TRACERx analysis identifies a role for FAT1 in regulating chromosomal instability and whole-genome doubling via Hippo signalling

Lu, Wei-Ting; Zalmas, Lykourgos-Panagiotis; Bailey, Chris; Black, James R. M.; Martinez-Ruiz, Carlos; Pich, Oriol; Gimeno-Valiente, Francisco; Usaite, Ieva; Magness, Alastair; Thol, Kerstin; Webber, Thomas A.; Jiang, Ming; Saunders, Rebecca E.; Liu, Yun-Hsin; Biswas, Dhruva; Ige, Esther O.; Aerne, Birgit; Grönroos, Eva; Venkatesan, Subramanian; Stavrou, Georgia; Karasaki, Takahiro; Al Bakir, Maise; Renshaw, Matthew; Xu, Hang; Schneider-Luftman, Deborah; Sharma, Natasha; Tovini, Laura; Jamal-Hanjani, Mariam; McClelland, Sarah E.; Litchfield, Kevin; Birkbak, Nicolai J.; Howell, Michael; Tapon, Nicolas; Fugger, Kasper; McGranahan, Nicholas; Bartek, Jiri; Kanu, Nnennaya; Swanton, Charles

doi:10.1038/s41556-024-01558-w

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Published: 30 December 2024

TRACERx analysis identifies a role for FAT1 in regulating chromosomal instability and whole-genome doubling via Hippo signalling

Nature Cell Biology volume 27, pages 154–168 (2025)Cite this article

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Abstract

Chromosomal instability (CIN) is common in solid tumours and fuels evolutionary adaptation and poor prognosis by increasing intratumour heterogeneity. Systematic characterization of driver events in the TRACERx non-small-cell lung cancer (NSCLC) cohort identified that genetic alterations in six genes, including FAT1, result in homologous recombination (HR) repair deficiencies and CIN. Using orthogonal genetic and experimental approaches, we demonstrate that FAT1 alterations are positively selected before genome doubling and associated with HR deficiency. FAT1 ablation causes persistent replication stress, an elevated mitotic failure rate, nuclear deformation and elevated structural CIN, including chromosome translocations and radial chromosomes. FAT1 loss contributes to whole-genome doubling (a form of numerical CIN) through the dysregulation of YAP1. Co-depletion of YAP1 partially rescues numerical CIN caused by FAT1 loss but does not relieve HR deficiencies, nor structural CIN. Importantly, overexpression of constitutively active YAP1^5SA is sufficient to induce numerical CIN. Taken together, we show that FAT1 loss in NSCLC attenuates HR and exacerbates CIN through two distinct downstream mechanisms, leading to increased tumour heterogeneity.

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Main

Chromosomal instability (CIN) is pervasive during cancer evolution, particularly in non-small-cell lung cancer (NSCLC), where it is associated with poor recurrence-free survival^1,2,3,4. CIN results in loss of heterozygosity (LOH) events, the burden of which correlates with the frequency of whole-genome doubling (WGD) events in solid tumours^5,6. WGD not only mitigates the effect of LOH resulting from CIN, but also fosters ongoing CIN^7,8,9,10. By duplicating the complete set of chromosomes, WGD is a key event during cancer evolution and correlates with poor prognosis and targeted therapy resistance^7,11,12,13. Despite the importance of CIN and WGD in accelerating cancer evolution by promoting intratumour heterogeneity⁷, genetic events responsible for the initiation and maintenance of CIN and WGD in NSCLC have not been systematically investigated.

Tracking Cancer Evolution Through Therapy/Rx (TRACERx) is a longitudinal cancer study that utilizes multiregional sampling and whole-exome sequencing (WES)³ to identify and time genetic alterations and their relationship with WGD and CIN³. Here, we identify mutations in cancer driver genes that co-occur with WGD and CIN and further characterize their respective involvement in the DNA damage response (DDR) and CIN. We demonstrate that genetic perturbation of six TRACERx driver genes identified in our screen (particularly FAT1) causes deficiencies in homologous recombination (HR) repair and elevated CIN. Using NSCLC cell line models, we further characterize the molecular mechanisms by which FAT1 alterations drive WGD and CIN—known mediators of drug resistance^12,14,15,16. These findings highlight the importance of FAT1 gene alterations in lung cancer evolution.

Results

Identification of DDR and CIN drivers in lung TRACERx

To identify alterations that correlate with WGD and CIN in NSCLC, we analysed multiregional WES data from tumours obtained from the first 100 patients within the lung TRACERx study³. Here, we identified 795 driver events in 91 genes, excluded known oncogenes that could not be modelled appropriately by genetic depletion approaches and focused on 37 tumour suppressor gene mutations that co-occurred with WGD (Fig. 1a). Pathway analysis revealed cellular processes related to genome maintenance, such as the DDR, transcription and chromatin remodelling (Supplementary Fig. 1)^17,18,19. To assess how these genes contribute to genome integrity, we performed a multi-parametric RNA interference (RNAi) screen using high-content imaging in four different lung cancer cell lines harbouring mutations in KRAS, EGFR and TP53 to reflect the mutational landscape of the TRACERx cohort (Fig. 1b (top) and Extended Data Fig. 1a). DNA double-strand breaks (DSBs) induced by ionizing radiation, as well as replication stress induced by hydroxyurea, were chosen to model genotoxic stress. In parallel, the impact on chromosome loss was investigated utilizing a human artificial chromosome (HAC) reporter system²⁰ (Fig. 1b (bottom) and Extended Data Fig. 1b). These combined approaches enabled the identification of six tumour suppressor driver genes—namely BAP1, CREBBP, FAT1, NCOA6, RAD21 and UBR5—as regulators of the DDR and maintenance of chromosomal stability in NSCLC (Fig. 1c). In addition, we also confirmed previously reported roles of WRN, FANCM, DICER1, SMARCA4/BRG1, ARID1B, ARID2, KDM5C and ATRX in the DDR^{21,22,23,24,25,26} (Extended Data Fig. 1a).

**Fig. 1: DDR and CIN loss-of-function screen of genome doubling-associated drivers from the TRACERx 100 cohort.**

The effect of these genes on the DDR was initially validated using two orthogonal approaches; the DR-GFP HR reporter assay²⁷ and a site-specific DSB-generating endonuclease system (DIvA)²⁸. Loss of BAP1, FAT1, NCOA6, RAD21 or UBR5, but not CREBBP, was associated with HR repair deficiency and impaired single-stranded DNA (ssDNA) resection—a step required for accurate HR repair (Fig. 1d,e and Supplementary Fig. 2). Next, we assessed early HR repair signalling 1 h after 6 Gy ionizing radiation in both A549 and H1944 cells. A marked decrease in the formation of RAD51 ionizing radiation-induced foci (IRIFs) was observed following depletion of BAP1, FAT1, NCOA6, RAD21 or UBR5 (Fig. 1f, Extended Data Fig. 2a,b and Supplementary Figs. 3 and 4). These alterations in HR efficiency could not be fully explained by cell cycle changes (Extended Data Fig. 2c), which dictate the selection of activating HR or non-homologous end-joining (NHEJ) repair, suggesting that these genes may be involved in HR directly. FAT1 was prioritized as a top candidate of clinical relevance, as inactivating mutations in FAT1 were highly recurrent (~10%) in the TRACERx 421 cohort (comprising 421 patients and 1,644 tumour regions), with a notable proportion of FAT1 mutations occurring early before WGD (Fig. 1g and Supplementary Fig. 5a–c). Notably, ~20% of the lung TRACERx cohort were found to harbour other inactivating mutations that impair HR efficiency (Fig. 1g and Extended Data Fig. 2d).

FAT1 alterations are positively selected in lung cancer

To quantify whether mutations in FAT1 were under positive selection, we measured the enrichment of FAT1 mutations before and after WGD using the ratio of the observed number of non-synonymous substitutions per non-synonymous site to the number of synonymous substitutions per synonymous site (dN/dS)²⁹. Estimates of dN/dS above or below 1 suggest positive or negative selection, respectively, whereas estimates overlapping 1 imply that there is no evidence of selection. FAT1 mutations, which occurred more frequently in lung squamous cell carcinoma (LUSC) compared with lung adenocarcinoma (LUAD), were under greater positive selection before WGD occurrence in LUSC (Fig. 2a and Extended Data Fig. 3a). In the TRACERx 421 cohort, an enrichment of copy number deletion events was identified around the FAT1 genomic locus 4q35.2 only in patients with clonal WGD, indicating positive selection of 4q35.2 loss (Fig. 2b and Extended Data Fig. 3b–d). In LUSC, FAT1 promoter hypermethylation events reducing FAT1 expression levels and FAT1 copy number loss events were observed in the same tumours (Extended Data Fig. 3e–g). Furthermore, we detected a significant occurrence of mirrored subclonal allelic imbalance (MSAI) at the 4q35.2 locus, suggesting parallel evolution (Fig. 2c). Among genes encoded at the 4q35.2 locus, FAT1 has the lowest Genome Aggregation Database loss-of-function score in germline samples, implying that FAT1 loss is the least tolerated event within 4q35.2 (Fig. 2d). These results highlight the importance of FAT1 alterations.

**Fig. 2: *FAT1* loss of function is enriched in the TRACERx 421 cohort and leads to an elevated mitotic error rate and WGD.**

FAT1 ablation reduces HR efficiency

Considering the frequency of FAT1 alterations in NSCLC (Figs. 1g and 2, Extended Data Fig. 3 and Supplementary Fig. 5) and its potential role in genome maintenance (Fig. 1d–f), we further elucidated at which stage FAT1 acts in the DSB repair pathway by systematically investigating which mediators were affected by FAT1 depletion 1 h post-ionizing radiation. FAT1 knockdown did not impact IRIFs of early DSB repair mediators, including phosphorylated ATM, γH2A.X and 53BP1 oligomerization, which are associated with NHEJ²² (Fig. 3a and Supplementary Fig. 6a,b). However, IRIF formation of CtBP interacting protein (CtIP), which is responsible for initiating ssDNA resection²², was significantly impaired by FAT1 knockdown (Fig. 3a and Supplementary Fig. 6c). FAT1 depletion also reduced IRIF formation of the key HR mediator breast cancer type 1 susceptibility protein (BRCA1) in G2/M cells, using centromere protein F-positive staining as a marker (Fig. 3a and Supplementary Fig. 6d). CRISPR knockout of FAT1 in H1944 or A549 cells impaired RAD51 IRIF formation, but not γH2A.X (Extended Data Fig. 4a,b and Supplementary Fig. 6e). A time-course post-ionizing radiation demonstrated that FAT1 depletion was associated with persistent DNA damage, as manifested by the increased frequency of 53BP1 nuclear bodies (Extended Data Fig. 4c).

**Fig. 3: *FAT1* loss attenuates HR repair.**

Although the size of full-length FAT1 (4,588 amino acids) limits its ectopic expression, the reduction in RAD51 foci formation could be partially rescued by overexpression of the FAT1 carboxy (C)-terminal intracellular domain³⁰ (HA–FAT1^ICD; amino acids 4,202–4,588), which exhibited nuclear localization, suggesting that nuclear FAT1 may promote efficient HR (Fig. 3b,c and Extended Data Fig. 4d,e). FAT1-knockout A549 cells were more sensitive to genotoxic stress induced by poly(ADP-ribose) polymerase inhibitors, cisplatin and hydroxyurea (Extended Data Fig. 5a,b). By analysing both the TRACERx and The Cancer Genome Atlas (TCGA) LUAD datasets, we observed a correlation between FAT1 loss and HR deficiency (HRD)-related genomic signatures, including elevated telomeric allelic imbalance (TAI)³¹, large-scale transitions (LST)³² and loss of heterozygosity (LOH)³³ (Fig. 3d–f). Using established reporters for distal and alternative end-joining activities, respectively³⁴, we confirmed that transient FAT1 siRNA depletion reduced the alternative end-joining efficiency without significantly reducing distal end joining (Fig. 3g,h). Utilizing WGS data from Genomics England, no significant difference was observed in ID6 and SBS3 mutational profiles (Extended Data Fig. 5c,d and Supplementary Fig. 7a,b), both of which are mutation signatures associated with NHEJ activity³⁵.

FAT1 ablation leads to structural and numerical CIN

Indeed, FAT1 depletion resulted in an increased fork collapse rate and HAC loss (Fig. 4a and Extended Data Fig. 1b). Our analysis of the TCGA and Genomics England datasets revealed that FAT1 loss correlated with an increased weighted genome instability index score and total mutational burden, indicating elevated structural and numerical CIN (Fig. 4b,c). To validate this observation, we used U2OS and type 2 pneumocyte (T2P) cells to investigate the formation of micronuclei and 53BP1 nuclear bodies in G1 daughter cells, both established markers of unresolved replication stress and HRD^36,37,38,39. FAT1 ablation at baseline and under replication stress induced by either low-dose aphidicolin or a short pulse of hydroxyurea exacerbated the formation of cyclin A-negative (G1-specific) 53BP1 nuclear bodies and micronuclei (Fig. 4d–h and Extended Data Fig. 6a). Notably, acentric micronucleus formation was significantly elevated following FAT1 depletion in A549 and U2OS cells following replication stress (Fig. 4f,g). Under these conditions, FAT1 ablation also resulted in an increased mitotic error rate at baseline and under replication stress, which manifested as an increased formation rate of chromatin bridges and lagging chromosomes (Fig. 5a and Extended Data Fig. 6b). Utilizing the DIvA site-directed DSB system^28,40, we further demonstrated that FAT1 depletion resulted in increased illegitimate translocation of two DSBs induced on chromosome 17 (Fig. 5b). Concurrently, higher rates of structural chromosomal aberrations, including radial chromosomes and chromatid gaps, were observed upon FAT1 loss (Fig. 5c and Extended Data Fig. 6c). FAT1 silencing also reduced mitotic fidelity, as evidenced by deviations in the modal chromosome number (Fig. 5d–f and Extended Data Fig. 6d).

**Fig. 4: *FAT1* loss elevates replication stress and micronuclei.**

**Fig. 5: *FAT1* loss increases structural CIN and chromosome numbers.**

Since FAT1 mutations are both common in lung cancer and evolutionarily selected before WGD (Fig. 2a–c), we experimentally validated the mitotic defect associated with FAT1 loss using the near-triploid (3N) LUAD cell line PC9 (which harbours an in-frame deletion at exon 19 of the epidermal growth factor receptor-encoding gene) and its isogenic WGD hexaploid (6N) clone¹². Transient FAT1 depletion in PC9 cells significantly elevated the rate of stalled replication forks and mitotic errors, further confirming the involvement of FAT1 in genome maintenance (Extended Data Fig. 6e–g). Despite resulting in an elevated replication fork collapse rate, reduced formation of interphase and mitotic Fanconi anaemia complementation group D2 (FancD2) foci was also observed in FAT1-depleted WGD PC9 cells, suggesting a failure to recover from replication stress, leading to structural CIN (Extended Data Fig. 6e,g–j).

FAT1 depletion leads to mitotic errors and WGD

WGD events were highly prevalent in the lung TRACERx 421 cohort (84% of LUSC and 77% of LUAD) and FAT1 driver mutations were selected before WGD in LUSC tumours (Fig. 2a). However, FAT1 mutations and WGD did not significantly co-occur in the TRACERx 421 cohort (Fisher’s exact test; P = 0.179) (Extended Data Fig. 7a). This was probably due to the presence of other HR-related gene alterations (~20% of patients; Fig. 1g and Extended Data Fig. 2d), which also can contribute to WGD. To investigate whether FAT1 alterations drive WGD, we used the PC9 lung cancer model to quantify the proportion of actively replicating cells with >6N genome content (the basal ploidy of PC9 is 3N). Increased 5-ethynyl-2′-deoxyuridine (EdU) incorporation rates (Fig. 6a,b), as well as a significant increase in loading of the replicative helicase MCM7 beyond 6N, were observed in FAT1-knockout PC9 cells (Extended Data Fig. 7b), both indicating a second replication event post-6N and suggesting that FAT1 loss is associated with WGD. These observations were independent of p53 mutational status, as similar results were obtained in TP53 wild-type, near diploid, untransformed retinal pigment epithelial-1 (RPE-1) cells immortalized with the human telomerase reverse transcriptase subunit (hTERT) (Extended Data Fig. 7c).

To identify the cause of WGD following FAT1 depletion, we monitored cells using live-cell microscopy, tracking at single-cell resolution. Reported causes of WGD include cytokinesis defects, endoreplication, mitotic bypass and cyclin B1 dysregulation during G2 (refs. ^41,42,43,44). No change in cyclin B1 level was observed upon FAT1 knockdown in G2, ruling out cyclin B1 dysregulation as the cause of WGD in the absence of FAT1 (Extended Data Fig. 7d,e). DNA synthesis, measured by EdU incorporation, was also unaltered in control versus FAT1-knockout WGD cells transiently blocked in mitosis using nocodazole (Extended Data Fig. 7f), thereby ruling out a role for FAT1 loss in driving WGD through endoreplication in a manner similar to that of cyclin E amplification reported recently⁴⁴. To investigate mitotic bypass, we used the hTERT RPE-1 cell line expressing both H2B-mTurquoise and fluorescent, ubiquitination-based cell cycle indicator (FUCCI) (hereafter FUCCI–RPE-1 cells) for live-cell imaging (Supplementary Video 1). FAT1 depletion, irrespective of the induction of aphidicolin-induced replication stress, did not cause a significant increase in the rate of mitotic bypass (Fig. 6c and Supplementary Fig. 8). In contrast, FAT1-depleted cells demonstrated an elevated rate of cytokinesis failure, suggesting defects in this final step of cell division as the cause of the WGD associated with FAT1 deficiency (Fig. 6d, Extended Data Fig. 7g and Supplementary Video 2). FAT1 depletion was also associated with an increased rate of nuclear shape abnormalities in daughter cells after normal mitoses (Fig. 6e and Supplementary Video 3). Similarly, after FAT1 depletion, increases in multinucleation and nuclear morphology alterations were observed in fixed U2OS and RPE-1 cells (Extended Data Fig. 7h,i) and WGD PC9 cells, respectively, after replication stress exposure (Extended Data Fig. 8a).

**Fig. 6: *FAT1* loss leads to an elevated mitotic error rate and results in WGD.**

To delineate whether structural CIN precedes nuclear shape abnormalities, we performed live-cell spinning-disk confocal microscopy on FUCCI–RPE-1 cells in G2, allowing us to determine the timing and outcome of the pending mitosis and the fate of respective daughter cells at high resolution with low phototoxicity. We observed that FAT1 depletion not only increases chromatin bridge formation rates (Extended Data Fig. 8b, left) but reduces the maintenance of normal nuclear morphology following mitotic chromosomal bridge formation (Extended Data Fig. 8b (right) and Supplementary Videos 4 and 5). Next, we investigated the long-term outcome of daughter cells with deformed nuclear morphology, by observing the heritability and recurrence of nuclear deformities and mitotic errors in respective daughter cells following mitosis (Extended Data Fig. 8c). In addition, the number of normal second mitoses was significantly reduced following an initial nuclear deformation event (Extended Data Fig. 8c). Furthermore, FAT1-depleted cells had an accelerated rate of metaphase entry (Extended Data Fig. 8d), suggestive of a potential role for FAT1 in regulating mitotic timing. Taken together, these results demonstrate that FAT1 loss contributes to both an increased rate of structural CIN and elevated mitotic defects in daughter cells following CIN, contributing to WGD.

Since elevated CIN is known to synergize with WGD to generate increased intratumour heterogeneity^3,45 and escape from targeted therapeutic pressure^12,14,46,47, we also investigated whether FAT1 loss might enable WGD PC9 cells to accelerate cancer evolution and bypass targeted therapy treatment. PC9 cells were treated with a sensitizing concentration of the epidermal growth factor receptor inhibitor osimertinib (>90% inhibitory concentration) for 5 weeks (Extended Data Fig. 8e,f). An increase in clonal survival and clonal derivation rate was observed for FAT1-knockout cells over 3 months (Extended Data Fig. 8g,h). Osimertinib-resistant FAT1-knockout clones exhibited elevated ploidies compared with control clones (Extended Data Fig. 8i). Taken together, these results suggest that FAT1 loss might fuel WGD and elevated CIN, which in turn exacerbates cancer evolution and targeted therapy resistance, as previously demonstrated^12,14,46,47.

FAT1 depletion leads to dysregulated Yes-associated protein 1 signalling

To determine the molecular mechanism by which FAT1 alterations lead to CIN, we investigated components of potential signalling pathways in which FAT1 has been implicated. FAT1 loss has been connected to dysregulation of the Hippo pathway, RAS–RAF–MEK–MAPK signalling and regulation of the level of Yes tyrosine kinase, which mediates hybrid–epithelial–mesenchymal transition^48,49. Under our experimental conditions, no consistent FAT1 siRNA-mediated alterations of MEK–ERK phosphorylation or Yes tyrosine kinase levels (Fig. 7a) were observed. However, FAT1 depletion resulted in elevated nuclear localization of the key Hippo pathway activator YAP/TAZ (Fig. 7b and Extended Data Fig. 9a,b). Despite increased Yes-associated protein 1 (YAP1) nuclear localization, no significant alteration in YAP1 phosphorylation could be consistently observed following LATS1 or FAT1 depletion (Extended Data Fig. 9c) in T2P or FUCCI–RPE-1 cells.

**Fig. 7: *FAT1* loss leads to dysregulation of the Hippo pathway.**

Orthogonally, we designed a neonGreen reporter with 14xTEAD binding sites to elucidate how FAT1 regulates the ultimate output of the Hippo pathway, TEAD transcriptional activity. This is particularly important since YAP1 and TAZ/WWTR1 are highly analogous, functionally overlapping⁵⁰ and both amplified in lung cancer^3,51 (Extended Data Fig. 9d). By scoring HA-tagged construct and neonGreen reporter double-positive cells, we confirmed that FAT1 loss elevated TEAD transcriptional activity, despite the presence of multiple copies of YAP/TAZ in hexaploid WGD PC9 cells (Fig. 7c, condition 1 versus condition 2). Notably, overexpression of the constitutively active YAP1 mutant construct⁵² further stimulated TEAD activity in FAT1-knockout cells (HA-YAP1^5SA; Fig. 7c, condition 3).

Next, we investigated which FAT1 domains are required to re-suppress TEAD transcriptional activity. In addition to the C-terminal HA–FAT1^ICD fragment capable of rescuing RAD51 IRIF formation (Fig. 3b,c), a FAT1 construct encompassing the extracellular and transmembrane domain was generated (mScarlet–HA–FAT1^WT in Fig. 7c), consisting of 3,735–4,588 amino acids of FAT1. Since FAT1 is known to cooperate with the E3 ligase MIB2 to regulate YAP/TAZ signalling⁵³, we also generated a FAT1 mutant lacking the MIB2 interaction domain (mScarlet–HA–FAT1^MIB∆ in Fig. 7c). Indeed, the mScarlet–HA–FAT1^WT fragment successfully re-repressed TEAD transcription, whereas the FAT1 construct lacking the MIB2 interaction domain failed to do so (Fig. 7c (conditions 4 and 5) and Extended Data Fig. 9e), highlighting the role of FAT1–MIB2 interaction in the modulation of TEAD transcription. Notably, the HA–FAT1^ICD construct, which lacks a transmembrane domain anchor compared with HA–FAT1^WT (Supplementary Fig. 9), failed to re-repress TEAD transcription (Fig. 7c, condition 6), despite successfully rescuing RAD51 IRIF formation in A549 cells (Fig. 3b,c).

Next, we investigated whether depletion of LATS1/2—crucial modulators of YAP1 nuclear localization⁵⁴—might lead to phenotypes resembling those of DDR and CIN observed following FAT1 depletion. Indeed, a marked reduction of the DNA end-resection rate, 53BP1 nuclear body formation and an increase in micronuclei formation were all observed following depletion of either LATS1 or LATS2 (Fig. 7d–f and Extended Data Fig. 10a). Using the site-directed endonuclease DIvA system, depletion of LATS2 caused illegitimate DSB repair, culminating in a translocation (Extended Data Fig. 10b). Notably, somatic copy number alteration (SCNA) loss of LATS2 genomic loci at 13q12.11 was positively selected for in LUSC (Extended Data Fig. 10c). Despite causing HRD, neither LATS1 nor LATS2 depletion disrupted the activation of early DDR signalling, such as phosphorylation of KAP1/TRIM28 or γH2A.X phosphorylation (Extended Data Fig. 10a). Similar to FAT1 depletion, LATS1 knockdown led to an elevated mitotic error rate (Fig. 7g).

FAT1 and YAP1 co-depletion reverses WGD but not HR defects

Given the involvement of FAT1 loss in YAP/TAZ activity (Fig. 7b,c), we systematically investigated whether WGD and numerical and structural CIN associated with FAT1 loss could be reversed by co-depleting YAP1. Despite FAT1/YAP1 co-depletion reversing the cell cycle arrest associated with YAP1 single knockdown (Extended Data Fig. 10d,e), co-depletion of FAT1 and YAP1 did not rescue HR activation defects after DSB formation (Fig. 8a–c). This result was orthogonally validated using the DR-GFP reporter assay, ssDNA formation after endonuclease-induced DSBs and RAD51 foci formation after ionizing irradiation (Fig. 8a–c). Next, since unresolved recombination intermediates due to HR repair deficiency can cause mitotic errors⁵⁵, we quantified the rate of mitotic errors. YAP1/FAT1 co-depletion failed to rescue the elevated mitotic error and chromosomal bridges observed in FAT1-knockout A549 cells (Fig. 8d,e and Extended Data Fig. 10f).

**Fig. 8: Co-depletion of FAT1 and YAP1 reverses cytokinesis failure but not HR deficiencies.**

Next, we investigated whether FAT1/YAP1 co-depletion might rescue the CIN phenotypes associated with FAT1 loss. Live-cell imaging experiments demonstrated that co-depletion of FAT1 and YAP1 reversed the cytokinesis failure phenotypes (Fig. 8f (left) and Extended Data Fig. 10g). However, the nuclear shape deformation observed following FAT1 depletion was only partially rescued (Fig. 8f, right).

Taken together, our observations suggest that FAT1 possesses dual roles. One outcome of FAT1 loss is HRD leading to unresolved recombination intermediates, replication stress, structural CIN and nuclear deformation (Figs. 3–5 and 8a–d); these events appear to be YAP1 independent. In contrast, hyperactive YAP1 leads to increased cytokinesis failure and WGD (Fig. 8f). To visualize the impact of constitutively active mScarlet–YAP1^5SA overexpression on WGD, we performed an EdU incorporation assay. Complementary to the literature⁵⁶, mScarlet–YAP1^5SA overexpression promoted WGD in RPE-1 cells and this was independent of TP53 status (Fig. 8g and Supplementary Fig. 10). Concurrently, we observed an emerging WGD population in FAT1 wild-type and FAT1-knockout PC9 cells (Fig. 8h). These results suggest that constitutively active YAP1 is sufficient to promote WGD. We postulate that FAT1 loss might drive genome instability through two different routes—one through WGD, dependent on YAP1, and the second through HRD, driving structural CIN—in a TEAD/YAP1-independent manner.

Discussion

We systematically analysed 37 NSCLC drivers previously reported in the lung TRACERx study³ that are associated with CIN and WGD. Particularly, we show that depletion of either BAP1, FAT1, NCOA6, RAD21 or UBR5 is associated with downregulation of multiple key steps required for HR repair and elevated loss of a HAC, thereby confirming previous reports of the roles of BAP1, RAD21 and UBR5 in genome maintenance^57,58,59. In addition, our DDR screen confirms previous reports characterizing the role of WRN, FANCM, DICER1, SMARCA4/BRG1, ARID1B, ARID2, KDM5C and ATRX in the DDR^{21,22,23,24,25,26}. Despite FAT1 being frequently altered in normal somatic skin cells⁶⁰ and in multiple cancers^61,62,63, as well as being associated with NSCLC mortality², the mechanistic links between FAT1 alterations, DDR and both numerical and structural CIN have remained elusive.

Through our comprehensive analyses, we have demonstrated that FAT1 ablation impacts HR efficiency, but not NHEJ efficiency, nor the early signalling events of the DDR. FAT1 loss results in hallmarks of HRD, such as increased sensitivity to replication stress inducers, elevated chromosome translocation, elevated fork collapse rates and increased HRD-predictive genetic scars in NSCLC. Our data suggest that FAT1 loss leads to end-resection deficiency after the chromatin remodelling step to initiate HR. The inability to accurately resolve replication stress or clastogenic breaks may lead to chromosome-level alterations³⁶, especially structural CIN.

FAT1 alterations have been detected in normal somatic tissue⁶⁰ and we hypothesize that FAT1 inactivation occurs early in tumorigenesis. Using the multiregional sequencing of the TRACERx study to time the occurrence of FAT1 alterations, we observed evidence of positive selection for tumour subclones with FAT1 alterations before WGD. Indeed, FAT1 depletion attenuates HR and causes unresolved replication stress, both of which contribute to mitotic errors and micronucleus formation³⁹. Using live-cell microscopy, we demonstrated that FAT1 depletion not only elevates structural CIN, such as mitotic bridges, but also increases subsequent nuclear shape deformation.

FAT1 has been proposed to be tumour suppressive and involved in various signalling pathways, including the CaMK2, WNT and Hippo signalling pathways^48,49,50,62. Components of the Hippo pathway have been reported to impact various aspects of genome maintenance, such as mitotic control, stabilization of replication forks, and modulation of nuclear shape, which can affect three-dimensional genome organization^56,64,65,66. We confirmed that depletion of LATS1 or LATS2 can significantly affect HR efficiency and contribute to CIN⁶⁴. We discovered that FAT1—at least in part through its intracellular C-terminal domain—is involved in the DDR and represses CIN through the modulation of YAP1, whereas its interaction with YAP1 is dependent on the interaction between FAT1 and the E3 ligase MIB2 (ref. ⁵³). Notably, despite being able to reconstitute HR-related functions, overexpression of the HA–FAT1^ICD construct, devoid of the transmembrane domain, failed to re-repress TEAD transcriptional activity in FAT1-knockout cells. In line with previous reports that HRD results in mitotic defects and structural CIN^39,55, FAT1/YAP1 co-depletion failed to rescue both the HRD-related phenotypes and the formation of chromosomal bridges, suggesting that FAT1 loss contributes to HRD and structural CIN in a YAP1-independent manner. It has been reported that FAT1 can interact with MST1 and LATS1 (ref. ⁴⁹), which can influence HR efficiency through their interaction with ATR and RASSF1A to control BRCA2 recruitment at DNA damage sites^64,67.

Conversely, FAT1/YAP1 co-depletion rescued both cytokinesis failure and WGD. Strikingly, overexpression of constitutively active mScarlet–YAP1^5SA was sufficient to drive WGD and increased numerical CIN. Taken together, our data reveal that FAT1 alterations may contribute to HRD, CIN and WGD through two distinct mechanisms (Extended Data Fig. 10h).

Multiple studies have suggested that WGD accelerates cancer evolution and promotes intratumour heterogeneity, which promotes targeted therapy resistance^6,12,46,47. Here, using PC9 cells that are sensitive to osimertinib as an experimental model, we illustrate that FAT1 loss, through elevated genomic instability, may provide the evolutionary advantages that fuel targeted therapy resistance. Taken together, genomic observations and experimental results suggest a role for early positive selection of FAT1 alterations in LUSC tumorigenesis, potentially by driving cancer evolution and WGD through elevated CIN.

Recent studies have also identified that the YAP/TEAD signalling axis promotes therapy resistance⁶⁸. In addition to FAT1, three other genes identified through our DDR and CIN screens—namely RAD21, BAP1 and NCOA6—have also been demonstrated to modulate the Hippo pathway^69,70,71. Although YAP1 is not amplified in the TRACERx dataset, amplification of the WWTR1/TAZ genomic locus (3q25.1) and loss of the LATS2 genomic locus (13q12) are prevalent in LUSC^3,51. Consistent with other studies^61,68, our data highlight the importance of FAT1 and Hippo pathway dysregulation in genome instability, targeted therapy resistance and cancer evolution.

Methods

Ethical approval

The TRACERx study (NCT01888601; Clinicaltrials.gov) is sponsored by University College London (UCL/12/0279) and has been approved by an independent Research Ethics Committee (13/LO/1546). TRACERx is funded by Cancer Research UK (CRUK; C11496/A17786) and coordinated through the CRUK and University College London Cancer Trials Centre.

Cell line, cell culture, transfection and CRISPR knockout generation

H1944, H1650, H1792, HCC4006, A549 (CCL-185) and U2OS (HTB-96) cells were obtained from Cell Services at the Francis Crick Institute. The FUCCI–H2B-mTurquoise–RPE-1 cells have been described previously⁴⁴ and were a kind gift from J. Diffley (Francis Crick Institute). The HA–ER–AsiSI–U2OS cells have been described previously²⁸ and were a kind gift from G. Legube (Paul Sabatier University). The ER–KRAS–V12–T2P cells (referred to as T2P) have been described previously³⁸ and were a kind gift from J. Downward (Francis Crick Institute). The TP53 wild-type and TP53-knockout RPE-1 cells have been described previously⁷⁴. The HCT116 iRFP cell lines have been described previously⁷⁵ and were a kind gift from K. Vousden (Francis Crick Institute). All of the cell lines were maintained in either McCoy’s 5A Medium, Dulbecco Modified Eagle′s Medium (DMEM) or RPMI 1640 Medium (all from Thermo Fisher Scientific) fortified with 10% foetal bovine serum in the presence of 1% penicillin–streptomycin (Thermo Fisher Scientific). Cells were grown at 37 °C under 5% CO₂. All cell lines used were negative for Mycoplasma contamination and are frequently tested in-house at the Francis Crick Institute. DNA was transfected using GeneJuice (EMD Millipore) according to the manufacturer’s instructions.

CRISPR knockout cell lines were generated using a single-cell cloning approach. To minimize the off-target effect, CRISPR plasmids and guides were transiently transfected using GeneJuice (EMD Milipore) and underwent puromycin selection for 5 days before single-cell sorting to generate knockout clones. Single-cell clones were then cultured, validated and frozen. A549 clones were generated with two individual guides (sgFAT1#1 (AAACCCGGGAAGTCGAAGTCCTTGC) and sgFAT1#2 (AAACACGCTGGATGTGTAATGTAAC)), whereas PC9 clones were generated with both guides. The sgNT sequence as follows: GCGAGGTATTCGGCTCCGCG.

RNAi

A list of the siRNAs used is provided in Supplementary Table 1. Dharmacon ON-TARGETplus siRNA pools (Horizon Discovery) were used for high-content screening, HAC assay and RAD51 foci experiments in H1994 cells (Extended Data Figs. 1 and 2b,c and Supplementary Fig. 4). Ambion Silencer Select siRNA (Thermo Fisher Scientific) was used for all of the other RNAi experiments, except siRNA against MRE11A (L-009271-00-0005; Dharmacon; Fig. 8a). siRNA transfection was carried out using Lullaby reagent (OZ Biosciences) or Lipofectamine RNAiMAX Transfection Reagent (Thermo Fisher Scientific) according to the manufacturers’ instructions.

ssDNA resection and translocation assays

ssDNA resection assays were carried out as described previously²⁸. Briefly, DIvA HA–ER–AsiSI–U2OS cells were plated overnight and siRNAs were transfected as described previously. Samples were subjected to a 4 h incubation in 300 nM 4-hydroxytamoxifen. Genomic DNA was extracted using the DNeasy Blood & Tissue kit (69504; Qiagen). For every 500 ng genomic DNA used, five units of RNase H1 (M0297; New England Biolabs) were added at 37 °C for 15 min. In vitro, restriction digestion with the BanI restriction enzyme was performed to assay for the presence of ssDNA around break sites. Then, 200 ng of samples were digested with 16 units of BanI at 37 °C for 12 h (New England Biolabs).

The following primers were used for the assay: 131-nucleotide forward (ACCATGAACGTGTTCCGAAT), 131-nucleotide reverse (GAGCTCCGCAAAGTTTCAA), 851-nucleotide forward (ACAGATCCAGAGCCACGAAA) and 851-nucleotide reverse (CCCACTCTCAGCCTTCTCAG),

The percentage of ssDNA generated by DNA resection was determined by quantitative PCR. ΔCT was defined as the difference in average cycles between a given digested sample and its undigested counterpart. To calculate the percentage of ssDNA, the following equation was used:

$${\rm{Percentage}}\,{\rm{of}} \,{\rm{ssDNA}}=1/[{2}^{(\Delta {\rm{CT}}-1)}+0.5]\times 100.$$

For the semi-quantitative DNA translocation assay⁴⁰, 150 ng genomic DNA was used to perform the PCR reactions using the following primers: TRIM37 forward (AATTCGCAAACACCAACCGT), TRIM37 reverse (TCTGAAGTCTGCGCTTTCCA), MIS12 forward (GACTGGCATAAGCGTCTTCG), control chr1_82844750 forward (AGCACATGGGATTTTGCAGG) and control chr1_82844992 reverse (TTCCCTCCTTTGTGTCACCA).

Legitimate and illegitimate re-joining frequencies between MIS12 and TRIM37 (chr17_5390209 and chr17_57184285) were tested by PCR. The uncut site at chromosome 1 was used as a negative control.

Plasmid construction

A gene fragment containing the C-terminal intracellular domain of FAT1 transcript (NM_005245.4) was ordered from Integrated DNA Technologies. The gene fragment was subsequently subcloned into the pCMV-SPORT6 expression plasmid containing a His-HA tag at the amino (N) terminus of the open reading frame. To generate the mScarlet–HA–FAT1^WT construct, gene fragments containing the mScarlet sequence and FAT1 sequences were ordered and ligated into the HA–FAT1^ICD construct using NEBuilder HiFi DNA Assembly (New England Biolabs). Similarly, deletion of the MIB2 binding domain and generation of the HA–FAT1^WT construct were achieved using NEBuilder HiFi DNA Assembly (New England Biolabs).

The TEAD transcriptional reporter comprises a multimerized TEAD-binding sequence (14xTBS), based on the sequence described by Schlegelmilch et al.⁷⁶, upstream of a minimal promoter and a nuclear localization signal in frame with four copies of mNeonGreen followed by a Myc-tag. The sequence components were assembled by GeneArt (Thermo Fisher Scientific) in a pDONOR221 backbone. The final construct was obtained by cloning the insert into pLenti X1 Zeo DEST using Gateway technology (Thermo Fisher Scientific). The YAP^5SA construct was obtained from Addgene (plasmid 27371)⁷⁷ and subcloned into a pCMV-SPORT6 expression plasmid containing a His-HA tag at the N terminus of the open reading frame. The His-HA-eIF4A1 plasmid was a kind gift from M. Bushell (CRUK Scotland Institute) and was documented by Meijer et al.⁷⁸.

All of the plasmids used in this study were then sequenced using Plasmidsaurus or Full Circle sequencing. The relevant sequences are included in Supplementary Information.

DNA fibre assay

DNA fibre assays were performed as described in ref. ⁵⁵. A549 and PC9 cells were incubated with 5-chloro-2′-deoxyuridine for 20 min, challenged with replication stress induced by 2 mM hydroxyurea incubation for 2 h and then labelled with 5-iodo-2′-deoxyuridine for 20 min.

MCM7 loading assay

PC9 or RPE-1 cells were trypsinized and treated with modified CSK buffer (10 mM HEPES (pH 7.9), 100 mM NaCl, 3 mM MgCl₂, 1 mM EGTA, 300 mM sucrose, 1% bovine serum albumin (BSA), 0.2% Triton X-100 and 1 mM dithiothreitol) on ice for 5 min to remove soluble proteins. Cells were washed with 1% BSA in phosphate-buffered saline (PBS) and fixed in 4% paraformaldehyde (Thermo Fisher Scientific) for 10 min. Cells were then washed, pelleted and permeabilized in 70% ethanol for 15 min. MCM7 staining was performed using a mouse anti-MCM7 antibody (sc-56324; Santa Cruz Biotechnology) followed by an Alexa Fluor 594 goat anti-mouse antibody (A11007; Invitrogen). DNA content was stained using 1 µg ml⁻¹ 4′,6-diamidino-2-phenylindole (DAPI) supplemented with 100 µg ml⁻¹ RNase A. Cell doublets were excluded from all of the analyses.

Metaphase spreads

To assess numerical CIN, transfected cells were incubated with KaryoMAX Colcemid at 0.1 μg ml⁻¹ for 1 h, collected and incubated for 7 min at 37 °C in pre-warmed hypotonic solution (75 mM KCl). Cells were pelleted and repeatedly resuspended in freshly prepared fixation buffer (3:1 methanol:glacial acetic acid) and spun three times, after which the cells were dropped onto glass slides. For hybridization, the slides were ethanol dehydrated and subsequently incubated overnight at 37 °C in a humidified chamber with All Human centromeric probes (KBI-20000G; Leica) according to the manufacturer’s instructions. Metaphase chromosome images were visualized using a 100× oil immersion objective on an Image Solutions DeltaVision microscope.

To assess chromosomal breakage and aberrations, metaphase spreads were prepared as previously described⁷⁹. Briefly, 250,000 wild-type or FAT1-knockout cells were seeded in 6 cm dishes for one day before treatment with 0.2 µM aphidicolin for 24 h. Cells were treated with 0.2 µg ml⁻¹ colcemid for 1 h at 37 °C before collection by trypsinization and incubation with 5 ml pre-warmed hypotonic swelling buffer (75 mM KCl) in 15 ml tubes at 37 °C for 10 min. 1 ml freshly prepared fixation buffer (3:1 methanol:acetic acid) was added to the 15 ml tube and mixed by inversion. Cells were pelleted by centrifugation at 300g for 4 min at room temperature. About 500 µl supernatant was left in the tubes and the cells were slowly resuspended before the addition of 5 ml fixation buffer, mixing and incubation for 10 min at room temperature. The fixation step was repeated by pelleting the cells, resuspension and the addition of fixation buffer, as above. To spread the metaphases, the fixed cells were dropped on glass slides at a 45° angle from a 30 cm height. Slides were left to air dry before staining with Giemsa (7% in 10 mM PIPES (pH 6.8)) for 10 min at room temperature, then mounted with DPX. Metaphase chromosome images were visualized using a 100× oil immersion objective on an upright Zeiss Axio Imager fluorescence microscope equipped with Volocity software.

ImageStream fluorescence in situ hybridization

Transfected cells were processed for fluorescence in situ hybridization in suspension, as described elsewhere⁸⁰. Briefly, following transfection, cells were harvested and fixed with freshly prepared 3:1 methanol:glacial acetic acid. Cells were subsequently hybridized with chromosome 15 satellite enumeration probe (LPE015G; CytoCell) performed in a thermocycler under the following conditions: 65 °C (2 h pre-annealing), 80 °C (5 min) and 37 °C (16 h) before analysis on an ImageStream X Mark II (Amnis).

Definition of FAT1 driver mutation in the TRACERx 421 cohort

FAT1 driver mutations in the TRACERx 421 cohort were defined as described in ref. ⁸¹. Briefly, FAT1 non-synonymous variants that were found to be deleterious (either stop–gain or predicted deleterious in two of the three computational approaches applied (that is, Sift⁸², Polyphen⁸³ and MutationTaster⁸¹)) were classified as a driver mutation. Any tumour with any such mutations in FAT1 was considered to have a FAT1 loss.

Determination of WGD events in TRACERx 421

The WGD status for each tumour was estimated in two steps, as described in ref. ⁸¹. Briefly, if the genome-wide copy number of the major allele was ≥2 across at least 50% of the genome, this was assumed to reflect a WGD event^13,84. A major allele copy number ≥3 across at least 50% genome was assumed to reflect two WGD events. Then, we leveraged additional information from the estimated copy number of mutations using a novel tool, ParallelGDDetect, available as an R package (https://github.com/amf71/ParallelGDDetect).

TRACERx 421 cohort

The TRACERx 421 cohort consisted of 233 males and 188 females (421 patients in total), corresponding to a 55:45 male:female ratio. 93% of the cohort was from a White ethnic background and the mean age of the patients was 69 years, ranging between 34 and 92 years. Written informed consent was obtained. None of the patients were compensated for their involvement in the study.

Mutation clonality in TRACERx 421

Reconstructed phylogenetic trees were used to classify mutations based on their inferred phylogenetic cancer cell fraction (phyloCCF)⁸¹. We classified as clonal in each tumour region every cluster whose 95% confidence interval of the phyloCCF of its mutations overlapped with the 95% confidence interval of the phyloCCF of the mutations in the mutation cluster within the trunk node of the tree (a minimum threshold of 0.9 was used for the left side of the 95% confidence interval on truncal mutations). Second, we defined as subclonal every mutation cluster in a tumour region whose mean phyloCCF across the corresponding mutations in that region was greater than 0 and not clonal (that is, the mutation cluster did not pass the previous tests). Lastly, any remaining mutation cluster was defined as absent in a tumour region otherwise. Furthermore, clonal mutations were defined as early or late depending on whether they occurred before or after WGD. A clonal mutation copy number consistent with the WGD ploidy was therefore considered early, or late, otherwise.

dN/dS analyses

The dN/dS point mutation estimate for FAT1 was calculated using the dndscv and geneci functions in the dNdScv²⁹ R package. dndscv was run on different subsets of mutations (clonal early versus clonal late) from different subsets of samples (LUAD versus LUSC). The 95% confidence interval of the dN/dS estimate was obtained using the geneci function.

Enrichment analyses

All enrichment analyses were performed by counting the tumours in each category (for example, LUAD versus LUSC, WGD versus no WGD or early clonal versus late clonal) and performing a chi-squared test.

SCNA detection and MSAI

The identification of genome-wide allele-specific copy number states for multiregion WES is described in ref. ⁸¹. Briefly, logR data were calculated using VarScan 2, GC corrected using a wave-pattern GC correction method developed by Cheng and colleagues⁸⁵ and processed using ASCAT (version 2.3)⁸⁶. Sequenza (version 2.1.2)⁸⁷ was utilized to offer supplementary assessments of tumour purity and ploidy for further examination. ASCAT was presented with the automatically selected models for ploidy and purity from either Sequenza or ASCAT, which were manually reviewed to generate SCNA profiles for each tumour area. Samples that had an estimated tumour purity below 10% were excluded. The data on ploidy, purity and copy number segmentation were fed into a multi-sample SCNA estimation approach⁵¹ to create a consistently minimal segmentation and genome-wide evaluation of LOH, as well as loss, gain and amplification copy number states in relation to sample ploidy.

The input SCNA profiles were used to identify allelic imbalance, which was subsequently employed to phase heterozygous single-nucleotide polymorphisms and re-estimate allele-specific copy numbers. Furthermore, MSAI, which occurs when SCNAs disrupt the same genomic region but affect different parental alleles within distinct tumour subclones, was detected as previously described⁵⁷. We identified a subset of these MSAI events as parallel SCNA events that refer to the same class of event (gain/amplification or loss/LOH) in multiple samples from a given tumour but with major alleles from distinct haplotypes in the samples demonstrating the event. We then quantified the proportion of tumours where MSAI events occurred per cytoband, providing an empirical P value for cytoband 4q35.2.

EdU incorporation assay and cyclin B loading assay

The Click-iT Plus EdU Alexa Fluor 647 Flow Cytometry Assay kit (Thermo Fisher Scientific) was used for EdU incorporation assays. Cells were incubated with 10 μM EdU for 30 min before being fixed and processed per the manufacturer’s instructions. Cyclin B1 antibody was obtained from Abcam (ab32053; Abcam). DNA was stained in staining buffer (1 µg ml⁻¹ DAPI and 100 µg ml⁻¹ RNase A in PBS). Cell doublets were excluded from all of the analyses.

Microscopy and DNA damage foci studies

DDR foci and DNA fibre assays

The preparation of slides for DDR immunofluorescence foci studies was carried out as described in ref. ⁸⁸. Briefly, cells were pre-seeded on glass coverslips, subjected to Cs-137 ionizing irradiation and allowed to recover. Cells were then washed once with PBS and pre-extracted with CSK buffer (100 mM NaCl, 300 mM sucrose, 3 mM MgCl₂, 10 mM PIPES (pH 6.8), 10 mM β-glycerol phosphate, 50 mM NaF, 1 mM EDTA, 1 mM EGTA, 5 mM Na₃VO₄ and 0.5% Triton X-100). Cells were then washed once with CSK buffer and fixed with 4% paraformaldehyde on ice for 20 min. Samples were then washed three times with 0.1% Tris-buffered saline (TBS)-Tween, blocked with 10% goat serum for 1 h, washed twice with 0.1% TBS-Tween and incubated with primary antibody overnight at 4 °C (see Supplementary Table 1). The samples were subsequently washed and incubated with secondary antibodies (Alexa Fluor 488/594/647; Invitrogen) with DAPI in 1% goat serum for 1 h at room temperature. Samples were then washed with 0.1% TBS-Tween and mounted with Vectashield (H1200; Vector Laboratories). The samples were then double-blinded for quantification.

Images were acquired using a Zeiss Axio Imager M1 microscope with a 40×/1.3 NA Plan-Neofluar or 63×/1.4 NA Plan-Apochromat objective, equipped with an ORCA-spark CMOS camera (Hamamatsu) and pE-300white LED light source (CoolLED) and controlled by Micro-Manager 2.0 software⁸⁹. Images were processed and counted in an unbiased way using the FindFoci ImageJ plugin⁹⁰.

Segregation errors

For segregation errors, cells were fixed and permeablized in PTEMF buffer (4% paraformaldehyde, 0.2% Triton X-100, 20 mM PIPES and 2 mM MgCl₂) for 15 min at room temperature. The samples were washed three times in PBS, blocked for 1 h with 3% BSA with 0.2% Triton X-100 and incubated overnight with the relevant primary antibodies. The samples were subsequently washed and incubated with secondary antibodies (Alexa Fluor 488/594/647; Invitrogen) with DAPI in 1% goat serum for 1 h at room temperature. Samples were then washed with 0.1% TBS-Tween and mounted with Vectashield (H1200; Vector Laboratories). Images were acquired using a Nikon Ti2 microscope with a 60×/1.45 NA Plan Apo TIRF objective and 1.5× intermediate magnification, equipped with a Prime BSI sCMOS camera (Teledyne Photometrics), motorized XY stage with piezo Z axis (Applied Scientific Instrumentation) and Spectra X LED light engine (Lumencor) and controlled with Micro-Manager 2.0 software⁸⁹. Z stacks were acquired (31 μm × 0.2 μm z steps) and deconvolved using Microvolution software with ten iterations.

Live-cell widefield microscopy

Live-cell widefield microscopy was performed using a Nikon Ti2 inverted microscope with a Perfect Focus System using 1.5× intermediate magnification, equipped with a Prime BSI sCMOS camera (Teledyne Photometrics), motorized XY stage with piezo Z axis (ASI) and Spectra X LED light engine (Lumencor) and controlled with Micro-Manager 2.0 software⁸⁹. Environmental conditions were maintained at 37 °C under 5% CO₂ using a chamber and CO₂ mixer (Okolab).

For live-cell imaging of the mitotic failure rate, the FUCCI–H2B-mTurquoise–RPE-1 cells were grown and transfected for 72 h on glass-bottomed plates (IBL) in DMEM culture media, as described above. Before imaging, the growth media was replaced with FluoroBrite DMEM (Thermo Fisher Scientific) with 10% foetal bovine serum to improve the image quality. To acclimatize to potential temperature fluctuations, the cells were allowed to settle for 1 h before imaging. Phase-contrast and fluorescence images were acquired with 20×/0.75 NA Plan Apo Ph2 objective every 20 min for over 72 h. The images were analysed using Fiji and the cells were tracked manually.

For the mitotic timing experiments, FUCCI–H2B-mTurquoise–RPE-1 cells were set up as above but imaged with a 40×/0.95 NA Plan Apo objective. Locations with high-level mVenus–geminin-expressing G2 cells were identified and recorded. Z stacks (25 μm × 0.5 μm z steps) of H2B-mTurquoise were taken at 5 min intervals over 12 h. Images were deconvolved over ten iterations using Microvolution software.

Live-cell confocal microscopy

Live-cell confocal microscopy was performed using either: (1) a Yokogawa CSU-W1 spinning-disk confocal scanhead on a Nikon Ti2 inverted microscope with a 40×/1.15 NA Apo objective and Prime 95B sCMOS camera (Teledyne Photometrics), controlled with NIS-Elements, at 37 °C under 5% CO₂ with an environmental chamber and gas mixer (Okolab); or (2) an NL5 slit-scanning confocal unit on a Nikon Eclipse Ti inverted microscope with a 40×/1.25 NA Apo objective and Quest qCMOS camera (Hamamatsu), controlled with Micro-Manager 2.0 software⁸⁹ at 37 °C under 5% CO₂ using a stage top incubator and gas mixer (Tokai Hit). Locations with high-level mVenus–geminin-expressing G2 cells were first identified and recorded. Z stacks (25 μm × 0.5 μm z steps) of H2B-mTurquoise were taken at 5 min intervals over 12 h.

For Extended Data Fig. 7h,i and Supplementary Videos 4 and 5, a higher resolution was required to delineate whether mitotic error precedes nuclear deformation. A mixture of widefield deconvolution microscopy (n = 28 cells (siCTRL) and n = 27 cells (siFAT1)) and confocal microscopy (n = 50 cells (siCTRL) and n = 27 cells (siFAT1)) techniques were utilized to obtain sufficient images for statistical analysis.

Statistics and reproducibility

No statistical methods were used to predetermine the sample size. The statistical test types and biological n numbers used are detailed in the relevant figure captions. For immunofluorescence data, the numbers of cells sampled per biological repeat are documented accordingly. All of the data points shown are independent samples. For the experiments using statistical tests that assume normal distributions, we assumed that the data distribution was normal, but this was not formally tested.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The RNA-seq, WES and reduced representation bisulfite sequencing (RRBS) data (in each case from the TRACERx study) used during this study have been deposited in the European Genome-phenome Archive, which is hosted by the European Bioinformatics Institute and Centre for Genomic Regulation, under the accession codes EGAS00001006517 (RNA-seq), EGAS00001006494 (WES) and EGAS00001006523 (RRBS). Access is controlled by the TRACERx data access committee. The Genomics England lung cohort is part of the 100,000 Genomes Project whose data are held in a secure research environment and are only available to registered users. For further information on how to obtain access, visit https://www.genomicsengland.co.uk/research/academic. Source data are provided with this paper.

Code availability

The code used to determine genome doubling is available at https://github.com/amf71/ParallelGDDetect. The code used to process data and generate figures is available at https://doi.org/10.5281/zenodo.13884283 (ref. ⁹¹).

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Acknowledgements

C.M.-R. is supported by the Rosetrees Trust (M630) and Wellcome Trust. K.L. is funded by the UK Medical Research Council (MR/V033077/1), Rosetrees Trust and Cotswold Trust (A2437), Royal Marsden Cancer Charity (thanks to the Ross Russell Family and Macfarlanes donations), Melanoma Research Alliance and CRUK (C69256/A30194). M.J.-H. received the CRUK Career Establishment Award, as well as funding from CRUK, the International Association for the Study of Lung Cancer International Lung Cancer Foundation, the Lung Cancer Research Foundation, the Rosetrees Trust, the UK and Ireland Neuroendocrine Tumour Society, the National Institute for Health Research (NIHR) and the NIHR University College London Hospitals Biomedical Research Centre. N.J.B. is supported by the Lundbeck Foundation (R272-2017-4040), Aarhus University Research Foundation (AUFF-E-2018-7-14) and Novo Nordisk Foundation (NNF21OC0071483 and NNF23OC0085954). L.-P.Z., T. A.W. and E.G. received support from the ERC Consolidator Grant THESEUS (grant agreement no. 617844). E.G. was further funded by the ERC Advanced Grant PROTEUS (grant agreement no. 835297). N.M. is funded by the Wellcome Trust and Royal Society (grant number 211179/Z/18/Z), and also receives funding from CRUK (DRCPFA-Nov23/100003), the Rosetrees Trust, the NIHR Biomedical Research Centre at University College London Hospitals and the CRUK-funded University College London Experimental Cancer Medicine Centre. W.-T.L. is grateful for support from the Kuok Foundation. N.K. is supported by the Breast Cancer Research Foundation (BCRF 23-157), Rosetrees Trust and CRUK. J.B. is funded by the Danish Cancer Society (R322-A17482) and Novo Nordisk Foundation (NNF20OC0060590) and is grateful for the support. C.S. is a Royal Society Napier Research Professor (RSRP\R\210001). His work is supported by the Francis Crick Institute, which receives core funding from CRUK (CC2041), the UK Medical Research Council (CC2041) and the Wellcome Trust (CC2041). For the purpose of open access, C.S. has applied a CC BY public copyright licence to any author accepted manuscript version arising from this submission. C.S. is funded by CRUK (TRACERx (C11496/A17786), PEACE (C416/A21999) and the CRUK Cancer Immunotherapy Catalyst Network), the CRUK Lung Cancer Centre of Excellence (C11496/A30025), the Rosetrees Trust, the Butterfield and Stoneygate Trusts, the Novo Nordisk Foundation (ID16584), the Royal Society Professorship Enhancement Award (RP/EA/180007 and RF\ERE\231118), the NIHR University College London Hospitals Biomedical Research Centre, the CRUK–University College London Centre, the Experimental Cancer Medicine Centre, the US Breast Cancer Research Foundation (BCRF-22-157), the CRUK Early Detection and Diagnosis Primer Award (grant EDDPMA-Nov21/100034), the Mark Foundation for Cancer Research ASPIRE Award (grant 21-029-ASP) and the ASPIRE Phase II award (grant 23-034-ASP). C.S. is in receipt of an ERC Advanced Grant (PROTEUS) from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement number 835297). The TRACERx study (NCT01888601; Clinicaltrials.gov) is sponsored by University College London (UCL/12/0279) and has been approved by an independent Research Ethics Committee (13/LO/1546). TRACERx is funded by CRUK (C11496/A17786) and coordinated through the CRUK and University College London Cancer Trials Centre, which has a core grant from CRUK (C444/A15953). We gratefully acknowledge the patients and relatives who participated in the TRACERx study. This research was made possible through access to data in the National Genomic Research Library, which is managed by Genomics England (a wholly owned company of the Department of Health and Social Care). The National Genomic Research Library holds data provided by patients and collected by the National Health Service (NHS) as part of their care, as well as data collected as part of their participation in research. The National Genomic Research Library is funded by the National Institute for Health Research and NHS England. The Wellcome Trust, CRUK and the Medical Research Council are involved in the research infrastructure of Genomic England. We thank G. Legube (Paul Sabatier University), J. Diffley (Francis Crick Institute), J. Downward (Francis Crick Institute) and K. Vousden (Francis Crick Institute) for providing materials. We thank J. Zeng, A. Bertolin, J. Diffley, T. Klockner, U. Baruchel (Francis Crick Institute), S.F. Bakhoum (Memorial Sloan Kettering Cancer Center) and the members of the C.S. laboratory for discussion and insights. We thank the scientific platforms in the Francis Crick Institute—particularly Advance Light Microscopy, Cell Services, Flow Cytometry and High Throughput Screening—for support. We are grateful for assistance from the Flow Cytometry unit of the CRUK City of London Centre at the Barts Cancer Insitute and University College London Cancer Institute. This work was supported by the Francis Crick Institute, which receives core funding from CRUK (CC2041), the UK Medical Research Council (CC2041) and the Wellcome Trust (CC2041). This work was also supported by the CRUK Lung Cancer Centre of Excellence and the CRUK City of London Centre Award (C7893/A26233).

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Open Access funding provided by The Francis Crick Institute.

Author information

These authors contributed equally: Chris Bailey, James R. M. Black, Carlos Martinez-Ruiz, Oriol Pich.
A full list of members and their affiliations appears in the Supplementary Information.

Authors and Affiliations

The Francis Crick Institute, London, UK
Wei-Ting Lu, Lykourgos-Panagiotis Zalmas, Chris Bailey, James R. M. Black, Oriol Pich, Ieva Usaite, Alastair Magness, Thomas A. Webber, Ming Jiang, Rebecca E. Saunders, Dhruva Biswas, Esther O. Ige, Birgit Aerne, Eva Grönroos, Takahiro Karasaki, Maise Al Bakir, Matthew Renshaw, Hang Xu, Deborah Schneider-Luftman, Mariam Jamal-Hanjani, Michael Howell, Nicolas Tapon & Charles Swanton
CRUK Lung Cancer Centre of Excellence, London, UK
James R. M. Black, Carlos Martinez-Ruiz, Francisco Gimeno-Valiente, Ieva Usaite, Kerstin Thol, Yun-Hsin Liu, Dhruva Biswas, Subramanian Venkatesan, Georgia Stavrou, Takahiro Karasaki, Maise Al Bakir, Natasha Sharma, Mariam Jamal-Hanjani, Kevin Litchfield, Nicholas McGranahan, Nnennaya Kanu & Charles Swanton
University College London Cancer Institute, London, UK
James R. M. Black, Carlos Martinez-Ruiz, Francisco Gimeno-Valiente, Ieva Usaite, Kerstin Thol, Yun-Hsin Liu, Dhruva Biswas, Subramanian Venkatesan, Georgia Stavrou, Takahiro Karasaki, Maise Al Bakir, Natasha Sharma, Mariam Jamal-Hanjani, Kevin Litchfield, Kasper Fugger, Nicholas McGranahan, Nnennaya Kanu & Charles Swanton
Department of Thoracic Surgery, Respiratory Center, Toranomon Hospital, Tokyo, Japan
Takahiro Karasaki
Barts Cancer Institute, Queen Mary University of London, London, UK
Laura Tovini & Sarah E. McClelland
Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
Nicolai J. Birkbak
Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
Nicolai J. Birkbak
Danish Cancer Society Research Centre, Copenhagen, Denmark
Jiri Bartek
Division of Genome Biology, Department of Medical Biochemistry and Biophysics, Science for Laboratory, Karolinska Institutet, Solna, Sweden
Jiri Bartek

Authors

Wei-Ting Lu
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Lykourgos-Panagiotis Zalmas
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Chris Bailey
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James R. M. Black
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Carlos Martinez-Ruiz
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Oriol Pich
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Francisco Gimeno-Valiente
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Ming Jiang
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Dhruva Biswas
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Hang Xu
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Kevin Litchfield
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Michael Howell
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Nicolas Tapon
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Nnennaya Kanu
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Consortia

TRACERx Consortium

Wei-Ting Lu
, Chris Bailey
, James R. M. Black
, Carlos Martinez-Ruiz
, Oriol Pich
, Francisco Gimeno-Valiente
, Ieva Usaite
, Kerstin Thol
, Dhruva Biswas
, Eva Grönroos
, Georgia Stavrou
, Takahiro Karasaki
, Maise Al Bakir
, Mariam Jamal-Hanjani
, Nicolai J. Birkbak
, Nicholas McGranahan
, Nnennaya Kanu
& Charles Swanton

Contributions

W.-T.L., N.K., L.-P.Z. and C.S. conceived of the project concept. L.-P.Z. and M.H. designed the high-throughput screening experiments. L.-P.Z., M.J. and M.H. executed the high-throughput screening experiments. L.-P.Z., H.X., R.E.S. and M.H. analysed the high-throughput screening experiments. C.B., J.R.M.B., C.M.-R., O.P., F.G.-V., I.U., K.T., Y.-H.L., D.B., T.K., M.A.B., D.S.-L., N.S., K.L., N.J.B. and N.M. performed the genomics and statistical analyses. A.M. and M.R. contributed to the imaging analysis and imaging method development. L.T. and S.E.M. developed the ImageStream fluorescence in situ hybridization methods. W.-T.L., L.-P.Z., T.A.W., E.I., B.A., S.V., G.S., K.F. and N.K. carried out the experiments and developed the methods. L.-P.Z., E.G., S.E.M., K.L., N.J.B., N.T., K.F. and N.M. provided feedback on the experimental design and data analyses. M.J.-H. designed the PEACE and TRACERx study protocols. L.-P.Z., E.G., M.J.-H., S.E.M., N.T., K.F. and N.M. provided feedback on the manuscript. W.-T.L. and N.K. jointly coordinated and designed the experiments, performed the experiment analyses and wrote the manuscript. J.B. and C.S. provided strategic oversight and helped to write the manuscript. L.-P.Z., T. A.W. and E.G. received support from the ERC Consolidator Grant THESEUS (grant agreement no. 617844). E.G. was further funded by the ERC Advanced Grant PROTEUS, (grant agreement no. 835297).

Corresponding authors

Correspondence to Wei-Ting Lu, Jiri Bartek, Nnennaya Kanu or Charles Swanton.

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Competing interests

D.B. reports personal fees from NanoString and AstraZeneca and has a patent (PCT/GB2020/050221) issued on methods for cancer prognostication. M.A.B. has consulted for Achilles Therapeutics. M.J.-H. has received funding from CRUK, the National Institutes of Health National Cancer Institute, the International Association for the Study of Lung Cancer International Lung Cancer Foundation, the Lung Cancer Research Foundation, the Rosetrees Trust, the UK and Ireland Neuroendocrine Tumour Society and the NIHR. M.J.-H. has consulted for, and is a member of, the Achilles Therapeutics Scientific Advisory Board (SAB) and Steering Committee, has received speaker honoraria from Pfizer, Astex Pharmaceuticals, Oslo Cancer Cluster, Bristol Myers Squibb and Genentech. M.J.-H. is listed as a co-inventor on a European patent application relating to methods for the detection of lung cancer (PCT/US2017/028013). This patent has been licensed to commercial entities and, under the terms of their employment, M.J.-H. is due a share of any revenue generated from such license(s). M.J.-H. is also listed as a co-inventor on a GB priority patent application (GB2400424.4) with title ‘Treatment and prevention of lung cancer’. K.L. has a patent pending on indel burden and checkpoint inhibitor response, has received speaker fees from Roche Tissue Diagnostics and research funding from the CRUK Therapeutic Discovery Laboratories–Ono Pharmaceutical–LifeArc alliance and Genesis Therapeutics and has held consulting roles with Ellipses Pharma, Monopteros and Kynos Therapeutics. N.J.B. is listed as a co-inventor on a patent related to the identification of responders to cancer treatment (PCT/GB2018/051912), has submitted a patent application (PCT/GB2020/050221) on methods for cancer prognostication and has a patent on methods for predicting anti-cancer response (US14/466,208). N.M. has stock options in, and has consulted for, Achilles Therapeutics and holds a European patent in determining human leukocyte antigen (HLA) LOH (PCT/GB2018/052004), has a patent pending on determining HLA disruption (PCT/EP2023/059039) and is a co-inventor on a patent on the identification of responders to cancer treatment (PCT/GB2018/051912). N.K. acknowledges grants from AstraZeneca. C.S. acknowledges grants from AstraZeneca, Boehringer Ingelheim, Bristol Myers Squibb, Pfizer, Roche VENTANA, Invitae (previously ArcherDX; a collaboration on minimal residual disease sequencing technologies), Ono Pharmaceutical and Personalis. He is Chief Investigator for the AZ MeRmaiD-1 and -2 clinical trials and is the Steering Committee Chair. He is also Co-Chief Investigator of the NHS Galleri Trial funded by GRAIL and a paid member of GRAIL’s SAB. He receives consultant fees from Achilles Therapeutics (and is also a SAB member), Bicycle Therapeutics (and is also a SAB member), Genentech, Medicxi, the China Innovation Centre of Roche (formerly the Roche Innovation Centre – Shanghai); Metabomed, until July 2022, Relay Therapeutics (and is also a SAB member), SAGA Diagnostics (and is also a SAB member) and the Sarah Cannon Research Institute. C.S. has received honoraria from Amgen, AstraZeneca, Boehringer Ingelheim, Bristol Myers Squibb, GlaxoSmithKline, Illumina, MSD, Novartis, Pfizer and Roche VENTANA. C.S. has previously held stock options in ApoGen Biotechnologies and GRAIL and currently has stock options in EPIC Bioscience, Bicycle Therapeutics, Relay Therapeutics and Achilles Therapeutics. C.S. is also a co-founder of Achilles Therapeutics. C.S. declares patents and patent applications relating to methods for the detection of lung cancer (PCT/US2017/028013), neoantigen targeting (PCT/EP2016/059401), the identification of patent response to immune checkpoint blockade (PCT/EP2016/071471), the identification of patients who respond to cancer treatment (PCT/GB2018/051912), the determination of HLA LOH (PCT/GB2018/052004), the prediction of survival rates of patients with cancer (PCT/GB2020/050221) and methods and systems for tumour monitoring (PCT/EP2022/077987). C.S. is an inventor on a European patent application (PCT/GB2017/053289) relating to assay technology to detect tumour recurrence. This patent has been licensed to a commercial entity and, under the terms of their employment, C.S. is due a share of any revenue generated from such license(s). The remaining authors declare no competing interests.

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Source data

Extended Data Fig. 10 Loss of LATS2 is enriched in the TRACERx LUSC cohort.

a, Representative western blot following LATS1/2 knockdown in the U2OS-AsiSI DIvA system reveals that DNA damage markers such as γH2A.X and phospho-KAP1 are activated proficiently upon damage. b, Middle, representative gel showing that transient knockdown of LATS2, but not LATS1, induces an illegitimate repair product in U2OS-AsiSI DIvA cells. PCR products generated from the legitimate repair product were used as the loading control (bottom). n=3 biological repeats. c, Plots of GISTIC scores illustrating that the SCNA loss of the LATS2 genomic loci (13q12.11) is positively selected in the LUSC but not the LUAD TRACERx421 cohort. Red lines denote q value <0.2. d,e, Histograms representing cell cycle profiles following loss of FAT1, YAP1 or combined loss of both FAT1 and YAP1 by siRNA in U2OS-AsiSI cells (d) and A549 FAT1 WT/KO cells (e), n=3 biological repeats, one-way ANOVA with Sidak corrections. f, Histogram quantifying the percentage of FAT1 WT or KO A549 cells with lagging chromosomes after aphidicolin treatment following transient depletion of YAP1 using RNAi, mean±SD, one-way ANOVA with Holm-Sidak multiple correction, biological repeats: FAT1 WT =3, FAT1 KO =4. g, Representative western blot showing YAP1 and FAT1 knockdown efficiency in the RPE1-FUCCI and A549 cells. h, Proposed model showing potential interaction between FAT1 and the Hippo pathway.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–10, uncropped blots for Supplementary Figs. 2 and 6, Supplementary Methods, full plasmid maps and sequences.

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Supplementary Video 1

Live-cell imaging showing an example of a typical tracking experiment with control siRNA knockdown in FUCCI–RPE-1 cells, without exogenous damage. Video tracking length: 56 h (1 frame is 20 min). The mitotic outcome was tracked manually. Blue, H2B; red, CDT1; green, geminin.

Supplementary Video 2

Live-cell imaging showing an example of the failed cytokinesis phenotype following FAT1 siRNA knockdown in FUCCI–RPE-1 cells. Blue, H2B; red, CDT1; green, geminin.

Supplementary Video 3

Live-cell imaging showing an example of the nuclear morphology deformation phenotype following FAT1 siRNA knockdown in FUCCI–RPE-1 cells. The cell proceeded to finish the second cell cycle and failed cytokinesis. Blue, H2B; red, CDT1; green, geminin.

Supplementary Video 4

Video showing control siRNA RPE-1 cells going through mitosis. The cells recovered normally after mitotic bridge formation.

Supplementary Video 5

Video showing FAT1 siRNA-depleted RPE-1 cells going through mitosis. The cells displayed aberrant morphology after mitotic bridge formation.

Supplementary Tables 1–3

Supplementary Table 1. Antibodies. Supplementary Table 2. siRNA sequences. Supplementary Table 3. Full list of members of the TRACERx consortium.

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Lu, WT., Zalmas, LP., Bailey, C. et al. TRACERx analysis identifies a role for FAT1 in regulating chromosomal instability and whole-genome doubling via Hippo signalling. Nat Cell Biol 27, 154–168 (2025). https://doi.org/10.1038/s41556-024-01558-w

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Received: 07 July 2023
Accepted: 16 October 2024
Published: 30 December 2024
Issue date: January 2025
DOI: https://doi.org/10.1038/s41556-024-01558-w

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Subjects

Abstract

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Main

Results

Identification of DDR and CIN drivers in lung TRACERx

FAT1 alterations are positively selected in lung cancer

FAT1 ablation reduces HR efficiency

FAT1 ablation leads to structural and numerical CIN

FAT1 depletion leads to mitotic errors and WGD

FAT1 depletion leads to dysregulated Yes-associated protein 1 signalling

FAT1 and YAP1 co-depletion reverses WGD but not HR defects

Discussion

Methods

Ethical approval

Cell line, cell culture, transfection and CRISPR knockout generation

RNAi

ssDNA resection and translocation assays

Plasmid construction

DNA fibre assay

MCM7 loading assay

Metaphase spreads

ImageStream fluorescence in situ hybridization

Definition of FAT1 driver mutation in the TRACERx 421 cohort

Determination of WGD events in TRACERx 421

TRACERx 421 cohort

Mutation clonality in TRACERx 421

dN/dS analyses

Enrichment analyses

SCNA detection and MSAI

EdU incorporation assay and cyclin B loading assay

Microscopy and DNA damage foci studies

DDR foci and DNA fibre assays

Segregation errors

Live-cell widefield microscopy

Live-cell confocal microscopy

Statistics and reproducibility

Reporting summary

Data availability

Code availability

References

Acknowledgements

Funding

Author information

Authors and Affiliations

Consortia

TRACERx Consortium

Contributions

Corresponding authors

Ethics declarations

Competing interests

Peer review

Peer review information

Additional information

Extended data

Supplementary information

Source data

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