Acquisition of chromosome instability is a mechanism to evade oncogene addiction
2020; Springer Nature; Volume: 12; Issue: 3 Linguagem: Inglês
10.15252/emmm.201910941
ISSN1757-4684
AutoresLorena Salgueiro, Christopher Buccitelli, Konstantina Rowald, Kálmán Somogyi, Sridhar Kandala, Jan O. Korbel, Rocı́o Sotillo,
Tópico(s)DNA Repair Mechanisms
ResumoArticle6 February 2020Open Access Source DataTransparent process Acquisition of chromosome instability is a mechanism to evade oncogene addiction Lorena Salgueiro Lorena Salgueiro Division of Molecular Thoracic Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany Search for more papers by this author Christopher Buccitelli Christopher Buccitelli Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany Search for more papers by this author Konstantina Rowald Konstantina Rowald Division of Molecular Thoracic Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany Search for more papers by this author Kalman Somogyi Kalman Somogyi Division of Molecular Thoracic Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany Search for more papers by this author Sridhar Kandala Sridhar Kandala Division of Molecular Thoracic Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany Search for more papers by this author Jan O Korbel Jan O Korbel orcid.org/0000-0002-2798-3794 Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany Search for more papers by this author Rocio Sotillo Corresponding Author Rocio Sotillo [email protected] orcid.org/0000-0002-0855-7917 Division of Molecular Thoracic Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany Translational Lung Research Center Heidelberg (TRLC), German Center for Lung Research (DZL), Heidelberg, Germany Search for more papers by this author Lorena Salgueiro Lorena Salgueiro Division of Molecular Thoracic Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany Search for more papers by this author Christopher Buccitelli Christopher Buccitelli Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany Search for more papers by this author Konstantina Rowald Konstantina Rowald Division of Molecular Thoracic Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany Search for more papers by this author Kalman Somogyi Kalman Somogyi Division of Molecular Thoracic Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany Search for more papers by this author Sridhar Kandala Sridhar Kandala Division of Molecular Thoracic Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany Search for more papers by this author Jan O Korbel Jan O Korbel orcid.org/0000-0002-2798-3794 Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany Search for more papers by this author Rocio Sotillo Corresponding Author Rocio Sotillo [email protected] orcid.org/0000-0002-0855-7917 Division of Molecular Thoracic Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany Translational Lung Research Center Heidelberg (TRLC), German Center for Lung Research (DZL), Heidelberg, Germany Search for more papers by this author Author Information Lorena Salgueiro1, Christopher Buccitelli2,4, Konstantina Rowald1, Kalman Somogyi1, Sridhar Kandala1, Jan O Korbel2 and Rocio Sotillo *,1,3 1Division of Molecular Thoracic Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany 2Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany 3Translational Lung Research Center Heidelberg (TRLC), German Center for Lung Research (DZL), Heidelberg, Germany 4Present address: Max Delbrück Center for Molecular Medicine, Berlin, Germany *Corresponding author. Tel: ++49 6221 42 3691; E-mail: [email protected] EMBO Mol Med (2020)12:e10941https://doi.org/10.15252/emmm.201910941 See also: D Bronder & SF Bakhoum (March 2020) PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Chromosome instability (CIN) has been associated with therapeutic resistance in many cancers. However, whether tumours become genomically unstable as an evolutionary mechanism to overcome the bottleneck exerted by therapy is not clear. Using a CIN model of Kras-driven breast cancer, we demonstrate that aneuploid tumours acquire genetic modifications that facilitate the development of resistance to targeted therapy faster than euploid tumours. We further show that the few initially chromosomally stable cancers that manage to persist during treatment do so concomitantly with the acquisition of CIN. Whole-genome sequencing analysis revealed that the most predominant genetic alteration in resistant tumours, originated from either euploid or aneuploid primary tumours, was an amplification on chromosome 6 containing the cMet oncogene. We further show that these tumours are dependent on cMet since its pharmacological inhibition leads to reduced growth and increased cell death. Our results highlight that irrespective of the initial CIN levels, cancer genomes are dynamic and the acquisition of a certain level of CIN, either induced or spontaneous, is a mechanism to circumvent oncogene addiction. Synopsis Cancer genomes are dynamic, and the acquisition of a certain level of chromosome instability is a source of genetic variability. This study reveals that genetic variability provides mechanisms to resist the strong selective pressure exerted by targeted therapy, and to circumvent oncogene addiction. Chromosomal Instability is a mechanism to circumvent oncogene addiction. Genetic alterations driving resistance were most probably acquired during the course of the treatment. The most predominant genetic alteration in resistant tumors of a Kras-driven breast cancer mouse model was an amplification of the cMet oncogene. The paper explained Problem Therapy resistance is one of the main causes of death in breast cancer patients. During the course of treatment, acquisition of new genetic alterations can favour oncogene independence and therefore progression of the disease. Chromosome instability (CIN) acts as a powerful source of variability, compromising the efficacy of targeted therapy. Results Using a Kras-driven breast cancer mouse model, we showed that genomically unstable tumours have high chances of persisting during treatment. Furthermore, initially stable tumours are also able to acquire CIN as a resistance mechanism. Impact This study points out the important role of CIN in acquisition of therapy resistance and highlights the necessity to combine current treatments with drugs that specifically impair progression of unstable cells. Introduction Chromosome instability (CIN) is a widespread phenomenon in malignancies characterized by the inability of a cell to maintain its diploid chromosome number, leading to a state of aneuploidy. Advances in DNA sequencing technologies have allowed for the high-throughput visualization of tumour genomes and revealed the pervasiveness and diversity of aneuploidy across cancers (Cancer Genome Atlas Research Network et al, 2013; Zack et al, 2013). Indeed, 90% of solid and 75% of hematopoietic tumours display aneuploidy (Duijf et al, 2013) and are thus thought to have gone through periods of CIN to obtain it. Though CIN is often reported to cause whole or partial chromosome changes, it is also known to cause smaller focal somatic copy number alterations (SCNAs) including amplifications and deletions (Janssen et al, 2011; Burrell et al, 2013). Although many SCNAs are clearly pro-tumorigenic (e.g. lower expression of TP53 via whole chromosome loss or focal amplification of KRAS), the majority are thought to be detrimental to the cell (Sansregret & Swanton, 2017). High levels of CIN and thus high rates of SCNA generation may lead to imbalanced expression of proteins encoded on the affected DNA regions, endangering the survival of a tumour's lineage. It is becoming clear that an optimal level of CIN can be achieved that is tolerated by tumour cells while promoting diversification of subclones and facilitating adaptation to selective pressures (e.g. drug treatment) during tumour development (Rowald et al, 2016). Despite recent advances in detection and therapy, breast cancer remains the second leading cause of cancer-related death in women and approximately 20% of breast cancer patients develop recurrent disease following treatment. Therefore, understanding the mechanism by which breast tumours recur and develop therapeutic resistance is critical. Several mechanisms have been observed to date such as activating point mutations in PIK3CA, loss of PTEN or overexpression of cMET (Nagata et al, 2004; Berns et al, 2007; Shattuck et al, 2008). Mouse models of human cancer provide a suitable setting to look at the molecular and temporal dynamics of tumour recurrence via evolution of oncogene independent subclones. In a recent study, using a mouse model of Braf-induced melanoma, Kwong et al (2017) show that under strong selective pressure, genetically stable tumours acquire treatment resistance by mutating and thereby reactivating the initiating oncogenic pathway whereas genomically unstable tumours acquire broad whole chromosome aneuploidies which presumably afford their oncogene independence via a yet unidentified mechanism. Whether this is a general phenomenon for all cancer types or whether it only applies to this model is not known. Mad2 is a central component of the spindle assembly checkpoint responsible for ensuring proper separation of sister chromatids. Its overexpression is commonly found in human cancers and leads to the hyperstabilization of kinetochore-microtubule attachments that can result in mitotic arrest and improper correction of erroneous attachments causing lagging chromosomes, misalignments and consequently, aneuploidy (Rowald et al, 2016). Moreover, this ongoing CIN induced by Mad2 overexpression can circumvent oncogene addiction, compromising the effectiveness of targeted therapies thereby facilitating tumour relapse and persistence in lung and breast cancer models (Sotillo et al, 2010; Rowald et al, 2016). Using a doxycycline-inducible mouse model of mutant Kras, we showed that mice develop multiple invasive mammary adenocarcinomas within 147 days, while the additional overexpression of Mad2 delays tumour onset (221 days) and increases the levels of CIN in the resultant breast tumours (Rowald et al, 2016). Moreover, downregulation of Kras or Kras and Mad2 in these fully formed breast tumours leads in some cases to the development of resistance (Rowald et al, 2016). These results suggest that withdrawal of the oncogenic driver event in the presence of ongoing CIN would not be deleterious as this would enable further gain or loss of chromosomal regions that could sustain tumour growth. Here, we show that both low CIN and high CIN breast cancer models may resist oncogene withdrawal by activating effectors in downstream or parallel pathways to the initiating oncogene and further characterize the temporal effects of CIN on disease progression. Furthermore, we demonstrate that the few initially low CIN tumours that manage to resist oncogene withdrawal do so by acquiring CIN and achieving similar SCNA levels as initially high CIN tumours when faced with the pressure of oncogene withdrawal. Results Oncogene independent Kras and Kras/Mad2 tumours show high levels of chromosomal instability Doxycycline-inducible mouse models of KrasG12D (K) and KrasG12D/Mad2 (KM) allow us to mimic targeted therapy since doxycycline withdrawal leads to complete silencing of the transgenes. Downregulation of Kras expression or Kras and Mad2 in fully formed mammary tumours results in their regression to a nonpalpable state in the vast majority of the cases. However, 6.6% of K and 21.3% of KM tumours did not fully regressed and were able to continue growing (Rowald et al, 2016). This indicates that, despite the initial detrimental effect of Mad2 overexpression, these resulting highly unstable tumours have increased chances to develop therapy resistance. Moreover, supporting the idea that chromosomal instability at primary tumour level confers advantages in strong pressure environments such as during targeted therapy, KM tumours needed on average 46 days to grow back while K tumours needed 93 days. To evaluate chromosome segregation fidelity in these tumours that did not regress upon doxycycline withdrawal or that partially regressed and resumed growth (termed non-regressed), we performed time-lapse microscopy of the non-regressed mammary tumour-derived cells. Interestingly, we found that, while KM primary tumours were significantly more unstable than K primary tumours (Rowald et al, 2016), K and KM non-regressed tumours showed similar percentages of mitotic errors, surpassing in both cases the already high level of CIN found in KM primary tumours (Fig 1A and B). Regardless of the genotype, we observed lagging chromosomes, chromosome bridges and misaligned chromosomes to be the major mitotic errors in the non-regressed tumours (Fig 1C). Interestingly, there was an increase in the percentage of chromosome bridges in the non-regressed tumours compared to the primary tumours, suggesting that non-regressed tumours acquire different mitotic errors. These results suggest that genomically stable tumours (K) are able to acquire a certain level of CIN during the course of acquiring therapeutic resistance, although initially unstable tumours (KM) have an increased chance of persisting during treatment. Figure 1. Non-regressed K and KM tumours show high levels of chromosomal instability A. Representative micrographs of a time-lapse microscopy of K and KM non-regressed tumour cells (H2B-GFP green). Top: mitotic cell with a chromatin bridge (yellow arrow). Bottom: mitotic cell with misalignment and cytokinesis failure resulting in binucleation (yellow arrow). B. Percentage of mitotic errors in K and KM primary tumours and in K and KM non-regressed tumours. Ns: not significant; *P < 0.01, ****P < 0.0001; one-way ANOVA, Turkey's multiple comparison test. Mean ± SEM. Exact P values are indicated in Appendix Table S1. C. Percentage of cells in K and KM primary tumours and in K and KM non-regressed tumours with the indicated mitotic errors. Scale bar 20 μm. Data information: K primary (n = 4; 74 cells), KM primary (n = 5; 84 cells), K non-Regr (n = 7; 219 cells), KM non-Regr (n = 5; 247 cells). Source data are available online for this figure. Source Data for Figure 1 [emmm201910941-sup-0003-SDataFig1.zip] Download figure Download PowerPoint Sequencing of oncogene independent Kras and Kras/Mad2 tumours reveals recurrent SCNAs To determine which alterations were involved in promoting oncogene independence, 7 K and 16 KM non-regressed tumours were subjected to low-pass whole-genome sequencing using an Illumina HiSeq platform, followed by somatic copy number alteration (SCNA) analysis (Fig 2A). Similar to what we found in primary tumours, genomes of KM non-regressed tumours were more frequently affected than K tumours. However, in line with the live imaging results, the mean of SCNAs was not significantly increased compared to K non-regressed tumours (Figs 2A and EV1A). Notably, some recurrent alterations in these tumours were a whole gain of chromosome 15 in 1.2% of K and 6.5% of KM non-regressed tumours (Fig EV1B and C) and a deletion, a partial chromosome loss and in 2 KM non-regressed tumours a gross rearrangement of chromosome 4. However, we found that the most frequent alteration in either cohort was a small ~2 megabase amplification on chromosome 6, present in 57% of K and 62.5% of KM non-regressed tumours. While whole gain of chromosome 15 and deletions and partial deletions of chromosome 4 were already present in some K and KM primary tumours (Fig EV1D and Rowald et al, 2016), amplifications in chromosome 6 were detected for the first time in the non-regressed tumours. Figure 2. Recurrent somatic copy number alterations in K and KM non-regressed tumours A. Somatic copy number alterations in each individual Kras and Kras/Mad2 non-regressed tumours. 7 K non-regressed and 16 KM non-regressed tumours were sequenced. Focal deletion (DEL, green), focal amplification (AMP, light green), whole chromosome gain (WCG, dark grey) and loss (WCL, light grey), partial chromosome gain (PCG, dark blue) and loss (PCL, light blue) and gross chromosomal rearrangement (GCR, red). B. Overlaying of the amplifications in chromosome 6 across multiple regions showing that all amplicons contained the cMet oncogene. Colour of the block corresponds to the segment mean (the degree to which a genome segment is lost (blue) or gained (red). C. Immunostaining of phospho-cMet in non-regressed tumours carrying or not the amplification in chromosome 6. Yellow numbers indicate the total number of cMet-positive cells/total number of cells counted. Scale bar 100 μm. Numbers of the K and KM non-regressed tumours are described in Table 1. Source data are available online for this figure. Source Data for Figure 2 [emmm201910941-sup-0004-SDataFig2.zip] Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Related to Fig 2. Somatic copy number alterations in K and KM primary and non-regressed tumours A. Average of somatic copy number alteration (SCNAs) in K and KM non-regressed tumours. Unpaired t-test, P = 0.34. Boxes and whiskers represent min to max values. The central line represents the median. B. Percentage of SCNAs per chromosome in K (green) and KM (blue) non-regressed tumours. C. Heatmap of all chromosomes in the non-regressed tumours. D. Frequency of somatic copy number alterations per chromosome in Kras and Kras/Mad2 primary tumours. Focal amplification (AMP, dark green), whole chromosome gain (WCG, dark grey) and loss (WCL, light grey), partial chromosome gain (PCG, dark blue) and loss (PCL, light blue) and gross chromosomal rearrangement (GCR, red). Source data are available online for this figure. Download figure Download PowerPoint In an attempt to investigate the mechanism driving resistance, we focused on those tumours that did not re-express oncogenic Kras, as reactivation of the oncogenic driver was most probably driving resistance. Moreover, we reasoned that, if the chromosomal alterations were already present in the primary tumour, they might represent genomic events that cooperated with the oncogenic driver in facilitating mammary tumour formation but not involved in resistance. Finally, since amplification of chromosome 6 was the most frequently detected alteration, we focused on those tumours that presented this amplification. Read depth analysis revealed variation in both the size and copy number pattern of the amplicons, with some presenting concise duplications and others showing complex copy number alterations resembling more elaborate mechanisms such as breakage-fusion-bridge cycles (Gisselsson et al, 2000; Fig EV2A). However, overlaying the amplifications across multiple regions revealed that all amplicons contained the Met oncogene (Fig 2B). Click here to expand this figure. Figure EV2. Related to Fig 2. cMet analysis in non-regressed tumours A. Genome-wide log2 of chromosome 6 of control mammary gland and log2-ratio plots of K and KM non-regressed tumour biopsies showing no amplification in the control (upper panels) and a small amplification in the non-regressed tumours (middle and bottom panels). B. Quantitative RT–PCR analysis of cMet in 10 K and 22 KM non-regressed tumours. C. Correlation analysis of cMet mRNA and gene amplification in K (green) and KM (blue) non-regressed breast tumours. Source data are available online for this figure. Download figure Download PowerPoint Met gene encodes for the receptor tyrosine kinase cMET, mainly expressed in cells of mesenchymal origin and implicated in the activation of proliferation, survival, motility and morphogenesis signalling pathways (Birchmeier et al, 2003). Deregulation of cMET by gene amplification, overexpression or activating mutations (Tokunou et al, 2001; Lengyel et al, 2005) has been found in a variety of carcinomas, although the most common alteration is gene amplification and consequently protein overexpression and activation (Di Renzo et al, 1995). Analysis of cMet mRNA in K and KM non-regressed breast tumours (Fig EV2B) revealed a direct correlation between gene amplification and mRNA levels (Fig EV2C). Moreover, immunohistochemistry against phospho-cMet was positive in those tumours containing the chromosome 6 amplification and negative in the ones where the amplification was not present (Fig 2C and Table 1). Table 1. Non-regressed tumours used in this study Genotype Name Sequenced # SCNA cMet ampl. by seq. cMet upreg. QPCR Fig EV2B cMet upreg. IHC Fig 2C Kras re-expressiona KrasG12D K1 1 YES YES 61% NO KrasG12D K3 2 YES YES 36% NO KrasG12D K4 3 YES YES 28% NO KrasG12D K5 4 YES YES 9% NO KrasG12D K6 n.d. n.d. YES n.d. NO KrasG12D K2 3 NO NO 0% NO KrasG12D K7 0 NO NO 0% YES KrasG12D K8 n.d. NO NO n.d. NO KrasG12D K9 n.d. NO NO n.d. YES KrasG12D K10 1 NO NO n.d. NO KrasG12D K11 n.d. n.d. n.d. n.d. n.d. KrasG12D/Mad2 KM2 3 YES YES 39% NO KrasG12D/Mad2 KM3 4 YES YES 18% NO KrasG12D/Mad2 KM4 n.d. n.d. YES n.d. NO KrasG12D/Mad2 KM5 4 YES YES 39% NO KrasG12D/Mad2 KM7 2 YES YES 28% YES. Low KrasG12D/Mad2 KM8 3 YES YES 44% yes KrasG12D/Mad2 KM11 2 YES YES 12% NO KrasG12D/Mad2 KM12 6 YES YES 13% NO KrasG12D/Mad2 KM15 1 YES YES 42% NO KrasG12D/Mad2 KM20 5 YES YES 11% NO KrasG12D/Mad2 KM21 1 YES YES 11% NO KrasG12D/Mad2 KM22 4 YES YES 35% YES KrasG12D/Mad2 KM1 n.d. n.d. NO 0% NO KrasG12D/Mad2 KM6 2 NO NO 0% NO KrasG12D/Mad2 KM9 n.d. n.d. NO n.d. Yes KrasG12D/Mad2 KM10 3 NO NO 0% NO KrasG12D/Mad2 KM13 n.d. n.d. NO n.d. NO KrasG12D/Mad2 KM14 1 NO NO n.d. NO KrasG12D/Mad2 KM16 2 NO NO n.d. NO KrasG12D/Mad2 KM17 n.d. n.d. NO n.d. YES KrasG12D/Mad2 KM18 n.d. n.d. NO n.d. YES KrasG12D/Mad2 KM19 0 NO NO n.d. YES Low KrasG12D/Mad2 KM23 n.d. n.d. n.d. n.d. n.d. n.d: not done. a Rowald et al (2016). cMet amplification is not clonally dominant in primary tumours To clarify whether the amplification on chromosome 6 was already present in the primary tumour, we first looked at the tumour evolution after doxycycline withdrawal and noticed that following a first period where tumours underwent a reduction in size, they continued to grow (Fig 3A). This suggests that in case cMet amplification was not present in the primary tumour, it could have been acquired during this timeframe, forced by the selective pressure that oncogenic silence exerted within the tumour. We then looked if cMet-positive KM tumours resumed growth faster than K tumours. In fact, KM tumours partially regressed after doxycycline withdrawal and they needed an average of 38 days to grow back while K tumours took 133 days (Fig 3A), suggesting that already CIN tumours were more predispose to acquire this genetic modification. Figure 3. cMet amplification is not found in primary tumours A. Tumour diameter before and after doxycycline withdrawal in 4 K and 5 KM breast tumours with cMet amplification. 0 indicates when doxycycline was removed. Each colour represents one tumour. Blue and green squares indicate the timeframe between doxycycline withdrawal and the moment in which tumours resumed growth. B. Genome-wide log2-ratio plots of chromosome 6 of two primary tumour biopsies and their corresponding non-regressed tumour showing no amplification in the primary tumour (upper panels) and a small amplification in the non-regressed tumours (bottom panels, yellow arrow). C. Immunostaining of phospho-cMet in 3 biopsied primary tumours (PT) and their corresponding non-regressed tumours (KM5, KM7 and KM8, which are also shown in Fig 2C). Scale bar 100 μm. D. Representative two-dimensional scatter plots constructed with overlaid dPCR data of the reference (VIC) and cMet (FAM) from one tumour without cMet amplification and one with cMet amplified. Dots represent results of independent PCRs in the wells of a digital PCR chip. Reactions in the bottom left corner (yellow) are negative for both targets, while the ones in the top right corner (green) are double positives. Reactions in the top left (blue) and bottom right (red) corners are positive for cMet and the reference targets, respectively. E. Representative photographs of FISH staining with a probe for Met (red signal) and a probe for a reference gene EML4 (green signals) The upper panel is a negative example for cMet amplification containing 2 red and 2 green dots (white arrows). The lower panel shows an example of a tumour with cMet amplification (several red dots) and 2 green dots (yellow arrow). Scale bar 10 μm. Source data are available online for this figure. Source Data for Figure 3 [emmm201910941-sup-0005-SDataFig3.zip] Download figure Download PowerPoint Then, we determined the presence or absence of the cMet amplification in the primary tumours. We compared the low-coverage sequenced genomes of two primary tumours that were biopsied and their corresponding non-regressed tumours, after withdrawal of the oncogenic driver. As shown in Fig 3B, we found no detectable alterations in that specific region in the primary tumours. Additional immunohistochemistry staining of 3 biopsied primary tumours whose corresponding non-regressed tumour contained a cMet amplification showed negative phospho-cMet staining in the primary tumour and positive in the non-regressed (Fig 3C). Additionally, we analysed a panel of primary tumours at the time point just before doxycycline withdrawal. Phospho-cMet staining in 34 primary tumours was negative in all the tumour cells analysed (more than 400,000 cells in total; Fig EV3). Click here to expand this figure. Figure EV3. Related to Fig 3. cMet staining in primary tumoursImmunostaining of phospho-cMet in one non-regressed tumour (KM15) as a positive control (as shown in Fig 2C) and 34 primary tumours (PT). Yellow numbers indicate the total number of cMet-positive cells/total number of cells counted. Scale bar 100 μm. Source data are available online for this figure. Download figure Download PowerPoint To further examine whether cMet amplification could be present in a small population of the tumours undetectable by whole-genome sequencing or immunohistochemistry, we resorted to analyse by digital PCR a panel of primary tumours at human endpoint (Fig 3D and Table 2). The measured ratio between cMet and a reference gene was never significantly different from 1 except for one primary tumour whose ratio was 1.5, a result explained by a whole chromosome 6 gain detected by whole-genome sequencing (PT KM15) (Table 2). Finally, we looked at the single-cell level for amplification of cMet by performing fluorescent in situ hybridization (FISH) with a probe against cMet. A probe recognizing EML4 gene was used as reference, given this gene is located in chromosome 17, which was almost never affected in the primary tumours. During the analysis, cells where 3 or more copies of cMet were accompanied by 3 or more copies of EML4 were discarded, since they might represent cells that underwent genome doubling. We found that the cMet:EML4 ratio on population level was close to 1 in all tumours, but on average 6.22% of cells in K and 5.67% of cells in KM tumours carried 3–5 copies of cMet (Fig 3E and Table 2). Although detection of cMet gene amplification by FISH has been previously considered positive when the number of copies exceed 5 in more than 15% of the nuclei (Cappuzzo et al, 2009), the FISH results were not sufficient to rule out the existence of cMet-positive cells. Therefore, we sought to test whether these primary tumour cells carrying more than three copies of cMet were indeed dependent on cMet by evaluating the effect of a cMet inhibitor on tumour growth. We first recapitulated the primary tumours by injecting tumour cells from K and KM tumours into the cleared fat pad of Rag2−/− immunocompromised animals (Fig 4A). When tumours reached 0.5 cm3, we treated them with the cMet inhibitor Tepotinib which has been proven to selectively inhibit the kinase activity of this protein, independently of its mechanism of activation (Bladt et al, 2013). We found no differences in tumour growth between control and treated mice (Fig 4B) and the results of FISH analysis of the treated tumours were similar to the untreated ones (Fig 4C). Moreover, the ratio treated/control obtained by dPCR was not significantly different from 1 in all tumours independently of the genotype (K or KM), suggesting that the presence of more than 2 copies of cMet in some of the cells did not lead to detectable difference in the sensitivity to the drug treatment in the tumour cell population. Table 2. Primary tumours used in this study Genotype Name FISH MET: EML4 dPCR Ratio % p-cMET+ Ratio %cells ≥3 copies KrasG12D PT K1 1.01 4.80 1.01 0/11313 KrasG12D PT K2 1.02 4.55 1.02 0/7555 KrasG12D PT K3 0.97 2.20 0.98 0/5307 KrasG12D PT K4 1.01 5.05 0.99 0/10199 KrasG12D PT K5 0.98 0/22759 KrasG12D PT K6 0.96 0.02 1.00 0/12067 KrasG12D PT K7 1.07 0/17125 KrasG12D PT K8 0.97 6.41 0.95 0/16602 KrasG12D PT K9 1.06 0/11755 KrasG12D PT K10 1.01 0/16521 KrasG12D PT K11 1.01 4.12 1.05 0/7591 KrasG12D PT K12 1.03 0/14508 KrasG12D PT K13 0.95 5.77 0.87
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