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[Preprint]. 2024 Aug 23:2024.08.21.609031.
doi: 10.1101/2024.08.21.609031.

Tracking clonal evolution of drug resistance in ovarian cancer patients by exploiting structural variants in cfDNA

Marc J Williams  1   2 , Ignacio Vázquez-García  1   2   3 , Grittney Tam  4 , Michelle Wu  1   2 , Nancy Varice  5 , Eliyahu Havasov  1   2 , Hongyu Shi  1   2 , Gryte Satas  1   2 , Hannah J Lees  1   2 , Jake June-Koo Lee  1   2 , Matthew A Myers  1   2 , Matthew Zatzman  1   2 , Nicole Rusk  1   2 , Emily Ali  1   2   6 , Ronak H Shah  4 , Michael F Berger  4   6 , Neeman Mohibullah  7 , Yulia Lakhman  8 , Dennis S Chi  5 , Nadeem R Abu-Rustum  5 , Carol Aghajanian  9 , Andrew McPherson  1   2 , Dmitriy Zamarin  10 , Brian Loomis  4   6 , Britta Weigelt  6 , Claire F Friedman  9 , Sohrab P Shah  1   2
Affiliations

Tracking clonal evolution of drug resistance in ovarian cancer patients by exploiting structural variants in cfDNA

Marc J Williams et al. bioRxiv. .

Abstract

Drug resistance is the major cause of therapeutic failure in high-grade serous ovarian cancer (HGSOC). Yet, the mechanisms by which tumors evolve to drug resistant states remains largely unknown. To address this, we aimed to exploit clone-specific genomic structural variations by combining scaled single-cell whole genome sequencing with longitudinally collected cell-free DNA (cfDNA), enabling clonal tracking before, during and after treatment. We developed a cfDNA hybrid capture, deep sequencing approach based on leveraging clone-specific structural variants as endogenous barcodes, with orders of magnitude lower error rates than single nucleotide variants in ctDNA (circulating tumor DNA) detection, demonstrated on 19 patients at baseline. We then applied this to monitor and model clonal evolution over several years in ten HGSOC patients treated with systemic therapy from diagnosis through recurrence. We found drug resistance to be polyclonal in most cases, but frequently dominated by a single high-fitness and expanding clone, reducing clonal diversity in the relapsed disease state in most patients. Drug-resistant clones frequently displayed notable genomic features, including high-level amplifications of oncogenes such as CCNE1, RAB25, NOTCH3, and ERBB2. Using a population genetics Wright-Fisher model, we found evolutionary trajectories of these features were consistent with drug-induced positive selection. In select cases, these alterations impacted selection of secondary lines of therapy with positive patient outcomes. For cases with matched single-cell RNA sequencing data, pre-existing and genomically encoded phenotypic states such as upregulation of EMT and VEGF were linked to drug resistance. Together, our findings indicate that drug resistant states in HGSOC pre-exist at diagnosis and lead to dramatic clonal expansions that alter clonal composition at the time of relapse. We suggest that combining tumor single cell sequencing with cfDNA enables clonal tracking in patients and harbors potential for evolution-informed adaptive treatment decisions.

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Conflict of interest statement

B.W. reports grant funding by Repare Therapeutics paid to the institution, outside the submitted work, and employment of a direct family member at AstraZeneca. C.A. reports grants from Clovis, Genentech, AbbVie and AstraZeneca and personal fees from Tesaro, Eisai/Merck, Mersana Therapeutics, Roche/Genentech, Abbvie, AstraZeneca/Merck and Repare Therapeutics, outside the scope of the submitted work. C.A. reports clinical trial funding to the institution from Abbvie, AstraZeneca, and Genentech/Roche; participation on a data safety monitoring board or advisory board in AstraZeneca and Merck; unpaid membership of the GOG Foundation Board of Directors and the NRG Oncology Board of Directors. M.F.B reports consulting fees (Eli Lilly, AstraZeneca, Paige.AI), Research Support (Boundless Bio) and Intellectual Property Rights (SOPHiA Genetics). BL reports Intellectual propery rights (SOPHiA Genetics) and licensing royalties (BioLegend/Revvity). C.F. reports research funding to the institution from Merck, AstraZeneca, Genentech/Roche, Bristol Myers Squibb, and Daiichi; uncompensated membership of a scientific advisory board for Merck and Genentech; and is a consultant for OncLive, Aptitude Health, Bristol Myers Squibb and Seagen, all outside the scope of this manuscript. D.S.C. reports membership of the medical advisory board of Verthermia Acquio Inc and Biom’up, is a paid speaker for AstraZeneca, and holds stock of Doximity, Moderna, and BioNTech. D.Z. reports institutional grants from Merck, Genentech, AstraZeneca, Plexxikon, and Synthekine, and personal fees from AstraZeneca, Xencor, Memgen, Takeda, Astellas, Immunos, Tessa Therapeutics, Miltenyi, and Calidi Biotherapeutics. D.Z. own a patent on use of oncolytic Newcastle Disease Virus for cancer therapy. N.A.-R. reports grants to the institution from Stryker/Novadaq and GRAIL, outside the submitted work. S.P.S. reports research funding from AstraZeneca and Bristol Myers Squibb, outside the scope of this work.

Figures

Figure 1
Figure 1. Clone-specific mutations and structural variations in scWGS
a) scWGS based copy number heatmap for patient OV-004. Each row is the copy number of a cell, cells are ordered according to a MEDICC2 computed single-cell phylogeny (shown on the left) b) Clone pseudobulk copy number at 10kb resolution for clone A and clone B in chr17. Truncal variants (TP53 missense and deletion) are annotated in purple, clone specific duplications and SNVs are annotated in red and blue respectively c) Phylogenetic trees annotated with cells that have support for variants shown in panel b). d) Clone pseudobulk copy number at 10kb resolution for clone A and clone B in chr8 showing different chromothriptic chromosomes. In b) and d) notable regions that are different between clones A and B are highlighted in gray.
Figure 2
Figure 2. Structural variants as highly specific markers of tumor DNA in cfDNA
a) Schematic of workflow illustrated with a translocation between chr8 and chr19 identified in OV-107. b) Distribution of VAFs for SVs and SNVs in baseline samples c) Schematic showing how patient specific error rates are calculated by applying probe sets to off target patients d) average background error rates in duplex, simplex and uncollapsed sequences. Each violin/boxplot is a distribution over SVs/SNVs where each data point is the error rate for an individual patient. Triangles show limit of detection (LOD) defined as 2X the largest observed patient error rate e) Fraction of SNV/SVs that have 0 background ie no read support in incorrect patient f) Mean SV VAF vs Tumor fraction computed from TP53 VAF
Figure 3
Figure 3. Detecting clone-specific SVs in cfDNA
a)-d) Single cell phylogeny on left hand side with tips coloured by clone membership, zoom in on copy number profiles of chromosomes of interest that have clone specific structural variants driven by a mutational process, above each copy number profile, the location of SVs are shown, right hand side shows the CCF of the 2 clones of interest in DLP, the number of clone specific structural variants and the VAF of those clone specific SVs in cfDNA at baseline. Shown are chromothripsis in OV-083, breakage-fusion bridge in OV-045, tandem duplication towers in OV-081 and chromoplexy in OV-002. e) Tumor fraction in baseline samples inferred from TP53 mutation f) VAF of all structural variants at baseline in cfDNA stratified by clonality. Black horizontal line shows mean value.
Figure 4
Figure 4. Clonal evolution of drug resistance in patients
Clonal evolution tracking in 4 patients. a) Anatomical sites sequenced with DLP, a phylogenetic tree of the clones, then clonal fractions, mean truncal SV VAF and TP53 VAF, CA-125 and treatment history over time for patient 044. Disease recurrences are annotated on the CA-125 track. b) ERBB2 copy number in clone B vs E across cells c) Pseudobulk copy number of clones B and E at 10kb resolution in chromosomes 2 and 17. A translocation specific to clone E and implicated in the ERBB2 amplification is highlighted. Below shows the read counts of this translocation across timepoints in cfDNA d) CT scan images from day 0 and day 84 from 2 sites. Orange/white arrows indicate site of disease e) Clonal tracking in patient 009, same as panel a). f) Diagram of mutations impacting the BRCA1 gene: location of frameshift deletion shown with red dashed line, large 1.37kb deletion shown in gray. Number of reads supporting the 1.37kb deletion in cfDNA across time. g) Clonal tracking in patient 107, same as panel a). h) NOTCH3 and CCNE1 single cell copy number distribution across clones i) Clonal tracking in patient 045, same as panel a). j) RAB25 and CCNE1 single cell copy number distribution across clones
Figure 5
Figure 5. Clone-specific transcriptional programs
a) Hallmark pathway variability across genomically defined clones in scRNAseq data. Each data point represent the maximal pathway score difference between clones in each patient. Data from 20 patients included. b) From left to right, clone frequencies inferred from cfDNA at baseline (B) and recurrence (R) for OV-107. UMAPs labelled by sites and clone mapping (inferred using TreeAlign). Distribution of NOTCH3 expression, VEGF pathway, hypoxia and HIF1A across clones c) Clone frequencies inferred from cfDNA at baseline (B) and recurrence (R) for OV-009 UMAPs labelled by sites and clone mapping (inferred using TreeAlign). Distribution of EMT pathway, VIM expression, JAK-STAT pathway and fraction of cells in each cell cycle phase.
Figure 6
Figure 6. Wright-Fisher modeling
a) Summary of approach used to accept/reject neutrality. Frequency of clones at baseline and changes in cancer cell population informed by CA-125 levels are used as input to a neutral wright-fisher model with varying population sizes. For each sample, 1000 simulations are generated and then the distribution of frequencies at the final time point are compared to observed values. b) Example simulated trajectories and observed frequencies for 3 patients: 009, 014 and 045. 009 and 045 have clones that deviate from the expectations in a neutral model, while clones in 014 are consistent with a neutral model. c) Summary of the results of the Wright-Fisher simulation based test in 10 patients. From bottom to top: change in clone frequencies between baseline and the final timepoint which had evidence of ctDNA (in most cases the final timepoint samples), p-values per clone, neutral/non-neutral classification based on a cutoff of p(adjusted) < 0.05.
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