Other putative drivers

Several other known cancer genes were recurrently mutated in the studied BCCs, albeit with low frequency. They were identified by searching known cancer related genes and known driver mutations in these genes in our sample set as described above.

PPP6C is mutated in 15% of BCCs (Figure 29) and 60% of these mutations are p.R264C substitutions (Figure 38a). This particular amino acid was found to be a SRM site by TumOnc (Table 7). This specific mutation, along with four additional ones also present in our sample set (p.P186S, p.P259S, p.S270L and p.L305F) have been observed in up to 12%

of melanomas (Hodis et al., 2012, Krauthammer et al., 2012) and have been shown to impair PPP6C’s phosphatase activity (Hammond et al., 2013, Hodis et al., 2012, Duman-Scheel et al., 2002). The PPP6C protein is involved in the inhibition of Cyclin D1 and reduces

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the phosphorylation of RB1 at Ser807/811, causing its inactivation (Stefansson and Brautigan, 2007). Interestingly, wt PPP6C has been shown to participate in the LATS1 activation process in the Hippo pathway (Couzens et al., 2013). Altogether, the data suggest that the PPP6C protein could be a regulator of BCC tumor growth at several mechanistic levels.

TumOnc also identified STK19 as a gene relevant in BCC (Table 7), as 10% of the tumors harbored mutations in this serine/threonine kinase (Figure 29). All mutations were p.D89N (Figure 38b), a specific mutation previously identified in 5% of melanomas, that has been suggested to play a role in oncogenesis (Hodis et al., 2012).

CASP8, a SMG in head and neck squamous cell carcinoma (HNSCC)(Cancer Genome Atlas, 2015) was mutated in 11% of the studied tumors (14% nonsense, 86% missense mutations) (Figure 29, Figure 38c). This gene encodes a caspase involved in apoptosis and regulated by the tumor necrosis factor receptors (van Raam and Salvesen, 2012). CASP8 identification in other cancers as well as its participation in apoptotic pathways make it an excellent candidate for a novel BCC gene.

Mutations in RB1 were observed in 10% of the studied tumors (Figure 29). 44% of the affected samples had loss of one allele either through heterozygous deletion of RB1 or a nonsense mutation (Figure 38d). RB1 is a well-known tumor suppressor gene that has been involved in several tumor types. Its wt form interacts with the E2F family of transcription factors to arrest cells in G1. When mutated, this inhibition cannot take place, causing this way, proliferation of mutant cells (Zhang et al., 1999).

KNSTRN, a recently identified oncogene in melanoma and cSCC (Lee et al., 2014), was found to be mutated in 4% of our samples (Figure 29). The studied BCCs harbored the same previously identified mutations p.R11K and p.S24F (Figure 38e). KNSTRN has shown a clear signature of UV-light induced mutagenesis in cSCC and melanoma, and was found to disrupt chromatin cohesion and chromosome segregation when mutated. Furthermore, it was shown to accelerate tumor growth in a mouse model of cSCC (Lee et al., 2014).

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The well-known ERBB2 p.S310F mutation (Stephens et al., 2005, Davies et al., 2005, Wen et al., 2015) was picked up by the SRM analysis and observed in 4% of our samples (Figure 29, Figure 38f). Interestingly, Li-Fraumeni syndrome and Paget's disease patients harboring this particular ERBB2 mutation are known to respond to anti-HER inhibitors (Vornicova et al., 2014, Jia et al., 2014). This could be relevant for the clinical management of BCCs with a high fraction of tumor cells with ERBB2 mutations when surgical resection is not indicated.

Furthermore, we identified well-described oncogenic mutations in genes of the mitogen-activated protein kinase (MAPK) pathway in a number of BCCs and observed gain-of-function (GoF) canonical mutations (p.G12C/D, p.G13R/V, p.Q22K, p.Q61R) in (K/N/H)-RAS genes in 2% of our tumors as well as PIK3CA mutations (p.V344K, p.N345K, p.E542K and p.E726K) in 2% of the studied BCCs. 1% of the tumors harbored the activating p.P29S mutation in RAC1, which has been previously reported in 9% of melanomas (Figure 29) (Hodis et al., 2012, Krauthammer et al., 2012). Active Rac1 was found to be an upstream activator of c-MYC and Cyclin D1 (Benitah et al., 2005, Page et al., 1999) suggesting its oncogenic function in BCC.

Mutations in NOTCH2 were found in 26% of the analyzed tumors (Figure 29). This gene shows a classical tumor suppressor profile, where 30% of the identified mutations were truncating mutations (Figure 38g). Furthermore, 22% of the samples with NOTCH2 mutations had two truncating events. NOTCH1, another member of the NOTCH gene family, was mutated in 29% of the tumors and 25% of the identified mutations were LoF mutations (Figure 29, Figure 38h). Out of the 8 NOTCH1 point mutations overlapping cnLOHs, 6 occurred before the LOH, and therefore, in these cases, no WT allele remained in the tumor.

NOTCH genes are frequently mutated in other epithelial cancers (Wang et al., 2011, Agrawal et al., 2011) and in patches of normal skin (Martincorena et al., 2015). Moreover, NOTCH1 conditional inactivation in mouse keratinocytes has been shown to induce Hh pathway activation through Gli2 mRNA upregulation, as well as the development of basal cell carcinoma-like tumors (Nicolas et al., 2003). These findings are in line with our observation of NOTCH mutations in BCC and suggest NOTCH1 and NOTCH2 are candidate BCC tumor suppressors.

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ARID1A harbored deleterious mutations in 26% of our BCC cohort (Figure 29). 32% of the identified mutations were truncating and one fourth of the tumors presented more than one mutation, a profile consistent with that of a tumor suppressor gene (Figure 38i).

Furthermore, ARID1A has been shown to be associated with the repression of c-MYC (Nagl et al., 2006) and ARID1A LoF mutations are frequently found in neuroblastomas (Sausen et al., 2013).

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Figure 38. Distribution of mutations in novel BCC driver genes. Distribution of mutations in the full sample set along the a. PPP6C, b. STK19, c. CASP8, d. RB1, e. KNSTRN, f. ERBB2, g.NOTCH2, h.NOTCH1 and i.ARID1A protein diagrams. The red lollipop represents truncating mutations, the green lollipop missense mutations and the purple lollipop both truncating and missense events in the same amino acid. Protein functional domains are represented by colored boxes. The most recurrent events per protein are labeled with the amino acid change. Protein diagrams were generated with cBioPortal (Cerami et al., 2012, Gao et al., 2013) with data from our study.

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143 of 299 4.2. Somatic copy number aberrations in BCC

SCNAs were investigated on the exome-sequenced tumors (see Materials and Methods).

The most common SCNA was the loss of one allele of PTCH1 through focal deletion of chromosome 9q (q=5.80x10^-6). There was also a frequent loss of a copy of 17p that overlaps TP53 (q=0.042) (Figure 39a,b).

6% of BCCs from our data set had copy gain of the full chromosome 2, which contains MYCN (chr2p24.3, Figure 39a,b). Furthermore, in an additional 6% of BCCs, focal copy gain was observed in MYCN but not GLI2, which is also located on chromosome 2p. It is worth mentioning that MYCN amplification is an event arising in a fraction of neuroblastomas (Brodeur et al., 1984) and medulloblastomas (Swartling et al., 2010) through focal amplifications. Moreover, MYCN amplification and overexpression in a fraction of BCCs has previously been reported (Freier et al., 2006), further underlining the relevance of this gene in BCC tumorigenesis.

LOHs events overlapping FBXW7 were found in 8% of the samples. This events, in conjunction with the inactivating mutations identified in this gene (see above), suggest that the inability of mutant FBXW7 to interact with its substrates could be an additional mechanism responsible for the partial impairment of MYCN degradation, resulting in its accumulation.

Finally, we observed chromosome 6 copy gain in 18% of the exome sequenced BCCs (q=6.15 x10^-6; GISTIC, Figure 39). Although the majority of the detected gain-of-copy events spanned the whole chromosome, 5% of them were limited to a region smaller than 64 Mb which includes CCND3 and E2F3 genes and that has been previously described in tumors with aberrant Hh pathway activation (Santos et al., 2007).

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Figure 39. SCNAs in BCC exomes. a. Overall profile of SCNAs in BCC. Frequently deleted regions are in the top panel(green) while frequently amplified regions are in the bottom panel (red). Chromosome numbers are indicated on the top gray panels and correspond to the X axis. Y axis corresponds to the frequency of SCNA in the sample set. The position of relevant genes inside the amplified regions are marked and labeled. b. Per-tumor SCNA profile. Each line represents a sample, each column a chromosome, indicated by the gray panel. Loss of an allele (LOH) is depicted in blue, cnLOH in black, copy number gain in yellow. Chromosomes X and Y have been excluded.

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SECTION 5. Relationship between drivers and phenotypes

Rationale and objectives

The observation of BCC behavior allowed to classify them based on their risk of recurrence.

Interestingly, risk of recurrence in BCC is closely correlated to histological subtype, as those tumors with a lower risk of recurrence are also nodular or superficial while high risk of recurrence tumors are of the morpheaform and metatypical subtypes. Furthermore, mutations in particular genes can directly impact the level of aggressivity in cancer, and have a dramatic effect on response to treatment, morbidity or mortality. In the case of BCC for example, mutations affecting the SMO drug binding pocket result in resistance to treatment with vismodegib.

The environment in which a mutation occurs is also important in cancer. Gorlin syndrome individuals, carrying a germline predisposing mutation, have tumors with a lower mutation rate on average, and tend to have multiple tumors, for example. The prevalence of the novel BCC drivers is unknown in Gorlin syndrome, and it would be interesting to know if, in spite of the germline predisposition, Gorlin syndrome BCCs are mutationally undistinguishable in the number and type of BCC drivers, from sporadic tumors.

Additionally, when multiple BCCs arise in Gorlin syndrome or sporadic BCC patients, these tumors are completely independent from each other, and only occur at the same time by chance. By studying the collection of somatic mutations in different BCCs from the same individual, it is possible to determine if the tumors have a common origin because if they do, they would share a fraction of their somatic mutations. The level of tumor clonality is important because, if elevated, may signify that, although BCC metastasis to other organs is rare, local micrometastasis are possible.

The objectives of this section of the study are to determine if specific mutations are associated to a particular risk of recurrence and therefore a specific BCC histological subtype, and to study the correlation between resistance to treatment with vismodegib and the BCC driver genes carried by a tumor. We were also interested in determining if the

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mutational profile of Gorlin syndrome tumors differs from the sporadic BCC profile, and how germline variation affects the mutational profile of BCC. Finally, we explore tumor evolution in individuals with clonal BCCs to determine how this clones arose and how similar they are to each other.

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5.1. Histological Subtypes, drug resistance and BCC drivers

We investigated the association of driver mutations with specific BCC phenotypes (Figure 40) and we observed that in the subset of BCCs with high risk of recurrence (non-clonal metatypical and morpheaform BCCs, 51 tumors), the profile of novel driver mutations was different from that of BCCs with low risk of recurrence (non-clonal nodular and superficial BCCs, 157 tumors). Specifically, we have observed that in the group of BCCs with increased risk of recurrence, MYCN, PPP6C and PTPN14 mutations were 1.8 (P= 3.4x10^-3), 2.7 (P=

2.3x10^-3) and 1.8 (P= 5.3x10^-2) times more frequent than in nodular BCCs, respectively (Figure 40). Moreover, when we considered only the MYCN mutation with the highest frequency (p.P44S/L), the difference increased to 2.1 fold (P= 2.2x10^-2). These correlations suggest a link between oncogenic mutations in MYCN, PPP6C and PTPN14 and the histological subtypes associated with an increased risk of recurrence, and this could be taken into account when determining a patient’s management and therapy. PTPN14 mutations were enriched in BCCs driven by SMO mutations over BCCs driven by PTCH1 mutations (1.7 fold; P= 6.2x10^-2), and when only LoF PTPN14 mutations were taken into account, the effect was stronger (2.5 fold; P= 2.1x10^-2).

Although the number of samples was small to draw significant statistics per histological subtype, preliminary analysis of our dataset showed significant enrichment of MYCN mutations in morpheaform and metatypical subtypes as compared to nodular and superficial subtypes. We also observed enrichment of PPP6C mutations in morpheaform tumors as compared to nodular and superficial subtypes. We did not observe however, significant differences between subtypes in other putative driver genes.

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Figure 40. Fraction of tumors with driver mutations per category. The bars represent the fraction of samples with driver mutations per BCC category indicated in the header (only non-clonal samples were used in this analysis). Number of samples in each category used for the analysis (non-clonal samples only): all tumors=283, High risk of recurrence (morpheaform/metatypical)=54, low risk of recurrence (nodular/superficial)=157, sporadic/naïve=231, sporadic/resistant=14, sporadic/sensitive=9, Gorlin/naïve=21, Gorlin/resistant= 14, Gorlin/sensitive= 2, PTCH1 mutated tumors= 181, SMO mutated tumors=

30. Naïve=naïve to vismodegib, Sensitive=sensitive to vismodegib, Resistant=resistant to vismodegib. Genes harboring driver mutations are on the x axis. MYCN-p.44 is a subcategory containing only tumors with MYCN p.44 mutations; PTPN14-tr corresponds to PTPN14 truncating mutations. The y axis depicts the fraction of tumors represented by the bars.

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PTPN14 mutations were significantly enriched in morpheaform as compared to nodular subtype (Figure 41). We were not able to see any statistically significant differences between metatypical and morpheaform, nor between nodular and superficial and therefore grouping the subtypes in high and low recurrence risk did not seem to mask interesting results and instead increasing our power.

Regarding treatment status, vismodegib resistant BCCs had at least 2.2 times higher frequency of SMO mutations than treatment-naïve BCCs in both sporadic (43% vs. 20% P=

5x10^-2) and Gorlin syndrome cases (83% vs. 0% P= 7.3x10^-5). We observed that PTPN14 mutations were more prevalent in BCCs driven by SMO mutations than on those driven by PTCH1 mutations (37% vs. 22% P= 6.18x10^-2; PTPN14tr 27% vs. 11% P= 2.11x10^-2 Fisher’s exact test) (Figure 40).

Figure 41. Fraction of mutations in putative driver genes in different BCC histological subtypes. The y axis represents fraction of total while the x axis displays the genes identified in this study. The height of the bar indicates the fraction of tumors of a specific subtype with mutations in the corresponding gene. SUP=superficial subtype, NOD= nodular, MET= metatypical and MOR=morpheaform. Significant differences are marked with asterisks: *= P<0.5 and **=

P<0.01.

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Some BCCs harbored mutations in two of the three primary BCC driver genes of the Hh pathway (PTCH1, SMO, SUFU). The analysis of the Variant Allele Frequency (VAF) of these mutations suggested that they do not correspond to different clones but rather to the same clone that accumulates mutations in more than one primary BCC gene (Figure 42).

5.2. Tumor clonality and germline variants

The incidence of BCC increases with age and UV light exposure (Rubin et al., 2005), and it is therefore not uncommon that patients develop several tumors at the same time or throughout their lifetime. We identified 38 individuals from out cohort contributing with more than one tumor to the study and we then searched for shared somatic variants among

Figure 42. Variant Allele Frequencies of somatic mutations in tumors with several driver mutations in SMO, PTCH1 and SUFU. Colored circles represent VAFs of the putative driver mutations. The exact amino acid changes are indicated next to the circles. Horizontal bars correspond to the average VAF of all somatic mutations in the given tumor. VAFs in driver genes were normalized to the ploidy of the loci in the tumor.

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the tumors. One patient had two cases of anatomically close tumors (patient VS037; 0.4 and 0.5 cm distance between tumors) that were clonal, sharing a considerable fraction (14% the first pair and 49% the second) of passenger mutations and at least one driver mutation. In both tumor pairs, a TP53 LoF mutation was identified and one of the pairs shared a PTCH1 mutation as well (Figure 43). There were available matched blood samples for these tumors and their sequencing data was used to re-confirm the somatic nature of the identified mutations. Conversely, none of the BCCs located in anatomical distant sites were clonal but we interestingly found two pairs of anatomically distant tumors (10-15 cm apart) that shared a unique TP53 LoF mutation but no other driver or passenger events. The mutation was not present in the matched blood sample of the individual. This observation suggests that TP35 mutations could be BCC primary drivers or could provide an enhanced background for subsequent oncogenic mutations through field cancerization, a phenomenon described by Slaughter et al. (1953) where a region of histologically (and now in this case genetically) abnormal tissue surrounds the area where the tumor arises. This possibility, along with recent findings regarding oncogenic mutations found in non-affected skin, is considered in the Discussion section of this thesis.

We identified germline variants in PTCH1 in 19 out of 20 patients with Gorlin Syndrome.

90% of these variants were truncating mutations. In sporadic BCCs we identified six germline PTCH1 variants, two of which were truncating and four missense of unknown clinical significance, as well as three missense germline variants in TP53. One of these TP53 variants (p.T377P), which was also reported in COSMIC as a somatic mutation, was detected in an individual with multiple BCCs but otherwise no other clinical features of Gorlin syndrome, suggesting that it may confer predisposition.

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Figure 43. Clonality in sporadic BCCs. Common and tumor-specific mutations found in four pairs of tumors. The trunk of the tree corresponds to the shared mutations (blue boxes) and the branches (green and orange) to those specific for each tumor. The length of the lines corresponds to the number of mutations/Mb. The shared and private driver mutations are in the table of the corresponding color. Gene= mutated gene, Mut= protein change, %= VAF. For common mutations, VAFs represent first the tumor on the left branch, and second that on the right. Patient IDs can be found at the bottom of each tree while tumor IDs are marked for each branch. The tumor’s anatomical location is noted in parenthesis next to tumor ID.

153 of 299 5.3. BCCs in Gorlin syndrome

The novel BCC drivers identified in the study were also found in tumors from individuals with Gorlin syndrome, including MYCN, PTPN14, LATS1, (K/N/H)-RAS, ERBB2, STK19 and PPP6C (Figure 20), however some differences were observed. In line with the fact that 19 treatment naïve Gorlin patients from our dataset had germline PTCH1 mutations, we observed no SMO mutations, which was significantly less than in treatment naïve sporadic BCCs (P= 0.025). Additionally, we observed significantly smaller fractions of TP53, NOTCH2, NOTCH1, ARID1A, and MYCN mutations in naïve Gorlin BCCs when compared to sporadic naïve BCCs (P= 8x10^-5, P= 2.7x10^-3, P= 5.8x10^-3, P= 7.2x10^-3 and P= 5x10^-2 ,respectively) (Figure 33).

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SECTION 6. Gene expression in BCC

Rationale and objectives

The study of gene expression in cancer is an exceptional complement to the study of somatic mutations since it is possible to identify specific genes or signaling pathways that are disrupted and that could point to novel drivers or mechanisms of oncogenesis. Gene expression studies can also be used as an independent confirmation of the relevance of a mutation. For example, mutations in an oncogene are expected to increase said gene’s expression. By observing elevated expression of the gene of interest or of its targets, we can infer a causality relation between the mutation and the elevated expression.

Bioinformatic tools for the analysis of gene expression allow us to explore pathways upregulated and specific target genes in the full dataset or in smaller groups of tumors with characteristics in common.

The objective of this section is to compare gene expression levels between BCCs and non-affected skin to identify the disrupted signaling pathways in BCC. Furthermore, we used this expression data to search for signs of Hh or Hippo pathway activation in order to validate the relevance of the identified MYCN and PTPN14 mutations in BCC.

155 of 299 6.1. Gene expression in BCC

In order to compare global gene expression between BCC tumors and non-affected skin, we performed RNA sequencing of sets of samples of these two groups and carried out gene ontology analysis on genes upregulated in BCC (see Materials and Methods). Pathway

In order to compare global gene expression between BCC tumors and non-affected skin, we performed RNA sequencing of sets of samples of these two groups and carried out gene ontology analysis on genes upregulated in BCC (see Materials and Methods). Pathway