Cancer, apart from being a mortal disease, is also a summary of cell evolution within a short time period.
Therefore, at the fundamental level, cancer research is not only helping the patient, but also helping to
uncover the laws of evolution, influenced by mutations, selection and other mechanisms. In turn,
progress in understanding these laws provides cues for understanding other diseases. Modern biology
entered the stage in which we can obtain personalized information about mutations in tumors of a given
patient, and this information may help design the right treatment.

In this project, we explore two major topics, related with increased availability of detailed molecular
data. One of them is the question how much can be learned about the past (and by extrapolation, of the
future) of a tumor, from the pattern of mutations in this tumor genome? Methodologically, this topic
belongs to the so-called Genetic Archeology, which has been fruitfully used for example in deciphering
early human population growth and migration rates. In case of cancer, the same techniques can help
estimating the growth and mutation rate of the tumor, which otherwise are not available. In particular,
we are interested in the differences between primary tumors and distant metastases. We will use data
from the publicly available Cancer Genome Atlas database as well as from sequencing of head-and-neck
and breast cancer specimens.

Another research topic we plan to explore is the connection between immunity and potential of the
tumor to form distant metastases. We developed a mathematical model connecting immunity with one
of the mechanisms of cancer metastasis, called EMT (epithelial-mesenchymal transition). We will use
this model among other to predict how drug-induced stimulation of the immune system can influence
(reduce) EMT. We will validate this research by cell line experimentation. Since immunity and EMT-
related genes are particularly frequently mutated in breast and head-and-neck cancers, there exists a
connection with the previous topic.

Aim 1: Develop and validate models of clonal evolution of cancer cell populations in the primary and secondary tumors. Validate the models using experimental data obtained from specimens of breast and head and neck cancers. Based on the same data and literature, propose a model of metastatic spread in these cancers.

Aim 2: Develop a model of tumor progression, using the example of the epithelial-mesenchymal transition (EMT) and its interaction with innate immunity (the NF-κB) regulatory module. Validate the model using cell-line experimentation, In addition, employ the models to develop a systemic approach to anticancer agents which explore immune system-related effects.

1. Generalize the Kimmel-Tavaré model of selective sweeps (see Preliminary Studies) in cancer to multiple sweeps. Employ birth-and-death process coalescent of Lambert to improve the study the patterns of tumor growth based on site frequency spectra.
2. Compare cancer genomes in primary solid tumor and matched concurrent metastasis to regional lymph nodes. We expect to identify genetic heterogeneity between primary tumor and local metastases, and to model evolution of cancer genomes during cancer spread/progression to evaluate the role of selection and new mutations.
3. Replicate and extend the NF-κB – EMT circuit experiments of Brasier laboratory using cell lines with breast cancer and head-and-neck cancer characteristics. Employ native-immunity modifying agents to further explore the connection between NF-κB – EMT and anti-EMT action of such agents.
4. Develop models of stimulation of the immune system for treatment of cancer, using literature data and own models of immune response and validate it based on data from Task 3.
5. Generalize the proof-of-concept model of maintenance therapy to include realistic patterns of competition, growth and killing of sensitive and resistant cells and validate it based on data from Task 3

Project manager

Marek Kimmel

Contractors

Paweł Kuś, Roman Jaksik, Monika Kurpas, Wojciech Bensz

1. We generalized the model of Kimmel and Tavaré's selective substitutions (in collaboration with the Institute of Cancer Dynamics at Columbia University) [Dinh et al. Stat Sci 2020] and applied it to various types of cancers (breast, larynx, lungs, colon, and AML [Dinh, Jaksik et al. Comp Syst Oncol]). Concurrently, Dr. Paweł Kuś introduced a new method and software, cevomod https://github.com/pawelqs/cevomod, in his doctoral thesis https://bip.polsl.pl/nadania_dr/pawel-kus/. This software found application in analyzing the spatial spread of bladder cancer, in collaboration with the MD Anderson Cancer Center [Bondaruk, Jaksik et al. iSci 2022].

2. We developed a DNA sequencing and analysis scheme for breast and laryngeal cancers in collaboration with the National Center for Oncology in Krakow and the Medical University in Poznan (see [Kuś et al. Recent Adv 2021]). We explored and confirmed the empirical law linking mutations in tumor suppressor genes and oncogenes with the amplification and deamplification of different parts of the genome. Additionally, we contributed to innovative analysis of Darwinian selection in cancers, based on our Tug-of-War type models (see Results and [Kurpas et al. Frontiers Ecol Evol 2022, Bobrowski et al. DCDS 2022]), using the Evolutionary trace method in collaboration with Lichtarge's laboratory at Baylor College of Medicine [Hsu et al. Genome Res 2020].

3. We conducted experiments and advanced bioinformatic analyses of the connections between the heat shock factor HSF and estrogen receptor signaling pathways, based on experiments with normal and malignant breast cell lines, in collaboration with the Laboratory of Wiesława Widlak at the National Center for Oncology in Gliwice [Vydra et al. E-life 2021].

4. We continued model analyses of experiments involving crosstalk with the NF-κB – EMT circuit (see [Kardyńska et al. PLoS CB 14 (4), e1006130]), in the context of the effectiveness of targeted immunotherapy on the cGAS/STING pathway [Gan et al. Front Immunol 12 (2022): 795401].

5. In collaboration with Prof. Świerniak's group at the Silesian University of Technology and Prof. Suwiński's Laboratory at the National Center for Oncology in Gliwice, we initiated the modeling of lung cancer chemotherapy under developing drug resistance [Kozłowska et al. PloS CB 2020] to establish reference points for assessing immunotherapy for lung cancer.

Other achievements:

• We utilized mutational analysis of hypermutated colon cancer DNA sequencing data to understand the distribution of DNA replication initiation sites in the human genome [Jaksik et al. BMC Biology 2023].
• We applied the mathematical modeling, bioinformatics, and statistical methods developed in our project to assess data on hematological cancers from King’s Lab at Baylor College of Medicine and Corey’s Lab at Cleveland Clinic, resulting in a series of publications in high-impact factor journals [Hormachea et al. Cell Stem Cell 2022, Florez et al. Cell Reports 2020, Wang et al. Leukemia 2022], with acknowledgments for our project.
• We used Site Frequency Spectra (SFS) analysis methods developed in our project to explain the molecular evolution of SARS-CoV-2 genomes in the first 100 weeks of the pandemic [Kurpas et al. Viruses 2022].