Abstract PR010: Linking proliferation rate to the evolution of single-cell primary and metastatic tumor clones
Authors: Lucas Olivia , Ward Sophia , Zaidi Rija , Bunkum Abigail , Frankell Alexander M. , Moore David A. , Hill Mark S. , Liu Wing Kin , Marinelli Daniele , Lim Emilia L. , Hessey Sonya , Naceur-Lombardelli Cristina , Rowan Andrew , Mann Sukhveer , Zhai Haoran , Dietzen Michelle , Ding Boyue , Royle Gary , McGranahan Nicholas , Jamal-Hanjani Mariam , Kanu Nnennaya , Swanton Charles , Zaccaria Simone 
Published: February 2024

Abstract
Proliferation is a key phenotypic feature of cancer, with higher rates associated with poorer clinical outcomes. Thus far, proliferation rates have been measured using pathological or experimental techniques on bulk tumor samples. However, tumors are heterogeneous compositions of distinct clones. Measuring the proliferation of individual clones has been unfeasible to date since proliferation and clonal diversity cannot easily be measured for the same set of cells, but it is potentially important as it may allow the identification of clones that develop more aggressive phenotypes (e.g., metastatic potential or treatment resistance). We have developed SPRINTER, a novel algorithm that uses single-cell whole-genome DNA sequencing (scDNA-seq) data to enable the accurate identification of actively replicating cells in both the S and G2 phases of the cell cycle and their assignment to distinct tumor clones, thus providing a proxy to estimate clone-specific proliferation rates. To evaluate SPRINTER’s accuracy, we generated a ground truth dataset of 8,844 diploid and tetraploid cancer cells by coupling scDNA-seq with 5-Ethynyl-2-deoxyuridine (EdU) labeling, and demonstrated that SPRINTER can accurately distinguish clone proliferation rates in contrast to previous approaches. We further generated a longitudinal, primary-metastasis matched dataset of 23,001 cancer cells obtained from 5 samples from the primary tumor and 5 samples from distinct metastases from a patient with non-small cell lung cancer, allowing us to integrate analyses of proliferation and cancer evolution through the metastatic disease course. We revealed widespread heterogeneity in clone proliferation rates both between and within samples, supported by multiple orthogonal analyses including Ki-67 pathology, nuclei microscopy imaging, and patient clinical imaging, with high proliferation seen in fast-growing metastatic lesions. We demonstrated an association between clones with high proliferation and increased metastatic potential, as well as increased shedding of circulating tumor DNA. We further illustrated SPRINTER’s broad applicability on previous datasets of 42,009 breast cancer cells and 19,905 ovarian cancer cells, revealing an association between high proliferation and increased rates of different genetic variants. In conclusion, SPRINTER infers the proliferation rates of distinct tumor clones from scDNA-seq data, allowing the identification of clones with potentially aggressive phenotypes, such as metastatic potential.
Citation Format: Olivia Lucas, Sophia Ward, Rija Zaidi, Abigail Bunkum, Alexander M. Frankell, David A. Moore, Mark S. Hill, Wing Kin Liu, Daniele Marinelli, Emilia L. Lim, Sonya Hessey, Cristina Naceur-Lombardelli, Andrew Rowan, Sukhveer Mann, Haoran Zhai, Michelle Dietzen, Boyue Ding, Gary Royle, Nicholas McGranahan, Mariam Jamal-Hanjani, Nnennaya Kanu, Charles Swanton, Simone Zaccaria. Linking proliferation rate to the evolution of single-cell primary and metastatic tumor clones [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr PR010.