1. Cancer Biology
  2. Computational and Systems Biology

Quantifying chromosomal instability from intratumoral karyotype diversity using agent-based modeling and Bayesian inference

  1. Andrew R Lynch
  2. Nicholas L. Arp
  3. Amber S Zhou
  4. Beth A Weaver
  5. Mark E Burkard  Is a corresponding author
  1. University of Wisconsin-Madison, United States
https://doi.org/10.7554/eLife.69799
Share this article
Cite this article
  1. Andrew R Lynch
  2. Nicholas L. Arp
  3. Amber S Zhou
  4. Beth A Weaver
  5. Mark E Burkard
(2022)
Quantifying chromosomal instability from intratumoral karyotype diversity using agent-based modeling and Bayesian inference
eLife 11:e69799.
https://doi.org/10.7554/eLife.69799
  2. Download BibTeX
  3. Download .RIS

Abstract

Chromosomal instability (CIN)-persistent chromosome gain or loss through abnormal mitotic segregation-is a hallmark of cancer that drives aneuploidy. Intrinsic chromosome mis-segregation rate, a measure of CIN, can inform prognosis and is a promising biomarker for response to anti-microtubule agents. However, existing methodologies to measure this rate are labor intensive, indirect, and confounded by selection against aneuploid cells, which reduces observable diversity. We developed a framework to measure CIN, accounting for karyotype selection, using simulations with various levels of CIN and models of selection. To identify the model parameters that best fit karyotype data from single-cell sequencing, we used approximate Bayesian computation to infer mis-segregation rates and karyotype selection. Experimental validation confirmed the extensive chromosome mis-segregation rates caused by the chemotherapy paclitaxel (18.5±0.5/division). Extending this approach to clinical samples revealed that inferred rates fell within direct observations of cancer cell lines. This work provides the necessary framework to quantify CIN in human tumors and develop it as a predictive biomarker.

Data availability

Single-cell DNA sequencing data from this study has been deposited in NCBI SRA (PRJNA725515). All data and scripts used for modeling and analysis have been deposited in OSF at https://osf.io/snrg3/.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Andrew R Lynch

    Carbone Cancer Center, University of Wisconsin-Madison, Madison, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0238-682X

  2. Nicholas L. Arp

    Carbone Cancer Center, University of Wisconsin-Madison, Madison, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8709-0667

  3. Amber S Zhou

    Carbone Cancer Center, University of Wisconsin-Madison, Madison, United States
    Competing interests
    No competing interests declared.

  4. Beth A Weaver

    McArdle Laboratory for Cancer Research, University of Wisconsin-Madison, Madison, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7830-3816

  5. Mark E Burkard

    Carbone Cancer Center, University of Wisconsin-Madison, Madison, United States
    For correspondence
    mburkard@wisc.edu
    Competing interests
    Mark E Burkard, declares the following: Medical advisory board of Strata Oncology; Research funding from Abbvie, Genentech, Puma, Arcus, Apollomics, Loxo Oncology/Lilly, and Elevation Oncology. I hold patents on microfluidic device for drug testing, and for homologous recombination and super-resolution microscopy technologies.I declare all interests without adjudicating relationship to the published work..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4215-7722

Funding

National Cancer Institute (R01CA234904)

  • Mark E Burkard

National Institutes of Health (R01GM141068)

  • Mark E Burkard

National Cancer Institute (P30CA014520)

  • Mark E Burkard

National Cancer Institute (F31CA254247)

  • Andrew R Lynch

National Institutes of Health (T32HG002760)

  • Andrew R Lynch

National Institutes of Health (T32GM81061)

  • Andrew R Lynch

National Institutes of Health (T32GM008692)

  • Nicholas L. Arp

National Institutes of Health (T32GM008688)

  • Amber S Zhou

National Institutes of Health (T32GM140935)

  • Nicholas L. Arp

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2022, Lynch et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 2,257
    views
  • 283
    downloads
  • 24
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Andrew R Lynch
  2. Nicholas L. Arp
  3. Amber S Zhou
  4. Beth A Weaver
  5. Mark E Burkard
(2022)
Quantifying chromosomal instability from intratumoral karyotype diversity using agent-based modeling and Bayesian inference
eLife 11:e69799.
https://doi.org/10.7554/eLife.69799

Share this article

https://doi.org/10.7554/eLife.69799

Categories and tags

Research organism

Further reading

    1. Cancer Biology

    Propionyl-CoA carboxylase subunit B regulates anti-tumor T cells in a pancreatic cancer mouse model

    Han V Han, Richard Efem ... Richard Z Lin
    Research Article

    Most human pancreatic ductal adenocarcinoma (PDAC) are not infiltrated with cytotoxic T cells and are highly resistant to immunotherapy. Over 90% of PDAC have oncogenic KRAS mutations, and phosphoinositide 3-kinases (PI3Ks) are direct effectors of KRAS. Our previous study demonstrated that ablation of Pik3ca in KPC (KrasG12D; Trp53R172H; Pdx1-Cre) pancreatic cancer cells induced host T cells to infiltrate and completely eliminate the tumors in a syngeneic orthotopic implantation mouse model. Now, we show that implantation of Pik3ca−/− KPC (named αKO) cancer cells induces clonal enrichment of cytotoxic T cells infiltrating the pancreatic tumors. To identify potential molecules that can regulate the activity of these anti-tumor T cells, we conducted an in vivo genome-wide gene-deletion screen using αKO cells implanted in the mouse pancreas. The result shows that deletion of propionyl-CoA carboxylase subunit B gene (Pccb) in αKO cells (named p-αKO) leads to immune evasion, tumor progression, and death of host mice. Surprisingly, p-αKO tumors are still infiltrated with clonally enriched CD8+ T cells but they are inactive against tumor cells. However, blockade of PD-L1/PD1 interaction reactivated these clonally enriched T cells infiltrating p-αKO tumors, leading to slower tumor progression and improve survival of host mice. These results indicate that Pccb can modulate the activity of cytotoxic T cells infiltrating some pancreatic cancers and this understanding may lead to improvement in immunotherapy for this difficult-to-treat cancer.

    1. Cancer Biology

    Single-cell profiling reveals the intratumor heterogeneity and immunosuppressive microenvironment in cervical adenocarcinoma

    Yang Peng, Jing Yang ... Liang Weng
    Research Article

    Background:

    Cervical adenocarcinoma (ADC) is more aggressive compared to other types of cervical cancer (CC), such as squamous cell carcinoma (SCC). The tumor immune microenvironment (TIME) and tumor heterogeneity are recognized as pivotal factors in cancer progression and therapy. However, the disparities in TIME and heterogeneity between ADC and SCC are poorly understood.

    Methods:

    We performed single-cell RNA sequencing on 11 samples of ADC tumor tissues, with other 4 SCC samples served as controls. The immunochemistry and multiplexed immunofluorescence were conducted to validate our findings.

    Results:

    Compared to SCC, ADC exhibited unique enrichments in several sub-clusters of epithelial cells with elevated stemness and hyper-malignant features, including the Epi_10_CYSTM1 cluster. ADC displayed a highly immunosuppressive environment characterized by the enrichment of regulatory T cells (Tregs) and tumor-promoting neutrophils. The Epi_10_CYSTM1 cluster recruits Tregs via ALCAM-CD6 signaling, while Tregs reciprocally induce stemness in the Epi_10_CYSTM1 cluster through TGFβ signaling. Importantly, our study revealed that the Epi_10_CYSTM1 cluster could serve as a valuable predictor of lymph node metastasis for CC patients.

    Conclusions:

    This study highlights the significance of ADC-specific cell clusters in establishing a highly immunosuppressive microenvironment, ultimately contributing to the heightened aggressiveness and poorer prognosis of ADC compared to SCC.

    Funding:

    Funded by the National Natural Science Foundation of China (82002753; 82072882; 81500475) and the Natural Science Foundation of Hunan Province (2021JJ40324; 2022JJ70103).

    1. Cancer Biology
    2. Immunology and Inflammation

    Unraveling the power of NAP-CNB’s machine learning-enhanced tumor neoantigen prediction

    Almudena Mendez-Perez, Andres M Acosta-Moreno ... Esteban Veiga
    Short Report

    In this study, we present a proof-of-concept classical vaccination experiment that validates the in silico identification of tumor neoantigens (TNAs) using a machine learning-based platform called NAP-CNB. Unlike other TNA predictors, NAP-CNB leverages RNA-seq data to consider the relative expression of neoantigens in tumors. Our experiments show the efficacy of NAP-CNB. Predicted TNAs elicited potent antitumor responses in mice following classical vaccination protocols. Notably, optimal antitumor activity was observed when targeting the antigen with higher expression in the tumor, which was not the most immunogenic. Additionally, the vaccination combining different neoantigens resulted in vastly improved responses compared to each one individually, showing the worth of multiantigen-based approaches. These findings validate NAP-CNB as an innovative TNA identification platform and make a substantial contribution to advancing the next generation of personalized immunotherapies.