Auditorium 2 - Symposium 30 - Americas Hub - Phylomedicine of Tumor 10:00am - 1:00pm Thursday, 8th July, 2021 Presentation type Oral

Symposia organisers: Sayaka Miura, Li Liu Please note, some speakers in this symposium also have a poster available to view. Oral presentations occur in the Auditorium listed and the Posters are available in one of two Poster Halls. Please do a search for "Presenters" via the search function of the online program for more information.

SYMP30-1 The mutual illumination of molecular evolution and cancer biology

Jeffrey P Townsend Yale University, New Haven, CT, USA

Abstract

Cancer progression is a molecular evolutionary process. The tools of molecular evolution—e.g. model-based phylogenetic inference, the detection of signals of selection—can be applied to enlighten cancer evolution. Phylogenetic tools are essential to understanding the evolutionary history of cancer and illuminating past trajectories, and the rules regarding the temporal progression of cancer that we learn from them illuminate both general molecular evolution and the special circumstances of cancer. The convergent selective effects— observed in tumor after tumor in large analysis of large datasets that enable us to “replay the tape” of evolution—provide an ability to predict the evolutionary trajectory of cancer based on heterogeneous underlying rates, selective impacts of , and epistatic interactions. Efforts to prevent or delay cancer will be enormously aided by molecular evolutionary approaches engaging these concepts. I’ll discuss a suite of concepts and tools from molecular evolutionary theory that can inform cancer biology in new and meaningful ways; highlight current challenges to applying these concepts; and propose ways in which incorporating these concepts could identify new therapeutic modes and vulnerabilities in cancer. SYMP30-2 The Role of Multi-level Genetic Diversity in Cancers

Li Liu1, Navid Ahmadinejad2 1Arizona State University, Phoenix, AZ, USA. 2Illumina Inc., San Diego, CA, USA

Abstract

Background: In ecology, genetic diversity has been associated with population fitness under fluctuating environmental conditions. A tumor is composed of multiple cell populations (i.e., subclones) that cooperate and compete during tumorigenesis and cancer treatment, forming a dynamic ecological system. Tumor mutational burden, as a gross measure of genetic diversity in cancers is predictive of responses to immunotherapies. However, no studies investigate clinical significance of genetic diversity at subclonal levels. Methods: We analyzed 5,754 tumor exomes from the TCGA project. For each tumor, we estimated genetic diversity in clonal, subclonal, ancestral, and derived cell populations. For each diversity measure, we tested its association with age of diagnosis, tumor stage and patient overall survival adjusted for tumor types and sex. P- values <0.05 indicated significant associations. Results: In pan-cancer analysis, subclonal counts and entropy were both positively associated with age of diagnosis and negatively associated with patient overall survival. In individual cancer types (colorectal cancers, liver cancers, ovarian cancers and head and neck cancers), the number of driver mutations in derived subclones was associated with patient overall survival. However, the number of driver mutations in ancestral clones showed no significant associations. Furthermore, although tumor mutational burden measures showed significant associations with age at diagnosis in multiple cancer types, such associations disappeared when adjusted for tumor stages. Discussion: Consistent with expectations from ecological biology, genetic diversity plays important roles in cancer development. However, diversity at the subclonal level instead of at the clonal level contributes to tumor fitness, especially as responses to treatments. SYMP30-3 Finding the evolutionary roots of cancer cells migrations

Antonia Chroni1, Lauren Hamilton1, Tracy Vu1, Jeffrey Townsend2, Sudhir Kumar1 1Temple University, Philadelphia, PA, USA. 2Yale University, New Haven, CT, USA

Abstract

Metastasis is the result of the ongoing evolutionary progression of cancer cells with metastatic potential. Deciphering the origin and trajectory of cancer cells is key for fundamentally understanding disease progression. Here, we present an analysis of migration trajectories of cancer cells based on clone phylogenies in patients with different types of metastatic cancer. We reconstructed migration histories of cancer cells seeding metastases and identified their potential sources during cancer progression for multiple cohorts. We found that majority of metastases are seeded by solitary clones that come from primary tumors. In many patients, metastases were seeded by primary tumors, but in a similar number of patients, we found metastases to be seeded by clones from other metastases. We also observed metastatic cascades involving multiple tumors and inter-tumor clone exchanges. Our findings are consistent with emerging experimental and clinical data that paint a more complex picture of metastatic migration networks. We suggest that the knowledge of inferring the migration history of cancer cells will be beneficial for identifying mutations, genes, and mutational signatures that modulate the dynamics of metastatic processes. SYMP30-4 Quantifying and Describing Contributors to Cancer progression and Therapeutic Resistance via Molecular Phylogenetic Analysis

J Nicholas Fisk1, Katerina Politi1, Scott Gettinger2, Alexander Dornburg3, Stephen G Gaffney1, Christopher Cross1, Amandeep R Mahal2, James Yu2, Jeffrey P Townsend1 1Yale University, New Haven, CT, USA. 2Yale School of , New Haven, CT, USA. 3University of North Carolina at Charlotte, Charlotte, NC, USA

Abstract

The acquisition by cancer of resistance to targeted molecular therapies remains a immense clinical challenge to implementing precision medicine in oncology. Mechanisms of cancer theraputic resitance are poorly understood—partly because they must be reconstructed from biopsies whose timing and content are dictated by patient care. Molecular evolutionary techniques, including phylogenetic analysis and calculation of selection intensities, are well suited to offer guiding insights in describing and overcoming therapeutic resistance. We developed a molecular phylogenetic approach to perform these evolutionary analyses on cancer tumor sequences, enabling precise determination of the genetic variants that are under selection and influence recurrence. We 1) infer ancestral states in tumor lineages to better identify clinical timelines of disease progression in individual patients, 2) trace the evolution of mutational signatures across a tumor phylogeny with superimposed clinical information to examine the shifting exogenous and endogenous contributors to cancer and 3) quantify the selective advantage conferred by somatic variants in a response to treatment, revealing how cancer evades elimination and recurs. To demonstrate this approach, we present results in EGFR-driven lung adenocarcinoma and in clear-cell renal cell carcinoma. In LUAD we reveal the strikingly high effect size for the EGFR T790M resistance mutation and note its consequences regarding theraputic strategies. In ccRCC, it remains unclear small renal masses all constitute viable precursors to large masses, or if there are distinct molecular etiologies associated with distinct evolutionary trajectories meriting distinct therapeutic approaches. To test this hypothesis of linear development, we apply a synergistic machine learning- evolutionary biology approach. SYMP30-5 Mutational processes in somatic cancer cell populations

Sayaka Miura, Sudhir Kumar Temple University, Philadelphia, PA, USA

Abstract

Mutational processes in somatic cancer cell populations are constantly changing, leaving their signatures in the accumulated genomic variation in tumors. The inference of mutational signatures from the observed genetic variation enables spatiotemporal tracking of tumor mutational processes that evolve due to cellular environmental changes, mutations, and treatment regimes. Ultimately, mutational patterns illuminate the mechanistic understanding of their evolution in cancer progression. We show that the integration of cancer cell phylogeny with mutational signature deconvolution enables higher-resolution detection of gain and loss of mutational processes within the phylogeny. This approach to analyzing somatic genomic variations in 61 lung cancer patients revealed a high turn-over of mutational processes over time and closely related clonal lineages. Some mutational signatures (e.g., smoking-related) showed a higher propensity to be lost, whereas others (e.g., AID/APOBEC) were gained during lung tumors evolution. In addition to the usefulness of phylogeny-aware approaches to reveal the turn-over of mutational processes, their usefulness in general will be briefly mentioned in other applications, such as reconstructing clone genotypes from bulk sequencing data, imputing missing data and correcting base calls in single-cell sequences, inferring clone phylogenies, and reconstructing cancer migration paths. SYMP30-6 Adapted Binary State-Dependent Speciation and Extinction Phylodynamic Model Infers Boundary-Driven Growth in Tumors

Maya Lewinsohn1, Trevor Bedford2, Alison Feder3 1University of Washington, Seattle, WA, USA. 2Fred Hutchinson Cancer Research Center, Seattle, WA, USA. 3University of California Berkeley, Berkeley, CA, USA

Abstract

Spatial properties of tumor growth have profound implications for cancer progression, therapeutic resistance and metastasis, yet how space governs tumor cell division remains an open question. Xenograft and organoid studies suggest that tumors expand preferentially on the periphery ( i.e.,“boundary-driven growth”), while sequencing efforts have suggested faster progression in the tumor interior. Boundary-driven growth likely affects the shape of tumor phylogenies and is therefore theoretically observable from multi-region sequencing data. However, phylodynamic methods have been largely under-utilized to infer growth dynamics in clinical tumors. In this study, we show that boundary-driven growth can be well-approximated by a two-state model permitting different growth rates in the tumor edge and center. We then adapt phylodynamic tools for inferring binary state-dependent speciation and extinction (BiSSE) to quantify these heterogeneous rates. We validate this approach on simulated tumors sampled across multiple spatial regions, and demonstrate its ability to quantify spatially-varying diversification rates under a range of growth conditions and sampling strategies. We then apply BiSSE to multi-region sequencing data from MMR-deficient gastro-esophageal cancers and find evidence that these tumors diversify more rapidly near the tumor edge than in the center. As multi-region and single cell sequencing increases in resolution and availability, this approach could interrogate proposed spatial growth dynamics in diverse clinically resected specimens and be extended to test other two-state growth models, e.g. metastasis or driver gene effects. More generally, this approach demonstrates the potential power of these phylodynamic models to quantify tumor evolutionary dynamics. SYMP30-7 Cancer evolution after whole genome duplication

Elle Loughran, Aoife McLysaght Trinity College Dublin, Dublin, Dublin, Ireland

Abstract

Whole genome duplication is the second most common genetic aberration in cancer after TP53 mutation, occurring in ~30% of primary tumours and most cases of metastatic cancer. Following WGD, the tetraploid cancer cell rapidly loses chromosomes, resulting in a highly aneuploid, genetically diverse and aggressive tumour.

It is not clear what factors govern the outcome of this rediploidisation process in cancer, but analyses of remnants left in the human genome from two ancestral vertebrate WGDs have revealed non-random duplicate gene retention and loss and suggested selection to retain dosage-constrained genes (the dosage balance hypothesis). Here we investigate to what extent the dosage balance hypothesis explains duplicate gene retention and loss after WGD in cancer.

Using copy number profiles of post-WGD tumour samples from The Cancer Genome Atlas, we found that WGD genes retained in duplicate in humans (ohnologs) are not more likely to be retained after cancer WGD, and that known dosage-sensitive genes in the human genome show little evidence of dosage constraint in cancer samples. Dosage balance does not appear to be a major factor in tumour rediploidisation; it remains to be seen whether other factors have a significant influence or whether the process is essentially random.