data: WGS/WXS, cytogenetics, fusion
• Transcriptomic data: bulk-RNA-Seq
• Spatial information: origin of tumor samples to enable assignment of geographic coordinates (X-Y coordinate-based barcode) and/or use margins of tumor samples
• Other: imaging data, sc-RNA-Sequencing, and/or sc-TCR-Sequencing
• Normal-Tumor matched samples
• Peripheral blood vs solid tumor
• Multi-regional longitudinal samples in the primary site

How long do you expect is needed to complete the analysis? Will it be a multi-step analysis?

Who will complete the analysis (please add a GitHub handle here if relevant)?

@AntoniaChroni

What relevant scientific literature relates to this analysis?

Balsat C, Signolle N, Goffin F, et al. Improved computer-assisted analysis of the global lymphatic network in human cervical tissues. Mod Pathol 2014;27:887–898.
Barsan, V., Xia, Y., Klein, D., Gonzalez-Pena, V., Youssef, S., Inaba, Y., Mahmud, O., Natarajan, S., Agarwal, V., Pang, Y. and Autry, R., 2022. Simultaneous monitoring of disease and microbe dynamics through plasma DNA sequencing in pediatric patients with acute lymphoblastic leukemia. Science Advances, 8(16), p.eabj1360.
Biswas, A., Ghaddar, B., Riedlinger, G. and De, S., 2022. Inference on spatial heterogeneity in tumor microenvironment using spatial transcriptomics data. Computational and systems oncology, 2(3), p.e21043. https://doi.org/10.1002/cso2.1043
Feichtenbeiner A, Haas M, Buttner M, et al. Critical role of spatial interaction between CD8( þ ) and Foxp3( þ ) cells in human gastric cancer: the distance matters. Cancer Immunol Immunother 2014; 63:111–119.
Jamal-Hanjani, M. et al. Tracking the evolution of non-small-cell lung cancer. N. Engl. J. Med. 376, 2109–2121 (2017).
Qazi, M.A., Bakhshinyan, D. and Singh, S.K., 2019. Deciphering brain tumor heterogeneity, one cell at a time. Nature Medicine, 25(10), pp.1474-1476. https://doi.org/10.1038/s41591-019-0605-1
Stanta, G. and Bonin, S., 2018. Overview on clinical relevance of intra-tumor heterogeneity. Frontiers in medicine, 5, p.85. https://doi.org/10.3389/fmed.2018.00085
Sun, YF., Wu, L., Liu, SP. et al. Dissecting spatial heterogeneity and the immune-evasion mechanism of CTCs by single-cell RNA-seq in hepatocellular carcinoma. Nat Commun 12, 4091 (2021). https://doi.org/10.1038/s41467-021-24386-0
Yan, T., Cui, H., Zhou, Y. et al. Multi-region sequencing unveils novel actionable targets and spatial heterogeneity in esophageal squamous cell carcinoma. Nat Commun 10, 1670 (2019). https://doi.org/10.1038/s41467-019-09255-1
Jiang, Y., Qiu, Y., Minn, A.J. and Zhang, N.R., 2016. Assessing intratumor heterogeneity and tracking longitudinal and spatial clonal evolutionary history by next-generation sequencing. Proceedings of the National Academy of Sciences, 113(37), pp.E5528-E5537. https://www.pnas.org/doi/pdf/10.1073/pnas.1522203113